From 67a4610785b933b3c92481267cbbc5ec60c4f143 Mon Sep 17 00:00:00 2001 From: Aditya Joardar <30207466+joardar-aditya@users.noreply.github.com> Date: Tue, 12 Feb 2019 07:59:31 +0530 Subject: [PATCH 01/13] Created using Colaboratory --- feature_sets.ipynb | 1564 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1564 insertions(+) create mode 100644 feature_sets.ipynb diff --git a/feature_sets.ipynb b/feature_sets.ipynb new file mode 100644 index 0000000..9fcae40 --- /dev/null +++ b/feature_sets.ipynb @@ -0,0 +1,1564 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "feature_sets.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "IGINhMIJ5Wyt", + "pZa8miwu6_tQ" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zbIgBK-oXHO7", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Feature Sets" + ] + }, + { + "metadata": { + "id": "bL04rAQwH3pH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objective:** Create a minimal set of features that performs just as well as a more complex feature set" + ] + }, + { + "metadata": { + "id": "F8Hci6tAH3pH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "So far, we've thrown all of our features into the model. Models with fewer features use fewer resources and are easier to maintain. Let's see if we can build a model on a minimal set of housing features that will perform equally as well as one that uses all the features in the data set." + ] + }, + { + "metadata": { + "id": "F5ZjVwK_qOyR", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "As before, let's load and prepare the California housing data." + ] + }, + { + "metadata": { + "id": "SrOYRILAH3pJ", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "dGnXo7flH3pM", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "jLXC8y4AqsIy", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1224 + }, + "outputId": "4a00a62d-0a2c-4189-f724-4213165122e8" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.5 2637.3 539.1 \n", + "std 2.1 2.0 12.6 2139.4 417.0 \n", + "min 32.5 -124.3 2.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1456.8 295.0 \n", + "50% 34.2 -118.5 29.0 2127.0 433.0 \n", + "75% 37.7 -118.0 37.0 3147.2 649.2 \n", + "max 42.0 -114.3 52.0 30401.0 4957.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1426.9 500.9 3.9 2.0 \n", + "std 1133.9 379.3 1.9 1.1 \n", + "min 3.0 1.0 0.5 0.0 \n", + "25% 792.0 281.0 2.6 1.5 \n", + "50% 1166.0 408.0 3.5 1.9 \n", + "75% 1718.2 607.0 4.8 2.3 \n", + "max 35682.0 4769.0 15.0 55.2 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "hLvmkugKLany", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Develop a Good Feature Set\n", + "\n", + "**What's the best performance you can get with just 2 or 3 features?**\n", + "\n", + "A **correlation matrix** shows pairwise correlations, both for each feature compared to the target and for each feature compared to other features.\n", + "\n", + "Here, correlation is defined as the [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient). You don't have to understand the mathematical details for this exercise.\n", + "\n", + "Correlation values have the following meanings:\n", + "\n", + " * `-1.0`: perfect negative correlation\n", + " * `0.0`: no correlation\n", + " * `1.0`: perfect positive correlation" + ] + }, + { + "metadata": { + "id": "UzoZUSdLIolF", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 383 + }, + "outputId": "2bb04c73-60a1-4012-85f9-d3f17d6a6874" + }, + "cell_type": "code", + "source": [ + "correlation_dataframe = training_examples.copy()\n", + "correlation_dataframe[\"target\"] = training_targets[\"median_house_value\"]\n", + "\n", + "correlation_dataframe.corr()" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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target-0.2-0.00.10.10.1-0.00.10.70.21.0
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" + ], + "text/plain": [ + " latitude longitude housing_median_age total_rooms \\\n", + "latitude 1.0 -0.9 0.0 -0.0 \n", + "longitude -0.9 1.0 -0.1 0.0 \n", + "housing_median_age 0.0 -0.1 1.0 -0.4 \n", + "total_rooms -0.0 0.0 -0.4 1.0 \n", + "total_bedrooms -0.1 0.1 -0.3 0.9 \n", + "population -0.1 0.1 -0.3 0.9 \n", + "households -0.1 0.1 -0.3 0.9 \n", + "median_income -0.1 -0.0 -0.1 0.2 \n", + "rooms_per_person 0.1 -0.1 -0.1 0.1 \n", + "target -0.2 -0.0 0.1 0.1 \n", + "\n", + " total_bedrooms population households median_income \\\n", + "latitude -0.1 -0.1 -0.1 -0.1 \n", + "longitude 0.1 0.1 0.1 -0.0 \n", + "housing_median_age -0.3 -0.3 -0.3 -0.1 \n", + "total_rooms 0.9 0.9 0.9 0.2 \n", + "total_bedrooms 1.0 0.9 1.0 -0.0 \n", + "population 0.9 1.0 0.9 -0.0 \n", + "households 1.0 0.9 1.0 0.0 \n", + "median_income -0.0 -0.0 0.0 1.0 \n", + "rooms_per_person 0.1 -0.1 -0.0 0.2 \n", + "target 0.1 -0.0 0.1 0.7 \n", + "\n", + " rooms_per_person target \n", + "latitude 0.1 -0.2 \n", + "longitude -0.1 -0.0 \n", + "housing_median_age -0.1 0.1 \n", + "total_rooms 0.1 0.1 \n", + "total_bedrooms 0.1 0.1 \n", + "population -0.1 -0.0 \n", + "households -0.0 0.1 \n", + "median_income 0.2 0.7 \n", + "rooms_per_person 1.0 0.2 \n", + "target 0.2 1.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "metadata": { + "id": "RQpktkNpia2P", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Features that have strong positive or negative correlations with the target will add information to our model. We can use the correlation matrix to find such strongly correlated features.\n", + "\n", + "We'd also like to have features that aren't so strongly correlated with each other, so that they add independent information.\n", + "\n", + "Use this information to try removing features. You can also try developing additional synthetic features, such as ratios of two raw features.\n", + "\n", + "For convenience, we've included the training code from the previous exercise." + ] + }, + { + "metadata": { + "id": "bjR5jWpFr2xs", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "jsvKHzRciH9T", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + "\n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "g3kjQV9WH3pb", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "varLu7RNH3pf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Spend 5 minutes searching for a good set of features and training parameters. Then check the solution to see what we chose. Don't forget that different features may require different learning parameters." + ] + }, + { + "metadata": { + "id": "DSgUxRIlH3pg", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 658 + }, + "outputId": "615d37ae-5f77-4244-9b60-e7cbeb0af16a" + }, + "cell_type": "code", + "source": [ + "#\n", + "# Your code here: add your features of choice as a list of quoted strings.\n", + "#\n", + "minimal_features = [\"median_income\",\"latitude\"]\n", + "\n", + "\n", + "assert minimal_features, \"You must select at least one feature!\"\n", + "\n", + "minimal_training_examples = training_examples[minimal_features]\n", + "minimal_validation_examples = validation_examples[minimal_features]\n", + "\n", + "#\n", + "# Don't forget to adjust these parameters.\n", + "#\n", + "train_model(\n", + " learning_rate=0.001,\n", + " steps=500,\n", + " batch_size=500,\n", + " training_examples=minimal_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=minimal_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 228.88\n", + " period 01 : 221.21\n", + " period 02 : 213.64\n", + " period 03 : 206.18\n", + " period 04 : 198.85\n", + " period 05 : 191.66\n", + " period 06 : 184.63\n", + " period 07 : 177.77\n", + " period 08 : 171.11\n", + " period 09 : 164.66\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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lUPkR0koy2Zm/h0APK1aTf5u1W4tkwGuT5KJdko12STb2kQLGAe3RqfQ6hdhw\nH/rH+JOTV0lKTlMhE+DtToi/h1PH9Hb1YljIEBQU9tsy2JGfREmNjRjvKFz0xjZtv1bIgNcmyUW7\nJBvtkmzsIwWMA9qzU3l5ujIyLhgXg46UQza2pxWQW1xFjy7euLo4MRuj6OjuE00//14cqTxGmi2T\n7fm78Xf3I8jD2g5ncGXJgNcmyUW7JBvtkmzsIwWMA9q7U+kUhe5dvBkcG8CRgkpSc2xsTsnDx+xK\naICHU3sgWVzNDAuOx0XvQpotk10Fe8irKqCbdxSuesf3adIqGfDaJLlol2SjXZKNfaSAccDl6lRm\nkwvX9g3Gw81Iak4JO9ILOZJfSY+uPri7Or4gV6foiPaOZGBAX46dPEG6LYsf83bi5WohxCPoqtgc\nUga8Nkku2iXZaJdkYx8pYBxwOTuVoihEh3oxpFcgJ4qqSM2xsWlfLmaTC10DPZ0qOjxdPLgmeDAe\nRhPpJZkkFe7jcOUxYrwjcTc49ywarZABr02Si3ZJNtol2dhHChgHXIlO5eFmZHifIHzMrqTm2NiV\nWcSBE+V0C/PGw83xBbmKohDp1ZXBgQPIryog/afNIV0NrnQ1h3XY2RgZ8NokuWiXZKNdko19pIBx\nwJXqVIqiEBFkYVjvIPJt1U2zMcl5uLnoiQi2OFV0mIzuDAkaiJ+7Lxm2bJKLUskszSbKKxxPF892\nOIv2JQNemyQX7ZJstEuysY8UMA640p3K3dXA0F6BBPqY2H/YRlJWMelHSokJ9cJscnxBrqIohJlD\nuCZ4MLbasubZGFCI8gpH14E2h7zS2YgLk1y0S7LRLsnGPlLAOEALnUpRFLpYPRnRN5iS8hpSc2xs\nTM5Dr1eICrGgc2I2xlXvykBrP8I8Q8gqPUhKSRr7itPoag7D27VjbA6phWzE+SQX7ZJstEuysY8U\nMA7QUqdyc9ET3zOQUH8P0o+Wsie7mH0HS4gJ8cLi4dzt0UEeVoYFx1NdX83+kky25u6ktv4U0d4R\nmt8cUkvZiJ9JLtol2WiXZGMfKWAcoMVOFeLvwbX9gimvOk3qIRsbk3NpbFSJCfNCp3N8NsaoN9LX\nvxfdvCM5WH6Y1JIMdhfsJcQjCH8Nbw6pxWyE5KJlko12STb2kQLGAVrtVC5GPQO7BxAZbCbjaBnJ\nB0rYk11ERLAFH/PFA26Jn7svw0OG0qg2sr8kg+35uymrLSPGOwqjBrcj0Go2nZ3kol2SjXZJNvaR\nAsYBWu9Ugb4mRsWFUF1bx75DTc+NOV3XQLcwL/R6xxfk6nV6Yn270cevJ4crjv68HYGbL0Eege1w\nBs7TejadleSiXZKNdkk29pE2APq7AAAgAElEQVQCxgEdoVMZDTriYvzp3sWbrGNlJB8sYWdmEV2t\nnvh5uTl1TC9XC8ODh2DUGUmzZbGrYC+5J/OJ8Y7CzeDcDE9b6wjZdEaSi3ZJNtol2dhHChgHdKRO\nFeDtzqh+IZyuayTlYAlbUvI4WV1H9y5eGJyYjdEpOmJ+2o7g+Mncpluu83biafQkzDPkij8AryNl\n05lILtol2WiXZGMfKWAc0NE6lUGvo2+UH70jfDlwopx9h0rYnlZASIAHVm/ntg7wdPFgaPAgLC5m\n0m2Z7ClK4VD5EaK9IzAZTW18BvbraNl0FpKLdkk22iXZ2EcKGAd01E7la3FjVFwwqgopB21sTc3H\nVlFLjy7eGA2O3x6tKArhli4MCRpIYXURaT89AM+oMxJh6XJFZmM6ajZXO8lFuyQb7ZJs7CMFjAM6\ncqfS63T0ivAlLsafQ7kVpByysSU1H6u3O8F+Hk4d093gxuDA/lhNAWSWHiC5eD9ptkwiLF2xuJjb\n+Axa1pGzuZpJLtol2WiXZGMfKWAccDV0Km9PV0b2C8agV0jNsbEtrYATRSfp3sUbNxeDw8dTFIVQ\nz2CGBcdTfqqCNFsmW3J30Kg2EukVjv4ybUdwNWRzNZJctEuy0S7Jxj4tFTCKqqpqe33wokWL2L17\nN/X19fz2t7+lb9++PProo9TX12MwGHj++ecJCAjg008/ZcWKFeh0OubMmcPs2bNbPG5RUWV7NZmA\nAHO7Hv9yyy2u4p11GRw4Xo6Hm4Gbx3ZjRN+gVl0CSi1O54PM1ZSeKiPIZOX2nrOJ8gpvw1Zf2NWW\nzdVCctEuyUa7JBv7BARcfKa/3QqYbdu28dZbb/HGG29QWlrKjBkzGDp0KKNHj2bKlCn85z//4cSJ\nEyxYsIAZM2aQmJiI0Whk1qxZ/Pvf/8bb2/uix5YCxjGNqsp3SSdI/OEgp0430DvChzsSYglwcpEv\nQG19LZ8cXMfGE1tRUBgVNpzroxLa9ZbrqzGbq4Hkol2SjXZJNvZpqYBpt0tIwcHBTJgwAaPRiIuL\nC8uWLePtt9+mR48e6HQ6jh8/TlZWFl5eXpSUlDB9+nQMBgMZGRm4uroSGRl50WPLJSTHKErTJpDD\negWRb6v+aXPIXFwMOiKDLU7Nxhh0Bvr4x9LDJ4ZD5YfZX5LBroK9BJmsBJj82+Esrs5srgaSi3ZJ\nNtol2dinpUtI7bZ4Qa/XYzI13XKbmJjIqFGjMJlM6PV6GhoaeP/995k+fTrFxcX4+v68/46vry9F\nRUXt1axOzc/LjT/M7sc903rhYtDzwYYDPPPv3RwvOun0MWO8I3k0/g8khI+l7FQ5S5LfYkXaB5ys\nq2rDlgshhBDncnxFp4PWr19PYmIiy5cvB6ChoYGFCxdyzTXXMGzYMNauXXvOz9tzRcvHx4TBiVuD\n7dXSlNXV4HqrhdHxXfnXmhQ27jnBk+/sZNbY7swZ382pW64B7gqazdgew3h953vsyE8iozSLuwbe\nzLAug9r0luurPZuOSnLRLslGuySb1mnXAmbTpk28/vrrvPnmm5jNTUE9+uijhIeHs2DBAgCsVivF\nxcXN7yksLKR///4tHre0tLrd2tyZrkv+alIP+kf78d5XmXzwTSYb9xznzsmxRId6OXU8D7z4Q9zv\n+O74Zj479DX//PEtvs3eys3dZ+DjdvE1TfbqTNl0JJKLdkk22iXZ2KelIq/dLiFVVlayaNEili1b\n1rwg99NPP8VoNHL//fc3/1xcXBwpKSlUVFRQVVVFUlISgwcPbq9miV/oH+PP078eypgBoeQWV/HM\ne7t5f30WtafrnTqeXqdnfNfR/HnIQ3T3jialOJ2nt7/IphPbaFQb27j1QgghOqt2uwtp5cqVLF68\n+JzFuLm5uVgsFjw9PQGIjo7mr3/9K+vWreOtt95CURTmzp3L9ddf3+Kx5S6k9pF5tJR31mVSYKvG\nz+LG/Mk96BPp5/TxVFVla94OVh/4nJr6WmK8I7ktdhaBpgCnjteZs9EyyUW7JBvtkmzsc0Vuo25P\nUsC0n7r6Bj7dcpgvtx2lUVUZ0SeIm8d1w9Pd6PQxy06V82HmGpKL92PQGZgaOYFxXUah1zm23qaz\nZ6NVkot2STbaJdnY54rcRt2e5Dbq9nNmO4L+3fzJyaskJcfGlpQ8fC1uhPh7OLUg183gxkBrHMGe\nQWSVHmBfcRqpxemEW7rg5Wqx+zidPRutkly0S7LRLsnGPrKVgAOkUzXx8nRlZFwwbkY9qTk2dqQX\ncrSgaTsCd1fntiMI9ghkWHA8ladPkmbLZGveTuoa64jyirBrNkay0SbJRbskG+2SbOwjBYwDpFP9\nTKcodAvzZkisleOFJ9l/2Mamfbl4uBvpGmh2ajbGRW8kLqA3UV7hHCg7RGpJOnsK9xHqGYyfu0+L\n75VstEly0S7JRrskG/tIAeMA6VTn83Q3MrxvEN5mV9IO29idWUTWsTJiwrycXhsT4O7H8JCh1DXW\nsb8kk235u6g4XUmMdyRG3YVneCQbbZJctEuy0S7Jxj5SwDhAOtWFKYpCRJCF4X2CKSytad6OwKBr\n2qZA59R2BHp6+fWgp28PciqOkFaSyY78JKwm/wveqSTZaJPkol2SjXZJNvaRAsYB0qla5u5qYEhP\nKyH+HmQcKWVPdjH7DpQQFWLBy9O5jRx93LwYHjIEvaIjrSSTnQV7KKgqJMY7Cle9S/PPSTbaJLlo\nl2SjXZKNfaSAcYB0qktTFIXQAE+u7RdCedXpn2Zj8qhraKRbmBd6nePPR9QpOrr5RBMX0IfjlSdI\ns2XxY+5OLC5mQj2DURRFstEoyUW7JBvtkmzsIwWMA6RT2c/FqGdg9wCiQyxkHisj+WAJOzOK6Gr1\nxM/Lzaljml08uSZ4MB5GE+m2TJKK9pFTcZRor0j8vbwkGw2SMaNdko12STb2kQLGAdKpHGf1MTEq\nLphTpxtIPVTC5pQ8KqpO072LN0aD47MxiqIQ6dWV+MAB5FcXkm7LYkveDtwMrgS7Bbfp5pCi9WTM\naJdko12SjX1aKmDkSby/IE9HbJ0DJ8p5+4t08kqq8TG7csekHsTF+Dt9PFVV2ZGfxMfZa6mqrybC\n0pXbYmcS6hnchq0WrSFjRrskG+2SbOwjT+J1gFTFreNrcWNUXAg6BVIP2fhxfwH5tmq6d/HG1ejY\n1gHQNBsTZg7hmuDB1FBFSmE6W3J3UN9YT5RXuMPbEYi2J2NGuyQb7ZJs7COXkBwgnar19DqF2HAf\nBnYP4HB+Jak5Njbvy8Pb7EpYgHPbEbjqXRjb4xr89VYOlOWQWpJOUuE+QjyD8HP3bYezEPaSMaNd\nko12STb2kQLGAdKp2o7Fw4WR/YIxuRpIPWxjZ3ohOXmVdA/zxuTm+HYEHh6ueGJheMgQ6hrrSPvp\nAXhltWVND8DTO7/hpHCejBntkmy0S7KxjxQwDpBO1bYURSE61IshvQLJLa5if46NjftycXcxEBHs\n2HYEZ7Ix6Az08utBb79YDlccI83WVMj4uHoR7BEoi3wvMxkz2iXZaJdkYx8pYBwgnap9eLgZGdY7\nCD8vN9JySknKKiLtSCkxoV6YTS6XPgDnZ+Pt6sXw4CG46F1It2WxuzCZo5XHifaOwN3g3l6nIn5B\nxox2STbaJdnYRwoYB0inaj+KohAeaGZE3yBKymubtyNAUYgOsaDTtTxzcqFsdIqOaO9IBlrjyKsq\nIN2WxdbcHbjoXQi3hMlszGUgY0a7JBvtkmzsIwWMA6RTtT83FwPxPQMJC/Ak40gpew8Usye7mIhg\nMz7mi3fWlrLxMJoYGjQQX3dfMm3ZJBfvJ92WRYSlCxaXi9+GJ1pPxox2STbaJdnYRwoYB0inunxC\n/D0YGRfMyeo6UnNsbNqXy6nTDcSEeWHQn/8AvEtloygKXcwhDAuOp+xUOWm2TLnl+jKQMaNdko12\nSTb2kQfZOUAeLnRlpB+28c66DIrKarF6uzM/oQc9I869PdrRbPaXZPDfjFWUnirD6u7PrbE30d0n\npq2b3unJmNEuyUa7JBv7yIPsHCBV8ZUR4O3OqLgQGhpU9h0qYUtqPqWVtT9tR9A0c+JoNlaT/y9u\nud5NaW0Z0d6RuMgt121Gxox2STbaJdnYR2ZgHCBV8ZWXk1fB21+kc7yoCi9PF+ZO6MGgHgGtyuZI\nxTH+k5HIiZN5mI2ezO5+PQOtcbLItw3ImNEuyUa7JBv7yAyMA6QqvvJ8zK6MjAvBoFdIzbGxPa2A\n40UniesWQGNDo1PHlFuu24+MGe2SbLRLsrGPzMA4QKpibcktruKdLzM4cKIcD3cjs6+LZmS/1u1I\nXVhdzAeZq8gsPYCL3oXroxIYHTYcneL4ztlCxoyWSTbaJdnYp6UZGClgfkE6lfY0qirfJZ1g1cZD\n1Jyqp0cXb+ZPjiXI1+T0MVVVZVv+blZnf0ZVfTXhli7cHjtLdrl2gowZ7ZJstEuysY8UMA6QTqVd\nitHAy/9NYu+BYgx6HdNHRDB5aNcL3nJtr8rTJ0nM/pRdBXvRKTrGdx3N5IjxssjXATJmtEuy0S7J\nxj6yBsYBcl1SuwL8POgT7t30ALyjTQ/AS8ouIjzIjK/ZzaljuupdGGDtS4Sly1m7XCfLLtcOkDGj\nXZKNdkk29pEH2TlAOpV2ncnmzAPwqmrqST1kY3NyHidr6ugW5oXR4NxsjNXkz/DgIdQ31sst1w6S\nMaNdko12STb2kUW8DpBpPe26UDaZR0t5Z10mBbZqfMyuzJvYg/7d/Fv1Ob+85XpW9+sZJLdcX5SM\nGe2SbLRLsrGPXEJygFTF2nWhbPy93BkdF4xC0y3X29IKOFFcRfcwL9xcDE59jtxy7RgZM9ol2WiX\nZGMfuYTkAOlU2nWxbPQ6HT3DfRjUPYCjhZXsz7GxKTkPT5ORroGeTs2cnNnlepC1f/Mu11tyd+Aq\nu1yfR8aMdkk22iXZ2EcKGAdIp9KuS2Vj8XDh2n7BmE0upB22sSuziMyjZcSEeeHp7tw6Fg+jiSE/\n7XKdZTtAcnEqabZMIixdZZfrn8iY0S7JRrskG/tIAeMA6VTaZU82iqIQFWJhWO8gispqSM2x8cPe\nXFAgOsSCTuf4zMmZXa6vCR5M2any5tmYusY6orwiOv0u1zJmtEuy0S7Jxj6yiNcBsrBKuxzNRlVV\ndmcW8Z9vsiivOk1ogAe/SoglOtSrVe3YX5LBB5mrsdWWEuDux609ZtLDt/Puci1jRrskG+2SbOwj\ni3gdIFWxdjmajaIohPh7MCoumKraelIO2di8L4+T1W17y/X2/N3YakuJ6aS3XMuY0S7JRrskG/vI\nDIwDpCrWrtZmk3m0lBXrMslv41uu38/4mOMnczvtLdcyZrRLstEuycY+Lc3AtOvudYsWLeLmm29m\n5syZfP311wC8++679O7dm6qqquaf+/TTT5k5cyazZ8/mo48+as8miU6sR1cfnrgrnutHRFBRdZpX\nPt7H0jWplJ885fQxwy1dWDj4Pm6MnkJtQy1v73+f1/a9ja22tA1bLoQQ4pece1CGHbZt20Z2djYr\nV66ktLSUGTNmUF1dTUlJCVartfnnqqurWbJkCYmJiRiNRmbNmsWECRPw9vZur6aJTsxo0HPjyCji\nY628sy6DXRmFpOXYmD0mmpFxIeicmDnR6/RMCL+O/gF9+W/mx+wvyeCp7S/ILtdCCNGO2u1P1vj4\neF5++WUALBYLNTU1jBs3jgcffPCc6fXk5GT69u2L2WzGzc2NgQMHkpSU1F7NEgKA0ABPHp07iLkT\nu9OoqqxYl8mi9/eQV1J16TdfRIDJj/v638O8nnMwKgYSsz/lH7uXcOJkXhu2XAghBLRjAaPX6zGZ\nTAAkJiYyatQozObzr2UVFxfj6/vzpnm+vr4UFRW1V7OEaKZTFMYODOPpXw9lQDd/so6V8X/Ld7J2\nSw71DY1OHVNRFK4JHszj1/yRwYH9OVJxjOd2vswnB7/kdENdG5+BEEJ0Xu12CemM9evXk5iYyPLl\ny+36eXvWFPv4mDAY2u/ZGy0tGhJXVntkExBg5okof7am5LFs1T5Wb8ohKbuYBXP6Exvu3I7UAZhZ\nGPpb9uSl8uau//L1ke/YV5LKbwbfRp/A2DY+gytPxox2STbaJdm0TrsWMJs2beL111/nzTffvODs\nC4DVaqW4uLj568LCQvr379/icUtLq9u0nWeTleHa1d7ZdA8289TdQ0j8/iDf781l4SubGDswjJtG\nR+Hu6txQCTOE82j8Q3x26Cu+O7aZJ79/mWuCB3NTzDQ8jKY2PoMrQ8aMdkk22iXZ2OeK3IVUWVnJ\nokWLWLZsWYsLcuPi4khJSaGiooKqqiqSkpIYPHhwezVLiBaZ3IzckRDLn24fSJCfiW+TjvPYm9vZ\nm1186TdfhKvehZndpvO/gxcQ5hnCtrxdPLnteXbkJ9k14yiEEOJ87fYcmJUrV7J48WIiIyObvzd0\n6FC2b9/O3r176du3L/3792fhwoWsW7eOt956C0VRmDt3Ltdff32Lx5bnwHROlzubuvpGPv/xMJ//\neISGRpXBsVZuG98Nb8+LP1jpUhoaG9hwbBOf53xDXWMdsT7duKXHTQSY/Nqu4ZeZjBntkmy0S7Kx\nT0szMPIgu1+QTqVdVyqbE0UnWbEukwMnynF3NTCnFbdcn1FcY2Nl5mrSbJkYdQYSIsYzvusoDLp2\nX5bW5mTMaJdko12SjX1kKwEHyOOdtetKZWPxcGFEv2AsHk27XO/OLCLjaBnRoRbMJhenjmkyuhMf\nOIAgDytZZQdJKU5jb1EqoZ7B+Lr5tPEZtC8ZM9ol2WiXZGMf2Y3aAdKptOtKZqMoCpHBP+9yvT/H\nxsbkXFQgOtTL6V2uQzyDGB48hJqGWtJKMtmWt4vyUxVEe0Vg7CD7KsmY0S7JRrskG/vIXkgOkGk9\n7dJSNrszC/n3N1mUnzxNqL8H8yfHEtPKXa4PlR/hvxkfk1uV37SvUrfpDArsr/l9lbSUiziXZKNd\nko195BKSA6Qq1i4tZRPi78GofsFUn7XLdWX1abqFeTu9y7WPmzcjQobgonch3ZbN7sJkciqOEmkJ\n1/Qt11rKRZxLstEuycY+cgnJAdKptEtr2RgNeuJi/OkZ7sPB3HJSDtn4cX8+Vh93gv08nDqmTtER\n7R3J4MD+FFQXkW7LYkvudhQUIixdNLmvktZyET+TbLRLsrGPFDAOkE6lXVrNxs/LjVFxIegUSD1k\nY1taASeKTtKtizduLs7dVWQymogPHEDgWYt89xXtJ8wcjI+btjY61WouQrLRMsnGPlLAOEA6lXZp\nORu9TiE23IdBPawcKzhJao6Njcl5eLgb6Bpodmody8+LfOOpqa9hvy2TH/N2UqGxRb5azqWzk2y0\nS7KxjxQwDpBOpV0dIRuLqemWay8PF/a30S3XRr2Rvv69iPXpxuGKo6TZMtmWvwtvVy+CPQKv+CLf\njpBLZyXZaJdkYx8pYBwgnUq7Oko2Z265Ht4nuM1uuQbwdfNmeMgQXHUupNuymhf5RnmFY7qCi3w7\nSi6dkWSjXZKNfaSAcYB0Ku3qaNm4uxoY2iuQsAAPMo6WkXyghKSsIroGmvG1uDl1zDOLfAdZz13k\nq0NHhKXrFVnk29Fy6UwkG+2SbOwjBYwDpFNpV0fNpumW6xCqT9WTcqik+ZbrmFDnb7n2OLPI1xRA\nVukh9pWkkVy0nzBzyGVf5NtRc+kMJBvtkmzsIwWMA6RTaVdHzsZo0J13y/XW1Dz8LG4E+5lascg3\nmOEh8VTXVzetjcnbRfnpysu6yLcj53K1k2y0S7KxjxQwDpBOpV1XQzZnbrk26BVSc0rZnl7AkfxK\nYsK8MLk5V3CcWeTbwyeGnIqjpJVksj1/Nz6XaZHv1ZDL1Uqy0S7Jxj5SwDhAOpV2XS3Z6HUKPbr6\nEN/TSm5xFak5Nn5IzsWg1xEZYnZ6l2tfNx9GhAzBqDOS8dMi38MVx4jyisBkdG/js/jZ1ZLL1Uiy\n0S7Jxj5SwDhAOpV2XW3ZeLobGd4niABvdzKOlLEnu5jk7GK6BprxMV980LZEp+iI8Y5koDWOgqpC\n0kubFvnqFX27Pcn3asvlaiLZaJdkYx8pYBwgnUq7rsZsFEWha6CZa/sFc7K6jpQcG5uSczlZU0e3\nMK9WLfIdEjQQqymArNID7CtuWuTbpR0W+V6NuVwtJBvtkmzsIwWMA6RTadfVnI2rUc+A7gH06OLN\nwdwKUg6VsDU1D3+v1i3yDfUMZnjIEKrqmhb5/pi3i4rTJ4n2jsCoa5tFvldzLh2dZKNdko19pIBx\ngHQq7eoM2fh7uzct8tUppObY2J5WyNGCk8SEemFyc25fJRe9kX4BPy3yLT/y091Ku/F18yHIZG31\nIt/OkEtHJdlol2RjHylgHCCdSrs6SzZnFvkOjv15ke/G5FyMBh2RwW2xyNfQ9CTfgr0cqTz+05N8\nnV/k21ly6YgkG+2SbOwjBYwDpFNpV2fLxmxyaV7km36ktGmR74FiwoNau8g3ikHWOPKrCki3ZbG5\nlYt8O1suHYlko12SjX2kgHGAdCrt6ozZnL3It7L6NKmHbGzal0tVTR0xbbDIN8DkT1bpQfYVp7Gv\nOI0wz1B83LwcO1YnzKWjkGy0S7KxjxQwDpBOpV2dORtXo56BPy3yPXCiaZHvj/vz8fdyb/Ui32Eh\n8VQ3L/LdSeXpKqK9w+1e5NuZc9E6yUa7JBv7SAHjAOlU2iXZNC3yHR0Xgl6nkJpTwva0gjZY5OtC\nv4DedPeObnqSry2D7Xm78bFzka/kol2SjXZJNvaRAsYB0qm0S7JpotcpxF5gka+LQUdEKxb5+rn7\nMDxkCAbFQHpp0yLfo3Ys8pVctEuy0S7Jxj5SwDhAOpV2STbn+uUi36TsYvYdKCEi2Iy3p3OLfPWK\njm4+UQyy9iOvqpB0W9OTfA06A+HmCy/ylVy0S7LRLsnGPlLAOEA6lXZJNuc7Z5Fv1WlSfpqNqaqt\nIya0NYt8PRgaNBB/dz+yyg6yr3g/+4rT6GIOxdv13EW+kot2STbaJdnYRwoYB0in0i7J5uLOLPLt\nfmaR78GmRb4B3u4E+3k4dUxFUQgzhzAsJP7nJ/nm7uRkXRVRXhEYdU1rbiQX7ZJstEuysY8UMA6Q\nTqVdks2lBXi7MzouGJ2isD/Hxra0Ao4WVNItzAt319Yu8o0ip+Io+0uaFvmeeZKv5KJdko12STb2\nkQLGAdKptEuysY9epyM2vGmR74mipkW+PyTn4mrQExlscXrrAD93358W+epJt2Wyq3AvRytP0Csw\nBur0bXwWoi3ImNEuycY+LRUwiqqq6mVsS5soKqpst2MHBJjb9fjCeZKN41RVZUtKPis3ZFNVW094\nkJn5CT2ICLK06rgF1UV8kLGKrLKDuOpdmBI5gTFh16LXSSGjJTJmtEuysU9AgPmir0kB8wvSqbRL\nsnFeRfVpPtpwgC2p+SgKjB/UhRtHRjp9WQmaiqPt+btZc+gLKk+dJMQjiFt63ES0d0TbNVy0iowZ\n7ZJs7CMFjAOkU2mXZNN66YdtvPtVJgWlNfiYXZk7oTsDuge06phuFoXlOz5iS+4OAIYFx3Nj9BQ8\nXZxbPCzajowZ7ZJs7NNSAeP0GpjDhw/j7e3tbJtaRdbAdE6STesFeLszun8IOkUh9VDbLPL1sZiJ\nNsXQ07c7RyuPN29J4GE0EeoZ7PSaG9F6Mma0S7KxT0trYFp8SMSdd955ztdLly5t/v+//OUvrWyW\nEOJKMBr03DgyiifuGkL3Lt7syS7mz29u55tdx2hsdH5CNsornEcG38/MmGnUN9bzn4xEXkp6nRMn\n89qw9UII0aTFAqa+vv6cr7dt29b8/x3wypMQ4iwh/h48ctsA7pwSi0Gn8N/12Tz17i6O5Ds/ra3X\n6RnbdRSPD/0j/QP6cqj8MM/tfJlV2Z9RW3+qDVsvhOjsWpwz/uXU79lFiz3TwosWLWL37t3U19fz\n29/+lr59+7Jw4UIaGhoICAjg+eefx8XFhU8//ZQVK1ag0+mYM2cOs2fPdvJ0hBCOUBSFkf1CiIvx\nZ+W3B/hxfz5PrtjJhMFNi3zdXJy8rOTmzT1957G/JIOVmWv49thGdhcmM7v7DcT595bLSkKIVnPo\nOeOO/KGzbds2srOzWblyJW+++SbPPPMMr7zyCrfddhvvv/8+4eHhJCYmUl1dzZIlS3jnnXd47733\nWLFiBWVlZQ6fiBDCeRaTC/dM78Ufb+lPgLc7X+88xmNvbmdPdlGrjtvbL5bHhj5MQsQ4Kk+f5I2U\nd3l939sU19jaqOVCiM6qxX9elZeX8+OPPzZ/XVFRwbZt21BVlYqKihYPHB8fT79+/QCwWCzU1NSw\nfft2nnjiCQDGjBnD8uXLiYyMpG/fvpjNTSuNBw4cSFJSEmPHjm3ViQkhHNcrwpen7h7CZ1uP8MW2\nIyz+OIWB3QO4bXw3fC1uTh3TRW9ketQk4gMHsDJzNaklGWRuf4GEiHGM7zoKg875W7mFEJ1Xi39y\nWCyWcxbums1mlixZ0vz/LdHr9ZhMJgASExMZNWoUmzdvxsXFBQA/Pz+KioooLi7G19e3+X2+vr4U\nFbX8rz4fHxMGQ/s9MKul27bElSXZXB6/menN5GujWJKYTFJWEelHbMyd3JOpI6LQ686fibUnl4AA\nM33CH2bzkZ28uzeRtYfWkVS0l18PvpXe1u7tcRoCGTNaJtm0TosFzHvvvdfqD1i/fj2JiYksX76c\niRMnNn//YouA7VkcXFpa3ep2XYzcm69dks3l5aaDB2f3Y8u+PD787gBvrEnlm21HmJ8QS3jQz3/w\nOppLrEdPHhvyR9YeWsemE9t44ruXGBI0kJtipmF28WyPU+m0ZMxol2Rjn5aKvBbXwJw8eZJ33nmn\n+esPPviAG264gfvvv5/i4uJLfvCmTZt4/fXXeeONNzCbzZhMJmprawEoKCjAarVitVrPOVZhYSFW\nq/WSxxZCtD+dojAyLikNzTQAACAASURBVIS/3XMNw3oHcTi/kidX7OSDb7OpPV1/6QNchMnozs09\nZvC/gxfQxRzKjvwkntj2PJtO/Eij2tiGZyCEuFq1WMD85S9/oaSkBICcnBxefPFFHnnkEYYPH87f\n/va3Fg9cWVnJokWLWLZsWfMD74YPH85XX30FwNdff83IkSOJi4sjJSWFiooKqqqqSEpKYvDgwW1x\nbkKINmLxaFrk+/AvFvnuzb70P2RaEm7pwsLB9zG72w2oqsoHmav5x+4lHKs80UYtF0JcrVrcSmD2\n7Nl89NFHALz++uvk5uby5JNPAjBv3rwWLzGtXLmSxYsXExkZ2fy95557jscee4xTp04REhLCs88+\ni9FoZN26dbz11lsoisLcuXO5/vrrW2y0bCXQOUk22nC6roHPfjzCl9uO0NCoMqxvMDNHRjq9yPeM\n8lMVfJy9lt2FySgojA4bzrSoSbgbWnfczkzGjHZJNvZxei+k+fPns2LFCgDuuusuZs2axZQpUwC4\n4447ePfdd9u4qfaRAqZzkmy05URxFe+uyyD7eDmuLnpuvDaS8YPD0OscejrDedJtWXyYuYbCmmK8\nXMzM7HY9A6395NkxTpAxo12SjX2cXgPT0NBASUkJR48eZc+ePYwYMQKAqqoqampq2raVQogOJdTf\ng0duH8j9c/pj1OtYueEAT7y9iwMnylt13J6+3fl/Qx5kauQEquprWL7/PyxJfovC6tZdrhJCXF1a\n3MzRz8+PX/3qV7z33nvce++9DB8+nNraWm699VZmzpzZ/JyXy002c+ycJBvtURSFvt2tDIz2paqm\njtQcG5v25VFaeYqYMC9cjM497kCv09PNJ5pB1jgKq4tIt2WxJXc7jWojkZau6HXt9xiFq4mMGe2S\nbOzT0maOLV5CAqirq+PUqVN4ev58e+PmzZu59tpr266FDpJLSJ2TZKNNZ+eSfbyM977K5HhRFZ7u\nRuaMiWFE36BWXf5RVZWkwn18nP0p5acrsbr7M6fHjfT0lWfHXIqMGe2SbOzj9BqY3NzcFg8cEhLi\nfKtaQQqYzkmy0aZf5lLf0Mj6Xcf5ZHMOp+oa6B7mxbxJPQgNaN0zXmrqa/n80Nd8f3wLKiqDrHHM\n7DYdL1dLa0/hqiVjRrskG/s4XcDExsYSGRlJQEAAcP5mjrKIV1xOko02XSwXW0Ut76/PJimrCL1O\nYWJ8F64fEYmrS+su/xyrPMF/M1dxpOIYbno3pkdNYlTYMHRK6xYPX41kzGiXZGMfpwuYTz75hE8+\n+YSqqiqmTp3KtGnTznns/5UiBUznJNlo06VyST5QzH++yaK4vBY/iyu3je/OgO4BrfrMRrWRLbnb\n+eTgOmrqa+hiDuXWHjcRbunSquNebWTMaJdkYx+nC5gz8vLyWL16NWvXriU0NJQbbriBCRMm4OZ2\nZZ7PIAVM5yTZaJM9uZyqa+CzrYdZt/0oDY0q/WP8uW1CN/z/f3v3HR/1ce/7/7VFQr2iioS66KL3\n3g3GyAhTjMHJubmOc52ck/jEuXGcOMaHXOfg4zxyfol9bcd2bmwcG0yH0JtopndEUZeQhPoKda20\nu78/jIkL4F3tSjsrfZ7/IaRh1u8Z+Pg7853x97Trz6411rE5ewenS8+jQcPEXmN4LP4RvNzsa7er\nkDmjLsnGOnYXMF+1fv163njjDUwmE2fPnrW7c+0hBUz3JNmoyZZcSiob+HjvTW4U1uCu1/LY+Fhm\nj+qNXmff8k+mIYd1NzdT2liOr7sPaYnzGBk2tNufHSNzRl2SjXXsLmBqa2vZtm0bmzZtwmQykZqa\nyrx585x2Z5EUMN2TZKMmW3OxWCyczChj3cEsahtbiezpzYpZyfTpHWhXP9rMbRwoPMKu/AO0mltJ\nDkxkSfLjhHt337vVZM6oS7KxTrsLmGPHjrFx40auXr3KrFmzSE1NJTnZ+a8uSgHTPUk2ampvLg3N\nrWw6nEv6hWIswLiB4Syemoift7td/alsqmZ95lauVl1Hp9Exs/dkZsdOx13nZle7rkjmjLokG+vY\n9RZSbGwsgwcPRnuf48F///vfO6aHNpICpnuSbNRkby65JbV8tOcGhWX1eHvoWTglgUmDI9HaeXbM\n5coMPsvcSk3LHYI9glicnMrAnv3a3aYrkjmjLsnGOu0uYE6fPg2AwWAgMPDrj3eLiopIS0tzUBdt\nIwVM9yTZqMkRuZjMZg6eL2bzkVyajSYSIv1YMbsPvcMe/JeXNZrbWtiZv49Dt45htpgZEjKQJ5Lm\nE+gRYFe7rkLmjLokG+u0u4A5e/Yszz//PC0tLQQFBfHuu+8SExPDxx9/zF/+8heOHDnSIR3+LlLA\ndE+SjZocmYuhroV1B7M4fb0cjQZmDI/m8YlxePbQ29Vucf1t1t7cTO6dfNx17syLm8WUqPFd/koC\nmTPqkmys0+4C5qmnnuI//uM/SEhI4MCBA3z00UeYzWb8/f15+eWXCQsL65AOfxcpYLonyUZNHZFL\nRl41a/bepNzQRICPO0/OSGZEnxC73ioyW8ycvH2OLTk7aGhtpJdPBEv7LCDeP9ZxHVeMzBl1STbW\nafdt1FqtloSEBACmT59OcXExTz/9NG+++abTihchRNc3IC6IVT8YReqEOOqb2nh7y1X++Nklyg2N\n7W5Tq9EyLnIkvx39C8ZFjKS4/jZ/OPd/+fv1DdS3Njiw90KIzvDQAuab/7cTERHBzJkzO7RDQggB\n4KbXkTohjlU/GMWAuCCu5lXzm/dPs+1YHq1t5na36+PuzVP9FvHvw54j0jucz2+fZtXJNzhRcgYb\nj8USQjiRTadHdfdDoYQQnS8syIt/XzyYH6UOwNtTz5Zjefz2g1Nk5Ffb1W5CQCwvjvwpCxIfxWhu\n5eMb6/nj+bcpqS91UM+FEB3poXtgBg0aRHBw8L1fV1VVERwcjMViQaPRkJ6e3hl9/BbZA9M9STZq\n6sxcmlra2Hw0lwPnirBYYFS/UJZOTyLAp4dd7Rqaa1iftY1LFVfRarRMiRrP3LiZeOqdc12Ko8ic\nUZdkY512b+ItLi5+aMO9evVqf6/sIAVM9yTZqMkZuRSU1vHRnpvk3a7Fs4eOtEkJTB3aC63WvqfE\nVyuvsz5zK5XN1fi7+7IgcR4jwoa47NNnmTPqkmys49C7kFQgBUz3JNmoyVm5mM0WDl8qYWN6Do0t\nbcSE+/L07D7ERfjZ1W6rqZV9hensLThEq7mNpIB4Fic/TqRPuIN63nlkzqhLsrHOwwoY3cqVK1d2\nXlcco7HR2GFte3v36ND2RftJNmpyVi4ajYa4CD/Gp0RQ22Dkal41Ry+VUNtoJKmXP2769p3xotPq\nSApMYETYUKqaq7lencnxklM0tzUT598bvda+M2k6k8wZdUk21vH2fvDysDyB+QapitUl2ahJlVxu\nFBhYs/cmt6sa8fN2Z8m0RMb0D7N7+edK5TXWZ26j6u6yUlriPIa7yLKSKtmIb5NsrCNPYGwgVbG6\nJBs1qZJLzwBPJg+JxN1Ny7W8as7cKCer6A7xkX74erX/gsgwrxDGR45Gp9Vxw5DF+fLLZNXk0ts3\nCl93Hwd+AsdTJRvxbZKNdR72BEYKmG+QQaUuyUZNKuWi1WpIjg5gdP8wKgxNXM2r5vDFEtpMZhIi\n/dHpbDo54h6dVkdyYAIjw4bcXVbK+mJZydRMnJ+6y0oqZSO+TrKxjiwh2UAe66lLslGTqrlYLBYu\nZFXy932ZGOpa6OnvwfJZyaQk9LS77a8vK/mRljSP4aGDlVtWUjUbIdlYS5aQbCBVsbokGzWpmotG\noyEi2JvJQyIxmSxczavmREYZReX1JPbyt+uCyHvLShrt3WWlS2TX5BHjF63UspKq2QjJxlqyhGQD\nGVTqkmzUpHouep2WAXFBDEsO4VZFPRl3l5Xc9FriInzRtvOpyZfLSiNCh1DZVMV1QxbHFFtWUj2b\n7kyysY4sIdlAHuupS7JRkyvlYrZYOH75NuvTc6hvaiUqxIenZ/chMcrf7ra/WFbaSlWzAX93PxYm\nzWOYk5eVXCmb7kaysY4sIdlAqmJ1STZqcqVcNBoNMeG+TBwcSX1T6xdnx1y+TXVtM0lRAbi7te/s\nGPhyWWnMvWWlc+WXyL6TT6xfFD5OWlZypWy6G8nGOrKEZAMZVOqSbNTkirm4u+kYmhRC/9hA8m/X\n3itkfLzciA71afdTk68uK1U0VXGjOpNjJadoMbUQ5xfT6ctKrphNdyHZWEeWkGwgj/XUJdmoydVz\naTOZ2X+2iK3H8mhpNZEY5c/ymcn0Dnvwo2trWCwWrlReY0PWNqqaDQT08CctcR7DQlM6bVnJ1bPp\nyiQb68gSkg2kKlaXZKMmV89Fq9WQGOXPuIHhVN1p/mKT76US6ptaSezl1+4rCTQaDWHeoYyPHIPW\nSctKrp5NVybZWEeewNhAqmJ1STZq6mq5XM2t4u/7MikzNOHn5caiqYmMGxhu91OTisYq1mdtJaPq\nBlqNlunRk3gkdjoe+gf/BW2vrpZNVyLZWEduo7aBDCp1STZq6oq5tLaZ2XumkO3H8zG2mR2+rLQ+\naxvVd5eVFiY9xtCQQR2yrNQVs+kqJBvryBKSDeSxnrokGzV1xVx0d68kGDsgnOrafy4rNTS1kuCA\nZaUJkaPRajTcqM7kXPklcu7kE+MXjY+7t0M/R1fMpquQbKzjtLeQMjMzWbJkCVqtlpSUFHJycvjX\nf/1XNm/ezPnz55k0aRJarZZt27bx0ksvsWHDBjQaDQMGDHhou1LAdE+SjZq6ci5eHnpG9QsjIdKP\n3JJaruRWc+zybXy93B3wtlIiw8OGUNFUyY27dyu1mIzEOvAQvK6cjauTbKzzsAKmfTebWaGxsZFV\nq1YxduzYe1974403+OEPf8jHH39MREQEu3btorGxkbfeeou//e1vrFmzhg8//JCampqO6pYQQths\nYHww//GD0aRNiqfZaOKDHdf5z7+fp7DMviWAUK+ePJfyP/jhoO/h38OPfYXprDr1BufLL+OCq/tC\ndKoOK2Dc3d157733CA0Nvfe1goICUlJSAJg4cSLHjx/n0qVLDBo0CF9fXzw8PBg2bBjnz5/vqG4J\nIUS7uOm1zBsXy++eGc3w5BCyiu7w6t/O8Mm+TBqbW9vdrkajYXDIAF4e/XMeiZ1OvbGeD65+zJsX\n36e0odyBn0CIrqXDTlXS6/Xo9V9vPjk5mcOHD/P4449z9OhRKisrqaysJCgo6N73BAUFUVFR8dC2\nAwO90LdzDdoaD9s0JJxLslFTd8olJMSXlYmhnL9RzrubL7P/XBFnb1bwL4/1Z+rwaLs24/6P8CeY\n038S/+/8Oi6WXuO1M3/ksT4zSOs/p91vK3WnbFyNZGOfTj0W8pe//CUrV65k06ZNjBo16r6PSK15\nbGowNHZE9wDZGa4yyUZN3TWX6GBPXvn+SPacLuQfn+fzx08v8I+juTxl59tKejz5n/2+x+WQDNZn\nbmPL9T2k555s19tK3TUbVyDZWOdhRV6nFjARERG8++67ABw9epTy8nJCQ0OprKy89z3l5eUMGTKk\nM7slhBDt8uWy0pgBYaw7kM25zApe/dsZpg+L4vGJ8Xh5tO+v2C+WlQbSLyiZPfkH2V94mA+ufkzf\nwCQWJ6cS5h363Y0I0cV12B6Y+/nTn/5Eeno6AJs2bWLatGkMHjyYK1euUFtbS0NDA+fPn2fEiBGd\n2S0hhLBLT39Pfpw2iOcXDyYkwJP954p46b2THL9y267NuO46dx5LeIRfj/53+gUlc8OQxf85/Ue2\n5uyixSRvsIjurcMOsrt69SqrV6+muLgYvV5PWFgYL7zwAqtWrcJisTBixAh+9atfAbB7924++OAD\nNBoNy5cvZ/78+Q9tWw6y654kGzVJLl/X2ma+t6xkbDOTFOXP8ll9iA617+oAi8XCpcoMNmRuw9BS\nQ2CPABYmPcaQkIEPXFaSbNQl2VhHTuK1gQwqdUk2apJc7q/yThNrD2RzPrMCrUbDtGG97FpW+pLR\nZLy3rNRmMdEvKJlFyamEeYV863slG3VJNtaRAsYGMqjUJdmoSXJ5uCt371YqNzTh5+3OoikJDrlb\nqayxgvWZW7lenYlOo2N67y/uVuqhc7/3PZKNuiQb60gBYwMZVOqSbNQkuXy31jYzu08XsqMTlpWe\nSHqMwXeXlSQbdUk21pG7kGwgxzurS7JRk+Ty3XRaDX2iAxgzIIyq2hYy8qo5crGE+qZWEnr546Zv\n3/sUGo2GcO9QxvcaDcD16kzOll8kr7aQGL9oQgMCJRtFybyxzsOuEpAnMN8gVbG6JBs1SS62++ay\n0uKpCYwd4NhlJb1Gx7y+M5gYMqHdh+CJjiPzxjqyhGQDGVTqkmzUJLm0T4cuK1VcZUPWdgwtNQT0\n8GdBwlyGhw2xu0ASjiPzxjpSwNhABpW6JBs1SS72+dbbSsN78fgE+99WajEZOVZxnG039tFmbiPB\nP45FyalE+0Y6qOfCHjJvrCN7YGwg65LqkmzUJLnYx8vDjVH9woiP9COn5A5Xcqs5duU2ft5uRIX4\ntPupiV6rY3RcCv19+2NoruG6IZPjJaeoNdYT698b96+8rSQ6n8wb6zxsD4wUMN8gg0pdko2aJBfH\nCAv0YvKQXrjptVzPr+bMjQquFxiIDffD37t9xYa3dw8w6hgeNoR4vxgK6oq4Vn2Tz0tO00PXg2if\nSLSaTj2QXdwl88Y6sonXBvJYT12SjZokF8dz1LLSN7MxmU0cLjrOjrz9NJuaifQOZ1FyKsmBCY7+\nCOI7yLyxjuyBsYEMKnVJNmqSXDrO5ZwqPtnf/reVHpRNrbGObTm7OXn7LBYsDAtNYUHiowR5BDr6\nI4gHkHljHdkDYwN5rKcuyUZNkkvHCQvyYvKQSNz0unvLSjcKDMRYuaz0oGx66HqQEjKAAcF9Ka4v\n5Xp1JseKT2GxWIj1i0an1XXExxFfIfPGOrKEZAOpitUl2ahJcukc7VlWsiYbs8XM6dLzbMnZSZ2x\nnmCPIBYmzSOl5wB57boDybyxjiwh2UAGlbokGzVJLp3rm8tKS6YmMmZA2H2LDVuyaWprZlf+fg7d\nOobZYqZvYBKLkucT7h3m6I8gkHljLSlgbCCDSl2SjZokl87X2mZi9+lb9w7BS47y56n7HILXnmxK\nG8rZkLWN69WZaDVapkSNZ27cDDz1no78CN2ezBvrSAFjAxlU6pJs1CS5OE9lTROfHsjiQlblfZeV\n2puNxWLhatV1NmRuo7K5Gl83H+YnzGFMxHB57dpBZN5YRwoYG8igUpdkoybJxfku51Txyb5Mymu+\nvqwUGupnVzatplYO3jrK7vwDGM2t9PaNYnFyKnH+MQ7sffck88Y6UsDYQAaVuiQbNUkuamhtM7H7\nVCE7ThTcW1b6yZKh+LjZ/8TE0FzDlpydnC27CMDo8OGkJszFv8eD/3ERDyfzxjpSwNhABpW6JBs1\nSS5q+fqyEkwdGkXqxDh8PN3sbju7Jo/1mVspqi/BQ9eDOXEzmBI1Hr3WvnubuiOZN9aRAsYGMqjU\nJdmoSXJR0+WcKj47lE1JZQM+nm6kTYpn0uBItFr7Xo02W8wcLznN9tzdNLQ2EurVkyeSUhkQ3MdB\nPe8eZN5YRwoYG8igUpdkoybJRV0Bgd58uvsa247n02I00TvUh2Uzk0mODrC77YbWRnbk7eVI0Qks\nWBjUsx9piY8R6tXTAT3v+mTeWEdO4rWBnI6oLslGTZKLunx9PYgM9GTCoAjqG1u5mvfFTdel1Y3E\nR/jh2aP9Sz/uOjcGBPdlcMhAShvKuV6dxfHikxjNrcT69ZZlpe8g88Y6chKvDaQqVpdkoybJRV3f\nzCan+A5/35dJfmkd7m5a5o2NZfaoaNz09l0dYLFYuFBxhU1Z/8DQUoO/ux8LEh9lRNgQOc33AWTe\nWEeWkGwgg0pdko2aJBd13S8bs8XC8cu32XA4h7rGVkICPFg6PYkhiT3tLjaMJiN7C9LZV5hOm7mN\neP9YFienEu3by652uyKZN9aRAsYGMqjUJdmoSXJR18OyaWxuZdvxfA6cK8JktjAwLognZyQREext\n959b2VTNpux/cKniKho0jI8cxWPxj+Djbn/bXYXMG+tIAWMDGVTqkmzUJLmoy5psSiob+HR/Jhn5\nBnRaDdOHRzF/fNxDL4m01o3qLNZnbqW0sRxPvSfz4mcxMXKM3HaNzBtrSQFjAxlU6pJs1CS5qMva\nbCwWCxezKvn0QBaVd5rx83Jj4ZQExg+KQGvnspLJbOJI8Ql25O2lqa2ZSO9wFiXPJzkw0a52XZ3M\nG+tIAWMDGVTqkmzUJLmoy9Zs7l0SeSIfY6uZuAhfls1MJiHS3+6+1Bnr2ZazmxO3z2DBwtDQFNIS\nHyXII9Dutl2RzBvrSAFjAxlU6pJs1CS5qKu92VTXNrM+PYdT18oAGD8onCcmJ+Dv8+BXWq1VUHuL\n9ZlbyastxE3rxqyYKczoPQV3nf0nBbsSmTfWkQLGBjKo1CXZqElyUZe92dwsNPDJ/ixuldfj4a5j\n/vg4ZoyIQq+z734ls8XMmdILbMnZSa2xjiCPQBYmzmNwyMBu89q1zBvrSAFjAxlU6pJs1CS5qMsR\n2ZjNFg5fKmHT4RwamtsIC/Ji2YwkBsUH292/5rZmduUf4NCtY5gsJvoEJvJE0nwifcLtblt1Mm+s\nIwWMDWRQqUuyUZPkoi5HZlPf1MqWo7kculCMxQJDEnuyZHoiYYFedrdd1lDOhqztXKu+iVajZXLU\nOObGzsTLzdMBPVeTzBvrSAFjAxlU6pJs1CS5qKsjsrlVXs+n+zO5UViDXqdh1sjezBsXg4e7fa9d\nWywWrlZdZ0PWdiqbqvBx82Z+wiOMjRiJVmPfkpWKZN5YRwoYG8igUpdkoybJRV0dlY3FYuHszQrW\nHcyiuraFAB93Fk1NZEz/MLv3sLSa2zhUeJRdBQcwmoz09u3FouTHifePcVDv1SDzxjpSwNhABpW6\nJBs1SS7q6uhsWlpN7DpZwM6ThbSZzCRG+fPUjGRiwh/8j461alrusCV7J2fKLgAwKnwYqQlzCOhh\n/yvdKpB5Yx2n3UadmZnJkiVL0Gq1pKSkcObMGV544QW2bt3Knj17mDRpEh4eHrz//vu89tprrF+/\nnrCwMGJjYx/artxG3T1JNmqSXNTV0dnodVr6xgQyZkAYhtoWMvKqOXKxhJr6FuIj/ejh1v4Tdz30\nHgwJHUTfwCSK6oq5Xp3JseKTWCwWYvyiXP40X5k31nHKbdSNjY08++yzxMbG0qdPH5YvX05aWhpv\nvPEG8fHxvPPOO2i1WubMmcNPf/pT1q5dS319PcuWLWPHjh3odA8enPIEpnuSbNQkuairs7O5ll/N\nJ/uzKKlswKuHnscnxjF1WC90Wvtfuz55+yzbcnZT11pPYI8AHk+Yw3AXvu1a5o11HvYEpsN2Rrm7\nu/Pee+8RGhp672uBgYHU1NQAcOfOHQIDAzl16hQTJ07E3d2doKAgevXqRXZ2dkd1SwghRAfpHxvE\nyn8ZyZMzkrAAn+zPYuVfz3Atv9qudrUaLeMiR/HK2P/NrJip1Bnr+H/XPuUP5/4veXcKHdN54XI6\nrIDR6/V4eHh87WsvvfQSP/7xj5k9ezbnzp1jwYIFVFZWEhQUdO97goKCqKio6KhuCSGE6EB6nZaZ\nI6L5/bNjmDQ4kpLKBt5Ye5G3Nl+hsqbJrrY99R6kJszh5TEvMDRkEHm1Bbxx7k3+lvEphuYaB30C\n4Srsv27UBqtWreLNN99k+PDhrF69mk8++eRb32PNilZgoBd6fcetfz7skZVwLslGTZKLupyVTQjw\ni6eDWXCrhnc3X+bczQqu5FSxcFoSaVMT7XrtOgRf+vV+jmvlWXx4cT1nyi5wqfIq8/vOYn7fmXjo\n7b/yoDPIvLFPpxYwN2/eZPjw4QCMGzeO7du3M2bMGPLy8u59T1lZ2deWne7HYGjssD7KuqS6JBs1\nSS7qUiEbfw8dv1g6hJPXyvjsUDaf7r3J3pP5LJ6WxIg+IXbtYQnRhPPvQ37MqdLzbMvZxYaMHezP\nPkZqwhxGhA1R+vwYFbJxBU7ZA3M/PXv2vLe/5cqVK8TExDBmzBjS09MxGo2UlZVRXl5OYmL3vmZd\nCCG6Eo1Gw9gB4bz2zBjmjonhToORt7dc5b8+vUBReb1dbWs1WsZGjOCVMf+bR2KmUd/awIfX1vLG\n2bfIvZPvmA8glNRhbyFdvXqV1atXU1xcjF6vJywsjOeff57XX38dNzc3/P39ee211/Dz82PNmjVs\n374djUbDz372M8aOHfvQtuUtpO5JslGT5KIuVbMpq25k7YEsLuVUodHAtKFRpE6Mw8fT/hupq5oM\nbM3ZybnySwAMDx1MasJcgj0D7W7bkVTNRjVykJ0NZFCpS7JRk+SiLtWzuZxTxacHsiirbsTH040F\nk+KZPDgSrdb+V6NzavLZmLWdgrpbuGn1TI+exMyYqcrsj1E9G1VIAWMDGVTqkmzUJLmoyxWyaTOZ\n2X+2iK3H82gxmugd6sOymckkRwfY3bbZYuZM6QW25uzijrEWf3dfHkuYw+jwYU7fH+MK2ahAChgb\nyKBSl2SjJslFXa6UTU19CxvTczh+tRSA0f3DWDQlgSA/j+/4ye/WYjKyvyCdfYWHaTW30tu3FwuT\n5pMYEGd32+3lStk4kxQwNpBBpS7JRk2Si7pcMZuckjt8si+TvNt1uLtpmTc2ltmjonFzwNEZhuYa\ntubsune/0tDQFB5PmEtPz6Dv+EnHc8VsnEEKGBvIoFKXZKMmyUVdrpqN2WLh+JXbbEzPobaxlZAA\nD5ZOS2JIUk+HXB2Qd6eADVnbya8tRK/VMy16IrNipuKpt/9pj7VcNZvOJgWMDWRQqUuyUZPkoi5X\nz6axuY1tx/M4cK4Ik9lCv5hAlk5PIjrUx+62LRYLZ8susjVnF4aWGnzdfZgf/whjIkZ0yv4YV8+m\ns0gBYwMZVOqSP7lQ4wAAFthJREFUbNQkuairq2RTUtnAuoPZXMn94rXrSYMjWTAxHj9vd7vbNpqM\nHCg8wt6CQxjNrUT5RLIw6TGSAxMc0PMH6yrZdDQpYGwgg0pdko2aJBd1dbVsLudUse5gFrerGvHs\noWPeuFhmDI/GTW//E5Oaljtsy9nNqdJzAAwOGciChEcJ8Qq2u+376WrZdBQpYGwgg0pdko2aJBd1\ndcVs2kxmDl8sYcvRXBqa2wgJ8GDx1ESGJdt3LcGXCmpvsSFrO7l38tFrdEyJnsAjsdPw1Hs6oPf/\n1BWz6QhSwNhABpW6JBs1SS7q6srZNDS3sv14/r39MX2iA1g6PYmYcPsvSLRYLJwvv8yWnJ1UNxvw\ncfNmXvxsxkeOctj+mK6cjSNJAWMDGVTqkmzUJLmoqztkU1rdyGcHs7mYXYkGGD8ogrTJ8QT42H/i\nrtHUysFbR9lTcBCjyUikdzgLkx6jb1CS3W13h2wcQQoYG8igUpdkoybJRV3dKZuM/GrWHsiiuKKB\nHm46Hh0bw6yR0bi72X9+zJ2WWrbn7uHk7bNYsDCoZ3/SEh8l1Cuk3W12p2zsIQWMDWRQqUuyUZPk\noq7ulo3JbObopdtsPppLXWMrwX49WDQ1kZF9Qx2yP6awroiNWdvJrslDp9ExOWocc2Kn4+XmZXNb\n3S2b9pICxgYyqNQl2ahJclFXd82msbmNHSfy2Xf2Fm0mC4m9/Fk6PYn4SD+727ZYLFysuMrm7B1U\nNVfj7ebFvLhZjI8cjU5r/dOe7pqNraSAsYEMKnVJNmqSXNTV3bMpNzSy/lAO5zIrABg7IJyFk+Md\ncr9Sq6mV9KLj7M4/QLOphXDvMBYmzqN/cB+rfr67Z2MtKWBsIINKXZKNmiQXdUk2X7hZaODTA1kU\nltXjrtcyZ0wMj4zuTQ8H7I+pNdbxj9w9fF5yBgsWBgT3JS1xHuHeoQ/9OcnGOlLA2EAGlbokGzVJ\nLuqSbP7JbP7ifqVNR3K502Ak0LcHT0xOYPSAMLQO2B9TVFfCxqztZNbkoNVomdRrLHPjZuL9gP0x\nko11pICxgQwqdUk2apJc1CXZfFtTSxs7Txaw5/Qt2kxm4iJ8eXJ6Mol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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "IGINhMIJ5Wyt", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "BAGoXFPZ5ZE3", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "minimal_features = [\n", + " \"median_income\",\n", + " \"latitude\",\n", + "]\n", + "\n", + "minimal_training_examples = training_examples[minimal_features]\n", + "minimal_validation_examples = validation_examples[minimal_features]\n", + "\n", + "_ = train_model(\n", + " learning_rate=0.01,\n", + " steps=500,\n", + " batch_size=5,\n", + " training_examples=minimal_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=minimal_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "RidI9YhKOiY2", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Make Better Use of Latitude\n", + "\n", + "Plotting `latitude` vs. `median_house_value` shows that there really isn't a linear relationship there.\n", + "\n", + "Instead, there are a couple of peaks, which roughly correspond to Los Angeles and San Francisco." + ] + }, + { + "metadata": { + "id": "hfGUKj2IR_F1", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 365 + }, + "outputId": "59ad9796-2b7b-4d70-dcab-50000792a0b8" + }, + "cell_type": "code", + "source": [ + "plt.scatter(training_examples[\"latitude\"], training_targets[\"median_house_value\"])" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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48803AQBTU1Og6fiutrq6Gh6PByMjI3C73YnPuN1ueDzKP1aXyw6LJbc/lJff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\nJjmUMRZVO1i12spSXhIfjSI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MhLgIhkaD+OItC9A3MIHPL/tl25luuG6WLimcXHD0jAfV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\nG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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "6N0p91k2iFCP", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Try creating some synthetic features that do a better job with latitude.**\n", + "\n", + "For example, you could have a feature that maps `latitude` to a value of `|latitude - 38|`, and call this `distance_from_san_francisco`.\n", + "\n", + "Or you could break the space into 10 different buckets. `latitude_32_to_33`, `latitude_33_to_34`, etc., each showing a value of `1.0` if `latitude` is within that bucket range and a value of `0.0` otherwise.\n", + "\n", + "Use the correlation matrix to help guide development, and then add them to your model if you find something that looks good.\n", + "\n", + "What's the best validation performance you can get?" + ] + }, + { + "metadata": { + "id": "wduJ2B28yMFl", + "colab_type": "code", + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 640 + }, + "outputId": "26cef08b-62bb-4a51-e818-64a10fe4dbed" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Train on a new data set that includes synthetic features based on latitude.\n", + "#First create a seperate feature function which will add a feature for each set of latitudes (Binning) and add 1 if the house exists for the latitude and 0 if the house doesn't exists in the given latitude and longitude\n", + "def new_features_for_training(prev_df):\n", + " new_df = pd.DataFrame()\n", + " new_df[\"median_income\"]=prev_df[\"median_income\"]\n", + " latitude_sets = zip(range(32,44),range(33,45))\n", + " for i in latitude_sets:\n", + " new_df[\"latitude_%d_to_%d\" %i] = prev_df[\"latitude\"].apply(lambda l: 1.0 if l>= i[0] and l < i[1] else 0.0) \n", + " return new_df\n", + "\n", + "new_df_training = new_features_for_training(training_examples)\n", + "new_df_validation = new_features_for_training(validation_examples)\n", + "\n", + "train = train_model(learning_rate = 0.01,\n", + " steps = 500,\n", + " batch_size=500,\n", + " training_examples=new_df_training,\n", + " training_targets=training_targets,\n", + " validation_examples=new_df_validation,\n", + " validation_targets=validation_targets)\n", + "\n" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 226.33\n", + " period 01 : 216.09\n", + " period 02 : 205.94\n", + " period 03 : 195.90\n", + " period 04 : 185.97\n", + " period 05 : 176.17\n", + " period 06 : 166.54\n", + " period 07 : 157.10\n", + " period 08 : 147.88\n", + " period 09 : 138.93\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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QN/d8wpaz20koSOTu2Hn0CuraTK1vXs2djXCM5KJeko16STZXp0kLmK1bt/LW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\nK80VZFhSCQsK7Y9LGjJk3HhHppB8IJ1KXZKNmiQXdd1pNr1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1ajAvXQjRT6SA\nEUIMKKfTyYcffsiUKVP4wQ9+wKuvvkpqauoth9uFhYXxxhtv3PTeDRs2YLFY+MUvfkFHRwdLly5l\n3rx5fPbZZxiNRjZt2kRtbS2LFi0C4P333yczM5P/+q//orOzky1btgz49Qoh/EMKGCGE39ntdtat\nWweAy+Vi6tSpPPLII/zqV7/iRz/60Y3XtbS04HK5gOvHe3zV2bNnefjhhwEwGo2MGzeO8+fPY7PZ\nmDJlCnD9YNbMzEwA5s2bx5tvvslzzz3HggULWL16tV+vUwgxcKSAEUL43RdrYL6submZoKCgW77/\nhaCgoFu+p2naTV+73W40TcPtdt901s8XRVBWVha7du3i+PHj7N69m9dff52NGzfe6eUIIRQgi3iF\nEIPCbDaTnJzMJ598AkBZWRm/+c1v+nzPxIkTOXDgAABtbW2cP3+esWPHkpWVxenTpwGoqqqirKwM\ngJ07d5KXl8fs2bN58cUXqaqqoqenx49XJYQYKHIHRggxaNavX89PfvIT/vjHP9LT08Nzzz3X5+vX\nrVvHCy+8wDe/+U26urp46qmnSE5OZsWKFXz88cesWbOG5ORkxo8fD0B2djYvvvgiwcHBuN1uvvOd\n72AwyH97QgwFchq1EEIIIQKOTCEJIYQQIuBIASOEEEKIgCMFjBBCCCECjhQwQgghhAg4UsAIIYQQ\nIuBIASOEEEKIgCMFjBBCCCECjhQwQgghhAg4/x/ZthjS8WN8MwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "pZa8miwu6_tQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "PzABdyjq7IZU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Aside from `latitude`, we'll also keep `median_income`, to compare with the previous results.\n", + "\n", + "We decided to bucketize the latitude. This is fairly straightforward in Pandas using `Series.apply`." + ] + }, + { + "metadata": { + "id": "xdVF8siZ7Lup", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def select_and_transform_features(source_df):\n", + " LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n", + " selected_examples = pd.DataFrame()\n", + " selected_examples[\"median_income\"] = source_df[\"median_income\"]\n", + " for r in LATITUDE_RANGES:\n", + " selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n", + " lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n", + " return selected_examples\n", + "\n", + "selected_training_examples = select_and_transform_features(training_examples)\n", + "selected_validation_examples = select_and_transform_features(validation_examples)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "U4iAdY6t7Pkh", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 2268 + }, + "outputId": "f225d61c-0e4a-44f6-e505-2ca13d766e35" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=0.01,\n", + " steps=500,\n", + " batch_size=5,\n", + " training_examples=selected_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=selected_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 226.40\n", + " period 01 : 216.24\n", + " period 02 : 206.18\n", + " period 03 : 196.19\n" + ], + "name": "stdout" + }, + { + "output_type": "error", + "ename": "KeyboardInterrupt", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0mTraceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mtraining_targets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_targets\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mvalidation_examples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mselected_validation_examples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m validation_targets=validation_targets)\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(learning_rate, steps, batch_size, training_examples, training_targets, validation_examples, validation_targets)\u001b[0m\n\u001b[1;32m 64\u001b[0m linear_regressor.train(\n\u001b[1;32m 65\u001b[0m \u001b[0minput_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_input_fn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_period\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 67\u001b[0m )\n\u001b[1;32m 68\u001b[0m \u001b[0;31m# Take a break and compute predictions.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow_estimator/python/estimator/estimator.pyc\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, input_fn, hooks, steps, max_steps, saving_listeners)\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0msaving_listeners\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_listeners_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msaving_listeners\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhooks\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow_estimator/python/estimator/estimator.pyc\u001b[0m in \u001b[0;36m_train_model_default\u001b[0;34m(self, input_fn, hooks, saving_listeners)\u001b[0m\n\u001b[1;32m 1215\u001b[0m return self._train_with_estimator_spec(estimator_spec, worker_hooks,\n\u001b[1;32m 1216\u001b[0m \u001b[0mhooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglobal_step_tensor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1217\u001b[0;31m saving_listeners)\n\u001b[0m\u001b[1;32m 1218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1219\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_train_model_distributed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaving_listeners\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + 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stop_grace_period_secs=stop_grace_period_secs)\n\u001b[0m\u001b[1;32m 509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, session_creator, hooks, stop_grace_period_secs)\u001b[0m\n\u001b[1;32m 932\u001b[0m super(MonitoredSession, self).__init__(\n\u001b[1;32m 933\u001b[0m \u001b[0msession_creator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshould_recover\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 934\u001b[0;31m stop_grace_period_secs=stop_grace_period_secs)\n\u001b[0m\u001b[1;32m 935\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 936\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + 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1123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1124\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_create_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.pyc\u001b[0m in \u001b[0;36m_create_session\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1125\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1126\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1127\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sess_creator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1128\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0m_PREEMPTION_ERRORS\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1129\u001b[0m logging.info('An error was raised while a session was being created. '\n", + "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.pyc\u001b[0m in \u001b[0;36mcreate_session\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 810\u001b[0m \u001b[0;31m# Inform the hooks that a new session has been created.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 811\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hooks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 812\u001b[0;31m \u001b[0mhook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mafter_create_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf_sess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoord\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 813\u001b[0m return _CoordinatedSession(\n\u001b[1;32m 814\u001b[0m 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info.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 194\u001b[0;31m \u001b[0mtrue_graph_def\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_graph_def\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madd_shapes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 195\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_write_plugin_assets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 196\u001b[0m elif (isinstance(graph, graph_pb2.GraphDef) or\n", + "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.pyc\u001b[0m in \u001b[0;36mas_graph_def\u001b[0;34m(self, from_version, add_shapes)\u001b[0m\n\u001b[1;32m 3148\u001b[0m \"\"\"\n\u001b[1;32m 3149\u001b[0m \u001b[0;31m# pylint: enable=line-too-long\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3150\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_as_graph_def\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfrom_version\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_shapes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3151\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.pyc\u001b[0m in \u001b[0;36m_as_graph_def\u001b[0;34m(self, from_version, add_shapes)\u001b[0m\n\u001b[1;32m 3118\u001b[0m \u001b[0mop\u001b[0m \u001b[0;34m=\u001b[0m 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<30207466+joardar-aditya@users.noreply.github.com> Date: Tue, 12 Feb 2019 08:08:02 +0530 Subject: [PATCH 02/13] feature_sets_solved From 60978f8a98546bc9bb0f6f4c27d57b238d0e63b5 Mon Sep 17 00:00:00 2001 From: Aditya Joardar <30207466+joardar-aditya@users.noreply.github.com> Date: Tue, 12 Feb 2019 21:26:56 +0530 Subject: [PATCH 03/13] Solved the feature_cross notebook --- feature_crosses.ipynb | 1565 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1565 insertions(+) create mode 100644 feature_crosses.ipynb diff --git a/feature_crosses.ipynb b/feature_crosses.ipynb new file mode 100644 index 0000000..336dfc8 --- /dev/null +++ b/feature_crosses.ipynb @@ -0,0 +1,1565 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "feature_crosses.