diff --git a/feature_crosses.ipynb b/feature_crosses.ipynb new file mode 100644 index 0000000..58b9dff --- /dev/null +++ b/feature_crosses.ipynb @@ -0,0 +1,1571 @@ +{ + "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": 1153 + }, + "outputId": "fb64cd82-1049-4e6e-c6e9-c7a7ba3ecf6f" + }, + "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": 19, + "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 2634.6 537.1 \n", + "std 2.1 2.0 12.6 2163.5 418.2 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1456.8 296.0 \n", + "50% 34.2 -118.5 29.0 2123.0 433.0 \n", + "75% 37.7 -118.0 37.0 3156.2 647.0 \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 1426.3 499.1 3.9 2.0 \n", + "std 1158.6 381.3 1.9 1.1 \n", + "min 3.0 1.0 0.5 0.0 \n", + "25% 789.0 282.0 2.6 1.5 \n", + "50% 1163.0 408.0 3.6 1.9 \n", + "75% 1715.0 603.0 4.8 2.3 \n", + "max 35682.0 6082.0 15.0 55.2 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "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": 619 + }, + "outputId": "ff290be6-3f0c-4406-c7df-bbbe7a6b37b0" + }, + "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": 23, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 254.50\n", + " period 01 : 111.19\n", + " period 02 : 123.73\n", + " period 03 : 120.12\n", + " period 04 : 377.18\n", + " period 05 : 294.93\n", + " period 06 : 275.06\n", + " period 07 : 247.23\n", + " period 08 : 220.72\n", + " period 09 : 169.73\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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rZ39NG9xqu6iB8f0DWbPnPF+vSWDmiDCG+g9ie9puVhzdyKiOw24l/HZNvjMt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\n2K6VtPWGzn90q8CYZEZ2MaBlJDm/o02BEQI5cVkmJxGRoWT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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(\n", + " latitude, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"latitude\"], 10))\n", + "\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", + " bucketized_median_income = tf.feature_column.bucketized_column(\n", + " median_income, boundaries=get_quantile_based_boundaries(\n", + " training_examples[\"median_income\"], 7))\n", + " \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", + " \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": 619 + }, + "outputId": "a7b7e74f-a70f-45dc-a487-29b147e4a75b" + }, + "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": 28, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 169.88\n", + " period 01 : 143.47\n", + " period 02 : 126.89\n", + " period 03 : 115.63\n", + " period 04 : 107.68\n", + " period 05 : 101.80\n", + " period 06 : 97.26\n", + " period 07 : 93.57\n", + " period 08 : 90.58\n", + " period 09 : 88.13\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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ERORGKcDY2c31ulDPO4RUy2Es3tks2aSFHkVERG6UAoydmU1mRjUbgoFBSLsT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\nJ36WCV8DE41GueOOO0hKSuJXv/oVJpOJBx98kK1bt5KXl8e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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(\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": "xZuZMp3EShkM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 619 + }, + "outputId": "464c400f-a8d3-426d-941d-e3121cce42da" + }, + "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": 30, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 163.83\n", + " period 01 : 135.56\n", + " period 02 : 118.46\n", + " period 03 : 107.19\n", + " period 04 : 99.24\n", + " period 05 : 93.39\n", + " period 06 : 88.86\n", + " period 07 : 85.26\n", + " period 08 : 82.40\n", + " period 09 : 80.06\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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EbQNaEeEdTrFnCoZ7FvNXJZgdSUREpEZQgTGRxWLhyqZ/zsIcYO3OIySn55qc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\n5LKWq/H6F8naJhmZGaJ/6hSHIkc4FDkCgLnCRFt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" + ] + }, + "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 diff --git a/feature_sets.ipynb b/feature_sets.ipynb new file mode 100644 index 0000000..611370c --- /dev/null +++ b/feature_sets.ipynb @@ -0,0 +1,1549 @@ +{ + "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": 1153 + }, + "outputId": "7068d60f-bd2e-4c14-8a56-c244fc6edecd" + }, + "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 -119.5 28.7 2644.6 540.1 \n", + "std 2.1 2.0 12.5 2173.7 423.2 \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 2134.0 435.0 \n", + "75% 37.7 -118.0 37.0 3156.0 648.0 \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 1431.5 501.9 3.9 2.0 \n", + "std 1162.5 385.5 1.9 1.2 \n", + "min 6.0 1.0 0.5 0.0 \n", + "25% 794.0 282.0 2.6 1.5 \n", + "50% 1168.0 409.0 3.5 1.9 \n", + "75% 1721.2 605.0 4.7 2.3 \n", + "max 35682.0 6082.0 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": 343 + }, + "outputId": "9b4acb59-ee77-41a1-8a36-da84d12b8396" + }, + "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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latitudelongitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomerooms_per_persontarget
latitude1.0-0.90.0-0.0-0.1-0.1-0.1-0.10.1-0.1
longitude-0.91.0-0.10.00.10.10.1-0.0-0.1-0.0
housing_median_age0.0-0.11.0-0.4-0.3-0.3-0.3-0.1-0.10.1
total_rooms-0.00.0-0.41.00.90.90.90.20.10.1
total_bedrooms-0.10.1-0.30.91.00.91.0-0.00.10.0
population-0.10.1-0.30.90.91.00.9-0.0-0.1-0.0
households-0.10.1-0.30.91.00.91.00.0-0.00.1
median_income-0.1-0.0-0.10.2-0.0-0.00.01.00.20.7
rooms_per_person0.1-0.1-0.10.10.1-0.1-0.00.21.00.2
target-0.1-0.00.10.10.0-0.00.10.70.21.0
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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 -0.1 -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 0.0 -0.0 0.1 0.7 \n", + "\n", + " rooms_per_person target \n", + "latitude 0.1 -0.1 \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 0.0 \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": 636 + }, + "outputId": "c6df7c51-ebec-4493-b0f1-31e080d21acd" + }, + "cell_type": "code", + "source": [ + "#\n", + "# Your code here: add your features of choice as a list of quoted strings.\n", + "#\n", + "minimal_features = [\n", + " \"latitude\",\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"households\",\n", + " \"median_income\"]\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=0.005,\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": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 181.82\n", + " period 01 : 180.38\n", + " period 02 : 179.07\n", + " period 03 : 173.48\n", + " period 04 : 178.27\n", + " period 05 : 179.28\n", + " period 06 : 181.64\n", + " period 07 : 184.09\n", + " period 08 : 208.31\n", + " period 09 : 167.13\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 15 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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jloDyJpE3pve6j9plOywon4lyTyWa0nr9/EOFBBghhBCimw40ldIS8BCos5OW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\n7f30Dx4ZbgaBQew2CxmBZIpzAoRSkgmmJpN5OKR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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": 364 + }, + "outputId": "9c08aae9-45b5-4b17-e78d-9e88d778c867" + }, + "cell_type": "code", + "source": [ + "plt.scatter(training_examples[\"latitude\"], training_targets[\"median_house_value\"])" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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ExP6eicfjl/W+XODDPBw2aSUtfyAE15g36S7c8eYnou81MzS+tGYhXC6v7Gu4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\n/H58HFLKpaMp+P1+GPR0Usvz+AUHRRKS32dLQxXGnB4cOpNZGf25yr/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kYOD2+sB6ONkP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\no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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": {} + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Train on a new data set that includes synthetic features based on latitude.\n", + "#\n", + "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": "zoukwC6L7iUM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 636 + }, + "outputId": "ce246a33-b12c-4782-bcd6-7f54c82c5d33" + }, + "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": 18, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 227.13\n", + " period 01 : 216.92\n", + " period 02 : 206.80\n", + " period 03 : 196.77\n", + " period 04 : 186.86\n", + " period 05 : 177.07\n", + " period 06 : 167.44\n", + " period 07 : 157.99\n", + " period 08 : 148.78\n", + " period 09 : 139.84\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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qOqYQQgjxt2QEpp5RFIXgADf8mjjw7eZ4Tp7UYGZtg33rBI6kx3I6J5GwFsNo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\nHE4XfXp0Zml2FskJYXxUeozNF97hZms9saExzO81iyEJAzr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+ "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": {} + }, + "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": 0, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/first_steps_with_tensor_flow.ipynb b/first_steps_with_tensor_flow.ipynb new file mode 100644 index 0000000..a35febf --- /dev/null +++ b/first_steps_with_tensor_flow.ipynb @@ -0,0 +1,1764 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 402 + }, + "outputId": "5e4ab12e-dda1-4669-b507-d119fb41e74d" + }, + "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": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
3821-117.933.827.02512.0506.01861.0511.04.2184.2
666-117.032.713.02132.0425.01345.0432.04.089.3
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15730-122.437.852.04265.0912.01555.0836.04.1298.3
..............................
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11080-121.039.114.0770.0116.0285.0116.03.6155.4
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17000 rows × 9 columns

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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "3821 -117.9 33.8 27.0 2512.0 506.0 \n", + "666 -117.0 32.7 13.0 2132.0 425.0 \n", + "6443 -118.3 34.2 48.0 1560.0 280.0 \n", + "5405 -118.2 33.8 29.0 2448.0 354.0 \n", + "15730 -122.4 37.8 52.0 4265.0 912.0 \n", + "... ... ... ... ... ... \n", + "7697 -118.4 34.2 37.0 1434.0 394.0 \n", + "3848 -118.0 34.0 22.0 1919.0 411.0 \n", + "878 -117.1 33.0 10.0 2577.0 347.0 \n", + "12279 -121.5 38.6 8.0 15309.0 2996.0 \n", + "11080 -121.0 39.1 14.0 770.0 116.0 \n", + "\n", + " population households median_income median_house_value \n", + "3821 1861.0 511.0 4.2 184.2 \n", + "666 1345.0 432.0 4.0 89.3 \n", + "6443 825.0 269.0 5.5 354.7 \n", + "5405 894.0 349.0 7.7 481.3 \n", + "15730 1555.0 836.0 4.1 298.3 \n", + "... ... ... ... ... \n", + "7697 1667.0 404.0 2.4 176.3 \n", + "3848 1203.0 363.0 4.3 144.1 \n", + "878 1193.0 365.0 6.5 264.1 \n", + "12279 7463.0 2885.0 3.9 129.7 \n", + "11080 285.0 116.0 3.6 155.4 \n", + "\n", + "[17000 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 284 + }, + "outputId": "f587e1f4-5aa3-433d-c289-5ddaf3956433" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.describe()" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
count17000.017000.017000.017000.017000.017000.017000.017000.017000.0
mean-119.635.628.62643.7539.41429.6501.23.9207.3
std2.02.112.62179.9421.51147.9384.51.9116.0
min-124.332.51.02.01.03.01.00.515.0
25%-121.833.918.01462.0297.0790.0282.02.6119.4
50%-118.534.229.02127.0434.01167.0409.03.5180.4
75%-118.037.737.03151.2648.21721.0605.24.8265.0
max-114.342.052.037937.06445.035682.06082.015.0500.0
\n", + "
" + ], + "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": 4 + } + ] + }, + { + "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", + "colab": {} + }, + "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\")]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "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\"]" + ], + "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": { + "base_uri": "https://localhost:8080/", + "height": 134 + }, + "outputId": "b3ba2324-3f6b-44eb-ab57-3b9831eafc6e" + }, + "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": 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" + ], + "name": "stdout" + } + ] + }, + { + "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + }, + "outputId": "08ebe62d-fd21-4a08-9b7f-d138f7f9b321" + }, + "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)" + ], + "execution_count": 10, + "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" + } + ] + }, + { + "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": { + "base_uri": "https://localhost:8080/", + "height": 84 + }, + "outputId": "4fc7058e-14fb-498f-a661-a354eb558e39" + }, + "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": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Min. Median House Value: 14.999\n", + "Max. Median House Value: 500.001\n", + "Difference between Min. and Max.: 485.002\n", + "Root Mean Squared Error: 237.417\n" + ], + "name": "stdout" + } + ] + }, + { + "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 284 + }, + "outputId": "bab7079b-b12f-49ad-875b-4be97370480e" + }, + "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": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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count17000.017000.0