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "ZTDHHM61NPTw", + "0i7vGo9PTaZl" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "g4T-_IsVbweU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Feature Crosses" + ] + }, + { + "metadata": { + "id": "F7dke6skIK-k", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Improve a linear regression model with the addition of additional synthetic features (this is a continuation of the previous exercise)\n", + " * Use an input function to convert pandas `DataFrame` objects to `Tensors` and invoke the input function in `fit()` and `predict()` operations\n", + " * Use the FTRL optimization algorithm for model training\n", + " * Create new synthetic features through one-hot encoding, binning, and feature crosses" + ] + }, + { + "metadata": { + "id": "NS_fcQRd8B97", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup" + ] + }, + { + "metadata": { + "id": "4IdzD8IdIK-l", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "First, as we've done in previous exercises, let's define the input and create the data-loading code." + ] + }, + { + "metadata": { + "id": "CsfdiLiDIK-n", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "10rhoflKIK-s", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ufplEkjN8KUp", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1224 + }, + "outputId": "c292ac02-172d-450c-c24f-5e439d74933b" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.6 2616.4 533.7 \n", + "std 2.1 2.0 12.6 2123.9 410.9 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1454.0 297.0 \n", + "50% 34.2 -118.5 29.0 2112.0 432.0 \n", + "75% 37.7 -118.0 37.0 3127.2 640.0 \n", + "max 42.0 -114.3 52.0 37937.0 5471.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1415.3 496.8 3.9 2.0 \n", + "std 1085.1 376.6 1.9 1.1 \n", + "min 6.0 1.0 0.5 0.1 \n", + "25% 786.0 281.0 2.6 1.5 \n", + "50% 1165.0 408.0 3.5 1.9 \n", + "75% 1705.0 598.0 4.8 2.3 \n", + "max 16122.0 5189.0 15.0 55.2 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "oJlrB4rJ_2Ma", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\"\n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "NBxoAfp2AcB6", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "hweDyy31LBsV", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## FTRL Optimization Algorithm\n", + "\n", + "High dimensional linear models benefit from using a variant of gradient-based optimization called FTRL. This algorithm has the benefit of scaling the learning rate differently for different coefficients, which can be useful if some features rarely take non-zero values (it also is well suited to support L1 regularization). We can apply FTRL using the [FtrlOptimizer](https://www.tensorflow.org/api_docs/python/tf/train/FtrlOptimizer)." + ] + }, + { + "metadata": { + "id": "S0SBf1X1IK_O", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " feature_columns,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " feature_columns: A `set` specifying the input feature columns to use.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "1Cdr02tLIK_Q", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 768 + }, + "outputId": "3ec2fe05-99b9-42c9-9f32-e0850ad1f1bb" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 165.51\n", + " period 01 : 120.92\n", + " period 02 : 131.34\n", + " period 03 : 208.18\n", + " period 04 : 129.76\n", + " period 05 : 119.78\n", + " period 06 : 119.61\n", + " period 07 : 119.78\n", + " period 08 : 122.31\n", + " period 09 : 133.58\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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UoIUAAZkx6W6Pn6rQQQQw6lzxLgDkZZ4r5GUdDBFRnzGBIRogh+hAqUGL1GgN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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "i4lGvqajDWlw", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## One-Hot Encoding for Discrete Features\n", + "\n", + "Discrete (i.e. strings, enumerations, integers) features are usually converted into families of binary features before training a logistic regression model.\n", + "\n", + "For example, suppose we created a synthetic feature that can take any of the values `0`, `1` or `2`, and that we have a few training points:\n", + "\n", + "| # | feature_value |\n", + "|---|---------------|\n", + "| 0 | 2 |\n", + "| 1 | 0 |\n", + "| 2 | 1 |\n", + "\n", + "For each possible categorical value, we make a new **binary** feature of **real values** that can take one of just two possible values: 1.0 if the example has that value, and 0.0 if not. In the example above, the categorical feature would be converted into three features, and the training points now look like:\n", + "\n", + "| # | feature_value_0 | feature_value_1 | feature_value_2 |\n", + "|---|-----------------|-----------------|-----------------|\n", + "| 0 | 0.0 | 0.0 | 1.0 |\n", + "| 1 | 1.0 | 0.0 | 0.0 |\n", + "| 2 | 0.0 | 1.0 | 0.0 |" + ] + }, + { + "metadata": { + "id": "KnssXowblKm7", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Bucketized (Binned) Features\n", + "\n", + "Bucketization is also known as binning.\n", + "\n", + "We can bucketize `population` into the following 3 buckets (for instance):\n", + "- `bucket_0` (`< 5000`): corresponding to less populated blocks\n", + "- `bucket_1` (`5000 - 25000`): corresponding to mid populated blocks\n", + "- `bucket_2` (`> 25000`): corresponding to highly populated blocks\n", + "\n", + "Given the preceding bucket definitions, the following `population` vector:\n", + "\n", + " [[10001], [42004], [2500], [18000]]\n", + "\n", + "becomes the following bucketized feature vector:\n", + "\n", + " [[1], [2], [0], [1]]\n", + "\n", + "The feature values are now the bucket indices. Note that these indices are considered to be discrete features. Typically, these will be further converted in one-hot representations as above, but this is done transparently.\n", + "\n", + "To define feature columns for bucketized features, instead of using `numeric_column`, we can use [`bucketized_column`](https://www.tensorflow.org/api_docs/python/tf/feature_column/bucketized_column), which takes a numeric column as input and transforms it to a bucketized feature using the bucket boundaries specified in the `boundaries` argument. The following code defines bucketized feature columns for `households` and `longitude`; the `get_quantile_based_boundaries` function calculates boundaries based on quantiles, so that each bucket contains an equal number of elements." + ] + }, + { + "metadata": { + "id": "cc9qZrtRy-ED", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def get_quantile_based_boundaries(feature_values, num_buckets):\n", + " boundaries = np.arange(1.0, num_buckets) / num_buckets\n", + " quantiles = feature_values.quantile(boundaries)\n", + " return [quantiles[q] for q in quantiles.keys()]\n", + "\n", + "# Divide households into 7 buckets.\n", + "households = tf.feature_column.numeric_column(\"households\")\n", + "bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " california_housing_dataframe[\"households\"], 7))\n", + "\n", + "# Divide longitude into 10 buckets.\n", + "longitude = tf.feature_column.numeric_column(\"longitude\")\n", + "bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " california_housing_dataframe[\"longitude\"], 10))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "U-pQDAa0MeN3", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Train the Model on Bucketized Feature Columns\n", + "**Bucketize all the real valued features in our example, train the model and see if the results improve.**\n", + "\n", + "In the preceding code block, two real valued columns (namely `households` and `longitude`) have been transformed into bucketized feature columns. Your task is to bucketize the rest of the columns, then run the code to train the model. There are various heuristics to find the range of the buckets. This exercise uses a quantile-based technique, which chooses the bucket boundaries in such a way that each bucket has the same number of examples." + ] + }, + { + "metadata": { + "id": "YFXV9lyMLedy", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " households = tf.feature_column.numeric_column(\"households\")\n", + " longitude = tf.feature_column.numeric_column(\"longitude\")\n", + " latitude = tf.feature_column.numeric_column(\"latitude\")\n", + " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n", + " median_income = tf.feature_column.numeric_column(\"median_income\")\n", + " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n", + " \n", + " # Divide households into 7 buckets.\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"households\"], 7))\n", + "\n", + " # Divide longitude into 10 buckets.\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"longitude\"], 10))\n", + "\n", + " #\n", + " # YOUR CODE HERE: bucketize the following columns, following the example above:\n", + " #\n", + " bucketized_latitude = tf.feature_column.bucketized_column(latitude, boundaries=get_quantile_based_boundaries(training_examples[\"latitude\"], 10))\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(housing_median_age,boundaries=get_quantile_based_boundaries(training_examples[\"housing_median_age\"], 10))\n", + " bucketized_median_income = tf.feature_column.bucketized_column(median_income, boundaries=get_quantile_based_boundaries(training_examples[\"median_income\"], 10))\n", + " bucketized_rooms_per_person =tf.feature_column.bucketized_column(rooms_per_person, boundaries=get_quantile_based_boundaries(training_examples[\"rooms_per_person\"], 10))\n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person])\n", + " \n", + " return feature_columns\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "0FfUytOTNJhL", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 640 + }, + "outputId": "be4a46da-d200-41a3-fedf-25be458106f8" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 170.93\n", + " period 01 : 144.56\n", + " period 02 : 127.90\n", + " period 03 : 116.56\n", + " period 04 : 108.53\n", + " period 05 : 102.54\n", + " period 06 : 98.03\n", + " period 07 : 94.37\n", + " period 08 : 91.46\n", + " period 09 : 89.03\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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cxCWEEELUlASYOvJAwHC0Kg0b4rahUyqZHBKIgkxuJ4QQQtwNCTB1xMXKiQHe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\nD7cuWKHp6vdEJwU3dQ7HFJpsowYrC0xYNVVqWGhi8byJDwt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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ZTDHHM61NPTw", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "JQHnUhL_NRwA", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "You may be wondering how to determine how many buckets to use. That is of course data-dependent. Here, we just selected arbitrary values so as to obtain a not-too-large model." + ] + }, + { + "metadata": { + "id": "Ro5civQ3Ngh_", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " households = tf.feature_column.numeric_column(\"households\")\n", + " longitude = tf.feature_column.numeric_column(\"longitude\")\n", + " latitude = tf.feature_column.numeric_column(\"latitude\")\n", + " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n", + " median_income = tf.feature_column.numeric_column(\"median_income\")\n", + " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n", + " \n", + " # Divide households into 7 buckets.\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"households\"], 7))\n", + "\n", + " # Divide longitude into 10 buckets.\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"longitude\"], 10))\n", + " \n", + " # Divide latitude into 10 buckets.\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " latitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"latitude\"], 10))\n", + "\n", + " # Divide housing_median_age into 7 buckets.\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " housing_median_age, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"housing_median_age\"], 7))\n", + " \n", + " # Divide median_income into 7 buckets.\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " median_income, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"median_income\"], 7))\n", + " \n", + " # Divide rooms_per_person into 7 buckets.\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " rooms_per_person, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"rooms_per_person\"], 7))\n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "RNgfYk6OO8Sy", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "AFJ1qoZPlQcs", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Feature Crosses\n", + "\n", + "Crossing two (or more) features is a clever way to learn non-linear relations using a linear model. In our problem, if we just use the feature `latitude` for learning, the model might learn that city blocks at a particular latitude (or within a particular range of latitudes since we have bucketized it) are more likely to be expensive than others. Similarly for the feature `longitude`. However, if we cross `longitude` by `latitude`, the crossed feature represents a well defined city block. If the model learns that certain city blocks (within range of latitudes and longitudes) are more likely to be more expensive than others, it is a stronger signal than two features considered individually.\n", + "\n", + "Currently, the feature columns API only supports discrete features for crosses. To cross two continuous values, like `latitude` or `longitude`, we can bucketize them.\n", + "\n", + "If we cross the `latitude` and `longitude` features (supposing, for example, that `longitude` was bucketized into `2` buckets, while `latitude` has `3` buckets), we actually get six crossed binary features. Each of these features will get its own separate weight when we train the model." + ] + }, + { + "metadata": { + "id": "-Rk0c1oTYaVH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Train the Model Using Feature Crosses\n", + "\n", + "**Add a feature cross of `longitude` and `latitude` to your model, train it, and determine whether the results improve.**\n", + "\n", + "Refer to the TensorFlow API docs for [`crossed_column()`](https://www.tensorflow.org/api_docs/python/tf/feature_column/crossed_column) to build the feature column for your cross. Use a `hash_bucket_size` of `1000`." + ] + }, + { + "metadata": { + "id": "-eYiVEGeYhUi", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " households = tf.feature_column.numeric_column(\"households\")\n", + " longitude = tf.feature_column.numeric_column(\"longitude\")\n", + " latitude = tf.feature_column.numeric_column(\"latitude\")\n", + " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n", + " median_income = tf.feature_column.numeric_column(\"median_income\")\n", + " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n", + " \n", + " # Divide households into 7 buckets.\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"households\"], 7))\n", + "\n", + " # Divide longitude into 10 buckets.\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"longitude\"], 10))\n", + " \n", + " # Divide latitude into 10 buckets.\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " latitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"latitude\"], 10))\n", + "\n", + " # Divide housing_median_age into 7 buckets.\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " housing_median_age, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"housing_median_age\"], 7))\n", + " \n", + " # Divide median_income into 7 buckets.\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " median_income, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"median_income\"], 7))\n", + " \n", + " # Divide rooms_per_person into 7 buckets.\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " rooms_per_person, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"rooms_per_person\"], 7))\n", + " \n", + " # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n", + " long_x_lat = tf.feature_column.crossed_column([bucketized_longitude, bucketized_latitude], 1000)\n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person,\n", + " long_x_lat])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "xZuZMp3EShkM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 640 + }, + "outputId": "798af178-f18a-45cc-bd09-870ec51db1f6" + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 164.03\n", + " period 01 : 135.74\n", + " period 02 : 118.66\n", + " period 03 : 107.39\n", + " period 04 : 99.54\n", + " period 05 : 93.75\n", + " period 06 : 89.23\n", + " period 07 : 85.71\n", + " period 08 : 82.92\n", + " period 09 : 80.59\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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AS4+uwMvDkQGdmpKeU8LG2GQrVyYiIlJ/KcDUsSD3QLr5d+J4QTK70vYwqlcL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\nLo2cN4mRAiYJ0qnUJdmoSXJR10JmE5wOcnb4XHzY6cJoNxE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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "0i7vGo9PTaZl", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "3tAWu8qSTe2v", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " households = tf.feature_column.numeric_column(\"households\")\n", + " longitude = tf.feature_column.numeric_column(\"longitude\")\n", + " latitude = tf.feature_column.numeric_column(\"latitude\")\n", + " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n", + " median_income = tf.feature_column.numeric_column(\"median_income\")\n", + " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n", + " \n", + " # Divide households into 7 buckets.\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " households, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"households\"], 7))\n", + "\n", + " # Divide longitude into 10 buckets.\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " longitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"longitude\"], 10))\n", + " \n", + " # Divide latitude into 10 buckets.\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " latitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"latitude\"], 10))\n", + "\n", + " # Divide housing_median_age into 7 buckets.\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " housing_median_age, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"housing_median_age\"], 7))\n", + " \n", + " # Divide median_income into 7 buckets.\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " median_income, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"median_income\"], 7))\n", + " \n", + " # Divide rooms_per_person into 7 buckets.\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " rooms_per_person, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"rooms_per_person\"], 7))\n", + " \n", + " # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n", + " long_x_lat = tf.feature_column.crossed_column(\n", + " set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000) \n", + " \n", + " feature_columns = set([\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person,\n", + " long_x_lat])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "-_vvNYIyTtPC", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_ = train_model(\n", + " learning_rate=1.0,\n", + " steps=500,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ymlHJ-vrhLZw", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Optional Challenge: Try Out More Synthetic Features\n", + "\n", + "So far, we've tried simple bucketized columns and feature crosses, but there are many more combinations that could potentially improve the results. For example, you could cross multiple columns. What happens if you vary the number of buckets? What other synthetic features can you think of? Do they improve the model?" + ] + } + ] +} \ No newline at end of file From 5980ee0cb60b9c2bc06a3631276f2e3b4522c48c Mon Sep 17 00:00:00 2001 From: Aditya Joardar <30207466+joardar-aditya@users.noreply.github.com> Date: Tue, 12 Feb 2019 23:38:52 +0530 Subject: [PATCH 04/13] Created using Colaboratory --- feature_crosses.ipynb | 320 +++++++++++++++++++++--------------------- 1 file changed, 157 insertions(+), 163 deletions(-) diff --git a/feature_crosses.ipynb b/feature_crosses.ipynb index 336dfc8..0832001 100644 --- a/feature_crosses.ipynb +++ b/feature_crosses.ipynb @@ -201,7 +201,7 @@ "base_uri": "https://localhost:8080/", "height": 1224 }, - "outputId": "c292ac02-172d-450c-c24f-5e439d74933b" + "outputId": "ee4ca52c-8e70-46ef-8c7f-d8319276a6fa" }, "cell_type": "code", "source": [ @@ -224,7 +224,7 @@ "print(\"Validation targets summary:\")\n", "display.display(validation_targets.describe())" ], - "execution_count": 4, + "execution_count": 24, "outputs": [ { "output_type": "stream", @@ -239,23 +239,23 @@ "text/plain": [ " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", - "mean 35.6 -119.6 28.6 2616.4 533.7 \n", - "std 2.1 2.0 12.6 2123.9 410.9 \n", - "min 32.5 -124.3 1.0 2.0 1.0 \n", - "25% 33.9 -121.8 18.0 1454.0 297.0 \n", - "50% 34.2 -118.5 29.0 2112.0 432.0 \n", - "75% 37.7 -118.0 37.0 3127.2 640.0 \n", - "max 42.0 -114.3 52.0 37937.0 5471.0 \n", + "mean 35.6 -119.6 28.5 2652.5 539.7 \n", + "std 2.1 2.0 12.6 2212.3 425.0 \n", + "min 32.5 -124.3 1.0 8.0 1.0 \n", + "25% 33.9 -121.8 18.0 1465.0 297.0 \n", + "50% 34.3 -118.5 28.0 2129.0 433.0 \n", + "75% 37.7 -118.0 37.0 3148.0 649.0 \n", + "max 41.9 -114.3 52.0 37937.0 6445.0 \n", "\n", " population households median_income rooms_per_person \n", "count 12000.0 12000.0 12000.0 12000.0 \n", - "mean 1415.3 496.8 3.9 2.0 \n", - "std 1085.1 376.6 1.9 1.1 \n", - "min 6.0 1.0 0.5 0.1 \n", - "25% 786.0 281.0 2.6 1.5 \n", - "50% 1165.0 408.0 3.5 1.9 \n", - "75% 1705.0 598.0 4.8 2.3 \n", - "max 16122.0 5189.0 15.0 55.2 " + "mean 1437.1 502.2 3.9 2.0 \n", + "std 1149.5 389.9 1.9 1.1 \n", + "min 3.0 1.0 0.5 0.1 \n", + "25% 788.0 282.0 2.6 1.5 \n", + "50% 1167.0 409.0 3.5 1.9 \n", + "75% 1724.0 605.0 4.8 2.3 \n", + "max 28566.0 6082.0 15.0 55.2 " ], "text/html": [ "
\n", @@ -304,11 +304,11 @@ " mean\n", " 35.6\n", " -119.6\n", - " 28.6\n", - " 2616.4\n", - " 533.7\n", - " 1415.3\n", - " 496.8\n", + " 28.5\n", + " 2652.5\n", + " 539.7\n", + " 1437.1\n", + " 502.2\n", " 3.9\n", " 2.0\n", " \n", @@ -317,10 +317,10 @@ " 2.1\n", " 2.0\n", " 12.6\n", - " 2123.9\n", - " 410.9\n", - " 1085.1\n", - " 376.6\n", + " 2212.3\n", + " 425.0\n", + " 1149.5\n", + " 389.9\n", " 1.9\n", " 1.1\n", " \n", @@ -329,9 +329,9 @@ " 32.5\n", " -124.3\n", " 1.0\n", - " 2.0\n", + " 8.0\n", " 1.0\n", - " 6.0\n", + " 3.0\n", " 1.0\n", " 0.5\n", " 0.1\n", @@ -341,22 +341,22 @@ " 33.9\n", " -121.8\n", " 18.0\n", - " 1454.0\n", + " 1465.0\n", " 297.0\n", - " 786.0\n", - " 281.0\n", + " 788.0\n", + " 282.0\n", " 2.6\n", " 1.5\n", " \n", " \n", " 50%\n", - " 34.2\n", + " 34.3\n", " -118.5\n", - " 29.0\n", - " 2112.0\n", - " 432.0\n", - " 1165.0\n", - " 408.0\n", + " 28.0\n", + " 2129.0\n", + " 433.0\n", + " 1167.0\n", + " 409.0\n", " 3.5\n", " 1.9\n", " \n", @@ -365,22 +365,22 @@ " 37.7\n", " -118.0\n", " 37.0\n", - " 3127.2\n", - " 640.0\n", - " 1705.0\n", - " 598.0\n", + " 3148.0\n", + " 649.0\n", + " 1724.0\n", + " 605.0\n", " 4.8\n", " 2.3\n", " \n", " \n", " max\n", - " 42.0\n", + " 41.9\n", " -114.3\n", " 52.0\n", " 37937.0\n", - " 5471.0\n", - " 16122.0\n", - " 5189.0\n", + " 6445.0\n", + " 28566.0\n", + " 6082.0\n", " 15.0\n", " 55.2\n", " \n", @@ -406,23 +406,23 @@ "text/plain": [ " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", "count 5000.0 5000.0 5000.0 5000.0 5000.0 \n", - "mean 35.6 -119.6 28.6 2709.2 553.2 \n", - "std 2.1 2.0 12.6 2307.8 445.7 \n", - "min 32.5 -124.3 2.0 18.0 3.0 \n", - "25% 33.9 -121.8 18.0 1475.8 297.0 \n", - "50% 34.2 -118.5 29.0 2174.0 437.5 \n", - "75% 37.7 -118.0 37.0 3196.2 668.0 \n", - "max 41.9 -114.6 52.0 32627.0 6445.0 \n", + "mean 35.6 -119.5 28.9 2622.5 538.7 \n", + "std 2.1 2.0 12.6 2100.3 413.0 \n", + "min 32.6 -124.3 2.0 2.0 2.0 \n", + "25% 33.9 -121.7 18.0 1453.0 295.0 \n", + "50% 34.2 -118.5 29.0 2124.0 436.0 \n", + "75% 37.7 -118.0 37.0 3162.0 648.0 \n", + "max 42.0 -114.5 52.0 30401.0 4957.0 \n", "\n", " population households median_income rooms_per_person \n", "count 5000.0 5000.0 5000.0 5000.0 \n", - "mean 1463.8 511.9 3.9 2.0 \n", - "std 1285.6 402.7 1.9 1.3 \n", - "min 3.0 4.0 0.5 0.0 \n", - "25% 798.8 283.0 2.5 1.5 \n", - "50% 1172.0 411.0 3.5 1.9 \n", - "75% 1758.2 619.2 4.7 2.3 \n", - "max 35682.0 6082.0 15.0 52.0 " + "mean 1411.6 498.8 3.9 2.0 \n", + "std 1143.8 371.3 1.9 1.2 \n", + "min 6.0 2.0 0.5 0.0 \n", + "25% 794.8 281.0 2.6 1.5 \n", + "50% 1166.0 410.0 3.5 1.9 \n", + "75% 1718.0 606.0 4.7 2.3 \n", + "max 35682.0 4769.0 15.0 41.3 " ], "text/html": [ "
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+ " 2124.0\n", + " 436.0\n", + " 1166.0\n", + " 410.0\n", " 3.5\n", " 1.9\n", " \n", @@ -532,24 +532,24 @@ " 37.7\n", " -118.0\n", " 37.0\n", - " 3196.2\n", - " 668.0\n", - " 1758.2\n", - " 619.2\n", + " 3162.0\n", + " 648.0\n", + " 1718.0\n", + " 606.0\n", " 4.7\n", " 2.3\n", " \n", " \n", " max\n", - " 41.9\n", - " -114.6\n", + " 42.0\n", + " -114.5\n", " 52.0\n", - " 32627.0\n", - " 6445.0\n", + " 30401.0\n", + " 4957.0\n", " 35682.0\n", - " 6082.0\n", + " 4769.0\n", " 15.0\n", - " 52.0\n", + " 41.3\n", " \n", " \n", "\n", @@ -573,12 +573,12 @@ "text/plain": [ " median_house_value\n", "count 12000.0\n", - "mean 207.8\n", - "std 116.3\n", + "mean 207.5\n", + "std 115.8\n", "min 15.0\n", - "25% 120.0\n", - "50% 180.5\n", - "75% 265.9\n", + "25% 120.1\n", + "50% 180.4\n", + "75% 265.0\n", "max 500.0" ], "text/html": [ @@ -610,11 +610,11 @@ " \n", " \n", " mean\n", - " 207.8\n", + " 207.5\n", " \n", " \n", " std\n", - " 116.3\n", + " 115.8\n", " \n", " \n", " min\n", @@ -622,15 +622,15 @@ " \n", " \n", " 25%\n", - " 120.0\n", + " 120.1\n", " \n", " \n", " 50%\n", - " 180.5\n", + " 180.4\n", " \n", " \n", " 75%\n", - " 265.9\n", + " 265.0\n", " \n", " \n", " max\n", @@ -658,12 +658,12 @@ "text/plain": [ " median_house_value\n", "count 5000.0\n", - "mean 206.0\n", - "std 115.2\n", - "min 15.0\n", - "25% 118.5\n", - "50% 179.8\n", - "75% 263.4\n", + "mean 206.9\n", + "std 116.5\n", + "min 27.5\n", + "25% 118.1\n", + "50% 180.2\n", + "75% 265.4\n", "max 500.0" ], "text/html": [ @@ -695,27 +695,27 @@ " \n", " \n", " mean\n", - " 206.0\n", + " 206.9\n", " \n", " \n", " std\n", - " 115.2\n", + " 116.5\n", " \n", " \n", " min\n", - " 15.0\n", + " 27.5\n", " \n", " \n", " 25%\n", - " 118.5\n", + " 118.1\n", " \n", " \n", " 50%\n", - " 179.8\n", + " 180.2\n", " \n", " \n", " 75%\n", - " 263.4\n", + " 265.4\n", " \n", " \n", " max\n", @@ -919,9 +919,9 @@ "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", - "height": 768 + "height": 640 }, - "outputId": "3ec2fe05-99b9-42c9-9f32-e0850ad1f1bb" + "outputId": "30383cf6-9021-4af9-f19b-df584d9f6f7d" }, "cell_type": "code", "source": [ @@ -935,30 +935,23 @@ " validation_examples=validation_examples,\n", " validation_targets=validation_targets)" ], - "execution_count": 10, + "execution_count": 28, "outputs": [ { "output_type": "stream", "text": [ - "\n", - "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", - "For more information, please see:\n", - " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", - " * https://github.com/tensorflow/addons\n", - "If you depend on functionality not listed there, please file an issue.\n", - "\n", "Training model...\n", "RMSE (on training data):\n", - " period 00 : 165.51\n", - " period 01 : 120.92\n", - " period 02 : 131.34\n", - " period 03 : 208.18\n", - " period 04 : 129.76\n", - " period 05 : 119.78\n", - " period 06 : 119.61\n", - " period 07 : 119.78\n", - " period 08 : 122.31\n", - " period 09 : 133.58\n", + " period 00 : 145.53\n", + " period 01 : 159.50\n", + " period 02 : 116.73\n", + " period 03 : 313.37\n", + " period 04 : 353.00\n", + " period 05 : 316.81\n", + " period 06 : 311.05\n", + " period 07 : 283.78\n", + " period 08 : 265.21\n", + " period 09 : 224.52\n", "Model training finished.\n" ], "name": "stdout" @@ -966,7 +959,7 @@ { "output_type": "display_data", "data": { - "image/png": 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UoIUAAZkx6W6Pn6rQQQQw6lzxLgDkZZ4r5GUdDBFRnzGBIRogh+hAqUGL1GgN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6KQVVhUzsMpYJncc0+9pEwa7+PB76ALZmNqxKWMtPp9ahV/QGy5qaqLlvQi/G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\nDElIJybIv2e2zS7azMKcv4HbpG57Bj3CenL3pHSCA60vBj90ynXpqjW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"text/plain": [ "
" ] @@ -1114,6 +1107,7 @@ " bucketized_median_income = tf.feature_column.bucketized_column(median_income, boundaries=get_quantile_based_boundaries(training_examples[\"median_income\"], 10))\n", " bucketized_rooms_per_person =tf.feature_column.bucketized_column(rooms_per_person, boundaries=get_quantile_based_boundaries(training_examples[\"rooms_per_person\"], 10))\n", " \n", + " \n", " feature_columns = set([\n", " bucketized_longitude,\n", " bucketized_latitude,\n", @@ -1135,7 +1129,7 @@ "base_uri": "https://localhost:8080/", "height": 640 }, - "outputId": "be4a46da-d200-41a3-fedf-25be458106f8" + "outputId": "4ebc66b2-8688-4901-b601-d3e3dc857220" }, "cell_type": "code", "source": [ @@ -1149,23 +1143,23 @@ " validation_examples=validation_examples,\n", " validation_targets=validation_targets)" ], - "execution_count": 17, + "execution_count": 31, "outputs": [ { "output_type": "stream", "text": [ "Training model...\n", "RMSE (on training data):\n", - " period 00 : 170.93\n", - " period 01 : 144.56\n", - " period 02 : 127.90\n", - " period 03 : 116.56\n", - " period 04 : 108.53\n", - " period 05 : 102.54\n", - " period 06 : 98.03\n", - " period 07 : 94.37\n", - " period 08 : 91.46\n", - " period 09 : 89.03\n", + " period 00 : 170.32\n", + " period 01 : 143.98\n", + " period 02 : 127.43\n", + " period 03 : 116.14\n", + " period 04 : 108.01\n", + " period 05 : 101.93\n", + " period 06 : 97.29\n", + " period 07 : 93.64\n", + " period 08 : 90.67\n", + " period 09 : 88.19\n", "Model training finished.\n" ], "name": "stdout" @@ -1173,7 +1167,7 @@ { "output_type": "display_data", "data": { - "image/png": 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cxCWEEELUlASYOvJAwHC0Kg0b4rahUyqZHBKIgkxuJ4QQQtwNCTB1xMXKiQHe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\nJXjyyScxMTGBf/zjH0v+fYlocbDAENGiGxoawq5duwAA4XAYV199Ne6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REal1VGBERETqmI0b11Xqfi+99DwpKclXvP0vf3moqiJVORUYERGROiQ1NYW1a9dU6r4P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\n2KjxVlPvq2V9Xh3VuRXYbrJAo/tmZfQISURE3jV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" ] @@ -1398,7 +1392,7 @@ "base_uri": "https://localhost:8080/", "height": 640 }, - "outputId": "798af178-f18a-45cc-bd09-870ec51db1f6" + "outputId": "b4f97380-8835-4641-bcb8-b6abc11c76b6" }, "cell_type": "code", "source": [ @@ -1412,23 +1406,23 @@ " validation_examples=validation_examples,\n", " validation_targets=validation_targets)" ], - "execution_count": 21, + "execution_count": 33, "outputs": [ { "output_type": "stream", "text": [ "Training model...\n", "RMSE (on training data):\n", - " period 00 : 164.03\n", - " period 01 : 135.74\n", - " period 02 : 118.66\n", - " period 03 : 107.39\n", - " period 04 : 99.54\n", - " period 05 : 93.75\n", - " period 06 : 89.23\n", - " period 07 : 85.71\n", - " period 08 : 82.92\n", - " period 09 : 80.59\n", + " period 00 : 163.60\n", + " period 01 : 135.47\n", + " period 02 : 118.48\n", + " period 03 : 107.22\n", + " period 04 : 99.25\n", + " period 05 : 93.33\n", + " period 06 : 88.78\n", + " period 07 : 85.17\n", + " period 08 : 82.21\n", + " period 09 : 79.75\n", "Model training finished.\n" ], "name": "stdout" @@ -1436,7 +1430,7 @@ { "output_type": "display_data", "data": { - "image/png": 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AS4+uwMvDkQGdmpKeU8LG2GQrVyYiIlJ/KcDUsSD3QLr5d+J4QTK70vYwqlcL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\nKMTDDz/Mk08+yfj4OH/5l3+Jz+ejsrKSyclJALxeL3/1V38FwMTEBNu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nVsE0tfUA4NPdX2MYhsmJREREnI8KjBO6NS4We04oKcXH2ZO13+w4IiIiTkcF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\n8AcX6PbN0D0YoNs3w1u9ft7qTR55KSqw0lZXyhb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" ] From 018b0d7e97d2c16acbf7dc6457e15b4102d16ac0 Mon Sep 17 00:00:00 2001 From: Aditya Joardar <30207466+joardar-aditya@users.noreply.github.com> Date: Sun, 17 Feb 2019 20:40:32 +0530 Subject: [PATCH 05/13] Created using Colaboratory --- logistic_regression.ipynb | 945 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 945 insertions(+) create mode 100644 logistic_regression.ipynb diff --git a/logistic_regression.ipynb b/logistic_regression.ipynb new file mode 100644 index 0000000..b7aad9c --- /dev/null +++ b/logistic_regression.ipynb @@ -0,0 +1,945 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "logistic_regression.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "dPpJUV862FYI", + "i2e3TlyL57Qs", + "wCugvl0JdWYL" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "g4T-_IsVbweU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Logistic Regression" + ] + }, + { + "metadata": { + "id": "LEAHZv4rIYHX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Reframe the median house value predictor (from the preceding exercises) as a binary classification model\n", + " * Compare the effectiveness of logisitic regression vs linear regression for a binary classification problem" + ] + }, + { + "metadata": { + "id": "CnkCZqdIIYHY", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "As in the prior exercises, we're working with the [California housing data set](https://developers.google.com/machine-learning/crash-course/california-housing-data-description), but this time we will turn it into a binary classification problem by predicting whether a city block is a high-cost city block. We'll also revert to the default features, for now." + ] + }, + { + "metadata": { + "id": "9pltCyy2K3dd", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Frame the Problem as Binary Classification\n", + "\n", + "The target of our dataset is `median_house_value` which is a numeric (continuous-valued) feature. We can create a boolean label by applying a threshold to this continuous value.\n", + "\n", + "Given features describing a city block, we wish to predict if it is a high-cost city block. To prepare the targets for train and eval data, we define a classification threshold of the 75%-ile for median house value (a value of approximately 265000). All house values above the threshold are labeled `1`, and all others are labeled `0`." + ] + }, + { + "metadata": { + "id": "67IJwZX1Vvjt", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "Run the cells below to load the data and prepare the input features and targets." + ] + }, + { + "metadata": { + "id": "fOlbcJ4EIYHd", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "lTB73MNeIYHf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Note how the code below is slightly different from the previous exercises. Instead of using `median_house_value` as target, we create a new binary target, `median_house_value_is_high`." + ] + }, + { + "metadata": { + "id": "kPSqspaqIYHg", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Create a boolean categorical feature representing whether the\n", + " # median_house_value is above a set threshold.\n", + " output_targets[\"median_house_value_is_high\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] > 265000).astype(float)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "FwOYWmXqWA6D", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "uon1LB3A31VN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## How Would Linear Regression Fare?\n", + "To see why logistic regression is effective, let us first train a naive model that uses linear regression. This model will use labels with values in the set `{0, 1}` and will try to predict a continuous value that is as close as possible to `0` or `1`. Furthermore, we wish to interpret the output as a probability, so it would be ideal if the output will be within the range `(0, 1)`. We would then apply a threshold of `0.5` to determine the label.\n", + "\n", + "Run the cells below to train the linear regression model using [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor)." + ] + }, + { + "metadata": { + "id": "smmUYRDtWOV_", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\"\n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "B5OwSrr1yIKD", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "SE2-hq8PIYHz", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_regressor_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " \n", + " # Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TDBD8xeeIYH2", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "linear_regressor = train_linear_regressor_model(\n", + " learning_rate=0.000001,\n", + " steps=200,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "JjBZ_q7aD9gh", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Can We Calculate LogLoss for These Predictions?\n", + "\n", + "**Examine the predictions and decide whether or not we can use them to calculate LogLoss.**\n", + "\n", + "`LinearRegressor` uses the L2 loss, which doesn't do a great job at penalizing misclassifications when the output is interpreted as a probability. For example, there should be a huge difference whether a negative example is classified as positive with a probability of 0.9 vs 0.9999, but L2 loss doesn't strongly differentiate these cases.\n", + "\n", + "In contrast, `LogLoss` penalizes these \"confidence errors\" much more heavily. Remember, `LogLoss` is defined as:\n", + "\n", + "$$Log Loss = \\sum_{(x,y)\\in D} -y \\cdot log(y_{pred}) - (1 - y) \\cdot log(1 - y_{pred})$$\n", + "\n", + "\n", + "But first, we'll need to obtain the prediction values. We could use `LinearRegressor.predict` to obtain these.\n", + "\n", + "Given the predictions and the targets, can we calculate `LogLoss`?" + ] + }, + { + "metadata": { + "id": "dPpJUV862FYI", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to display the solution." + ] + }, + { + "metadata": { + "id": "kXFQ5uig2RoP", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n", + "validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + "\n", + "_ = plt.hist(validation_predictions)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "rYpy336F9wBg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Train a Logistic Regression Model and Calculate LogLoss on the Validation Set\n", + "\n", + "To use logistic regression, simply use [LinearClassifier](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearClassifier) instead of `LinearRegressor`. Complete the code below.\n", + "\n", + "**NOTE**: When running `train()` and `predict()` on a `LinearClassifier` model, you can access the real-valued predicted probabilities via the `\"probabilities\"` key in the returned dict—e.g., `predictions[\"probabilities\"]`. Sklearn's [log_loss](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html) function is handy for calculating LogLoss using these probabilities.\n" + ] + }, + { + "metadata": { + "id": "JElcb--E9wBm", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_classifier_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear classification model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearClassifier` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a linear classifier object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_classifier = # YOUR CODE HERE: Construct the linear classifier.\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " \n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss (on training data):\")\n", + " training_log_losses = []\n", + " validation_log_losses = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions. \n", + " training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n", + " training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n", + " \n", + " validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n", + " \n", + " training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_log_losses.append(training_log_loss)\n", + " validation_log_losses.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_log_losses, label=\"training\")\n", + " plt.plot(validation_log_losses, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "VM0wmnFUIYH9", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000005,\n", + " steps=500,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "i2e3TlyL57Qs", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see the solution.\n", + "\n" + ] + }, + { + "metadata": { + "id": "5YxXd2hn6MuF", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_classifier_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear classification model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearClassifier` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a linear classifier object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0) \n", + " linear_classifier = tf.estimator.LinearClassifier(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " \n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss (on training data):\")\n", + " training_log_losses = []\n", + " validation_log_losses = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions. \n", + " training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n", + " training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n", + " \n", + " validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n", + " \n", + " training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_log_losses.append(training_log_loss)\n", + " validation_log_losses.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " \n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_log_losses, label=\"training\")\n", + " plt.plot(validation_log_losses, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "UPM_T1FXsTaL", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000005,\n", + " steps=500,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "i-Xo83_aR6s_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Calculate Accuracy and plot a ROC Curve for the Validation Set\n", + "\n", + "A few of the metrics useful for classification are the model [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification), the [ROC curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) and the area under the ROC curve (AUC). We'll examine these metrics.\n", + "\n", + "`LinearClassifier.evaluate` calculates useful metrics like accuracy and AUC." + ] + }, + { + "metadata": { + "id": "DKSQ87VVIYIA", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n", + "\n", + "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n", + "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "47xGS2uNIYIE", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "You may use class probabilities, such as those calculated by `LinearClassifier.predict`,\n", + "and Sklearn's [roc_curve](http://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics) to\n", + "obtain the true positive and false positive rates needed to plot a ROC curve." + ] + }, + { + "metadata": { + "id": "xaU7ttj8IYIF", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + "# Get just the probabilities for the positive class.\n", + "validation_probabilities = np.array([item['probabilities'][1] for item in validation_probabilities])\n", + "\n", + "false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(\n", + " validation_targets, validation_probabilities)\n", + "plt.plot(false_positive_rate, true_positive_rate, label=\"our model\")\n", + "plt.plot([0, 1], [0, 1], label=\"random classifier\")\n", + "_ = plt.legend(loc=2)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "PIdhwfgzIYII", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**See if you can tune the learning settings of the model trained at Task 2 to improve AUC.**\n", + "\n", + "Often times, certain metrics improve at the detriment of others, and you'll need to find the settings that achieve a good compromise.\n", + "\n", + "**Verify if all metrics improve at the same time.**" + ] + }, + { + "metadata": { + "id": "XKIqjsqcCaxO", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# TUNE THE SETTINGS BELOW TO IMPROVE AUC\n", + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000005,\n", + " steps=500,\n", + " batch_size=20,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "\n", + "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n", + "\n", + "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n", + "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "wCugvl0JdWYL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a possible solution." + ] + }, + { + "metadata": { + "id": "VHosS1g2aetf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "One possible solution that works is to just train for longer, as long as we don't overfit. \n", + "\n", + "We can do this by increasing the number the steps, the batch size, or both.\n", + "\n", + "All metrics improve at the same time, so our loss metric is a good proxy\n", + "for both AUC and accuracy.\n", + "\n", + "Notice how it takes many, many more iterations just to squeeze a few more \n", + "units of AUC. This commonly happens. But often even this small gain is worth \n", + "the costs." + ] + }, + { + "metadata": { + "id": "dWgTEYMddaA-", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000003,\n", + " steps=20000,\n", + " batch_size=500,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "\n", + "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n", + "\n", + "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n", + "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From c6377e761f82b804593d9cf9c909e28144437501 Mon Sep 17 00:00:00 2001 From: Aditya Joardar <30207466+joardar-aditya@users.noreply.github.com> Date: Sun, 17 Feb 2019 23:18:07 +0530 Subject: [PATCH 06/13] Created using Colaboratory --- logistic_regression.ipynb | 696 +++++++++++++++++++++++++++++++++++++- 1 file changed, 679 insertions(+), 17 deletions(-) diff --git a/logistic_regression.ipynb b/logistic_regression.ipynb index b7aad9c..ea99620 100644 --- a/logistic_regression.ipynb +++ b/logistic_regression.ipynb @@ -223,7 +223,11 @@ "metadata": { "id": "FwOYWmXqWA6D", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1224 + }, + "outputId": "f24b25b6-61da-4833-e0cc-013dcba900c8" }, "cell_type": "code", "source": [ @@ -246,8 +250,513 @@ "print(\"Validation targets summary:\")\n", "display.display(validation_targets.describe())" ], - "execution_count": 0, - "outputs": [] + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.6 2645.6 539.5 \n", + "std 2.1 2.0 12.6 2204.2 425.1 \n", + "min 32.5 -124.3 2.0 8.0 1.0 \n", + "25% 33.9 -121.8 18.0 1459.0 296.0 \n", + "50% 34.2 -118.5 29.0 2127.0 433.0 \n", + "75% 37.7 -118.0 37.0 3143.2 648.0 \n", + "max 42.0 -114.3 52.0 37937.0 5471.