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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": 12 + } + ] + }, + { + "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 361 + }, + "outputId": "184abb4c-ebaa-401a-bb56-b0a163967011" + }, + "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": 15, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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Zs0a1RqWraIKy3JTq+FGFiv6g+4NrjiFLdr/41LHRT9FGs46tZIaipCSHa8Mh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\n5XQrn072CcDu2ka43F6sWDhOtjLbqLKBKA5z3ro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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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 939 + }, + "outputId": "65ea0f9f-32a4-4cc7-d2a9-1cd9ff1f9a7c" + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.00001,\n", + " steps=1000,\n", + " batch_size=100\n", + ")" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 225.63\n", + " period 01 : 214.42\n", + " period 02 : 204.04\n", + " period 03 : 194.62\n", + " period 04 : 187.07\n", + " period 05 : 180.80\n", + " period 06 : 176.56\n", + " period 07 : 172.35\n", + " period 08 : 169.46\n", + " period 09 : 167.93\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 112.9 207.3\n", + "std 93.1 116.0\n", + "min 0.1 15.0\n", + "25% 62.4 119.4\n", + "50% 90.8 180.4\n", + "75% 134.6 265.0\n", + "max 1619.9 500.0" + ], + "text/html": [ + "
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LDqB7TKTreRGRUzKXruPgw89gz84hpO9ltHrhMfybNvJ2WFU2e/ZstmzZgs1m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1Dn7n3uILdj651oVJE0mNs+2RTBbEqTNhIhPTZ/G9HhPbN3kz2zHKiq2ZQInP52JgIHjZP/Nm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\nDZEp0djYSENDA7/zO7+Doih8/vO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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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 939 + }, + "outputId": "a0d7af70-6cd2-4550-bb04-59ae1b4dde83" + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE\n", + "train_model(\n", + " input_feature=\"population\",\n", + " learning_rate=0.00002,\n", + " steps=2000,\n", + " batch_size=200\n", + ")" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 214.62\n", + " period 01 : 196.26\n", + " period 02 : 183.70\n", + " period 03 : 177.49\n", + " period 04 : 176.20\n", + " period 05 : 176.05\n", + " period 06 : 176.64\n", + " period 07 : 177.18\n", + " period 08 : 177.46\n", + " period 09 : 177.61\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 143.6 207.3\n", + "std 115.3 116.0\n", + "min 0.3 15.0\n", + "25% 79.3 119.4\n", + "50% 117.2 180.4\n", + "75% 172.8 265.0\n", + "max 3583.2 500.0" + ], + "text/html": [ + "
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na10nx8C7ZP97nxyDilV2H1FrLSX8/f154403zpr+r3/966xpiYmJJCYm1lZV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\nRNsMRFsNRFuNRFsN2G0GYqwGom1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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 diff --git a/improving_neural_net_performance.ipynb b/improving_neural_net_performance.ipynb new file mode 100644 index 0000000..515c598 --- /dev/null +++ b/improving_neural_net_performance.ipynb @@ -0,0 +1,1857 @@ +{ + "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": { + "id": "Cl6KubJea19u", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "" + ] + }, + { + "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": 1153 + }, + "outputId": "71d077e0-ad83-442c-cddc-8ec2c032aa84" + }, + "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 -119.5 28.5 2636.5 538.1 \n", + "std 2.1 2.0 12.6 2144.2 416.5 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1462.0 298.0 \n", + "50% 34.2 -118.5 28.0 2129.0 434.0 \n", + "75% 37.7 -118.0 37.0 3153.0 648.0 \n", + "max 42.0 -114.3 52.0 32627.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1431.7 500.7 3.9 2.0 \n", + "std 1155.4 380.5 1.9 1.2 \n", + "min 3.0 1.0 0.5 0.1 \n", + "25% 791.0 282.0 2.6 1.5 \n", + "50% 1170.0 409.0 3.5 1.9 \n", + "75% 1727.0 606.0 4.8 2.3 \n", + "max 35682.0 6082.0 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": 770 + }, + "outputId": "6a34fecd-e3b0-4f10-faac-9f54bc3c9be4" + }, + "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 : 159.19\n", + " period 01 : 148.46\n", + " period 02 : 130.44\n", + " period 03 : 115.31\n", + " period 04 : 108.54\n", + " period 05 : 104.21\n", + " period 06 : 101.10\n", + " period 07 : 102.10\n", + " period 08 : 99.01\n", + " period 09 : 100.14\n", + "Model training finished.\n", + "Final RMSE (on training data): 100.14\n", + "Final RMSE (on validation data): 101.26\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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IqP1ggNETlh0M8frcQbAxV2L/+WRsPpKAmuaEGJO6IebizUgtVktERKRbDDB6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\nMnrivHRrceAwZ0/ZZNuQEqKx+zR7W6qp7agnqAQxaPVUOSp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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": 653 + }, + "outputId": "b8de1608-9c4f-4982-a82a-53679ad3bbd0" + }, + "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", + " # Your code here: normalize the inputs.\n", + " #\n", + " features = pd.DataFrame()\n", + " features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n", + " features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n", + " features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n", + " features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n", + " features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n", + " features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n", + " features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n", + " features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n", + " return 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 : 225.63\n", + " period 01 : 195.41\n", + " period 02 : 143.06\n", + " period 03 : 116.60\n", + " period 04 : 112.93\n", + " period 05 : 108.92\n", + " period 06 : 104.14\n", + " period 07 : 98.36\n", + " period 08 : 91.80\n", + " period 09 : 85.43\n", + "Model training finished.\n", + "Final RMSE (on training data): 85.43\n", + "Final RMSE (on validation data): 88.42\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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6stv/+tfnblakm04FRkREpBKJj49jxYqlV/Xap5+eQGBgrctuf/PN925WrJuuQt5KQERE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\nCkXhZqSfq8uKiou1kSbWRbpWuzblLjMuvagKQPb3uccfw6k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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": 653 + }, + "outputId": "fa83c9ce-555a-48e4-a219-f29d4e3d8b4a" + }, + "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": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 92.06\n", + " period 01 : 73.85\n", + " period 02 : 71.89\n", + " period 03 : 70.59\n", + " period 04 : 76.92\n", + " period 05 : 70.05\n", + " period 06 : 69.92\n", + " period 07 : 69.55\n", + " period 08 : 69.73\n", + " period 09 : 69.68\n", + "Model training finished.\n", + "Final RMSE (on training data): 69.68\n", + "Final RMSE (on validation data): 71.13\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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ERMS7FFR6qGM/FU/jN6eCioiIiDcpqPRQx6AyLMhOZNAw8h2FGIbh48pEREQG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\nImbNyaDyj//4j2zfvp0dO3Zw5syZaBdHjPDDH/6Q7du388ADD7Bnz55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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "vpeFbHV1dT-w", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 653 + }, + "outputId": "40d84dcb-b459-4467-c18b-a76032ca7cb7" + }, + "cell_type": "code", + "source": [ + "# by Adam\n", + "_, 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": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 229.40\n", + " period 01 : 185.54\n", + " period 02 : 119.06\n", + " period 03 : 113.88\n", + " period 04 : 107.55\n", + " period 05 : 98.29\n", + " period 06 : 85.57\n", + " period 07 : 74.21\n", + " period 08 : 71.31\n", + " period 09 : 70.36\n", + "Model training finished.\n", + "Final RMSE (on training data): 70.36\n", + "Final RMSE (on validation data): 71.68\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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qy+1dnoiISKOlAGNjg2MjiGrqzcbdJyjK8mTANb1JL8lk8ZHl9i5NRESk0VKA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\nWWb5nZAcN6FRTYF55ZVXsNlsPP7449hsNr74xS+Snp7Od7/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+ "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": "c1e68aaa-95b1-479e-f019-6cf7e3fe4d23" + }, + "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": 14, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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ysfH0wmXgYFI2/YDTqT3Ua9SGqxdSiD2dQNNWnpaOJ4QQogYy2ax1UzL3rPVb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\n+gtRAapK3dRXTRq3/xKn4PyoOkdiydZ/c0eAY+0BOvpD6XLZrCp1lR7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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": "a69098b6-5e89-44c0-c10d-e2b84e7daa71" + }, + "cell_type": "code", + "source": [ + "_ = normalized_training_examples.hist(bins=20, figsize=(18, 12), xlabelsize=10)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "display_data", + 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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": 653 + }, + "outputId": "69217104-9c44-4b65-c256-efda2cfe6c79" + }, + "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", + " \n", + " processed_features = pd.DataFrame()\n", + "\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\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", + " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\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": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 225.31\n", + " period 01 : 164.61\n", + " period 02 : 113.39\n", + " period 03 : 111.92\n", + " period 04 : 110.10\n", + " period 05 : 107.87\n", + " period 06 : 104.90\n", + " period 07 : 101.22\n", + " period 08 : 96.51\n", + " period 09 : 91.64\n", + "Model training finished.\n", + "Final RMSE (on training data): 91.64\n", + "Final RMSE (on validation data): 94.58\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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xxx/z7rvv8vzzz/Paa6/xwAMP8NFHHxEVFcXixYspLy9n7ty5zJ8/nwULFvDB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\nC4jP5hGbLTT85M8bcKxzAacx3DTcX4uAYzQY4VX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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": { + "base_uri": "https://localhost:8080/", + "height": 653 + }, + "outputId": "aebbfe75-1889-4980-9162-4d281d840aec" + }, + "cell_type": "code", + "source": [ + "#\n", + "# YOUR CODE HERE: Train the network using only latitude and longitude\n", + "#\n", + "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", + " \n", + " processed_features = pd.DataFrame()\n", + "\n", + " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n", + " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\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": 18, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 233.12\n", + " period 01 : 223.08\n", + " period 02 : 201.36\n", + " period 03 : 165.22\n", + " period 04 : 122.92\n", + " period 05 : 111.31\n", + " period 06 : 109.65\n", + " period 07 : 108.53\n", + " period 08 : 107.53\n", + " period 09 : 106.48\n", + "Model training finished.\n", + "Final RMSE (on training data): 106.48\n", + "Final RMSE (on validation data): 109.82\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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kNrUjDcxtMPcP1aXMIrYfTGH/iUx0egV7GwsGhDSmf4gvTnaWNzw/Mfs4Xx5b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\n+sxELvWmcxRgukBvqsil3kSm3u5LrctL7t7TfJJ3Fp8/SGq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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "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 diff --git a/intro_to_neural_nets.ipynb b/intro_to_neural_nets.ipynb new file mode 100644 index 0000000..e8bf00c --- /dev/null +++ b/intro_to_neural_nets.ipynb @@ -0,0 +1,1178 @@ +{ + "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": 1153 + }, + "outputId": "5cba7705-2e72-4c80-9b2e-31e0158d572c" + }, + "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 2651.4 541.1 \n", + "std 2.1 2.0 12.6 2192.8 422.2 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1469.8 297.0 \n", + "50% 34.2 -118.5 29.0 2129.0 435.0 \n", + "75% 37.7 -118.0 37.0 3149.0 648.0 \n", + "max 42.0 -114.3 52.0 32627.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 12000.0 12000.0 12000.0 12000.0 \n", + "mean 1433.6 502.9 3.9 2.0 \n", + "std 1138.3 386.2 1.9 1.2 \n", + "min 3.0 1.0 0.5 0.0 \n", + "25% 790.0 282.0 2.6 1.5 \n", + "50% 1166.0 410.0 3.5 1.9 \n", + "75% 1717.2 606.0 4.8 2.3 \n", + "max 28566.0 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": 653 + }, + "outputId": "4d6cd99f-5c64-4937-a561-0c114270c094" + }, + "cell_type": "code", + "source": [ + "dnn_regressor = train_nn_regression_model(\n", + " learning_rate=0.005,\n", + " steps=1000,\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": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 230.89\n", + " period 01 : 156.59\n", + " period 02 : 161.09\n", + " period 03 : 140.12\n", + " period 04 : 129.73\n", + " period 05 : 122.65\n", + " period 06 : 119.43\n", + " period 07 : 122.14\n", + " period 08 : 108.27\n", + " period 09 : 106.58\n", + "Model training finished.\n", + "Final RMSE (on training data): 106.58\n", + "Final RMSE (on validation data): 107.34\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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iykRERC4fCjAXqE1kAEH+nmzZnU213U788bvy6qZ2IiIiDUcB5gKZTSZ6xIRR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\nERERGXEUYERERGTEGdYAU19fz+LFi3nssccAaG5uZtWqVax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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": 34 + }, + "outputId": "1d60703b-8e50-46f1-f77c-01eba704b278" + }, + "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", + "\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": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Final RMSE (on test data): 104.89\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 diff --git a/intro_to_pandas.ipynb b/intro_to_pandas.ipynb new file mode 100644 index 0000000..1eaf5b1 --- /dev/null +++ b/intro_to_pandas.ipynb @@ -0,0 +1,1415 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "9d1d0a4f-0186-47ba-e197-e011c017097f" + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import pandas as pd\n", + "pd.__version__" + ], + "execution_count": 2, + "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84 + }, + "outputId": "07a27f40-4925-4db3-a320-c1b87e4f9d9c" + }, + "cell_type": "code", + "source": [ + "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])" + ], + "execution_count": 3, + "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": 3 + } + ] + }, + { + "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 136 + }, + "outputId": "7a24918e-b424-4092-9184-05e15274bcfe" + }, + "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": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulation