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1432.2 501.2 3.9 2.0 \n", + "std 1156.5 388.7 1.9 1.2 \n", + "min 3.0 1.0 0.5 0.0 \n", + "25% 790.0 281.8 2.6 1.5 \n", + "50% 1166.0 409.0 3.5 1.9 \n", + "75% 1720.0 603.0 4.8 2.3 \n", + "max 35682.0 5189.0 15.0 55.2 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] }, { "metadata": { @@ -436,7 +945,11 @@ "metadata": { "id": "TDBD8xeeIYH2", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 640 + }, + "outputId": "0460c7f6-1552-4dce-f4c9-7e1a88553d6b" }, "cell_type": "code", "source": [ @@ -449,8 +962,40 @@ " validation_examples=validation_examples,\n", " validation_targets=validation_targets)" ], - "execution_count": 0, - "outputs": [] + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 0.45\n", + " period 01 : 0.45\n", + " period 02 : 0.45\n", + " period 03 : 0.44\n", + " period 04 : 0.44\n", + " period 05 : 0.44\n", + " period 06 : 0.44\n", + " period 07 : 0.44\n", + " period 08 : 0.45\n", + " period 09 : 0.44\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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vMv/aOwghhOiQJPERFlFVX82qY++RU3Ge6MCRzO07q8MkPRdE+V6Y7pJRHyGE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\nEXaVvYtSoeRO0y1Ouea+3GqGRsa4ZXGcPfQ4kl7nxfVzI7n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FgF6vp6ioyFFlC+GWlAoF\ndyxL4LWPynjr0wre+rSCGKM/mbOMZM4KIzTIOQvACTEVBHkFkhmeQWZ4xuVf7ADvHalidMzK+iVx\nKJXS2uNuHBZ8WltbSU1NtX8dHBxMS0uLPfhkZ2eTmZlJVFTUhPc99dRTPPbYY+zatct+zNfXF4DR\n0VFeffVVvvvd7wIwNDTEww8/TF1dHTfffDP/9E//dMma9Hpf1GrHdYWFhspUY3c1HZ7NfTfPYv0N\nSeQWNfLJyTpOnG3mzY/LefPjcmbEBrF8XjTL5kYSMoVC0HR4LtOVPJur0941wMH8BsKCfVl/QxJq\n1eRvgirP5to4bXCz1Wq1/7qjo4Ps7GxeeOEFmpqa7Md37drFvHnziImJuej9o6OjPPLIIyxevJgl\nS5YA8Mgjj3D77bejUCj4+te/zsKFC0lLS/vSGiyWvkm8o4lCQ3W0tHQ77Pzi6k23Z5MaG0RqbBC9\nAzPIO9tCbnEzZyotlFR38MfdhcyIDmTRrDAWphgJ9Lv2hd8cZbo9l+lEns3Ve+3DUoZHxlibGYOl\nvXfSzy/P5spcKhw6LPgYjUZaW1vtXzc3NxMaGgrA4cOHaW9v54EHHmBoaIjq6mq2b99Oc3MzNTU1\nHDhwgMbGRrRaLeHh4WRlZbFlyxbi4uL43ve+Zz/n1772NfuvFy9eTElJySWDjxDTiZ+3huVzI1k+\nN5KuviGOn20h93QTJTUdlNR28uoHJaTE6rludhgZM0Lx95Fp8EI4UlffEAdO1qHXeZE1J8LV5Ygv\n4bDgs3TpUnbs2MGmTZsoKirCaDTau7nWrl3L2rVrAaitrWXLli1s3bp1wvt37NhBVFQUWVlZ7N69\nG41Gww9+8AP775eXl/Pss8/y61//mtHRUfLy8uznFMLTBPhqWTk/ipXzo7B0D3KsuJnc4ibOVFk4\nU2Xh5X1nSU0IZlGKkfnJofh6y0oWQky293NrGBoe494VcWjUk9/FJSaHwz79MjIySE1NZdOmTSgU\nCrZt20Z2djY6nY41a77aBnGvvvoqg4ODbN68GbANln788ccJDw/nnnvuQalUcuONN37hdHkhPI1e\n58WaRTGsWRRDa2c/R4ubyT3dTL65jXxzG2rVWdISg7ludhhzTSF4aWUJCCGuVU//MB/m1RLgp2V5\nurT2uDOF9cLBN9OcI/tFpd/VfcmzsWlq7yP3TBO5Z5qpa7WNPdBqlMxLCiFzVhhpicFoHDj4//Pk\nubgveTZf3a6D5ez+rJL7Viax9rpYh11Hns2VcckYHyGEewkL9uW2pQnctjSB2pYecs80c3Q8COWe\nacbHS8X85FAyZxmZHR/skNkoQkxH/YMjfHCsFn8fDSvnR13+DcKlJPgI4YGiQ/2JDvXnruUJVDf1\n2FuCDhU2cqiwET9vNQtmhpI5K4yZsUGolBKChPgyH+XV0jc4wobrE6XreAqQ4COEB1MoFMSF64gL\n13HPChPl9V0cOdPE0eJmPjnVwCenGgjw1bAwxbZQYlJ0oCy/L8QFBodG2Zdbg6+XmlULol1djrgC\nEnyEEIAtBJmiAjFFBbLpxmRKazts3WHFzXyUV8dHebZpuovGQ1BChE52nBYeb/+JOnr6h7l9aTw+\nXvIjdSqQpySEuIhSqWBmrJ6ZsXruX5NMcVUHR840kXe2hfeP1vD+0RpCAr3JnBVG5iwjMUZ/CUHC\n4wwNj7IvtxovrYrVCy9eeFe4Jwk+QohLUimVpCYEk5oQzDdunklhRTtHzzSRV9rKu4erePdwFeHB\nvvZ9wyJDZPNU4RkO5jfQ2TvEusVxskDoFCLBRwhxxdQq2/T3eUkhDA2Pkm9uI7e4mfyyVnZ/Vsnu\nzyqJDvWztwQZ9b6uLlkIhxgeGePdw1Vo1UpuypTWnqlEgo8Q4qpoNSoWphhZmGJkYGiEk2Wt5J5u\nprCijexPysn+pJz4cB2Zs8JYlGLEEOjt6pKFmDSHChuwdA9y06IYAnzdd088cTEJPkKIa+atVbN4\ndjiLZ4fTNzBMXkkrucVNnK6wUNnYzV/3l5EUFUjmLCOLUowE+nu5umQhrtrI6Bjv5FShVikdulih\ncAwJPkKISeXrrWFZegTL0iPo7hvieIlt89Sz1R2U1XXylw9KmRkbxOrr4kiLC3LqatFCTIYjp5to\n7RzgxowogiTETzkSfIQQDqPz1bJiXhQr5kXR0XNu89Rmiqs7KK7uwBDgzb0rTSxKMcqsMDEljI1Z\n2ZNThUqp4Jbr4lxdjrgKEnyEEE4R5O/F6oUxrF4YQ1vnAIfONLP7EzO/f6uID47VsmlVMomRAa4u\nU4hLOlrcTFN7H9fPjZBxa1OUrEMvhHA6Q6A3/3xbKr/81nUsmBFKWV0nv/i/Yzy/u4i2zgFXlyfE\nFxqzWtmTU4lSoWDdknhXlyOukrT4CCFcxqj35bsb0jhbbeG1D8s4fLqJ4yUt3JwZy7rFsXhr5SNK\nuI8TJa3UtfSyJDUcY5CPq8sRV0lafIQQLjczVs9j/7iQb946Cz9vNXsOVbLlD4c5eKqesTGrq8sT\nAqvVytuHKlAA67NkbM9UJsFHCOEWlAoFS9MieOLbS7h9aTz9gyO88F4x//niUYqrLK4uT3i4gvI2\nqpt6WDTLSIRBViefyiT4CCHcipdWxZ3LE9n+7cUsSQ2nurmHX/3lBDvezKepvc/V5QkPZLVaefuz\nSgDWy9ieKU860IUQbik4wJtv3Tab1Qujee3DUk6UtpJvbmPVgmhuWxqPn7fsjSSc43SVBXN9F/OT\nQ4g2+ru6HHGNpMVHCOHWEiICePSBDB68cw56nRfvH63h0d/n8MGxGkZGx1xdnvAAe8Zbe25bGu/S\nOsTkkBYfIYTbUygULEwxMjfJwAfHann7UCWvflDK/hN13LcyiXSTQRZAFA5RUtPB2ZoO0hINxIfL\nOlPTgbT4CCGmDI1axS2L43jyX5awYn4Uje19/M8b+fxm50lqm3tcXZ6Yht7+rAKQ1p7pRIKPEGLK\nCfDT8o2bZ/If/5xJakIwRZUWtr2Qy0t7i+nsHXJ1eWKaMNd3UlRpYVacnqSoQFeXIyaJdHUJIaas\n6FB/fnzfXArK29n5USkfn6znyOkm1mfFs2ZhtGyAKq6JfWxPVrxL6xCTS4KPEGJKUygUpJsMzI7X\n8/HJet76tII3Dpg5cKKOe1bIBqji6lQ1dnPK3EZydCAzY4NcXY6YRNLVJYSYFtQqJasWRPPEvyzm\npkUxWLoH+f1bRTzx5zzK67tcXZ6YYvbkVAK2sT0SnKcXCT5CiGnFz1vDplXJ/OJb15ExI5Sy2vEN\nUN8uor1LNkAVl1fX0sPxsy0kROhIjQ92dTlikklXlxBiWgrT+/K9DWkUV1l47aNSDhc1kXfWtgHq\nLbIBqriEPTlVANyWlSCtPdOQtPgIIaa1lDg9P/vHRfzzuln4eKt5+1AlW54/zKf5DYxZZQNUMVFj\nex+5Z5qIMfozN8ng6nKEA0jwEUJMe0qFgmXpETzx7cXclhVP/8AIf3r3DP/54lHOVssGqOK8d3Iq\nsVptM7mktWd6kuAjhPAY3lo1d11/bgPUMKqbenjq1RM8k11Ak0U2QPV0LR395BQ2ERniR8bMUFeX\nIxxEOrmFEB7HtgFqKqsWxPDah6XklbRwqqyVVQuiuX1pPL6yAapHeu9wFWNWK+uXxKGU1p5pS1p8\nhBAeKzEygC1fz+A7d6Se3wD1D4f58Hgto2OyAaonae8a4NOCBox6HxbNMrq6HOFADg0+27dvZ+PG\njWzatIn8/PwvfM3TTz/N5s2bJxwbGBhg9erVZGdnA9DQ0MDmzZu5//77+eEPf8jQkG1J+t27d3P3\n3Xdz77338vrrrzvyVoQQ05RCoSBzVhi//NZ13LPCxMjoGH/+ewk/+2Mu+eZWrDIA2iO8d6SakVEr\nty6JQ6WUNoHpzGFPNzc3l6qqKnbu3Mkvf/lLfvnLX170mrKyMo4ePXrR8eeee47AwPP7ovzud7/j\n/vvv59VXXyUuLo433niDvr4+nn32WV588UVefvllXnrpJTo6Ohx1O0KIaU6jVrFucRxP/MsSVsyL\npLG9j9++ns9v/nqK2hbZAHU66+wZ5JNT9RgCvFmSGu7qcoSDOSz45OTksHr1agBMJhOdnZ309Ez8\n8HjyySd56KGHJhwzm82UlZWxYsUK+7EjR46watUqAFauXElOTg6nTp0iLS0NnU6Ht7c3GRkZ5OXl\nOep2hBAeItBPyzfWpvAf/5RJaryeoop2tv0pl//bW0yXbIA6Le3LrWF4ZIx1S+JQq6S1Z7pz2BNu\nbW1Fr9fbvw4ODqalpcX+dXZ2NpmZmURFRU1431NPPcWjjz464Vh/fz9arRYAg8FAS0sLra2tBAef\nX1Hz8+cXQohrEW3058cb5/HDe9IJ0/ty4GQ9W57P4b3DVQyPyPif6aK7b4j9J+rQ67xYlhbh6nKE\nEzhtVteF/eQdHR1kZ2fzwgsv0NTUZD++a9cu5s2bR0xMzBWd50qOX0iv90XtwN2aQ0N1Dju3uDby\nbNzTVHguq40BrMiMY29OJa/uK+b1A2Y+yW/gn9ankpUeMW3XepkKz2Yy7H3vDIPDo3zj1llERgRe\n/g1uwFOejaM4LPgYjUZaW1vtXzc3NxMaalsX4fDhw7S3t/PAAw8wNDREdXU127dvp7m5mZqaGg4c\nOEBjYyNarZbw8HB8fX0ZGBjA29ubpqYmjEbjF55/3rx5l6zJ4sB1OkJDdbS0dDvs/OLqybNxT1Pt\nuVw3M5Q5cUG8/VklHx6v5cn/O0pydCCbViWTEBHg6vIm1VR7Nlerb2CYtw+aCfDVkGEyTIl79pRn\nc60uFQ4dFnyWLl3Kjh072LRpE0VFRRiNRvz9/QFYu3Yta9euBaC2tpYtW7awdev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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] }, { "metadata": { @@ -487,6 +1032,44 @@ "Click below to display the solution." ] }, + { + "metadata": { + "id": "yiVTBuHIcIXx", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "ea6b2617-6ea4-41b8-961f-9a2c0028e70c" + }, + "cell_type": "code", + "source": [ + "predict_validation = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "validation_predictions = linear_regressor.predict(input_fn=predict_validation)\n", + "validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + "\n", + "_ = plt.hist(validation_predictions)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, { "metadata": { "id": "kXFQ5uig2RoP", @@ -567,7 +1150,10 @@ " # Create a linear classifier object.\n", " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", - " linear_classifier = # YOUR CODE HERE: Construct the linear classifier.\n", + " linear_classifier = tf.estimator.LinearClassifier(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " optimizer=my_optimizer\n", + " )\n", " \n", " # Create input functions.\n", " training_input_fn = lambda: my_input_fn(training_examples, \n", @@ -628,7 +1214,11 @@ "metadata": { "id": "VM0wmnFUIYH9", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 640 + }, + "outputId": "3905c6bb-1b9d-42b7-fbb7-2586e2de6f15" }, "cell_type": "code", "source": [ @@ -641,8 +1231,40 @@ " validation_examples=validation_examples,\n", " validation_targets=validation_targets)" ], - "execution_count": 0, - "outputs": [] + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.60\n", + " period 01 : 0.58\n", + " period 02 : 0.57\n", + " period 03 : 0.55\n", + " period 04 : 0.55\n", + " period 05 : 0.54\n", + " period 06 : 0.54\n", + " period 07 : 0.53\n", + " period 08 : 0.54\n", + " period 09 : 0.53\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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HV746FUFCTmKFMXs7M/eP7YIBfLA2moKiEusUKSIicoMpzNQhrvYu3NlpCsWl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\nHOs5jEfbB/Qfjgp1JZIbm8XVWQUkRcT69PMlm+9Xb+3hpQ9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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] }, { "metadata": { @@ -863,27 +1485,67 @@ "metadata": { "id": "XKIqjsqcCaxO", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 677 + }, + "outputId": "2d1e1c73-75e8-4e4d-8448-49dfd03f6506" }, "cell_type": "code", "source": [ "# TUNE THE SETTINGS BELOW TO IMPROVE AUC\n", "linear_classifier = train_linear_classifier_model(\n", - " learning_rate=0.000005,\n", - " steps=500,\n", - " batch_size=20,\n", + " learning_rate=0.000003,\n", + " steps=50000,\n", + " batch_size=50,\n", " training_examples=training_examples,\n", " training_targets=training_targets,\n", " validation_examples=validation_examples,\n", " validation_targets=validation_targets)\n", "\n", - "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n", + "#I hope that it doesn't overfit\n", + "\n", + "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation)\n", "\n", "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n", "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])" ], - "execution_count": 0, - "outputs": [] + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.50\n", + " period 01 : 0.49\n", + " period 02 : 0.48\n", + " period 03 : 0.48\n", + " period 04 : 0.47\n", + " period 05 : 0.47\n", + " period 06 : 0.47\n", + " period 07 : 0.47\n", + " period 08 : 0.46\n", + " period 09 : 0.46\n", + "Model training finished.\n", + "AUC on the validation set: 0.80\n", + "Accuracy on the validation set: 0.78\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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MGYObmxsdOnTgww8/ZNmyZbz88ss1nsvd3QGNRm3Wz+Pl5Vyn46dEdOC9tafY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\nUjEeuDUOk0dEYfPuPBw4UYrjP1Ug6bY4TB8bAxFPLycKCL/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" + ] + }, + "metadata": { + "tags": [] + } + } + ] }, { "metadata": { From c5341e3f677fc6afde915f53f0731179979f3b1f Mon Sep 17 00:00:00 2001 From: Aditya Joardar <30207466+joardar-aditya@users.noreply.github.com> Date: Sun, 17 Feb 2019 23:56:35 +0530 Subject: [PATCH 07/13] Sparsity and l1 Regularisation completed! --- sparsity_and_l1_regularization.ipynb | 1141 ++++++++++++++++++++++++++ 1 file changed, 1141 insertions(+) create mode 100644 sparsity_and_l1_regularization.ipynb diff --git a/sparsity_and_l1_regularization.ipynb b/sparsity_and_l1_regularization.ipynb new file mode 100644 index 0000000..dfcfda5 --- /dev/null +++ b/sparsity_and_l1_regularization.ipynb @@ -0,0 +1,1141 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "sparsity_and_l1_regularization.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "yjUCX5LAkxAX" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "g4T-_IsVbweU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Sparsity and L1 Regularization" + ] + }, + { + "metadata": { + "id": "g8ue2FyFIjnQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Calculate the size of a model\n", + " * Apply L1 regularization to reduce the size of a model by increasing sparsity" + ] + }, + { + "metadata": { + "id": "ME_WXE7cIjnS", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "One way to reduce complexity is to use a regularization function that encourages weights to be exactly zero. For linear models such as regression, a zero weight is equivalent to not using the corresponding feature at all. In addition to avoiding overfitting, the resulting model will be more efficient.\n", + "\n", + "L1 regularization is a good way to increase sparsity.\n", + "\n" + ] + }, + { + "metadata": { + "id": "fHRzeWkRLrHF", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "Run the cells below to load the data and create feature definitions." + ] + }, + { + "metadata": { + "id": "pb7rSrLKIjnS", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "3V7q8jk0IjnW", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Create a boolean categorical feature representing whether the\n", + " # median_house_value is above a set threshold.\n", + " output_targets[\"median_house_value_is_high\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] > 265000).astype(float)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "pAG3tmgwIjnY", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1224 + }, + "outputId": "f23c8fd3-fc57-4f90-8310-aa821841bdb0" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.5 2641.0 538.3 \n", + "std 2.1 2.0 12.6 2175.0 420.9 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1463.0 297.0 \n", + "50% 34.2 -118.5 29.0 2128.0 433.0 \n", + "75% 37.7 -118.0 37.0 3156.0 647.0 \n", + "max 42.0 -114.3 52.0 37937.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1422.1 500.0 3.9 2.0 \n", + "std 1121.6 383.7 1.9 1.3 \n", + "min 3.0 1.0 0.5 0.0 \n", + "25% 790.0 282.0 2.6 1.5 \n", + "50% 1161.0 408.0 3.6 1.9 \n", + "75% 1710.0 604.2 4.8 2.3 \n", + "max 28566.0 6082.0 15.0 55.2 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "gHkniRI1Ijna", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "bLzK72jkNJPf", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def get_quantile_based_buckets(feature_values, num_buckets):\n", + " quantiles = feature_values.quantile(\n", + " [(i+1.)/(num_buckets + 1.) for i in range(num_buckets)])\n", + " return [quantiles[q] for q in quantiles.keys()]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "al2YQpKyIjnd", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\"\n", + "\n", + " bucketized_households = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"households\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"households\"], 10))\n", + " bucketized_longitude = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"longitude\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"longitude\"], 50))\n", + " bucketized_latitude = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"latitude\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"latitude\"], 50))\n", + " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"housing_median_age\"),\n", + " boundaries=get_quantile_based_buckets(\n", + " training_examples[\"housing_median_age\"], 10))\n", + " bucketized_total_rooms = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"total_rooms\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"total_rooms\"], 10))\n", + " bucketized_total_bedrooms = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"total_bedrooms\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"total_bedrooms\"], 10))\n", + " bucketized_population = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"population\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"population\"], 10))\n", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"median_income\"),\n", + " boundaries=get_quantile_based_buckets(training_examples[\"median_income\"], 10))\n", + " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n", + " tf.feature_column.numeric_column(\"rooms_per_person\"),\n", + " boundaries=get_quantile_based_buckets(\n", + " training_examples[\"rooms_per_person\"], 10))\n", + "\n", + " long_x_lat = tf.feature_column.crossed_column(\n", + " set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000)\n", + "\n", + " feature_columns = set([\n", + " long_x_lat,\n", + " bucketized_longitude,\n", + " bucketized_latitude,\n", + " bucketized_housing_median_age,\n", + " bucketized_total_rooms,\n", + " bucketized_total_bedrooms,\n", + " bucketized_population,\n", + " bucketized_households,\n", + " bucketized_median_income,\n", + " bucketized_rooms_per_person])\n", + " \n", + " return feature_columns" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "hSBwMrsrE21n", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Calculate the Model Size\n", + "\n", + "To calculate the model size, we simply count the number of parameters that are non-zero. We provide a helper function below to do that. The function uses intimate knowledge of the Estimators API - don't worry about understanding how it works." + ] + }, + { + "metadata": { + "id": "e6GfTI0CFhB8", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def model_size(estimator):\n", + " variables = estimator.get_variable_names()\n", + " size = 0\n", + " for variable in variables:\n", + " if not any(x in variable \n", + " for x in ['global_step',\n", + " 'centered_bias_weight',\n", + " 'bias_weight',\n", + " 'Ftrl']\n", + " ):\n", + " size += np.count_nonzero(estimator.get_variable_value(variable))\n", + " return size" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "XabdAaj67GfF", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Reduce the Model Size\n", + "\n", + "Your team needs to build a highly accurate Logistic Regression model on the *SmartRing*, a ring that is so smart it can sense the demographics of a city block ('median_income', 'avg_rooms', 'households', ..., etc.) and tell you whether the given city block is high cost city block or not.\n", + "\n", + "Since the SmartRing is small, the engineering team has determined that it can only handle a model that has **no more than 600 parameters**. On the other hand, the product management team has determined that the model is not launchable unless the **LogLoss is less than 0.35** on the holdout test set.\n", + "\n", + "Can you use your secret weapon—L1 regularization—to tune the model to satisfy both the size and accuracy constraints?" + ] + }, + { + "metadata": { + "id": "G79hGRe7qqej", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Task 1: Find a good regularization coefficient.\n", + "\n", + "**Find an L1 regularization strength parameter which satisfies both constraints — model size is less than 600 and log-loss is less than 0.35 on validation set.**\n", + "\n", + "The following code will help you get started. There are many ways to apply regularization to your model. Here, we chose to do it using `FtrlOptimizer`, which is designed to give better results with L1 regularization than standard gradient descent.\n", + "\n", + "Again, the model will train on the entire data set, so expect it to run slower than normal." + ] + }, + { + "metadata": { + "id": "1Fcdm0hpIjnl", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_classifier_model(\n", + " learning_rate,\n", + " regularization_strength,\n", + " steps,\n", + " batch_size,\n", + " feature_columns,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " regularization_strength: A `float` that indicates the strength of the L1\n", + " regularization. A value of `0.0` means no regularization.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " feature_columns: A `set` specifying the input feature columns to use.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `LinearClassifier` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 7\n", + " steps_per_period = steps / periods\n", + "\n", + " # Create a linear classifier object.\n", + " my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate, l1_regularization_strength=regularization_strength)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_classifier = tf.estimator.LinearClassifier(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value_is_high\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " \n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss (on validation data):\")\n", + " training_log_losses = []\n", + " validation_log_losses = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n", + " training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n", + " \n", + " validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n", + " \n", + " # Compute training and validation loss.\n", + " training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_log_losses.append(training_log_loss)\n", + " validation_log_losses.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_log_losses, label=\"training\")\n", + " plt.plot(validation_log_losses, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " return linear_classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "9H1CKHSzIjno", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 603 + }, + "outputId": "b3934f8a-25de-4c47-8e2c-9b6d17df5be1" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.1,\n", + " # TWEAK THE REGULARIZATION VALUE BELOW\n", + " regularization_strength=0.9,\n", + " steps=300,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "print(\"Model size:\", model_size(linear_classifier))" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on validation data):\n", + " period 00 : 0.33\n", + " period 01 : 0.29\n", + " period 02 : 0.27\n", + " period 03 : 0.26\n", + " period 04 : 0.25\n", + " period 05 : 0.25\n", + " period 06 : 0.24\n", + "Model training finished.\n", + "Model size: 559\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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iPj65qs7TTT4eTkwZHkZhcRlLt5yupypFREQalsJMIze09c1E+HbhxJV4vr64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\niw9yJbeonisWEWkZFGZEbGA2mZna5Xb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UW8yZNTlbONp6jLr+KwCk66zsyNlGqbmYKPXd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\nyHmmnMyZEUIIEdFUKhVWnQWrzsKmjA34/X76xwamlZu6/sv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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "yjUCX5LAkxAX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see a possible solution." + ] + }, + { + "metadata": { + "id": "hgGhy-okmkWL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "A regularization strength of 0.1 should be sufficient. Note that there is a compromise to be struck:\n", + "stronger regularization gives us smaller models, but can affect the classification loss." + ] + }, + { + "metadata": { + "id": "_rV8YQWZIjns", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.1,\n", + " regularization_strength=0.1,\n", + " steps=300,\n", + " batch_size=100,\n", + " feature_columns=construct_feature_columns(),\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)\n", + "print(\"Model size:\", model_size(linear_classifier))" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From d0244772b860409b881aad25a37aa6b56bb866d6 Mon Sep 17 00:00:00 2001 From: Aditya Joardar <30207466+joardar-aditya@users.noreply.github.com> Date: Mon, 18 Feb 2019 00:04:55 +0530 Subject: [PATCH 08/13] intro_to_pandas solved! --- Copy_of_intro_to_pandas.ipynb | 1625 +++++++++++++++++++++++++++++++++ 1 file changed, 1625 insertions(+) create mode 100644 Copy_of_intro_to_pandas.ipynb diff --git a/Copy_of_intro_to_pandas.ipynb b/Copy_of_intro_to_pandas.ipynb new file mode 100644 index 0000000..152ab6e --- /dev/null +++ b/Copy_of_intro_to_pandas.ipynb @@ -0,0 +1,1625 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Copy of intro_to_pandas.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "YHIWvc9Ms-Ll", + "TJffr5_Jwqvd" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "JndnmDMp66FL" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "hMqWDc_m6rUC", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "rHLcriKWLRe4" + }, + "cell_type": "markdown", + "source": [ + "# Intro to pandas" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "QvJBqX8_Bctk" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Gain an introduction to the `DataFrame` and `Series` data structures of the *pandas* library\n", + " * Access and manipulate data within a `DataFrame` and `Series`\n", + " * Import CSV data into a *pandas* `DataFrame`\n", + " * Reindex a `DataFrame` to shuffle data" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TIFJ83ZTBctl" + }, + "cell_type": "markdown", + "source": [ + "[*pandas*](http://pandas.pydata.org/) is a column-oriented data analysis API. It's a great tool for handling and analyzing input data, and many ML frameworks support *pandas* data structures as inputs.\n", + "Although a comprehensive introduction to the *pandas* API would span many pages, the core concepts are fairly straightforward, and we'll present them below. For a more complete reference, the [*pandas* docs site](http://pandas.pydata.org/pandas-docs/stable/index.html) contains extensive documentation and many tutorials." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "s_JOISVgmn9v" + }, + "cell_type": "markdown", + "source": [ + "## Basic Concepts\n", + "\n", + "The following line imports the *pandas* API and prints the API version:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "aSRYu62xUi3g", + "outputId": "b7efd0ab-e05a-4af2-93ad-e7ac5b9ce519", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import pandas as pd\n", + "pd.__version__" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "u'0.22.0'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 2 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "daQreKXIUslr" + }, + "cell_type": "markdown", + "source": [ + "The primary data structures in *pandas* are implemented as two classes:\n", + "\n", + " * **`DataFrame`**, which you can imagine as a relational data table, with rows and named columns.\n", + " * **`Series`**, which is a single column. A `DataFrame` contains one or more `Series` and a name for each `Series`.\n", + "\n", + "The data frame is a commonly used abstraction for data manipulation. Similar implementations exist in [Spark](https://spark.apache.org/) and [R](https://www.r-project.org/about.html)." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "fjnAk1xcU0yc" + }, + "cell_type": "markdown", + "source": [ + "One way to create a `Series` is to construct a `Series` object. For example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "DFZ42Uq7UFDj", + "outputId": "1fc176a3-a09d-46a2-c7e7-7177171c619e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 90 + } + }, + "cell_type": "code", + "source": [ + "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 San Francisco\n", + "1 San Jose\n", + "2 Sacramento\n", + "dtype: object" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "U5ouUp1cU6pC" + }, + "cell_type": "markdown", + "source": [ + "`DataFrame` objects can be created by passing a `dict` mapping `string` column names to their respective `Series`. If the `Series` don't match in length, missing values are filled with special [NA/NaN](http://pandas.pydata.org/pandas-docs/stable/missing_data.html) values. Example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "avgr6GfiUh8t", + "outputId": "2e452a81-6b72-4ccd-cad4-acbce1557f0a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + } + }, + "cell_type": "code", + "source": [ + "city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\n", + "population = pd.Series([852469, 1015785, 485199])\n", + "\n", + "pd.DataFrame({ 'City name': city_names, 'Population': population })" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " City name Population\n", + "0 San Francisco 852469\n", + "1 San Jose 1015785\n", + "2 Sacramento 485199" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "oa5wfZT7VHJl" + }, + "cell_type": "markdown", + "source": [ + "But most of the time, you load an entire file into a `DataFrame`. The following example loads a file with California housing data. Run the following cell to load the data and create feature definitions:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "av6RYOraVG1V", + "outputId": "733e8298-c448-41d1-91e6-42225cda9115", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + } + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "california_housing_dataframe.describe()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
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mean-119.56210835.62522528.5893532643.664412539.4108241429.573941501.2219413.883578207300.912353
std2.0051662.13734012.5869372179.947071421.4994521147.852959384.5208411.908157115983.764387
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max-114.31000041.95000052.00000037937.0000006445.00000035682.0000006082.00000015.000100500001.000000
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms \\\n", + "count 17000.000000 17000.000000 17000.000000 17000.000000 \n", + "mean -119.562108 35.625225 28.589353 2643.664412 \n", + "std 2.005166 2.137340 12.586937 2179.947071 \n", + "min -124.350000 32.540000 1.000000 2.000000 \n", + "25% -121.790000 33.930000 18.000000 1462.000000 \n", + "50% -118.490000 34.250000 29.000000 2127.000000 \n", + "75% -118.000000 37.720000 37.000000 3151.250000 \n", + "max -114.310000 41.950000 52.000000 37937.000000 \n", + "\n", + " total_bedrooms population households median_income \\\n", + "count 17000.000000 17000.000000 17000.000000 17000.000000 \n", + "mean 539.410824 1429.573941 501.221941 3.883578 \n", + "std 421.499452 1147.852959 384.520841 1.908157 \n", + "min 1.000000 3.000000 1.000000 0.499900 \n", + "25% 297.000000 790.000000 282.000000 2.566375 \n", + "50% 434.000000 1167.000000 409.000000 3.544600 \n", + "75% 648.250000 1721.000000 605.250000 4.767000 \n", + "max 6445.000000 35682.000000 6082.000000 15.000100 \n", + "\n", + " median_house_value \n", + "count 17000.000000 \n", + "mean 207300.912353 \n", + "std 115983.764387 \n", + "min 14999.000000 \n", + "25% 119400.000000 \n", + "50% 180400.000000 \n", + "75% 265000.000000 \n", + "max 500001.000000 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "WrkBjfz5kEQu" + }, + "cell_type": "markdown", + "source": [ + "The example above used `DataFrame.describe` to show interesting statistics about a `DataFrame`. Another useful function is `DataFrame.head`, which displays the first few records of a `DataFrame`:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "s3ND3bgOkB5k", + "outputId": "bd0aa8ed-5571-481f-8e18-6caff065bac5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + } + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.head()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
0-114.3134.1915.05612.01283.01015.0472.01.493666900.0
1-114.4734.4019.07650.01901.01129.0463.01.820080100.0
2-114.5633.6917.0720.0174.0333.0117.01.650985700.0
3-114.5733.6414.01501.0337.0515.0226.03.191773400.0
4-114.5733.5720.01454.0326.0624.0262.01.925065500.0
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "0 -114.31 34.19 15.0 5612.0 1283.0 \n", + "1 -114.47 34.40 19.0 7650.0 1901.0 \n", + "2 -114.56 33.69 17.0 720.0 174.0 \n", + "3 -114.57 33.64 14.0 1501.0 337.0 \n", + "4 -114.57 33.57 20.0 1454.0 326.0 \n", + "\n", + " population households median_income median_house_value \n", + "0 1015.0 472.0 1.4936 66900.0 \n", + "1 1129.0 463.0 1.8200 80100.0 \n", + "2 333.0 117.0 1.6509 85700.0 \n", + "3 515.0 226.0 3.1917 73400.0 \n", + "4 624.0 262.0 1.9250 65500.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "w9-Es5Y6laGd" + }, + "cell_type": "markdown", + "source": [ + "Another powerful feature of *pandas* is graphing. For example, `DataFrame.hist` lets you quickly study the distribution of values in a column:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "nqndFVXVlbPN", + "outputId": "f0d57711-259e-4f37-cb01-4127e4e7950d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 399 + } + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.hist('housing_median_age')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[]],\n", + " dtype=object)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "XtYZ7114n3b-" + }, + "cell_type": "markdown", + "source": [ + "## Accessing Data\n", + "\n", + "You can access `DataFrame` data using familiar Python dict/list operations:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "_TFm7-looBFF", + "outputId": "c1cd77fa-8787-463a-cf68-66eb85b9fa5a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 109 + } + }, + "cell_type": "code", + "source": [ + "cities = pd.DataFrame({ 'City name': city_names, 'Population': population })\n", + "print(type(cities['City name']))\n", + "cities['City name']" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 San Francisco\n", + "1 San Jose\n", + "2 Sacramento\n", + "Name: City name, dtype: object" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "V5L6xacLoxyv", + "outputId": "b3f245ac-049f-4f23-a90b-69163c97d2ef", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54 + } + }, + "cell_type": "code", + "source": [ + "print(type(cities['City name'][1]))\n", + "cities['City name'][1]" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'San Jose'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "gcYX1tBPugZl", + "outputId": "2166cc30-ad36-4415-db65-941b9624efcf", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 130 + } + }, + "cell_type": "code", + "source": [ + "print(type(cities[0:2]))\n", + "cities[0:2]" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulation
0San Francisco852469
1San Jose1015785
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" + ], + "text/plain": [ + " City name Population\n", + "0 San Francisco 852469\n", + "1 San Jose 1015785" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "65g1ZdGVjXsQ" + }, + "cell_type": "markdown", + "source": [ + "In addition, *pandas* provides an extremely rich API for advanced [indexing and selection](http://pandas.pydata.org/pandas-docs/stable/indexing.html) that is too extensive to be covered here." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "RM1iaD-ka3Y1" + }, + "cell_type": "markdown", + "source": [ + "## Manipulating Data\n", + "\n", + "You may apply Python's basic arithmetic operations to `Series`. For example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "XWmyCFJ5bOv-", + "outputId": "5b0fc03e-46d1-43c6-f218-65908fae75d8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 90 + } + }, + "cell_type": "code", + "source": [ + "population / 1000." + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 852.469\n", + "1 1015.785\n", + "2 485.199\n", + "dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TQzIVnbnmWGM" + }, + "cell_type": "markdown", + "source": [ + "[NumPy](http://www.numpy.org/) is a popular toolkit for scientific computing. *pandas* `Series` can be used as arguments to most NumPy functions:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "ko6pLK6JmkYP", + "outputId": "7b21b0e9-b3ab-4cb5-df15-411463a285f0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 90 + } + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "np.log(population)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 13.655892\n", + "1 13.831172\n", + "2 13.092314\n", + "dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "xmxFuQmurr6d" + }, + "cell_type": "markdown", + "source": [ + "For more complex single-column transformations, you can use `Series.apply`. Like the Python [map function](https://docs.python.org/2/library/functions.html#map), \n", + "`Series.apply` accepts as an argument a [lambda function](https://docs.python.org/2/tutorial/controlflow.html#lambda-expressions), which is applied to each value.\n", + "\n", + "The example below creates a new `Series` that indicates whether `population` is over one million:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Fc1DvPAbstjI", + "outputId": "62c07efd-2723-4d98-c1d0-7c0c67e434c2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "cell_type": "code", + "source": [ + "population.apply(lambda val: val > 1000000)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "pandas.core.series.Series" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "ZeYYLoV9b9fB" + }, + "cell_type": "markdown", + "source": [ + "\n", + "Modifying `DataFrames` is also straightforward. For example, the following code adds two `Series` to an existing `DataFrame`:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "0gCEX99Hb8LR", + "outputId": "ec254f3c-ee7c-4e5d-82e6-a0296499864c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + } + }, + "cell_type": "code", + "source": [ + "cities['Area square miles'] = pd.Series([46.87, 176.53, 97.92])\n", + "cities['Population density'] = cities['Population'] / cities['Area square miles']\n", + "cities" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation density
0San Francisco85246946.8718187.945381
1San Jose1015785176.535754.177760
2Sacramento48519997.924955.055147
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" + ], + "text/plain": [ + " City name Population Area square miles Population density\n", + "0 San Francisco 852469 46.87 18187.945381\n", + "1 San Jose 1015785 176.53 5754.177760\n", + "2 Sacramento 485199 97.92 4955.055147" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 15 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "6qh63m-ayb-c" + }, + "cell_type": "markdown", + "source": [ + "## Exercise #1\n", + "\n", + "Modify the `cities` table by adding a new boolean column that is True if and only if *both* of the following are True:\n", + "\n", + " * The city is named after a saint.\n", + " * The city has an area greater than 50 square miles.\n", + "\n", + "**Note:** Boolean `Series` are combined using the bitwise, rather than the traditional boolean, operators. For example, when performing *logical and*, use `&` instead of `and`.\n", + "\n", + "**Hint:** \"San\" in Spanish means \"saint.\"" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "zCOn8ftSyddH", + "outputId": "02ff55bc-b37d-41a6-a42b-45224f370350", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 182 + } + }, + "cell_type": "code", + "source": [ + "#Important logic for adding and generating series in DataFrame\n", + "cities['new_column'] = (cities['Area square miles']>50)&cities['City name'].apply(lambda name: name[0:3]=='San')\n", + "print(cities.head())" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + " City name Population Area square miles Population density \\\n", + "0 San Francisco 852469 46.87 18187.945381 \n", + "1 San Jose 1015785 176.53 5754.177760 \n", + "2 Sacramento 485199 97.92 4955.055147 \n", + "\n", + " new_column \n", + "0 False \n", + "1 True \n", + "2 False \n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "YHIWvc9Ms-Ll" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "T5OlrqtdtCIb", + "colab": {} + }, + "cell_type": "code", + "source": [ + "cities['Is wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n", + "cities" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "f-xAOJeMiXFB" + }, + "cell_type": "markdown", + "source": [ + "## Indexes\n", + "Both `Series` and `DataFrame` objects also define an `index` property that assigns an identifier value to each `Series` item or `DataFrame` row. \n", + "\n", + "By default, at construction, *pandas* assigns index values that reflect the ordering of the source data. Once created, the index values are stable; that is, they do not change when data is reordered." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "2684gsWNinq9", + "outputId": "83620eae-ea29-43f6-9ea6-5b6493432917", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "cell_type": "code", + "source": [ + "city_names.index" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 24 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "F_qPe2TBjfWd", + "outputId": "d593928e-f1e9-4ef4-c4ae-a51b6bb1b365", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "cell_type": "code", + "source": [ + "cities.index" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 25 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "hp2oWY9Slo_h" + }, + "cell_type": "markdown", + "source": [ + "Call `DataFrame.reindex` to manually reorder the rows. For example, the following has the same effect as sorting by city name:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "sN0zUzSAj-U1", + "outputId": "7fcd04b9-9460-4cc1-933d-5fc0a7771f2c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + } + }, + "cell_type": "code", + "source": [ + "cities.reindex([2, 0, 1])" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "2 Sacramento 485199 97.92 4955.055147 \n", + "0 San Francisco 852469 46.87 18187.945381 \n", + "1 San Jose 1015785 176.53 5754.177760 \n", + "\n", + " new_column \n", + "2 False \n", + "0 False \n", + "1 True " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 26 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "-GQFz8NZuS06" + }, + "cell_type": "markdown", + "source": [ + "Reindexing is a great way to shuffle (randomize) a `DataFrame`. In the example below, we take the index, which is array-like, and pass it to NumPy's `random.permutation` function, which shuffles its values in place. Calling `reindex` with this shuffled array causes the `DataFrame` rows to be shuffled in the same way.\n", + "Try running the following cell multiple times!" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "mF8GC0k8uYhz", + "outputId": "3cc02e8d-9eff-42ac-a96a-d18954ae727f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + } + }, + "cell_type": "code", + "source": [ + "#important for training and reindexing\n", + "cities.reindex(np.random.permutation(cities.index))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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0San Francisco85246946.8718187.945381False
1San Jose1015785176.535754.177760True
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "2 Sacramento 485199 97.92 4955.055147 \n", + "0 San Francisco 852469 46.87 18187.945381 \n", + "1 San Jose 1015785 176.53 5754.177760 \n", + "\n", + " new_column \n", + "2 False \n", + "0 False \n", + "1 True " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 30 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "fSso35fQmGKb" + }, + "cell_type": "markdown", + "source": [ + "For more information, see the [Index documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html#index-objects)." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "8UngIdVhz8C0" + }, + "cell_type": "markdown", + "source": [ + "## Exercise #2\n", + "\n", + "The `reindex` method allows index values that are not in the original `DataFrame`'s index values. Try it and see what happens if you use such values! Why do you think this is allowed?" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "PN55GrDX0jzO", + "outputId": "4d3fd3aa-8a07-42df-bf08-bbfe84ffd68c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + } + }, + "cell_type": "code", + "source": [ + "cities.reindex([3,5,6])" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " City name Population Area square miles Population density new_column\n", + "3 NaN NaN NaN NaN NaN\n", + "5 NaN NaN NaN NaN NaN\n", + "6 NaN NaN NaN NaN NaN" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 31 + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "TJffr5_Jwqvd" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "8oSvi2QWwuDH" + }, + "cell_type": "markdown", + "source": [ + "If your `reindex` input array includes values not in the original `DataFrame` index values, `reindex` will add new rows for these \"missing\" indices and populate all corresponding columns with `NaN` values:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "yBdkucKCwy4x", + "colab": {} + }, + "cell_type": "code", + "source": [ + "cities.reindex([0, 4, 5, 2])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "2l82PhPbwz7g" + }, + "cell_type": "markdown", + "source": [ + "This behavior is desirable because indexes are often strings pulled from the actual data (see the [*pandas* reindex\n", + "documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.reindex.html) for an example\n", + "in which the index values are browser names).\n", + "\n", + "In this case, allowing \"missing\" indices makes it easy to reindex using an external list, as you don't have to worry about\n", + "sanitizing the input." + ] + } + ] +} \ No newline at end of file From a594dce5d643429e8130a8e4ad8debc0d09e850f Mon Sep 17 00:00:00 2001 From: Aditya Joardar <30207466+joardar-aditya@users.noreply.github.com> Date: Mon, 18 Feb 2019 00:09:20 +0530 Subject: [PATCH 09/13] First Steps with tensorflow solved! --- Copy_of_first_steps_with_tensor_flow.ipynb | 1763 ++++++++++++++++++++ 1 file changed, 1763 insertions(+) create mode 100644 Copy_of_first_steps_with_tensor_flow.ipynb diff --git a/Copy_of_first_steps_with_tensor_flow.ipynb b/Copy_of_first_steps_with_tensor_flow.ipynb new file mode 100644 index 0000000..75bb337 --- /dev/null +++ b/Copy_of_first_steps_with_tensor_flow.ipynb @@ -0,0 +1,1763 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Copy of first_steps_with_tensor_flow.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "ajVM7rkoYXeL", + "ci1ISxxrZ7v0" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "4f3CKqFUqL2-", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# First Steps with TensorFlow" + ] + }, + { + "metadata": { + "id": "Bd2Zkk1LE2Zr", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Learn fundamental TensorFlow concepts\n", + " * Use the `LinearRegressor` class in TensorFlow to predict median housing price, at the granularity of city blocks, based on one input feature\n", + " * Evaluate the accuracy of a model's predictions using Root Mean Squared Error (RMSE)\n", + " * Improve the accuracy of a model by tuning its hyperparameters" + ] + }, + { + "metadata": { + "id": "MxiIKhP4E2Zr", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The [data](https://developers.google.com/machine-learning/crash-course/california-housing-data-description) is based on 1990 census data from California." + ] + }, + { + "metadata": { + "id": "6TjLjL9IU80G", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "In this first cell, we'll load the necessary libraries." + ] + }, + { + "metadata": { + "id": "rVFf5asKE2Zt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ipRyUHjhU80Q", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll load our data set." + ] + }, + { + "metadata": { + "id": "9ivCDWnwE2Zx", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "vVk_qlG6U80j", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "We'll randomize the data, just to be sure not to get any pathological ordering effects that might harm the performance of Stochastic Gradient Descent. Additionally, we'll scale `median_house_value` to be in units of thousands, so it can be learned a little more easily with learning rates in a range that we usually use." + ] + }, + { + "metadata": { + "id": "r0eVyguIU80m", + "colab_type": "code", + "outputId": "bc8685d2-a49e-484d-f25c-7a57fe7fcefb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + } + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))\n", + "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n", + "california_housing_dataframe" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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11469-121.238.714.03713.0637.01845.0635.04.3143.4
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5927-118.234.136.02000.0533.01234.0535.03.7241.7
..............................
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "6775 -118.3 33.7 36.0 3135.0 746.0 \n", + "11469 -121.2 38.7 14.0 3713.0 637.0 \n", + "15693 -122.4 38.4 33.0 1066.0 191.0 \n", + "16627 -122.7 38.4 30.0 2099.0 406.0 \n", + "5927 -118.2 34.1 36.0 2000.0 533.0 \n", + "... ... ... ... ... ... \n", + "4956 -118.1 34.1 45.0 1106.0 226.0 \n", + "4901 -118.1 33.9 36.0 1124.0 217.0 \n", + "11053 -121.0 37.6 7.0 8489.0 1673.0 \n", + "16928 -124.1 41.0 19.0 1734.0 365.0 \n", + "11461 -121.2 37.8 7.0 5151.0 867.0 \n", + "\n", + " population households median_income median_house_value \n", + "6775 1815.0 697.0 3.8 300.0 \n", + "11469 1845.0 635.0 4.3 143.4 \n", + "15693 403.0 163.0 6.8 240.8 \n", + "16627 1156.0 401.0 2.8 152.3 \n", + "5927 1234.0 535.0 3.7 241.7 \n", + "... ... ... ... ... \n", + "4956 779.0 205.0 4.5 244.8 \n", + "4901 707.0 234.0 4.4 174.5 \n", + "11053 5807.0 1575.0 2.9 127.8 \n", + "16928 866.0 342.0 3.0 81.7 \n", + "11461 2553.0 805.0 4.1 195.0 \n", + "\n", + "[17000 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "metadata": { + "id": "HzzlSs3PtTmt", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Examine the Data\n", + "\n", + "It's a good idea to get to know your data a little bit before you work with it.\n", + "\n", + "We'll print out a quick summary of a few useful statistics on each column: count of examples, mean, standard deviation, max, min, and various quantiles." + ] + }, + { + "metadata": { + "id": "gzb10yoVrydW", + "colab_type": "code", + "cellView": "both", + "outputId": "fa104dd5-e32b-4e84-e0df-2ce1a653784f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + } + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.describe()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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mean-119.635.628.62643.7539.41429.6501.23.9207.3
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50%-118.534.229.02127.0434.01167.0409.03.5180.4
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "count 17000.0 17000.0 17000.0 17000.0 17000.0 \n", + "mean -119.6 35.6 28.6 2643.7 539.4 \n", + "std 2.0 2.1 12.6 2179.9 421.5 \n", + "min -124.3 32.5 1.0 2.0 1.0 \n", + "25% -121.8 33.9 18.0 1462.0 297.0 \n", + "50% -118.5 34.2 29.0 2127.0 434.0 \n", + "75% -118.0 37.7 37.0 3151.2 648.2 \n", + "max -114.3 42.0 52.0 37937.0 6445.0 \n", + "\n", + " population households median_income median_house_value \n", + "count 17000.0 17000.0 17000.0 17000.0 \n", + "mean 1429.6 501.2 3.9 207.3 \n", + "std 1147.9 384.5 1.9 116.0 \n", + "min 3.0 1.0 0.5 15.0 \n", + "25% 790.0 282.0 2.6 119.4 \n", + "50% 1167.0 409.0 3.5 180.4 \n", + "75% 1721.0 605.2 4.8 265.0 \n", + "max 35682.0 6082.0 15.0 500.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "metadata": { + "id": "Lr6wYl2bt2Ep", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Build the First Model\n", + "\n", + "In this exercise, we'll try to predict `median_house_value`, which will be our label (sometimes also called a target). We'll use `total_rooms` as our input feature.\n", + "\n", + "**NOTE:** Our data is at the city block level, so this feature represents the total number of rooms in that block.\n", + "\n", + "To train our model, we'll use the [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor) interface provided by the TensorFlow [Estimator](https://www.tensorflow.org/get_started/estimator) API. This API takes care of a lot of the low-level model plumbing, and exposes convenient methods for performing model training, evaluation, and inference." + ] + }, + { + "metadata": { + "id": "0cpcsieFhsNI", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 1: Define Features and Configure Feature Columns" + ] + }, + { + "metadata": { + "id": "EL8-9d4ZJNR7", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "In order to import our training data into TensorFlow, we need to specify what type of data each feature contains. There are two main types of data we'll use in this and future exercises:\n", + "\n", + "* **Categorical Data**: Data that is textual. In this exercise, our housing data set does not contain any categorical features, but examples you might see would be the home style, the words in a real-estate ad.\n", + "\n", + "* **Numerical Data**: Data that is a number (integer or float) and that you want to treat as a number. As we will discuss more later sometimes you might want to treat numerical data (e.g., a postal code) as if it were categorical.\n", + "\n", + "In TensorFlow, we indicate a feature's data type using a construct called a **feature column**. Feature columns store only a description of the feature data; they do not contain the feature data itself.\n", + "\n", + "To start, we're going to use just one numeric input feature, `total_rooms`. The following code pulls the `total_rooms` data from our `california_housing_dataframe` and defines the feature column using `numeric_column`, which specifies its data is numeric:" + ] + }, + { + "metadata": { + "id": "rhEbFCZ86cDZ", + "colab_type": "code", + "outputId": "254ca935-1bec-4451-ef0d-9039c2628847", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "cell_type": "code", + "source": [ + "# Define the input feature: total_rooms.\n", + "my_feature = california_housing_dataframe[[\"total_rooms\"]]\n", + "\n", + "# Configure a numeric feature column for total_rooms.\n", + "feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]\n", + "\n", + "{key:np.array(value) for key,value in dict(my_feature).items()}\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'total_rooms': array([3135., 3713., 1066., ..., 8489., 1734., 5151.])}" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 26 + } + ] + }, + { + "metadata": { + "id": "K_3S8teX7Rd2", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE:** The shape of our `total_rooms` data is a one-dimensional array (a list of the total number of rooms for each block). This is the default shape for `numeric_column`, so we don't have to pass it as an argument." + ] + }, + { + "metadata": { + "id": "UMl3qrU5MGV6", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 2: Define the Target" + ] + }, + { + "metadata": { + "id": "cw4nrfcB7kyk", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll define our target, which is `median_house_value`. Again, we can pull it from our `california_housing_dataframe`:" + ] + }, + { + "metadata": { + "id": "l1NvvNkH8Kbt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Define the label.\n", + "targets = california_housing_dataframe[\"median_house_value\"]\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "4M-rTFHL2UkA", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 3: Configure the LinearRegressor" + ] + }, + { + "metadata": { + "id": "fUfGQUNp7jdL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll configure a linear regression model using LinearRegressor. We'll train this model using the `GradientDescentOptimizer`, which implements Mini-Batch Stochastic Gradient Descent (SGD). The `learning_rate` argument controls the size of the gradient step.\n", + "\n", + "**NOTE:** To be safe, we also apply [gradient clipping](https://developers.google.com/machine-learning/glossary/#gradient_clipping) to our optimizer via `clip_gradients_by_norm`. Gradient clipping ensures the magnitude of the gradients do not become too large during training, which can cause gradient descent to fail. " + ] + }, + { + "metadata": { + "id": "ubhtW-NGU802", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Use gradient descent as the optimizer for training the model.\n", + "my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n", + "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + "\n", + "# Configure the linear regression model with our feature columns and optimizer.\n", + "# Set a learning rate of 0.0000001 for Gradient Descent.\n", + "linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + ")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "-0IztwdK2f3F", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 4: Define the Input Function" + ] + }, + { + "metadata": { + "id": "S5M5j6xSCHxx", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "To import our California housing data into our `LinearRegressor`, we need to define an input function, which instructs TensorFlow how to preprocess\n", + "the data, as well as how to batch, shuffle, and repeat it during model training.\n", + "\n", + "First, we'll convert our *pandas* feature data into a dict of NumPy arrays. We can then use the TensorFlow [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) to construct a dataset object from our data, and then break\n", + "our data into batches of `batch_size`, to be repeated for the specified number of epochs (num_epochs). \n", + "\n", + "**NOTE:** When the default value of `num_epochs=None` is passed to `repeat()`, the input data will be repeated indefinitely.\n", + "\n", + "Next, if `shuffle` is set to `True`, we'll shuffle the data so that it's passed to the model randomly during training. The `buffer_size` argument specifies\n", + "the size of the dataset from which `shuffle` will randomly sample.\n", + "\n", + "Finally, our input function constructs an iterator for the dataset and returns the next batch of data to the LinearRegressor." + ] + }, + { + "metadata": { + "id": "RKZ9zNcHJtwc", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model of one feature.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(buffer_size=10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "wwa6UeA1V5F_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE:** We'll continue to use this same input function in later exercises. For more\n", + "detailed documentation of input functions and the `Dataset` API, see the [TensorFlow Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets)." + ] + }, + { + "metadata": { + "id": "4YS50CQb2ooO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 5: Train the Model" + ] + }, + { + "metadata": { + "id": "yP92XkzhU803", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "We can now call `train()` on our `linear_regressor` to train the model. We'll wrap `my_input_fn` in a `lambda`\n", + "so we can pass in `my_feature` and `target` as arguments (see this [TensorFlow input function tutorial](https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model) for more details), and to start, we'll\n", + "train for 100 steps." + ] + }, + { + "metadata": { + "id": "5M-Kt6w8U803", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_ = linear_regressor.train(\n", + " input_fn = lambda:my_input_fn(my_feature, targets),\n", + " steps=100\n", + ")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "7Nwxqxlx2sOv", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 6: Evaluate the Model" + ] + }, + { + "metadata": { + "id": "KoDaF2dlJQG5", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's make predictions on that training data, to see how well our model fit it during training.\n", + "\n", + "**NOTE:** Training error measures how well your model fits the training data, but it **_does not_** measure how well your model **_generalizes to new data_**. In later exercises, you'll explore how to split your data to evaluate your model's ability to generalize.\n" + ] + }, + { + "metadata": { + "id": "pDIxp6vcU809", + "colab_type": "code", + "outputId": "5ea6fda6-8ca1-4e09-d8ca-0ad37d2e90bb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 90 + } + }, + "cell_type": "code", + "source": [ + "# Create an input function for predictions.\n", + "# Note: Since we're making just one prediction for each example, we don't \n", + "# need to repeat or shuffle the data here.\n", + "prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n", + "\n", + "# Call predict() on the linear_regressor to make predictions.\n", + "predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n", + "\n", + "# Format predictions as a NumPy array, so we can calculate error metrics.\n", + "predictions = np.array([item['predictions'][0] for item in predictions])\n", + "\n", + "# Print Mean Squared Error and Root Mean Squared Error.\n", + "mean_squared_error = metrics.mean_squared_error(predictions, targets)\n", + "root_mean_squared_error = math.sqrt(mean_squared_error)\n", + "print(\"Mean Squared Error (on training data): %0.3f\" % mean_squared_error)\n", + "print(\"Root Mean Squared Error (on training data): %0.3f\" % root_mean_squared_error)\n", + "predictions" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Mean Squared Error (on training data): 56367.025\n", + "Root Mean Squared Error (on training data): 237.417\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0.15674996, 0.18564996, 0.05330002, ..., 0.42444986, 0.0867 ,\n", + " 0.25754994], dtype=float32)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 34 + } + ] + }, + { + "metadata": { + "id": "AKWstXXPzOVz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Is this a good model? How would you judge how large this error is?\n", + "\n", + "Mean Squared Error (MSE) can be hard to interpret, so we often look at Root Mean Squared Error (RMSE)\n", + "instead. A nice property of RMSE is that it can be interpreted on the same scale as the original targets.\n", + "\n", + "Let's compare the RMSE to the difference of the min and max of our targets:" + ] + }, + { + "metadata": { + "id": "7UwqGbbxP53O", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "min_house_value = california_housing_dataframe[\"median_house_value\"].min()\n", + "max_house_value = california_housing_dataframe[\"median_house_value\"].max()\n", + "min_max_difference = max_house_value - min_house_value\n", + "\n", + "print(\"Min. Median House Value: %0.3f\" % min_house_value)\n", + "print(\"Max. Median House Value: %0.3f\" % max_house_value)\n", + "print(\"Difference between Min. and Max.: %0.3f\" % min_max_difference)\n", + "print(\"Root Mean Squared Error: %0.3f\" % root_mean_squared_error)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "JigJr0C7Pzit", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Our error spans nearly half the range of the target values. Can we do better?\n", + "\n", + "This is the question that nags at every model developer. Let's develop some basic strategies to reduce model error.\n", + "\n", + "The first thing we can do is take a look at how well our predictions match our targets, in terms of overall summary statistics." + ] + }, + { + "metadata": { + "id": "941nclxbzqGH", + "colab_type": "code", + "cellView": "both", + "outputId": "381526f4-b195-405d-f1b7-7b48d1dec42e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + } + }, + "cell_type": "code", + "source": [ + "calibration_data = pd.DataFrame()\n", + "calibration_data[\"predictions\"] = pd.Series(predictions)\n", + "calibration_data[\"targets\"] = pd.Series(targets)\n", + "calibration_data.describe()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 0.1 207.3\n", + "std 0.1 116.0\n", + "min 0.0 15.0\n", + "25% 0.1 119.4\n", + "50% 0.1 180.4\n", + "75% 0.2 265.0\n", + "max 1.9 500.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 35 + } + ] + }, + { + "metadata": { + "id": "E2-bf8Hq36y8", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Okay, maybe this information is helpful. How does the mean value compare to the model's RMSE? How about the various quantiles?\n", + "\n", + "We can also visualize the data and the line we've learned. Recall that linear regression on a single feature can be drawn as a line mapping input *x* to output *y*.\n", + "\n", + "First, we'll get a uniform random sample of the data so we can make a readable scatter plot." + ] + }, + { + "metadata": { + "id": "SGRIi3mAU81H", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "sample = california_housing_dataframe.sample(n=300)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "N-JwuJBKU81J", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll plot the line we've learned, drawing from the model's bias term and feature weight, together with the scatter plot. The line will show up red." + ] + }, + { + "metadata": { + "id": "7G12E76-339G", + "colab_type": "code", + "cellView": "both", + "outputId": "260cea61-17ee-476f-ab63-424a335fe38f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 361 + } + }, + "cell_type": "code", + "source": [ + "# Get the min and max total_rooms values.\n", + "x_0 = sample[\"total_rooms\"].min()\n", + "x_1 = sample[\"total_rooms\"].max()\n", + "\n", + "# Retrieve the final weight and bias generated during training.\n", + "weight = linear_regressor.get_variable_value('linear/linear_model/total_rooms/weights')[0]\n", + "bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", + "\n", + "# Get the predicted median_house_values for the min and max total_rooms values.\n", + "y_0 = weight * x_0 + bias \n", + "y_1 = weight * x_1 + bias\n", + "\n", + "# Plot our regression line from (x_0, y_0) to (x_1, y_1).\n", + "plt.plot([x_0, x_1], [y_0, y_1], c='r')\n", + "\n", + "# Label the graph axes.\n", + "plt.ylabel(\"median_house_value\")\n", + "plt.xlabel(\"total_rooms\")\n", + "\n", + "# Plot a scatter plot from our data sample.\n", + "plt.scatter(sample[\"total_rooms\"], sample[\"median_house_value\"])\n", + "\n", + "# Display graph.\n", + "plt.show()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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jZWutR85ffdSJxlZfNJO+XGKjloh0EtaUricb5JIScx3XyROR6kD+5JNP4pvf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\nQQAKrF3vt0LhMNZsrsET/98u2cIuAOCV2TVtX00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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "t0lRt4USU81L", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "This initial line looks way off. See if you can look back at the summary stats and see the same information encoded there.\n", + "\n", + "Together, these initial sanity checks suggest we may be able to find a much better line." + ] + }, + { + "metadata": { + "id": "AZWF67uv0HTG", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Tweak the Model Hyperparameters\n", + "For this exercise, we've put all the above code in a single function for convenience. You can call the function with different parameters to see the effect.\n", + "\n", + "In this function, we'll proceed in 10 evenly divided periods so that we can observe the model improvement at each period.\n", + "\n", + "For each period, we'll compute and graph training loss. This may help you judge when a model is converged, or if it needs more iterations.\n", + "\n", + "We'll also plot the feature weight and bias term values learned by the model over time. This is another way to see how things converge." + ] + }, + { + "metadata": { + "id": "wgSMeD5UU81N", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(learning_rate, steps, batch_size, input_feature=\"total_rooms\"):\n", + " \"\"\"Trains a linear regression model of one feature.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " input_feature: A `string` specifying a column from `california_housing_dataframe`\n", + " to use as input feature.\n", + " \"\"\"\n", + " \n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " my_feature = input_feature\n", + " my_feature_data = california_housing_dataframe[[my_feature]]\n", + " my_label = \"median_house_value\"\n", + " targets = california_housing_dataframe[my_label]\n", + "\n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column(my_feature)]\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda:my_input_fn(my_feature_data, targets, batch_size=batch_size)\n", + " prediction_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + "\n", + " # Set up to plot the state of our model's line each period.\n", + " plt.figure(figsize=(15, 6))\n", + " plt.subplot(1, 2, 1)\n", + " plt.title(\"Learned Line by Period\")\n", + " plt.ylabel(my_label)\n", + " plt.xlabel(my_feature)\n", + " sample = california_housing_dataframe.sample(n=300)\n", + " plt.scatter(sample[my_feature], sample[my_label])\n", + " colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " root_mean_squared_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n", + " predictions = np.array([item['predictions'][0] for item in predictions])\n", + " \n", + " # Compute loss.\n", + " root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(predictions, targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " root_mean_squared_errors.append(root_mean_squared_error)\n", + " # Finally, track the weights and biases over time.\n", + " # Apply some math to ensure that the data and line are plotted neatly.\n", + " y_extents = np.array([0, sample[my_label].max()])\n", + " \n", + " weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n", + " bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", + "\n", + " x_extents = (y_extents - bias) / weight\n", + " x_extents = np.maximum(np.minimum(x_extents,\n", + " sample[my_feature].max()),\n", + " sample[my_feature].min())\n", + " y_extents = weight * x_extents + bias\n", + " plt.plot(x_extents, y_extents, color=colors[period]) \n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.subplot(1, 2, 2)\n", + " plt.ylabel('RMSE')\n", + " plt.xlabel('Periods')\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(root_mean_squared_errors)\n", + "\n", + " # Output a table with calibration data.\n", + " calibration_data = pd.DataFrame()\n", + " calibration_data[\"predictions\"] = pd.Series(predictions)\n", + " calibration_data[\"targets\"] = pd.Series(targets)\n", + " display.display(calibration_data.describe())\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "kg8A4ArBU81Q", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Achieve an RMSE of 180 or Below\n", + "\n", + "Tweak the model hyperparameters to improve loss and better match the target distribution.\n", + "If, after 5 minutes or so, you're having trouble beating a RMSE of 180, check the solution for a possible combination." + ] + }, + { + "metadata": { + "id": "UzoZUSdLIolF", + "colab_type": "code", + "cellView": "both", + "outputId": "ac6e3e85-1e9d-46a9-b41e-92ccd3ab8675", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 977 + } + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.0001,\n", + " steps=300,\n", + " batch_size=1000\n", + ")" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 204.04\n", + " period 01 : 179.23\n", + " period 02 : 167.79\n", + " period 03 : 166.39\n", + " period 04 : 166.53\n", + " period 05 : 166.32\n", + " period 06 : 166.39\n", + " period 07 : 166.32\n", + " period 08 : 166.53\n", + " period 09 : 166.53\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 137.5 207.3\n", + "std 113.4 116.0\n", + "min 0.1 15.0\n", + "25% 76.0 119.4\n", + "50% 110.6 180.4\n", + "75% 163.9 265.0\n", + "max 1973.1 500.0" + ], + "text/html": [ + "
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Ytdqzv0JvbypCUeCaWdHSK+EEhSV1vLghD38/NUtvG4DWR/50CSG6lsYhmjmF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\nwoXk5OQwcODAtsZUgwcPZvPmzYwcOdLf59QjtCYLBuUmsHbLgaPuPzU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ai525L+5Dp1Ux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\nPkpBQQEej4e77rqLkSNHBjqs04PRwq1evdq47bbbDMMwjB07dhhXX311g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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ajVM7rkoYXeL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "id": "T3zmldDwYy5c", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.00002,\n", + " steps=500,\n", + " batch_size=5\n", + ")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "M8H0_D4vYa49", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "This is just one possible configuration; there may be other combinations of settings that also give good results. Note that in general, this exercise isn't about finding the *one best* setting, but to help build your intutions about how tweaking the model configuration affects prediction quality." + ] + }, + { + "metadata": { + "id": "QU5sLyYTqzqL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Is There a Standard Heuristic for Model Tuning?\n", + "\n", + "This is a commonly asked question. The short answer is that the effects of different hyperparameters are data dependent. So there are no hard-and-fast rules; you'll need to test on your data.\n", + "\n", + "That said, here are a few rules of thumb that may help guide you:\n", + "\n", + " * Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.\n", + " * If the training has not converged, try running it for longer.\n", + " * If the training error decreases too slowly, increasing the learning rate may help it decrease faster.\n", + " * But sometimes the exact opposite may happen if the learning rate is too high.\n", + " * If the training error varies wildly, try decreasing the learning rate.\n", + " * Lower learning rate plus larger number of steps or larger batch size is often a good combination.\n", + " * Very small batch sizes can also cause instability. First try larger values like 100 or 1000, and decrease until you see degradation.\n", + "\n", + "Again, never go strictly by these rules of thumb, because the effects are data dependent. Always experiment and verify." + ] + }, + { + "metadata": { + "id": "GpV-uF_cBCBU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Try a Different Feature\n", + "\n", + "See if you can do any better by replacing the `total_rooms` feature with the `population` feature.\n", + "\n", + "Don't take more than 5 minutes on this portion." + ] + }, + { + "metadata": { + "id": "YMyOxzb0ZlAH", + "colab_type": "code", + "outputId": "c8091026-424f-4fbd-a8bd-44d0287dfbc9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 977 + } + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.0001,\n", + " steps=500,\n", + " batch_size=1000,\n", + " input_feature=\"population\"\n", + ")" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 209.51\n", + " period 01 : 188.58\n", + " period 02 : 177.30\n", + " period 03 : 175.95\n", + " period 04 : 175.92\n", + " period 05 : 175.95\n", + " period 06 : 175.92\n", + " period 07 : 176.06\n", + " period 08 : 175.99\n", + " period 09 : 175.92\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 122.9 207.3\n", + "std 98.7 116.0\n", + "min 0.3 15.0\n", + "25% 67.9 119.4\n", + "50% 100.3 180.4\n", + "75% 148.0 265.0\n", + "max 3068.1 500.0" + ], + "text/html": [ + "
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IIU4lZ07izDmqUJuP1ZOI87b+jNqVDWo1plFD/dpF9o8WFAWGh+jSDbvDwxdf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5PUaZMB8PnD7NzrpqjAzLi8sy2xUDh6WMOgV/j2PdMpGpfWL+Bw6nSR3pkiOnXkjwTTNI2n/9DI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\nSJA0OwB0qsrKyjLuWp5C3SE7GRYTzf/6G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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ci1ISxxrZ7v0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "id": "SjdQQCduZ7BV", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.00002,\n", + " steps=1000,\n", + " batch_size=5,\n", + " input_feature=\"population\"\n", + ")" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 00406f09cecb5e53890f96ca47983dd889141a90 Mon Sep 17 00:00:00 2001 From: Aditya Joardar <30207466+joardar-aditya@users.noreply.github.com> Date: Mon, 18 Feb 2019 00:11:51 +0530 Subject: [PATCH 10/13] synthetic features and outliers solved --- Copy_of_synthetic_features_and_outliers.ipynb | 1398 +++++++++++++++++ 1 file changed, 1398 insertions(+) create mode 100644 Copy_of_synthetic_features_and_outliers.ipynb diff --git a/Copy_of_synthetic_features_and_outliers.ipynb b/Copy_of_synthetic_features_and_outliers.ipynb new file mode 100644 index 0000000..ceb2ed1 --- /dev/null +++ b/Copy_of_synthetic_features_and_outliers.ipynb @@ -0,0 +1,1398 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Copy of synthetic_features_and_outliers.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "i5Ul3zf5QYvW", + "jByCP8hDRZmM", + "WvgxW0bUSC-c" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "4f3CKqFUqL2-", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Synthetic Features and Outliers" + ] + }, + { + "metadata": { + "id": "jnKgkN5fHbGy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Create a synthetic feature that is the ratio of two other features\n", + " * Use this new feature as an input to a linear regression model\n", + " * Improve the effectiveness of the model by identifying and clipping (removing) outliers out of the input data" + ] + }, + { + "metadata": { + "id": "VOpLo5dcHbG0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's revisit our model from the previous First Steps with TensorFlow exercise. \n", + "\n", + "First, we'll import the California housing data into a *pandas* `DataFrame`:" + ] + }, + { + "metadata": { + "id": "S8gm6BpqRRuh", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup" + ] + }, + { + "metadata": { + "id": "9D8GgUovHbG0", + "colab_type": "code", + "outputId": "cfced8aa-fd63-4c2f-eafd-13976c87fddf", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + } + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import sklearn.metrics as metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))\n", + "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n", + "california_housing_dataframe" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "11441 -121.2 38.8 5.0 9137.0 1368.0 \n", + "1033 -117.1 32.7 47.0 771.0 224.0 \n", + "2208 -117.4 34.1 7.0 5059.0 780.0 \n", + "14787 -122.2 37.5 40.0 2959.0 389.0 \n", + "7482 -118.4 34.1 36.0 2963.0 838.0 \n", + "... ... ... ... ... ... \n", + "709 -117.0 32.8 37.0 1184.0 178.0 \n", + "9642 -119.5 36.7 19.0 3351.0 589.0 \n", + "3619 -117.9 34.6 7.0 681.0 125.0 \n", + "671 -117.0 32.6 5.0 2329.0 542.0 \n", + "16215 -122.5 37.8 50.0 2159.0 437.0 \n", + "\n", + " population households median_income median_house_value \n", + "11441 3667.0 1294.0 5.5 229.6 \n", + "1033 637.0 212.0 2.0 90.3 \n", + "2208 3253.0 801.0 4.9 140.5 \n", + "14787 985.0 365.0 9.9 500.0 \n", + "7482 1129.0 745.0 2.6 500.0 \n", + "... ... ... ... ... \n", + "709 529.0 192.0 4.8 161.7 \n", + "9642 1578.0 542.0 3.3 160.1 \n", + "3619 485.0 104.0 2.7 125.6 \n", + "671 1213.0 514.0 4.0 225.6 \n", + "16215 1111.0 417.0 3.6 346.4 \n", + "\n", + "[17000 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "metadata": { + "id": "I6kNgrwCO_ms", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll set up our input function, and define the function for model training:" + ] + }, + { + "metadata": { + "id": "5RpTJER9XDub", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model of one feature.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(buffer_size=10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "VgQPftrpHbG3", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(learning_rate, steps, batch_size, input_feature):\n", + " \"\"\"Trains a linear regression model.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " input_feature: A `string` specifying a column from `california_housing_dataframe`\n", + " to use as input feature.\n", + " \n", + " Returns:\n", + " A Pandas `DataFrame` containing targets and the corresponding predictions done\n", + " after training the model.\n", + " \"\"\"\n", + " \n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " my_feature = input_feature\n", + " my_feature_data = california_housing_dataframe[[my_feature]].astype('float32')\n", + " my_label = \"median_house_value\"\n", + " targets = california_housing_dataframe[my_label].astype('float32')\n", + "\n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(my_feature_data, targets, batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n", + " \n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column(my_feature)]\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + "\n", + " # Set up to plot the state of our model's line each period.\n", + " plt.figure(figsize=(15, 6))\n", + " plt.subplot(1, 2, 1)\n", + " plt.title(\"Learned Line by Period\")\n", + " plt.ylabel(my_label)\n", + " plt.xlabel(my_feature)\n", + " sample = california_housing_dataframe.sample(n=300)\n", + " plt.scatter(sample[my_feature], sample[my_label])\n", + " colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " root_mean_squared_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period,\n", + " )\n", + " # Take a break and compute predictions.\n", + " predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", + " predictions = np.array([item['predictions'][0] for item in predictions])\n", + " \n", + " # Compute loss.\n", + " root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(predictions, targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " root_mean_squared_errors.append(root_mean_squared_error)\n", + " # Finally, track the weights and biases over time.\n", + " # Apply some math to ensure that the data and line are plotted neatly.\n", + " y_extents = np.array([0, sample[my_label].max()])\n", + " \n", + " weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n", + " bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", + " \n", + " x_extents = (y_extents - bias) / weight\n", + " x_extents = np.maximum(np.minimum(x_extents,\n", + " sample[my_feature].max()),\n", + " sample[my_feature].min())\n", + " y_extents = weight * x_extents + bias\n", + " plt.plot(x_extents, y_extents, color=colors[period]) \n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.subplot(1, 2, 2)\n", + " plt.ylabel('RMSE')\n", + " plt.xlabel('Periods')\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(root_mean_squared_errors)\n", + "\n", + " # Create a table with calibration data.\n", + " calibration_data = pd.DataFrame()\n", + " calibration_data[\"predictions\"] = pd.Series(predictions)\n", + " calibration_data[\"targets\"] = pd.Series(targets)\n", + " display.display(calibration_data.describe())\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)\n", + " \n", + " return calibration_data" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "FJ6xUNVRm-do", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Try a Synthetic Feature\n", + "\n", + "Both the `total_rooms` and `population` features count totals for a given city block.\n", + "\n", + "But what if one city block were more densely populated than another? We can explore how block density relates to median house value by creating a synthetic feature that's a ratio of `total_rooms` and `population`.\n", + "\n", + "In the cell below, create a feature called `rooms_per_person`, and use that as the `input_feature` to `train_model()`.\n", + "\n", + "What's the best performance you can get with this single feature by tweaking the learning rate? (The better the performance, the better your regression line should fit the data, and the lower\n", + "the final RMSE should be.)" + ] + }, + { + "metadata": { + "id": "isONN2XK32Wo", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE**: You may find it helpful to add a few code cells below so you can try out several different learning rates and compare the results. To add a new code cell, hover your cursor directly below the center of this cell, and click **CODE**." + ] + }, + { + "metadata": { + "id": "5ihcVutnnu1D", + "colab_type": "code", + "cellView": "both", + "outputId": "f6c044bc-ea13-4960-aa32-7d190d435cea", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1384 + } + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE\n", + "#\n", + "california_housing_dataframe[\"rooms_per_person\"] = california_housing_dataframe[\"total_rooms\"]/california_housing_dataframe[\"population\"]\n", + "\n", + "calibration_data = train_model(\n", + " learning_rate=0.00005,\n", + " steps=500,\n", + " batch_size=5,\n", + " input_feature=\"rooms_per_person\"\n", + ")\n", + "california_housing_dataframe" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 237.51\n", + " period 01 : 237.49\n", + " period 02 : 237.46\n", + " period 03 : 237.44\n", + " period 04 : 237.41\n", + " period 05 : 237.39\n", + " period 06 : 237.36\n", + " period 07 : 237.34\n", + " period 08 : 237.31\n", + " period 09 : 237.29\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 0.3 207.3\n", + "std 0.1 116.0\n", + "min 0.1 15.0\n", + "25% 0.2 119.4\n", + "50% 0.3 180.4\n", + "75% 0.3 265.0\n", + "max 6.2 500.0" + ], + "text/html": [ + "
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17000 rows × 10 columns