0San Francisco852469
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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": 4 + } + ] + }, + { + "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 284 + }, + "outputId": "977058b1-71db-4ea7-a9f4-50180b9ccfd3" + }, + "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": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
count17000.00000017000.00000017000.00000017000.00000017000.00000017000.00000017000.00000017000.00000017000.000000
mean-119.56210835.62522528.5893532643.664412539.4108241429.573941501.2219413.883578207300.912353
std2.0051662.13734012.5869372179.947071421.4994521147.852959384.5208411.908157115983.764387
min-124.35000032.5400001.0000002.0000001.0000003.0000001.0000000.49990014999.000000
25%-121.79000033.93000018.0000001462.000000297.000000790.000000282.0000002.566375119400.000000
50%-118.49000034.25000029.0000002127.000000434.0000001167.000000409.0000003.544600180400.000000
75%-118.00000037.72000037.0000003151.250000648.2500001721.000000605.2500004.767000265000.000000
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": 5 + } + ] + }, + { + "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", + "colab": {} + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.head()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "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", + "colab": {} + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.hist('housing_median_age')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 101 + }, + "outputId": "176677cb-2940-4e5c-d3cd-6813035a3b6c" + }, + "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": 6, + "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": 6 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "V5L6xacLoxyv", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + }, + "outputId": "e4e302d1-837b-438f-c8a0-74f8795830a9" + }, + "cell_type": "code", + "source": [ + "print(type(cities['City name'][1]))\n", + "cities['City name'][1]" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'San Jose'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "gcYX1tBPugZl", + "colab": {} + }, + "cell_type": "code", + "source": [ + "print(type(cities[0:2]))\n", + "cities[0:2]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "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-", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84 + }, + "outputId": "8293ade8-dfa4-4e0e-a028-23f54827d0fb" + }, + "cell_type": "code", + "source": [ + "population / 1000." + ], + "execution_count": 8, + "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": 8 + } + ] + }, + { + "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84 + }, + "outputId": "3cfb1515-cd07-4253-fa3e-51b0f90e102a" + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "np.log(population)" + ], + "execution_count": 9, + "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": 9 + } + ] + }, + { + "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84 + }, + "outputId": "118c4433-5ad9-4b3f-d70a-88973af2fe9c" + }, + "cell_type": "code", + "source": [ + "population.apply(lambda val: val > 1000000)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 False\n", + "1 True\n", + "2 False\n", + "dtype: bool" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 136 + }, + "outputId": "889c6b71-8850-4bcb-bc31-f501d70c83bb" + }, + "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": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation density
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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": 11 + } + ] + }, + { + "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 136 + }, + "outputId": "c33194c8-e8c8-47f0-d4c9-374ffeded0db" + }, + "cell_type": "code", + "source": [ + "# Your code here\n", + "cities['named saint and area greater than 50'] = cities['Area square miles'] > 50 & cities['City name'].apply(lambda name: name.startswith('San'))\n", + "cities" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densitynamed saint and area greater than 50
0San Francisco85246946.8718187.945381True
1San Jose1015785176.535754.177760True
2Sacramento48519997.924955.055147True
\n", + "
" + ], + "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 \n", + "\n", + " named saint and area greater than 50 \n", + "0 True \n", + "1 True \n", + "2 True " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "78a15322-4e06-4682-ff93-0597cda459ed" + }, + "cell_type": "code", + "source": [ + "city_names.index" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "F_qPe2TBjfWd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "31549988-cc31-4b61-965d-c06d1a824858" + }, + "cell_type": "code", + "source": [ + "cities.index" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=3, step=1)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 136 + }, + "outputId": "174703f4-5aaf-41ea-93ab-c54bc115e574" + }, + "cell_type": "code", + "source": [ + "cities.reindex([2, 0, 1])" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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City namePopulationArea square milesPopulation densitynamed saint and area greater than 50
2Sacramento48519997.924955.055147True
0San Francisco85246946.8718187.945381True
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", + " named saint and area greater than 50 \n", + "2 True \n", + "0 True \n", + "1 True " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 15 + } + ] + }, + { + "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", + "colab": {} + }, + "cell_type": "code", + "source": [ + "cities.reindex(np.random.permutation(cities.index))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 195 + }, + "outputId": "671f9e67-3c53-4fed-8800-82e7ed80e2fc" + }, + "cell_type": "code", + "source": [ + "# Your code here\n", + "cities.reindex([4,2,0,3,1])" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " City name Population Area square miles Population density \\\n", + "4 NaN NaN NaN NaN \n", + "2 Sacramento 485199.0 97.92 4955.055147 \n", + "0 San Francisco 852469.0 46.87 18187.945381 \n", + "3 NaN NaN NaN NaN \n", + "1 San Jose 1015785.0 176.53 5754.177760 \n", + "\n", + " named saint and area greater than 50 \n", + "4 NaN \n", + "2 True \n", + "0 True \n", + "3 NaN \n", + "1 True " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "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 diff --git a/logistic_regression.ipynb b/logistic_regression.ipynb new file mode 100644 index 0000000..e4bf4a1 --- /dev/null +++ b/logistic_regression.ipynb @@ -0,0 +1,1620 @@ +{ + "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": { + "base_uri": "https://localhost:8080/", + "height": 1153 + }, + "outputId": "7b5b7606-1822-4b7a-9c30-c30aa4702302" + }, + "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 2644.8 538.8 \n", + "std 2.1 2.0 12.6 2167.8 419.9 \n", + "min 32.5 -124.3 1.0 11.0 3.0 \n", + "25% 33.9 -121.8 18.0 1462.0 297.0 \n", + "50% 34.2 -118.5 29.0 2137.0 434.0 \n", + "75% 37.7 -118.0 37.0 3159.0 649.0 \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 1430.5 500.7 3.9 2.0 \n", + "std 1159.4 382.8 1.9 1.1 \n", + "min 8.0 2.0 0.5 0.0 \n", + "25% 790.0 282.0 2.6 1.5 \n", + "50% 1168.0 408.0 3.6 1.9 \n", + "75% 1720.0 605.2 4.8 2.3 \n", + "max 35682.0 6082.0 15.0 52.0 " + ], + "text/html": [ + "
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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "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": { + "base_uri": "https://localhost:8080/", + "height": 737 + }, + "outputId": "ac7c3eff-25e9-4906-eef9-224f81d9fd56" + }, + "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": 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 : 0.45\n", + " period 01 : 0.45\n", + " period 02 : 0.46\n", + " period 03 : 0.45\n", + " period 04 : 0.45\n", + " period 05 : 0.45\n", + " period 06 : 0.45\n", + " period 07 : 0.44\n", + " period 08 : 0.44\n", + " period 09 : 0.45\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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UH1OHc4Nh/d2wt7HgcEoeTTq9qcMRQghxhyTBEZ3iy0OXqG/UMSfSHysLjanD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\nqXDPYQzzCkGtUpNUdMbEUX0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PhjKmrtAJuea7kVHffDcyytfdngUxQYQEuJh9s4+jrSW/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\nsGAB3t7ejBgxgsjISEwmE3V1dUybNq33cW699VZeeOGFKz53fb11Lwk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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "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": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "cd5f03f3-4cd5-481b-e9a3-3ce4af255684" + }, + "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": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "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", + " # YOUR CODE HERE: Construct the linear classifier.\n", + " linear_classifier = tf.estimator.LinearClassifier( feature_columns=construct_feature_columns(training_examples), 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": "VM0wmnFUIYH9", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 619 + }, + "outputId": "bf667e19-fe46-4ba3-904b-23dde3b50567" + }, + "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": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.61\n", + " period 01 : 0.59\n", + " period 02 : 0.58\n", + " period 03 : 0.56\n", + " period 04 : 0.55\n", + " period 05 : 0.55\n", + " period 06 : 0.54\n", + " period 07 : 0.54\n", + " period 08 : 0.53\n", + " period 09 : 0.53\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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nB2+D983ILeW5D49jaaHmhQV34GhnZcJMW5ccmjVPUhfzJbUxT1IXw7XKaSbR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\nnz9lkRFCiHhzu9NRZ3sbOdd7nuaBFpoHWnjt4ttROx2VpFG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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "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": { + "base_uri": "https://localhost:8080/", + "height": 50 + }, + "outputId": "d42e4921-4347-41dd-cc50-03bc378a2644" + }, + "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": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "AUC on the validation set: 0.76\n", + "Accuracy on the validation set: 0.52\n" + ], + "name": "stdout" + } + ] + }, + { + "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": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "aba17a99-1900-4aaf-f6d0-744e54d5e111" + }, + "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": 14, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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370B+9asnAejffwAffLCKgwf387e/vc0999zLXXdNv+i2KzEYWj+CfvPNt9vcvm3bFsf4\nCNFdJH95huyCanIKay+6T6fVEB/Vi8hgH5ImD1AhnehoiqJwsPgoa7M302RtIs5/AIvi59Lbo+fN\nfn+o25Ww2mpqqgkODkGv17Nnzy5sNjsWi+WSj42K6kty8kcoikJJSbFjtvrd0ojTpt3J8eNHiI83\nXVeGuDgTH3zwHlarldraGv7nf17liSd+CUBjYwM6nY7evQMpKSkmIyMdq9XKjh2f0qdPOBMmTMLP\nrxdfffU5BoPhottiY+Mv+7pRUf04dy6XqqpK/P0DeO+9v3PvvTOvK7sQHaXFbMPy7UFTx7LK+L9P\nMgDQaOByV7x/ev4Qgnp5EOTn3mNnRuJiNS11rMpcz6nyNIw6I/PjZjKuz2in+DuWEv43w4ePYuXK\nf/Hznz/C+PETufXWcbz++quXfOyAAQPp3z+Gn/70x0RGRjFwYCwADz/8KK+++nu2bNmIXm/g179+\n3rGO8LUIC+vDHXfczc9//giKovDTnz7muM/PrxcjRozi4YeXMmDAQBYuXMKf//wmv/71C/zv//4R\nDw9PtFotTz75n7S0tPD666+0uS0tLfWyr+vu7s4vfvE0zzzzC4xGAwMHxhEYGHTNuYW4UXa7wv60\nYvTGcvafLOT4mfLLPnZAeOs5n3ZFIbFfAHeMjJKrSDkpRVE4VHKMtVmbaLQ2EdsrhkWmuQR6BKgd\nrcPIKkoCkHHsCDKG18dqs/PXjakcy7584Yb19iSst5fj658f3x2PTztX6XF2zvJ7WGuuY3VGCifK\nT2PUGrh/wD2MDx+NVnPlZQ07gqyiJIRwWnZF4fVVx8jIr25zu0YDcyYPxMuoIzrMl7Denuivso6s\ncD6KonCk9ATJWRtpsDQysFd/FpvmEujRW+1onUJKWAjRJSxWG6+tPEZuUdsDqR6ebmJMYigajcZp\nZnHixtSZ61mduYHjZacwaA3MHXgfEyLGdMnsVy1SwkKIDmO3K6TmVpBbVIfdrnAip5yy6masNjsW\nq73NY5fcEcdtQ8NVSiq6myMlrbPfeksDMX79WGxKItgzUO1YnU5KWAhxQxqaLVwoa+CrYxc4U1BN\nRW3LFR/v520EBR6abmJQtHN+tCiuX525njVZGzlWehKD1sDsgTOYFDHWqWe/PyQlLIS4LvtSi3l3\na9pl7+8b4oO/jxuThoaj1UK/UF/cjTr5fldc5FjpKVZnplBvaaC/Xz+WmOYS7OlaZ2RICQshrqik\nspFvUouorG0hM7+qzYw3LrIXPp4GRiWEMiw20CnO2xSdr97cQHLWRo6UnsCg1TNrwHRuixznMrPf\nH5ISFkJcUkFpPS+8f/CS9wX6ufPao2NkTVxx3Y6XpbI6I4U6Sz3RvlEsMSUR4uW6i8JICQshgNZT\nQ45mlVNZ18yqHdkX3f/k3JvxctcTFeKDQe96MxbRPvWWBtZmbeJwyXH0Wj0zB9zD5MjxLjn7/SEp\nYSFcnM1uJ+dCLa+tPHrRfX5eRp5/YDgBvu4qJBPO4kTZaVZlrqfOXE8/3yiWmOYS6hWidqxuQUpY\nCBd2oaye599r+5FzQj9/xg0OY8iAQNyN8hYhblyDpZG1WZs5VHIUvUbH/TF3MzlyPDqt7uo/7CLk\n/zAhXNDRrDLe2ZCK/QdXrZ0yLIIRpmBiI3upmEw4i1PlaazKWE+NuY6+PpEsSUgiTGa/F5ESFsIF\nWKx26pss7E8rZu1XORfd/+dfjJc1dkWHaLQ0si57CweKj6DT6Li3/51MjZoos9/LkBIWwom1mG08\n/94BymuaL7ovNsKPhdNiiQq59IXlhbheqeXpfJSxnhpzLVE+4SwxzaOPd6jasbo1KWEhnFCL2cbH\n+/PYuvec4zZfTwP+vu4Mjwti4pBwmfmKDtNoaWL9mS3sLzqMTqNjRv87mBY1SWa/10BKWAgncqG8\ngef/eeCi23+1YCjxff1VSCSc3emKTD7KWEd1Sw2R3n1YkjCPcO8wtWP1GFLCQjiJ9z9OZ8+poja3\nzZ8ykNtHRKqUSDizJmsTKdlb2Vt0CK1Gyz3R07ij72SZ/V4nKWEhnMDLyw+Tc6F1iUB/Hzde/PEI\nfD2NKqcSziq9IosVGWupbqkh3DuMJaZ5RPr0UTtWjyQlLEQPpCgKTS1WzFY7T7/9Dd+daBQb2Yvn\nFg1TNZtwXk3WZjac2co3hQfRarTc3W8qd/SbjF4rVXKjZOSE6CFazDYKyurZfaKQr08WXXT/tOGR\nLJg6UIVkwhVkVGazIn0tVS3V9PEKZWnCPCJ9ZD3o9pISFqIH2Lr3HCm7z150+9CBgVTWtfDAnXH0\nC/VVIZlwds3WZjbkbGPPhf1oNVru6jeFO/tNkdlvB5FRFKKbamqx8oeVR8kvrW9z+9CBgdw6KIyh\nAwPRamUVI9F5MivPsDJjLRXNVYR5hbDUNI8o3wi1YzkVKWEhupny6ib+37qTXChvaHP7qIQQHpmR\nIGv2ik7XbG1hU842dl/Yh1aj5Y6+k7kreioGmf12OBlRIboBRVH46PNsvjha0OZ2L3c9P7ornlvi\nXHe9VdG1sqtyWJ6+lormSkK9QlhqSqKvr5zm1lmkhIVQ0bniWnYdL2TX8cI2tw8dGMjQgUGMGywX\nPRBdo8VmZlPOJ+wq+AYNGm7vext395uKQSdXVutMUsJCdDG7XeGVFUeoqTdTUdv2ms73jOnLrAn9\n5SNn0aWyq86yImMt5U0VhHgGs8SURLRflNqxXIKUsBBdpKC0npdXHKHFbHPc5uWup6HZygs/Gk5U\nsI8caCW6lNlmZnPOdnYWfAPA1KiJTI++XWa/XUhKWIguUFXXwgvvH3Tsuxt1/GRGAkMHBqmYSriy\nM9W5rEhPpqypghDPoG9nv33VjuVypISF6CRHMku5UNbAxj25bW7/29MTMRrk+rpCHWabhS1nt/PV\n+T0ATImcwPT+d2CU2a8qpISF6GBl1U0s+8d+bHalze19Ar149L5EKWChmrM1eSxPX0NpYznBHoEs\nNiUR06uf2rFcmpSwEB0k+3wVv/vnfqrrzY7b+ob4cNfoKIbFBqHXaVVMJ1yZ2WZha+6nfJn/NQCT\nI8czo/8dGHWyyIfapISFaKfiykZe/vAwDc1Wx239Qn2YMymGhH4BKiYTAnJr8lienkxJYxlBHr1Z\nbEpiQK9otWOJb11TCb/yyiucOHECjUbDsmXLGDx4sOO+lStXsnnzZrRaLYMGDeI3v/lNp4UVorvZ\nsvccG35wTefYyF78fNZNeHvI92tCXRabhY9zP2dH/i4UFG6LGMe9MXfK7LebuWoJHzx4kLy8PNas\nWUNOTg7Lli1jzZo1ANTX1/Pee+/x2WefodfrefDBBzl+/DhDhgzp9OBCqC1l91m27j3n2F/z8t00\n1DVf/geE6CJ5tef5MG0NxY2lBLoHsNg0l4H+MWrHEpdw1RLet28fU6dOBSAmJoaamhrq6+vx9vbG\nYDBgMBhobGzE09OTpqYm/Pz8Oj20EF1NURSKKxu5UNbAgbQSjmSVtbn//ecm4+lukBIWqrLYrXx0\nciOb0j9DQWFixK3cF3M3bjL77bauWsLl5eUkJiY69gMCAigrK8Pb2xs3Nzcee+wxpk6dipubG/fc\ncw/R0fJdg3AuZouN11Ye5Vxx3UX3TRkWwcJpsoavUF9e7XmWpydT1FBCb3d/FpvmEus/QO1Y4iqu\n+8AsRfn+tIv6+nr+/ve/s337dry9vXnggQfIyMggPj7+sj/v7++JXt+xp2gEBfl06PO5KhnHi9nt\nCo++9gVFFa0rGg2K6U1EsA8Th4aTEN37oitcyRi2n4zh9bHYLKxP28bG9M+wK3ZuHzCBxYNn4m5w\nVztaj9ZVv4dXLeHg4GDKy8sd+6WlpQQFtV7lJycnh8jISAICWo8AHT58OKmpqVcs4aqqxvZmbiMo\nyIeysotnKOL6yDhe7GxhLS99eNix/+h9iYw0hTj2KyrarvMrY9h+MobXJ7+ugOVpyRQ2FBPg7s/i\n+LmMixtKWVkddVjUjtdjdcbv4eVK/aolPHbsWN566y3mz5/P6dOnCQ4OxtvbG4Dw8HBycnJobm7G\n3d2d1NRUJk6c2KHBhehK+SV1FJY3sP1APvml35fsf9w/iOHxspyg6B6sdivbz33Jp3lfYlfsjOsz\nipkD7sFdL7PfnuaqJTxs2DASExOZP38+Go2GF198kZSUFHx8fJg2bRoPPfQQS5cuRafTMXToUIYP\nH94VuYXoUC1mG0++tYcWi+2i+/7+zCQMernQhugeztcVsjx9DRfqi/B368Ui0xxMAbFqxxI3SKP8\n8EveLtAZU3z5+Kr9XG0c65ssfLg9A7PVTkZ+FWaL3XFfsL8HoxNCGJMYSrC/xzUvK+hqY9gZZAwv\nz2a3sT3vS7af+wK7Ymdsn5HMHDAdj3+b/coYtl+3+jhaCGfSYrbxzsZUTp2tuOg+N6OO3yy+hYhg\nbxWSCXF5BXWFLE9PpqC+kF5ufiyKn0NC7zi1Y4kOICUsXMaqHdl8fvh8m9sem3kTcVG9cDfq5NrO\notux2W18lvcVn5z7AptiY0zYCGYPnI6H3kPtaKKDSAkLl3Aks9RRwB5uOn4yI5EhAwJVTiXE5V2o\nL2J5ejLn6y7gZ/RlkWkOib0vf+aJ6JmkhIVT++CTdHafKGpz21+ekiP4Rfdls9v4PH8n23J3YFNs\njA4dzuyBM/A0yOzXGUkJC6f18b5zjgKODvPFzaDl6flyXXPRfRXWF7M8PZn8ugL8jD4sjJ/DoECT\n2rFEJ5ISFk7JYrWxflfr6kbRYb48/4CcOie6L5vdxhf5u/k49zOsio1RobcwZ+AMPA2eakcTnUxK\nWDgdq83OT1/f5diXAhbdWVFDCcvTk8mrPY+v0YeF8bO5KTBB7Viii0gJC6fz5J/3OLZf/eloFZMI\ncXl2xc4X+bvZmvsZVruVESFDmRt7H14y+3UpUsLCqWzak0tjixWA5xYNI8Rf3tBE91PcUMqK9GRy\na/PxMXqzIG42NwclXv0HhdOREhZOI6ewhk17cgFwN+qIjeylciIh2rIrdr48/zVbzn6K1W5leMgQ\n5sbeh7fBS+1oQiVSwsIp2O0KL394BACdVsNfnpqgciIh2ippLGNFejJna/LwNnixIGEBQ4JvUjuW\nUJmUsOjxjmSW8pcNqY79Vx8Zfc3Xexais9kVOzvP72Hz2e1Y7FaGBQ8mKfZ+fIxyeVQhJSx6sJr6\nFl776Bglld+vUb30jjgCe8lFDUT3UNpYxvL0tZytOYe3wYulCfMZFjxY7ViiG5ESFj3SyZxy/rT2\npGM/po8vz8wfiptRp2IqIVrZFTu7CvayKecTLHYLQ4NuYl7cTJn9iotICYse53BGKe9s/MHHzz8d\nLUdBi26jrLGCFRnJnKnOxcvgyRJTEreE3Kx2LNFNSQmLHmX7gXySvzrj2H/3V5PQaWX1I6E+u2Jn\nd8E+NuVsw2y3cHPQIObHzcTXeOl1ZIUAKWHRg1isNkcBB/t78LsHR0oBi26hvKmCFelrya4+i5fe\nk0Xxc7glZIgcICiuSkpYdHuNzVZ2nyhk7c7vZ8Cv/XSMiomEaGVX7Oy5sJ8NOdsw28wMDkxkftws\n/Nxk9iuujZSw6NZe+vAwZwtr29z2s/sHqZRGiO9VNFWyImMdWVVn8NR7sCBhPiNChsrsV1wXKWHR\n7djsdnIL63hlxZE2t8+ZFMOEm/vg7WFQKZkQoCgKewoPsOHMVlpsZm4KNLEgbjZ+br5qRxM9kJSw\n6HZ+8sedbfbnTIrh7tF91QkjxA9UNFXxUcY6Mqqy8dB7sNQ0j5Ghw2T2K26YlLDoFuyKwsH0Et7d\nkua4bcLNYdw9ph/BcvENoTJFUdhbeJCUM1tptrUwqHc8C+Jn08vNT+1oooeTEhbdwp6TRXzwSYZj\n/z/uH8Tw+GAVEwnRqqq5mpUZ60ivzMJD785iUxKjQ2+R2a/oEFLCQnVl1U2OAg729+DXi4bh5+2m\ncirh6hRFYV/RIdZnb6XZ1kxCQBwL42fj7y6rc4mOIyUsVPf8ewcc2y89PAq9Ts79Feqqaq7mo4z1\npFVm4q5zZ1H8XMaEDZfZr+hwUsJCNbuOX+Bf2zMd+395aoIUsFCVoijsLzrM+jNbaLI2YwqIZVH8\nHJn9ik4jJSxUYbbY2hTw/CkD8XCTX0ehnuqWGj7KWM/pigzcdW4sjJ/NrWEjZfYrOpW864kuV1XX\nwtN/+cax/89nb0Mrb3RCJYoAlV7wAAAgAElEQVSicLD4KGuzN9NkbSLefyCLTHMIcPdXO5pwAVLC\nokvtPHaBDz/9fgb87MKhUsBCNTUttazKXM+p8nTcdEbmx81iXJ9RMvsVXUZKWHSZ7ILqNgX8p8fH\n4etlVDGRcFWKonCo5BhrszbRaG0i1n8Ai+Pn0NsjQO1owsVICYsukZFXxR9XHXPsy0fQQi01LXWs\nzkzhZPlpjDoj82JnMi58FFqNHBQoup6UsOh0tY3mtgX8Kylg0fUUReFIyXGSszbRYG1kYK/+LDYl\nESizX6EiKWHRqTLzq/jDR98X8F9/ORGtVgpYdK1acx2rMzdwoiwVo9bA3Nj7mBA+Rma/QnVSwqLT\nHM4o5Z2NqY79Nx4bi5tRp2Ii4WoUReFo6QnWZG2kwdJIjF80S0xJBHn2VjuaEICUsOgkD772ZZv9\nf/znJLkQh+hSdeZ6Vmdu4HjZKQxaA3MG3svEiFtl9iu6FSlh0eE+3nfOsT0iPpiF02KlgEWXOlp6\nkjWZG6i3NBDj14/FpiSCPQPVjiXERaSERYd6a/1JjmWXAzD2plAeuidB5UTCldSbG1iTtYGjpScx\naPXMHjiDSRFjZfYrui0pYdFhrDa7o4C9PQz86K54lRMJV3K89BSrMzdQZ6mnv19fFpuSCPEMUjuW\nEFckJSw6hMVq539+cBrSn38xXsU0wpXUWxpIztzIkdITGLR6Zg64h8mR42X2K3oEKWHRIf740VFy\nCmsBeHzWTSqnEa7iRFkqqzJTqDPXE+0bxRJTEiFewWrHEuKaSQmLdmtqsToK+CfTExgaKx8Bis7V\nYGlkbdYmDpUcQ6/Vc3/M3UyJmiCzX9HjSAmLG2ax2vmvf+6nrLoZAKNey5hBoSqnEs7uZNlpVmWm\nUGuuo69vJEtNSYR6hagdS4gbIiUsbthfNpxyFDDAK4+MVjGNcHaNlkbWZm/mYPFR9Bod9/W/iylR\nE9Bp5QIwoueSEhY3ZN/pYk7mVADw4N0mxg0OUzmRcGanytNYlbGeGnMdUT4RLDEl0cdbPnURPZ+U\nsLhuqbkVvLslDYDQAE8pYNFpGi1NrMvezIHiI+g0Omb0v5NpURNl9iuchpSwuGaKovDqyqOcKahx\n3Pa7h0aqmEg4s9MVGXyUsZ7qlhoifcJZYkoi3Fv+wSecyzWV8CuvvMKJEyfQaDQsW7aMwYMHO+4r\nKiril7/8JRaLhYSEBH73u991WlihrjfWHG9TwO89exsaWZJQdLAmaxPrs7eyr+gQOo2O6dF3cHvf\nSTL7FU7pqsfzHzx4kLy8PNasWcPLL7/Myy+/3Ob+1157jQcffJB169ah0+koLCzstLBCXWnnqgC4\nbWg47z83WQpYdLjjRWm8dOBN9hUdIsK7D8+OeIK7oqdIAQunddWZ8L59+5g6dSoAMTEx1NTUUF9f\nj7e3N3a7nSNHjvDmm28C8OKLL3ZuWqGaDz7JcGwvuSNOxSTCGTVZm0nJ3sreooNoNVruiZ7GHX0n\nS/kKp3fVEi4vLycxMdGxHxAQQFlZGd7e3lRWVuLl5cWrr77K6dOnGT58OE8//fQVn8/f3xO9vmP/\nxwoK8unQ53NVlxvHd9afYPeJ1k847p8YI+N9BTI21+9kcTp/PbycisYq+vqF89ioB+jnH6l2rB5N\nfg/br6vG8LoPzFIUpc12SUkJS5cuJTw8nEceeYSdO3cyadKky/58VVXjDQW9nKAgH8rK6jr0OV3R\n5caxvLqJT/aeA2DKLRHcO6avjPdlyO/i9Wm2NrPhzMfsKTyAVqPlrn5TWTL8Pqoqm2Qc20F+D9uv\nM8bwcqV+1RIODg6mvLzcsV9aWkpQUOtlCf39/enTpw9RUVEAjBkzhuzs7CuWsOg57PbWo6EBevu6\ns2harMqJhLPIqMxmZcY6Kpur6OMVypKEJKJ8ItDr5IQN4VquemDW2LFj+fTTTwE4ffo0wcHBeHt7\nA6DX64mMjOTcuXOO+6OjozsvregyiqLw8B+/oqquBYBnFgxROZFwBs3WFlZnbuCt4+9S3VLDnf2m\n8OyIJ4jyiVA7mhCquOo/O4cNG0ZiYiLz589Ho9Hw4osvkpKSgo+PD9OmTWPZsmU899xzKIpCbGws\nkydP7orcopNt25/n2F40LZYQf08V0whnkFV1hhXpa6loriLMK4QlpiT6+sp3v8K1XdNnP88880yb\n/fj47xdr79u3L6tWrerYVEJ1W/e2lvDP7h/EiHhZGk7cuBabmU0529hVsBcNGm7vext3R0/DoJWP\nnoWQ/wvERf66MZUWiw1ACli0S3ZVDivS11LeXEmoZzBLEpLo5xuldiwhug0pYeHQYrGx7B/7Hd8D\nJ/TzVzmR6KlabGY253zCzoJv0KBhWtQk7omehkFnUDuaEN2KlLAAILewhife2OXYHxYbxM9n3aRi\nItFTnanOZXl6MuVNFYR4BrPElES0n8x+hbgUKWEBwIv/2OfYfnLuYAbHBKqYRvREZpuZzWe3s/P8\nNwBMjZrIPdG3Y5TZrxCXJSUs2LY/z/ER9NtPjsfTXd40xfU5W3OO5WnJlDaVE+wZyBJTEv39+qkd\nS4huT0rYxeUV17FuZw4At4+IlAIW18Vss7Dl7Ha+Or8HgMmR45nR/06Z/QpxjaSEXVhVXQu//eAQ\nAEa9lvlTBqqcSPQkZ2vyWJ6+htLGcoI8erPENI+YXv3UjiVEjyIl7KIqa5t55p29jv21r06noqJe\nxUSip7DYLGzN/Ywv8ncDcFvkOO7tfydGnVHlZEL0PFLCLupIVplj+09PjEOrlbWBxdXl1uSzPD2Z\nksZSAj16s8SUxIBecqlaIW6UlLALUhSFVTuyAXgq6WZ8PWUGI67MYrPwce7n7MjfhYLCxIix3Bdz\nF24y+xWiXaSEXdAL7x90bPcNlXVHxZXl1Z7nw/RkihtKCHQPYLFpLgP9Y9SOJYRTkBJ2IRarnZ++\nvtOxv3DqQJkFi8uy2K18kruDz/N3YlfsTAi/lfti7sJd76Z2NCGchpSwC3lj9THH9j1j+jJ1uKxg\nIy4tv7aA5enJFDYU09vdn8WmucT6D1A7lhBOR0rYRexLLSaroAaA/1o6nP59fFVOJLojq93KJ+e+\n4LO8r7ArdsaFj2ZmzN24693VjiaEU5ISdgEWq413t6YB4ONpkAIWl5RfV8DytNbZr79bLxab5hIf\nIOeOC9GZpIRdwD+3pju2//T4OBWTiO7Iarey/dyXfJr3JXbFztg+o5g54B48ZPYrRKeTEnZyq3Zk\ncyijFIDFt8ei0cj5wOJ7BXWFfJi+hgv1Rfi79WJR/BxMvWPVjiWEy5ASdmJFFQ18fvg8AIF+7kwe\nFqFyItFd2Ow2Ps37kk/OfYFdsXNr2EhmDbwHD72H2tGEcClSwk6q2WzlN+8ecOz/8We3qphGdCcX\n6otYnraG8/WF9HLzY2H8HBJ7x6kdSwiXJCXshM4U1PDKiiOOffkeWEDr7PezvJ18cm4HNsXGmLAR\nzB44XWa/QqhIStgJffhphmP7iTmD8fWSC3K4usL6YpanryG/7gJ+Rl8Wxs9mUKBJ7VhCuDwpYSeT\nV1xHQVkD0DoDlgJ2bTa7jR35u9iW+zlWxcao0FuYM3AGngZPtaMJIZASdio19d+vDxwZ7C0F7OKK\nGkpYnpZMXt15/Iw+LIifzU2BCWrHEkL8gJSwE3l9zXHH9rMLh6qYRKjJZrfxxfndfHz2M6yKjZGh\nw5g78F6Z/QrRDUkJO4naRjMXvvsY+olxeLobVE4k1FDcUMKH6cnk1Z7H1+jDgrhZDA5KVDuWEOIy\npISdgM1u58k/7wFAq9HIykguyK7Y+SJ/N1tzP8NqtzI8ZAhzY+/D2+CldjQhxBVICTuB11d9/zH0\nbx8aqWISoYaShlKWp68ltzYPH4M38xNnMSRokNqxhBDXQEq4hyurbiLzfDUAT8weTHigzHxchV2x\n8+X5r9l69lMsdiu3BN9MUuz9eBvld0CInkJKuIc7fa4SgIggb4YMDFQ5jegqJY1lrEhP5mxNHt4G\nLx5IWMDQ4JvUjiWEuE5Swj3c5j25AEwbLteFdgV2xc7Ogm/YnPMJFruVYcGDSYq9Hx+jt9rRhBA3\nQEq4B8vMr6K63gzA2JvCVE4jOltpYzkr0teSU5OLt8GLpQnzGRY8WO1YQoh2kBLuwdbvOgtAdJgv\nWq0sUeis7Iqd3QX72JizDYvdwpCgm5gfN1Nmv0I4ASnhHuxsYS0Aj82UI2GdVXlTBSvS15JdfRYv\ngydLTHMZFnyzrAsthJOQEu6hahvM2BUFo0FLgK+72nFEB7Mrdr6+sJ+NZz7GbLdwc9Ag5sfNxNfo\no3Y0IUQHkhLugRRF4cm3Wi/O0VsK2OmUN1WyIj2Z7OqzeOo9WBg/h+EhQ2T2K4QTkhLugY5kljm2\nn0q6WcUkoiPZFTt7LhxgQ87HmG1mBgcmMj9uFn5uMvsVwllJCfdAKz7PAuD2EZEE+smC7M6goqmK\nlRlryaw6g6fegwUJ8xkRMlRmv0I4OSnhHub4mXJqG1pPS7p3bD91w4h2UxSFPYUH2HBmKy02M4N6\nm1gQP4tebn5qRxNCdAEp4R7m80PnAZg2PFJWSurhKpurWJm+joyqbDz07iw1zWNk6DCZ/QrhQqSE\ne5C6RjPpeVUAzJs8QOU04kYpisLeooOkZG+l2dZCYu94FsbPltmvEC5ISrgHeWdDqmNbLs7RM1U1\nV7MyYx3plVm469xZHD+X0WHDZfYrhIuSEu5BFEUB4L9/PELlJOJ6KYrCvqLDrM/eQrOtmYSAOBbG\nz8bfvZfa0YQQKpIS7iG2H8gnq6AGgPAgWaquJ6luqWFlxjrSKjJx17mxKH4OY8JGyOxXCCEl3BOU\nVDWS/NUZoLWAdVqtyonEtVAUhQPFR1iXvZkmazPx/gNZZJpDgLu/2tGEEN2ElHAP8Ou/73ds//6h\nUSomEdequqWGVRnrSa3IwE1nZEHcLMb2GSWzXyFEG9dUwq+88gonTpxAo9GwbNkyBg++ePm0N954\ng+PHj7N8+fIOD+nK9qcVO7bf+eUEFZOIa6EoCgeLj7I2ezNN1ibi/AewKH4uvT1k9iuEuNhVS/jg\nwYPk5eWxZs0acnJyWLZsGWvWrGnzmDNnznDo0CEMBjlvtSMpisI/NqcBMDoxBHejfHDRnVU11fD3\nU//iVHk6Rp2R+XEzGddntMx+hRCXddV39X379jF16lQAYmJiqKmpob6+Hm/v79cyfe2113jqqad4\n++23Oy+pi7Ha7Dzzzl7H/qJpsSqmEVeiKAqHSo6x7sxmGsyNxPaKYZFpLoEeAWpHE0J0c1ct4fLy\nchITEx37AQEBlJWVOUo4JSWFkSNHEh4efk0v6O/viV6vu8G4lxYU5HwXuH/k1R2Oy1POnTKQfpGd\n/4bujOPY2aqba3n38EccunACN52Rh4bNZ9qA8Wg1cvDcjZLfw/aTMWy/rhrD6/5887tzVQGqq6tJ\nSUnh//7v/ygpKbmmn6+qarzel7yioCAfysrqOvQ5u4Oi8gYAHrzbxNibQjv9z+is49hZFEXhSMlx\nkrM20WBtZGCv/jwx9kdom9yp+PbvTlw/+T1sPxnD9uuMMbxcqV+1hIODgykvL3fsl5aWEhQUBMD+\n/fuprKxk0aJFmM1m8vPzeeWVV1i2bFkHxXZNqbkVAPh6GRk3OEzlNOLf1ZnrWZ2ZwvGyVIxaA3Nj\n72NC+BhCvP0oa5I3PyHEtbtqCY8dO5a33nqL+fPnc/r0aYKDgx0fRd95553ceeedABQUFPDrX/9a\nCrgDrPisdanCIQMCVU4i/t2RkhMkZ22k3tJAjF80S0xJBHn2VjuWEKKHumoJDxs2jMTERObPn49G\no+HFF18kJSUFHx8fpk2b1hUZXYqiKJRWNQFyMFZ3UmeuZ03WRo6VnsSgNTBn4L1MjLhVvvsVQrTL\nNX0n/Mwzz7TZj4+Pv+gxERERco5wB3ht5VHHtkEvb/DdwbHSU6zOTKHe0kB/v34sMc0l2DNI7VhC\nCCcgJ552I1v3niP72+tDz5kUo3IaUW9uIDlrI0dKT2DQ6pk9YDqTIsfJ7FcI0WGkhLuRlN1nARg/\nOIy7R/dVOY1rO16WyuqMFOos9UT79mWJaS4hXsFqxxJCOBkp4W4iI6/Ksf3ju00qJnFt9ZYG1mZt\n4nDJcfRaPTMH3MPkSDnvVwjROaSEu4k/rjoGwIh4mW2p5UTZaVZlrqfOXE8/3yiWmJIIldmvEKIT\nSQl3A58ezHds/+iuiw96E52rwdLI2qzNHCo5il6r5/6Yu5kSNUFmv0KITiclrDKL1caaL1vXCh4Q\n4YeHm/yVdKVT5Wl8lLGeWnMdfX0iWZKQRJhXiNqxhBAuQt7xVZb8ZY5j+9mFQ1VM4loaLY2sy97C\ngeIj6DU67ut/F1OiJqDTdux1zYUQ4kqkhFX2xdECAJ6ZPwSdVj7+7Aqp5el8lLGeGnMtUT7hLDHN\no493qNqxhBAuSEpYRc+8841jO6GfLHvX2RotTaw/s4X9RYfRaXTM6H8H06ImyexXCKEaKWGV2Ox2\nKmtbADkYqyucrsjko4x1VLfUEOkTzhJTEuHesjiGEEJdUsIq+e5grD6BXky4uY/KaZxXk7WJlOyt\n7C06hFajZXr07dze9zaZ/QohugUpYRVU1bWw43Drd8EjTXIeamdJr8hiRcZaqltqiPDuwxJTEhE+\n8g8eIUT3ISWsguPZZY7tGbf2Uy+Ik2qyNrPhzFa+KTyIVqPl7n5TuaPfZPRa+XUXQnQv8q7UxRRF\nYfm36wUvuSMOjUajciLnklGZzYr0tVS1VBPuHcYS0zwiZfYrhOimpIS72Iavcx3bYxLlohAdpdna\nzIYzH7On8ABajZa7+k3hzn5TZPYrhOjW5B2qC1msNrbuPQfAbcPCcTfK8HeEzMozrMhYS2VzFX28\nQlliSiLKN0LtWEIIcVXSAl3o9LnvV0paPC1WxSTOodnawqacbey+sA+tRsudfSdzZ/RUDDL7FUL0\nEPJu1YX+vO4kAPeO7SffBbdTdlUOy9PXUtFcSahXCEtNSfT1jVQ7lhBCXBcp4S7yt02pjm05L/jG\ntdjMbMrZxq6CvWjQcHvf27g7eprMfoUQPZK8c3WBoooGDqaXAq0HYwX4uqucqGfKrjrLivRkypsr\nCfEMZmlCEv18o9SOJYQQN0xKuAu8vvq4Y/snMxJVTNIzmW1mNudsZ2dB67W2p0VN4p7oaRh0BpWT\nCSFE+0gJd7LTuZVU1bVeI/qdX05QOU3Pc6Y6lxXpyZQ1VRDiGcQSUxLRfn3VjiWEEB1CSrgTpezO\nYevePABCAzzllKTrYLaZ2XL2U746vweAKVETmB59B0aZ/QohnIi0QifZcfi8o4ABfvvgSBXT9Cxn\na86xPC2Z0qZygj0CWZKQRH+/fmrHEkKIDicl3ElW7cgGWldJeunhUSqn6RnMNgtbcz/ly/yvAZgc\nOZ4Z/e/AqDOqnEwIITqHlHAnOJlTgfLtthTwtcmtyWN5ejIljWUEefRmsSmJAb2i1Y4lhBCdSkq4\ngzU0W/jT2hMADI+XZQqvxmKz8HHu5+zI3wXAbRHjuDfmTpn9CiFcgpRwBzuQVuLY/sn0BBWTdH/n\navNZnpZMcWMpge4BLDYlMdC/v9qxhBCiy0gJd7CUXWcBeHz2TRj0WpXTdE8Wu5VtuZ/zed5OFBQm\nRozlvpi7cJPZrxDCxUgJd6CquhYaW6wARAZ7q5yme8qrPc/y9GSKGkro7e7PYlMSsf4xascSQghV\nSAl3oPc+TgPAzagj0M9D5TTdi8VuZXvuDj7L34ldsTMhfAz3xdyNu95N7WhCCKEaKeEOlPbtUoW/\nXjRM5STdS35dAcvTkilsKCbA3Z/F8XOJCxigdiwhhFCdlHAHOZ5dDoAGiArxUTdMN2G1W9l+7gs+\nzfsKu2JnXPhoZsbcjbteFrAQQgiQEu4wf17fulbwTTG9VU7SPZyvK2R5+hou1Bfh79aLxaa5xAcM\nVDuWEEJ0K1LCHWD3iULH9sMuflqSzW5je96XbD/3BXbFztg+I5k5YDoeMvsVQoiLSAm3U32ThQ8+\nyQBgcExvvD1cd4GBgrpClqcnU1BfiL9bLxbFz8HUO1btWEII0W1JCbdDbYOZJ9/a49h/Ys5gFdOo\nx2a38VneV2w7twO7YufWsBHMGjgdD70cIS6EEFciJdwOPyzgNx4bi1ajUTGNOi7UF7E8PZnzdRfo\n5ebHwvjZJPaOVzuWEEL0CFLCN2j9rhzH9v/+fCx+3q51vqvNbuPz/J1sy92BTbExOmw4swfMwNMg\ns18hhLhWUsI3YOfxC3y8r3Wt4DtGRrpcARfWF7M8PZn8ugL8jL4sjJ/NoECT2rGEEKLHkRK+TgVl\n9Xy4PRMAXy8j8ya7zmk3NruNHfm72Jb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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "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": { + "base_uri": "https://localhost:8080/", + "height": 653 + }, + "outputId": "b86b23ed-be22-4109-9c24-5e8ca1638059" + }, + "cell_type": "code", + "source": [ + "# TUNE THE SETTINGS BELOW TO IMPROVE AUC\n", + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.000004,\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": 21, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss (on training data):\n", + " period 00 : 0.50\n", + " period 01 : 0.48\n", + " period 02 : 0.47\n", + " period 03 : 0.47\n", + " period 04 : 0.47\n", + " period 05 : 0.47\n", + " period 06 : 0.47\n", + " period 07 : 0.47\n", + " period 08 : 0.47\n", + " period 09 : 0.47\n", + "Model training finished.