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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "11441 -121.2 38.8 5.0 9137.0 1368.0 \n", + "1033 -117.1 32.7 47.0 771.0 224.0 \n", + "2208 -117.4 34.1 7.0 5059.0 780.0 \n", + "14787 -122.2 37.5 40.0 2959.0 389.0 \n", + "7482 -118.4 34.1 36.0 2963.0 838.0 \n", + "... ... ... ... ... ... \n", + "709 -117.0 32.8 37.0 1184.0 178.0 \n", + "9642 -119.5 36.7 19.0 3351.0 589.0 \n", + "3619 -117.9 34.6 7.0 681.0 125.0 \n", + "671 -117.0 32.6 5.0 2329.0 542.0 \n", + "16215 -122.5 37.8 50.0 2159.0 437.0 \n", + "\n", + " population households median_income median_house_value \\\n", + "11441 3667.0 1294.0 5.5 229.6 \n", + "1033 637.0 212.0 2.0 90.3 \n", + "2208 3253.0 801.0 4.9 140.5 \n", + "14787 985.0 365.0 9.9 500.0 \n", + "7482 1129.0 745.0 2.6 500.0 \n", + "... ... ... ... ... \n", + "709 529.0 192.0 4.8 161.7 \n", + "9642 1578.0 542.0 3.3 160.1 \n", + "3619 485.0 104.0 2.7 125.6 \n", + "671 1213.0 514.0 4.0 225.6 \n", + "16215 1111.0 417.0 3.6 346.4 \n", + "\n", + " rooms_per_person \n", + "11441 2.5 \n", + "1033 1.2 \n", + "2208 1.6 \n", + "14787 3.0 \n", + "7482 2.6 \n", + "... ... \n", + "709 2.2 \n", + "9642 2.1 \n", + "3619 1.4 \n", + "671 1.9 \n", + "16215 1.9 \n", + "\n", + "[17000 rows x 10 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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//Oc/tT6j1WrNGo9SWdyIozFOLpchJ6fQKtvWmT2yB7r6yBCXdAmLvzjBdI67\nNMU5INN4DmyP58D2eA4MMxWo4a8YERERWcXRo0fx6aef4osvvoBMJsOhQ4cAVKUlREZGIjU1Vf/Z\n5ORkDBw40OB2IiIi0LlzZwBAWFgYMjIy4O3tDYVCof/MzZs34e3dsmcO6NI53ogNhtyN3TmIiKhl\nYFCCiIiILK6wsBArVqzAZ599Bjc3NwDAmjVrcP78eQDA2bNn0bVrV/3n09PT0aNHj1rb0Wq1mDlz\nJlQqFYCqWhL33XcfBgwYgMOHD6OsrAw3b97ErVu30L179yY4Mtvr0s4Vi2c+gBB/OTKuFmDxxp9x\n7vdcWw+LiIioQZi+QURERBa3d+9eKJVKvPDCC/r3Fi5ciCVLlkAkEkEqlWLFihX6ZSqVCi4uLvrX\nR44cwdWrVxETE4MpU6Zg5syZcHJygo+PD55//nk4OTlhypQpmDZtGgQCAd566y2zCmS2FM5SBzxT\nrTvHh+zOQUREzZRAa24Sph1hjo7tMVfKvvB82BeeD/vC82FZzb14l7X+Fmz5d/bnDRXWJ6QjJ78U\n9/m2xZxW2p2D/9Ztj+fA9ngObI/nwDDWlCAiIiJqoaqnc1xiOgcRETUzDEoQERERNXO6dI7HI/xQ\nWlaBD3ecxTc/XIamstLWQyMiIjKJQQkiIiKiFsBQd44VX6UhT1Vq66EREREZxaAEmUVdrsEtZTHU\n5RpbD4WIiIhMuDud461Np5jOQUREdovdN8gkTWUl4pIykZaRgzyVGh6uEgT6yfHclEBbD42IiIiM\nMNSdY9SAezBhKLtzEBGRfeGvEpkUl5SJxJSryFWpoQWQq1IjMeUqNu7+1abj4swNIiIi03TpHG/G\nhkDuJsXen5jOQURE9oczJcgodbkGaRk5Bpf9lJ6NkQ90gsRR1KRjMjZzIzqsO5/8EBERGXBPOxkW\nz3wAm/dfQMqFW3hr0yn835he6NPN09ZDIyIi4kwJMq6gSI08ldrgMkV+CQqKDC+zJmMzN+KSMpt8\nLERERM2Fs9QBz4zrjWkPV3Xn+Ojrs4g/zO4cRERkewxKkFFtXSTwcJUYXObl5oS2LoaXWYupmRtp\nGQqmchAREZkgEAgQFlSVzuHt5sR0DiIisgsMSpDe3XUaJI4iBPrJDX52QED7Jk/dMDVzQ1lYapOZ\nG0RERM3NPe1kWDSzP0J6eOu7c/xymd05iIjINlhTgkzWaYgO6w6gaiaCsrAU7jIpAv288MTY3sjL\nu92k49TN3Mg1EJhwl0mbfOYGERFRc6VL50ju7Ibt31/CR1+zOwcREdkGgxKkr9Ogo6vTAAAx4X6I\nCffDxNBuKChSo62LBBJHEUTclXk9AAAgAElEQVSipr9g0c3cqD5WnUA/ryafuUFERNSc6dI5unVo\ni/UJ6dj701/IuJqPOY/0hoer1NbDIyKiVoKh8FbO3DoNEkcRvN2dLX7jX9/WntFh3REe4gtPVymE\nAsDTVYrwEF/9jA4iIiKqn3vaybB4VlU6R6Y+nUNh62EREVErwZkSrZw5dRq83Z0tvt+GtvYUCYUG\nZ24QERFRwzlJqtI5Dnd2w3+/v4SPvv4FIwd0xoQh98LBBrMjiYio9eCvTCtnqsOGNes0NLa1p7Vm\nbhAREbVWAoEAw3XdOdydsO+nK+zOQUREVsegRCtnqsOGteo0sLUnERGR/bqnnQyLZ/bHAz29kXmt\nAIs3/oyzmUznICIi62BQgqxSp8FUrQi29iQiIrJvThIHPP1Ib8RG+kNdXomP43/BjuRMVGgqbT00\nIiJqYVhTgixap8GcWhFs7UlERGT/BAIBhgd2xL3tXbF+Zzr2n7yCS1fz8cy4AHbnICIii+FMCdKz\nRJ0Gc2pF2CJlhIiIiBqmejrH5WsqLN74M84wnYOIiCyEQQmymPrUijCVMlLfNqFERERkXbp0jul3\n0jlWM52DiIgshOkbZDH1aS9qKGXEQSRoUJtQIiIisj6BQIBhgR1xbwdXrE/4O51jziMB8GzLdA4i\nImoY3umRxTSkvWj1lJHGtgklIiIi6+vsI8Oiaukcb236GWcuMZ2DiIgahkEJspjG1Ipgm1AiIqLm\nQ5/OEXUnneObX7AjiekcRERUf0zfIIvStRFNy1BAWVgKd5kUgX5edbYXrU/qBxEREdmeQCDAsH66\n7hy/Yv/PVekcT4/rDa+2TrYeHhERNRMMSpBFNbS9KNuEEhERNU+dfWRYNCMEWw9cxMnfbmLJplOY\nPboX+t3nZeuhERFRM8D0DbKK+rQX1VRW4psfLuN2abnB5WwTSkREZN+cJA54amwvzKiWzrH9+0tM\n5yAiojpxpgTZnK7A5d2kYhEe6tO+ztQPIiIisj2BQIDQfh3R9U46x8FTWci8VoA5TOcgIiITOFOC\nbMpUgUtniQMmhnZjO1AiIqJmRJfOMaCXD36/rsKSTaeQdsnwbz0RERHv9simTBW4zC9So6DI8DIi\nIiKyX04SBzw5thdmjuyBsopKrPnmHNM5iIjIIAYlyCR1uQa3lMVWa8mpK3BpCAtcEhERNV8CgQBD\n+3bAgukh8PFwxsFTWXj336ehyC+x9dCIiMiOsKYEGaSprERcUibSMnKQp1LDw1WCQD85osO6WzSd\nQuIoQqCf3GBNCRa4JCIiav46ebtg0YwQbDt4ET/9ehNvbTqF2aN7ItBPbuuhERGRHeBMCTJIV3wy\nV6WGFkCuSo3ElKuIS8qs8TlLzKSIDuuO8BBfeLpKIRQAnq5ShIf4Wq3ApbVnfxAREVFNThIHPDmm\nKp2jXFOJNd8ynYOIiKpwpgTVYqr4ZFqGAhNDu0GjqcRXiRkWmUkhEgoRE+6HiaHdUFCkRlsXiVVm\nSDTV7A8iIiKqTZfOcW97V6zfmY6Dp7Jw6WoBnhnXG15u7M5BRNRa8U6MajFVfFJZWIqCIjU27v7V\nrJkU9SFxFMHb3dlqKRvmzv4gIiIi6/H1dsHCGSEY2NsHf2Sr8NamUzht5GEIERG1fAxKUC11FZ90\nkjjgp/Rsg8vTMhR2mRZR1+wPexwzERFRSyUVO+D/xvTCrDvpHJ98ew5fJWYwnYOIqBViUIJq0RWf\nNCTQzwsl6grkGKmcrZtJYW/Mmf1BRERETUcgEGBI3w5YOD0E7T2dkZhyFe/+O9XoNQYREbVMVg1K\nlJaWIjw8HN9++y2ys7MRGxuLmJgYzJ8/H2VlZQCAXbt2YeLEiZg8eTK+/vpraw6H7jCn0KOp4pNt\nXSSQG8n9tNc2nmw9SkREZJ/+Tudohz+yC/HWplNIvch0DiKi1sKqhS7Xr1+Ptm3bAgBWr16NmJgY\njBw5EqtWrUJ8fDzGjx+PtWvXIj4+Ho6Ojpg0aRIiIiLg5uZmzWG1WvUp9Giq+KRICAwIaI9dR3+v\ntQ97bePJ1qNERET2qyqdoyd63OOG/xzMwNr/nUN4iC+mDO8OBxEn9hIRtWRW+3/5y5cvIzMzE8OG\nDQMAnDx5EiNGjAAADB8+HCdOnMDZs2dx//33QyaTQSqVIigoCKdPn7bWkFq9hhR6NFZ88omxvREW\n3BFS8d/vS8UiaLVaaCrtMx+0qVuPEhERkfkEAgGG9OmABTP+TudYti0Vt5jOQUTUolktKLF8+XK8\n9tpr+tclJSUQi8UAAE9PT+Tk5EChUMDDw0P/GQ8PD+TkcLqeNVi60KNIJIRQIEBp2d/rlZZp8H3q\nNbvtZqGb/bH0yQex7KkBWPrkg4gJ92M7UCIiIjviK69K5xgU0A5/3ijEkk2nkHrxlq2HRUREVmKV\n9I2EhAT069cPnTp1Mrhcq9XW6/27ubs7w8GB0+3rI1txG3mFxgs9isSOkHu1MXt7pWUV+OVyrsFl\nv1zOxdMTnSAVWzU7qFF8bT0AK5DLZbYeAlXD82FfeD6Imhddd44end3x74MXsfZ/6QgP9sXk4d3h\n6MCHCURELYlV7hoPHz6MrKwsHD58GDdu3IBYLIazszNKS0shlUpx8+ZNeHt7w9vbGwqFQr/erVu3\n0K9fvzq3r1QWW2PYLZqmXAMPmQS5BjpQuMuk0JSVIyen0OztVQiEyFEank6pyC/B5T9z4e3u3ODx\nUv3I5bJ6nT+yLp4P+8LzYVkM8FBTeqhPe3RtL8O6hHQkpl7FpWsFeGZ8ALyNFNwmIqLmxyqh5o8+\n+gjffPMNduzYgcmTJ2Pu3LkYNGgQDhw4AAA4ePAghgwZgr59++LcuXNQqVS4ffs2Tp8+jZCQEGsM\nqdWrq81nfQs9uruymwURERFZX0e5CxbN6I/BAe3w141CLNn0M1IuMJ2DiKilaLL5b88//zwSEhIQ\nExOD/Px8jB8/HlKpFC+99BJmz56NWbNm4dlnn4VMxicw1mLJQo9SsYNFgxxERERExkjEIswe0wtP\njOoJjUaLdQnp+M/BDJRX2GdxbSIiMp9Aa24hBzvCabiNoy7X1GrzWd/PyeUy3LhZcKfFqALKwlK4\ny6QI9PMy2GLUUmMiwzg93b7wfNgXng/Lau7pG9b6W+DfWdO5llOE9Tt/xXXFbdzTToZnxvWGt7sz\nz4Ed4DmwPZ4D2+M5MMzU9YP9ViIkq9G1+TRGU1l5J9iQgzyVGh6uEgT6yWsFG3TdLCaGdmtwQMHc\nfREREREBVekcC6eH4D+HMnDsXDaWbD6FWSN7YmQzD5gREbVWDEpQLXFJmUhMuap/natS61/HhPvV\n+nxdQQ5L7ouIiIhIIhbhidE94d/ZDdsOXsS6hHRcybmNsQPvYXcOIqJmhv+vTTWoyzVIy8gxuCwt\nQwF1uaZZ7ouIiIhansH3t8fCGf3R0asN9hz/A8u2peIWu7QRETUrDEpQDQVFauQZaBsKAMrCUhQU\nGV5mirpcg1vK4lpBBmvsi4iIiFqXjl5tsGB6CML7d8ZfNwuxZPMpnGJ3DiKiZoPpG1RDW5eqVp+5\nBoIF9W31aaheRJ/uXggP9oWHq9Si+yIiIqLWSyIWYf7UQNzj3QbbDl7E+oR0XAzqiOiw7nB0YAFt\nIiJ7xqAE1SBxFCHQT16jzoNOfVt9GqoXkXz6GpJPX4PnnYKWfe/zQlLqtXrvi906iIiI6G6D72+P\nru1dsT4hHUmnryHzWgGeGR8AnwbWviIiIutjUIJqiQ7rDgAGW32ay1S9CODvgpYjgjsiPMTX7H2x\nWwcRERGZ0sGrDRbMCMFXhzJw9JdsLNl0CrNG9UT/Ht62HhoRERnAoATVYolWn6bqRVR35lIulj75\noNn7YrcOIiIiqovEUYRZo6q6c2w9UJXOcSGoI6YynYOIyO7w0TIZpWv12ZD0CF29iLroClqasy92\n6yAiIqL6GBTQHotm9EdHeRskn76Gf21LxU125yAisisMSpBV6GpT1MW1jRhOEvMm7LBbBxEREdVX\nhzvdOYb2bY8rN4uwZNMp/Hz+pq2HRUREdzAoQVYTHdYd4SG+8HSVGv1MflEZ3t58Cl8lZkBTWWly\ne6ZmX7BbBxERERkjcRRh5sieeHJML2i1wKc7f8W2AxdRXsFZlkREtsaaEmQ11WtT5KlKkZiShV8u\n5yFXVVrjc+bWhbBkZxAiIiJqfQYGtEOX9jKsS0hHcto1XNZ15/Bgdw4iIlvhTAmyOomjCO092yA2\nsgcWzQyBu5EZDebUhag++0IoADxdpQgP8a1XZxAiIiJqvdp7VkvnuFWEJZuZzkFEZEucKUFNqkRd\ngXwjtR90dSG8TfQSt0RnECIiImrddOkc/p3dsXX/RXy681dcuJKPx0awOwcRUVPjTAlqMupyDcrK\nNRapC9GYziBEREREADCwdzssmhkCX3kbHE67hn9tTcXNPHbnICJqSpwpQVanqaxEXFIm0jJykKdS\nQyI2HAuzRF0IdbmGMyiIiIjIbLp0jq8SL+HI2et4a/MpzIzqgQd7+dh6aERErQKDEmR1cUmZNYpT\nlpZVddmQikUoK9fAXSZFoJ9Xo+pC3B348HCVINBPjuiw7hAJOSGIiIiIjBM7ijBzZA/06OyGLQcu\n4rNdv+JiFtM5iIiaAoMSZFXqcg3SMnIMLnOWOOCN2GDI3ZwaPavh7sCHuR09iIjIelasWIHU1FRU\nVFTg6aefhlwux4oVK+Dg4ACxWIyVK1fi+vXrWL58uX6dzMxMrF27FkFBQbW2t337dnz++edISkrC\n1atXMXbsWAQEBAAA3N3dsXr16iY7NmqZBvRuh3vaybA+4Vccrtadox27cxARWQ2DEmRVBUVq5KkM\nF7bML1JD7CC0SMqGscBHWoYCE0O7MZWDiKiJ/fTTT7h06RLi4uKgVCoxYcIE9OnTBytWrECnTp3w\nySefYMeOHZgzZw62bdsGAFCpVJg7dy769etXa3u5ubk4dOhQjfe6du2qX5fIUqrSOYLx3+8v4Ycz\n17Fk8ynMiPLHgF7tbD00IqIWifPayaraukgsUtjSFFOBD11HDyIialr9+/fHxx9/DABwdXVFSUkJ\nPvzwQ3Tq1AlarRY3b95Eu3Y1b/I2bNiAGTNmQGgg7W7lypWYN29ek4ydSOwowoyoHnjqkV4AgM93\n/Yat+y+grI7W5UREVH8MSpBVSRxFCPSTG1zWo7ObRfbRFIEPIiKqH5FIBGfnqinv8fHxGDp0KEQi\nEY4cOYKoqCgoFAo88sgj+s+Xlpbi2LFjGDFiRK1tnTx5EhKJBH379q3xvkKhwLx58zB16lTs2rXL\nugdErdKAXu2weGZ/dPJ2weEz1/Gvbam4we4cREQWJdBqtVpbD6K+cnIKbT2EVk8ulxk9D3d3wPi7\nCKUCysJSiB1FALQoLauEp4UKUn6VmFGjpoROeIhvq6gpYep8UNPj+bAvPB+WJZfL6vX5xMREfPbZ\nZ9i4cSNksqp1tVot3n//fchkMsyZMwcAsGfPHvzxxx94/vnna6xfVlaGWbNmYd26dWjbti3CwsKQ\nlJSEoqIiHDhwAI888ggKCwsxefJk/Pe//4W3t7fJ8VRUaODAwoVUT+pyDb7cmY79J/6Ek0SEZyf1\nQ2iQr62HRUTUIrCmBFmMqQ4YMeF+mBjaDdsOXMSP6Tf061iqIOX4IV1RXFqBC38pkV+ktkhHDyIi\napyjR4/i008/xZdffgmZTIZDhw4hIiICAoEAkZGRWLNmjf6zycnJeOyxx2pt4/z581AoFHjyyScB\nALdu3cI//vEPfPjhh5g4cSIAwMPDAwEBAfj999/rDEooldZ5ys3gl+1Z+xxMCb0XneXO2LL/It7/\nTypO/ZqNx0bcd+dhCwH8d2APeA5sj+fAMFMPNRiUqMPdT/2bq6Y4DnM6YFy8ojS4bkMLUhoKhAzs\n3Q6PRfjBWcI/byIiWyksLMSKFSuwefNmuLlVpeutWbMGvr6+6NmzJ86ePYuuXbvqP5+eno4ePXrU\n2k7fvn1x4MAB/euwsDB8+OGH+Omnn5CcnIzXX38dxcXFuHDhQo3tEVnDgF7t0KWdK9YnpOOHM9dx\n+ZoKz4zvjfaebWw9NCKiZot3bUaYeurfmDSDptZUx2FOBwxzClJ6u9ev5ZahQMjx9Btwkjq0irQN\nIiJ7tXfvXiiVSrzwwgv69xYuXIglS5ZAJBJBKpVixYoV+mUqlQouLi7610eOHMHVq1cRExNjcPsh\nISFISEhAdHQ0NBoNnnrqKfj4+FjvgIjuaOfhfKc7RyYOp13D21tSMCPSHwN6szsHEVFDMChhhDlP\n/ZuDpjoOcwIOuoKUuQY+15CClGwFSkRkv6KjoxEdHV3r/e3btxv8/IkTJ2q8Hjp0qMHPJSUlAQAc\nHBzw3nvvNXKURA3j6CDC9Eh/+Hdyw+b9F/D57t9w4Uo+YsKZzkFEVF/N55F/E6rrZlfdTNpBNeVx\nmNMBw1QnjkA/r3oHENgKlIiIiGzpwV4+eOtOd44jZ69j6dYUZOfetvWwiIiaFQYlDGgpN7tNeRzm\nBhyiw7ojPMQXnq5SCAWAp6sU4SG+DSpIyVagREREZGs+d9I5hgd2xNWc23h7cwpO/Hqj7hWJiAgA\n0zcMsnSaga009XHoAgu61p+GOmCIhEJ9J47GFt7UBUIMtQJtyMwLIiIiooZwdBAhNtIf/p3dsHnf\nBXyx+zdcvKJETLgf0zmIiOrAoIQBLeVmt6mPoz4BB4mjqN5FLQ2JDusOrVaL4+duoLSsKh1FKhai\nUquFprKyWRUlJSIioubtgZ4+uKedDOv/l44jZ7Px+3UVnhkfwO4cREQm8I7NCEumGdhSQ45DXa7B\nLWVxg2tO6AIOTRG8EQmFEAgE+oAEAJSWVSIp9RrikjKtvn8iIiKi6nzcnfHm9GAMD2I6BxGROThT\nwghLphnYUn2Oozm2QWUHDiIiIrI3jg4ixD58pzvHnXSOC38pERPhx+sSIqK7MChRB0ulGdiaOcfR\nHNugmlPMsyWcPyIiImp+9OkcCek4+ks2fs9WYS7TOYiIarDPx9/U5OylDWp9U0fYgYOIiIjsmY+7\nM96MrUrnuKZL50hnOgcRkQ5nShAA6884UJdrTKaPNDR1pKUUJSUiIqKWS5fO0aOzOzbtPY8v9vyG\nC1eYzkFEBDAoQXdYq32oucGGxqSOmNOKlIiIiMjW+vfwRmcfF3ya8Ks+neOZcQHo4MV0DiJqvRiU\nIADWm3FgTrChscUqW0pRUiIiImr5fNyd8UZsMHYkZeL701fx9pZTmB7pj0EB7W09NCIim2BNCQJQ\nFRgYHtgRw4M6WqwNqrl1KsxJHTFHU7YiJSIiImooRwchHn/YD3PHB0AkFODLPeexce/5JqvhRURk\nTzhTopUrVpfjq0OXcOGvPCgLy+DhKkGfbp4ID+kED1dpo27wza1TYa3UESIiIiJ7FnInnWN9wq84\n9ks2/riuwjPjmc5BRK0LZ0q0UprKSnyVmIF/rv0RP6bfQF5hGbSoSq9ITruO5LRrjZ5xYG5nDF3q\niCEsVklEREQtmfeddI4RQb64priNt7ecwvFz2bYeFhFRk2FQopXS1XooLTM8TbCxbUA1lZX45ofL\nuF1abnD53cGG6LDuCA/xNZo6Ut9WoURERETNxd3pHBu+YzoHEbUeTN9ohUzVetBpbBvQuwtc6kjF\nIjzUp32tOhXGilXqZnTUt1UoERERUXOjT+fY+Xc6x5zxAejIdA4iasF4V9cKmar1oNOYWg6mgh5O\nYhGG9u2ACo1W/9nqMyDuLlapC27kqtT69JLElKuIS8ps0NiaO84YISIiatm83Z3xxrRgjAiuSud4\nh+kcRNTC1WumREZGBq5cuYLw8HCoVCq4urpaa1xkRaYKS+o0ppaDyQKXRWVYvOFnuMvEaOMkRnFp\nudEZEI1tFdqSaCorEZeUyRkjRERErYCjgxCPR/jBv5MbNu07jw3fncfFK/l4/GG/VnPtQ0Sth9lB\nic2bN2PPnj0oKytDeHg41q1bB1dXV8ydO9ea4yMr0BWWrE96RX3UFfTQAsgrLENeYZn+Pd0MCACI\nCfcDYH73jtbg7nQYQ98XERERtSw10jnOZeOPbKZzEFHLY/Yj1j179mDHjh1o27YtAOCVV17B4cOH\nrTUusrLahSUlGBzQDu8/Oxgx4X6NevpuqptGXaoX2DS3e0dLV9eMEaZyEBERtVxM5yCils7smRJt\n2rSBsNqNqlAorPGamhdjhSUtRTfTIi1DgVxVqdnrVZ8BYWpGR2tqFWrOjBHfJh4TERERNR2mcxBR\nS2Z2UKJz58745JNPoFKpcPDgQezduxfdunWz5tioCegKS1qaLugxdlAXLN7wM/Jvl9W9EmrPgKge\n3FAWlsJdJkWgn1ej0kuaG1PpMK1pxggREVFrF9LDG53bybA+IZ3pHETUYpgdlFi0aBG2bt0KHx8f\n7Nq1C8HBwXj88cetOTZqAUrUFSgwMyAB1J4BYe0ZHc0BZ4wQERGRjrebE96YFowdyZn4PvUq3tly\nCrEP+2Pw/e1tPTQiogYxOyghEokwa9YszJo1y5rjoRbG1FN+kRBwc5FAWaiucwaEtWZ0NBecMUJE\nREQ6unSOHp3dsHHvBWz47jwuXFFiWoQ/JGI+rCCi5sXsoESvXr0gEAj0rwUCAWQyGU6ePGmVgVHL\nIHEUwVnqaDAo0cHLBW/EBrfaGRD1wRkjREREdLdgf2908pHh04R0HD93A39kF+IZpnMQUTNjdlDi\nwoUL+v8uKyvDiRMncPHiRasMiloOdbkGt0sMp2/cLikHgFY9A6K+WvuMESIiIqrJ280Jr08LxtfJ\nmUhkOgcRNUMNap8hFosRGhqK48ePW3o81MIUFKmhLDQclMgvUqOgyHBXCSIiIiIyj6ODEDERfnh2\nQgBEQiE2fHceG777Deoytg0nIvtn9kyJ+Pj4Gq9v3LiBmzdvWnxA1LKYqinh5iJh5wgiIiIiC2E6\nBxE1R2bPlEhNTa3xv4KCAnz00UfWHBu1ALrOEYYUqyvwzQ+XoamsbOJREREREbVMunSO8GBfXFfc\nxjtbTuH4uWxbD4uIyCizZ0q8++671hwHtWC6DhHHfslGabVphKVlGn2by5hwP5uMjYiIiKil0aVz\n+LM7BxE1A3UGJUJDQ2t03bjb4cOHLTkeaoFEQiEmhnZDWkZOjaCETlqGAhNDu7GbBBEREZEFMZ2D\niJqDOoMSX331ldFlKpXK6LKSkhK89tpryM3NhVqtxty5c9GjRw+88sor0Gg0kMvlWLlyJcRiMXbt\n2oUtW7ZAKBRiypQpmDx5csOOhuxWQZEaeQbqSgCAsrAUBUVqdpUgIiIisjB9d47DmUhMqerOMS3C\nHw/1YXcOIrIPdQYlOnbsqP/vzMxMKJVKAFVtQZcuXYp9+/YZXC85ORkBAQF48sknce3aNTzxxBMI\nCgpCTEwMRo4ciVWrViE+Ph7jx4/H2rVrER8fD0dHR0yaNAkRERFwc3Oz0CGSPTBV8NJdJmXBSyIi\nIiIrcXQQIibcD/6dqtI5Nu49j4tXlJj2MNM5iMj2zK4psXTpUhw/fhwKhQKdO3dGVlYWnnjiCaOf\nHzVqlP6/s7Oz4ePjg5MnT2LJkiUAgOHDh2Pjxo3o2rUr7r//fshkMgBAUFAQTp8+jbCwsIYeE9mA\nulyDgiI12rpIjKZh9OjsjuPpN2q9H+jnxdQNIiIiIisL9vdGZx8ZPt2ZjuPpN/DHDaZzEJHtmR2U\nOHfuHPbt24fY2Fhs27YN6enpOHToUJ3rTZ06FTdu3MCnn36KWbNmQSwWAwA8PT2Rk5MDhUIBDw8P\n/ec9PDyQk5Njcpvu7s5wcOBNrK3J5TJoNJXYuPtX/JSejZz8EsjdnDAgoD2eGNsbIpGwxvJbyhI4\nSUQABFCXVcDrrs82pdKyCihVari7SiAVm/3PwK7J5TJbD4Gq4fmwLzwfRERV5HfSOXYkM52DiOyD\n2XdjumBCeXk5tFotAgICsHz58jrX2759O86fP4+XX34ZWq1W/371/67O2PvVKZXFZo6a6sucGQ9A\n1QV+Tk4hvkrM0HfQAIBbyhLsOvo7ikvKEBPuV2t5ibqq0OWggHaIjfSHxFGEvLzb1jugu2gqKxGX\nlIm0jBzkqdTwcJUg0E+O6LDuEAmbNjBiSbrzQfaB58O+8HxYFgM8RM2fg0iXzuGOjXvPM52DiGzK\n7KBE165d8Z///AchISGYNWsWunbtisJC4xd56enp8PT0RPv27dGzZ09oNBq0adMGpaWlkEqluHnz\nJry9veHt7Q2FQqFf79atW+jXr1/jjorqrSE36+pyDdIyDM9qSctQILJ/J6RcuGVw+cUr+RYbe33E\nJWXWCJLkqtRsS0pEREStUrC/HJ19XGqmc4zrjY5yF1sPjYhaEbMfDb/99tsYPXo0XnzxRTz66KO4\n55578Omnnxr9fEpKCjZu3AgAUCgUKC4uxqBBg3DgwAEAwMGDBzFkyBD07dsX586dg0qlwu3bt3H6\n9GmEhIQ08rDobupyDW4pi6Eur92SE/j7Zj1XpYYWf9+sxyVlGt2mqY4auapSvLMlFflFZUaX56lK\n630cjVFXEMXYd0NERETUUunSOSJCOuG64jbe2ZKCY79k23pYRNSKmD1TYsqUKRg3bhxGjx6NRx55\npM7PT506FW+++SZiYmJQWlqKRYsWISAgAK+++iri4uLQoUMHjB8/Ho6OjnjppZcwe/ZsCAQCPPvs\ns/qil9R45syAqOtmfWJot1qpHKVlFSgr1xjtqAEAqmLDAQmdxJQsxEb2aMBRNQzbkhIRERHV5iAS\n4rHw++Df2Q0bvmM6BxE1LbODEq+++ir27duHCRMmoEePHhg3bhzCwsL0tSbuJpVK8cEHH9R6f9Om\nTbXei4qKQlRUVD2GTeYyJ12hPjfruiDHL5dzkaMsgUTc8DoMv1zOg7pc0ySdN9TlGpNBFLYlJSIi\notYuyE+OTt5/p3P8nsU4Kl4AACAASURBVK3C3PEBTOcgIqsy+44yODgYCxYsQFJSEmbOnImjR49i\n6NCh1hwbNZI56Qqaykoc+PkKBALD27j7Zl0X5LilLIEWQGlZJQBAKhZBKADc63Fjn1dYipz8ErM/\n3xCaykp8lZiBBV/8hMUbT+F2abnBz7EtKREREVHNdI7s3GKmcxCR1dWrF6JKpUJiYiL279+PrKws\nREdHW2tcZAHmzIBITL2K5LTrRrdR/Wa9sLgMqRcMBzmcJQ54IzYYbduI8fbmU0ZTOqrTaoGPdpxB\nkL+31bpf3D1TpHoQpaxcA3eZFIF+XogO627xfRMRERE1R9XTOTYynYOIrMzsoMTs2bNx6dIlRERE\nYM6cOQgKCrLmuMgC2rpITKYrOEkcjM6kEAqA0MCOiA7rrk/ZSLlwy2jhyvwiNcQOQsicxQj0k9cI\nBJiSV1hmte4XpmaK6IIocjcnzpAgIiIiMoDpHETUFMx+ND19+nQkJydj4cKFtQISX3zxhcUHRo0n\ncRQh0E9ucFmgnxdK1BVGZ1JotUBk/04QCYX62QbGAhJAzTSP6LDu6ORdvx8ra3S/MDVTRBdEYUCC\niIiIyDhD6RxHfzE+y5aIqL7MDkqEhoZCJDJ8A3f06FGLDYgsKzqsO8JDfOHpKoVQAHi6ShEe4ovo\nsO76mRSGeLhWBRlMzTaornqaR4VGi2IjtRuM0aWTWJKp42NhSyIiIiLz6NI5nnv0fjiIhNi09wK+\n3PMb1GVsp05EjVevmhLGaLVaS2yGrEAkFCIm3A8TQ7uhoEiNti4SffBAJITRVAv/zm4ATM82AKoK\nWwb3kNeoyVDXOga3Y4UggW6miKHjY2FLIiIiovoJ8pOjs7cL1u/8FT+m38Af2So8Mz4AvkznIKJG\nsEhlQYGx1g1kkLpcg1vKYounK5gicRTB29251o149ZkUAlQVgJSKRTiRfgMLvvgJB05lwV1muO2r\nAMD93dxrFak0NUNBaqQ4krWCBKZmihARERFR/Xi5OeH1aUF4uH9VOsfSO+kcfEhJRA1lkZkSZB5d\nwci0jBzkqdTwcJUg0E9utc4T5qg+k+LfBy7iePoN/bJclRrJp6+hk7cL8gpr15PQAjhy9gbEjg41\nilSamqEw+P52qNQCZzIUyL+thkcd3S/U5ZpaMzwaenyN2Q4RERERVXEQCTF1xH3w61TVnWPT3gu4\neCUf/4gJtvXQiKgZYlCiCd3dnjJXpbZa54mGuHBFafD94tJyDO3XHsfOZqPSQBA8LUOBiaHdatzs\n64IMaRkKKAtL4S6Tot99ntAC+CVTAWWRGm4uYvTp5mEwKFOfAI45gQvdTBEiIiIisoy70zmufPQD\nnhrbi+kcRFQvFglKdOnSxRKbadFMFYw0dFPf1HLyS4zWgVAWqvFgDx8cOZNtZHlVkcrqN/2GZih8\n88NlfF8tKJNfVIbktOsQiYS1gjLmBHDsceYJERERUWuiS+eIP3wZB09lYemWFMRE+GFIn/ZM8SYi\ns5h953bt2jXMmzcPsbGxAIAdO3bgzz//BAC8/fbbVhlcS2Kq+KM1Ok+YS1NZia8SM/DRjjMwlgno\nLpPC19sFng3oZFF9hoKpoEz1+hp1BXB0n9UFLnJVamjxd+AiLinz/9m78/Cm6nx/4O+TpEkoTfdW\noAVaWnYoOwrKKgguLI5KR5RRcRTEmdGZ+V3nzlwQmNFxBK96dXRQZBEUQXEGUVGgLApI2aFUllL2\nvVu60WZpkt8fJSFtzjk5aZOmy/v1PPe5ND3n5Juexun3k88i8UqIiIiIyN+c5Rz/89RgaNQqLP+u\nejqHyVIV7KURUROgOCgxZ84cTJo0ydXEJjk5GXPmzAnYwpqbxjqe0rmxF+sZ4dSvSywMoVr06xIn\n+X2pLA9nU0/5TIyaQRklARylgQsiIiIiahh39GqLedMHoVO7cOz++Tr+unw/LuaVB3tZRNTIKQ5K\nWK1W3H333a40rEGDBgVsUc2Rs/mjmGCNp5Tb2ANATLiuxqQKXyZZODMwZi/OxJ8/yMTbnx+GTmLy\nhntQxma3Y+PeC5DK9nMe21gzT4iIiIhastiIVvjvx/pj3OD2uFZUgVdW7McPhy9zOgcRSfKpp0Rp\naakrKHHq1CmYzdz4+UKs+aPc5In6UNL8UW5jLwB44eE0JMYbXI+594lQa0Ngs1glr127J4S3TAzn\nddZszcW2Q1e8HuvMPCkUWX8wM0+IiFqSc+fOsa8UEXnQqFVIH90ZXdtHYcm3x/Dx9ydx8kIxpo3r\nilY69tknopoU/1fh+eefx5QpU5Cfn48JEybAaDRi4cKFgVxbs9MQ4yl9af4ot7HXadWIjmgl+hy6\nEDXiYlsjP79M9PtyGRh6rRqt9RoYy8weQRm581QCMKJfgutYubGjwco8ISJqjl58cRbefvt919fv\nv/8+Zs2aBQB4+eWXsWLFimAtjYgaub6dYzHvqcFYtD4bmceu4+y1Mjw3qSc63GbwfjIRtRiKgxJ3\n3HEH1q1bh5ycHGi1WiQnJ0On46fRdRHI8ZS+jB2V29ibLDas23GmTqNKi0pNooEOALBYbXjh4d4o\nLDGja4dIxLgFPuQyNxwOYNyg9jUCKw2ZeUJE1FLZbDV79GRmZrqCEkzHJiJvYiL0+NPU/vjPj2fw\n3Z4LeGXFAUwd0xkj+rbjdA4iAuBDUCI7Oxv5+fkYNWoU3nrrLRw+fBi//e1vMXDgwECuj3xQl7Gj\nk4clY2fWVZgsno0hvY0qlSoRyTjgGeRwEgRg4WeHYXdUZz8kxIXhf37VH1qNRjZzIzrcsySjITJP\niIhautqbBvdABDcURKSERq3CI6NS0aV9JD765hhWbDyJExeMeGJ8N5ZzEJHyRpevvPIKkpOTsX//\nfhw9ehRz5szBO++8E8i1kY/q0vyxvMIKs0hAQu4cm92OxeuOuppYzl6ciVUZObDZ7TBbbcjKLZBc\no80O2G/+PWt3ABfzyvHqioMA6t4M1Jl5woAEEVHgMRBBRHXVJzUW86cPRmpiBPYez8P85ftw/pp4\nOTARtRyKQ5M6nQ5JSUlYs2YNpkyZgtTUVKhUimMa1ADq0vyxLufIlYiMGZAoGRiRcjm/HGUVFhhC\ntaIlGWkp0RjVLwFmq42BByKiBlZaWooDB/bV+DozMxMOhwOlpaVBXBkRNUXR4Xq89Gg/rNtxFhsy\nz+PVlfvx6N2dMbJfAoOeRC2U4qBEZWUlvvvuO2RkZOD5559HcXEx/xhpZHxp/uheeuFLw0hvJSIT\nhiZJBjmk2B3A8g3HMesXvWuUZBSVmpBx4BKycguw/dAV2aadREQUGAaDAcuXf1Tj6/fee8/1byIi\nX2nUKjw8MsVVzrFyUw6OXyjGk+O7IVTPcg6ilkbxu/4Pf/gDVqxYgd///vcICwvDu+++iyeffDKA\nS6O6SB+dCpvdgcM5BSi+YUZ0reaPYtM5+nSOxd0DEnD4VKFsw0ib3Y6VG09KBhyMZSZUmqskgxxy\nDuUWYs3WXFdjTV2IGtsOXca2g5ddx8g17SQiosB4990PanwdF8dABBH5R1pKDOY9NQgfrP8Z+0/k\n4cK1Mjw3uRc6tuF/Z4haEsVBicGDB2Pw4MEAALvdjueffz5gi6K6cQYcsnILYCw3IzJMi7SU6BqZ\nBWKlF1sPXMaYgYl45ZnbRRtGOrMqNu67iJ+yr0k+vzZEjbDQEMmpGMfOF+FKfoXk+QdP5rsaa9al\naScREfnfjRvl+Oabr5Ce/hgAYPXq1fjss8/QsWNHvPzyy4iNjQ3yComoKYsO1+OlqdXlHN/uri7n\nSB/dGaP7s5yDqKVQHJTo0aNHjf8wCIIAg8GAPXv2BGRh5LvaAYficgu2HboCtbq6JEJuo7//RB4m\nDE2qMarUPauisNQMlZf/XageI3oWU8d0qTEVo5VOg0pzFYb0jMffPj4oeb6xzIyScjPio0IVNe0M\n1FhVIiK6ZcGCv6Nt23YAgAsXzuPNN9/E22+/jQsXLuDVV1/FW2+9FeQVElFTp1ap8NCIFHRtH4kP\nvz6GTzfn4MQFI566tzvLOYhaAMXv8hMnTrj+bbVa8dNPP+HkyZMBWVRLITVSs67X8pZZILfRLy63\nYN7SfRjQ7VbPhtVbTmHLgVvlE3YF4+jdsxg0agEZBy65SkWiDFroQlQwW+2i50YZdK7GmkobcPrz\nZ0hERJ6uXLmM+fP/DgDYvn0Lxo8fj6FDh2Lo0KH49ttvZc9dsGABDhw4gKqqKsyYMQNxcXFYsGAB\nNBoNtFotFi5ciCtXruD11193nZObm4v33nsP/fv397je6tWr8eGHH2Lr1q0AgI8++gjff/89BEHA\nb37zG4wYMcKPr5yIGlqvTjGYP30wPlj/Mw6czMeF62WYOakXktuGB3tpRBRAdQo9hoSEYMSIEVi6\ndCmeffZZf6+p2RPr61DfBo5KMgvkNvoAYCy/1bPhoREp2HVUulRDSlGpCWcul6BTQgS+/OF0jcyN\nojKL7Ln9u8a5Agvemnba7A4s+eYYTlww+u1nSEREnkJDb2WlHTp0AFOn/tL1tVxqdWZmJk6dOoU1\na9bAaDTiwQcfRFpaGhYsWID27dvjn//8Jz7//HPMnDkTK1euBFA92WPWrFno27evx/UKCwuxefNm\n19cXL17Ehg0bsHr1apSXl2Pq1Km46667oFYzQE3UlEUZdPivR/viq53n8O1P5/D3lQcwZXQqxgxI\nZDkHUTOlOCixdu3aGl9fu3YN169f9/uCWgK5kZp1beCoJLNAbqPv7lBOAe7oeRtMFpvP6xAE4I3V\nhxFl0KLCLH6+TqOCAw5Yqm6lXui0KjgcDtjsdldQQaw3RZ/OMXA4HPh/7+2EyXIr44JNMImIAsNm\ns8FoLEJFRQWys4/izjvfBQDcuHEDlZWVkucNGjQIaWlpAIDw8HBUVlbirbfeglqthsPhwPXr1zFg\nwIAa5yxZsgRPPPGE6MjxhQsX4ne/+x1+//vfAwD27NmDYcOGQavVIjo6GgkJCcjNzUXXrl399dKJ\nKEjUKhV+MbzTzXKOn/FZximcvFCM6fd1Q6g+JNjLIyI/UxyUOHDgQI2vw8LC8Pbbb/t9Qc1doBo4\nKh0H6tzo7z+Rh+Jy8cyFolITLl0v83kNwK0SD7msCHOVHVqNCsCtoITZYseWA5chCIIrqOA+HtSZ\n6VE7+6I2sZ8hSzyIiOrusceewOOPPwKTyYTp059FREQETCYTpk6diilTpkiep1arXVkWa9euxfDh\nw6FWq/Hjjz/i1VdfRadOnTBx4kTX8SaTCTt37sQLL7zgca09e/ZAp9OhT58+rscKCgoQHR3t+jo6\nOhr5+flegxJRUaHQaALzvwWcTBJ8vAfB5897MDLOgLRut+GNTw7gYE4+LhXcwJ+mDUSXDlF+e47m\niO+D4OM98I3ioMRrr70GACguLoYgCIiIiAjYopqzQDZwlJp64T7a07nRnzA0CfOW7oOx3HMtggB8\n/H2O5PPoQlR4/Td34avtucg6XYSiUhMEQVnPCSdLlXhfCbGggi5EjfioUNmAjpP7zzAQZTJERC3N\nkCF34quvNsJsNqF16zAAgF6vx3/913/hrrvu8np+RkYG1q5di6VLlwIAhg8fjmHDhuGNN97Ahx9+\niJkzZ7qOGzlypEeWhMViwTvvvIP3339f9nkcDmX/I2Q0Sk+Bqo+4OAPy8+sW0Cf/4D0IvkDdgxce\n6o31u87i613n8NK7O/DIqFSMHchyDjF8HwQf74E4uUCN4qDEwYMH8dJLL+HGjRtwOByIjIzEwoUL\n0bt3b78ssqVQ2sCxLsQyC5yb+9rZAoZQLQZ0E8+s8BZcuDOtLVISozBtXDeYrTacuVyCN1YfrvO6\n3ckFZuQCOk7uP8NAlMkQEbU0167d6i9UVlYOq7X6D61OnTrhypUraNeuneS5O3bswKJFi/DRRx/B\nYDBg8+bNGDt2LARBwLhx4/Duu++6jt22bRseffRRj2scP34cBQUFeOaZZwAAeXl5+P3vf49hw4bh\n7NmzruOuX7+O+Pj4er9eImp8VCoBk4d1Qpeb0zlWbzmFkxeMmH5/d7RmOQdRk6c4KPG///u/eP/9\n99GlS/Vm7tixY3j11Vfx6aefBmxxzZHSMov6PodzUy+XLeCeWSGX7aC6+Xi0QYf+XeNqZF7oQtTo\nlBAh20DTF3KBGW+NOoFbP8NAlckQEbU0jzwyAR06dERMTCwAQKO5lckgCAJWrFghel5ZWRkWLFiA\n5cuXIzIyEgDw7rvvIjExEd27d8eRI0eQnJzsOj47OxvdunXzuE6fPn2wceNG19ejR4/GW2+9hStX\nrmDZsmX47W9/C6PRiLy8PKSmpnqcT0TNR4+kaMx/ahA+/PoYDp0qwLyl+zBzck+ktGMGN1FTpjgo\noVKpXAEJAOjRowc7XNeRkjILf/GWLeDMrJDLdnAA+K9f9kWnhAjRjbzSBppKyAVm5J5Hr1XjrrS2\nrp9hIMtkiIhaktmz5+P7779FRUUFxowZh1/+8qEavRykbNiwAUajES+++KLrsTlz5mD+/PlQq9XQ\n6/VYsGCB63ulpaUICwtzff3jjz/i0qVLmDp1quj127VrhylTpuDxxx+HIAiYN2+eaINMImpeIsJ0\n+GN6X3zz0zl8tfMs/vHJQTw8MgX3DGrPcg6iJsqnoMSmTZswdOhQANV/LDAoUTdyZRb+pDRbwFu2\nQ7RBLxmQcPIMtOhww2StMSFDjF6rhsVqUxyYqf08kWE6dOsYhaljOyNUdyt9L5BlMkRELcm4cfdh\n3Lj7cP36NXz33Td47LHHkJCQgEmTJmHs2LHQ6/Wi56WnpyM9Pd3j8dWrV4sev3v37hpfDx8+XPS4\nrVu3uv49bdo0TJs2TelLIaJmQqUSMPGuZHRuH4kP1/+MNVtzq6dz3N8dYa1YzkHU1AgOhZ2hzp07\nh7/97W/IysqCIAjo27cvZs+ejQ4dOgR6jR7YOESZPGMF/vxBJsRusEoA/v7sHTWyBVZl5IhmIYwZ\nmOjRg0GqgYt77wq5SRkx4dVBiMnDklFeYfU5MKNkooYvr6epY0OdxoX3o3Hh/fCvuDgDvvjiC7zx\nxhuw2WzYv39/sJfkk0D9LvD3LPh4D4IvGPeg5IYFi7/+GcfOGRETrsOMSb2QmtByyzn4Pgg+3gNx\nfml0mZSUhCVLlvhlQdQwfM0WEMt26NYhCpOHdVL0fLUDBWLX65wYiXG3d0Cb6FBXMME9w0Ep974Z\nUhqyTIaIqLkrKyvDpk0bsGnTBthsNsyYMQMPPPBAsJdFRC1cRGst/jClL77dfQ7rdp7F658exC9G\ndMK4wR2gYjkHUZOgOFNi9+7dWLFiBcrKymqM3QpGo0tGnpSrS7ZAhdmKVZtP4cT5IhjLLKKjNN0j\ngN5Gbyq5XiApyapo6hiRbVx4PxoX3o/62bs3E99++xVOnDiOESNG49FHH6nRY6qpYaZE88V7EHzB\nvgcnLxixaP3PKCm3IC0lBr9+oEeLK+cI9j0g3gMpfsmUmD9/PmbNmoU2bdr4ZVHUMOqSLbBux1n8\nlH1rBJy3UZremmn6ej1/U5JVQURE4v74x9+iffsO6N27D4qLjVi2bFmN77/22mtBWhkRUU1dO0Rh\n/lODsfibY8g6XYi5S/fiuUm9kJrYcss5iJoCxUGJhIQETJw4MZBroQDwtammr6M0vR0/YWiS5PcP\nnszH8D7tEBfZqtlmMBARNXXvvLMIAFBSUoyIiEhERt4K8l66VP+pS0RE/hTeWovfT+mDDbvP4z87\nzuAfN8s5xt/Ocg6ixsprUOLixYsAgIEDB2LNmjUYPHgwNJpbp7Vv3z5wqyOvlJYmKM0WUDJKMyJM\nh6sFN2C7+dxyx1/KK5f8flGZGXOX7G3wcg4iIlJOpVJh7ty/wGw2IyoqCh99tBgdO3bEJ598gg8/\n/BC/+MUvgr1EIqIaVIKAB4YmoXNiBD5Y/zPWbj+NkxeK8esHusMQqg328oioFq9BiSeeeAKCILj6\nSHzwwQeu7wmCgC1btgRudSTJWx+HupJrjhkZpsPGfReRlVuAojIzog06pKXGIsqgRVGZxeP4KIMe\nifFhktcDAAcavpyDiIiU+/DD9/H22+8jKSkZO3f+gJdffhl2ux0RERH44osvgr08IiJJXTtEYd70\nwfjom2M4eqYQ85btw4yJPdGlfWSwl0ZEbrwGJdzngUtZt24dJk+e7JcFkTJyfRyUlmqI0YWo0a9L\nnGhzzNatQrDt4OUaz7nt4GW0jw8TDUr06xILQ6hW8nq1iZWHEBFRcKlUKiQlJQMA7rprBN577238\n6U9/wtixY4O8MiIi78JDtXjxkT74fs8F/PuHM1iw6hAeHJ6Me+/oyHIOokZCcU8JOf/+978ZlGhA\ncn0cdmZdxcGTeZJTLqTKPdwfdzbBPHgyH8YyM6IMOvRJjUHW6ULR56wwWTGqXztknS4Sbabp3myz\nqNQEqXEvzvIQNqUkImo8hFp/tLdt25YBCSJqUlSCgPvu6IjUhOpyji9/OFNdzjGhB8JZzkEUdH4J\nSiicKkp+ItfHwWSxwWSxAaiZPZE+OlW03OPhkZ2wdvuZGo/37RwLBwDn36GCAJitdpneEWaMG9wB\nU0Z3Fg14uDfbzDdW4P/WZomWc0QZ9IgI09XjJ0NERIFWO0hBRNRUdGkfiXlPDcKSb48j63Qh5i3d\nixkTe6Jrh6hgL42oRfNLUIJ/oDQsub4PYg7lFMBms2PboSuux5wBi+Pnjbicf6PG41sOXK5xfmGp\nGT9lX4Neq3YFPNw5gwnemmnqQtRIjDdIlnP06xLL0g0iokYmOzsLv/jF/a6vi4uNGDlyJBwOBwRB\nwPbt24O3OCIiHxlCtfjdw2nYuPcCvtx+Bgs+O4TJwzrh/iEs5yAKFr8EJahhyfV9EFNUZsKhUwWi\n33MPSNRVWkq0Tz0s3Ms5xMo9iIio8Vi16ssaX0dHtw7SSoiI/EMlCLj39o7onBCJReuz8Z8fzyDn\nghHPTOiJ8NYs5yBqaAxKNFGeG3sdbpisMFnsHsdGttbBWK4sq0KO2WLDnb3a4MSFYhjLTIgM06F1\nqxBknS7E9kNXFE8AcS/n8CWYoXT8KRER+U+bNm1rfB0XZwjSSoiI/Cs1MQLznhqMJd8cw5HThZi7\nbC9mTOiJbh1ZzkHUkPwSlAgLC/PHZcgHtTf2YaEheP3TQ7iYV+5xbN8usThyKl90QoYvosP1eHxc\n1+rn14bgs43HPaZx+DLa01u5h1Ogxp8SERERUcsW1irkZjnHRXz5w2ksXH0Ik+9Kxv1DkqBSsZyD\nqCEoDkrk5+djw4YNKCkpqdHY8oUXXsD7778fkMWRd86N/aqMHNGARPv4MEwd0xlmiw0/ZV+r13O5\n93wwhOtw5JT4BJCDJ/P9OtpTbvypkuAHEREREZEUQRAw/vYOSE2MwKKvsvGfHWeRc7GY5RxEDUTx\nx8wzZszAiRMnoFKpoFarXf9HwSc3IrTCVIUqmwNTx3aGXuv9fqkEYGS/thg9IAEx4XqoBCAmXI8x\nAxORPjoVZqsNecYKXCuskMy8KCozo8QP5SKA/Gs7lFMAs9Wz8SYRERERka9SE6rLOfqkxODnc0bM\nXbYXJ84bg70somZPcaZEaGgoXnvttUCuherAZrdj5caTkpM4jGUmlJSbER8VirvS2nptjjmiXwKm\n3VNdovHIyFs9HDRqoUYJRUykXvY6rXR1qwyq3TdCbvyp+2sjIiIiIqovlnMQNTzFO8c+ffrg9OnT\nSElJCeR6mpWGaMy4ZmuubFmGc1wnULM5ZlGpCbqbmRMWq010AoZ7z4dVGTk1AhoFxSbZdZXcsMAQ\nqjzdTapvxORhyZLjT91fGxERERGRP7Ccg6hhKQ5K7NixA8uXL0dUVBQ0Gg3nk8toqMaMcqUNTu59\nIMSmXgDwGjhR8jwe3PqOKCHXN0Jq/Kn7ayMiIiIi8idnOQencxAFluKgxL/+9S+Px0pLS/26mOai\noRozypU2AMCdvdrUyHxwqj31wlv5g7fnqU2vVSPOh5IKb30j5j892PXv6vGnnlkdRERERET+xnIO\nosBTHJRISEhAbm4ujMbqZi8WiwWvvPIKvvvuu4AtrinytsH251SKiDCdZGlDtEGHx8d19Utmhtzz\niBnau41Pr9Fb34jyCotHhgczJIiIiIioIbCcgyiwFAclXnnlFezatQsFBQXo0KEDLl68iOnTpwdy\nbU1SQzZm1IWoJUsb+neN89i417XHhdzztI8Pw41KK4xlZkQZdOjfNc7nDAa5oId734jaGR5ERERE\nRA2F5RxEgaE4KHH06FF89913mDZtGlauXIns7Gxs3rw5kGtrkpRusP3FvXmlVGmDP3pc1H6e2MhW\nSEuJQfroVFTZHPXKYJALerBvBBERERE1FiznIPI/xUEJrbY6NclqtcLhcKBXr154/fXXA7awpqqh\nN9hizStrP4c/elzUfp6UpBiUlVTe/J73vhTeKAmuEBEREREFG8s5iPxLcVAiOTkZn376KQYOHIin\nnnoKycnJKCsrkz1nwYIFOHDgAKqqqjBjxgz07t0bL730Emw2G+Li4rBw4UJotVqsX78eH3/8MVQq\nFaZMmYJHHnmk3i8smKQ22JOHdUKesSIgPRGkShv82eOirMKCS3nliI9qBWOpGTarDYD36R1KKAmu\nEBERERE1Fs5yjo++OYYslnMQ1ZngcCib3ehwOFBSUoLw8HB8++23KCwsxPjx49GmTRvR4zMzM7Fk\nyRIsXrwYRqMRDz74IIYMGYLhw4fj3nvvxZtvvok2bdpg8uTJePDBB7F27VqEhITg4YcfxieffILI\nyEjJteTnywdDGgtnD4ew0BCs23E24CNCxZ670lyF+cv3ix6jEoC/P3uH1ywHS1UVXl1xEJfyyuH+\ny6ILUUEQAJPFjpgGeE0kLS7O0GTeFy0B70fjwvvhX3FxhmAvoV4C9bvA37Pg4z0IvpZ6D+wOBzbd\nLOewOxxBLedo4aQfkwAAIABJREFUqfegMeE9ECf394PXTIljx46hR48eyMzMdD0WGxuL2NhYnD17\nVjIoMWjQIKSlpQEAwsPDUVlZiT179mD+/PkAgFGjRmHp0qVITk5G7969YTBUL7J///44ePAgRo8e\nrfwVNlLO7IVVGTkBHxF6KwCixbodZ1wBEJ1WOkCgtMfFqysO4mJeuchz2l3/DtTYUyIiIiKixkzF\ncg6ievEalFi3bh169OiB999/3+N7giBgyJAhouep1WqEhlZ/Ar927VoMHz4cO3fudPWmiImJQX5+\nPgoKChAdHe06Lzo6Gvn54uUGTVGgR4TWbmKp06pgstwKFrj/u7a0lGivz11cbhYNSEiRek1KJ3/U\ndUIIEREREVEwsZyDqG68BiX+8pe/AABWrlxZpyfIyMjA2rVrsXTpUtxzzz2ux6WqRpRUk0RFhUKj\naRob1qsFN1BUJj0iVK0NQVxs6zpff/G6ozWyMOSCELVNuaeb1zTcxd8e92k9haUmQKN2Xddms2Pp\n1z8jM/sq8osrERfZCnf0aovpE3pCrVbdXHMVCoor8fWOM9h//LrkcSSvqadUNzf1vR8mSxWMpWZE\nheug1ypu/0MS+P4gIqKG4JzOsWnvRazdzukcREp4/Ut32rRpEATpN9CKFSskv7djxw4sWrQIH330\nEQwGA0JDQ2EymaDX63H9+nXEx8cjPj4eBQUFrnPy8vLQt29f2TUZjRXelt1o2Kw2RBukR4TaLNY6\n1xxVmK3YtOd8nddmqbTIPrfZasOJc0U+X/fzzScx7Z6uAOBRupJnrMT6HWdQUWlB+uhUV5ZH7Z+P\n+3EsB/GOtWuNS33uhz9G+FJNfH/4FwM8RETyXOUcCRFYtJ7lHETeeP0Ld9asWXjuuefQuXNndOnS\nBb/61a/w+OOPo1OnTujZs6fkeWVlZViwYAE++OADV9PKoUOHYuPGjQCATZs2YdiwYejTpw+OHj2K\n0tJS3LhxAwcPHsTAgQP99PKCzzkiVEx9R4Su2nwKJoutzufn3xzpKaWk3IxiiSwPOVm5hTBbbV5L\nV1ZlnELG/kuiARv348zWur9GoqbGOcK3sNQMB271a1mzNTfYSyMiIiIfpCZWl3OkpcTg53NGzF22\nFyfOG4O9LKJGx2umhLNnxJIlS/DRRx+5Hr/nnnvw3HPPSZ63YcMGGI1GvPjii67H/vGPf2D27NlY\ns2YN2rVrh8mTJyMkJAR//OMf8fTTT0MQBDz//POuppfNhdSIUOfjcqR6LJitNpw473sWg7t312Zh\nUPfbJD+BjQjTITpcPMtDTlGZCSXl1ecUSZxbVGrC4ZwC0e+5M968lrcJIU7sSUFNWaB70BAREVHD\nYjkHkXeKC5WvXbuGs2fPIjk5GQBw4cIFXLx4UfL49PR0pKenezy+bNkyj8fGjx+P8ePHK11Kk6NW\nqTB1TBc8NCJF8YbZWwp3SbkZxjKL5PlajQqWKvn+EiU3rLITM3QhavTqFI0fDl8Vub4AS5V4/w8B\nwMa9F/DQyFTJoEZEmBbF5d6DHUonhDDlnZqDknKzZCDP1wAdERERNQ4s5yCSp3i39uKLL+LJJ5/E\nHXfcgaFDh2Lq1KmYNWtWINfW7DhHhCr5pFMqhXvV5hzkGSvQSqdBdLj4Zl2vVeOvTw9GZJiy/8iJ\nlUjY7HasysjBnmPXRc+RCkgAgN0BbDt0Bet2nJEuXekcK7n+GscpLHFhyjs1B87sJDFKA3RERETU\nOLGcg0ic4kyJMWPGYMyYMSguLobD4UBUFEfbBIpcCvcPh69g+6EriA7XIVQfIpqF0L9LHCLCdBjY\nLb5Gk0kpYp/AOjf59XEopwD/86v+qDBV4cR5I4rLzTVKV9Rq6eeICfetxIUp79QcOHvQiL0v6tuD\nhoiIiIKP5RxEnhQHJS5fvozXX38dRqMRK1euxBdffIFBgwYhKSkpgMtrmeRSuO03ExQKS80oLDUj\nMb41Kk02FJWaoNNWb1h2Z1/DyQtG9EqJxpBet+Hk+WLJsaRA9SewrXQa5BkrXJ/ESm3yfVFYasJf\nl+9HSbkFkQYdhvRsg0fHdkGorvrXTqzXRlpqDMYMSER0uF7xBowp79Sc1KcHDRERETV+LOcgqklx\nUGLOnDl47LHHXD0hkpKSMGfOHKxcuTJgi2upfGkwWVBswuszh+DzrbnYlX3N9XhhqRk/HKruBRFt\n0KJtdCiKykwwWz37TLTSq/HX5ftcvRg6tjH43NxSSnF5dd8LY5kZu7KvQa9T47Gx1eNC69JrQ4zc\nz4sp79TU+Ot9QURERI2bs5zjo2+OIet0IeYu24sZE3qiW0dmpFPLorinhNVqxd133w1BqE4rGjRo\nUMAW1VSYrTbkGSv8PrJSboxobSaLDVcKbuDEBel6tKIyC64WVYgGJADgUt6NGr0YDiqYilFXu45e\n8/h5+dJrQ0wgx64SBUt93xdERETU+DnLOaaMSkXZDSsWrj6Er3edhd0u3b+NqLlRnCkBAKWlpa6g\nxKlTp2A2++fT9KamISY9uKdwF5aaZI99/z/ZKKu0+uV5vdFrVdCoVSivrBL5nhoWqw0atfRkDpPF\nhvziSiTGhfl1XUx5JyIiIqKmiOUc1NIpDko8//zzmDJlCvLz8zFhwgQYjUYsXLgwkGtrtGo3gXRO\negDER2vWhXsKd35xJV5dsV8y0yGQAQmVADgcQJRBh24do/DwyBS8umK/aFAiVKdB39QYZB7Lk7+o\nw/+RX6a8ExEREVFTxnIOaqkUf6yfnJyMBx98EE899RQ6duyIyZMn48CBA4FcW6PkbdJDIEo5EuPC\ncGdaW79eVykHgP/3y7549dk78OsHesBitUk3lSw348SFYtnr6bVqxAWw6SRT3omIiIioqXKWczwy\nKoXlHNRiKA5KPPPMMzh37hyqqqqQmpoKjUaDqirPT8ubOyWTHmrzR++JR+/ujDEDExHpJYUrPNS/\nKV7RBj06JUS4NvnOppJiIlvrXI0tpQzt3aZGwCBQfTmIiIiIiJoilSDg3ts74r8f648ogw7/2XEW\nb31+GKU35P/OJmqqFJdvREZG4rXXXgvkWpoEXyY9+LP3hLM8YcLQJMxduld08x8Trsd/P9YPr6zY\nj5IbniUdKqF6pKjz/0cbdOjbJRYCqhtQmiyegYHajSKdTSXdy1ecWrfSoOSGGVKB3FH9E/Do3Z0B\nNExfDiIiIiKiporlHNRSqOfNmzdPyYHl5eU4f/48QkNDcePGDZSVlaGsrAwGgyHAS/RUURG8KKFG\nrUJBiQlnrpR6fO/O3m3Qr/OtKRCrt5xCxv5LqDRXb/YrzTacuVKKwhITeiZHQ6P2ffOtC1GjsFT6\n+W/v0QZFZWbR74/s1w4zJ/XEA0OTMLJvO9w3JAn9O8chLSUWo/onoLjcghuVVpitNkSH63Fn7zZI\nH50K1c3mpk49kqIgqFQoKK6E2VKF6HA9YiP0uJR/A1KJZboQFZLahqNXcjRUgiD5s6k0V6F3pxif\nfy4tXevWuqC+L6gm3o/GhffDv1q3btpjlgP1u8Dfs+DjPQg+3gP/04aoMbjHbdBp1Th8qhC7sq9C\nJQCdEyNdAwjc8R4EH++BOLm/HxRnSpw8eRJff/01IiMjXY8JgoDt27fXa3FNkZJJD3K9J37KvoaT\nF4x1zgxwf/6iUhMiwrTo1/nW88utz/lchlplHqG6EPz6gR4wW21eG0WqVSo8M7k37h3cHiXlZrTS\nafDX5ftk12y22l3ZFQ+NSJHty/HQiBT2hCAiIiIiwq1yjs4JkZzOQc2S4qDEkSNHsG/fPmi1/MVX\nMulBrvcEUL+JHWqVCpOHdUJ5hQUnLthRUm5B1ulCqNW5rsBDXSdROBtF+nJsnrFC9rW6O5RTgOF9\n2nnty6F0DY2BkkAOEREREVF9sJyDmivFQYlevXrBbDYzKOFGbgMv13vCna+ZAc5eDDuOXKkxItQZ\n5LDZHZh2T1ev66sr5wbcENHK9ZjS1wpUBx3gcCjuy9GYsS8GERERETUk53SOjXsv4MvtZ7Bw9SFM\nvisZ9w9JgkrlWc5B1BQoDkpcv34do0ePRkpKCtTqWxvoTz/9NCALa+rkGkK68zUz4LMtp7D1wGXJ\n7/9w6DLgcGDq2C5+3RjX3oDHRbVCWkoM0kenKn6tQHXQIS4qVPL42o01G7M1W3NrvIb6ZL8QERER\nESnhXs7xr69qlnPExXk/n6ixURyUmDlzZiDXEXSBSMGfPCwZFaYqHD9XBKPEqEypzACx9ZitNvx0\n9Krsc9odwLZDV6BWq3zaGHt7/bU34HnGyhob8Np9LLQhaslpHgAwql8CbHYHsnILJftyNGZyPUPY\nF4OIiIiIAi01MQLzp9cs5/jTtEFoE9E0so6JnBQHJQYPHhzIdQSNkhR8XwMWYtdsGx2Kq0UVHsfW\nzgyQW0++sQImi93jGmLcN8Zy61f6+pVswN37WISFhmDdjrM1mm327RwDu8OB2YszXc+VlhKDMQPb\nIzpc36Q28XI9Q5piXwwiIiIianpql3PMXrQLk1jOQU2M4qBEcyWXgp8+OrVOPQPErgkA7ePDUGGy\noqjMjGjDrWspXc/wPu0Uvy5jmQlFpSZsO3RZdv1KShB82YC797Go3Wzzyx9OY0ut56pLVkdjINdH\noyn1xSAiIiKips29nOPDb47hPzvO4uTNco4ITuegJqBFd+PzlgGwKuMUMvZfQmGpGQ7c2rCv2Zpb\np2vmF1fCbrfD4QAcDofP64lorYVeqyybIDJMh4wDl0TXv2pzjqLnM1uryy+cG3Ax3jbg7kEKJc/V\nVDj7aIhpSn0xiIiIiKh5SE2MwP/9YSTSUmJw7JwR85buxfHzxmAvi8irFh2UkMsAKCo14XBOgej3\n5DbRctc0WWwwllurr19m8QhwFJWaJCdYGMtMqDRX4c7ebSRfj7sbJit2Z4v3n/jh8BWs3HQSRaUm\n6ddfZsKZyyUwW21+2YArybZoatJHp2LMwETEhOuhEoCYcD3GDExsMn0xiIiIiKh5CW+txe8eTsOU\nUakoq7DijdWHsH7nWdjtnh+IEjUWLbp8Qy4FPyJMi2KJjbJczwBfxmMCNXsyZOy/KHlclEGPVjoN\nRvdPhN3uQNbpQlcZSKg+BHnGihojQt3/XZvdAWw7WD3BQ2qtAoCFqw8j5mbJx8MjO7nWaywzITby\n1vSNW88p3buiOZY7qFUqjxIVZkgQERERUTCpBAHjb++A1IQILFqfjXU7q8s5np3Icg5qnFp0UEJu\nlGW/zrHIOl3o8ybal/GYwK0AR0SYDlmnCyWP02vV+OvyfbcaRKbGYsyARESH6wEAsxdnwmz1Ldsg\nK7cQvVKi8cMhz4wKZzDVWfJhs9kxbnAHTBiahEpzFVKSYlBWUglAWbNMuZ9LWkp0k97Uu5eoEBER\nERE1BqmJEZj31GAs+eYYjpwuxLyle/HsxJ7o3jEq2EsjqkE9b968ecFehK8qKsTHa9ZFj6QoVJqr\nUFJugdlShehwPe7s3QaPjumMwlIzzlwp9Tjnzt5t0K9zzXIGs9WGolITNBoV0lJiUGmuwpWCG6iy\nyadKRYfrcd+QjigpN+Obn85LHldWYUWlubpkpNJsw7mrZVCpBPTrHIeiUpPsuVLMliq0jWmNS3k3\nvB574XoZMvZfwt7j11FpsWFI73aorKwuRVm9pbr3hvv6zlwpRXmlFX1SYl3XqP2zjjLoEBvZCpfy\ny/HNT+ex++drKCgxoUdSFFQCuwX7onVrnV/fF1Q/vB+NC++Hf7Vu3fQy29wF6neBv2fBx3sQfLwH\nwVf7HmhD1Li9x23QazU4kluAXdlXIQDonBgJgX9vBwTfB+Lk/n5o0ZkSgHwKvrM0wX2sZb8usTVK\nFqSyBCYPS8bBk3kwWeQbODp7Mvha9uFc10MjUmTP1YWoYK2yQ6yMLMqgw0mFzW9qZ05otRo8PLyT\nbLPMHw5dBhwOTB3bBWqVyuNnvXHfRVcZifu1ATS5aRxERERERI2R4CznSIzAoq/cyjkm9GiSJdTU\n/LT4oISTWAq+kp4BUiM1K0xVMJZJR8giw7QY2C3eFeDwtewDqDn284bJKnrMsD7tYLM7amz+nbp1\niMJP2dcUP5+77zPPodJkxZgBiZINLO0OiI78dAZhsnLFG4nuzLqKycM6IVTHX08iIiIiIn9ITagu\n51j67XEczi3A3GX7MGNCD3RPig720qiFa/HlG0po1Cq0bhUCjbrmsBKz1YZVm3NcZQvublRaEarX\niH4vKkyH+dMHY2DX+BplCs7yBmOp2WuGBVBd+nHDZMXWg5c9ykT0WjVG9ktA+uhU9O4UI1qi8sio\nVOw5dk10jd44HMC5q2VwOBworbDIXqOk3IIRfdvV+PnJlZxU2RwoKTejv8TED/LENLHGhfejceH9\n8C+Wb4jj71nw8R4EH+9B8Hm7B9oQNQZ3j0cr3c1yjqPVH1B2YTmH3/B9II7lGwEiN+ayuNyMIT3b\nYJdIJsKAbnEwhHp2vnVmZkwYmoS5S/eiuFz+lzktNUYy2yBUp8FDI1JcjSalMj6ksjPUKsAmPcDD\nJet0EdJSY0UzMZzEppVEhOkQZdCiSCKb5MQFo2scKRERERER+YcgCBg3+OZ0jq+y8dXOs8hhOQcF\nkcr7ISTF2ctBTJRBj0fHdsGYgYmICddDJQAx4XqMGZhYoyeFGENodWmHFOd15EonisvNKKk10tRZ\nouK+0U8fneqxxlH9ExSPCyoqM6F/aixG9G0HlURwVWxaiS5EjW4dpVPFjGWe6yciIiIiIv9ISYjA\n3KcGo29qLI6fN2Lusn04dq4o2MuiFoiZEvUgO1K0SyxCdRqvPSmkiDXZTEuJxpiB7REdrocuRA2z\n1SbZ4FIbokaYSDZGbWJ9M0rKzdguk/ngTgDwv58fQUy4Dm1jW+NyvuckD2czz9qmju2Mgzn5oqUq\ncmNXiYiIiIio/sJaheC3D/XG5n0X8cX20/jf1Ycx4c4kTLwzGSqpTxyJ/IxBiXpSMqFDrImmN0qa\nbMoFRUwWG9btOKN4ioX7Gn2ZBOI+lQMwo3182M0mn+I/C3ehuhDcldZWMqjD0g0ioqZtwYIFOHDg\nAKqqqjBjxgzExcVhwYIF0Gg00Gq1WLhwIa5cuYLXX3/ddU5ubi7ee+899O/f3/XYli1b8OGHHyIk\nJATR0dFYuHAh8vPzMWHCBPTq1QsAEBUVhXfeeafBXyMRUVMnCALuGdwBKYkRWLTuZ6zfda66nGNi\nT0TyQ0JqAAxK1JOS4EF9yAU0bHY7qmQaPzhHhvq6Hrlgh15bnaEhCIBd5KkrTFV4+cmBqDRXKfpZ\nKAnqEBFR05OZmYlTp05hzZo1MBqNePDBB5GWloYFCxagffv2+Oc//4nPP/8cM2fOxMqVKwEApaWl\nmDVrFvr27VvjWitWrMBHH30Eg8GAP//5z9i0aRP69euH5ORk17lERFQ/Ke0iMG/6ICz99jgOnSrA\nvKV78czEnujJ6RwUYAxK+Ent4IHZagtIkMLdmq252H7oiuT3xRpMKpU+OhU2uwOHcwpQfMOM6JvB\ngsnDOuH81VIsXH1Y8jkrzVWKnzPQQR0iIgqOQYMGIS0tDQAQHh6OyspKvPXWW1Cr1XA4HLh+/ToG\nDBhQ45wlS5bgiSeegEpVs+XVxx9/DACoqqpCfn4+brvttoZ5EURELUxrfQh+84ve2Lz/Er7Ylos3\nWc5BDYCNLv3MZrdjVUYOZi/OxJ8/yMTsxZlYlZEDm1hagQJmqw15xgqYrTaPxw/l5MueGxmmq1Nf\nBpvdjjVbc5GVWwBjuRkRrbVIS4lG+uhUhOo06JQQgfioVqLn1rUXhFgTTiIiarrUajVCQ6sD1GvX\nrsXw4cOhVqvx448/Yvz48SgoKMDEiRNdx5tMJuzcuRN333236PX+/e9/Y8yYMejQoQMGDx4MACgo\nKMDvfvc7/PKXv8T69esD/6KIiFoAQRBwz6D2+PPjAxATocf6XefwxupDKGYTegoQweFwOIK9CF/l\n55cFewkutTMiVmXkiJY9jBmYKNrfQSqjwhkYOJSTj6JSM6LDdejXJQ7po1OhVqmQZ6zAnz/IhNzN\n02vVuCutrescpaRew6h+7TBtXDcAwLpd57B+xxnFr5MCKy7O0KjeFy0d70fjwvvhX3FxBp+Oz8jI\nwAcffIClS5fCYKg+1+Fw4I033oDBYMDMmTMBAN988w3Onj2L3/72t5LXqqqqwp/+9CeMHDkSo0aN\nwsaNGzFx4kSUlZXhkUcewWeffYb4eOnpVdXXsEGjYQCciEiJ8goL/m/NIWRmX0NkmA5/mNof/brK\n/3eWyFcs36gjsaBBWkoMsk4Xih5fu7+Dt6DDmq25NQIDhaVm19dTx3RR1IzSZLHVOEcJuQyMHw5f\nAQQBU8d0xvQJPVFRaZHtBaGkhKUhylyIiCg4duzYgUWLFrn6QWzevBljx46FIAgYN24c3n33Xdex\n27Ztw6OPPupxDbPZjD179mD48OHQaDS4++67sXfvXkyYMAEPPfQQACA6Ohq9evXCmTNnvAYljMYK\n/77Imxj8Cj7eg+DjPQi+QNyDZ+7vjuTbDPh8Wy7mfrgb9w9NwqS7knz60LMl4ftAnNyHGgxK1JFY\n0GCbD/0d5IIOD41IkQwMuAc3pJpRyp3jTUm5GUUSgQ67A9h28DLUKgEvPDpAsheEt4CL0mOIiKjp\nKisrw4IFC7B8+XJERkYCAN59910kJiaie/fuOHLkCJKTk13HZ2dno1u3bh7XUavVmDNnDj7//HPc\ndtttyMrKQnJyMjIzM7Ft2zb8+c9/RkVFBU6cOFHjekRE5B+CIGDsoPZITYzAv9Zl45ufzuHUzekc\nUQZO56D6Y1CiDuSyCVTCrTGZ7qIMOlisNldvCLmgw/C0tpKBAffgxsMjO+HkhWJczi8XfU6xc7xR\nkoFxKKcAJksVAPHpIN6yPJQeQ0RETdeGDRtgNBrx4osvuh6bM2cO5s+fD7VaDb1ejwULFri+V1pa\nirCwMNfXP/74Iy5duoSpU6fir3/9K55//nlotVrExsbihRdeQEhICNatW4f09HTYbDY8++yzbIBJ\nRBRAyW3DMe+pQVi64QQO5uRj3rK9eGZCD/RKjgn20qiJY1CiDrxlE4i5YbJi7tJ9iA7XoWuHKNmg\nAwRBMjDg3khy7fYzuJhX7nW9vjSfVJKBUVhqQkFxJXQiDXjlAjbOjI3qf3vPBCEioqYrPT0d6enp\nHo+vXr1a9Pjdu3fX+Hr48OGuf48YMQIjRozwOOcf//hHPVdJRES+CNWH4PkHeyHjwCV8vjUXb605\ngvuHdsSku5KZ7Ux1xt+cOnBmE4iJNugwqn8CYsL1UAnVzSYBwGSxw4HqjICfsq9BpxXfdEcZ9IiL\nbIV+XeJEv9+vSyx0IWpF0zdqn6NU+uhUjOrXDnJTf74WaXIJAPnFlZJZFs6MDbmgjvMYIiIiIiJq\nfARBwNiB7fGXadXTOb756TwWfnYYxjL+DU91w6BEHTizCcT07xqHafd0xSvP3I550wcjVOfbJ/7O\nAEL66FSMGZjoCm7EhOsxZmCiq5Gk3MYeAAQAUWHVARL35pNKqFUqTBvXDcP6tJM8Zv/x6zXGlNrs\ndnyy+SReXbFf8hxnxoZcUKeuI0WVkhqxSkREREREyjnLOQZ0iUPOxWLMW7YX2WfEm/4TyWH5Rh05\nN/pS0yd0IWpoNSoYyyyi55stNtzZqw1OXCgWPV+tUkk2kgTkez/oQlTQa9UwlpuRlVsAtUqoUwPJ\newa1r564IaKguNKjcefWA5dlr+eesSFVItKvSywAIM9Y4deJHGysSURERETkX6H6EMx6sBe2HLiE\nNVtz8ebnR3D/kI6YPIzlHKQcgxJ15C1oAMgHDqLD9Xh8XFcA1SUPcDgQFxXq8eYVayTpfFxqY2+2\n2mG22gHUr4FkdLgeMRLrj41s5cpoMFttOHgyT/ZaI/u1rZGxIRbU6dM5Bg6HA7MXZ/o9cMDGmkRE\nRERE/icIAsYMbI+UhOrpHN/uPo9TF4sxY1IvTucgRRi+qidn0EDsE325Mo9+XWKhUQv48ofT+L8v\njmDu0n2YvTgTqzJyYLPbvT6v2WrDqH4JGNWvnavEI9qghTZEvBHEgRP5KKsQz9qQe21S67+jV1vX\nay4pN6NIIiPEyWJ11AgsOIM6rzxzO/7+7B145ZnboRIEbDlwGYWlZlf/jYz91VHX+vDWfJOlHERE\nRERE9eMq5+gah5xLJZi7lOUcpAyDEgEm1xvC+em9L5twm92OVRk5mL04E7MX70HW6UL0TonGHT1u\ng91RvfkXYyw3Y+7SvYqDHt7WP31CT9cxEWE6RBu0stc5cd4ouvl3zwQJVOCAjTWJiIiIiAIvVB+C\nWZN74bGxXWCyVOHNz4/gyx9O+7T/oJaH5RsBJlXmoWR0plj2hVgZwvZD4n0faisut/hcsuBc/4Sh\nSbiUV47E+DAYQrVQq2/FszRqAa1baWWzJYpvTt0QK0UBlAUOpM71Rq6MJtCNNYmIiIiIWhJBEHD3\ngESkJIS7yjlyLhZjxsSeiA7XB3t51AgxU6KB1C7zqMun976MAZUjl3lQezqFMzPjr8v34Y3Vh/HX\n5fuqsy1sdtfxyzacwMW8ctnn9Lb5D+REDm9lNP5qpklERERERNWS2oRj7pODMbBrHE5dKsG8ZfuQ\ndZrlHOSJmRJBUpdP772NAVVKLPNAajqF3eGoMVXDWWKi14fAZLLiUE6+6GuozdvmX65xpz8CB96m\npVBNZqtNsoErEREREZESoXoNnpvcC1sPXsaarafw9hdHcN8dHfHgcE7noFsYlAiSumzC5QIZvhAL\nekhNp1BL/LciY+8FmCze+zxEtA7BoO63Kdr8BzJwoGRaCnF0KhERERH5l7OcI/XmdI4Nmedx6hLL\nOegWBiUCTO4TZ1834XKBDF+kpcbUWItcWYhNoieNkoAEAKh82Mg2ROBAasQqVePoVCIiIiIKhI5t\nDHj5yUGXHciPAAAgAElEQVRY/v0J7D+Rh3nL9uHXD/RAWkpMsJdGQcagRIAo+cS5LptwsUBG384x\ncAA4cqoQxjITIlrrYJSZKGG22GCz213r8FdZiBhjme+bWgYOgqOuzVcbI5afEBERETU+oXoNnpvU\nE9s7ROKzLdXlHPfe0QEPDusEjVSKNjV7DEoEiC+fOPuyCZcLZDwysnoj1kqnwZ8W7ZbMZvgp+xpC\n9RrXOvxVFiKnqW1qW6JATkBpKCw/ISIiImrcBEHAqP6J6NSuupzju8wLOHWxBDMnsZyjpeJf6QHg\n7RNnqckXvqg9zcP9MW2IGoBD9nz3dchNp/AXqYki1HgEcgJKQ3EGAwtLzXDgVjBwzdbcYC+NiIiI\niNx0bGPA3KcGYVC3eOReLsHcpXtxJLcg2MuiIGBQIgDqMu7T389vskg0g5BYR/roVIwZmIiYcD1U\nAhATrkf7+DDFz6kSgOF92yKmiW9qW7KmPjq1IYKBREREROQ/rXQazJzUE9PGdYXZasf/rc3C59ty\nUSXV2I6aJZZvBEBdxn36UyudBpFhWhSXWySPqb0OsbIQjVq4mQp/q39FqF6Di3nlHtcb0bcdpo3r\nhlUZOQEb60mB15RHpzaH8hMiIiKilkYQBIzql4BObcPxr6+y8f2eCzh1qRjPTerFco4WgkGJOpJr\npKdRCwjVh4gGJQK5OXevp5cLSMito3Z/C6lARdbpQhQUV3psWoO5qWVzw/pryqNTgx0MJCIiIqK6\n69jGgLlPDsLH35/A3uN5mLt0L55+oAf6psYGe2kUYAxK+EhJI71VGadEswnax4cFdHNeu7mmO5UA\n2B1AjNt6lRILVMx4qBVOnyv02LQGY1PL5ob+1xQnoMiNzGWmDhEREVHj10qnwYyJPdGtQxRWZZzC\nO2uzMP72DvjFcE7naM4CGpTIycnBrFmz8OSTT+Lxxx/H1atX8dJLL8FmsyEuLg4LFy6EVqvF+vXr\n8fHHH0OlUmHKlCl45JFHArmsepGbqpE+OhWrNufgh8NXRM+tMFWhyuZAIN5PcvX0UWE6/GVaf9js\nDr8FCfRajeymtSE3tb5MOqHmrSmXnxARERFRdTnHyH4J6NQuHP9ad6ucY+bEXoiJYDlHcxSwoERF\nRQX+9re/YciQIa7H3nnnHUydOhX33nsv3nzzTaxduxaTJ0/Ge++9h7Vr1yIkJAQPP/wwxo4di8jI\nyEAtrc68NdKz2R3Ydkg8IAEEtq5drp6+5IYZNrsD8VGhMFttyDNWNLq0/LqWXni7J2JjSFnm0Xw1\n5fITIiIiIrqlw20GvPzkIKzYeBJ7jl3HvGV78fT9PdC3M8s5mpuABSW0Wi0WL16MxYsXux7bs2cP\n5s+fDwAYNWoUli5diuTkZPTu3RsGgwEA0L9/fxw8eBCjR48O1NLqTG7jX1RqwuEc+RE2IRqVorr2\numyavdXTh4VqsSojxy8lDmarDVcLbsBmtdV7w1ff0gtfmhuyzKPlaIrlJ0RERERUUyudBs9O6IFu\nHSLx6eZTeOfLLIwb3B4PjUhhOUczErCghEajgUZT8/KVlZXQarUAgJiYGOTn56OgoADR0dGuY6Kj\no5GfL/7Jd7DJbfwjwrQo9jLq09tom/psmuXq6bt2iMSX23NrZHHUpcShwmzFqs2ncOJ8EYzlFkQb\n6r+pr2/phS/NDVnmQURERETUtAiCgBF9E5DcNhz/+upnbNx7EbmXSjBjUk/ERrQK9vLID4LW6NLh\ncPj0uLuoqFBoNMFJyb6zTwLW7zjj8fjQtHbYf/w68oyVkufa7ECVoEJinKHG4yZLFYylZqzbeUZ0\n0xzaSotnJvf2urbfTOmH0FZaZGZfRb6xEnqdGoCA3T9fgyCIn5N1uhAzHmoFvVb6V8Fms2Pp1z9j\n894LqDRX1Xl9tZksVcg6XVjndTlJ3ZM7+7RDYrtIvz5XYxZX6/eKgov3o3Hh/SAiImraOtxmwMtP\nDMTKjSeReew65i/bh+n3d0e/znHBXhrVU4PuwkJDQ2EymaDX63H9+nXEx8cjPj4eBQW3yh7y8vLQ\nt29f2esYjRWBXqqkCUM6oKLS4tFI78G7kmCxVElOv3AyGm+gtaY6QuCeGVFYaoZKInCw68gV3Du4\nvaJSicl3JuHewe3xycaT2JV9zfW4VKynoLgSp88VSqa6m602j2v5uj6pcpQ8YwXyJYI4+Ub5dbmT\nuicThnRAfn6Z1+fy9jNoCuLiDK7XSsHH+9G48H74FwM8REQULK10GjwzoQe6dYzCp5tz8O6XR3HP\noPZ4eCTLOZqyBg1KDB06FBs3bsSkSZOwadMmDBs2DH369MHs2bNRWloKtVqNgwcP4i9/+UtDLssn\nco300kenwmazSza71GvViIu8lWJUu5zALhE4qEuDzBMXjIqOq13i4OQMmBw8mYeiMovsNaTWV/sa\n0QYt+neNd5V7tNJpbpa9eF5fEICN+y5i6pjOXktDlDQ39KXMg4iIiIiIGidBEDC8T7vqco512di0\n7yJOXSrBc5N6IjaS5RxNUcCCEtnZ2Xj99ddx+fJlaDQabNy4EW+88Qb++7//G2vWrEG7du0wefJk\nhISE4I9//COefvppCIKA559/3tX0sjETa6SnVqkwbVw3OABsFwlM3Nm7jWuzLDc1ojZtiNqnTbNc\n88fa+nWJFc1wqB0wkSO1qf9syylsPXDZ9XVRmQUZ+y+hym6HRqXCoZx80YAEUB2g2Xaw+txxg9or\navop19xQrueG1M+AiIiIiIgap/bxYXj5yepyjt0/X8e8Zfvw9P3d0a8LyzmamoAFJXr16oWVK1d6\nPL5s2TKPx8aPH4/x48cHaikN7rGxXaBRV2+6i8rMroaQk4clu8ZxlpSbRT+19we5rACVUF3KER1e\nXeKQPjrV45iyCgv2Hb+u+PnENvVlFRbsOnJV9Pgdh6/AS89Plx8OXca2g5cR44dJGc7XWrvMQ+xn\nQEREREREjZteq8GvH+iBbh2i8MnmHLz776MYO7A9HhnFco6mpGl39mukapcThIVqsW7HGcxdstc1\nVaOVTgMBgPe2noDZYvOpfEMXokZaaqwr08DdiH4JkpkHNrsdq7ecws4jV2Gu8h410GvVuCutbY1N\nvbNkY9/x65LXUBqQAG6VtPhjUoaSMg8iIiIiImo6BEHAMGc5x1fZ2Lz/InIvF2PmpF41Suep8WJQ\nIoCc5QSrMnI8pmoAyrMkosOV9zxwBgWOnKouDVEJ1Rt7JZkGa7bmYssBz0CG53p06NslHr8YloxQ\nXc1fIV/KPuriUE4BHhqRUq9gglyZBxERERERNT2J8WGY88RArNyYg90/X8O8Zfsw/b7uGNCV5RyN\nHYMSAeZL7wgpvvQ8kGqemZYSI5thoHSdd/S8DU+M74bEdpEe3ez98Vq9qUvTTyIiIiIiav6qyzm6\no1vHSHy6KQfv/ecoxgxMxJRRqSznaMR4ZwLMl6aTANA2OhQx4XqoBCAmXI8xAxNrlEeYrTbkGStg\ntto8zpULCmSdLhI9x9d1ThiaBF2IGiZLlcc6lF4jMb616OPt48MQE66HIEByPGp9JmXI/eyIiIiI\niKjpEwQBw9LaYc4TA9E2JhQZ+y/h7ysPIK+4MthLIwnMlAgwuaaTYsxWG+Y+NQiV5qoaPQ+cZRmH\ncvJdfSlql2PIBQW8ZRgoWWe0QYeIMB1WZeQg63Qh8o2ViA7XoVuHKDw6tovXa0SH69C/SxweHtkJ\na7efEW04WWVzoKTcjI17L4iOVlWaNWK22lx9IzRqwevPjoiIiIiImo+EuDC8/MQgfLLpJHZlX8P8\nZfsw/b5uGNA1PthLo1oYlAgwuVGUYorLzag0V3kED2qXZYg1fpQLCnjLMFCyzv5d47BuxxmPdezK\nvoYDOXm4K60d+naOFe1LMbRXG0wb19UVUJBqOKlWAfFRoZg6tgvUapXPkzLEgjeh+hBczCuvseb6\nNs0kIiIiIqLGTadV4+kHeqBrhyh8sukk3vtPNu4eUF3OEaLhh5ONBYMSDcBzFKUOxeVm0SkUYsED\nubIM98aPcoEFqQwD94yC9NGpcDgc2HX0KkyWW4vThahwZ1pbTB6WjLlL9oquw2SxI2P/JYwekIAx\nAxNFgwm1sxLkGk7WdVKGWPBGKnPDH00ziYiIiIiocbsrrS2S2xrwr69+xpYDl5B7uQTPTe6FeE7n\naBQYlGgAYhvsL384rTh44EtZhmcARDzDQKoc5Jd3d8bDI1ORX1wJS5UNWrUKcVGh0IWokWes8Noz\n4sipQrzyzO1+G7vpy6QMXxttsmkmEREREVHLkBAXhjm/GohPN+dg59GrmL9sL566tzsGdmM5R7Ax\nKNGA3DfYtYMHkWE6dOsYhcnDkj3O86UsQ61S4aERKRjepx3gcLgCCrV5KwdJjAtzfc/ZILKVTuO1\n74T7Rr+hN/u+NhWtT9NMIiIiIiJqWnRaNabf3x1dO0Ri5aaTeH9dNu7un4gpo1nOEUwMSjQg91IJ\nABgzIBH33dERX24/jRMXjNidfQ0nLxg9mjAqLctQ0gzTuQ4l5SBS/RnkghLB3Oj72lTUl1GrRERE\nRETUPNzZuy2S2oZj0bpsbDnoLOfoyQzqIGFQogHU3tzrtGoADpgsdui1qhr9G6SaMCopy1DSDBNQ\nXg6yanNOjQkYzv4M7ePDUFBiQqW5yuP8YG705YI37ePDUGGq8qlpJjUc94AdA0VE9cP3ExERkXcJ\nsa0x+4mb5RxZVzF/+T48eW93DGI5R4NjUKIB1A4WmCw2t3+LdLuEZxNGsb4UAFBYYnL9W0n2A+C9\nHCQsVIuVm07ih8OeIzkBoMJUhQ/+NBqL/p2FE+eNKC43N5qNvlzwxjlulH+oNx5Ks3uIyDu+n4iI\niHyjC1Fj+n3d0a1DJFZsPIl/rcvGif4J+OXoVIRouF9oKAxKBJivzRed3DMWan/qFROh9/jDs2uH\nKMXNML2Vg6zbcQbbDnqO9XS/nslqx68f6NHoPpGTm9rhHDdKjYfS7B4i8o7vJyIioroZ2qstktqE\n419fZWPbwcs4fXM6x23cOzQIBiUCzNfmi07VGQshWJWR4/Gpl8PhwJYDt4IGhaVm/JR9DboQFcxW\nz8wLsT4PUo0277ujA15dccDr2qLCdSgrqfRpOkZDaqzroluU9jYhIu/4fiIiIqqfdrGtMftXA/FZ\nRg5+PHIV85ftw5P3dsPg7rcFe2nNHvM5A8xZKuGr6oyFs8jYfwmFpWY4cOtTr11Hr4meIxaQcF6r\n9h+jzoyC+U8PxpCebSAIwO7sa/jb8gNeG0X26xILvZbxLKofJb1NiEgZvp+IiIjqTxeixpP3dscz\nD/SAwwEs+upnrNx4Ev+/vTuPjqLO+z3+qXTShJBAEsjCPhAkaJAlLFd2F5Rx5lxEZCQgwXnOXK7I\neK7OUR8xilGH4RyiIy4wLKIDRIUgIqMzgCyC4GWTZQJEQFkGDWAWiULYQpJ+/gjddJLqToidVCd5\nv/iDTi2/+lZVd/Krb/+Wq8UlVe+MGiMpUcucXSWqEmy3KcCQWjYP1h2JbTXo1tbaeyTXdFv3MSm8\nHztAw/u28zrOw6qtx/X/D/7gSnwUeKm4BhjSHb3bWD5uBBoGbwk7pmsFbgyfJwAAfGdA91i98Pu+\nahfVTJv2ndJfluxRztmLVofVYJGUqANj7+yi4X3bqWXzYAUYZQmIYLtNhsqSEMP7ttOrfxyoP/+f\n/6UecZHafzRfL//9K509X/SLjlt0tVQlpQ6P6290vIthvdsqeUQ3BkyDT3hL2DFdK3Bj+DwBAOBb\nrVuWdecY2rONvsst1IuLvtLOr3OsDqtBog1+HfA0c0bFgRhXbT1RbgpOTzyNHVGRQ9KmvadkCzDK\nDXLmHJyyqLjU63gX4aF2nbtQ5Dcza6Dhqc5UtwCqh88TAAC+ZQ+y6ff3dlO3DuFa/NkRzf8kS0e+\nK1DSXTfJTsLfZ0hK1KGKgy+6v67pLB3V4RzkLNBmlJu1IyLMriZ2m2l3kJbNg/XC7/vq0pViv5lZ\nAw2Pt9lSANwYPk8AANSO2xJi1TE2THNXZWnzv0/r2OlzenRUd8VGMrC+L9AO309UNUuHobJuH5Ln\nAS09cQ5y5pwuzjl+xNnzRR7Hp+jdtZXCQuyKjgihUota50zY8V4Dfjk+TwAA+F5Zd44+ur1XG32f\nW6iXFn2lHV+bT0CAG0NSwk94G6SsZfMmem5iopoF16xhS/NmdtkCDI8tMYLtNrVs3sQ10GZVg2Pe\niCtXS5RbcFFXrjJiLQAAAID6yx5k08Rfd9P/HXmLJGnBJ19r8drDKuJZ5xeh+4afcA5StmF3dqV1\nvbtGKbSp3WtLCm9+KizSjPS9HmfWKLpaopQJibIH2XzW3LektLRcV5HI5k3Uu2uUxt7ZhYEyAQAA\nANRbt90Sq1/FNtfcVQf1xb9P69ipc3p0VIJat2xmdWj1Ek+HfqTiLB3urRa8taSoDm9TfUaEBSsq\nIsSnzX0rdhX58dwVbdidrYzPj/qkfAAAAACwSmxkSFl3jt5tlZ1XqJcX7db2LLpz1AQtJfyIt0HK\nbAHy2JLil/L1dHHeBu10DrpJX2cAAAAA9VlQoE0TR8SrW4dwLVpzWG9/+rVO5l7Q6MG/YnaOG0BL\nCT/iHH9BkmmrBfeWFM6BL4PtNhmGFGBU/zjhofYajR9R3fEhvA3a6Rx0EwAAAAAagv43xyj19/3U\nITpU63ae1PQlu3XmxwtWh1Vv0FKiDl25WmI6TVt1x18wa0khScdP/axXlv27WjHUZKpPs/gG9Wyr\n/z2gg+n4EM6uJj+aJCYiwoJdcQMAAABAQxATGaLnJvbRqm0ntWbbf/Tyot2a+Ot4DUiItTo0v0dS\nohZUTD5UlXRwjr/g5Bx/QZLGD+9aqXzndG9Ondu2UEsPSYCKnFN9hoXYq30+ZvF9svW4Ll4q8hif\n50E7fdtVBAAAAAD8QVCgTVMe6KmOUc1c3TmOfFeg8cO70p3DC5ISPuQp+VDqcOjzPadc27knHR4Y\nFveLx1/wlgQItttUdLVEEWHB6t211Q1P9VnT8SGcx9n3Tb4Kzl+u8fEBAAAAoD7pf3OMOsaEae6q\ng9qSeUbHT5/To6O6MzuHByQlfMhTi4dgu/nQHfu+ydfQHq2rHH/BvVWEJ56SAKOGdFLhxas1nuqz\nOuNDmMXnbdBOAAAAAGjInN05lm08qk37TtGdwwuSEj7irUXB5aJS0+UF5y9LhuGT8Re8JQFCmgRV\n8ywq+6XjQ1TsagIAAAAAjUFQoE3JI+IV7zY7B905KmP2DR/x1qLAk4iwYEWFN1XvrlGm62sy/oIz\nCeCrVgnOriFmGB8CAAAAALzrf3OMUv+rbHaOLZlnmJ2jApISPuJsUWAm2G7+4O58qHef6rMmU3XW\nNrP4Rg7p7DfxAQAAAIA/i4ko685xR2JbZedd0MuLdmt71g9Wh+UX6L7hI94Gmxx0a6wMw/A46KO/\nj79gFl+7NuHKyztvdWgAAAAAUC8EBdqUfE+84tvTncMdSQkf8jbjhC0goMqkg7+Pv+Dv8QEAAACA\nv+t/c4w6xoZp7sfMziGRlPCpqlo88FAPAAAAAHB251j2+VFt2tu4Z+dgTIla4OvBJgEAAAAADYuz\nO8fk+xJkGNLbn36tRWsOqehqidWh1SlaSgAAgFqRlpamPXv2qLi4WI888oiioqKUlpamwMBA2e12\nvfLKKzp9+rRmzpzp2ufo0aOaM2eOEhMTXcs2btyoBQsWKCgoSJGRkXrllVfUpEkTLVy4UGvXrpVh\nGHrsscc0bNgwK04TAIBfxNWdY1Xj7M5BUgIAAPjcjh079O233yojI0MFBQW6//771aNHD6Wlpal9\n+/aaPXu2li9frsmTJys9PV2SdO7cOU2ZMkW9evUqV9aSJUu0cOFChYWF6dlnn9W6devUq1cvrV69\nWsuWLVNhYaHGjx+vwYMHy2ajlSIAoP6JiQjRc8mNszsH3TcAAIDP9evXT2+88YYkqXnz5rp06ZJm\nzZql9u3by+FwKCcnR7Gx5Sta77zzjh5++GEFBJSvnixevFhhYWEqLi5WXl6eYmJitHPnTg0ZMkR2\nu12RkZFq27atjh49WmfnBwCArzXW7hy0lAAAAD5ns9kUElI2uPOKFSs0dOhQ2Ww2bdmyRX/5y1/U\nuXNnjRw50rX95cuX9eWXX+rxxx83LW/lypV68803deedd6p///7au3evIiMjXesjIyOVl5en+Ph4\nr3FFRIQoMLB2WlNERYXVSrmoPu6B9bgH1uMeWO+X3oPfRoWp9y2xmrlkt7ZkntF3uRf038l91T6m\nYd5bkhIAAKDWbNiwQStWrNC7774rSRo6dKiGDBmiV199VQsWLNDkyZNd291+++2VWkk4jR49WiNH\njtQzzzyjTz/9tNJ6h8NRrXgKCi7W8Ey8i4oKU17e+VopG9XDPbAe98B63APr+eoeBEl6ZlwvV3eO\nP836ol535/CWqKH7BgAAqBVbt27VvHnz9PbbbyssLEzr16+XJBmGoREjRmjPnj2ubTdt2qQBAwZU\nKuPKlSvasmWLJCkwMFB33XWX9uzZo+joaOXn57u2y8nJUXR0dC2fEQAAdcesO8ffVx/SlQbWnYOk\nBAAA8Lnz588rLS1N8+fPV3h4uCTprbfe0qFDhyRJmZmZ6tSpk2v7gwcPqlu3bpXKsdlsmjZtmnJy\nciRJ+/fvV6dOnXTbbbdp8+bNKioqUk5OjnJzc9WlS5c6ODMAAOpW/5tjlPpf/dQhJlRb95/R9CW7\ndebHC1aH5TN03wAAAD63evVqFRQU6IknnnAtmzZtml566SXZbDYFBwcrLS3Nte7cuXMKDQ11/bxl\nyxZlZ2dr/Pjxevnll/XHP/5RdrtdrVq10uOPP66mTZvqwQcf1IQJE2QYhl588UWPXT8AAKjvGvLs\nHIajup0w/Qj9pKxHfzX/wv3wL9wP/8L98K36PoBabb0XeJ9Zj3tgPe6B9bgH1quLe/DV4Vz9ffUh\nXS4q0ZAerTX+7q5qEuTfU2J7qz/QUgIAAAAAgHqiX7dodYgJ1dxVB7V1/xkdP3NOU0Z1V+uWzawO\nrUZo5wgAAAAAQD3i7M5xZ2Jbncq7oJcX7db2rB+sDqtGSEoAAAAAAFDPBAXaNOGeeD06qnu9np2D\n7hsAAAAAANRT9b07By0lAAAAAACox+pzdw6SEgAAAAAA1HP1tTsH3TcAAAAAAGgg6lt3DlpKAAAA\nAADQgJh25zjon905SEoAAAAAANDAVOrO8U//7M5B9w0AAAAAABooZ3eOeauy/LI7By0lAAAAAABo\nwGIiQpSS3Ed3Jbbzu+4cJCUAAAAAAGjgggID9NA9Xf2uO4ffdN+YMWOGMjMzZRiGUlJS1KNHD6tD\nAgAAAACgQfG37hx+kZTYtWuXTp48qYyMDB07dkwpKSnKyMiwOiwAQDU4HA73H8r/7/bafZHZetNl\n5V6arzfb33311SCHin8uLL+wtNRt29JKxctR6vbarXyP5+Jw2848Vkep+zL38t22cxXsfi4Vzs+h\n8tuZlB8YEa6g1rECAAAw4+zOsfzzo9q4N1svL9qtiSPiNaB73dcf/CIpsX37dg0fPlySFBcXp59/\n/lmFhYUKDQ2tsxh2rT2o0E8WK6D4atkCT5VLJ+dyR6WFpssMuVcsJcOkTOd6w1NZlWKpxvEdZrFX\nKrT8S7P1F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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "i5Ul3zf5QYvW", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "Leaz2oYMQcBf", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n", + "\n", + "calibration_data = train_model(\n", + " learning_rate=0.05,\n", + " steps=500,\n", + " batch_size=5,\n", + " input_feature=\"rooms_per_person\")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ZjQrZ8mcHFiU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Identify Outliers\n", + "\n", + "We can visualize the performance of our model by creating a scatter plot of predictions vs. target values. Ideally, these would lie on a perfectly correlated diagonal line.\n", + "\n", + "Use Pyplot's [`scatter()`](https://matplotlib.org/gallery/shapes_and_collections/scatter.html) to create a scatter plot of predictions vs. targets, using the rooms-per-person model you trained in Task 1.\n", + "\n", + "Do you see any oddities? Trace these back to the source data by looking at the distribution of values in `rooms_per_person`." + ] + }, + { + "metadata": { + "id": "P0BDOec4HbG_", + "colab_type": "code", + "outputId": "970d1490-b35c-4eaf-9081-299ef4778234", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 2397 + } + }, + "cell_type": "code", + "source": [ + "calibration_data = train_model(\n", + " learning_rate=0.00005,\n", + " steps=500,\n", + " batch_size=5,\n", + " input_feature=\"rooms_per_person\"\n", + ")\n", + "plt.scatter(calibration_data[\"predictions\"],calibration_data[\"target\"])" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 237.51\n", + " period 01 : 237.49\n" + ], + "name": "stdout" + }, + { + "output_type": "error", + "ename": "KeyboardInterrupt", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0mTraceback 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\u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1317\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1318\u001b[0m return self._call_tf_sessionrun(\n\u001b[0;32m-> 1319\u001b[0;31m options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m 1320\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1321\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m 1405\u001b[0m return tf_session.TF_SessionRun_wrapper(\n\u001b[1;32m 1406\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1407\u001b[0;31m run_metadata)\n\u001b[0m\u001b[1;32m 1408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1409\u001b[0m \u001b[0;32mdef\u001b[0m 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FC/50jwIpRj3Sk/Wij6WZDEjUJ8iO0JjwQUTxiAEsCui1GkwekyX62OQxmehwdEqO0BrP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\nwoUL1fXr15VSSn366adq8+bNSimlgoKC1LFjx5RSSuXk5Kg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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "jByCP8hDRZmM", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "s0tiX2gdRe-S", + "colab_type": "code", + "outputId": "56e6c703-2e13-4b50-98bb-ad0772336506", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 392 + } + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize=(15, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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eo9r5y0vsePRrNQgLgymDRf3+tlHTh1KZdSMDDjD5AEZEpAW6CES9QQHdCndp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\nyWYx4qE7qjHDPW3Co53JzNk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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "kMQD0Uq3RqTX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The calibration data shows most scatter points aligned to a line. The line is almost vertical, but we'll come back to that later. Right now let's focus on the ones that deviate from the line. We notice that they are relatively few in number.\n", + "\n", + "If we plot a histogram of `rooms_per_person`, we find that we have a few outliers in our input data:" + ] + }, + { + "metadata": { + "id": "POTM8C_ER1Oc", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "plt.subplot(1, 2, 2)\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "9l0KYpBQu8ed", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Clip Outliers\n", + "\n", + "See if you can further improve the model fit by setting the outlier values of `rooms_per_person` to some reasonable minimum or maximum.\n", + "\n", + "For reference, here's a quick example of how to apply a function to a Pandas `Series`:\n", + "\n", + " clipped_feature = my_dataframe[\"my_feature_name\"].apply(lambda x: max(x, 0))\n", + "\n", + "The above `clipped_feature` will have no values less than `0`." + ] + }, + { + "metadata": { + "id": "rGxjRoYlHbHC", + "colab_type": "code", + "outputId": "9069e58e-14ea-47c8-e203-a8f438d71137", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 996 + } + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe[\"rooms_per_person\"]=california_housing_dataframe[\"rooms_per_person\"].apply(lambda x: min(x,10))\n", + "calibration_data = train_model(\n", + " learning_rate=0.0001,\n", + " steps=500,\n", + " batch_size=5000,\n", + " input_feature=\"rooms_per_person\"\n", + ")\n", + "plt.scatter(calibration_data[\"predictions\"],calibration_data[\"targets\"])\n", + "\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 237.49\n", + " period 01 : 237.44\n", + " period 02 : 237.39\n", + " period 03 : 237.34\n", + " period 04 : 237.29\n", + " period 05 : 237.24\n", + " period 06 : 237.18\n", + " period 07 : 237.13\n", + " period 08 : 237.08\n", + " period 09 : 237.03\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 0.6 207.3\n", + "std 0.2 116.0\n", + "min 0.1 15.0\n", + "25% 0.5 119.4\n", + "50% 0.5 180.4\n", + "75% 0.6 265.0\n", + "max 2.4 500.0" + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Final RMSE (on training data): 237.03\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 23 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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5kaO2g9QixVchMFbHJiIipbPaBew/WiP3MLxYbK3tSCMd6Hdtm2hbU8JFrm0T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5fpmXo8wyZYsH3/ybzOlRlnrVg0+rATEVHOMCiRhXS7AFMb3IbbZhmRGLxI11s7MchgZCLOtqiU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\nlZeXWx0SAADAsOlrk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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "WvgxW0bUSC-c", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "8YGNjXPaSMPV", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The histogram we created in Task 2 shows that the majority of values are less than `5`. Let's clip `rooms_per_person` to 5, and plot a histogram to double-check the results." + ] + }, + { + "metadata": { + "id": "9YyARz6gSR7Q", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 5))\n", + "\n", + "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "vO0e1p_aSgKA", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "To verify that clipping worked, let's train again and print the calibration data once more:" + ] + }, + { + "metadata": { + "id": "ZgSP2HKfSoOH", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "calibration_data = train_model(\n", + " learning_rate=0.05,\n", + " steps=500,\n", + " batch_size=5,\n", + " input_feature=\"rooms_per_person\")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "gySE-UgfSony", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From b96950f477b782e20a07a2edde203af412382e09 Mon Sep 17 00:00:00 2001 From: Aditya Joardar <30207466+joardar-aditya@users.noreply.github.com> Date: Mon, 18 Feb 2019 00:36:27 +0530 Subject: [PATCH 11/13] into_to_neural_nets solved! --- intro_to_neural_nets.ipynb | 1175 ++++++++++++++++++++++++++++++++++++ 1 file changed, 1175 insertions(+) create mode 100644 intro_to_neural_nets.ipynb diff --git a/intro_to_neural_nets.ipynb b/intro_to_neural_nets.ipynb new file mode 100644 index 0000000..a9c8b04 --- /dev/null +++ b/intro_to_neural_nets.ipynb @@ -0,0 +1,1175 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "intro_to_neural_nets.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "O2q5RRCKqYaU", + "vvT2jDWjrKew" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "eV16J6oUY-HN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Intro to Neural Networks" + ] + }, + { + "metadata": { + "id": "_wIcUFLSKNdx", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Define a neural network (NN) and its hidden layers using the TensorFlow `DNNRegressor` class\n", + " * Train a neural network to learn nonlinearities in a dataset and achieve better performance than a linear regression model" + ] + }, + { + "metadata": { + "id": "_ZZ7f7prKNdy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "In the previous exercises, we used synthetic features to help our model incorporate nonlinearities.\n", + "\n", + "One important set of nonlinearities was around latitude and longitude, but there may be others.\n", + "\n", + "We'll also switch back, for now, to a standard regression task, rather than the logistic regression task from the previous exercise. That is, we'll be predicting `median_house_value` directly." + ] + }, + { + "metadata": { + "id": "J2kqX6VZTHUy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "First, let's load and prepare the data." + ] + }, + { + "metadata": { + "id": "AGOM1TUiKNdz", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "2I8E2qhyKNd4", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "pQzcj2B1T5dA", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1224 + }, + "outputId": "d66fafff-9fb9-4ab8-f854-c91543948ad3" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.6 2657.7 542.3 \n", + "std 2.1 2.0 12.6 2263.0 436.3 \n", + "min 32.5 -124.3 1.0 2.0 2.0 \n", + "25% 33.9 -121.8 18.0 1461.0 296.0 \n", + "50% 34.2 -118.5 29.0 2117.5 431.0 \n", + "75% 37.7 -118.0 37.0 3152.2 650.0 \n", + "max 42.0 -114.5 52.0 37937.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1438.8 504.2 3.9 2.0 \n", + "std 1193.6 398.1 1.9 1.1 \n", + "min 3.0 2.0 0.5 0.0 \n", + "25% 790.8 280.8 2.6 1.5 \n", + "50% 1168.0 407.0 3.5 1.9 \n", + "75% 1724.0 607.0 4.8 2.3 \n", + "max 35682.0 6082.0 15.0 55.2 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "RWq0xecNKNeG", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Building a Neural Network\n", + "\n", + "The NN is defined by the [DNNRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNRegressor) class.\n", + "\n", + "Use **`hidden_units`** to define the structure of the NN. The `hidden_units` argument provides a list of ints, where each int corresponds to a hidden layer and indicates the number of nodes in it. For example, consider the following assignment:\n", + "\n", + "`hidden_units=[3,10]`\n", + "\n", + "The preceding assignment specifies a neural net with two hidden layers:\n", + "\n", + "* The first hidden layer contains 3 nodes.\n", + "* The second hidden layer contains 10 nodes.\n", + "\n", + "If we wanted to add more layers, we'd add more ints to the list. For example, `hidden_units=[10,20,30,40]` would create four layers with ten, twenty, thirty, and forty units, respectively.\n", + "\n", + "By default, all hidden layers will use ReLu activation and will be fully connected." + ] + }, + { + "metadata": { + "id": "ni0S6zHcTb04", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zvCqgNdzpaFg", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a neural net regression model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "U52Ychv9KNeH", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_nn_regression_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " hidden_units,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a neural network regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A `DNNRegressor` object trained on the training data.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a DNNRegressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " dnn_regressor = tf.estimator.DNNRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " hidden_units=hidden_units,\n", + " optimizer=my_optimizer,\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " dnn_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % training_root_mean_squared_error)\n", + " print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n", + "\n", + " return dnn_regressor" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "2QhdcCy-Y8QR", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Train a NN Model\n", + "\n", + "**Adjust hyperparameters, aiming to drop RMSE below 110.**\n", + "\n", + "Run the following block to train a NN model. \n", + "\n", + "Recall that in the linear regression exercise with many features, an RMSE of 110 or so was pretty good. We'll aim to beat that.\n", + "\n", + "Your task here is to modify various learning settings to improve accuracy on validation data.\n", + "\n", + "Overfitting is a real potential hazard for NNs. You can look at the gap between loss on training data and loss on validation data to help judge if your model is starting to overfit. If the gap starts to grow, that is usually a sure sign of overfitting.\n", + "\n", + "Because of the number of different possible settings, it's strongly recommended that you take notes on each trial to help guide your development process.\n", + "\n", + "Also, when you get a good setting, try running it multiple times and see how repeatable your result is. NN weights are typically initialized to small random values, so you should see differences from run to run.\n" + ] + }, + { + "metadata": { + "id": "rXmtSW1yKNeK", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 677 + }, + "outputId": "9456e61e-73a5-4871-b48d-cc8338607171" + }, + "cell_type": "code", + "source": [ + "dnn_regressor = train_nn_regression_model(\n", + " learning_rate=0.001,\n", + " steps=5000,\n", + " batch_size=10,\n", + " hidden_units=[10, 10],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 189.98\n", + " period 01 : 162.18\n", + " period 02 : 158.25\n", + " period 03 : 146.24\n", + " period 04 : 151.64\n", + " period 05 : 134.54\n", + " period 06 : 127.59\n", + " period 07 : 124.71\n", + " period 08 : 121.16\n", + " period 09 : 106.83\n", + "Model training finished.\n", + "Final RMSE (on training data): 106.83\n", + "Final RMSE (on validation data): 103.87\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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1exK1DkcIIYQwC1LAXMRab83DA+7GSmfFl0eXk1ViHrcwj4log7ODNev3JJFX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\n0kJ2w6BfxualbMxJuZiLx+vhUH0hOyv2UNx8jM4e1xmvB1g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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "O2q5RRCKqYaU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see a possible solution" + ] + }, + { + "metadata": { + "id": "j2Yd5VfrqcC3", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE:** This selection of parameters is somewhat arbitrary. Here we've tried combinations that are increasingly complex, combined with training for longer, until the error falls below our objective (training is nondeterministic, so results may fluctuate a bit each time you run the solution). This may not be the best combination; others may attain an even lower RMSE. If your aim is to find the model that can attain the best error, then you'll want to use a more rigorous process, like a parameter search." + ] + }, + { + "metadata": { + "id": "IjkpSqmxqnSM", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "dnn_regressor = train_nn_regression_model(\n", + " learning_rate=0.001,\n", + " steps=2000,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "c6diezCSeH4Y", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Evaluate on Test Data\n", + "\n", + "**Confirm that your validation performance results hold up on test data.**\n", + "\n", + "Once you have a model you're happy with, evaluate it on test data to compare that to validation performance.\n", + "\n", + "Reminder, the test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv)." + ] + }, + { + "metadata": { + "id": "icEJIl5Vp51r", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "bedbaf6c-3cc0-4748-ea0b-2b0704339c86" + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "\n", + "# YOUR CODE HERE\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "input_function = lambda: my_input_fn(test_examples, \n", + " test_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "predictions = dnn_regressor.predict(input_fn=input_function)\n", + "predictions = np.array([item['predictions'][0] for item in predictions])\n", + "root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(predictions, targets))\n", + "\n", + "print(\"Final RMSE: %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Final RMSE: 107.04\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "vvT2jDWjrKew", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below to see a possible solution." + ] + }, + { + "metadata": { + "id": "FyDh7Qy6rQb0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Similar to what the code at the top does, we just need to load the appropriate data file, preprocess it and call predict and mean_squared_error.\n", + "\n", + "Note that we don't have to randomize the test data, since we will use all records." + ] + }, + { + "metadata": { + "id": "vhb0CtdvrWZx", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n", + "\n", + "test_examples = preprocess_features(california_housing_test_data)\n", + "test_targets = preprocess_targets(california_housing_test_data)\n", + "\n", + "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n", + " test_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n", + "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n", + "\n", + "root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(test_predictions, test_targets))\n", + "\n", + "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 44284e3505c451ee62c8a66fdcbab101f4aa01ec Mon Sep 17 00:00:00 2001 From: Aditya Joardar <30207466+joardar-aditya@users.noreply.github.com> Date: Mon, 18 Feb 2019 01:16:03 +0530 Subject: [PATCH 12/13] improving_neural_net done --- improving_neural_net_performance.ipynb | 1780 ++++++++++++++++++++++++ 1 file changed, 1780 insertions(+) create mode 100644 improving_neural_net_performance.ipynb diff --git a/improving_neural_net_performance.ipynb b/improving_neural_net_performance.ipynb new file mode 100644 index 0000000..8c09a3a --- /dev/null +++ b/improving_neural_net_performance.ipynb @@ -0,0 +1,1780 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "improving_neural_net_performance.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "jFfc3saSxg6t", + "FSPZIiYgyh93", + "GhFtWjQRzD2l", + "P8BLQ7T71JWd" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "JndnmDMp66FL" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "cellView": "both", + "colab_type": "code", + "id": "hMqWDc_m6rUC", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "eV16J6oUY-HN" + }, + "cell_type": "markdown", + "source": [ + "# Improving Neural Net Performance" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "0Rwl1iXIKxkm" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objective:** Improve the performance of a neural network by normalizing features and applying various optimization algorithms\n", + "\n", + "**NOTE:** The optimization methods described in this exercise are not specific to neural networks; they are effective means to improve most types of models." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "lBPTONWzKxkn" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "First, we'll load the data." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "VtYVuONUKxko", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", + "\n", + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "B8qC-jTIKxkr", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_features(california_housing_dataframe):\n", + " \"\"\"Prepares input features from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the features to be used for the model, including\n", + " synthetic features.\n", + " \"\"\"\n", + " selected_features = california_housing_dataframe[\n", + " [\"latitude\",\n", + " \"longitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\"]]\n", + " processed_features = selected_features.copy()\n", + " # Create a synthetic feature.\n", + " processed_features[\"rooms_per_person\"] = (\n", + " california_housing_dataframe[\"total_rooms\"] /\n", + " california_housing_dataframe[\"population\"])\n", + " return processed_features\n", + "\n", + "def preprocess_targets(california_housing_dataframe):\n", + " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n", + "\n", + " Args:\n", + " california_housing_dataframe: A Pandas DataFrame expected to contain data\n", + " from the California housing data set.\n", + " Returns:\n", + " A DataFrame that contains the target feature.\n", + " \"\"\"\n", + " output_targets = pd.DataFrame()\n", + " # Scale the target to be in units of thousands of dollars.\n", + " output_targets[\"median_house_value\"] = (\n", + " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n", + " return output_targets" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "Ah6LjMIJ2spZ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1224 + }, + "outputId": "b19a9b97-9ca6-4c34-8675-50d27b6e37d2" + }, + "cell_type": "code", + "source": [ + "# Choose the first 12000 (out of 17000) examples for training.\n", + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "# Choose the last 5000 (out of 17000) examples for validation.\n", + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "\n", + "# Double-check that we've done the right thing.\n", + "print(\"Training examples summary:\")\n", + "display.display(training_examples.describe())\n", + "print(\"Validation examples summary:\")\n", + "display.display(validation_examples.describe())\n", + "\n", + "print(\"Training targets summary:\")\n", + "display.display(training_targets.describe())\n", + "print(\"Validation targets summary:\")\n", + "display.display(validation_targets.describe())" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training examples summary:\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " latitude longitude housing_median_age total_rooms total_bedrooms \\\n", + "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n", + "mean 35.6 -119.6 28.6 2634.2 537.6 \n", + "std 2.1 2.0 12.6 2176.3 418.5 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1452.0 295.0 \n", + "50% 34.2 -118.5 29.0 2121.0 434.0 \n", + "75% 37.7 -118.0 37.0 3146.0 647.2 \n", + "max 42.0 -114.5 52.0 37937.0 5471.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1426.9 499.5 3.9 2.0 \n", + "std 1142.5 381.4 1.9 1.2 \n", + "min 3.0 1.0 0.5 0.1 \n", + "25% 786.0 280.0 2.6 1.5 \n", + "50% 1168.0 410.0 3.5 1.9 \n", + "75% 1721.0 603.0 4.7 2.3 \n", + "max 35682.0 5189.0 15.0 55.2 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "NqIbXxx222ea" + }, + "cell_type": "markdown", + "source": [ + "## Train the Neural Network\n", + "\n", + "Next, we'll train the neural network." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "6k3xYlSg27VB", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns(input_features):\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Args:\n", + " input_features: The names of the numerical input features to use.\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " return set([tf.feature_column.numeric_column(my_feature)\n", + " for my_feature in input_features])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "De9jwyy4wTUT", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a neural network model.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "W-51R3yIKxk4", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_nn_regression_model(\n", + " my_optimizer,\n", + " steps,\n", + " batch_size,\n", + " hidden_units,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a neural network regression model.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " as well as a plot of the training and validation loss over time.\n", + " \n", + " Args:\n", + " my_optimizer: An instance of `tf.train.Optimizer`, the optimizer to use.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n", + " training_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for training.\n", + " training_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for training.\n", + " validation_examples: A `DataFrame` containing one or more columns from\n", + " `california_housing_dataframe` to use as input features for validation.\n", + " validation_targets: A `DataFrame` containing exactly one column from\n", + " `california_housing_dataframe` to use as target for validation.\n", + " \n", + " Returns:\n", + " A tuple `(estimator, training_losses, validation_losses)`:\n", + " estimator: the trained `DNNRegressor` object.\n", + " training_losses: a `list` containing the training loss values taken during training.\n", + " validation_losses: a `list` containing the validation loss values taken during training.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + " \n", + " # Create a DNNRegressor object.\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " dnn_regressor = tf.estimator.DNNRegressor(\n", + " feature_columns=construct_feature_columns(training_examples),\n", + " hidden_units=hidden_units,\n", + " optimizer=my_optimizer\n", + " )\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n", + " validation_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " training_rmse = []\n", + " validation_rmse = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " dnn_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n", + " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n", + " \n", + " validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n", + " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n", + " \n", + " # Compute training and validation loss.\n", + " training_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(training_predictions, training_targets))\n", + " validation_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(validation_predictions, validation_targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " training_rmse.append(training_root_mean_squared_error)\n", + " validation_rmse.append(validation_root_mean_squared_error)\n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"RMSE\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(training_rmse, label=\"training\")\n", + " plt.plot(validation_rmse, label=\"validation\")\n", + " plt.legend()\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % training_root_mean_squared_error)\n", + " print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n", + "\n", + " return dnn_regressor, training_rmse, validation_rmse" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "KueReMZ9Kxk7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 805 + }, + "outputId": "dd7f43d6-ad8f-4b8b-ed89-c8fd1a9b81b5" + }, + "cell_type": "code", + "source": [ + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n", + " steps=5000,\n", + " batch_size=70,\n", + " hidden_units=[10, 10],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 150.63\n", + " period 01 : 135.21\n", + " period 02 : 123.19\n", + " period 03 : 115.75\n", + " period 04 : 109.58\n", + " period 05 : 108.87\n", + " period 06 : 108.01\n", + " period 07 : 110.06\n", + " period 08 : 107.97\n", + " period 09 : 109.78\n", + "Model training finished.\n", + "Final RMSE (on training data): 109.78\n", + "Final RMSE (on validation data): 109.73\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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6WPVCYuEN3C5JkSAhERFRx8AC04ZMlAaIGOKKsspaHDqf2uQ24e71ozAHb3MU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\nHT+lfbDD7BJFRMY2wNTX17N8+XIef/zx0WW//vW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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "flxmFt0KKxk9" + }, + "cell_type": "markdown", + "source": [ + "## Linear Scaling\n", + "It can be a good standard practice to normalize the inputs to fall within the range -1, 1. This helps SGD not get stuck taking steps that are too large in one dimension, or too small in another. Fans of numerical optimization may note that there's a connection to the idea of using a preconditioner here." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Dws5rIQjKxk-", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def linear_scale(series):\n", + " min_val = series.min()\n", + " max_val = series.max()\n", + " scale = (max_val - min_val) / 2.0\n", + " return series.apply(lambda x:((x - min_val) / scale) - 1.0)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "MVmuHI76N2Sz" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Normalize the Features Using Linear Scaling\n", + "\n", + "**Normalize the inputs to the scale -1, 1.**\n", + "\n", + "**Spend about 5 minutes training and evaluating on the newly normalized data. How well can you do?**\n", + "\n", + "As a rule of thumb, NN's train best when the input features are roughly on the same scale.\n", + "\n", + "Sanity check your normalized data. (What would happen if you forgot to normalize one feature?)\n" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "yD948ZgAM6Cx", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 677 + }, + "outputId": "e3e4e31f-4fee-4080-ee1f-9db0bd51091a" + }, + "cell_type": "code", + "source": [ + "def normalize_linear_scale(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n", + " #\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + " processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n", + " processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n", + " processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n", + " processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n", + " processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n", + " return processed_features\n", + " #\n", + " pass\n", + "\n", + "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n", + " steps=5000,\n", + " batch_size=70,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 230.10\n", + " period 01 : 206.71\n", + " period 02 : 159.93\n", + " period 03 : 118.77\n", + " period 04 : 115.06\n", + " period 05 : 111.25\n", + " period 06 : 106.80\n", + " period 07 : 101.55\n", + " period 08 : 95.33\n", + " period 09 : 88.40\n", + "Model training finished.\n", + "Final RMSE (on training data): 88.40\n", + "Final RMSE (on validation data): 88.26\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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REZGbogJTg5hNJiYPacGonk1Iycznw88SmNLsQXxd6rH6zAZWnFqjEiMiInWC\nCkwNYzKZGNe3GZMGNCfzUiELlp9jUqP7CXYP4qv4bXxyfDmlRqmjY4qIiNiVCkwNNeyuxjw4vDW5\n+UW8s/w0o4Pvo7FXA3Ym7eWDw7qStYiI1G4qMDVY346h/GpMO4qKS3nn05MM9JlA83pNOZB6iHe/\nX6grWYuISK2lAlPD3dkmmMcm3IEJeG9lDN1dRtHevzXH0mN467sF5BcXODqiiIjIbacCUwt0aObP\nk/d0wtnJzIerT9KqZDDdgjtxJusc62I3OTqeiIjIbacCU0u0bFSPp+/rgoebE4s3niIg+y58Xerx\ndcJO0gsyHB1PRETktlKBqUXCQrx4dmoXfL1cWPH1OYILO1FcWsya2C8dHU1EROS2UoGpZer7e/Ds\n1C4E+7pxYLcLPhZ/diftJyk32dHRREREbhsVmFoowMeNp+7rjLPVQt7Z5hgYrDq93tGxREREbhsV\nmFrKz9uVwd0akZ1UD19zfb5PO8KZrLOOjiUiInJbqMDUYiN6NMbD1YmME00BWHlqnS41ICIitYIK\nTC3m7urEiB5h5Gd4408Yp7NiOXLxuKNjiYiI3DIVmFpuUNeG+Hq5kHK0MSZMfH56na6VJCIiNZ4K\nTC3n7GRhTO+mFF7ywL8knMTcC+y9cMDRsURERG6JCkwd0KtDCCF+7iQcbojFZOGL2I262KOIiNRo\nKjB1gMVsZlzfZpRedqVeQUvSCzLYkbDL0bFERERumgpMHdG1VSBN63tx/kh9nM0urD/7lS70KCIi\nNZYKTB1hMpmY0C8cip1xz27JpaJcvorb5uhYIiIiN8Vqz52/8sor7N+/n+LiYn71q1/RoUMHnn76\naUpKSggMDOSvf/0rzs7OrFq1io8++giz2cykSZOYOHGiPWPVWW2a+NGuqR9Hjhfjd9cZvorfRt+G\nEXg7ezk6moiISKXYbQZm165dnDx5kiVLlvD+++8zZ84c3njjDSZPnswnn3xCWFgYy5cvJy8vj3nz\n5rFw4UIWLVrERx99RGZmpr1i1XkT+oVDqRVLaksKSwpZf/YrR0cSERGpNLsVmO7du/P6668D4O3t\nTX5+Prt372bQoEEADBgwgG+//ZaDBw/SoUMHvLy8cHV1pUuXLkRHR9srVp0XFuLFnW2CSD0diJel\nHjsSdpOWf9HRsURERCrFbqeQLBYL7u7uACxfvpy+ffuyY8cOnJ2dAfD39yc1NZW0tDT8/Pxs2/n5\n+ZGamlruvn193bFaLfaKTmBg7T6lMm1sB/bPTaU0sSUlwXv4MmEzj0X83NGxKqS2j01NpXGpvjQ2\n1ZfG5tbYdQ0MwKZNm1i+fDlhh5EFAAAgAElEQVQffPABQ4cOtd1/vWvyVORaPRkZebct308FBnqR\nmppjt/1XB05A346hbDlQSv3QQHbE7aV3cC8aeYU6Olq56sLY1EQal+pLY1N9aWwqprySZ9d3IW3f\nvp133nmHBQsW4OXlhbu7OwUFV966m5ycTFBQEEFBQaSlpdm2SUlJISgoyJ6xBBjdqwnOThYunQkH\nYNXpdQ5OJCIiUnF2KzA5OTm88sorvPvuu9SrVw+Anj17smHDBgA2btxInz596NixI4cOHSI7O5vc\n3Fyio6Pp1q2bvWLJf9XzdGFIt0bkJPvgb27A0fQTxGScdnQsERGRCrHbKaS1a9eSkZHBb3/7W9t9\nL7/8MrNmzWLJkiWEhoYyduxYnJycmDlzJtOmTcNkMjF9+nS8vHResCoMvyuMrQcSuHi8KbRMYOXp\ntTzVdQYmk8nR0URERMplMiqy6KSased5w7p2XnL97jiWbjlF4ztPkEosD7ePolNQB0fHuqa6NjY1\nhcal+tLYVF8am4px2BoYqf4GdW2Ar5cLSUcaYcbMqjPrKSktcXQsERGRcqnA1HFOVgtjezelKNcd\n/5LmJOelsuvCPkfHEhERKZcKjNCzQwj1/d2JPxSK1WRlbewmCkuKHB1LRETkulRgBIvZzLi+4RiF\nrvjktyLzchZfn//G0bFERESuSwVGAOjSMoDwUG/OHwnB1ezKhnNbyCuy3wcGioiI3AoVGAHAZDIx\noX84lDjhmtmK/OJ8Np7b6uhYIiIi16QCIzatGvvSvpkfSSeC8LB4sfX8DjIvZzk6loiIyFVUYKSM\nCf3CwbBgSm5JUWkxa2O/dHQkERGRq6jASBmNg73o0TaY1DP++Fj8+DZpH8m5KY6OJSIiUoYKjFxl\nbJ+mWMwWLse1oNQoZdWZDY6OJCIiUoYKjFwlyNedfp1CyUioh58lhO9SD3E2O87RsURERGxUYOSa\nRvdqiouTlexT4QB8fmodNfCyWSIiUkupwMg1+Xg4M6R7I3JSvQg0NyYm8zTH0mMcHUtERARQgZFy\nDL+rMZ5uTqQeC8OEic9Pr6PUKHV0LBERERUYuT43FyujIsLIz/IgkHDOX0okOvmgo2OJiIiowEj5\nBnRpgL+3CwmHGmIxWVh9ZgPFpcWOjiUiInWcCoyUy8lqYUzvZhTnu+Jf1JK0gnS+Sdzj6FgiIlLH\nqcDIDfVsH0KDAA/iDoXgbHZmXewmCoovOzqWiIjUYSowckNms4lx/ZphFLngnduanKJLbInf7uhY\nIiJSh6nASIV0ah5A8wY+xB8Jws3izpdxW8kpvOToWCIiUkepwEiFmEwmJvQPh1IrrumtuVxSyIaz\nmx0dS0RE6igVGKmwlo3qcUe4P4kxAXhZfdie8C0X89MdHUtEROogFRiplPH9wjEZZoykVhQbJXwR\nu9HRkUREpA5SgZFKaRTkSY92waTG+uJrDWTvhQMkXEpydCwREaljVGCk0sb2aYbFbCb/bHMMDFad\nXufoSCIiUseowEilBdZzo3/nBmQkehNgacDhi8c5lRnr6FgiIlKHqMDITRndswkuzlYyYpoCsPLU\nWgzDcHAqERGpK1Rg5KZ4ezgT2b0Rly56EmRuSmz2Ob5PO+roWCIiUkeowMhNi7yzMZ5uTiQfaYQJ\nE6vOrKfUKHV0LBERqQNUYOSmublYGd2zCQU57gQZLbmQm8zupP2OjiUiInWACozckv6dGxDg48r5\nQ/Wxmqysif2SopIiR8cSEZFaTgVGbomT1czYPk0pLnDFr7A1GZcz+Tphp6NjiYhILWfXAhMTE8Pg\nwYNZvHgxAHv37uW+++4jKiqKX/3qV2RlZQHw/vvvM2HCBCZOnMjXX39tz0hiBz3ahtAg0IO4Q0G4\nml3ZeHYL+cX5jo4lIiK1mN0KTF5eHrNnzyYiIsJ230svvcSLL77IokWL6Ny5M0uWLCE+Pp61a9fy\nySef8O677/LSSy9RUlJir1hiB2azifH9wjGKnfG81Jrc4jy+PKciKiIi9mO3AuPs7MyCBQsICgqy\n3efr60tmZiYAWVlZ+Pr6snv3bvr06YOzszN+fn40aNCAU6dO2SuW2EnHcH9aNPQh/mggHhZPNsdv\nJ+tytqNjiYhILWW3AmO1WnF1dS1z3x/+8AemT59OZGQk+/fv5+677yYtLQ0/Pz/bc/z8/EhNTbVX\nLLETk8nEhP7hUGrB+WJrikqLWHt2k6NjiYhILWWtyoPNnj2bt956i65duzJ37lw++eSTq55TkU9z\n9fV1x2q12CMiAIGBXnbbd20WGOjFndGJ7DlaSmhvf3Ym7mFix+HU9wq68caVOIZUPxqX6ktjU31p\nbG5NlRaYEydO0LVrVwB69uzJ6tWr6dGjB7Gx/7uOTnJycpnTTteSkZFnt4yBgV6kpubYbf+13aiI\nxuw9eoGi+OaUBu/mo32fMq391Nuyb41N9aRxqb40NtWXxqZiyit5Vfo26oCAANv6lkOHDhEWFkaP\nHj3YunUrhYWFJCcnk5KSQvPmzasyltxGDQM9iWgfQuq5evhbg4lO+Z647POOjiUiIrWM3WZgDh8+\nzNy5c0lISMBqtbJhwwb+/Oc/M2vWLJycnPDx8WHOnDl4e3szadIkpk6dislk4k9/+hNmsz6epiYb\n26cpe44lkxvbHBol8/npdTza+WFHxxIRkVrEZNTASwjbc9pN03q3x783neTLffGERRwlpSSORzs9\nTGu/Fre0T41N9aRxqb40NtWXxqZiqs0pJKk7RvYMw9XZwsUTTQD4/PRaXehRRERuGxUYsQtvd2eG\n3dmYS+nuhJibE5eTwIGUQ46OJSIitYQKjNjN0Dsb4eXuROLhhphNZlafWU9JqT5lWUREbp0KjNiN\nq7OV0T2bcPmSK0GlrUjNv8jOpD2OjiUiIrWACozYVf/ODQjwcSX++/o4mZ1YG7uJyyWFjo4lIiI1\nnAqM2JXVYubuvs0ovuyMX0Ebsgtz2BK/w9GxRESkhlOBEbu7q20wDQM9Ofd9EG4WN748t5VLRbmO\njiUiIjWYCozYndlkYkL/ZhilVjyz21BQUsDGs1scHUtERGowFRipEh2a+dOyUT3ijvrjZfXm64Sd\npBdkODqWiIjUUCowUiVMJhMT+oeDYcGS2pri0mLWxH7p6FgiIlJDqcBIlWnewIfOLQJIOumLr1MA\nu5P2k5Sb7OhYIiJSA6nASJUa1y8ck8lEUXwLDAxWnV7v6EgiIlIDqcBIlWoQ4EGv9vVJjfMm0BrK\n92lHOJN11tGxRESkhlGBkSo3pndTrBYL2afDAVh5ah018KLoIiLiQCowUuX8fVwZ2KUBmckehFia\ncjorliMXjzs6loiI1CAqMOIQo3o2wc3FQsqxxpgw8fnpdZQapY6OJSIiNYQKjDiEp5sTw+5sTG6m\nGyGmFiTmXmDvhQOOjiUiIjWECow4zNDujfH2cCbhUAMsJgtfxG6kqLTY0bFERKQGUIERh3FxtvCz\nXk24nOdCUEkb0gsy2JGwy9GxRESkBlCBEYfq2zGUwHqunPs+GBezC+vPfkV+cYGjY4mISDWnAiMO\nZbWYubtvM0oKnfAtaMOloly+itvm6FgiIlLNqcCIw93ZJpjGQZ6c/T4AD6sHX8VvI7swx9GxRESk\nGrvpAnP27NnbGEPqMrPJxPj+4RilVtwz21JYUsj6s185OpaIiFRj5RaYhx56qMzt+fPn2/77+eef\nt08iqZPaN/WjdeN6xB3zxcepHjsSdpOWf9HRsUREpJoqt8AUF5d9S+uuXf97h4g++l1uJ9N/Z2Ew\nzJgvtKbEKGH1mQ2OjiUiItVUuQXGZDKVuf3j0vLTx0RuVXioD11bBpJ42gd/pyD2JX9HfE6io2OJ\niEg1VKk1MCotYm93922GyWTiclxLAFadXufgRCIiUh1Zy3swKyuLb7/91nY7OzubXbt2YRgG2dnZ\ndg8ndU9ogAe9O9Rn+/eJhDVuxNH0E8RknKalb7ijo4mISDVSboHx9vYus3DXy8uLefPm2f5bxB7G\n9G7Kt0eSyTrZFJrEs/L0Wp7qOsPRsUREpBopt8AsWrSoqnKI2Ph5uzK4a0PW74mjWXg457JPczD1\nMEOCejo6moiIVBPlroG5dOkSCxcutN3+z3/+w5gxY3jsscdIS0uzdzapw0ZEhOHmYuXCkUaYMbPq\nzHpKSkscHUtERKqJcgvM888/z8WLVz6LIzY2lldffZVnnnmGnj178uKLL1ZJQKmbPN2cGNGjMXnZ\nroSYWpGcl8qW2G9vvKGIiNQJ5RaY+Ph4Zs6cCcCGDRsYNmwYPXv25N57763QDExMTAyDBw9m8eLF\nABQVFTFz5kwmTJjAAw88QFZWFgCrVq1i/PjxTJw4kWXLlt3qa5JaYnDXRvh4OBP/fX2czE4sObSK\nvKJ8R8cSEZFqoNwC4+7ubvvvPXv20KNHD9vtG72lOi8vj9mzZxMREWG7b+nSpfj6+rJ8+XJGjBjB\nvn37yMvLY968eSxcuJBFixbx0UcfkZmZebOvR2oRF2cLP+vdlMJ8Z0KKOpJ1OYcvYjc6OpaIiFQD\n5RaYkpISLl68SFxcHAcOHKBXr14A5Obmkp9f/l/Czs7OLFiwgKCgINt9W7Zs4Wc/+xkA99xzD4MG\nDeLgwYN06NABLy8vXF1d6dKlC9HR0bf6uqSW6HNHfYJ83Tj9nR9B7oFsO79TH24nIiLlF5iHH36Y\nESNGMHr0aB555BF8fHwoKChg8uTJjB07ttwdW61WXF1dy9yXkJDAtm3biIqK4oknniAzM5O0tDT8\n/Pxsz/Hz8yM1NfUWXpLUJlaLmXF9m1FSYsY7swsGBktjVlBqlDo6moiIOFC5b6Pu168fO3bs4PLl\ny3h6egLg6urKU089Re/evSt9MMMwaNq0KTNmzGD+/Pm8++67tG3b9qrn3IivrztWq6XSx6+owEB9\nxk11Mtzfky3fJXLouww6D23L8cyjHMs9Sv+mETfeWKqEfmaqL41N9aWxuTXlFpjExP9N1f/4k3eb\nNWtGYmIioaGhlTpYQEAA3bt3B6B37968+eab9O/fv8yC4JSUFDp16lTufjIy8ip13MoIDPQiNTXH\nbvuXm3PvgObM/mgv579rjHOzk3x84FOauoTj7uTm6Gh1nn5mqi+NTfWlsamY8kpeuQVm4MCBNG3a\nlMDAQODqizl+/PHHlQrSt29ftm/fzvjx4zly5AhNmzalY8eOzJo1i+zsbCwWC9HR0fzhD3+o1H6l\n9gsL8WJEr6Z8sSOWjs27ElO0iy9iNzCpZfmnMkVEpHYqt8DMnTuXzz//nNzcXEaOHMmoUaPKrFcp\nz+HDh5k7dy4JCQlYrVY2bNjA3/72N1588UWWL1+Ou7s7c+fOxdXVlZkzZzJt2jRMJhPTp0/XZQrk\nmqYOa8O2Awkc21uP4B4BbDv/LRH1u9PIq4Gjo4mISBUzGRVYdJKUlMSKFStYvXo1DRo0YMyYMQwZ\nMuSqRbpVxZ7TbprWq74CA71YtfUkC1YfpUXrIs57f0VT7zCe7PobzKZKXVhdbiP9zFRfGpvqS2NT\nMeWdQqrQv/r169fnkUceYd26dURGRvLCCy/c1CJekVvVo20wrRvX4+RxJ5q6tSI2+xy7k/Y7OpaI\niFSxChWY7OxsFi9ezLhx41i8eDG/+tWvWLt2rb2ziVzFZDIxZWgrLGYTFw43wdnszMrTa8krst/C\nbhERqX7KXQOzY8cOPv30Uw4fPszQoUN5+eWXadmyZVVlE7mmBgEeDO3eiHW74+jQojOninaz+swG\n7ml1t6OjiYhIFSm3wPziF7+gSZMmdOnShfT0dD788MMyj7/00kt2DSdyPaN7NWHX0WSO7atHSEQA\n2xN2ERHancZeDR0dTUREqkC5BeaHt0lnZGTg6+tb5rHz58/bL5XIDbg6W7lvUAvmrzyM84UOGL5b\nWHpiJU92fUQLekVE6oBy/6U3m83MnDmT5557jueff57g4GDuvPNOYmJieO2116oqo8g1dW0VSPtm\nfpw56UIT11bEZsexSwt6RUTqhHJnYP7xj3+wcOFCwsPD+eqrr3j++ecpLS3Fx8eHZcuWVVVGkWsy\nmUxMGdKS597fQ+L3jXFuHcvnp9fSMbAdHk7uN96BiIjUWDecgQkPDwdg0KBBJCQkcP/99/PWW28R\nHBxcJQFFyhPs686IHo3JyrTQoLQTl4pyWX1mg6NjiYiInZVbYEwmU5nb9evXZ8iQIXYNJFJZI3qE\nEVjPleP7fPF3CWBHwi7isrVGS0SkNqvUasefFhqR6sDZycKUIS0pLTVhTmiPgcF/YlZQapQ6OpqI\niNhJuWtgDhw4QP/+/W23L168SP/+/TEMA5PJxNatW+0cT6Ri7ggPoEvLQKJjUmndoBXnsk/wbdJe\neoXe5ehoIiJiB+UWmPXr11dVDpFbdt+gFhyOvcj5g41wbhvL56fX0Smwgxb0iojUQuUWmAYNdJVf\nqTn8fVwZ3bMJn359hlZFnYgr3cOqM+u5r9U4R0cTEZHbTJ/4JbVK5J2Nqe/vTkx0PfxdAvgmYTfn\nsuMdHUtERG4zFRipVawWM1OHtsIwzJTGt8PAYMmJlVrQKyJSy6jASK3TJsyXu9oGk3jWjUZOLTmX\nE8+3iXsdHUtERG4jFRiple4Z2BxXZwvnDzbG2ezM56fXcako19GxRETkNlGBkVqpnqcLd/dpRt4l\nK0GXO5JbnMeq03pXnYhIbaECI7XWwK4NaBTkycnvfPF3DmBn4h4t6BURqSVUYKTWspjNRA1tBYaZ\nonNXFvT+54Q+oVdEpDZQgZFarXlDH3rfUZ/keDcaWlsSl3OenYl7HB1LRERukQqM1HoT+ofj4Wol\n/rtGuJhdWHV6PZcKtaBXRKQmU4GRWs/b3Znx/cMpyHPCL7/DlQW9Z9Y5OpaIiNwCFRipE/p2DKVp\nfW/OfO+Hn1MgOxP3EpsV5+hYIiJyk1RgpE4wm0xERbbEhJnCs20wMFgaowW9IiI1lQqM1BlNQrwZ\n0LkBqQnuhFpaEpeTwDeJux0dS0REboIKjNQp4/o2w9vdifgDDbWgV0SkBlOBkTrF3dWJSQObU1jg\njM+l9uQV5/P5aS3oFRGpaVRgpM6JaBdCy0b1OHfY/8qC3qQ9xGadc3QsERGpBBUYqXNMJhNTh7bE\nbLKQd7oVAEtiVmpBr4hIDaICI3VSw0BPhnZvRMYFT+qbWhKfk8COBC3oFRGpKVRgpM76We8m+Hq5\nEPfDJ/SeWU9O4SVHxxIRkQqwa4GJiYlh8ODBLF68uMz927dvp1WrVrbbq1atYvz48UycOJFly5bZ\nM5KIjauzlXsHtaD4shOe2e3J14JeEZEaw24FJi8vj9mzZxMREVHm/suXL/Pee+8RGBhoe968efNY\nuHAhixYt4qOPPiIzM9NesUTK6NYqkHZN/Th/1B9fayDfJu3ljBb0iohUe3YrMM7OzixYsICgoKAy\n97/zzjtMnjwZZ2dnAA4ePEiHDh3w8vLC1dWVLl26EB0dba9YImWYTCamDmmJ1WIh99SVWcGlJ/QJ\nvSIi1Z3dCozVasXV1bXMfbGxsRw/fpzhw4fb7ktLS8PPz89228/Pj9TUVHvFErlKsJ87w+4KIyvF\nk2BaEH8pkR0JuxwdS0REymGtyoO99NJLzJo1q9znGIZxw/34+rpjtVpuV6yrBAZ62W3fcmvsNTYP\n/qw9e4+nEP9dI7y7xvFF7AYGt4nAx9XbLserbfQzU31pbKovjc2tqbICk5yczJkzZ/jd734HQEpK\nClOnTuXRRx8lLS3N9ryUlBQ6depU7r4yMvLsljMw0IvU1By77V9unr3H5p6BzXlj+fe4prcj0yea\nf+5ZRlSbSXY7Xm2hn5nqS2NTfWlsKqa8kldlb6MODg5m06ZNLF26lKVLlxIUFMTixYvp2LEjhw4d\nIjs7m9zcXKKjo+nWrVtVxRKx6dQ8gE7NA0g6EYCvNZBdSfs4k3XW0bFEROQa7FZgDh8+TFRUFCtW\nrODjjz8mKirqmu8ucnV1ZebMmUybNo2HHnqI6dOn4+WlaTVxjMmDW+BstZId0xKAJSdWUlJa4uBU\nIiLyUyajIotOqhl7TrtpWq/6qqqx+WLnWT7bdobG3U6Taj7JxJZj6N+wl92PW1PpZ6b60thUXxqb\niqkWp5BEaorIOxsT4udO/MGGuJhd+OLMBn1Cr4hINaMCI/ITTlYzU4e2xChyweViW/KLC1h5aq2j\nY4mIyI+owIhcQ9smftzZJojkk4HUswSy68I+TmeedXQsERH5LxUYkeu4Z2ALXJ2dyDrRAoAlMSu0\noFdEpJpQgRG5Dl8vF8b2bkpeujcBJS1IuJTEdn1Cr4hItaACI1KOQd0a0jDQ478Lel1ZfWYD2YV6\n54CIiKOpwIiUw2I2M3VoKyh2wSm1DQUlWtArIlIdqMCI3EDLRvXo1SGE1NNB+JgD2X1hP6cyYx0d\nS0SkTlOBEamAif2b4+7yvwW9S2P0Cb0iIo6kAiNSAd4ezozvH05+hjd+Rc1JuJTEtoRvHR1LRKTO\nUoERqaB+HUNpEuJFwqHGuJhd+eLMRrIua0GviIgjqMCIVJDZbCIqshWmYmfMya2vLOg9vcbRsURE\n6iQVGJFKaFrfm/6dG5AeG4yPKZA9F6I5mXHG0bFEROocFRiRShrXrxle7s6kH9eCXhERR1GBEakk\nD1cnJg1oTmGWN/UuNycx9wJfJ+x0dCwRkTpFBUbkJvRsH0KLhj4kHbmyoHfNmS/Jupzt6FgiInWG\nCozITTCZTEQNbYW5xAWSrizoXaFP6BURqTIqMCI3qWGQJ4O7NSTzXDDepkD2JmtBr4hIVVGBEbkF\nY3o3pZ6nC+lHtaBXRKQqqcCI3AI3Fyv3DmpBUY433gX/XdB7/htHxxIRqfVUYERuUffWQbRt4kvy\n0ca4mFxZE/slmZezHB1LRKRWU4ERuUUmk4kpQ1piKXWhNLEVBSWXWXFKn9ArImJPKjAit0F9fw+G\n3dWY7PgQvAhkX/J3nMw47ehYIiK1lgqMyG0yqmcT/L3dSD/aHIAlWtArImI3KjAit4mLk4XJQ1pQ\nfMkHz7xwknKT2aoFvSIidqECI3IbdW4RSMdwf1KPh+FscmVN7EYt6BURsQMVGJHbbPKQljjhSklC\nKy6XFGpBr4iIHajAiNxmgfXcGBURxqXzIXgaVxb0xmSccnQsEZFaRQVGxA6G3RVGsK87F39Y0HtC\nC3pFRG4nFRgRO3CympkytCWluT645zbjQl4KW87vcHQsEZFaQwVGxE7aN/WnW+sgLp5ogrPJlbX6\nhF4RkdtGBUbEju4b1AIXsxtF8S25XFLIZye/cHQkEZFawa4FJiYmhsGDB7N48WIAkpKSePDBB5k6\ndSoPPvggqampAKxatYrx48czceJEli1bZs9IIlXK18uFMb2akpdYH4/SAPanHOREuhb0iojcKrsV\nmLy8PGbPnk1ERITtvtdee41JkyaxePFihgwZwocffkheXh7z5s1j4cKFLFq0iI8++ojMzEx7xRKp\ncoO7NaRBgCfpR1sAsDRmJcWlxQ5OJSJSs9mtwDg7O7NgwQKCgoJs9/3xj38kMjISAF9fXzIzMzl4\n8CAdOnTAy8sLV1dXunTpQnR0tL1iiVQ5q8VMVGQrSvN8cMsJ50JeChvPbaFIJUZE5KZZ7bZjqxWr\ntezu3d3dASgpKeGTTz5h+vTppKWl4efnZ3uOn5+f7dSSSG3RslE9erYPYeexQny6JbAm9kvWxm7C\n382PEPcgQjyCCP7v/4e4B+Hu5OboyCIi1ZrdCsz1lJSU8PTTT9OjRw8iIiJYvXp1mccNw7jhPnx9\n3bFaLfaKSGCgl932LbemJo/Nryd05ODLaRTFdKPPwBLSClI5n32BwxePcfjisTLP9XH1pqF3CKFe\nwTTwDqGhd31CvYPxd/PFZDI56BVcX00el9pOY1N9aWxuTZUXmGeffZawsDBmzJgBQFBQEGlpabbH\nU1JS6NSpU7n7yMjIs1u+wEAvUlNz7LZ/uXm1YWzu7tuMxRtjSDzgR7+OvQhu6Y6HZykXL18kOS+F\nC7kpXMhLITk3laMpJzmSElNmexeLM8HugQS7B/93tiaQEI8gAt0CsJjtV+rLUxvGpbbS2FRfGpuK\nKa/kVWmBWbVqFU5OTjz22GO2+zp27MisWbPIzs7GYrEQHR3NH/7wh6qMJVJl+ndqwM7DFzh8Jp3D\nZ9IBMAF+3q6E+LkR7NeSVn6d6Bfijm89KyWWS6QUpHIhN8VWcBJzk4nLSSizX7PJTKCbPyHuQQT/\n9zRUiEcQQe6BuFldHfBKRUTsy2RU5JzNTTh8+DBz584lISEBq9VKcHAwFy9exMXFBU9PTwDCw8P5\n05/+xPr16/nnP/+JyWRi6tSp/OxnPyt33/ZsrWrF1VdtGZvLRSUcPZtOcno+F9LzSE7P40JGHlmX\nCq96rtViIsjXnWBfN0L83An2cyfI1xVnj8tcKs0gOS+F5LxU28xNfnH+Vfuo5+JD8H9nakLc/7fW\nxtvZ67acjqot41IbaWyqL41NxZQ3A2O3AmNPKjB1U20fm/zLxaRklC01yel5XEjPI//y1ddRcnOx\nEOzrXqbY+PhAiXMOmUUXr5Sa3CsFJ+Py1R9N4GZ1vVJm3IMI9gi0zdr4u/pV6nRUbR+XmkxjU31p\nbCqm2pxCEpHrc3OxEhbiRVhI2R9YwzDIySv6SbHJJzk9j/Oplzh74ep/BH08nQnxbUSwXyta+Lnj\nF2DB4p5HoSWb1PxU2+mouJzznM2OK7Ot1WQh0D2gzLuigj0CCXYPwsXibNevgYhIRanAiFRzJpMJ\nbw9nvD2cadmoXpnHSksNLmYX2GZqktPzbTM3MfGZnIjP/Mm+INDHh2C/+v/f3r0HR3XX/x9/nr1l\ns7dkN9kkJCEkJDThVqAXFQRbtdVRZ4r2RkVQ//g543T8Q6deGGytnTo61Ms4tZ2qtZ3p4NcpSr3U\nUXvxp1WmpbV+oQECgWPEgvcAABiNSURBVJD7fXPZzSa7m+ue3x8bQgKVHy2G3YXXY2aH4ezZk8/p\nh6UvPp/3+XyoDuSy0Z+D0zsJOWOMmWFC8bP1Nr2xfjhnRQN/Tn4q1Jx57NtVhMNbhWmSkU9HiciV\nS1NI59CwXuZS37wzU9Mzs1NSCfrD8bkRnP7hONH41Hnn222WuVqbIn8uefkmFleMGVuU8OyUVH88\nxMjk+X3gtrkWFA+fGbnxO/OxGNpyLV30nclc6puLoykkkauQ3WalLOihLOg57734+BT98+tt5o3g\ndA3Ezjvf7SygJLCU5QEXAb8Vp3cc0zFKwogQNcN0hHtoHWmnZaRtweccFnvqsW93ESVnHv12FxHM\nLcBm0V8/IvLu6W8QkauQy2mnaomdqiW+BcdN0yQyNrmgiPjM01JtfaM090TPvRKFeQGC+WuoKsjB\nkzeJzRVjyhZlZGY4VWsTD9E51rPgU2ce+55fZ5Oalgri1GPfInIRFGBEZI5hGPi9Ofi9OdQt8y94\nbyaZZHDkTL1NYm7kZiCS4ER7mBPt88/OwW4ro9hfw4qAk/xAEocnTtIxRsKIMDg+mFqwLz7AkcGG\nBT8nPyfvvKmoEncxHrtbdTYiMkcBRkQuitViodjvotjv4trqs8eDQS9d3ZG5aai511Dq14VTUnYg\niM9dRnEgl8KAQW7eOJbcGJPWEUamhwklBmgMN9EYblrw81VnIyLzKcCIyCXLcVj/4yPgkbFJ+oZi\n9A3H6Z0Xbk53jtDUeeZMKxDAaimgyH8tKwI2vP5J7O44M45RYoQZGh+8qDqbM2vaFLkKVWcjcgXT\nt1tEFs38KamVlYEF701Nz6QKiYfOH7npHTqz35kFyAPycDtrKCnIIb9gGqcvATkxJiwjjEwP/cc6\nm8LcwNniYdXZiFxRFGBEJC3sNivlQQ/l5zwlNX/hvvlTUb3Dcdp64iS7TVLBxgt4MYxyCvOclAWT\nuPMnsLpiTNuijJlhBsYHCcUbVGcjcgVSgBGRjHKhhfumZ5IMRBILR2tmfz3RNEVqa0zP7KuU3BwL\nwUIrvsAkOd44Zs4YCSKEp4b+Y51NqaeEck8pZd5Syj2lLHEXaSpKJAPpWykiWcNmtbCkwM2SAvd5\n740lphaM2Jx59fTF6eg2APfsqxgD8Odb8QencM0rIo7ODHM60kpTpGXuulbDSom7iHJPKeWzoabc\nswSX3XW5bltE3oYCjIhcETy5dmrK8qgpy1twPJk0GRxJnBdueofjNDfNAK7ZVxCA3FyTwpJpPIEE\nRm6UuJFaz6Z7rJc3+v537roBp38uzJwJNgGnX1NQIpeJAoyIXNEsFoMiv4uicx7/htQO4OdOR/UM\nxuhui5NstQM+oBxI4i+cIT84jsM3xrQ9QmQqtYbN/PqaXFtuKtBoCkpk0elbJSJXrdwcG1VLfOet\nSDw1PUPPYGq379QrRldojNYTdlLFw0uAOmw5UxSUTOD2p0ZrYoamoEQuFwUYEZFz2G1vv65NND5J\nd2iMzoFYKtiExujujtHf7gWKUidZpnH7E+QHJ3B4x5i0hemPDWgKSuS/TAFGROQi+VwOfJWBBWva\nJJMmoUiCrlBqtKYzNEb3QIzuxgRQCFQCSSzOOPlFE7j8cXBGiU0OaQpK5BLoWyEicgksFoOSgIuS\ngIsb6ormjo9PTtM9N1JzdsRmuGP67IftE+R4x8gLjmP3jDFpidAUadEUlMhFUIAREVkEToeN6rI8\nquc9FWWaJuHRiVRNzZn6mtAYvU1xZpJm6iTLNEbuGJ5AHFd+HDM3St/Y+VNQBbNTUGVzoaaUgDNf\nU1By1VCAERG5TAzDIOBzEvA5uba6YO749EySvqE4nXOhJhVw+jsnZs8wMZwxbO5RPAXj2D2jjI6H\nqR9voP7tpqBmQ806+zXkmB5tdilXJAUYEZE0s1ktlBd5KC9auK3CWGKK7tmnoDpna2y6W2NMTM2k\nTrBPYHFFcfpi5ObHMZ0jC6ag9p6AHKuDZb4KqnwVVOVVUOmrwOvwnNsEkayjACMikqE8uXZqK/zU\nVvjnjiVNk8FIYu7R7s7ZgBPqjGPC3BSU1R3FHYiDJ8yp8GlOhU/PXaPQGaAyr4Iq3zKq8ioo8yxR\nobBkHf2JFRHJIhbj7MJ8110TnDs+MTVDz+C8UBMao7M9RiwxBdYpLO4RrN4RXP5RImaYf4+/xb/7\n3wLAZrFR4S2j0ldBVd4yqnwV+J35/6kJIhlBAUZE5AqQY7eetyhfYaGHhlMhWnqjtPZEae2N0n5i\njOmZGQxnHIs7gj1vBCNvlJZkBy0j7dB5AEjt2F0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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "jFfc3saSxg6t" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Ax_IIQVRx4gr" + }, + "cell_type": "markdown", + "source": [ + "Since normalization uses min and max, we have to ensure it's done on the entire dataset at once. \n", + "\n", + "We can do that here because all our data is in a single DataFrame. If we had multiple data sets, a good practice would be to derive the normalization parameters from the training set and apply those identically to the test set." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "D-bJBXrJx-U_", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def normalize_linear_scale(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + " processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n", + " processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n", + " processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n", + " processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n", + " processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n", + " return processed_features\n", + "\n", + "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.005),\n", + " steps=2000,\n", + " batch_size=50,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "MrwtdStNJ6ZQ" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Try a Different Optimizer\n", + "\n", + "** Use the Adagrad and Adam optimizers and compare performance.**\n", + "\n", + "The Adagrad optimizer is one alternative. The key insight of Adagrad is that it modifies the learning rate adaptively for each coefficient in a model, monotonically lowering the effective learning rate. This works great for convex problems, but isn't always ideal for the non-convex problem Neural Net training. You can use Adagrad by specifying `AdagradOptimizer` instead of `GradientDescentOptimizer`. Note that you may need to use a larger learning rate with Adagrad.\n", + "\n", + "For non-convex optimization problems, Adam is sometimes more efficient than Adagrad. To use Adam, invoke the `tf.train.AdamOptimizer` method. This method takes several optional hyperparameters as arguments, but our solution only specifies one of these (`learning_rate`). In a production setting, you should specify and tune the optional hyperparameters carefully." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "61GSlDvF7-7q", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 677 + }, + "outputId": "aefe89df-c727-45e8-e0b6-b243b7886201" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Retrain the network using Adagrad and then Adam.\n", + "#\n", + "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.5),\n", + " steps=500,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 93.44\n", + " period 01 : 75.53\n", + " period 02 : 75.13\n", + " period 03 : 73.52\n", + " period 04 : 71.01\n", + " period 05 : 71.45\n", + " period 06 : 70.28\n", + " period 07 : 71.60\n", + " period 08 : 69.35\n", + " period 09 : 68.68\n", + "Model training finished.\n", + "Final RMSE (on training data): 68.68\n", + "Final RMSE (on validation data): 67.68\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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4g4LKSTp0llp7dEd/ivpUREREep+Cykk6dN2fekux+lRERES8REHlJCWFJhBm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\nt7/9LQ0NDezatUv6u27TvAsqVqsVl8sV+d7hcGCxWKI4IjH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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "jKR0mzr16pHy", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 677 + }, + "outputId": "14084931-ef23-4634-bdfb-e0f307258bc2" + }, + "cell_type": "code", + "source": [ + "_, adam_training_losses, adam_validation_losses = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdamOptimizer(learning_rate=0.009),\n", + " steps=500,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 208.53\n", + " period 01 : 121.05\n", + " period 02 : 113.57\n", + " period 03 : 106.19\n", + " period 04 : 96.16\n", + " period 05 : 82.63\n", + " period 06 : 73.38\n", + " period 07 : 70.93\n", + " period 08 : 70.15\n", + " period 09 : 69.89\n", + "Model training finished.\n", + "Final RMSE (on training data): 69.89\n", + "Final RMSE (on validation data): 69.36\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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6vRHPUF98l3rju9Sb5mkq5LXqUUhpaWkMHjwYgBEjRrBt2zYcDgeFhYXu5+Tl\n5R017dTWpCTF4CqPpqiuiKKaYm+XIyIi0i61aoCx2+3u9S1bt24lMTGRYcOGsXbtWurr68nLyyM/\nP59zzjmnNcs6o35YBwOwQ4dTi4iIeITHppC2bdvG4sWLycrKwmq1snr1av72t7+xYMEC/Pz8iIiI\n4N577yU8PJxZs2YxZ84cTCYTf/3rXzGb2+7paWKjgohwJVDDDnYUpTE6YZi3SxIREWl3TEYbPO+9\nJ+cNz8S85PLVaXzh/H8EBjt5cMxfsZjb3iHhvkhzxr5JffFd6o3vUm+ax2fWwJwtfrg6db2rjn3l\nB71djoiISLujAOMBPTtHYZTrsgIiIiKeogDjAUEBVpLDkzBcJrYW7PJ2OSIiIu2OAoyH9EuKw1UZ\nSVZVFpX1Vd4uR0REpF1RgPGQI69OvUvTSCIiImeUAoyHdHKEElQfD8B2BRgREZEzSgHGQ0wmEykd\numA0+LO9MK1Zl0gQERGR5lGA8aCUZDvOMjtVjZVkV+V6uxwREZF2QwHGg/ocsQ5mR1Gal6sRERFp\nPxRgPCg0yI+EwC4AbC9UgBERETlTFGA8bECXeFxV4ewp20+ds97b5YiIiLQLCjAedvjq1NG4cLK7\nZI+3yxEREWkXFGA8LKlDGH7VsYAuKyAiInKmKMB4mMVsppejK4bTossKiIiInCEKMK2gf1IMrnIb\nRXVFFNWUeLscERGRNk8BphUcXgfzw9WpdTSSiIjI6VKAaQVRYQHEWDoDOpxaRETkTFCAaSUDOiXi\nqg1iV0kGTpfT2+WIiIi0aQowraRv8uGz8ta76thfnuntckRERNo0BZhW0q1jJKYqB6B1MCIiIqdL\nAaaV+FnNdIvsiuEy8V2+DqcWERE5HQowrWhAUhyuykiyqrOobKjydjkiIiJtlgJMK/phHQxAWvFu\nL1cjIiLSdinAtKLYqGDCXQkAbC/SOhgREZFTpQDTyvolJGM0+LOtIA3DMLxdjoiISJukANPK+iXZ\ncZZFU+WsJKcqz9vliIiItEkKMK2sZ2IkVMQAsEOHU4uIiJwSBZhWFuhvpUtIEgBbdTi1iIjIKVGA\n8YIBXTriqgpjb/l+6p313i5HRESkzVGA8YIfrk7twsnu0n3eLkdERKTNUYDxgo4xIQTVxwGwo0jT\nSCIiIi2lAOMFJpOJvrHnYDgtuqyAiIjIKVCA8ZL+yQ5cFTaK64sori3xdjkiIiJtikcDTHp6OpMm\nTWLFihUANDQ0MH/+fGbOnMk111xDWVkZAG+99RaXX345V1xxBa+++qonS/IZvbv8eFmBnUXpXq5G\nRESkbfFYgKmurmbhwoUMHz4/pUdyAAAgAElEQVTcfd8rr7xCVFQUK1euZMaMGWzcuJHq6mqWLFnC\n888/z/Lly3nhhRcoLS31VFk+IzTIj4SALgBsLdA0koiISEt4LMD4+/uzbNkyHA6H+75PPvmEiy66\nCICf/exnTJw4kS1btpCSkkJYWBiBgYEMGjSI1NRUT5XlU/p17IyrNoi0kgycLqe3yxEREWkzrB7b\nsNWK1Xr05rOysvjss8944IEHsNvt/OUvf6GwsBCbzeZ+js1mo6CgoMltR0UFY7VaPFI3QExMmMe2\nfaQxQzrz/pt26gMzKbMU0cPetVX225a1Vm+kZdQX36Xe+C715vR4LMCciGEYJCUlMW/ePJYuXcpT\nTz1F7969j3vOTykpqfZUicTEhFFQUOGx7R8pKtCKX00sBpl8sWcTNsPx0y86i7Vmb6T51Bffpd74\nLvWmeZoKea16FJLdbmfo0KEAjBo1ioyMDBwOB4WFhe7n5OfnHzXt1J6ZzSZ62s7BcJnYosOpRURE\nmq1VA8yYMWNYt24dANu3bycpKYn+/fuzdetWysvLqaqqIjU1lSFDhrRmWV7VPzkOV2Uk2dXZVDV4\nbmRJRESkPfHYFNK2bdtYvHgxWVlZWK1WVq9ezYMPPsg//vEPVq5cSXBwMIsXLyYwMJD58+dz7bXX\nYjKZuOGGGwgLO3vmBfsmRePaZMcSXsKu4t0Mju3v7ZJERER8nslozqITH+PJeUNvzEveufx9yhLW\ncF7sEH7eZ1ar7rst0Zyxb1JffJd647vUm+bxmTUwcmL9E7piNPixrXBXsxYxi4iInO0UYHxAv+Ro\nnOV2qpyV5FTlebscERERn6cA4wPO6RiJufLwkVc7i3VZARERkZ+iAOMD/KxmuoUfPondlrydXq5G\nRETE9ynA+Ij+XTriqg5jX8V+6p0N3i5HRETEpynA+IiUZBvOMjsunGSU7vV2OSIiIj5NAcZHOKKC\nCXfGA7C9MM3L1YiIiPg2BRgf0i+uG4bTwncFuqyAiIhIUxRgfEi/ZAeuChvF9YWU1JZ6uxwRERGf\npQDjQ3p2joRyO6DDqUVERJqiAONDAv2tdA5OBtDVqUVERJqgAONjBnROxFUXSHrJblyGy9vliIiI\n+CQFGB/TL9mOq8xOvVHHgfJMb5cjIiLikxRgfExCTAiBdXEAbC/S4dQiIiInogDjY0wmE31jemAY\nJrbkaR2MiIjIiSjA+KD+SXG4KiPIrsmiuqHa2+WIiIj4HAUYH9S7iw1XmR0w2FWS4e1yREREfI4C\njA8KDfKjg38iAFvzdXVqERGRYynA+KhBCedgNPqxrTAdwzC8XY6IiIhPUYDxUX272nGWRVPtqiC3\nOt/b5YiIiPgUBRgflRQXjl91LAA7dDi1iIjIURRgfJTZbKJHVDcANudqHYyIiMiRFGB82MAunXBV\nh7K/cj/1zgZvlyMiIuIzTjnA7N+//wyWISfSN9mGs8yOCyd7Svd5uxwRERGf0WSA+eUvf3nU7aVL\nl7r/fPfdd3umInGLDA0g2tQJgK2FOiuviIjID5oMMI2NjUfd/uqrr9x/1qG9raN/h24YTjPf5SvA\niIiI/KDJAGMymY66fWRoOfYx8Yz+ybG4KmyUNBRSUlvq7XJERER8QovWwCi0tL5uHSMwVToA2Fm8\n28vViIiI+AZrUw+WlZXx5Zdfum+Xl5fz1VdfYRgG5eXlHi9OwGoxkxzalf3sYHPuDkbED/V2SSIi\nIl7XZIAJDw8/auFuWFgYS5Yscf9ZWsfgxC7szQ8kvSwDl+HCbNLR7yIicnZrMsAsX768teqQJvTt\naufl3XYaAg5xoPwQSRGdvV2SiIiIVzX5T/nKykqef/559+2XXnqJiy++mBtvvJHCwkJP1ybfc0QG\nEeaMB2C7DqcWERFpOsDcfffdFBUVAbBv3z4efvhhbrvtNkaMGME//vGPVilQDktx9MAwTGzO02UF\nREREmgwwmZmZzJ8/H4DVq1czbdo0RowYwZVXXtmsEZj09HQmTZrEihUrjrp/3bp19OjRw337rbfe\n4vLLL+eKK67g1VdfPZX30e4NTO6AqzKC3NpsqhtqvF2OiIiIVzUZYIKDg91//vrrrxk2bJj79k8d\nUl1dXc3ChQsZPnz4UffX1dXx9NNPExMT437ekiVLeP7551m+fDkvvPACpaU638mxenSKggo7BgZp\nJRneLkdERMSrmgwwTqeToqIiDh48yKZNmxg5ciQAVVVV1NQ0PQrg7+/PsmXLcDgcR93/5JNPMnv2\nbPz9/QHYsmULKSkphIWFERgYyKBBg0hNTT2d99QuBfhb6BiYBMAWTSOJiMhZrsmjkK677jpmzJhB\nbW0t8+bNIyIigtraWmbPns2sWbOa3rDVitV69Ob37dvHrl27uOmmm3jggQcAKCwsxGazuZ9js9ko\nKChocttRUcFYrZYmn3M6YmJ88xDxMT378lL2WnaWpGO3h56VJxb01d6c7dQX36Xe+C715vQ0GWDG\njh3L+vXrqaurIzQ0FIDAwED+9Kc/MWrUqBbvbNGiRSxYsKDJ5zTnGkslJdUt3ndzxcSEUVBQ4bHt\nn47k2DCcO6OptOay7cBe4kIcP/2idsSXe3M2U198l3rju9Sb5mkq5DUZYLKzs91/PvLMu8nJyWRn\nZxMfH9/sIvLy8ti7dy9//OMfAcjPz2fOnDn84Q9/OGpBcH5+PgMGDGj2ds8mCfYQAuviaCSXHUVp\nZ12AERER+UGTAWbChAkkJSW5F9weezHH//znP83eUWxsLB999NFR216xYgW1tbUsWLCA8vJyLBYL\nqamp3HnnnS19H2cFk8lEb1t3vmMzqTk7mNB5tLdLEhER8YomA8zixYt58803qaqq4vzzz+eCCy44\nar1KU7Zt28bixYvJysrCarWyevVqHnvsMSIjI496XmBgIPPnz+faa6/FZDJxww036DIFTRic3JnN\nGaEc4AANzgb8LH7eLklERKTVmYxmLDrJycnh9ddf5+233yYhIYGLL76YyZMnExgY2Bo1HseT84a+\nPi9ZVdvALa8twxq3nz8MuI6etm7eLqnV+Hpvzlbqi+9Sb3yXetM8Ta2BadZVATt06MD111/Pe++9\nx9SpU7nnnntOaRGvnL6QQD9irYkAbMnX4dQiInJ2alaAKS8vZ8WKFVx22WWsWLGC3/72t6xatcrT\ntclJDIzvjuEyszVf10USEZGzU5NrYNavX8///vc/tm3bxpQpU7jvvvvo3r17a9UmJzGgayyrv7RR\nEllIaV0ZkQER3i5JRESkVTUZYH7961/TpUsXBg0aRHFxMc8999xRjy9atMijxcmJJcaGYa12YEQW\nsqMojRHx53q7JBERkVbVZID54TDpkpISoqKijnrs0KFDnqtKmmQ2m+gW0Y10dpCas1MBRkREzjpN\nBhiz2czNN99MXV0dNpuNp556isTERFasWMHTTz/NZZdd1lp1yjEGJyaxKyuQjLIMXIYLs6lZy5lE\nRETahSYDzD//+U+ef/55unbtyscff8zdd9+Ny+UiIiKCV199tbVqlBNISY7GtcNOg+MQBysO0SW8\ns7dLEhERaTVN/rPdbDbTtWtXACZOnEhWVhY///nPefzxx4mNjW2VAuXEIkIDsNERQEcjiYjIWafJ\nAHPs1Y47dOjA5MmTPVqQNF+/2J4YBmzO0/lgRETk7NKihRPHBhrxrkFdO+CqjCS3NpuaxhpvlyMi\nItJqmlwDs2nTJsaNG+e+XVRUxLhx4zAMA5PJxNq1az1cnjSla0IEpnUxEFZKWnEGAxwp3i5JRESk\nVTQZYN5///3WqkNOgdViJikkmQPs5tvcHQowIiJy1mgywCQkJLRWHXKKhnTuzv7CNewqSnePjImI\niLR3OnlIG5eSbMdZFk21UUF+dYG3yxEREWkVCjBtXExkEKGN8QBsK0zzcjUiIiKtQwGmHehr7wHA\ntzk7vFyJiIhI61CAaQcGJ3fGVR1KZvUBGlyN3i5HRETE4xRg2oEenSMxKuy4aGRP6T5vlyMiIuJx\nCjDtQICfhYSALgBsztVZeUVEpP1TgGknBif0xHCZ2Vqg6yKJiEj7pwDTTvTvGourIopSZyFldeXe\nLkdERMSjFGDaifjoYAJq4wDYUZTu5WpEREQ8SwGmnTCZTPSM6gbAxuztXq5GRETEsxRg2pEhiV0x\n6gPYU74Hl+HydjkiIiIeowDTjvRJsuEqi6GBWjIrsrxdjoiIiMcowLQjwYF+2C2dANiSr8OpRUSk\n/VKAaWcGxvXEMGBzrg6nFhGR9ksBpp0Z1DUBoyqCvLosahprvV2OiIiIRyjAtDOJcWFYqmLBZJBW\nnOHtckRERDxCAaadMZtMdA3rCuhwahERab8UYNqhoYndMRqt7CpJxzAMb5cjIiJyxinAtEMpyTG4\nyqOpMSrYWZyuc8KIiEi7Y/V2AXLmRYT4E+lMpII8lmx5lnD/MPpG96SvvRc9bd0JsPh7u0QREZHT\n4tEAk56ezvXXX88vfvEL5syZQ05ODnfccQeNjY1YrVYeeOABYmJieOutt3jhhRcwm83MmjWLK664\nwpNlnRWGxg3kvZ2NRHUsptaUxxc53/BFzjdYzVa6R3UlJboXfe29sAVGebtUERGRFvNYgKmurmbh\nwoUMHz7cfd+//vUvZs2axYwZM/jvf//Lc889x7x581iyZAkrV67Ez8+PmTNnMnnyZCIjIz1V2llh\nwqCOpGeWsntbGdADU0gZkfElWKIK2FGUxo6iNF5Of4OE0A7fh5neJIZ3xGzSrKKIiPg+jwUYf39/\nli1bxrJly9z3/eUvfyEgIACAqKgotm/fzpYtW0hJSSEsLAyAQYMGkZqayoQJEzxV2lkhKiyAO+YM\npqyyji17iti8u5Dt+4pp2J2Eyb+G4NhiQh3F5FTmklWZw/sH1hDmH0rf70dmekZ1I9Aa4O23ISIi\nckIeCzBWqxWr9ejNBwcHA+B0OnnxxRe54YYbKCwsxGazuZ9js9koKChocttRUcFYrZYzX/T3YmLC\nPLbt1hYTE8Y5SXYun9SD2vpGtqQXsGF7Lt/syCMvMwHMvfC3lWDvVEaNOZsvc77hy5xv8DNb6ePo\nzuD4fgyOT8EeYvvpnbWC9tSb9kR98V3qje9Sb05Pqy/idTqd3HrrrQwbNozhw4fz9ttvH/V4cw77\nLSmp9lR5xMSEUVBQ4bHte1tybCjJsefws/Fd2ZddzuaMQjbtLiR7UxXQFXNIGdGdyjBF5LM5dweb\nc3fwbOpLR0w19SIxvJNXpprae2/aKvXFd6k3vku9aZ6mQl6rB5g77riDxMRE5s2bB4DD4aCwsND9\neH5+PgMGDGjtss46ZpOJrgkRdE2I4PKxXckrqWbL7kI2ZxSSnlaGy0jE5F9DRIcSgh3F5FTm/DjV\n5BdKH3tPUqIPH9WkqSYREWltrRpg3nrrLfz8/Ljxxhvd9/Xv358FCxZQXl6OxWIhNTWVO++8szXL\nEiA2Kpgp53ZmyrmdqaxpYOvew+tmtu4NpfRAPJh7EmwvJSqhlGpzNl/lbOSrnI1YTRa6RXUlxd6b\nvtG9iA7SUU0iIuJ5JsNDp2rdtm0bixcvJisrC6vVSmxsLEVFRQQEBBAaGgpA165d+etf/8r777/P\ns88+i8lkYs6cOVx00UVNbtuTw24a1jtao9NF2sFSNu0uYHNGIcXldYCBNaycmM7lGGF5lLl+HEGL\nD4k7HGbsvehyhqea1BvfpL74LvXGd6k3zdPUFJLHAownKcB4h2EYZOZXsnl3IZsyCjmQe/hzMvnX\nEN2pnEB7IaVk4zScAIT6hdA3uhcp9l70tHUj0Bp4WvtXb3yT+uK71Bvfpd40j0+tgZG2y2Qy0Tk2\njM6xYVw0Koni8lr3Ido79xfTuCcWzD0Iiy0jKr6MSnMWX+Vu5KvcH6ea+tp7kRLdW1NNIiJyWjQC\ncwyl4lNTU9fI9n3FbM4o5Ls9RVTWNAAGARGVOBLLcYbmUtL44+Hx8SFxh8OMvXezp5rUG9+kvvgu\n9cZ3qTfNoxEY8bigACtDejoY0tOB0+ViT1b54amm3QVkfhcGJGAKqCEusRL/6ELyqg+RfSCXDw58\nQqhfCH2ie5Ji702vMzDVJCIi7Z8CjJxxFrOZ7p0i6d4pklkTziGnqIrNGYVs3l1IRnoZBjFg7oYt\noZKIDqWUuzLZkPstG3K/xWKy0C0ymRR7b1LsvYgO8o0T6ImIiG/RFNIxNKznWeXV9XyXUcTmjEK2\n7SuivsEFGITYqnEkltMQnENRQ777+R1CYt1hZmhyH4qKqrxXvJyQfjO+S73xXepN8+gopBbQl6r1\nNDQ62XmgxH1UU1llPQDWwFrik6uwRBVQ2HiIRqMRgPCAUHrbetLP3odetm74W/y9Wb58T78Z36Xe\n+C71pnkUYFpAXyrvcBkGB3Irvl83U8ihgsrDD5gbiUusITS2mDJLJpUNh+/3M1vpaetGP3sf+tp7\nEe6va4p4i34zvku98V3qTfMowLSAvlS+obC05vC6mYxC0g6W4nQZgEGko5roTuXUBB6ipKEIABMm\nuoR3pp+9N/1iehMb7MBkMnn3DZxF9JvxXeqN71JvmkcBpgX0pfI91bWNbN1bxK7MMjbuzKWq9vCU\nUkBILXFJ5RjheRQ2ZmNw+KvsCLKTYu9Nv5g+JEckeuXCk2cT/WZ8l3rju9Sb5lGAaQF9qXxXTEwY\nuXllZBwq+350poi84sNXJjdZ64nrUkmAvZBiMmlwNQAQ4hdM3+he9IvpQy9bdwK0buaM02/Gd6k3\nvku9aR4FmBbQl8p3nag3ucXVbN5dyJaMQnYfKsNlGGByEhlXQWRCKRV+h6h2Hl43YzVb6Rl1jnvd\nTERAuDfeRruj34zvUm98l3rTPDqRnbRbcbZgpp3XmWnn/XgV7S0ZhWzdG8D+nEggkYDIShydy2gI\nyWFb0S62Fe2CNOgS3vnwVJO9Nx1CYrVuRkSkDVGAkXYjNMiP4X3iGN4njkani92ZpWzOOBxoDp8N\nuCPmgGocXcqxRBVwoDyT/eUHeXvv+9iDog8vArb3JjmiCxazxdtvR0REmqAppGNoWM93nWpvDMMg\np6iaLd8f1ZSRVYZhAJZ6IuPLCI0rptycRYNx+Dw0IdZg+th/PN+MLm3QNP1mfJd647vUm+bRFJKc\n1UwmE/H2EOLtIUwflkhFdT3f7Tk8MrNtXxClmTFg6kZQdAm2jmXUmrP5OjeVr3NTsZosdLcdXjeT\nYu9FZECEt9+OiIigACNnobBgf0amdGBkSgcanS7SDpayOaOQLRnBZG2OBpIwh5QT07kcInLZUZTG\njqI0XkqDxLBO3x+i3Zv4kDitmxER8RJNIR1Dw3q+y9O9MQyDrMIq91TT3qxyDMDkX01EfCmBMYWU\nm3Lc55uJDrS5T57XNSLprF03o9+M71JvfJd60zw6jLoF9KXyXa3dm/KqerbsKWRLRhHb9xVT1+AE\nSwNB9mIi4kuo9s92r5sJtgbRJ7qn+3wzQWfRuhn9ZnyXeuO71Jvm0RoYkVMQHuLP6H7xjO4XT0Oj\nk13uqaZQcjfFgqk71ohibAllNITm8E3eJr7J24TVZKFbVFf3upmowEhvvxURkXZHIzDHUCr2Xb7S\nG8MwyMyv/H6qqYh9OeWAgSm4nIj4EvxsBVRS5H5+57CE788304eE0A7tbt2Mr/RFjqfe+C71pnk0\nAiNyBplMJjrHhtE5NowLRyZRWlnHd3uK2Ly7kB37i6nP6ILJv4agmEJC4orJrMjhYEUW7+77EFtg\nFCn23gyMSeGcyKR2F2ZERFqLAozIaYoMDWBM/3jG9I+nvsHJzgMl34/ORFCQ1QksDfhFFhKRUEI5\nuXx66HM+PfQ58SFxjOk4gnPjBukaTSIiLaQppGNoWM93tbXeGIbBwbzK7y88WciB3AowuTCHFRPe\nMY/60CwMXARZgxjeYQhjO47AHhTt7bJbrK315Wyi3vgu9aZ5NIUk4gUmk4nEuDAS48K4eFQSJRV1\n7kO0t++KwWnpSmCHLBpjD7Emcx2fZK6nT3RPxnUcSQ/bOZhNZm+/BRERn6UAI9JKosICGDcwgXED\nEyitrGPdlmw+3RJBcWYSFlsuIZ0Osa1oJ9uKdhIbHMOYjiMYFjdYlzIQETkBTSEdQ8N6vqs99sbl\nMvhuTxGfbMpi294iCCkjKD4TorIxcBFoCeC8DkMYmzCc2BCHt8s9ofbYl/ZCvfFd6k3zaApJxEeZ\nzSYGdLMzoJudgtIaPt2czbrv7FTs64bVcQhLh0PuRb+9bN0Z23EEfaJ7anpJRM56CjAiPiImMoiZ\n47py8agkvk3PZ+2mWNJTk7BE5REQn8lO0tlZnI49KJqxCcMZ1mEowX5B3i5bRMQrFGBEfIyf1cyw\n3nEM6x1HVkElazdl88X2jtRaSvCLPUiRPYf/ZbzD23tXc26HwYxNGEF8aJy3yxYRaVVaA3MMzUv6\nrrO5N3X1TjbszOOT1CwOFBVhjTmEf1wmhl8NAN0juzK200j62Xu3+vTS2dwXX6fe+C71pnm0Bkak\njQvwtzCmfzyj+3Vgf24Fn6RmsWFrLs7QXPziDpDOHtJL9xAVEMmYjsMZEX8uoX4h3i5bRMRjNAJz\nDKVi36XeHK2qtoEvtubyyaYs8mrysMYexGrPBrMTq9nK0NiBjO04kk5h8R6tQ33xXeqN71Jvmqep\nERjLX//61796asfp6en87Gc/w2w2069fP3Jycrj++utZuXIln332GRMnTsRisfDWW29x5513snLl\nSkwmE3369Glyu9XV9Z4qmZCQAI9uX06denM0f6uFrgkRTBiUQPcOsdQURpO1046z3h8CKzlUc4D1\n2V+RVrwbf4s/scExHpleUl98l3rju9Sb5gkJCTjpYx6bQqqurmbhwoUMHz7cfd+jjz7K7NmzmT59\nOg8//DArV67kkksuYcmSJaxcuRI/Pz9mzpzJ5MmTiYyM9FRpIu2KyWSiV2IUvRKjKK3sxrrvcvh0\n8yFKTYewxh5kD/vZU7afCP9wRicMZ1TCeYT5h3q7bBGR0+Kx1X7+/v4sW7YMh+PHk29t2LCBiRMn\nAjB+/Hi+/PJLtmzZQkpKCmFhYQQGBjJo0CBSU1M9VZZIuxYZGsCFI7pw/+9GMm/SJHo6p1L33Sga\n8zpTVlPFO/tW8+fP/8ELO17iQHmmt8sVETllHhuBsVqtWK1Hb76mpgZ//8NX3Y2OjqagoIDCwkJs\nNpv7OTabjYKCAk+VJXJWOPoEed35dHM2n207QE3wAayxB/k6N5Wvc1NJDOvEuE4jGeToh9WsNf0i\n0nZ47f9YJ1s73Jw1xVFRwVitljNdkltTi4bEu9SblouJCaN3Nwe/vjSFL7fm8O4X+9h1IB1r7AEO\nGJm8sOMlXs94lyndRjO56xiigiJOaR/im9Qb36XenJ5WDTDBwcHU1tYSGBhIXl4eDocDh8NBYWGh\n+zn5+fkMGDCgye2UlFR7rEatDPdd6s3p69Uxgl6zBpBVcA5rN2fzxa4MGiL3UxZziJXbV/Ha9vcZ\n4EhhfKeRJIUnYjKZfnKb6ovvUm98l3rTPE2FvFY949WIESNYvXo1AB988AGjR4+mf//+bN26lfLy\ncqqqqkhNTWXIkCGtWZbIWSchJpSrJ3fn4eumMqfvJTiyL6R+X28aq4NJzd/CQ98u5d6vH+HLnI00\nOBu8Xa6IyHE8dh6Ybdu2sXjxYrKysrBarcTGxvLggw9y++23U1dXR3x8PIsWLcLPz4/333+fZ599\nFpPJxJw5c7joooua3LbOA3N2Um88a19OOWtSD/FN5k6w78cclYfJBEGWIEYnDGNMx+FEBR5/dKD6\n4rvUG9+l3jRPUyMwOpHdMfSl8l3qTev44QR5a7btpsgvDavjECZrAyZM9I3uzcTOozgnMtk9vaS+\n+C71xnepN82jSwmISLOFBPoxeWgnJg3pSNrBgXy86QDfFX2H2XGArWxna9F2YgIcTO4ymqFxA71d\nroicpTQCcwylYt+l3nhPWWUdn27JZm36VqpCM7DY8jCZDPxNAUw6ZxRjYkfq5Hg+SL8Z36XeNI+m\nkFpAXyrfpd54n8tl8N3eIj7aspvd1d9hcWRi8qvHij/nJ09iQudROp+MD9FvxnepN82jANMC+lL5\nLvXGtxSW1vDJ5kw+PfQVLkcaJmsDYZZIrup9Mf3svZt1CLZ4ln4zvku9aR6fOYxaRNoPe2QQV4zr\nzrO/vY6R1qtw5idS3ljG01tfYPFXT5BdmevtEkWkHVOAEZHTEhrsz9UT+rJw2q/oUXMhzrJoMmv2\n848N/+TfW16lsqHK2yWKSDukACMiZ0RMZBA3XTCKW8/7HTHFo3HVBfFt0Tfc+dki3stYi9Pl9HaJ\nItKOKMCIyBmVHB/BXy6/gGvP+R2BhSk0Ol28c3AVd6y9n815O7xdnoi0EwowInLGmUwmhnSPY/Hl\nV3OR7ReYixOpNEpYtv157lm3lJzKfG+XKCJtnAKMiHiM1WJm2pDu3H/hbznPegVGRTQ5Dfu5Z8ND\nPPnNK1TVa32MiJwaBRgR8bigACvXjB3KPeNvIrluAq66QLZWbOT2zxbx5s5PtD5GRFpMAUZEWo0t\nPJD506dx66CbsVUOwGk4+SDnPW5ds5hvDm33dnki0oYowIhIq0uKi2ThRbP5RZffEVDRhRpTKc+n\nv8Bf1iwlszTP2+WJSBugACMiXnNut0QevPD3TIuajanaRiH7uW/jw/xr/UtU1lV7uzwR8WEKMCLi\nVWaziYsGDeDBKX9kgN8UjMZAdtencvuni3gx9WOtjxGRE1KAERGfEOhv5brRk1g48lY6OgfhMjXy\neelq5n+4mE8ztnq7PBHxMQowIuJTosNCuGPyldyUciPhdUk0+JXyysHl3LF6Cel52d4uT0R8hAKM\niPikHnEdWDT99/ys0zX41dko9zvAv7Y+yqKPX6SostLb5YmIlynAiIhPG9OtDw9NvZVRETMwO/05\nZNrM3Z8vZtn61dQ1NEiF5NoAABL2SURBVHi7PBHxEgUYEfF5FrOZqwaP475xd9LdbyhYGthc/zF/\n/OAB3tqUisswvF2iiLQyBRj5/+3dfXBU9b3H8fc5u5un3SQbQgKEQIAgpBCCQqioICqIvWUKFWpD\nqal3bq9trzpjO8gVYwVtO73FO3Y6VS7VqbY2jiWtqNSqgI6ijCQogoCRZxUl5JEs5Gk3ye45948s\nITyosZjsLvm8ZpY95/zOnv3u/BL48PudPUckZnjiE7hz5k0sL1xKhp2LlXiCjb61LPvnI2w/fCTS\n5YlIP1KAEZGYM8Kbwf2zf8x/jPshiaF0Au5PeeLDNax4sZQjdSciXZ6I9AMFGBGJWVOzx/PgnGV8\nY9i3cBDH8cQ9rNrxW3676SVOtLRHujwR6UMKMCIS00zD5Ftfm8n/XlPC5OTpGM4ODjs3U/L6Q5Ru\n2UZ7hy6EJ3IxUoARkYtCgiuBH01byIrpdzHcNRbDfYKKznXc9c/VbNx5AMvSib4iFxMFGBG5qAzx\nDKZk5o/4r/z/xEM6lvco6xv+xH8/+xfeO1QT6fJE5CuiACMiF6X8zHH8z7XLWJCzAJfpwj+okkcP\n/h8PPLeeIzVNkS5PRC6QAoyIXLRMw2Ru7lX8ZtY9fD39Csy4DupS3+I3Fat5+MUtNDYFIl2iiPyL\nFGBE5KKX6Ezklsk3svKKuxiVdAlmio+9CS9w74bH+OvmPfjbg5EuUUS+JAUYERkwMpMGs2z6rdwx\n+Va8znTMwUfZ0vk0/72ulE3bPyYYsiJdooj0kgKMiAw4X0u/hF/OvItFuQuIc7iwhu7lubo/s3zt\n87y5q4pAh0ZkRKKdM9IFiIhEgsN0cF3OVUzPuoznD25ia3U5/mEV/PWTSv66K5vJ6QVcO2kM40Z4\nMQwj0uWKyFkUYERkQEtyJbFkwre5LudK1u1/ib3sw3bvZbe9j/e2DybpzVHMHHUpV08aQXpqQqTL\nFZGwfg0wra2t3H333Zw8eZLOzk5uv/12MjIyuP/++wEYP348DzzwQH+WJCICwFB3JrdP+XdaOlp5\np/Y93vxkG3VGDe3eel5p28mGl4eS7cxjdt4kpo7PJN7liHTJIgOaYdv9dx/6p556itraWpYuXUpt\nbS233HILGRkZLFu2jIKCApYuXcr8+fOZNWvW5x6nvr65z2rMyEju0+PLv059E50u5n6pbq1l69Ht\nlFe/i99qAcAKJGGeyKYgbTKzC8aRm5UStVNMF3PfxDr1Te9kZCR/Zlu/jsCkpaWxf/9+AJqamvB6\nvVRVVVFQUADAtddeS3l5+RcGGBGR/jDMPYRF4+dx47h/44DvMG8c2cb7vg+whh5gNwfY+U4aHv9o\nZuRcxqxJo0hLjo90ySIDRr8GmHnz5vHss89y/fXX09TUxJo1a/jFL37R3Z6enk59fX1/liQi8oVM\nwyRv0CXkDbqEQLCdnXV7eP3jbVTZR/Cn+NjU9h4bNmSS7fwac8ZfytRxmbicmmIS6Uv9GmDWr19P\nVlYWjz/+OPv27eP2228nOfn08FBvZ7PS0pJw9uFfDp83ZCWRpb6JTgOrX5IZMexa5k++lobWRl49\nVM6rh96iKb2Gamr4yydbeWrPcKYNK2TB1y9lbHZkv8U0sPomtqhvLky/BpgdO3YwY8YMAPLy8mhv\nbycYPH29hdraWjIzM7/wOD5fW5/VqHnJ6KW+iU4Du19czM66muuGzeTjpk957eMKdh/fQzDjQ94O\nfkjFKy/jCYxmxohCrpk0hlR3XL9WN7D7Jrqpb3onas6BycnJYdeuXdxwww1UVVXhdrsZPnw427dv\np7CwkE2bNlFcXNyfJYmIXDDDMBidOpIfTh5Jp7WQPfUf8OqHFRyxD9Pm3s3G1j1s2DiY4c485o4v\nZMrYITgduo6oyIXo128htba2UlJSwvHjxwkGg9x5551kZGSwYsUKLMti8uTJ3HPPPV94HH0LaWBS\n30Qn9ctna+5oYevRHbz5yducsOoAsINOzJNZTEq7lG/kF5AzNKXP3l99E73UN73zeSMw/RpgvioK\nMAOT+iY6qV96p7q1llcOl7OzfhcdRivQ9ZVsT2AUM7ILmT1pPJ5E11f6nuqb6KW+6R0FmC9BP1TR\nS30TndQvX45lW3zQcJBNh8v5sHU/thHq2t6cxnDHeG4YdzlTxg7DYV74FJP6Jnqpb3onas6BEREZ\n6EzDJD9jPPkZ4wkEA2w9+h5vHNlGQ3IV1VTwp0/e5i/vD2Oit4B5kwoZoW+qiJyXAoyISIQkOBO4\nbtR0rhs1neP+RjYdqmB73U4C3ir2UMXud1/DHRjFVdmFzM2fQFLCVzvFJBLLNIV0Fg3rRS/1TXRS\nv3y1bNvmkO9jXj6wlYMte7HMjq7tbSlkmeOYO246hWNGYJpffG0Z9U30Ut/0jqaQRERihGEYXDJo\nNJdMH02nFWTbp7t57eMKahOPUG1s589H3qW0MpMJqZOYn385wwenRrpkkYhQgBERiVIu08mMnCnM\nyJlCU3szmw5uY1vNu7Ql1/K+VcueHZtxt+dwxbCpfCN/sqaYZEDRFNJZNKwXvdQ30Un90v+OnDjG\ni/veYl/z+4QcfgDs9iSGGeOYO/YKpuWOwjQM9U0UU9/0jr5G/SXohyp6qW+ik/olcizb4p2jH/Dq\n4QqOBQ+D2fWVbLMtnbzkfBYVXo3biMeT6Iro/ZjkXPq96R2dAyMichEyDZPLR+Rz+Yh8/J0BNh54\nm4pj22lOquGD0Bt8sO0N7KATOhJxhtwkkILHkYo33svgxEEMTU4nMzmZVE88Xk+cgo7EFI3AnEWp\nOHqpb6KT+iX6HGuq54UP3qLKf5SW4Ek6jBbs8OjM2exOF3Z7ElZ7IkZHIglGMh5HKmnxaQxOSmOQ\nx43XE4fXE4/XE09qOOiYCjoXRL83vaMRGBGRASQrJYMfT/929z+Stm3T0tlKvb+RYyfrqW5uoK71\nOI3tPpqNk7Q5mzA9JwHoBHzhx4eA3RSPVZ+I3Z6I3dH1bHQk4XGmkhbvxetOwJvcFW687ji8yfGk\nhp8VdKQvKcCIiFzkDMMgOc5DcpyHMakjz2m3bIumjmaO+30cDzRy3N9ITUsD9W2NNDp9tMSdxE4+\nccZr2oFqG451JGC3JWL7TgWcpK6w056IGUwk1RNPqrtrisqbHA45nvjuaSuvJx5PkoKOfHkKMCIi\nA5xpmHjjU/HGp5LLqHPaQ1aIE+1NXeEm4OO4v5HGgI8GfyMN/kZOxp8aszmLbRDoTKQ1kMjRQCJ2\n9elRHKs9ETrjAQOHaZDiPjVNdfo5NTxt5fXEkeKOI97lwOU0cZiGztURBRgREfl8DtNBemIa6Ylp\n520PWkEaAydoDIebhkBj9/LxgI+muOOYKee+zrAduCw3RmcSQX8CVa3xfHI8AetY1ygOQRdwblAx\nAJfTxOU0cTpNXI6u5Tino3u7q8d251nr5+z7OfudsW94e2+ugix9TwFGREQuiNN0kpk0mMykwedt\n7wh10thj9Ob0c9dyq6MGEsCRBo6ex8VFgpGMK+TGCCZhh0xsy8QOmViWgRU0CYUMOkIG/k6DYMgg\nFDCwQia2bYIVftgmtuXoXsY2OF8w6i2HaZwRnHoGobjudcdn7uNymqQkJ9DW1oFpGhgGmIaBaRqY\nRteUX9d6z+Vz20zDCK8TPk5XmxFu62on/NrzLHcf98y28+0bjSNeCjAiItKn4hwuhrqHMNQ95Lzt\n/mDgjBGbrvNwTj+32I0Q98XvY4YfveEwnDhw4jAcmHQ9DBwY9qnnU+HH0R2EbMvEOhWgQgaWZWIF\nDdqCBlbIIBg0CPkN7O7QFA5MlqPHcvjBhYWo/mbQMySB0R12oCA3nVu/NbHfa1KAERGRiEp0JjDc\nM4zhnmHntNm2jT/ox9d+kqAVpNMK0ml1di8Hw+vdy6EgQauTTvv0eldbj316vO70eicdlp+g3UnQ\nDn/l/FQacpxT1nk5er/rOYzuMHNqyThv29l/ftZrwAC7x3JP9ln7wbn72j2feh6ra6PdY7+GuFGA\nAoyIiEg3wzBIciWR5Erqt/e0bIugFQqHnlB3+Dl/EOrZdp5A1SMonV4P4nQadHQG6boSmx3OB/YZ\n63ZXUui5BvYZa5y6lJt9Vnv3Fvuz2s587eljnfneXe3W6f16Hi9cb2Z6ZC4npwAjIiLSg2mYxDlM\n4hx9d3NMXcjuwvV2ulBEREQkaijAiIiISMxRgBEREZGYowAjIiIiMUcBRkRERGKOAoyIiIjEHAUY\nERERiTkKMCIiIhJzFGBEREQk5ijAiIiISMxRgBEREZGYowAjIiIiMUcBRkRERGKOYZ+6n7aIiIhI\njNAIjIiIiMQcBRgRERGJOQowIiIiEnMUYERERCTmKMCIiIhIzFGAERERkZijANPDr3/9a4qKili8\neDG7d++OdDnSw4MPPkhRURGLFi1i06ZNkS5HeggEAsyZM4dnn3020qVID//4xz+YP38+CxcuZPPm\nzZEuR4DW1lbuuOMOiouLWbx4MVu2bIl0STHNGekCosXbb7/NkSNHKCsr4/Dhw5SUlFBWVhbpsgSo\nqKjg4MGDlJWV4fP5uPHGG5k7d26ky5KwNWvWkJqaGukypAefz8fq1atZt24dbW1tPPzww1xzzTWR\nLmvAe+655xg9ejRLly6ltraWW265hQ0bNkS6rJilABNWXl7OnDlzAMjNzeXkyZO0tLTg8XgiXJlM\nmzaNgoICAFJSUvD7/YRCIRwOR4Qrk8OHD3Po0CH94xhlysvLueKKK/B4PHg8Hn75y19GuiQB0tLS\n2L9/PwBNTU2kpaVFuKLYpimksIaGhjN+mAYNGkR9fX0EK5JTHA4HSUlJADzzzDNcffXVCi9RYtWq\nVSxfvjzSZchZjh49SiAQ4Cc/+QlLliyhvLw80iUJMG/ePI4dO8b111/PzTffzN133x3pkmKaRmA+\ng+6wEH1effVVnnnmGZ544olIlyLA888/z6WXXsqIESMiXYqcx4kTJ3jkkUc4duwYP/jBD3j99dcx\nDCPSZQ1o69evJysri8cff5x9+/ZRUlKic8cugAJMWGZmJg0NDd3rdXV1ZGRkRLAi6WnLli384Q9/\n4I9//CPJycmRLkeAzZs38+mnn7J582ZqamqIi4tj6NChXHnllZEubcBLT0/nsssuw+l0MnLkSNxu\nN42NjaSnp0e6tAFtx44dzJgxA4C8vDzq6uo0HX4BNIUUdtVVV7Fx40YAKisryczM1PkvUaK5uZkH\nH3yQRx99FK/XG+lyJOx3v/sd69at429/+xs33XQTt912m8JLlJgxYwYVFRVYloXP56OtrU3nW0SB\nnJwcdu3aBUBVVRVut1vh5QJoBCZsypQpTJw4kcWLF2MYBitXrox0SRL20ksv4fP5+OlPf9q9bdWq\nVWRlZUWwKpHoNWTIEG644Qa++93vAvDzn/8c09T/VyOtqKiIkpISbr75ZoLBIPfff3+kS4pphq2T\nPURERCTGKJKLiIhIzFGAERERkZijACMiIiIxRwFGREREYo4CjIiIiMQcBRgR6VNHjx4lPz+f4uLi\n7rvwLl26lKampl4fo7i4mFAo1Ov9v/e977Ft27Z/pVwRiREKMCLS5wYNGkRpaSmlpaWsXbuWzMxM\n1qxZ0+vXl5aW6oJfInIGXchORPrdtGnTKCsrY9++faxatYpgMEhnZycrVqxgwoQJFBcXk5eXx969\ne3nyySeZMGEClZWVdHR0cN9991FTU0MwGGTBggUsWbIEv9/Pz372M3w+Hzk5ObS3twNQW1vLXXfd\nBUAgEKCoqIjvfOc7kfzoIvIVUYARkX4VCoV45ZVXmDp1KsuWLWP16tWMHDnynJvbJSUl8dRTT53x\n2tLSUlJSUnjooYcIBAJ885vfZObMmWzdupWEhATKysqoq6tj9uzZALz88suMGTOGBx54gPb2dv7+\n97/3++cVkb6hACMifa6xsZHi4mIALMuisLCQRYsW8fvf/5577723e7+WlhYsywK6bu9xtl27drFw\n4UIAEhISyM/Pp7KykgMHDjB16lSg68asY8aMAWDmzJk8/fTTLF++nFmzZlFUVNSnn1NE+o8CjIj0\nuVPnwPTU3NyMy+U6Z/spLpfrnG2GYZyxbts2hmFg2/YZ9/o5FYJyc3N58cUXeeedd9iwYQNPPvkk\na9euvdCPIyJRQCfxikhEJCcnk52dzRtvvAHARx99xCOPPPK5r5k8eTJbtmwBoK2tjcrKSiZOnEhu\nbi47d+4EoLq6mo8++giAF154gT179nDllVeycuVKqqurCQaDffipRKS/aARGRCJm1apV/OpXv+Kx\nxx4jGAyyfPnyz92/uLiY++67j+9///t0dHRw2223kZ2dzYIFC3jttddYsmQJ2dnZTJo0CYCxY8ey\ncuVK4uLisG2bW2+9FadTf+2JXAx0N2oRERGJOZpCEhERkZijACMiIiIxRwFGREREYo4CjIiIiMQc\nBRgRERGJOQowIiIiEnMUYERERCTmKMCIiIhIzPl/HIIv1IwQesEAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "FSPZIiYgyh93" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "X1QcIeiKyni4" + }, + "cell_type": "markdown", + "source": [ + "First, let's try Adagrad." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Ntn4jJxnypGZ", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.5),\n", + " steps=500,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "5JUsCdRRyso3" + }, + "cell_type": "markdown", + "source": [ + "Now let's try Adam." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "lZB8k0upyuY8", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_, adam_training_losses, adam_validation_losses = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdamOptimizer(learning_rate=0.009),\n", + " steps=500,\n", + " batch_size=100,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "twYgC8FGyxm6" + }, + "cell_type": "markdown", + "source": [ + "Let's print a graph of loss metrics side by side." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "8RHIUEfqyzW0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 376 + }, + "outputId": "61f3cfb2-2101-4478-8d5c-d437e96b4e08" + }, + "cell_type": "code", + "source": [ + "plt.ylabel(\"RMSE\")\n", + "plt.xlabel(\"Periods\")\n", + "plt.title(\"Root Mean Squared Error vs. Periods\")\n", + "plt.plot(adagrad_training_losses, label='Adagrad training')\n", + "plt.plot(adagrad_validation_losses, label='Adagrad validation')\n", + "plt.plot(adam_training_losses, label='Adam training')\n", + "plt.plot(adam_validation_losses, label='Adam validation')\n", + "_ = plt.legend()" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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FvXXrquYiRqORgoKCWo9/6X0Ggy9vvrmCWbMeYMuWzeTmXtme9PL2oYWF1fcb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\nb7ovH/I9dKdHhX2ql7TeD0Ewqsd+zqcE8XkRVCuEmw6QSfh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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "UySPl7CAQ28C" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Explore Alternate Normalization Methods\n", + "\n", + "**Try alternate normalizations for various features to further improve performance.**\n", + "\n", + "If you look closely at summary stats for your transformed data, you may notice that linear scaling some features leaves them clumped close to `-1`.\n", + "\n", + "For example, many features have a median of `-0.8` or so, rather than `0.0`." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "QWmm_6CGKxlH", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 715 + }, + "outputId": "410b0f05-c726-4f81-c8f9-f407284d8606" + }, + "cell_type": "code", + "source": [ + "_ = normalized_training_examples.hist(bins=20, figsize=(18, 12), xlabelsize=10)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Xx9jgEMHKxlJ" + }, + "cell_type": "markdown", + "source": [ + "We might be able to do better by choosing additional ways to transform these features.\n", + "\n", + "For example, a log scaling might help some features. Or clipping extreme values may make the remainder of the scale more informative." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "baKZa6MEKxlK", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def log_normalize(series):\n", + " return series.apply(lambda x:math.log(x+1.0))\n", + "\n", + "def clip(series, clip_to_min, clip_to_max):\n", + " return series.apply(lambda x:(\n", + " min(max(x, clip_to_min), clip_to_max)))\n", + "\n", + "def z_score_normalize(series):\n", + " mean = series.mean()\n", + " std_dv = series.std()\n", + " return series.apply(lambda x:(x - mean) / std_dv)\n", + "\n", + "def binary_threshold(series, threshold):\n", + " return series.apply(lambda x:(1 if x > threshold else 0))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "-wCCq_ClKxlO" + }, + "cell_type": "markdown", + "source": [ + "The block above contains a few additional possible normalization functions. Try some of these, or add your own.\n", + "\n", + "Note that if you normalize the target, you'll need to un-normalize the predictions for loss metrics to be comparable." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "8ToG-mLfMO9P", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 677 + }, + "outputId": "e89b4710-be8a-486b-aef0-562d695ce1b3" + }, + "cell_type": "code", + "source": [ + "def normalize(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized.\"\"\"\n", + " #\n", + " # YOUR CODE HERE: Normalize the inputs.\n", + " #\n", + " processed_features = pd.DataFrame()\n", + "\n", + " processed_features[\"households\"] = log_normalize(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = log_normalize(examples_dataframe[\"median_income\"])\n", + " processed_features[\"total_bedrooms\"] = log_normalize(examples_dataframe[\"total_bedrooms\"])\n", + " \n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + "\n", + " processed_features[\"population\"] = linear_scale(clip(examples_dataframe[\"population\"], 0, 5000))\n", + " processed_features[\"rooms_per_person\"] = linear_scale(clip(examples_dataframe[\"rooms_per_person\"], 0, 5))\n", + " processed_features[\"total_rooms\"] = linear_scale(clip(examples_dataframe[\"total_rooms\"], 0, 10000))\n", + "\n", + " return processed_features\n", + " pass\n", + "\n", + "normalized_dataframe = normalize(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n", + " steps=5000,\n", + " batch_size=70,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 209.00\n", + " period 01 : 129.63\n", + " period 02 : 114.60\n", + " period 03 : 112.91\n", + " period 04 : 110.93\n", + " period 05 : 108.50\n", + " period 06 : 105.22\n", + " period 07 : 100.94\n", + " period 08 : 95.97\n", + " period 09 : 90.72\n", + "Model training finished.\n", + "Final RMSE (on training data): 90.72\n", + "Final RMSE (on validation data): 91.17\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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RtToMMERERNTqMMAQERFRq8MAQ0RERK0OAwwREVEbs2PH141639KlzyMvL7fB1x999MHm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\nJXddCWNTU1JITUkNt6+qIRZPcHowREdfIDNTc7L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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "GhFtWjQRzD2l" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "OMoIsUMmzK9b" + }, + "cell_type": "markdown", + "source": [ + "These are only a few ways in which we could think about the data. Other transformations may work even better!\n", + "\n", + "`households`, `median_income` and `total_bedrooms` all appear normally-distributed in a log space.\n", + "\n", + "`latitude`, `longitude` and `housing_median_age` would probably be better off just scaled linearly, as before.\n", + "\n", + "`population`, `totalRooms` and `rooms_per_person` have a few extreme outliers. They seem too extreme for log normalization to help. So let's clip them instead." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "XDEYkPquzYCH", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def normalize(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized.\"\"\"\n", + " processed_features = pd.DataFrame()\n", + "\n", + " processed_features[\"households\"] = log_normalize(examples_dataframe[\"households\"])\n", + " processed_features[\"median_income\"] = log_normalize(examples_dataframe[\"median_income\"])\n", + " processed_features[\"total_bedrooms\"] = log_normalize(examples_dataframe[\"total_bedrooms\"])\n", + " \n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + "\n", + " processed_features[\"population\"] = linear_scale(clip(examples_dataframe[\"population\"], 0, 5000))\n", + " processed_features[\"rooms_per_person\"] = linear_scale(clip(examples_dataframe[\"rooms_per_person\"], 0, 5))\n", + " processed_features[\"total_rooms\"] = linear_scale(clip(examples_dataframe[\"total_rooms\"], 0, 10000))\n", + "\n", + " return processed_features\n", + "\n", + "normalized_dataframe = normalize(preprocess_features(california_housing_dataframe))\n", + "normalized_training_examples = normalized_dataframe.head(12000)\n", + "normalized_validation_examples = normalized_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.15),\n", + " steps=1000,\n", + " batch_size=50,\n", + " hidden_units=[10, 10],\n", + " training_examples=normalized_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=normalized_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "b7atJTbzU9Ca" + }, + "cell_type": "markdown", + "source": [ + "## Optional Challenge: Use only Latitude and Longitude Features\n", + "\n", + "**Train a NN model that uses only latitude and longitude as features.**\n", + "\n", + "Real estate people are fond of saying that location is the only important feature in housing price.\n", + "Let's see if we can confirm this by training a model that uses only latitude and longitude as features.\n", + "\n", + "This will only work well if our NN can learn complex nonlinearities from latitude and longitude.\n", + "\n", + "**NOTE:** We may need a network structure that has more layers than were useful earlier in the exercise." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "T5McjahpamOc", + "colab": {} + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Train the network using only latitude and longitude\n", + "#" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "P8BLQ7T71JWd" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a possible solution." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "1hwaFCE71OPZ" + }, + "cell_type": "markdown", + "source": [ + "It's a good idea to keep latitude and longitude normalized:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "djKtt4mz1ZEc", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def location_location_location(examples_dataframe):\n", + " \"\"\"Returns a version of the input `DataFrame` that keeps only the latitude and longitude.\"\"\"\n", + " processed_features = pd.DataFrame()\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " return processed_features\n", + "\n", + "lll_dataframe = location_location_location(preprocess_features(california_housing_dataframe))\n", + "lll_training_examples = lll_dataframe.head(12000)\n", + "lll_validation_examples = lll_dataframe.tail(5000)\n", + "\n", + "_ = train_nn_regression_model(\n", + " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.05),\n", + " steps=500,\n", + " batch_size=50,\n", + " hidden_units=[10, 10, 5, 5, 5],\n", + " training_examples=lll_training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=lll_validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "Dw2Mr9JZ1cRi" + }, + "cell_type": "markdown", + "source": [ + "This isn't too bad for just two features. Of course, property values can still vary significantly within short distances." + ] + } + ] +} \ No newline at end of file From 8814736d02478b7f0d3da4d17b1debe24552db11 Mon Sep 17 00:00:00 2001 From: Aditya Joardar <30207466+joardar-aditya@users.noreply.github.com> Date: Mon, 18 Feb 2019 01:21:14 +0530 Subject: [PATCH 13/13] Assignment 5 Solved! -: Final notebook MNISIT Solved! --- ...classification_of_handwritten_digits.ipynb | 2094 +++++++++++++++++ 1 file changed, 2094 insertions(+) create mode 100644 multi_class_classification_of_handwritten_digits.ipynb diff --git a/multi_class_classification_of_handwritten_digits.ipynb b/multi_class_classification_of_handwritten_digits.ipynb new file mode 100644 index 0000000..6a9b5fc --- /dev/null +++ b/multi_class_classification_of_handwritten_digits.ipynb @@ -0,0 +1,2094 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "multi-class_classification_of_handwritten_digits.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "266KQvZoMxMv", + "6sfw3LH0Oycm" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "mPa95uXvcpcn", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Classifying Handwritten Digits with Neural Networks" + ] + }, + { + "metadata": { + "id": "Fdpn8b90u8Tp", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "![img](https://www.tensorflow.org/versions/r0.11/images/MNIST.png)" + ] + }, + { + "metadata": { + "id": "c7HLCm66Cs2p", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Train both a linear model and a neural network to classify handwritten digits from the classic [MNIST](http://yann.lecun.com/exdb/mnist/) data set\n", + " * Compare the performance of the linear and neural network classification models\n", + " * Visualize the weights of a neural-network hidden layer" + ] + }, + { + "metadata": { + "id": "HSEh-gNdu8T0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Our goal is to map each input image to the correct numeric digit. We will create a NN with a few hidden layers and a Softmax layer at the top to select the winning class." + ] + }, + { + "metadata": { + "id": "2NMdE1b-7UIH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "First, let's download the data set, import TensorFlow and other utilities, and load the data into a *pandas* `DataFrame`. Note that this data is a sample of the original MNIST training data; we've taken 20000 rows at random." + ] + }, + { + "metadata": { + "id": "4LJ4SD8BWHeh", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 236 + }, + "outputId": "7959881e-725e-402c-b364-0931bc22b187" + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import glob\n", + "import math\n", + "import os\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format\n", + "\n", + "mnist_dataframe = pd.read_csv(\n", + " \"https://download.mlcc.google.com/mledu-datasets/mnist_train_small.csv\",\n", + " sep=\",\",\n", + " header=None)\n", + "\n", + "# Use just the first 10,000 records for training/validation.\n", + "mnist_dataframe = mnist_dataframe.head(10000)\n", + "\n", + "mnist_dataframe = mnist_dataframe.reindex(np.random.permutation(mnist_dataframe.index))\n", + "mnist_dataframe.head()" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 72\n", + "6849 0\n", + "562 0\n", + "3824 0\n", + "1958 0\n", + "4523 0\n", + "... ..\n", + "4517 0\n", + "2893 0\n", + "3727 0\n", + "1209 0\n", + "8838 0\n", + "\n", + "[10000 rows x 1 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "metadata": { + "id": "vLNg2VxqhUZ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Now, let's parse out the labels and features and look at a few examples. Note the use of `loc` which allows us to pull out columns based on original location, since we don't have a header row in this data set." + ] + }, + { + "metadata": { + "id": "JfFWWvMWDFrR", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def parse_labels_and_features(dataset):\n", + " \"\"\"Extracts labels and features.\n", + " \n", + " This is a good place to scale or transform the features if needed.\n", + " \n", + " Args:\n", + " dataset: A Pandas `Dataframe`, containing the label on the first column and\n", + " monochrome pixel values on the remaining columns, in row major order.\n", + " Returns:\n", + " A `tuple` `(labels, features)`:\n", + " labels: A Pandas `Series`.\n", + " features: A Pandas `DataFrame`.\n", + " \"\"\"\n", + " labels = dataset[0]\n", + "\n", + " # DataFrame.loc index ranges are inclusive at both ends.\n", + " features = dataset.loc[:,1:784]\n", + " # Scale the data to [0, 1] by dividing out the max value, 255.\n", + " features = features / 255\n", + "\n", + " return labels, features" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "mFY_-7vZu8UU", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 350 + }, + "outputId": "be81a869-0655-47b9-b0eb-2f58e8a1b9e4" + }, + "cell_type": "code", + "source": [ + "training_targets, training_examples = parse_labels_and_features(mnist_dataframe[:7500])\n", + "training_examples.describe()" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ScmYX7xdZMXE", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Build a Linear Model for MNIST\n", + "\n", + "First, let's create a baseline model to compare against. The `LinearClassifier` provides a set of *k* one-vs-all classifiers, one for each of the *k* classes.\n", + "\n", + "You'll notice that in addition to reporting accuracy, and plotting Log Loss over time, we also display a [**confusion matrix**](https://en.wikipedia.org/wiki/Confusion_matrix). The confusion matrix shows which classes were misclassified as other classes. Which digits get confused for each other?\n", + "\n", + "Also note that we track the model's error using the `log_loss` function. This should not be confused with the loss function internal to `LinearClassifier` that is used for training." + ] + }, + { + "metadata": { + "id": "cpoVC4TSdw5Z", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def construct_feature_columns():\n", + " \"\"\"Construct the TensorFlow Feature Columns.\n", + "\n", + " Returns:\n", + " A set of feature columns\n", + " \"\"\" \n", + " \n", + " # There are 784 pixels in each image.\n", + " return set([tf.feature_column.numeric_column('pixels', shape=784)])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "kMmL89yGeTfz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Here, we'll make separate input functions for training and for prediction. We'll nest them in `create_training_input_fn()` and `create_predict_input_fn()`, respectively, so we can invoke these functions to return the corresponding `_input_fn`s to pass to our `.train()` and `.predict()` calls." + ] + }, + { + "metadata": { + "id": "OeS47Bmn5Ms2", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def create_training_input_fn(features, labels, batch_size, num_epochs=None, shuffle=True):\n", + " \"\"\"A custom input_fn for sending MNIST data to the estimator for training.\n", + "\n", + " Args:\n", + " features: The training features.\n", + " labels: The training labels.\n", + " batch_size: Batch size to use during training.\n", + "\n", + " Returns:\n", + " A function that returns batches of training features and labels during\n", + " training.\n", + " \"\"\"\n", + " def _input_fn(num_epochs=None, shuffle=True):\n", + " # Input pipelines are reset with each call to .train(). To ensure model\n", + " # gets a good sampling of data, even when number of steps is small, we \n", + " # shuffle all the data before creating the Dataset object\n", + " idx = np.random.permutation(features.index)\n", + " raw_features = {\"pixels\":features.reindex(idx)}\n", + " raw_targets = np.array(labels[idx])\n", + " \n", + " ds = Dataset.from_tensor_slices((raw_features,raw_targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " if shuffle:\n", + " ds = ds.shuffle(10000)\n", + " \n", + " # Return the next batch of data.\n", + " feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n", + " return feature_batch, label_batch\n", + "\n", + " return _input_fn" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "8zoGWAoohrwS", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def create_predict_input_fn(features, labels, batch_size):\n", + " \"\"\"A custom input_fn for sending mnist data to the estimator for predictions.\n", + "\n", + " Args:\n", + " features: The features to base predictions on.\n", + " labels: The labels of the prediction examples.\n", + "\n", + " Returns:\n", + " A function that returns features and labels for predictions.\n", + " \"\"\"\n", + " def _input_fn():\n", + " raw_features = {\"pixels\": features.values}\n", + " raw_targets = np.array(labels)\n", + " \n", + " ds = Dataset.from_tensor_slices((raw_features, raw_targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size)\n", + " \n", + " \n", + " # Return the next batch of data.\n", + " feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n", + " return feature_batch, label_batch\n", + "\n", + " return _input_fn" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "G6DjSLZMu8Um", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_linear_classification_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a linear classification model for the MNIST digits dataset.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " a plot of the training and validation loss over time, and a confusion\n", + " matrix.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate to use.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " training_examples: A `DataFrame` containing the training features.\n", + " training_targets: A `DataFrame` containing the training labels.\n", + " validation_examples: A `DataFrame` containing the validation features.\n", + " validation_targets: A `DataFrame` containing the validation labels.\n", + " \n", + " Returns:\n", + " The trained `LinearClassifier` object.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + "\n", + " steps_per_period = steps / periods \n", + " # Create the input functions.\n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " # Create a LinearClassifier object.\n", + " my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " classifier = tf.estimator.LinearClassifier(\n", + " feature_columns=construct_feature_columns(),\n", + " n_classes=10,\n", + " optimizer=my_optimizer,\n", + " config=tf.estimator.RunConfig(keep_checkpoint_max=1)\n", + " )\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss error (on validation data):\")\n", + " training_errors = []\n", + " validation_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " \n", + " # Take a break and compute probabilities.\n", + " training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n", + " training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n", + " training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n", + " training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n", + " \n", + " validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_predictions]) \n", + " validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n", + " validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10) \n", + " \n", + " # Compute training and validation errors.\n", + " training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_errors.append(training_log_loss)\n", + " validation_errors.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " # Remove event files to save disk space.\n", + " _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n", + " \n", + " # Calculate final predictions (not probabilities, as above).\n", + " final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n", + " final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n", + " \n", + " \n", + " accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n", + " print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.plot(training_errors, label=\"training\")\n", + " plt.plot(validation_errors, label=\"validation\")\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + " # Output a plot of the confusion matrix.\n", + " cm = metrics.confusion_matrix(validation_targets, final_predictions)\n", + " # Normalize the confusion matrix by row (i.e by the number of samples\n", + " # in each class).\n", + " cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n", + " ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n", + " ax.set_aspect(1)\n", + " plt.title(\"Confusion matrix\")\n", + " plt.ylabel(\"True label\")\n", + " plt.xlabel(\"Predicted label\")\n", + " plt.show()\n", + "\n", + " return classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ItHIUyv2u8Ur", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Spend 5 minutes seeing how well you can do on accuracy with a linear model of this form. For this exercise, limit yourself to experimenting with the hyperparameters for batch size, learning rate and steps.**\n", + "\n", + "Stop if you get anything above about 0.9 accuracy." + ] + }, + { + "metadata": { + "id": "yaiIhIQqu8Uv", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1121 + }, + "outputId": "f874ef65-4856-4e93-d856-aded315e671d" + }, + "cell_type": "code", + "source": [ + "classifier = train_linear_classification_model(\n", + " learning_rate=0.02,\n", + " steps=1000,\n", + " batch_size=10,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 16.08\n", + " period 01 : 12.14\n", + " period 02 : 8.51\n", + " period 03 : 8.39\n", + " period 04 : 8.29\n", + " period 05 : 6.89\n", + " period 06 : 7.34\n", + " period 07 : 6.24\n", + " period 08 : 6.74\n", + " period 09 : 6.12\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.82\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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Bfu5al6EdEhJitxN35zPxmhqNzc/r5+eGiorBvTLZ3Ekh+OlQHr7Z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\nVlfnwKqJ6EYwtIkGmJghPlhzfyyGhnr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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "266KQvZoMxMv", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "id": "lRWcn24DM3qa", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Here is a set of parameters that should attain roughly 0.9 accuracy." + ] + }, + { + "metadata": { + "id": "TGlBMrUoM1K_", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_ = train_linear_classification_model(\n", + " learning_rate=0.03,\n", + " steps=1000,\n", + " batch_size=30,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "mk095OfpPdOx", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Replace the Linear Classifier with a Neural Network\n", + "\n", + "**Replace the LinearClassifier above with a [`DNNClassifier`](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier) and find a parameter combination that gives 0.95 or better accuracy.**\n", + "\n", + "You may wish to experiment with additional regularization methods, such as dropout. These additional regularization methods are documented in the comments for the `DNNClassifier` class." + ] + }, + { + "metadata": { + "id": "rm8P_Ttwu8U4", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Replace the linear classifier with a neural network.\n", + "#\n", + "def train_nn_classification_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " hidden_units,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a neural network classification model for the MNIST digits dataset.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " a plot of the training and validation loss over time, as well as a confusion\n", + " matrix.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate to use.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n", + " training_examples: A `DataFrame` containing the training features.\n", + " training_targets: A `DataFrame` containing the training labels.\n", + " validation_examples: A `DataFrame` containing the validation features.\n", + " validation_targets: A `DataFrame` containing the validation labels.\n", + " \n", + " Returns:\n", + " The trained `DNNClassifier` object.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " # Caution: input pipelines are reset with each call to train. \n", + " # If the number of steps is small, your model may never see most of the data. \n", + " # So with multiple `.train` calls like this you may want to control the length \n", + " # of training with num_epochs passed to the input_fn. Or, you can do a really-big shuffle, \n", + " # or since it's in-memory data, shuffle all the data in the `input_fn`.\n", + " steps_per_period = steps / periods \n", + " # Create the input functions.\n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " # Create the input functions.\n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n", + "\n", + " # Create a DNNClassifier object.\n", + " my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " classifier = tf.estimator.DNNClassifier(\n", + " feature_columns=feature_columns,\n", + " n_classes=10,\n", + " hidden_units=hidden_units,\n", + " optimizer=my_optimizer,\n", + " config=tf.contrib.learn.RunConfig(keep_checkpoint_max=1)\n", + " )\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss error (on validation data):\")\n", + " training_errors = []\n", + " validation_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " \n", + " # Take a break and compute probabilities.\n", + " training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n", + " training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n", + " training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n", + " training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n", + " \n", + " validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_predictions]) \n", + " validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n", + " validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10) \n", + " \n", + " # Compute training and validation errors.\n", + " training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_errors.append(training_log_loss)\n", + " validation_errors.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " # Remove event files to save disk space.\n", + " _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n", + " \n", + " # Calculate final predictions (not probabilities, as above).\n", + " final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n", + " final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n", + " \n", + " \n", + " accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n", + " print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.plot(training_errors, label=\"training\")\n", + " plt.plot(validation_errors, label=\"validation\")\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + " # Output a plot of the confusion matrix.\n", + " cm = metrics.confusion_matrix(validation_targets, final_predictions)\n", + " # Normalize the confusion matrix by row (i.e by the number of samples\n", + " # in each class).\n", + " cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n", + " ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n", + " ax.set_aspect(1)\n", + " plt.title(\"Confusion matrix\")\n", + " plt.ylabel(\"True label\")\n", + " plt.xlabel(\"Predicted label\")\n", + " plt.show()\n", + "\n", + " return classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TOfmiSvqu8U9", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Once you have a good model, double check that you didn't overfit the validation set by evaluating on the test data that we'll load below.\n" + ] + }, + { + "metadata": { + "id": "evlB5ubzu8VJ", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "mnist_test_dataframe = pd.read_csv(\n", + " \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n", + " sep=\",\",\n", + " header=None)\n", + "\n", + "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n", + "test_examples.describe()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "PDuLd2Hcu8VL", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Calculate accuracy on the test set.\n", + "#\n", + "classifier = train_nn_classification_model(\n", + " learning_rate=0.03,\n", + " steps=1000,\n", + " batch_size=50,\n", + " hidden_units=[100, 100],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "6sfw3LH0Oycm", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a possible solution." + ] + }, + { + "metadata": { + "id": "XatDGFKEO374", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The code below is almost identical to the original `LinearClassifer` training code, with the exception of the NN-specific configuration, such as the hyperparameter for hidden units." + ] + }, + { + "metadata": { + "id": "kdNTx8jkPQUx", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_nn_classification_model(\n", + " learning_rate,\n", + " steps,\n", + " batch_size,\n", + " hidden_units,\n", + " training_examples,\n", + " training_targets,\n", + " validation_examples,\n", + " validation_targets):\n", + " \"\"\"Trains a neural network classification model for the MNIST digits dataset.\n", + " \n", + " In addition to training, this function also prints training progress information,\n", + " a plot of the training and validation loss over time, as well as a confusion\n", + " matrix.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate to use.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n", + " training_examples: A `DataFrame` containing the training features.\n", + " training_targets: A `DataFrame` containing the training labels.\n", + " validation_examples: A `DataFrame` containing the validation features.\n", + " validation_targets: A `DataFrame` containing the validation labels.\n", + " \n", + " Returns:\n", + " The trained `DNNClassifier` object.\n", + " \"\"\"\n", + "\n", + " periods = 10\n", + " # Caution: input pipelines are reset with each call to train. \n", + " # If the number of steps is small, your model may never see most of the data. \n", + " # So with multiple `.train` calls like this you may want to control the length \n", + " # of training with num_epochs passed to the input_fn. Or, you can do a really-big shuffle, \n", + " # or since it's in-memory data, shuffle all the data in the `input_fn`.\n", + " steps_per_period = steps / periods \n", + " # Create the input functions.\n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " # Create the input functions.\n", + " predict_training_input_fn = create_predict_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " predict_validation_input_fn = create_predict_input_fn(\n", + " validation_examples, validation_targets, batch_size)\n", + " training_input_fn = create_training_input_fn(\n", + " training_examples, training_targets, batch_size)\n", + " \n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n", + "\n", + " # Create a DNNClassifier object.\n", + " my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " classifier = tf.estimator.DNNClassifier(\n", + " feature_columns=feature_columns,\n", + " n_classes=10,\n", + " hidden_units=hidden_units,\n", + " optimizer=my_optimizer,\n", + " config=tf.contrib.learn.RunConfig(keep_checkpoint_max=1)\n", + " )\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"LogLoss error (on validation data):\")\n", + " training_errors = []\n", + " validation_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " classifier.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " \n", + " # Take a break and compute probabilities.\n", + " training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n", + " training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n", + " training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n", + " training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n", + " \n", + " validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n", + " validation_probabilities = np.array([item['probabilities'] for item in validation_predictions]) \n", + " validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n", + " validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10) \n", + " \n", + " # Compute training and validation errors.\n", + " training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n", + " validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n", + " # Add the loss metrics from this period to our list.\n", + " training_errors.append(training_log_loss)\n", + " validation_errors.append(validation_log_loss)\n", + " print(\"Model training finished.\")\n", + " # Remove event files to save disk space.\n", + " _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n", + " \n", + " # Calculate final predictions (not probabilities, as above).\n", + " final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n", + " final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n", + " \n", + " \n", + " accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n", + " print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.ylabel(\"LogLoss\")\n", + " plt.xlabel(\"Periods\")\n", + " plt.title(\"LogLoss vs. Periods\")\n", + " plt.plot(training_errors, label=\"training\")\n", + " plt.plot(validation_errors, label=\"validation\")\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + " # Output a plot of the confusion matrix.\n", + " cm = metrics.confusion_matrix(validation_targets, final_predictions)\n", + " # Normalize the confusion matrix by row (i.e by the number of samples\n", + " # in each class).\n", + " cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n", + " ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n", + " ax.set_aspect(1)\n", + " plt.title(\"Confusion matrix\")\n", + " plt.ylabel(\"True label\")\n", + " plt.xlabel(\"Predicted label\")\n", + " plt.show()\n", + "\n", + " return classifier" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ZfzsTYGPPU8I", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "classifier = train_nn_classification_model(\n", + " learning_rate=0.05,\n", + " steps=1000,\n", + " batch_size=30,\n", + " hidden_units=[100, 100],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "qXvrOgtUR-zD", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we verify the accuracy on the test set." + ] + }, + { + "metadata": { + "id": "scQNpDePSFjt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "mnist_test_dataframe = pd.read_csv(\n", + " \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n", + " sep=\",\",\n", + " header=None)\n", + "\n", + "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n", + "test_examples.describe()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "EVaWpWKvSHmu", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "predict_test_input_fn = create_predict_input_fn(\n", + " test_examples, test_targets, batch_size=100)\n", + "\n", + "test_predictions = classifier.predict(input_fn=predict_test_input_fn)\n", + "test_predictions = np.array([item['class_ids'][0] for item in test_predictions])\n", + " \n", + "accuracy = metrics.accuracy_score(test_targets, test_predictions)\n", + "print(\"Accuracy on test data: %0.2f\" % accuracy)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "WX2mQBAEcisO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Visualize the weights of the first hidden layer.\n", + "\n", + "Let's take a few minutes to dig into our neural network and see what it has learned by accessing the `weights_` attribute of our model.\n", + "\n", + "The input layer of our model has `784` weights corresponding to the `28×28` pixel input images. The first hidden layer will have `784×N` weights where `N` is the number of nodes in that layer. We can turn those weights back into `28×28` images by *reshaping* each of the `N` `1×784` arrays of weights into `N` arrays of size `28×28`.\n", + "\n", + "Run the following cell to plot the weights. Note that this cell requires that a `DNNClassifier` called \"classifier\" has already been trained." + ] + }, + { + "metadata": { + "id": "eUC0Z8nbafgG", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "print(classifier.get_variable_names())\n", + "\n", + "weights0 = classifier.get_variable_value(\"dnn/hiddenlayer_0/kernel\")\n", + "\n", + "print(\"weights0 shape:\", weights0.shape)\n", + "\n", + "num_nodes = weights0.shape[1]\n", + "num_rows = int(math.ceil(num_nodes / 10.0))\n", + "fig, axes = plt.subplots(num_rows, 10, figsize=(20, 2 * num_rows))\n", + "for coef, ax in zip(weights0.T, axes.ravel()):\n", + " # Weights in coef is reshaped from 1x784 to 28x28.\n", + " ax.matshow(coef.reshape(28, 28), cmap=plt.cm.pink)\n", + " ax.set_xticks(())\n", + " ax.set_yticks(())\n", + "\n", + "plt.show()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "kL8MEhNgrx9N", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The first hidden layer of the neural network should be modeling some pretty low level features, so visualizing the weights will probably just show some fuzzy blobs or possibly a few parts of digits. You may also see some neurons that are essentially noise -- these are either unconverged or they are being ignored by higher layers.\n", + "\n", + "It can be interesting to stop training at different numbers of iterations and see the effect.\n", + "\n", + "**Train the classifier for 10, 100 and respectively 1000 steps. Then run this visualization again.**\n", + "\n", + "What differences do you see visually for the different levels of convergence?" + ] + } + ] +} \ No newline at end of file