\n", + "AUC on the validation set: 0.81\n", + "Accuracy on the validation set: 0.80\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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Id6ACpwOZvUKI9I0gu/QQlXVVbWrD1cXE+NhwKmvq2ZVdaOMIRUREugYVOB1s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\nI0SMsWjNXDtqNteOmk2/p59j7SeDQ1i7G/ayu2EvGkXNxKT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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "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 diff --git a/multi_class_classification_of_handwritten_digits.ipynb b/multi_class_classification_of_handwritten_digits.ipynb new file mode 100644 index 0000000..35ecbd8 --- /dev/null +++ b/multi_class_classification_of_handwritten_digits.ipynb @@ -0,0 +1,2625 @@ +{ + "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": 224 + }, + "outputId": "6727be2d-8928-45d7-c184-feab0321c55a" + }, + "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": 1, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 72\n", + "6406 0\n", + "1550 0\n", + "3887 0\n", + "9921 0\n", + "6073 0\n", + "... ..\n", + "7777 0\n", + "9709 0\n", + "3517 0\n", + "1226 0\n", + "5314 0\n", + "\n", + "[10000 rows x 1 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 2 + } + ] + }, + { + "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": 313 + }, + "outputId": "a81b1223-3014-4c95-8f33-28d6232c7ce1" + }, + "cell_type": "code", + "source": [ + "training_targets, training_examples = parse_labels_and_features(mnist_dataframe[:7500])\n", + "training_examples.describe()" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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0.0 0.0 \n", + "\n", + " 784 \n", + "count 2500.0 \n", + "mean 0.0 \n", + "std 0.0 \n", + "min 0.0 \n", + "25% 0.0 \n", + "50% 0.0 \n", + "75% 0.0 \n", + "max 0.0 \n", + "\n", + "[8 rows x 784 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "metadata": { + "id": "wrnAI1v6u8Uh", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Show a random example and its corresponding label." + ] + }, + { + "metadata": { + "id": "s-euVJVtu8Ui", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 360 + }, + "outputId": "54296759-b18e-4fce-a343-5e9acbea10dd" + }, + "cell_type": "code", + "source": [ + "rand_example = np.random.choice(training_examples.index)\n", + "_, ax = plt.subplots()\n", + "ax.matshow(training_examples.loc[rand_example].values.reshape(28, 28))\n", + "ax.set_title(\"Label: %i\" % training_targets.loc[rand_example])\n", + "ax.grid(False)" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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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": 1088 + }, + "outputId": "2518bb84-d9fb-4cc1-cb4e-388b06339b53" + }, + "cell_type": "code", + "source": [ + "classifier = train_linear_classification_model(\n", + " learning_rate=0.02,\n", + " steps=500,\n", + " batch_size=100,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 11, + "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 : 4.84\n", + " period 01 : 4.02\n", + " period 02 : 3.65\n", + " period 03 : 3.56\n", + " period 04 : 3.72\n", + " period 05 : 3.38\n", + " period 06 : 3.58\n", + " period 07 : 3.55\n", + " period 08 : 3.48\n", + " period 09 : 3.33\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.90\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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ZSJ7NR3LdeqtS13IgL/GK9lWrFHT6SwdCHewzgFt73djsflFRY1izZh233XY7P/30K1FR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\nmy1mtpz6gMN1xxgVnMiaG+zFbrFYON54ko/OfkZ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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": "c78IMMfHgRuc", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 970 + }, + "outputId": "c0282441-0a63-44ac-81ba-4197949b7849" + }, + "cell_type": "code", + "source": [ + "classifier = train_nn_classification_model(\n", + " learning_rate=0.03,\n", + " steps=100,\n", + " batch_size=100,\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": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 10.51\n", + " period 01 : 10.54\n", + " period 02 : 6.89\n", + " period 03 : 4.86\n", + " period 04 : 5.64\n", + " period 05 : 4.77\n", + " period 06 : 4.60\n", + " period 07 : 3.74\n", + " period 08 : 3.76\n", + " period 09 : 3.36\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.90\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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E7rrrLv70pz/Zc/OiHVSKwqB4PbXVGrp5dudk5RmKjSWOLksIIQSgsefGvvji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\n7Vo8rb48ceOvWX96MzvyvseKFW1dGFU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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "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": { + "base_uri": "https://localhost:8080/", + "height": 313 + }, + "outputId": "8e2a01b4-1830-41c2-d740-64206c4ea6c6" + }, + "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": 15, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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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": { + "base_uri": "https://localhost:8080/", + "height": 1171 + }, + "outputId": "bd6a44d2-a4ec-4459-9431-a0400186fbcc" + }, + "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": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "['dnn/hiddenlayer_0/bias', 'dnn/hiddenlayer_0/bias/t_0/Adagrad', 'dnn/hiddenlayer_0/kernel', 'dnn/hiddenlayer_0/kernel/t_0/Adagrad', 'dnn/hiddenlayer_1/bias', 'dnn/hiddenlayer_1/bias/t_0/Adagrad', 'dnn/hiddenlayer_1/kernel', 'dnn/hiddenlayer_1/kernel/t_0/Adagrad', 'dnn/logits/bias', 'dnn/logits/bias/t_0/Adagrad', 'dnn/logits/kernel', 'dnn/logits/kernel/t_0/Adagrad', 'global_step']\n", + "weights0 shape: (784, 100)\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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Hb6E3WcYzzKOGbfmcNSG2qlbkO5mYm7I8ZnnMZej/M3ZtQs+xiQt57akhlp/Z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IIgCEINIh/OCIIgCIIgCIIgCIIg1CDy4YwgCIIgCIIgCIIgCEINIh/OCIIgCIIgCIIgCIIg1CDy\n4YwgCIIgCIIgCIIgCEINIh/OCIIgCIIgCIIgCIIg1CDy4YwgCIIgCIIgCIIgCEINIh/OCIIgCIIg\nCIIgCIIg1CDy4YwgCIIgCIIgCIIgCEINIh/OCIIgCIIgCIIgCIIg1CDy4YwgCIIgCIIgCIIgCEIN\nIh/OCIIgCIIgCIIgCIIg1CAW//bg+e8+07F3z2D2WN7dJzquLK7QsV29Oiwv+0qqjr26Ber4aVU1\ny6sqr8JzYtJ07NjQleX99e0BHXcf0k7HHm39dFyWU8yeY+/lreOC5GSSV8Lytn+/X8f+bm74PfMG\nsLyKIjyvPL9Ux5b2ViyvKDlfx0+rnuo46eADltfkjSgde3r2V6Zm6/TpOq5ja8seCxkXruPK4nId\nWzpYs7w9i/fq2M4ajxWU8PewZetQHecl5urYJ7o+y8s6naTj+s830/GF707pOGxQM/ac1MNxOk7M\nytJxm1GRLM8hwBmv4SHyzvx+juWl5+XpeMyHz+k4ZV8sy3tagbF67xFet/G9jHytg47rNRimTMn3\nEybo2NaaX5tG4ZhXJ47H6HjcV+NZXmZMgo5dmnnpuKKwjOXFrDyr48eZmTpu27U5yyuOw/X17BWE\n1/DTSR33WzCQPac0G3OzsgTrRuo+Pie8+2C9oesLfY5SSp3YiNcaPba9jktSC1jeU0w/9fDSIx1n\nFxayvAEfYq77BAxRpiY7G2Mw6/599phtXQcdpxx+qOOr5+6yvMGfYGw9/uumjksS+d/s2t5Xx++9\nveIfX9Oknj11HPEmxrCtA8bVqUXr2HNiU7Guj/p8pI5jlp1keRVVWNcDOmOMFD/OZ3nuUfV0bO1k\no+OqskqWZ+OK/aBWLWxf7z03l+Ut27VSxw4OjZQp+WHiRB37uvL9KaAbxu3B3/BeNPXzY3m27nY6\ndm7hqeOrW66wvM7vdtdxzs10HTsEurA8M0tzHZtb430pSsEaVxCXw55j54e9upZZLR2fWMOvYfN2\nWNMtyB5Xy4J/t+PaHH9H2jHMsbhrj1le+xlddPzTO+t1PG7hSJZnZoHXZOr1VCml7p1cq+MnJ/lr\nDCR7Ui1z/J3VFVUs78FPV/GccVgf6V6qlFK1XXG98+5jTS1J5+uPjae9jl3DMGbyHmG+OQXWY895\nuOm8jg8cvajj0bMGs7ytX+zSMd1DOg/m+2f2FZy/YuLj8dqs+PkmehzWij9W7NGxlQU/Vg57B2ea\nwJZjlSk58eGHOk7NzWWP1a2D8W0fgNjO34nllefiDFcYm63juMQ09U90ew9r5qXlp9hjDQc10bFL\nqI+Ojy78S8etJrRlz8m5/ve/y8bbgf274B7OM05kvhnHb50wnF+ryfnFvQ0fO3R9TTuC81VtMg6V\nUsqxAX6ef6Phf/ta/wsXVy/V8fEjfA2c/MM8Hf8562sdh7VrwPLMa2PcOTbE6/394+0sr2V9nEXN\nzDC3zWrVYnlPyaHBxgF7kv/wxvh/Z77+58Qm6rhuWEsd16plyfIsLPD+7nh3sY6bDeRnXo/whjou\nTMMasH/ZAZbXbQrW1Lw7uDcrTeZngubTRunY3j5EmRI6F5vP5GPk4V+HdezfJ0LHDzaeYXm3ruHc\n06Ae7tus3flZOzcBe1nkLOwN5eUZLC/1FMZ0RR7OuW6tfdQ/cXYl9r+IMa11nH0xheVdvnpPx0F1\n6yIeHMbyTq09reOosZj3VzZfZnlNeuJ5lUXYP/JuZ7I8v6E4zwSGj/mHv+L/zk1ydnpyLok95hkd\noGN6j2jtYsPyNs3bpuPCUqyvXZs0YXluHbEelWfjXjL+fDzLo/d79OcNm9FPx/Q+QSmlEvbjfE33\nu9o+fG0LGobPEcpLMK5ybqezPHpN6oR66NihLl9Ti3MwTiqKMOac6gWyvIoKnM08PHoqI1I5IwiC\nIAiCIAiCIAiCUIP8a+XM43v4BMirK//UJ+4Yvum2Jd+oWLvxTzhdW+HTz9h1+Fbfs4M/y9v+00Ed\n9+mPT7Ie7bzD8kbMx7fZ9NO5LuRTsxxS1aOUUsnZx3TcdnQbHeff459Ivvj5aB0nbLutY/oNrVJK\nXf4Wn4TWi8CnZj7dGrK8dd9u1vFLy8fpeNeqQyzP6jv8fM+PTF85QytQbA3fxNBvTLOv4pP5xzeT\nWd7gT4bq+OLSYzruNqc3y8u+hW+A6DdUuTH8U0iPrgE6/m3uH/h/R0cdG9+nzl3xTUSrzhg/xSn8\nW/hM8mmvfRCqaFp25Z/aurbE2EzejU9ZrT3sWF7sRXz63vcjVIJknOPfVqUdj9dxPf6lzn+m0yjM\niSs7Ythjl89hjrRtgm+508/yapQjW1G1MWHFLPz/kjUsj35KPW75TOTN/5nlBbZHJUR+LJ4TSr7Z\nvbfyIntOfAa+2WjeFd8U1B/LvzEyt0IlQGkWqm0e7+XVJq4OGM+3d6GKpNnIliwveQ+qofJJtVfL\nyFCWl08qrXwClMkpLcI8yDgazx6zcMSn+/bBGLdO1/maemcFvm3KLsA3Y1aW/Nu5Jq3wTf4b/fAN\nA/1GUCmlMsnPsLHHt/UlRXh9xm/NR3yMSrPiVMy/DnMnsrxbv+Jby8CufXS89rX5LC+iEt/upsZj\njARH84n0lHzZ9/m0H3T8yvA+LO/BLnyzGD7atJUzlaQayKuZN3usJAnvBf2G9m4K/9atXSTG56Gf\nj+t48Ie80qyiAN+8WNFvp/iXvOrSt/j2/mE6xpgbmR9d3+rOnpNyAOtDdiKvqqEkXMe3wY37Yw2l\n1TrGf9uSqpzIyCiWd2H5CR13CCXzzzAuS7NIVaaJ11OllDq2Ft+QupN9RymlCr9FRV79/hg/7mH8\nhfg9h8csyDf3xoqYcvKt7ZPjqGAMHN+C5eXF4kxy5Qvsf5XVmB9ZBfzb5opKVD+89uMMHVdXG6p3\nyBx+/qupOi4t4lUbeddJVTQZ692ndmV5TgFYKzo2wvvg1pZ/K530J75hDuTL8n8mZAq+2a5L1m6l\nlKoqJWdCUg2UZ6ggCxmP6mGfaOxJ5ht4pW1uMipzcm5hjaob6M7ySsm1L62Lb0fDx7TSMa3WUUqp\n5BvkrB2KihhaBayUUmVkTuSQqvT6Y5qyvPjfb+jYLhDnsNQjD1meTw+MZ7q+ZF9KZXlWzvybcVMT\nfwNrTJ/nO7PHigqx53uSaijHUP6+39qIKjaXW5hH9IyglFIW5linPKMwhs2s+XpWnoNrFNSnh45j\n/0QVefAgfn6wsMU4u7B4g44j3hnE8g7Pw97VeRZ+9skl/MzbPQJVOo7eeK09Xu/G8mxIJeaOr/6+\nyl0ppRwO4ec3G2zaypkEUmXtfIJXk3mQ9zn5JMamZR3++ui1cm2DdaROiBvLS1+Be7DjC/E+l1Xw\n6onmwzC3HYNQbXpxFdZQTzc+x+p5Ylw5BuA5R1YfZ3lDFuJeNGHrLR27N+Jz0d0Rf69Vndo6btSJ\n3y96RWENvfPtMR3X7czvle28+V5lajzb4nVtXrOfPTZhONbHOvUCdPz40CWWR+fYoBHROq7Xnb83\n9Cz6JAZr083ERJY3di7Om44+qAhPPIw5f/s4rzCvIj+70zhsPJmX+L3tmte/03G/cahA+2PVPvVP\nRIfhfbCvz+8D7fyxRsUfwH1HRh4fP/dJ9fmiHVI5IwiCIAiCIAiCIAiC8P8r5MMZQRAEQRAEQRAE\nQRCEGkQ+nBEEQRAEQRAEQRAEQahB/rXnjDnpZJ6yn/evaDcLvUby4qCdenKa66/2noQW7fm34R5g\n58N1c+FEn+/UBJ2vq8u4O0Iu6URO+2uYkR4VzaZ34n/IV9C4u4ahT0Ztd95bhLoyNHgePT4OzNvC\n8loOQ7fxsswiHa+Y/APLa98Q2r0Lnx/VMf1bleKOJs+CY+ug/+w8luv/S5+gn0c16fvQdgbX/dLO\n1Y1GQSdv7DtQ2w3v6Z7l6PsQPbQNyzv7OxwmnpuGfhGn1xGtv4cHe457W/QyyboCjfbRfbyviTXp\nvdGyGtfbvSN3THlK/l7P7uiptO2zXSzPywma7Yyz6BcQc+Amy8sg7k+tX1YmpYJo1OnvUUqp/pOI\nowtxOnNqxN+/PpOhUz4yH31mmo9vzfIKSbf6gizM+/CJ3GEi+xp+VxXp+RRzDz16oke0Y8/JPYz5\n8vAMNKYeBheJJOKYRbXwvCuFUg07QjNvS5ziyvO4pv8W0bCO+gIX59hCw9yO4mPE1FxYBt2psfdL\n+4nQnRal4RoP+GwWy7u5/ncdf/HNDh2H+vqyvLrbcP2DRkLrW/qkiOXtWLxRx8Pt8X5+MOwdHb/6\nzgj2nLJc9D4oeoxeDInZJ1heOemR8OvrC3RsdMny6oF5GuwB161Ln+9meWEDJum4+ilcBZq/PJ7l\npT/k+l5T0nMyem9QN0KluJuPcwR6R/QY1pjlUXcyS+JuQ/srKaVUWSZZn4nDYfwO3ostmPQ7q5eJ\nceBAdPY/vvsbe07nxnhNDg7oa9S6LZ8DFQXoXVKUiJ46jg24Y9Q5MrYfpGFt6D2yI8sL7AhHK9pb\nKvY33kvLtwd3iDQ1Qz6FO1TmtQT2mIM/+hAUJmEuxm0/y/KsyRni4DdwJIkawtfUfevx3tD9/vwc\n7gzYKgjzwDsKvQZi9qFvgbGHRloOeqgkHoEG3+hK1L4z+npVVuI6bpi1meWNWYL35dHH2PftffnP\n+/2t1TruOAB/b/wx3tckqC/vy2FKqsrRb6foMd8XaQ8jG284dHj35uOK7hWWdliXEh/yXjwBYZhX\nF/6E00o3Qy+nWsT1J2kX+u1Qp7PEO7wHVfgEOGbZ1iV99z74k+UNWITeC7eIS1T8JsNZJANjolkP\n4ua4ijux+fXFvlBFnBDLDb07Sgx9/UwNHdO1LPl3xkl7yHtIellcWXeB5TXqgZ4dwd3huuhyYg/L\n82+PvhLHFvyoY2cXPq9CJyHv/jb8jPukB6El6SGilFK+HTEP2s/FfcjNdRtYnncI9oZ7P+D8amnO\nz9MXl8Dhq/0HOLdc/Pl3lhfSDutGh/akx2Q9fp9lvJ8yJQ2bBejYO4r3d7z1NXqK0v5Zwc9zB9CM\nG9hPPVthfzK6XYUMQM8Pzxa4Hyst5fef175Cb5pGLyHPvxH62RQk8nWjpBz7HXUyHb1sOsurqCB9\nFl/E2S3j1nWW12oGxkHmFfQ7uXLoBsujbkV2gTjLFjzIZnmZxOm27jzT9yh9uBnzynhGvf/7NR0X\nlyEvuCvvX1RNnldB1tesO3yfvbcD65Zx7FNiye8NGoYxTM8313/lP/ul2XAMe0h6cNUfwcdmy3u4\nH6drdP8BHVje8UNY811aYv6e232V5VlfwHmu42Rce/9Mfu5+8C0/2xqRyhlBEARBEARBEARBEIQa\nRD6cEQRBEARBEARBEARBqEFqPTXWLRHunVyLfxjSStJQ/unSDCU+Rcm8RMyaWPClH4v/xxfi2R1l\neRdXw+aM2oIqpVSPXij/LM9EGdjhCyiJdrbjcqWm/igPPnEbFtkTl73A8p4Qa2R3UtqdeZlbb8Xs\nR4lUqyGw6Lqxi5epBTaBVMPSCeWPj87Gsbzrj/F7523dqkzNuqmwzWzRh1uZOTeG9OFpNa5x0l/3\nWJ5jKKzs9m9AaWy/SdzSzzHQVce05Djt2COWV5GPcsFLV/G7ugyHDGbPei5NcLFHaXJkX0irrN34\n9c48hfczYDT+3o3vcwlLgDss88IGoxT04R5uyRb+OqRg1NpW1eJ+toXxKCUO6ztZmZLYs7/q2MqR\n2w/e/AXldnFkvjTw9GR5EW/DstHKCtc94Si3PfRqD2nLg/UouQ0ey0v104hVd922ATo2N0d58NnF\nO+hTVOu3o3W8fTbG+vPL57C8rERSKkjWni2L+M8bNB2SuAqDlIniGIxxmX0D5erOjeuyvNp1UCbp\n5hatTE1lJUoby8u59ev3r8zWca8xKIcsuJfJ8vyHETtDV8yDBSNeY3lz1n+sY2trXO+RbfuyvOfa\nYc4F+8MauvXMaTrOSDzKnuPmg5LPt/uP0fH4MdzS2rUVft79DShNjXi7N8t7uBG2teZ2KC3168dt\nsG9/w22E/4d9QB32b5/eKLP18hloTP9PxF1Zr+OUfVzCUVaE9YHaxjduFMDyKosgG/Aj1/NpJS87\nzzHtKKMAACAASURBVLyAEmaXcC8d//zRHyyvsBRj38cFY7jXaEiKLv7Fy29vkH3Hg1jUGm3TI1tA\nlnLgNNaa58b3YHkXd+HndxiHNbOqnP9Nl7bgZ0QMgdVp+nFellyH7Dktn+cl5aYgLgZSg9qufA9J\nJLKxfHKm8ekSyPJSj8frOGAoyvAri7iNtY0H9q4iIpOqrqhmedS+c+Tr/fCcBDzHziBVsCT7wcHv\nj+g4ql8Ey/OJRjn3oQVYe8OHc39rc2IJnnsD+4lXtyCWl3oYY//AfpS4tw7ieVSe1qjrRGVKbu2H\nLOXKDi6La9Ebe79zGNa/lIN8ztK5mJ0GiWYdO1uWF/Iqzp53VmC9CjbYoe/+BBIYOhfta+MMaG7O\nvxe1C4aMrpScra3d+WuoQ+yjqbSxyetcPnznO8jGG0/DYxeXHmN5Ld/AOk7PNnQMKKXUE7IOhY96\nQ5maS2uW6tjCgZ9viuMx9s1t8bqM701JcsHfPmb8efQs4FwPc3bf3FUsr5xY1LcaijlSGI8x0nj0\nc+w5VVXY3/MzYQGevJfLF5tOGK3j66uxn+SncvkYfQ0+EbifsPXiEqyipL+XnT2t4uuLcxPMg8CW\nY//2Of9XcnOxrp9atJE9Rse+YwjmhJ0fl0paEwl72lHcMzQez/fw8nK0t7j4OazDm7/O2zYcW4zW\nCqm5uG4vfv2Kjkty+PnKwR1rvLk59oWYL7mUzG84xg69X7Kw4xIseyL/9W2LuWgcb+2nR+u4tiOe\nU11dwvKqyrFeefma9myjlFIZGXjPfpj6I3usYyNi952ENSEymt9XUrmRLbH+/sLw816aCov5O4ew\nnhnPIFVECkcfi3p/go5z0/j994VvcV8TPXeYjmN/49JOKzesFWZEUplznX/2YE3u4d074POBwkc5\nLM++PtbyO1sgcYt4rT3LO/QZbMpfXLlSGZHKGUEQBEEQBEEQBEEQhBpEPpwRBEEQBEEQBEEQBEGo\nQf5V1nRj5/c6LjM4fNy+hNLQ3guG6LgkM5flZV9D9223VuiQXZxSoP4JR1IStWYGd5jo0x9lYT49\nG6i/49PxK9i/R/WCRCA/A7/3ESk7V0qpXtPRcdupHjo4737/F5bX9+NxOk48Ajeq+LNcuuPujb9j\n70nIQ95Y9TrL2zlnk44nruKlbqYgIwOl0uWF3A2Eyo0cQ1DuaWXoQn/jF/ydoc9BAlQniEtnrnwB\nx4qwiZDBUBcTpZQqIePJknTItvVECVz8Fu5AQDtpZz9AKeK641z+9MZ4jMcLZ27pOMDg/hQyCHKC\nDV/u1LGVBS/pHf4WOqL/tQIlf23CeIdyL+ICERg+RpmSQ3Mg+3Ftxt/zB2cxF3t+hI7+v735Bctr\nRNx8LIgTm89A/ndknoeML/sxOsU7utizPJdWkFm4NMFrurgU16PL/FfZc4qKUP6Zex9lg84NuUPM\nPuKQ1npEKx2bW/Nr4xKCEtS0S5AsOoW4s7zTy7gs53806s5lM6e2ozz/1Z9//tvn/BfmDoZj3YT5\n3AHJtzHkRp+ORvn/rPXfsrylL0BuNPWHt3Qct4M7ybgRSRF1VbOw5WW3VM5Yko6SeipHyTjM1zZL\nF6wPl6/gmg4nTi9KKRW/HaWms7+EQ9iXi6axvK3rsW706w5XMOcWfKy7N8ac/XnaVzoePJNLtaaP\n/0zHO69dU6Yk7irkMMZ17doGlHZT15Y6trwE/yZxD+v7KtxeKou5HMbaBc87swpluo2j+Jyt7YHy\na5u6mKcX1mBMBIR4s+fkGSTI/yNwaBj7d8bReB3HJ2A/tzSsk8zNhqy7Dby8WJ5DXZTk+z+H31UQ\nz8uDT/0Kp41nsS/umDlTx60NDo/5DyE5LCVzwtqNX0fqZOXaHH+ntQMv11/1GsqWx38JOcHlr3iJ\ntVcT/Axnss5TJ0kqdVNKqYBBcDyhriZ3vj/G8sxtcL2oFOrxbS7b7vwBSs0tLXEmyHpocBfJgwzG\n0gF7c1VpJcsrIw5kTQdNVabkyrpleD05XNZq5UykFA0hkbv++xWW15K4ECZuRWl9TgF3lAsdDFkY\nff9qGeTN1HHNgkg0c65i7pSm8/P0UyJvSyPyC+P+ZO2K8VcYh72ZyuaV4nL1UuL4lh3D3eVsvDEX\n7YjbobnBhfPxNrwvHefOV6Zm97vv6jhiOnd3qyrDeKokjlIOntzh8foyyMk8u+H8XsuMXx8q2bKv\nh3laYZAiUmJWYh1tPwdSpgd/8PlLHUXjNuP86t6Kr730OtL99/o2Ls0L64cxV68dJDspV7hTlZkF\n1ge6/td24nLf4gyMGf8wvlf/V85+uUjH9gYnP8+2OBvn3MN6U5LK7wNdyBpKXfJyrnLntNREyJqo\n9CtiNJfeV5MzDHWVrO2J94hKU5VSqvUsSM4e7cO5MaAXd+95uBPnXO+uOIfm3n3C8qiLXPpdnHkr\nKvk62W4WXCB3z8X9iLFNRwvi7Fa/2Whlamb2gTS9tkFe1LslZMiOYVhTs2P49bnyCOdFuj4GGu7B\nGnRvqP4OS0PrhnRyBvHohDYlfpGQx1/9Zg19imowAQ7BNjYBOi4p4fJpc3OMhcpKjJF7359jeU3e\nwH3gzeVw9HVpy+d2Ndn/3NvgvsbY8uXAikM6/rt7DamcEQRBEARBEARBEARBqEHkwxlBEARBEARB\nEARBEIQaRD6cEQRBEARBEARBEARBqEEs/u3B8lxoeJ2acMvZvoNg07hjNjT4Xad1ZXm7tkKT2acY\n2l5ql6qUUlVE75lHNHvUQksppX75DbZpszpDV1qcBu2iUctXWQCdaiX5eTlFXPebcQpaNOcx6GfT\nYWpnlndyEWxMm46FxV6wA/+bSlKgWQ7xhi6tqopro+1tbNSzZNcH2/A6/HzYYzfj8Dc/PQntq/F9\nH7n0RfIvPJZ89A7LS8hELxiv69Ah2vpw+0/aA6M0A9fBwQ+axJJM3h/HlmgXL8fBjjy/mOc9rSR/\nB2mp5FKX62///BZWZlENoX0MHsFt4Wy98Nr7TUZ/iCcnH/9jnqlpPgO/N/U0t/qmFuMJRzHfRi59\nieUd/gh9XJoQLXPd0EiWl7Ib89nJC+9Zk4ncNtLGBmMpM/OYjtvPGa7jnHSuoT77Ne8P9D+8Pfl7\nSVXiycT6NOTFcJZXnEP61jSiOns+t9tMgV64ljl+euyv/PWFh/NeHqbm+Rno5+Dk588eWzUJdsFd\nwtCL4/j8r1je1B/QK2Nqb9iaWlnyXjJTstGHxT4I9n5ffcltLiMCoZde8gv6a+07hD4fjV7vwp5j\naQlNOe0Ls87QI6yZHzS36w6iD0wtg5Xs3EFrdXx1zQ86NvZgObcYa2+vF9AnZNvnu1jejhhuG21K\n4v5AL4GCEm5z6RsCzbxDMN6jXT8fYXm2RMtNbZbv/8z7YQQMRc+J8AGw7K02WG7/9RN69tBeMN7O\nuO4xV7mda5cJvLfD/8i+ksL+ffN+vI67voK90M6Xr6cJW9BnpscrGC+0N4ZSSp05hDlXmYfrG5/O\ne8A5Gvr0mJqo92BVnXiA91OJPY81h1p3Xk/gevUR06FDz7yEXjDmNtyGc9J36LF0cclfOvZtzXtt\neUejN8O+eeg7kFmA8w21pVVKqQcx8Tr2D8Bc9OzObb9pzxlnf9hde6TxtbckG/12MlPQO+BpJT8T\neLXCGfDgvLU69q3Pz4rePYPVs8KW9Emx9uC9GbJO43okXUOfC0fDeSvmJ9hOZ+TDktja0FMp/z7e\nl9greF+CmvJrGPgc9tOsW8jz6obrYWHLz4pl5KydvhK9pe4fucfywsehp4ZTU7zPyTvuszw6N/Pu\n4Uxm7I/jFo5zKe0+mX6aj/OAkU3Us6TLfPRhfLD/L/ZYUE/0wDi1EFa8oeNYmnKJwNqbRfrmWRjO\n5fduxOv4BpnPc9bPZ3lXv8D8az8HVry7SA/K/oueZ8+58BmeQ8/Cj/bwta3DWGJvvhn9HFv0MZw9\nPdETyNY2QMcujRNZXkkW6YtVB/Ogdm1fllft9s99df4rN25jrJdf4+NxZFucr2m/UY92fO48Oc//\nrv9BewgppdSf75/RcagPzqE7yZleKaXGfIq+fo6B2I8zr2KPa/ACP1Oam2N9COzTTccPdh5keRWk\nx5V9Hfx9Zk35ulFJ7m2vxcfruGt7/nutrTF++388VMc5d3ifqANfo1fJ5DWm7zkzaQ56EdF+a0op\n5dkR14H223tiODO8sAzzImk/7ldojyullEo6hH3Wk/RrcgxyZXnUjvvwEvT9rEWsr0Nf7saeU5SF\nNeDMYvR8M/YUbTYFn0sUJKDvXcNX27K8mytwxnRsip6WGYb7wHxyJly/Cn2wjPfUA9vx+y4jUjkj\nCIIgCIIgCIIgCIJQg8iHM4IgCIIgCIIgCIIgCDXIv8qafHuhxN/cnJcjlWSjTK/tEFjdVhTysrnR\n76KM38wKUpa0Q3Esz6snymw/fQNl7VMmDmJ5GzeipCtpN0rnioglGy3TV0op13Yoe/Mj1nluh7nd\n27ETKIW/FYNyKz83N5YXNhLl5R9Px2t1tedWw8P6oQQ8pDHK91ZO/YnljZs7TD1L2pMSysukhFIp\nbnMam5r6t/+vlFJXvkApmX9vjIvKogqWV59YpZWQ8sWg3r1Z3q1ft+rYPgDXpDgdJfANJ0aw5+QR\n++xRHTEuhpVwGVvCLpTRdeyFn3Fk93mW128cSu/vHYA8a838TSzPnNhOU0vcAHdu1+yXRyQO3AH4\nP3NtGcZ9k9fbscfoNShJxnueG8dLkylODfHaa9fmVnDBEyHVc3CCrMLMjJcHFxWhjNXeHmWd93eg\nlK9ue1622n3+Czp+8AdsCn37cUs9ix24Hs7NUL5t58bLdDNv41pXk7L74kRuW2cfhLmefweySbu6\nfM6aWXMLUVNj54vyTGdnfh1f+RHv9f2DkO/c3Mct5cvL8Pq/3Pq+jpP38lJiapHbnNgPfr37R5a3\n+PlZOt6w+CMdr16KOfrOGm6Bu3EmrP9WbINs8p1Ro1hewxcxluKJtWhtL76fWPTEdaDyibObuWVo\nuxEoBW3YFXXtgZ36sbw1kybr+GUT2zA3egXSgrIcLqk8thKyvXah2Dc6RHB7ak8icaD28I/SuRwm\n1B2/6/IveC9avcRLbqlUg5Z5W1jiZzcxvIaU/djjvtmDOfvRJ5NZXmgy1gcqwbq2/DTL82kfoGNq\nX+vbK5TltSb207b1MB/auPDS9YrCMvUsST+P+VJmKN8O69lYx8VJkLp4N+b7oj2Rj1jVgdzIwoYf\nrVLOQDZlZ4O84sR8xcH606wLXgO1/7UxrFmWjvh5KfshXXMM5KXh+XGQ5RQ+gXzAyZtfHysrrJXJ\n+37Vcdw9buGdfRnnhc7vQ3py/0e+zx75GpK7CT+MUKYklZwj3dpwybZFHdixOhLNjplB2uPqjDEY\nOgDynRPrz7A8en7188E5h9onK6XUpc8hvbexxmtwa4vX59G6AXtO2mEiCVyAde32j3tZnkcI1oPc\ndKynnj35mbcsB2cRl+Y4jCTv5tLGRxswLt061CPP4ePcxoWPJVPz4AAkA7HH+WusLMb5rgGRnFcb\nZHZWxE48JxPzKiU2h+VRGbwnkX1m3LjF8ugoyUuK13F4b7yGxMNchtph7st4rXcxD7wacwlpzPeQ\n/w79/B287nQuxy0m9zWFhVivHB352Xj77Nk6fuGb+Tre9vanLM+B7BODvuB75n/F3QF7el1ffs90\n7jPsLx0/gBRnzWtfsrwufbG/e7TFWe/ksqMsr1cL3IO5RWB/8unUmOVVlOL9i1t/DQ8Q+/K67bi8\nvCAX46CEyOMbPcetx9PuH9PxsQWrddzhfb7GXdj1p47rkXtJrx5BLO/GN5DzPUjC2tp2JJe/tAjn\na4epOfITzjC0HYdSSp3dg/HefTLun9xb8bU39RTWZSsX3DPVCeH3TK5EEvr0Kc4Fa9/k8ngq1aZn\nnfy7uCdM3M3Pv3RcJGVh77Mw52f867Nxv1fPFevc5bg/WF6PZs10XJdIaN0i+d9uS6zTXyafFTgG\n8L/96pfH1L8hlTOCIAiCIAiCIAiCIAg1iHw4IwiCIAiCIAiCIAiCUIP8q6zp/sqLOi4s5q4UhaXo\nVB0UiZLKX77dyfI86qDs97n3Bui4PI+XLBeRUqCIIJR7ZdxMY3mzf/tQx7mJKJ2ytEP5aO3tt9lz\nXJqhRPPgRyiva9g8gOUNf2+gjtcuQElTl9e4U8mpH4gjTlSUjh1ceLnx/O/X6fi9oei+/doPr7K8\nqgru3mRqqFOSs0F6VVmFjtuR3Zrr2KkxL8GiZerJf6F8zNyWD6HkbMiSmr2B92bD9MUsr2kTjBlb\nUhpOS/yLUnjJd/IRXG/qXtFucgeW59sd4+fhPrgdNPLlkhhKLnHuaurHpThU1tT+LYwFWmqulFLx\nxMXFj6t0/jPODd3+8TFzK7y+8iqU+pYZ3K76LsK4o1Kmqio+F2O+gVyhyUSUGlo7cTeM4nTM2cSt\nkCH5j0JpeMqBB+w5jceiQ70bKVvNf5DF8vyHoDz1+Kfokt/rI6OTFkppj36B8nlanqiUUveI+0qL\noXgNnuHchSLzLnfHMDU5tyEHdXDjDjFPn2IuLpiPMtnl2z9gebe/Rbl02DTIWxqN4RLQb3vDravz\nB3istJTLE95eO0fHNjYo8XVshDH329u/s+fQORJJyj3bDecluAXEqWfTQayb73zHpTNUMhc4EiXb\nxWlcbmJeG45U2dn4eQ9/v8jy+i/k74UpoQ5SZ1ZzaU/rARhb1O2wupS7K93egPL1u8mQn0UZpEf7\nP96j/o6qcv7z7qbALaF1B/yM5T9tR9IBxTh+BrKNHZuW69jahbskpeRAFuBJ1mSfKF4ObmGHa/OU\nrEO2trx8W1WjvJy6ytA9RinuqvYsoDKV2Fg+J6we4dxBJU5Fj3JZXsFj/HvrN7hWXkQuoZRSnSbB\nWezYlnM67jKcSxvvrIBTj30wfoZvV6xTCbu59MHaDeuyCynlNsrC8mOxxq5bgnHha1grvZwgM66m\nboeGs4PvQMihHm3BNbVy525IA6ZwqaMpoc4YbkSqoJRSZhbYF82JO1yDya1YXiG5hnY+GI9163A3\nMjdSul/ZBLKmoiR+TgkZjXNUzjWMI8dgvM/W1h7sOZ7dMEcqK/HzGk7kTqF0j3Dxwt+RksldEOna\nY07GuZdB/uQegrL7ykqstY8P8fU07jqub5eP+XnYFJz967KOe0zjriuXfsJ8aUykS2ZW/Ltl6vBC\nz6HR47mkKP0gzpHUNTDj+D/LwLMuYY0OHhqt45Mf833RyvWEjm/8ifcsKIK7NaUmQprschGSnYM/\n8+tIJRwt++CMuu3XwyyvMTnbxqyAmxR1mlNKqcgZndSzIuo9XDdray6HOfDhWh3vmrNGx839+R5C\nWxzYu0LmGtiYnyODiLw55RTOnk+u83YZeTfxvtO5lHoG0qWqUt6aIX4TzvEWjnj/sq9sYHl25LVG\nzR6s4/Jyfq17vYeWDlnE1YjukUop5fcc9pmcnzAX7+3icruwES3Us6TzGNy37Vh9iD3WdzjmEpVI\nno/lUsReQ8m93w+7dfzmqkks7/PxcCKlTspjhnVnedZuOJPQtcKV7Hfbd5xgzym4ABm4L5GTjZw7\nhOXt/AyvL2oK/r4oxdeN8nzsp3WC8fNuLufy1xZv4Xqvfu1bHU9Yzt1znfz4GcGIVM4IgiAIgiAI\ngiAIgiDUIPLhjCAIgiAIgiAIgiAIQg0iH84IgiAIgiAIgiAIgiDUIP/ac8aiDvR2pbncmvZ6AvSZ\njXtBKzd8CNej7t4DPZaZBbSvrq25JrEO6alBdZY7/x/2/iu+qqr7HocX6b2SShohJIEQSug99N6b\nIB1EVFQUQVFUBAVUxAoqiCJNVLqg9N57J0BIIJV00hsJ783/t8cc5/vgxevhw80aV1P33Cf77L3W\nXGsfxpjjHFs/7x11xYhn/IC+BbZOOD9mGvcgubXjDyNuNxWay0fFbPvtVQf9Ap57GfrOz9/8ifJu\npUCf/nxH6Bgb1WMd8bzp4424LAU9UqoecY8ZKxvWcpsbeVehe/aK9KFjgb1hi517HTauWceTKM9a\n2FJKa3LnENbNeT8MMeKCu9C4N+/CvUJ820NPam0H7aaDA/5/9uNTdI60sJW9aeJXnKe82mOg+fZr\ngH5D+XdyKS9+H/qLRIajh4ZXO+458/sXsLir/uKAEXeYxbrIpDuwv2upzIuTB6BfjsniXjIpSdC4\nyv44AVb826tlLHoTpMWjAYWliX10aQXmhWNN3L/Nb6+ivIgAjIPUHNxb34foA1BsYhWbcQs9Os78\ngufbZFgM5VUWQt/ZaU5vI77+/U7Kc4qAzrznR7CkL83lHjYOh2D77eCHHky/vvYd5YX6YH7U5XYQ\nZoGF0Ljf23+MjpWKeyW14naObGva6l3UvVvb0TsiajDXvUk9MD6lFnv2DP7Oqw+jN9aNTbAVDOqB\nXjIj6nNte3v050b87vsTjLiygPtc7N6E5z16WDcj7lh/JOUdurbOiO/8esmIW7/H/blq1MBYvbYe\n53ia2Bkqk/4T5sTvH8Ma07QvhbSyl7hmYmvv5YIxOPRd9MdJ33eX8nq+j7GfewV1fOuX3ItG9hxY\nsmKjEc94Cbae106y1WRFJbT2yWdwfaXHWD/e5QNcX34CapytJ/emufA76nBQiK84wppsn44hRlyY\niH42nk14T3D9G8yPuuwcbhbknsN3cXXg79JwHPp5ZJ9Bv4lbN/g5Vl7FvKoU/dsatmV76n++Qb3t\nMhrz9ISJVfzgTycZ8Z4PMb7tfbFHsHHnni6WdpgT1zdjnWj6AhewlMvYt2QXoNbUq8VzJ6gl+kDY\ni351rnW471nOFfRPCBL9Z2wdeI9RXsL28OaEX138rfzr2XSsqgwWzEFD6+H/lz+ivMyjeKb+PcLw\neSW8zt5di3trJ3ogyD4/SilVkYf9XXAfjKN729HHJccpnc4J7oZnVfIQNufZ57gXklsDzCvvEIyj\n4lReZ0tED0c7L6z79t681ywrw9+qEvXgykHu29h1dg/1NNGyO/Zsfy7ivpWTl04z4qtf7TZiSyve\nt+ScwDwdvAh9jn5+dSXlDZ6OmrrnB+zn2vVvTnlyzjkI+/rSIjyTyCEN6ZyMg/eMOGY0Ps/JpL/E\nn3/i77YMwjtJVgE/x1btsW92DMY+uXND3k+HT8Xfyr6A++Ae40t5Z79Cn7b+i83bl01am+emxdEx\na2Ff3HIqxq1brXqUV1yAevrgIvYBPh1CKO/O2pNG/LhK2GJ35B421aI3W3U15nNhHPaHtdrxM4x+\naZgRX/4W+yE7P547ng1gPW9rizp0fjHvUYO6w/o66TRqTT3xPJVSqkC8n9T0xbGSXK5DhaJ3mHoK\n62LaQeyVQ7y496hbPfHfoh/ZsKFsYf7zBxuMeNIH2IMsf2UV5T03AntUOSfWb+ReN7N+wj6wnT3e\n/TxC8Hd7t+FeYrJn4j+/oR9NVQXX/+4TY43YxgU9rUzXicfVqOu75+4w4pbPcZ/FD0d8ZMTz/vhY\nPQm2nvZPPKaUZs5oaGhoaGhoaGhoaGhoaGhoPFPoH2c0NDQ0NDQ0NDQ0NDQ0NDQ0niH+VdaUkgg6\nanIOywTGzIQdVfZJUCM9mzNFdtgLsJVKWA9Jkm+nEMpLXINjTYZA4tCggOlSblGgj91ZecGIpSW0\nZ8MLdE7tnsL+2BLynNyUy5SXm4RrsPeBRe/E0X0ozzEYVHZp1fao0EQm1Qa0twOnIaHZ8xLTLF/5\nYbJ6mpB2gVLGpJRSqXtAYa8qBY0rIY7ptL7CXtNe2BenmdDwbT1A1Sq4IaiD/epS3s6PhFRI0ONG\nfjkD58ezDMkxEBTrrNMYc/WmxVKetAOubolxsXsny6QqHuH7RpRDjrH67wOU99psSDBK02FxZ0qP\nazbxKXAM/z80rgsLzHuJTImWEpjmQtJn78ZSlISD/xhxnc6wjT/xMctc6sbiWeWnQN62+9IlyrOo\nAavb8BDIKuQ4cm/I11CRj/sszze1JS8TVM7Ce5A+ONRmKmj6aVyfo6Dge4abeJnHIry0AlbUNWqw\nXe+ddL635kb8fkhLtpw+TcccbFGbBrUAVXLzrBWU16If6qNfR8gAky79RXm3k0Bvnv8bpJ1ffPoq\n5T1+DDry1o2HjHigmJemUoqfDuDzLv8AWdQfe45SnrQUdhP2sxezuEZLa/fdGaCuR2aypeucMUuM\n+N0vYMv44uj5lDdv/PNG7Du7nzIneg7HHDO1nc45i3tekAWJU4OwEMqr1Qdz7OQPuGcNezNd3cbe\nw4h3rcM9b1Wfx/epG1hfesdgfGzeDGvWnjFN6Jxp/bGuRU6FPLeyjKn1uWItKElhebNEszEYs1J2\nevlXfoYhrTBmz+3Fmlv/Nu8xXJ0c1dNEXi6+p29tpm+nC7tdacncegxLhS5sgOxaSkr9YmtTXuMk\n/K0MYdlbx4clQKUFqD9SNvvgMKjmy1ZsoXPm/vK6EcdMwhqUdZrX8B92Y161DMc+4J2lSynv9/AF\nuB6x3v366WbKG9QfVqMeUZBPPExiO1tp6+wXoMwKKWmwD3CmY8G9Md7vbYfkriytiPIqhJWuvGe9\n5w/nvBKM/ZxLeE5OASxt9PDFM6isxJj2ag25tJs/z99UsRYEtkZ9se5oR3kJG7AGF4RAxuVuIjt9\ncAb7o7r1MGZL83iOSep+SRrqVVht3scnb4dMxX+aMjvq9IbkdUhtlgCV5EDOmSLeQ+SeVCml3EVr\nhOzLmGP3s7IoL3EbrJfb9m5qxFvWsj11q7qo0Z3mzTTivFxIalxCPeic02uwx0xdi+fT8oU2lDeo\nJ55x0hZcz6DhsZQX0DXKiG9+C2lGrf68jy9IxH3JO4+xmZbJz7vxiKbqaeHyN5AwW1jwv/tLm+0D\n83cZsZ/7VcqLeBHyLL8YyFRSz/BeSb6DebfEvEpYy+90vl1Rh8vyML4bTxtrxOXlvOe7+uOfWNI2\n1wAAIABJREFURhw4EHLNknReFx+KNgY3dmENjxzPEn35HtP0NTx3Tx+WoV+/Axlr9IuQVqWc5j1V\n3E5Ya8eMUWbH8VvYS4T7saS+PB+13KsV7ruVLe8POzdoYMQX1mD97z+iI+V5t8G7WotLqFmWJuPH\nUrzjSOl43Bq80/h1r0PnJG9BzVq6ATKrhkHctkL+rVIh7XSy49qbnI353Lgrvt/qJVspT76PLZ2y\n2Iifm9Gf8qof/bv0XjNnNDQ0NDQ0NDQ0NDQ0NDQ0NJ4h9I8zGhoaGhoaGhoaGhoaGhoaGs8Q/ypr\niugE6mXZnmt0LP865Dx+3dHh/sZapqvXDIGbyvVk0JaiopnSJR1UHLxAFdz09nq+qG0Ih3w62oiP\nL0T3ZKecUnmGsrHB5xUVgbJl7WxLeZVFkFwU3ISky0U4wijFUqaiB6BYNZzenvIK7oNSeDsNzga2\n1taUV23SFdrcsHLA39s/7x86ViTkPK36go7Xago/n4u/gFboEAd6V81WzFM+vBzUy+Z9QSsuzSqm\nvF7voWN+uXD3uf4LKGL1xrGcTDq11G4IumdWFsuQ7uxEt3SvFri+5z8aRnnpe+KNeNdhUO96Nm5M\nedJ9QX5e9rlUyvOPDVNPC4GDQK8M92IvqPwEjMcM4bLlHs3OOd7NQee7KVx5bB15Hjy8hM8rEQ5C\nH3wwkfIKroEufOA85AndHDHeQkfyvUzciLzu8+Dys+eDXyivbmtQFGs2A8X66O8sTSO5nXCiSNzJ\nDjHuDUG7rz8czhBB9x5S3m0Tpxpzo9fCOUYcW5pMx4oLIF2wcwRF+4JJ9/+TgqLf1hpzwrYmS2y6\nz4TDxvAlkAsm7uf5cmjer0Y8cGisEc/97Gd8lsmcOLUVcg7p7vXmdy9QnrUTxlbGUXy/QeMGU96r\nfTDXl4n562jLY/O9JZAyPRLyua+XvEl50mnD3Eg9hTkWPYU79buEYa2QjmP7luylvFAPjEEpZXIw\nkUgc/hhSklYNsB5fi2fXoFELIcHIuQK6dfeWA424JINlos7+qGXVwong4W2WAcT/Ddp9cDvIKx38\nWUZSlokaXyYc5SL6sDS5+D7kIVL25lSH5QwHt2DN4ZXVPJAOIjVMnO0qM3H96XlCVhnkQnmNB2ON\ni/sWe5Aq4RKilFLW7qBIe0agnhWYSLkcXHFMrncH/ka9fvH5vnROYQKe60OxNynOZPnO2NhYI/5p\nL8ZjUGgo5b286FsjfqEHakj/HizN2LsHa2bkNayFzV7hvUN1Od8Lc6K6Ep/tHMYSk9I87FMcAvDc\npIuTUkq5+KHWJp/EvAp8xHuWomSM26CObY34zmZ2FrHsiW21rx8klY8fS+cYlsCf33rRiO/vx76k\n/nh2IAkbJWRrlyApT1zP8pB6EyBfKcvPU0/ChR8g0ZEOZaYw3eeZGycXrDbilu+wk19VFeZih9fR\noiA/jutUeS5qWIlwrwrw5P27ndh/79mCfcK4ubw/dPARMvpU7GuTNsPJ6tj563SOlE9s/e1rI3b0\n5Wu4dxt/t7gM1+2fyTXw93WYpy8vHmfEV1ayy5t0zx0+E/KJUJ9GlHfjB5wX3laZFSm5qENtR7LE\n/8pXkDx1/RBrUtLfVyjvYRzqV6EdPs8nhl2dUg5gvtg7QhpTnMf7vjt/YF7EfgRpWmUlPtvJiT/b\nszmer1zDZXsIpZRK24V5Wib2QFJqqZRSAW0xrxJ34T7kuWyivPABvYxY1gfT99QWr3VQTxM9OuJ6\nfTvz2pB9BrLPVYtw/Q4m+7QhL2LdqNyPOnXzCDtGesZgvfv7An47GNSS33Ee3sG4cBKyxwrx7vjt\n7NV0TpKQM/72OWTvPp1Ycpx/E3nSuTbzQhrlyff2qlLInwb24fUu5SbOqy0k3CXp7OR5eh8kqjHP\nq/8DzZzR0NDQ0NDQ0NDQ0NDQ0NDQeIbQP85oaGhoaGhoaGhoaGhoaGhoPEPoH2c0NDQ0NDQ0NDQ0\nNDQ0NDQ0niH+tefM9nUHjXjI1B50LO0A+gfs3gsdo7cra+b9m0BT1m1SrBGXF7Cet7IYGjtL0S8g\nzNeX8hq90dmId8yBDrv3POgsfXy4V0ncIfROSNsHm0dHP9bM1x7WEHF36OSvrWCrrOx0aHhr+qLn\nxW9v/UZ5nYfCwnD869BZesewjeK22bCinbRioDI7LGAX7OHEvRiaj4G2z8oemrrkjTcor8EQaFfL\n86Dzc/BlDX73t2Gdniis028lcX8W2UtC9qzo+zF6UZSWcF8Fdw/czytblxlx4c1syms4DdrhUtHX\nI+P4Tcqr1Qd2hE6noU0N68LPJ+Eg+pBkXYSe0KMe21cWJqF/gDcf+s9wFJarVlZ8zy2s8P2zb6BX\nUr2h3Ncjfg96ebhGQmfvGMRz9sIf6GlSswxzZPGvrJH96s93jbiXD2xv6wxCh4hrX3O/lOjpGN83\nf/7biDu8043yziyBBXCZsHNt/xxrmWU/oLIc1JTH1az7zTqJPiFuUXg4gV3YXvjQX6zlNjdKSu4Z\n8ZyhM+nYQGGfHSQs7y8kJlLe5KWvGXHmFYzpi2vZsrhE9JMasAg9fPKvZlKetBPf+DvsRJf/87ER\nFyVzb54l764y4q/+wbgoKGANecZZ2BlmX4Il6uwhQyiv7hg8h6UB0OfXm9ib8l7ohp4zs14QfVbu\ncg3ovnCBelqQtUvaoCqllFtDzNPHj2BR3mEyd03JvYJ7UXgHdcNCaJ6VUsrHG/pqC3ss1wMX8Nx2\ncUG/MO9emLM3d0KH7dGIbTFdXXHP0+5iLj4q4n4YjSZjjUg/gPVz21qT3kVXUUObhqH/lqkefeTr\n6MNx8jDGS1hN7k3TKDhYPU1cEz3wujXjnhrxV7H2hIbjmL0v7xmsHGGb6e+BnidWdtxXrljozWX/\nDt9OrOm3sYGld8J+WM4OnI5+BGsXsZV2qFgbYvpgnY4c14XyTi9C/6I3Bwww4pspbLndbRDW2Wox\nhi2seWy2q49eDSEjYS1adJ97nCTvQ8+BMG4l8J+RkZT9xGPxt/G9es0Te4IHlygvoCP6s9jWxDpm\nbc1WzW51Mf/u7sDeuM5A7jmQfAh1OLXwSyP2aIy9bOIf3KvEX/ReshN9utRjXseOfrLdiB+WoBdL\nx5fYorb0AdZM5xB8dtZ5tg2OmYpnffFH9HMLNemfJ/t7PQ08eIj1JSeR+1vausKmV65VcQduUV7T\n8Rhcq+bBDvmFz7ihQ8Yx7AX6tIo14urKasqzt0f92fMR9psxfTHHgpO86Jw/vl1oxMf+wT6qiwdb\nDdfyxhpXXop62/ztUZRX9TH6wf00G/03X/h8NOXlLEZ9KRI9qEz7WTZ/m/v5mBONW2HfnH6A9ywO\nLvj+B+djDHd4tzvlVVfgeu+L3j7uEWx/LPtGVVfjfaTZLN5X3FqDnj0pV0W/TTGOvMPZ+vrGVqxJ\nLV5Hf5ftH22nvJFfvmrElpaoG5e+4vfABtNQ48P74T01P5dtvwsfYq+UdgA18/wRrhV93uP3W3Pj\nyEl8/wGN+f3b2g290yZ9gP2Xpe2Tf0qoNxH19c+P+F16/+d7jHhC7664hgtcA7a9h315zybYtxSW\n4tnPWDaFzrmwDP2H/tx3zIiLd3CPsMGiv41XFL5vzYb83e3vokb5d0F9fHPwJ5Q3Z/4kI5b7g6Qd\nXK/shOX2/4JmzmhoaGhoaGhoaGhoaGhoaGg8Q+gfZzQ0NDQ0NDQ0NDQ0NDQ0NDSeIf5V1tS7P6wT\n3SJYp1GeDUplNyEPshPyBqWUOrtHWOxOB21J0hOVUsozrK4Rn1goaPKCuqmUUgcEJc7FHlS5R4Ia\neO/a73SOpJffF/ZaqbeYZjSqNejGeUWgndt5s0WtdSZs+iIn4jtlfsxWpTf3CQtSYZ9p58004q5v\nsKTD3Ij7CxSxJhOZVyxpV5KaVu/VWMo78zko1tKC9OxfFylP2hTeE/faVO528Bqu6YVRoOkdX7jb\niH0CatI59Sfh+VTkgs7mHMk2hSc+ARU0QNDGz+xnGmHuNtjaDRgPCvjtvXGU5+OHz3eqK2zcctmy\n3cKGad/mhI2NsPs8dpyOHd0Iy9m+78Fm9eDcpZQXEgtpy48fgno5qAtbpLacjP9+eBUyqVZJ4ZR3\nbAmo3X0WgOJZWnrPiJvOmiRPUfm5GC9hY+RY5N+JpX3ttSTQkKtPMs17wBjIHKWNey0PtlV1cAQd\nszIQcp/yEray7TGe6eHmxss9XzLid94ZS8dyBeU8qBlkDAevLqa85zJRP0JaQfZzaRPPRTnHiqbD\n1nP80g8oT8rkYlIgJ6ssQU3duZStoNtFwtrdwgI15NOxiyhv8mxQXzedAm2+VTiPpfahsEj1eAES\nifTLpylv3XHUh2u/Y56vPMASm929UVO+3LVLmRO2Yj1wi/ahY1c2QzLhI2qercm66BCANbPuaMgi\n7v11jvL8hfQyfS+ozqZ21z7tIVmqrgbl28IONd3WiW1ar25cacSV+cLOtStLGh4cElJgIYG0seLt\nQ0wd1JeUHMyriZ07U56toPi3aAkpU0Ue19OrYt7HKvNj9BJICO5vYup4WSWsMr07Qt6QeYSltsdP\nY47JmpNziW04PQU9vLIA9SfjINP/g9+C/ObeETxvKf19feU7dE7qcViQZp1CbQhsz3X9XAKeo5SM\n7b/CUkS5N+vcF1LLwjiulUHDo4zY3gP7w4xjfI/uPnignhbkc4q7yX+35weoAYmbMa+827JEwsoK\nc9G1LtZZKZdQiq3Nq4QMv7wsg/JqNsVez9UTUvnb2/9CTjN/OqfoNvaOXh1wfTYuLAmUdsWNw2DT\nemL5McqT+63oVyBdCow1kXAsg6wgrwhSqIJb/KyVtAd+Cr72rg6oqXLPoRRLKfKvou7dSWeJVjsv\nSPZ7tYcd8KEveW1oNhQyi4dCals7thflpV45ZMQNO6NO5d/ANTR9vjmdI62Xx4yebsS31u6mPPmM\n7b1x3RYWLIc8feeO+l+wdmRJREwvjDMpQcvYx/WlNBPS70aDo//nZ///C89mGPfZJuuTgyPWv6ie\nqBsPb7LE2i8Gzy0pAc/Nw2Ruhw3AO1NJEb6jhcma5BgCaaJffUiULCwwptJv76dzQttj/bv7M/ZU\n/+cd5iO0owhuFmLEDab1pbzEHUeNOLAn9jb2TiwzTj2Bv/XgGsb2gE+41YWUUD0NyFYiXg0i6VjG\nRayTm75Ay4JeI9neO+MUJMNhIzE227VvSHnl6RiPTnXFnv0CpakJ/fG8P12z0YgX//CmEWedTqZz\nmr+Ba4p/G/O8zxSW+65dss2Ip0zCfP7tQ27jMOg11Iecy3g+pvv4khRIDEtT8FtBUB9ulxFQWaX+\nDZo5o6GhoaGhoaGhoaGhoaGhofEMoX+c0dDQ0NDQ0NDQ0NDQ0NDQ0HiG+FdZk+xoXSHoekqxHMa7\nNSh6dk7c4dhSOAA5+yFPUkmVUmr99CVGLGUznV9lClLcOlC/4gVdtkp0JXf2Y8pocBRo416NQRMv\nzWVKnXQkKRAOQH5d61De1eOQQ539DN2nm73SlvJsnEHVrCiEPCtN0NOVUspG0LyD2bDCLGj+Knio\nF0UHa6WUihgEaqOkW7uEsSykZjCkPel3QTutNnETkO5P978E7a1xm3qU5y5ojo9KQE3OLBA0sJqB\ndM7NVei2bu2M8efThl094g7i+TzYAn5cx1FM8963BnTDjStB7x0/bzjl2XuBzliWC8qajRt34I//\nCd35azN77z8j5fRJIy4Vzh9KKeUuHLiqynAvbSxZZrVvA+RQPRo3NmJbH3bwyhbU+PIsjNtOPZtR\nXqNRU43Y0hL3IjcV0g57+zI6J3knno1rPTgdBDTtRHmteqEje9EdULlr9WNqYPYZXGv7iZjnVg5M\nD97wCebp0K6Quh1YyHTjEH8hU+HSYxZIKYh3K6bXS/llTjrm6eefvkp59/8EtdTvHbgdPCxmB7wZ\nMyHbyD4LmYWtLUtUR7eB7CRUUFp7twD9u/9b7JrkUgsU5vREOP1ICZpSSoW0AMX3pemQCYR3H0Z5\nSRfxGZIanircgZRSau9PoLF2Hou6JuVOSim14LnJ6mnh7k3QZ9t147WhQT/UUzsv1LjHVewEcuYX\nSLxsPLBOBPbm8Z30F6SxUZNxL4sL7lHe8U/nG3GdsZjbxYliTfNix7ywPqAKP34Miu1vb3xJeX3n\n4O8e+QIU8HaNeLFyqA0KuY07aONWDkzB3/8NPsNJSJP9s1hGYmXxdP/taMW0VUYcHcRzsVzIZQrj\nUX/CRrWmvJvX7xmxlIN6hERR3p3NcIhwjYB0Jnw87xkepEH60vQ11DNvf8inj330KZ3T8l3UYf82\nuJ7U0ywJnLL0BSPOuoi8Abksx44di7/rINypSlN53ZGSm+Uvf2fE/cZwLe809inoYP4/BNdGvbqf\nyPKpzBOQQgQNwFg9tpAlmr4+WJPqvYiib2nJrogpJ7AGR48FlT3hKLu4lAp3wTvx2H9Yu+J+WTny\n+pRwD/U5oD9qQHFqAeU1DoWUybcb1rHaXrzhsBSubxniPjiF5FNe+BSM54BMSIkvrjxFeXVNHCzN\njajhqFl+DXhOpF2BZMtrIvaE9nv4HUIiJx11L3Y6yyrPLMPnDVgMp5W8PJ4vQU2w5v25GlLg/ote\nwTnJ7CqTcx7PsboSrl2B/U3kIcfxTI6vxbiq68+f5+mM7+hsh5qaeYrX2cdCdiY7RqTnsnNa0Wns\nxxqx4d9/xg3xbibfF5RSyiMGEh4bV3yPsizes6RfhPxQ1uDccywTPboGMu0BC0bgnAIe3zVj8C4Y\nvxu1VUo0nSL4WvMv4f2mhjXWoLsZLLcb9g6cl+T7Z8ZllonKB3LnF4yxyMlcJ+WcjRiCfcTmd1he\nI8fE8G/4M8wBe+EiVFnJ8kY74WYnWw/Ymzgfy3sVKfbiPu34XS3pTzhyXdiN+xbixS5oDoGoxfM+\nxDrmGor6b2/yHpNzCdKj4jKMe+lGq5RStqIVx7nv8Y7UdyLXjazjmHMpSfjtoOnYFpQnx7eFGD+2\nHtwepbKA341MoZkzGhoaGhoaGhoaGhoaGhoaGs8Q+scZDQ0NDQ0NDQ0NDQ0NDQ0NjWeIf5U1XRPy\nnUa2LJGwFZTtmz+CvufZiGVNUvJ04wfQSf16MB2803hQX7d9D4nJkWWHKO+DH3804kOX1xvxndWQ\nUti52NE54RNBH8o4A5q8rSfLUioL0YHftzMoo+n7mVo/YBFoVT+9/IURN3fnrtqZV0DZqmGJ38HK\n0oooL/4qKI6NWVFjFpz8Gg4stRsxffv4asgnIuqAMlppImNzrQ+aWVWpoHwnMDXrxCp8XmzrRkY8\nZOKMJ16flaDR/b4MNFOn2uwuYtsUtLBHwknmkbgepZRqNh7SqodCnlZwmyl6XZ4HfTtxH7riZxzl\nzvCezSHhyDh0z4jtazGVz9qdx5054V4PUpTyHKb/F5Xiv0sFTTSoJzvieKZgbh76B3O2bx/Os3YC\n/fr6GiHVasPU6aNzFxhx1GtwOXL2BnXxyo9/0jkBffG3JBU3J/ks5Ukpk3TjKs9l9zZJd1z58R9G\n/PLicZTXWrgDOQgKZk1nfoYpGdnqaWJ0B3SQzzrN1OT9W0ElP74QcpavfptNeRXiHpSVgbo58JNB\nlLdg7DdGPGYY5E+J5zZTXv/m6FDf62PQ9b9/EbXNN4klMTFd4DpQpzfkMW3rsXxx+RTM+ztpoCZP\nD2Tng6AmcFbJzQK1NMVE1mQppC7SLa2wkN12+vRmCaM50WoSPjt+DTvAlZSjbkaNhjOKXFuUUiq6\nL2jLDr6g41ZXPqK85Ou4747BZ4zYuwnLDNybYG6f+AwuFw2GoAY71nKjc/IzMcZ2LoAEtccb3Skv\n9xrkIjHDIXUrfcDrWMYFXOu1ZEi/TJ0ZY2pDmlGzJWpr/lWWGeeZyPTMDUchE0g1kfa4CPcY10is\nfY8e8XfuOgNjP/cK7lN5LrtuWVjhHlxbjZoa2ILX4+BukHTcWof9UmIBKN+JmXyf7JavNmKbmrju\niIH9KS9uE6SdV47DkbDbyyb07RN4dn9+B7lh/YAAygu3xXiaKlwz8hLiKe/SWtyLSDOz8H27YJ9W\nsYn3LJcPYP/lGIK9RP1+DSjvcTXWoUePMOZurzlCeRFjcZ8ur1xlxA5BLH9KvYJ54BuGdVtKOwoT\nWG4S1QGyl0whebEzoer7dsf3lZT+uO/Yran22EbqfyHnLMtDrv+OfXOjCaDns1j9/16HuVEtJL13\ndu6kY3X7QFa5YTr2HJ1e5sHk4Iy6EvshZGxnFq2iPPn8CwquGrFpnTq3ZJkRS+e9I/PxebdNHKMG\nzcQ65h8JKWJ+PjspNhn5uhF7xmCPVHT/IeWNegXvRbd+xLro0yaE8pK2Yax7tkBNjbTlV7wyk5pt\nTrSYiVpYmMpuTadW4NrDorE/tK3JUg+vFqgxTqI+15/EtcxhzyEjls5L6+espLxOXbBeHdyPujv+\n6wlGLCX5SilVloZrlW6Hzcr5nTU/Dt9x2x94x5qw4DnKk20I5DgvzmUZpkc06sONbyF1Mx2XoSbv\ncOZGw5daGfHPr62hY106436+/vl4I1769mrK6xWDvc+937E3CxzA+5awyfg8m21Cwj2O97IFeTiW\ntg9tQZJ3QQbo14mfT2A7fI8RwvHNI4KlVZ5fQBJfJORPl7fx3i40EmMzIBjtD8pNXCalfMm6EO9S\nt1ezBZWsHWEtxyhTaOaMhoaGhoaGhoaGhoaGhoaGxjOE/nFGQ0NDQ0NDQ0NDQ0NDQ0ND4xlC/zij\noaGhoaGhoaGhoaGhoaGh8Qzxrz1nHgub5OuH4uiY1Eu9sAR6KWsH1u+VZsHabP95aLgGR7NVVs4Z\naGGfX4DGK3/N/4vyfp0zx4hzL0GzF/UqLAGdnFjXFr8TFswuddG/ojSDNe1xB/Adq/fju9fvzDZ4\nuQnIG/J2PyM+/ekWyrMTFl2PqmBV6h1jYvXdIFo9TUhr8qpS7mng5wbduF/PMCO2Fz2FlFLq5lJY\nwLlHQ0d9YR9rnSVqeyNv3EDux9OtMTTRDiHQbNu4Y/zknOE+F47BwtJaPDs3kz5H8tqDeqBPSuJm\n7gOQtB/aeP8m0Oma2jA7BeDv3hf6Qkt7nj53b6CHSEtlXlRXQqtasymPn4HdoO9M2AStqktkTcq7\neAj6zCGzMW7/+YJtiPNL0NPETvQDql/I/YWui74Sdr+hZ8zuw7jP7SJ57sjvURCPHkDSGl0ppZq+\n8SKuJx+fLfsDKKXUlW9Ej6Mo2Nde/4WfdevZqCmyT9T4b6ZQXsbZ2+ppInIS7Mhzr7LmuMtg1LB+\nfuj7sWn+NsqTGuS8OPTIaTl7AuXNWTfTiF1cMA8KC9muU9rXzx6C/jbyfraawvamn01fYcQviEdy\n/OZNyps7dbQRD+iG3gFr3+deRBH+6Kcie430/Yi15hVfog9EeQ7Gae6dRMpLEb1amivzQvad8ohm\nW/I7e7DG3fpshxH3nsR9PR4Vo09W8nb0dvPtUpvyAuthrtu4oTZuf3ct5cneJ9IK01n02rizki0k\ng4ehL0Ojhqj9x5YdpryIGPS5cIvAuu0eyXX3oegZ01BYU2cXsgWzd3scy78B3X7AQO5XFJb+dPs/\nNQ3F9wqb0ISOJW/HGn/3N/R7CXuee3mk74X+3c4PfTl+Xsz2p2/8iDpjLfTvpv0E0i+gN4VbQ+ja\nixLRo6RZVGM6J/N0ihHb++Marv60gfLChcW1V0vo502vQV5fW1G/3aN4z3Zg3kYjbjISde3Eryco\nr+vbPdTTwumf8Leaj+NV1/YE5ou0QTXtNejZDPciU/QktA/kXjLp5zAO0uJRu23ucy+76LG4Fyd/\nPGrEtUSvwarqajrn9B30vOvaBecf23yG8lp0Rh13CcVeNvwlrnKy95xPG8y3igLuyyP75qXvwX4o\najCP82u/oV9CWPPRytw4L/YP4S24d0TCPuzfo5vWNeJjy49SXkQUnnFVGfa5DV7rQHmJf2KO1WqO\n71+jBvdBa/oG5uz+D5YY8dUk/J2Rc7g3hmsA6qjsheLs/OQ9vm9t1O4fF7xOx9qL/aZ7U/Qkke9m\nSikVPLCBOIZ3jYfXufeLY23uO2ZO3PgWz6NmW+5P1fuTSUacfQs9SIqTuMfOhW/R7yVqIMb6rvdX\nUJ6NeKeRPRhHvM/Pw84T7wIdy3Ff4ldhXkVM6kjn3L2Jfe32sxiXMXV4XMYfw7vPrM8nG/GDg7wX\nCRmMZ1+ajbXQ1Zv3xrf/xDgPHYXv7psWQnll2dx30dz4ZSZ6uXaLbUbHCkRPJN9Y7FX6t+D6498P\nPR7vb8GeMGkz7w+tHPGuVXcsevkdnc+9gx6KPWG/Ra8Z8clPfjHimDFv0DnpqduNWNpbZ125S3ny\ns5tPxh5c9t5USqnMkxgXLr4YV6XpJj1kd2M/l1eEY11mcS+/cDEe/xc0c0ZDQ0NDQ0NDQ0NDQ0ND\nQ0PjGUL/OKOhoaGhoaGhoaGhoaGhoaHxDPGvsqbwSNAhfTqw/VTtI7D72zoXtPtGYUzLPngJVnWl\nwma0hgX/LhQ6GjSu3CuQTA1exLZkWefvGXHBLdCeb3wHG9o6o55M+7IU1nLSMlIppRoNAbX57O+g\nszn4M7218C5sNwuu4xqcnJgu69OZ78X/Q1km06Ae7AfNKrieafZ/R2NhkXhq+XE6Ft4KNExpm+xs\nz9/FrQHo+4Vx+P6vLhpLeWk7QM+1DwRltEVpGOX9dQb3d1LnYUZsKyjVDrX4vstjORcgg7v8J1uU\nhdQXdnx1QMOTkhqllIqaDCpe2h5BTzeRdB38GLKfyHag1dp6sg1g0yiWOJgTOz6GRELKxZRSqlb7\nECP26Yj4xHdsBWpthbF/4ntQUC1M5mL/ybCAjN8Fev/iGUw17N8MlMf78ZizbSMgK/RowNe6YNr3\nRuzv4fE/z1FKqcoCUOblOAho2I3ynDwhI4mYEGvEpjaoibsw7l/4HtIdU7psYJC43l6qJ45+AAAg\nAElEQVTK7Mg6g5pz7egtOnb0Buwwx/fGMxj3zauUV1WF+rbuzZ+NuMVjliwWpYDSHH8Qz644m+tP\nxSOcJy3H41IhDdo6aQmds2I/LBY3vsXHJOyETbRPCKQ9w2cyvd5OzCUrO1Bd7R1DKK/FTBzLOA36\n8A8f/UZ50z7lumRO5KSjpoREsHQwqjbWzLspmBMOflzLLKww56orQG9N3cpjwq8P6k0NC8hPTO2U\n132A+TLus5FGfGc5anqTmc/TOZeW4J7ZeKHed/uwD+Ud/gT1r6oEcixXE5lLpRhHAd1Q708t+4fy\nPA9iXZDWlbbneM1pP6uLepqIF9Ls8uWVdCw9DzKimD6QeJxfcZLyIrqBmn5mG9ahye8Np7y7q1Gn\nUh9gzxAZy3Uv7xyuqe4U1Ne4rdhHJWWz3GvQB5D+efpD2nP+y58pz8YG9TZ9v5CumfgmyzXk8jHQ\n0KNfZ/r/wZ1Yw5s7Qkrn7crykF0LYMc9aQXLDv4r3B2xVksJg1JKpSVijnh7Qs7hJr6fUkrZCsp7\n0nnIlSoqeEw4eqOWNZ8Gi+Pbv/D+oygR+yN5LxyDUQPW/LGXzuncALIUKUdu62siVxK2vNnCut70\nn1mL7mL8VmRJaQxLEaV9fe5DfHZmKlvLO9oyxd/csBR7kHpDB9OxzW99asQdXoM07+QJludWCZlA\ndQX2ejVq8GtO0ABssisrcZ+Ks9gW+6GwSnYWts7hfpAXVVfxnvLxY4yZkhJI5MrL2cI8+cIeIz63\nHhKbTsNaU9765Zg7DuIZ1BXXoJRSqbl4Xj3HYp5a2vDAcDR5lzEnyivx3R+b7LUvfYGWD/ZiT2Dt\nwnJ2/8bYu+ffxP1vNJilnFXlWGucgyHdLU7NpzxrJ3y+fIfNElLQ/CR+D5y9DBbq86dONeJrQs6m\nlFJjOsWKv4NnI9dIpZSyshLXl4K/m7qD22A4hXuIY5DXu9TnPYZnE3725saUpRON+MznB+hYi5lY\nky0s8J0danPNL7iNNSpoANZI+UyVUurk/ktGHFwCGX379ydR3pZZXxnxwbnLjVjuH8rKMuicJGHN\nHT1+lBFn1WDZbWY+xox9TYxNOwduHxF3Ha0SIlthbf7754OUN+x9tPBIPyBksq78LlSYzm07TKGZ\nMxoaGhoaGhoaGhoaGhoaGhrPEPrHGQ0NDQ0NDQ0NDQ0NDQ0NDY1niH+VNZ0+D1pQJ9HVXSmlXOuD\n0hyQjG71jnW4G3izQnS4Dh+GrtW27kxhTtsHWUnIIEicyh8WUF7SIeSVVVQYcc8FbxlxZiJLd24c\nAVW8lpAhnbvLXZsHDwbdsfNsOAyk7DSRHxwFRXngG9A+5Mcx3VhS1/9Zts+I+7zB7gXSueNpIOci\n6JopOews0MgHlG03V1C6KstMKL1BoK1VFeO+r/+EqXnNhAOGdSFob0FtQihvQi9Q3f76HhTPXuNi\njdhUCpC4HtRut4agiPl5uFOebyfIye6sBm0u6pVWlFch3Idc6oE6+NiEqhrRBhR96RLl1SKQ8uJ+\nAu0tghng/xm+wlWr5Tss9SsuuGfE+XcwBkPqMi0vbBQcdwqFZCVu/SXKyzgMyeJNkTd+GI/bYS+9\nbcQbf/zMiC3sUFZO771M51QLl4GJCyG/yDSRGD5+hGdQkooaYBXDYyJiAm508j7Qyytz2VnKux3k\nJofmrTJiU7p2chLLRcyNsL49RNyTjrW9CwlKSEPIIj4Zzs874yFkNc/Fgl5fWsouAXNe/taIi0pB\nbX/vxVGUZ5mEOtW0L+jDWeshDXu5J1/rd5M/MOLZv/9uxC3+WU5563/YacQdzoHSO3v1asrrKSRy\nEf4Yt63HMM076xioxU1ehXTpLeG2ppRSDs4swzUnpNwrsCSIjj2uwviWc9bCmv8dpDwXz+PI36gb\nbTo0pLwNn0Ey3L0rJA7DJ/Fc/PJzSJSufg+Jr6s/6vajR+yaJGUbIV0hq7iwhCWB0f1xTdJVcf9q\ndkvpMg6uKHkXIQ8Z/f4QyitOwXw+9AcoxvX8oyhv03ubjXjarwOUudHldUgHHbyZll1zN/Y+fq1x\nXRknuU79vmKXEU9ZDNfKhF+5ptr6QHLTJLapEeeeZbnD0r8hARtVhLXmgZjz/Wf1pnOca0L6du6z\nn4xYyoqVUurUglVG7BKMsVmezq6VJWl4PvUiMY/OfM5SnAAhS92+GPKLQE9PyisR+zRzo9543MvK\nIpZKRol9pK0HZJPVFSz/TD+CullZiWMhg56sMV/+FmSdpmtI5nnU8ZouWK8u78Me8JKJq91zvbCO\n2dXEWHGu7UF59xPgdCNdJT2j2EnGORgU/2ThllKWwZLW0DHY/xUsg2Sv4RR2vjJ1LjE3UoQs58Ft\n3r83HYpn7BmIGjhyMY/vvFuoOXIPZ2HB7psJ67AnCRsPp0tnH14zci9j32wpXGXqRGGPm/AHS6ti\n50HScGnND7jupixFKRVzrFF/dsaSkHOp0zis9cX32eWozxA42KScRf2+eyKB8lq3fnrrYlAP1KH8\na7yPCh4KZ8DKQszT3AssJQsbhfU+8yJaJDgF8XtlwirUVysHSJdM3xnuiecjJdaBffH+kbCWa/Uv\n771nxHXHYj9kv5TXO7cYuOkd/gbyn85vdaU8KUOXyM/h9bimF94nfMU7h5UVyzV3vAenxrHfD1Pm\nxuZ34KTZ843uJkexv/lkNOSGzU2crELDIU+zFO8Dh/ewY+TweZAwWljDgSvpJDtGWgpHwdBukN7b\n++CZJh5jZ2fvttibxW2C7PvikRuUN/xz7K8f3kbdrPTh9SRQjG8JJyF5VEqpcuGwJt+bE7acorwb\n5+COF/LDiP/zuZo5o6GhoaGhoaGhoaGhoaGhofEMoX+c0dDQ0NDQ0NDQ0NDQ0NDQ0HiG0D/OaGho\naGhoaGhoaGhoaGhoaDxD/GvPGVthvesQwJrsSxugHWv1CrSQWadZk71T6G8/XL/eiL96/UXKu3EL\nfS5yE9AXpe2c8ZQXOxd9QyorYYF1cxN0chcPX+dzpsYasexlEezIGvcZ49A345Ve6CXjEc0WWCfi\nYC/c/hz0ohfOxFGe1znoHwe+D7vL+FUXKc+zGfcGMTdc66E/UBdr1hLbeqD3z5k4aDz7TmXdZPou\n9OexEvrbbrHNKM/KGfpPqS/MfMga2amLoM/vOgDP1L8FNJ6ZV/g5usdAt3tmGz673dg2lPfbh5uM\nePRCaPlyLrG+f8dqWKC5COtwRxMNYfuXoAe3Ft/d1EYx+vX26mmhzTvo+bF/7io61v5dWN+6QY6p\n8kz0vHHLDxlxuLARPxbH4zZO2P1NE/Pg/GnWya9f8JERT3wX+tPKcmg1XxrE1qmxUZhzx7+FrrTT\nHO5pUnQfFpflD6HhjD+2gfIq89FbRlqlO4SY9JCoi/4DxWXQ1vu5c7+iqDEx6mliyXjomUdMY8vi\nOu3Qm+PuGdTKt9Z8RXlLJ80y4kOXoam2WGVJeaPaYzw6i/E97u2PKc9F9EaJCoBWWPZSCBzK/RcG\nZEAfnZcHK9DcMzzHpixAfxsXf+iShyVyf5w+IzHHAjrgGRycx887elgTI76wZJURn7jOY/jVn59s\n7/1f0WtMrBHb+znRMc8Y1PL9okfH8ZlrKW/0DPRQGb5gqBGf+ZK11v2fh3VswhFolC/vOU1542Jx\nTbY18azzUlF308/xupNfAi18/h2suQ4mPTS2/4ReGW7CurhA9DFSSqmEXdyb7f9h0zvcX0ja5k79\nGPbe+XFssznkE7bUNTeyxV4l4TL3uQiJwjzIugxbUwc3B8qbvBDjO+869OobjvPnjRsC7f6mr9Gf\nJbeQ+w68O3ucEa9fif4zD8WzapPANscO3ugzVl6O/i7J57nfRHRP9BWy88JzrI6sorziJIwZCxvU\nlKM3WKs/fgaeTz3R76U8i3sshNiGqKeFG7+gX1Nwd+4J8ODgPVyTsPmtesze4e3eG23EBTF41rd/\n5flSJj6jW2Ps+9Yd5h5N3RpirVlzGPP54iX0tphgsi4WPEAPEtsLqKHuDX0oz9oFc9M5FP1oKsvy\nKE9+RY/mqEnVFfysT3+Na6/bGZuHmyvPUV6k6M2i2I3bLGgfi33f+V+4ttlaY8+VfQQ9xxrP4F5s\nRU6YF4kbsXe8+zevDVFj0cNmz0foiVanDvctk70jWr47xYjLyvB8qiu5j0R6AuZ26AD0TykrYptf\nW9FX6OpW9MBJNukJWccHz//yZoyfdm90orwds7824ob9MP5CYrgnmpUt9/o0J06sx71o2pP76Fxf\ni36A9Z/HGu7bOZTysq+iZvnEYM+RfSOe8uxqYd21dcN+/fYanrMekXj38WyK53trGfYsF032ItKe\nOdIe+2RPZ+5xVJyAOtm4B/qp5sfzM/RsiHeioDZ4bgUmPUrtRU1e+ybWzHq1eFw26lxfPU1E1caY\nKTRZax6LwtInBjUhsCfXXrlupP2NZ+dqz+Pv5Neoj80mYb7IHmFKKVVZhbol15fyTPRLixzCve3u\n7ES/Pv9uuL6rx3mfsuntP4z4UTXeIQbO6Ud5FeI9RFqCD1rI/WJuLcPaf/IW1pMhM3i/X/P2v/e3\n1MwZDQ0NDQ0NDQ0NDQ0NDQ0NjWcI/eOMhoaGhoaGhoaGhoaGhoaGxjPEv8qaBn4M6qW0P1NKqai+\noMiWZoNa5NeZLbXGloDO+3Jt0H/mzVlBee/PnWTEuechx7iybCPl2QdAKnTnNKQ2PedPNuKjey7Q\nOb9/CnpTt+4tjNgloibluTuBKudaF5TR+Z/9SnkzRuK+LFu73YgX/DaT8iQl7MBiUNzDw9mC+eYB\n0C4bDlRmh4M3vpeFZQ065hwIqZCkrF3dyPZyV5NAJx08FpInaeeolFJph0AR7P5cuydeU9410Dz3\nbwcdcmwn0M/S9zEtu46wffQXcpRzv52lvA6tpBU7qGh5Fx5QXmwH0CvPngZlOzySqaAFgqZYcB10\ntpCR0ZSXewN0V29Wwv1n1KiBqRrZm2mNjx8/EjH+v2MI2w/WHwDr4dOfQyozairT7SR1OmE77kub\nPk0p78YhjNvNf0FGkrwTVD47N5aInb2CY12fx/jIPHmf8izFuKoQtsNNRr5OednZoGXv+RCWzt3m\nMsWxKA+1oulzoKoW3GQpRc5Z2CTXZldjsyA9D/Tzu/8w3XrNV3g+DYNhefnCmPmU9/lMUKzlM5ZW\nhEop5VwIG84vPltnxCtmzaA8a1c8b/fG4Kw3j+prxDk379I5Dy9i/kZ0xo16VLmd8lbOgSzp+emQ\ndo7/4nnKc/UArT0vA5LFOq2Z9iwpzF7tMU/7t+OamnoFUpywlmOUOSFtop2CIuiYhRWeQdd3YHed\nM2cT5SX9jXlwLRnyGlNbxjBBD5Z53i5sGXrlPuZPeTzkF+HClrz4fj6dI//WxpW7jfiFL/h+1fgF\ncbaQ4bR/rhXlPa5G8bHzBC3Zpx7rIJxDUbstbVHXju/idTsmFX/L7y3zW2l7tcEcq2Eyd6T1+aNi\n3M+abXmc2Xlgbc3MwfOp48vfuVYPWKNuWfidES95cTLllabB6ljKxtwccD/d6vHisuaNn4145CJY\nqza0YXnanV+wTlo1B9U+8xDXXsdgjK1z51CjXvlyPOXZuWGPdH4xxk8DExvmxPVX1NOCe6D7E48F\nD4YsIvcy6tVjEzny/QOwyK3VAXsM7xYsJ7i5H/ciR8yD5zt2oLw7qdgHfLPhXSO+vAJynehxLAcv\nEtbINYX8Iu8Gy2FCB2LO3Vp1yIgfFVZSXtAwsUcQW76LOy5TXt36qKG394Pu3+G9vpS3fx720EHf\nmt++N6gfrI39TKQu8t3j3M/YK947eIjy8q9hLXetg7EZEVub8uJ+gmSrUTfIrIM6cT07tRByh0vf\nwzpdtkZIuG9iBZ0NyUXIMMwxR1e+hi2/bjXiAR9gXbT//iTlxUxra8RlufhsJw++R64OuC+12+PZ\nFRby8z792R4j7vt5N2VOSOmJrP9KKdXhfcg1b29ErfBsxnPs0mbIkrpG4ztm7mfpUZO3sEbZ2+Mz\nAubzuC0shLwt9y7aNkRPh1zfYuluOuf8bbHXEWtaw+nc6sHDA+0ULqz61oj9u/I78Nb3thhxy+74\nHo7BLL2XUqD6QspkZWn5xLyngeQHmEemf8tRWJpHv46xaWHBa82xBZALdnwf7/1NrFgGfnIB5N55\nwrreua4n5TXrj3c1t/pY/36ZCfm/lJQrZbLfaYvn2GoUr097Vh4y4kFiLqbv5/fPvES8zweKZ2xr\ny2t9nUmQe2UvgVw14zCvs6E9wtW/QTNnNDQ0NDQ0NDQ0NDQ0NDQ0NJ4h9I8zGhoaGhoaGhoaGhoa\nGhoaGs8Q/yprkpTK/KvcWbhUSA3uZ4EGdSOZ3ZqmLR5vxL/PB71r9qyxlJdzCnKCuDTQQrtO60x5\nGxaADtizP2hlV7/FZ7/wwyd0zq3tOJZ2AU40fp2YGjiuKzppS4rUgBYtKO9RARwR3vscEoMjn+2j\nvBYT0X26+5xe6kl4vOHxE4+ZA0XJoMyaSoCCg0DJGvMlqIflBewiEXwYdLzNq/E9Kx49ojzZWX/C\nWFDR1n3I8rTgmpCUPfcx3EoOf7LLiJuOZ/rZ0a/hrlSvPeQE5WeZLlaSBZld4V1Q0cpLKyjv+nlI\ntSQN3aU+y91u7gQ1Un5fayFNUOr/dp43J9a9CW1B56Gt6diDE6Dfye7lUq6ilFKWlpAxyO/48ApT\np31iQ4w4tD/o0fbejpTXPBC0zOJkUAgLhLOIcxBTN7uPBQXcTsjtnP2Zkph+Evc8oCuu4fLmpZSX\ndQ51o92rsUa8f94Wyot9F/LK0+tBL3c2kZHEfjBSPU2MHwAqcfNpb9Cxhnd3GPHORXB9WLZsFuUF\ntsI9TNgNueTuTewQ88IyfH7EukNG/Kic5+zcpb8Z8T9XQY+uqICcL33PXjonZiZkSTd2YmyeiWdX\nBSk/DGgGR6bS0iTKq67G3Mw+hxod3J2d2G4sh4NNizenG/H1Lasoz6EWy37MicABqD02ruw+sGYm\n7mUf4er02MQh5uxdUKdlzYxuxJRoJWSoI9+GtOdxFX/epi9BI+47DmtmnpAIe7dhuaZPe8h6bNfh\nGmydWQ4Z8QrWv+rvMHdqWLBENuso1n73GNTTLVvZgWrY86CHF92DzK9Nd3ZKc6vvpZ4mdnyKe1Zf\nuJQppVStXpDX5l2CjG3Njzso79WvJhhx3FmM/ZJyloGn7cPz/vLlF4w4aChLVDNPYF7M/BJ5y4Tj\nVfo+lhg+/wVcawqFy51bKH+n2qMgPyxJA9261ORaXZ0gx2hSH3KsTULaopRSvsLlLawxxpKUqiml\nVGomu5eYE6l3sXa5RPK6Ld2afDtDVlJlUv/KhRQl7cRVIy5JLqA8VyEt6zSjixHb2LDMzPXAMSN2\n8oIMzt4Gko2s0yl0jlsDfMaNpZhjkS82p7zMy5AeebbE87V15zokXWvCR0MyalGD56xrFP5ubjLG\nzvGFf1NeUCC7RpkbG9/G/tDFgSUxdqI+ugu3OJ9WXCtLkrAHSbyMeXT6yFXKkw5IlYVYdxY8/wHl\nvb16thFnCTm2lDsM+mw6nXPoI7Rr8E7H+Inb+ifljfkGLRCkLL1mADvJOLmjDn0x9S0jHvsCy3ea\nz4Q0PX4f3pH+Xse1t1qsQ/wJ/x01xNhyNHH3vbXhH9N0pZRSR7/n64tsjHkqpTJNZ02kvBu/4X7K\nVhpetWIpT0pO8m+eMGLp7BnzJr+LWq+EPN7NC1L+R49YFpx0fbMRFyXiHcvRld8DSiowxqRM6Nzf\nLDlrXAJpYqD4Tm7hvA4mrhMyUfOrfVWryZArrZ7P721dxX3LzsX9uJXGLp2yZURRJvYFlna8NjR5\nE3sVKyvhBJnG9XHVQsjCJ7yL98WOUVg/kzazm6xjbaxPVRWYY1KWrpRSnYZjj5m8TbRq2Mf76Wmf\n4/1YPsecxGuUl3MO9yKkJcbzw2v8G4pcd/4XNHNGQ0NDQ0NDQ0NDQ0NDQ0ND4xlC/zijoaGhoaGh\noaGhoaGhoaGh8Qyhf5zR0NDQ0NDQ0NDQ0NDQ0NDQeIb4154zh9dDo9eiE/vKujWEbtOnClr2pOXZ\nlHdhBazhRn0MC769n7F9WdcZ6MXglwud855vuI/Li9+/ZsQ3vsMxh9rQOCYcZL2s9Bd290Heo1K2\nH5Q6xI0LYQkbVNPEcrsZ7Kf//h7X0LJZPcrLOQ/tWVkqLDLzCosoL7wv687NDWsX9NXo+mE/OlZR\nBN3bgxPQzB/deoby6vrhO0ttaZ/WbAkZOgb65o3vQSc47E1WuNrVhL4w9R9Y3AXXg4Xco2LuEdNA\n2B7mX4LWvPEk7k2Tcx59SDwaQnOaeIKt0WLHwcr5yiZYh5dlshbQ0Rba14Bw3IeTJ1lr+OgUtM1v\nrR+hzIm2sbD4rHhYRscuHYXddVdh91dVWUV5NWpAux3VD9b1F3O/o7yCO+gRYOWIc0ztEZP/gj66\n3st4BvHHMI5sTfrU2IrPKMvEPLCyYz2mHB82NniGtTpwybp7FH/rzho8w87v96Y8GxvUq84zUWv2\nfsp16K930T9l7Pfca8ocCOiLfiW/vvQaHWs3Dlrf8csWGnHCEbanzrmHfgJ1evY04tbXufZmXce4\n6D8ePRIcajlTXnfRJyY3B/0SbGxhRxr1Gvf+srVFr4LqCoyzGWt/oryz331jxDVqQKd7Z+0JynOJ\nQI11DoONYt497uvkEolj986jr1BA50aU5+7OvRrMCStH9OQoecB9KQZPx7irFparwz8cRHm/z4Ve\nvcsAzJ37p7l/lncH9PI4+j1s46U2XymuydIq2E48a9P1TvaJsq+F/k8WFtyHacOMVUbsKno+FO/m\nXiUBLbAPcBFWtuNmDaa8CxtgZZuUjTFbrxbbqt45jd4qoU1GKXOjURjuYcSL7elY3FLRC0H0/Xl5\nEduMy1oZ7IM54e3KPRccRA+G3UfQayA3h8dP63fQO2LfRxgjM35+2Yg3vs39K2r1QF8KG1c8u6qq\nYso79zUso1u/DZt3C4sblOfTFmPuxEn0eWvZlPc3d26hl4Ccv9kXUymvdiT3vjEn2ryNupa4nns4\nuDUUNUqshRkH7lGe7CFVLtZWBz+ukw5peIYp+9HfwMaVbX5L07GuFWbgHkVMRP+KxNV8rak3sFe0\nt0F9ufDNMcprICy4HX3QiyLn2j3Kc/ZFz63EDdinmNry2oi+dC5umNtOdT0oz8XE2tbcaNIAe/5T\nl+LoWM+x6FUme1Yc/Jj7P11LQp+ZxiEhRjxC9L1USqlHlein+Oc76Knx/obFlLfxra+MuFE7WH3b\niPp/16QXW8tZ6C2ZfhzfI2x8E8qrUQPf48DclUYc+8Foysu8hbV+6ofoh+cVGf3Ez7PzwnPsP4Xt\nsrOPcK83c0L28knddYcPinewiNHo/+fbidex3eK9sDwd9St4ZAPKc6mHse/sifp37Y9fKS9iEJqy\nlInPCx+E9yAbG+7p4t4Ee/y0a4eMOLQZ9yM8uQ420FkFqOOWX22mvF4TYo34xAb0k/IyWSPco7HP\nlXU87yr3SHGOfLpzMX0X9oPDJvegY86hqAueqdg/BKUFUt7FI1hTMhPQk9anLvfnCh8h7ckxRkz3\nKj2bYf78/AnWv+GjMb5vHuF+TTFi71RRgL3Kz1t4z79g4zwjtvW8Z8Rvj+Vejze+OWDEh2/g+/Xq\nw30Ryx9gnIW+hL2dnRdf351/UB8a/4/XRc2c0dDQ0NDQ0NDQ0NDQ0NDQ0HiG0D/OaGhoaGhoaGho\naGhoaGhoaDxD/KusSdoL25pIGh5Xg4K0cTloQrlFLNk5chP0z5prBNUyk2UMV36CjKbpm7CKbdI0\ngvIufgHJkpMvaKdW9vgq3s2ZKnflq0NGHDkZtNCkzUzn9WoHatbYabArLn/I9OD7G3FeTB3Yppla\nKR9bBmp0sDeocxcSWF6T+DPuRWQsW8aZAzlnYUuW75xFx2zchb2yoKJbWvDvdtIatGN9yLCqStmW\n8vZyUNaHfzbciLMvMNW5hqCKezaDjfKuZZCJxQYy7c9VUGuzhV2ZtDVTSqlHxaDEnf8B8ol6A5gK\namkPyY6UU53ce5Hy/IQdcGSHECMeKujkSilVUcByI3NC2uBaietWSqnSFNB0y4Q9m38M0+0Ovf++\nEUvrzfqTWAKUnwopSZGwp7z802nKC+0WbsTlefi7DQZBYlJZyNIHKVWTsZSAKKWUax3QH+O3YUzk\n32Zb1nr9QHctFBKD28tZlielN96dQozYz41tg5vPYtmfuVGUBMtFUxv6qjKM24oKzNMVSzZRnrR0\ntbQALbuRoHIrpdT2BX8Y8YyfXjLifz5iOni/iZAGlGahfh9difve6f0hdM6NraD0XtwP2vzX3/BY\nktatk0Xs1ZZtnS1EPQhsAAnknUO/U957H/xoxOuObTDiF7tNoLzVxw6opwW5bgQOYKnHwS/EWC3G\nutGsaSTlxXaBbXTxfcyxgEYs7ZFzves7oBgnbWPbSCkZPr8MFpCtZnUy4uzzXINDu4JefuUH3Mvi\nbM7rMg7rsYUl1oWKh6WUl3YSlHm3+pi/1s42lHc/C2M7XMhlJTVcKaV6fWhus1eGpIcnbj5Hx9yb\nY01KP3bPiHMusGWoezSo/DaeWEtDujMNP1Pcm6x8PO8Br/ekvGML/jJiaU/axRJzfvB8lsjJ2pn0\nJ8amX68wyms8GRTr7Ku4nqIyXrds7LEPqN0sxIjL0nhv12sextzB+VvVkxDZOfKJx/4rMk5ABng3\nnsdtSImwKG6LvV3oaJbol2ZhntoLGW5JeiHllYu84vsYqwEDeI/qKiReUmJdpxckLyXd+LP9rDCv\n8q5AxuDfjZ9hcSr+rpUD1hIbN5YiVpXhu+cU4m+1e4vlqYXCyt5C7KHdotg6+9T3kMTV/oblHeZA\ngykDjdh2yx46dnsXap1/ON5Jmk9oRXl1LmBN8RbSPAsL3i+lHcD+e+QX4434zNldDowAACAASURB\nVKK1lOflgvcVayEzuSXklvl37tE5/ZqgbuxYf8iIX17+NuXdXA9b+soq7E3u72X7Xu82+B6FiblG\n/OAi71H3rsK7Rl3x3hbxfGPKi54+UD0tyP2MnTe/L1bkocbc3oA18vaVe5TXew5kncVpGOsnlx6l\nvOh+2Ms/eoTx7S7aGCilVG7aeSN2jcaaVFQESYmNDcuEiu9jXsWMhlX6gwe8b6r7HOqI62HUIdO9\nrG8M8npH4D3w/iZ+/0zegnGeJWyq67Th98oL+7DfajRUmR12/pA4+7Tg/c3VrzA3/brguux8nShP\njmkpXa7/HI/H5MOnjNhCSBbPbblAeXLPeysVNdXCGnWzdjjvnV596XMj/uzDqUY8sl07ynv8GO8o\n+TdxrU4mdvByf9J/BKSWuZczKM9HyKny7t4zYvleqpRSTaa2Vv8GzZzR0NDQ0NDQ0NDQ0NDQ0NDQ\neIbQP85oaGhoaGhoaGhoaGhoaGhoPEP8q6wpQ9BvGzdnGnr8atDFBo0GVXLXH9xdvl0z0HudQiEh\niClg6VH4IKYB/z9UZLFzTmkFpBARvSGr2LlgpxEP68yd0W2s8DXTBaUxOYmlVXG3QfUd2AzSnWXv\nrKA82fF+xAzIIFK2cpf5rnNAY932/pNpv12Gt3niMXNANEpX7lHcLfvSz5B/RA4EVXDwouGUl3kW\ntD3PRqCiOzibuobg9759H8K5xZQ63fI5OOFYC8eA5s1Bo6s0kQlJGZKrcBMoz+Ex8rgKX1hSLXPO\nMSW9KBs07dr98Xf7d61DeSe+AWV0/9egZIZ48728KtwCpq8xr7uIlEyl77tLx0JGwMXqmJDw2Xkz\n1TB4KMZ0dTloh9bW7pTnUwdSiMoS0Bi7zp9OeaWlGBM5t3BNvo0hHdz41jd0Tre3IKWQz/P0D1w3\npNtJnTHsxCMhXThKhLyr0kQyVFYJSqGvoELey2KZX6OyXPU0ISV445bOo2NXVoJWbet5xYifH9Gd\n8rZsxXj8YCPkKBd+4Xtdff26EV/7Dq55bcdyvbEX7g5WDkLqVx800XVvsgvT2K+nGbGdkAJ0C+Tu\n/nnXQfkMjoU8xs6OKai5uXj+hz9cZMSe0Uyv9/cEBbmyEuvT4t/fobwzX39pxG1mvKfMCVmvpIRI\nKaXKxTiLCoSUwt6P56KNB2SFV06jBnfty+4aWafg9mIv3GPqjGQ3qkcVqIHetVAbSzJQ44I7dKJz\ncu5jDXeJhBTjwZF7lOcSjnt+cysc6eRarJRS6Q9BB492hOSgLJfr86DXsC7e3QZqd/dp7Jgk18yp\nP/dX5oaU9EoZllJK3fgV9yZ6CtaqxLVXKW/P35B69h6B8S1lTEopVRgP+cj05S8asZSDKqWUXxjG\nu1wzUw+Aym5Kj758EvuOrm9g/Nh5slNe3g3MxcxDqN2x77NVxO8zIB3s8To+76HcSJhcU0gUHJmc\nQnk92fQjZO8NB76szAkbF8zF9tNi6ViGGMfSGTB5DzvJ2NlCdld3CtauonvJlOfXGfuCXOGg4hYS\nTHmytrn3xl40bhPGc2BPlnolbIDToFcb1A1TaVWVcDE5Ltb6Bj2jKK9ayM1DW2KPlnUmhfI8GmMv\nl/QP5Mx317GbVKPBLEcwNzLjINM5c4jnWIWQSHi6CqmRsy3lWYuxIN1esq6x49++HZBS2O7GPM8U\n7ztKKTVmOpx+3CIhR2knasXtX1h+8dnUH4y4azT207c37Kc8SyEha/1aR/Uk2DlAJvXHckiYI/39\nKa+F2DfL8VOWxS0ZrEK5JpgTYQMxBn0b8Pq094PlRtzydVEn7/L+a9cnaFtRvwHGbWQsSweL7qKe\nntgDdyQptVdKKcdaGC8R3Z434tJSzAMbG65Xcn0/OvdDI/bvx5/9zzfYG/t7YM2t05bfHxL+wh7h\nURHWzHvx/D7i4YQ9QucPxxqxbDOglFLdmvIey9zw7wIpZXEmO0XV6o1WDrt/hHQ8NYfbDdgLh1sp\nSTq/mtsNlIt9+m4h1YttwL8HNBqP8fRYrEOvzYKj2sxBLPeVnyHbsoQM41pZmIIxuHnLISN+IcaP\n8ho+Byl66QOsJ7YuLCl1CsbvHPfWoZbVN3E8LcnmsW8KzZzR0NDQ0NDQ0NDQ0NDQ0NDQeIbQP85o\naGhoaGhoaGhoaGhoaGhoPEPoH2c0NDQ0NDQ0NDQ0NDQ0NDQ0niH+tedMZAyssrLOs/5270nowyb0\nfc6I20Sz9da1m4lGPGLy60Z8ZCtrz2KEte9doeN0rsc2Z3+thuXl7qnQ6c74YpIRX/+arfjCJkEr\nViisbOt7s/6yNA363o2zfjHiqUvZpjVVaJbzLkGTFza5KeVZWUFD2LI7+mZs3nCQ8nLOCO3hU3AP\n9esE7WbBXe6pkSxszh5twv0MbhhIeQG9oLd8XAWruMrKbMqrqoLGtcEw6JQz9iZS3vWt6KkR3gV6\nUpf60PZ6NWQLuYLkdCM+exh694HdWGto5QgNechQ6AsLEvi7X1t5xIhvfg97tmGfsG2wtO07uwka\nZUeTni59B3BvEHPiyE+wEqwpLB6VUqque6wRN+gJnaV7rfqUF7cefZnqDEPfkcSDuygvJBbWyjln\nhW2d5WHKyziCvgWB/aGhv/EL7GCHLn6Nzjm9aL0RR7+CvhSX7t2jvJGT0Zfi8gr0dajbj79TUSK0\nxzZCKxw+ohnlZQk7efcI9EeQ/aOUYpvpicvN3+fi1Fro3V3DuLaFDBPPzh12f4FRfI07d0DDnJ19\nyIiL7rFm/uX50Fi71UZfhD9n/kJ5VdWYz9FB6C3WfDZ6Y/h3ZRv1B+cxf9MOYm7bu9pTXos33zDi\n9MR/jPjj6bMo7731C/HZD6E7b9VvPOW9ZIsla0zsFCNeMPdFynOszRbp5oRPhxAjNmnDoWI6wh6x\nPBdW03Y1ea3ZOHeLEQ+cBfvQLcJKWSml2nVBDT28/oQRty3mfi+OgejRdOEK1qdeovbnpnIfCXsv\nnGMpbCylFbBSSt0X9syyz0zrF9mScsdiPN/H1bgxh3/gutFtFjTzAW1DjFjOUaWU6vNuH/U04eCH\nOpp7OZ2OFZbi2RUlC1vTCdx748Ic9LCrzIclZ1AfrlMWFqhNJxehDnt68zh1bYD1r8cw7Ftq1MD5\nZfmsVQ/uh74mxxeifrV6i3sM1WqGsXlxE/ZYFt9y/e/wHPLO/ox61W0u96a59hV6yUjb7iJhRauU\nUuMXmd962fhbCaj/cgwrpVToUPQpKC/ENZWmsyV46EjszXIuYy9WXcZ9y26uOGvEIYPwfCtKud9C\n7h2MCTvR68BN9PtLP8J942zFXvSc6MvQ7g1+hgc/32vEwT74PJe6vJY410EPDHsPjLH7O7ifS+Zx\nrOFyHQgw6btXlCieaawyO6T1d+ue3DMyrA/2AlkJ2H+l/s29OKImwCa6uho9Z26u2kl5zevgu10R\nfQJf/WEy5SVtg7Wxg+j39cHkr4140Z9ske17EeunTwvsa4szub+lRyDG3K0/UPNLTezqI17Eejpk\nLr7fATEOlFIqsAu+U/pujK1LtxIor30BalTDQdxD5b8i+zTqd9Zx7rlVtzP+lo0j7mWtpvyeUccb\n+0hrJ+zjrZ25r0exk3iPG437UlHBczH+D+ybbT3x3lWShpp+ZMNKvobeuIb0XNQX1ySua73fwDrm\n4IPvVGVSNyqLcM9VjRr4O8NbUF7qEayzJxeil6C9jQ3lWVqCUxEwd7AyN2QfRwcv7sdzbjn2INJa\n+qVPx1Be0mbMneIirKV5xdwDKaYf1tOOr6PWWTvydy7L/d+f0bY+6nDkCO5NWc8Sny1r/i9z/6C8\nRsHYG9tao+fin4u2U97AaT2NOOFIvBG3eYff+x7eQW+3GhZ43ikHuPYmnsTcDPpymDKFZs5oaGho\naGhoaGhoaGhoaGhoPEPoH2c0NDQ0NDQ0NDQ0NDQ0NDQ0niH+VdZ0+fQtI27ei+m8r/wIK9Ujn4D+\n02QCU7XOLwb9R9rq2plQtXYtBkW2QT1QsRNPsRzmtW8gX6oU1G5pqRjxCl9DmbBari4H5axSUPyU\nUsrOB9TSXm+DwvTg6D3Kq90PtN+MS6Ci7Zv3N+VlF0ImNWBmbyOevJBtls//eEI9TeRcBGXb9DsX\nCrvOIGE5Li3flFKq/CHy4tdC/hQ6gi3PpM2ne11YStqb0PqjvUHDzL4NiZJLCCj11dV8rRXiGnq9\nhedjZeNMeal/QXL3MA90tqgxMZTX6x3QZSW9N+dCKuXlnMP96zUP9ooZJ3ls3t0M6+IwHoL/GXX9\nYetW98VmJkdBQ/RuDoregytnKatmK8h5Hj/GPLh7kK1FH5XgGbo1AHXaN5JlDDc3QCZxbwls9Tq9\nB7pnWdl9OqdmFKxi0/aDfvvSF2Mpz84NtGzHIMgvJE1QKaV82ocY8VFxDUEVLCvwaQW6cWUZaJGx\nU55sY/k0MGDRS0acdYtlJrNfQX3s2Rj1tkksz7Hx8yEvcHdvacSlFWzXKeteSqKYsz5sTx3cG/Tr\n65txTZWVoPQ6egTROQV3QR+OmIh5NbonW1qvEnKHbZ+jPo4Y0ZXyHqaABnvmDsZj01NHKO/GAeSt\nOQR7zh3v/Up5jbrxPTMn4n4Gtf7UHZ47EzpDirn1a8h8KoUdrFJK9RBryJ6v9xlx/VpsMV5dCalB\nzzcxrx7eYJp8ylas1d2nwrIxcSNq0vkEprjXEePg8n3M0+CaLGuKaoDvFN0LEs/zK09RXpdxsEjN\nOgMZdLPeTDe+vgKyDWmL2fCV1pQn7XCfBkrSQcuWMhCllGrZ0NeI0/aiTt39O47ypNTA0gbywz1z\nmRLdZgpqZ2hn2JEWxrPU9sw2yI26C8teJ3fEl3/eTed4CLv53CKsd6l7eWxa2t8z4pAwWPHG3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V8cv74SwvOTNTxy061NexXW1nllfBykzHBRl5iFNyWd7J36/oePDCPjp+sSdEx/WmtWCPubzi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kmJjKsODiZ5xbkYwwbmRjJ+svU+yavUvZqM4y9h/FgHO5O8ghScvxoDJgptMqRhQxn/dnwR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\nPs8tzNtWw3vNlxTsaytU92V5RlbGWmxghO9kW4m3CsmMxGdUbD1C6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Eir9hyn9IkDY7DO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\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vZxiGYRiGYRiGYRiGYRoQ/nKGYRiGYRiGYRiGYRimAeEvZxiGYRiGYRiGYRiGYRoQ/nKG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\nuq+BzOtlFORzC3pxeXfHetj3WzfFbwUtvbi8+9wCvGvlnMb7S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8sU3zXiAOqtnp0Ry7Pv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fOPPypjEnUc0rx1y/ayY1P+HK/j3V/Aut6J2OgqpZQDkSW518f1vHbqLsvr+yNkLpN6Qc44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\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3nSf30oRekBL/ceYCe05YEPYTpWS/9duyXSyvfwesme49sDaXpPP55ckevKeKCkiu067H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\nDuy8gwZGU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tyNrvUfDqEPh7Z7eAkhxIsj2FPmf6K2vC69MC9EK710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\nvT6D+dTpKZ1fdI3/dXvynzFywPiOO017JdnVQS+Y0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4zV3mFCCPF2H3rs1B6F+aVHp6Yk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\nDHa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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "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?" + ] + }, + { + "metadata": { + "id": "l2AHvnWGi4Qp", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 970 + }, + "outputId": "687e6798-b290-4861-d257-453bf4ed0817" + }, + "cell_type": "code", + "source": [ + "# Training for 10, 100 and respectively 1000 steps \n", + "classifier = train_nn_classification_model(\n", + " learning_rate=0.05,\n", + " steps=1000,\n", + " batch_size=30,\n", + " hidden_units=[10, 100],\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "LogLoss error (on validation data):\n", + " period 00 : 6.01\n", + " period 01 : 4.63\n", + " period 02 : 3.90\n", + " period 03 : 4.24\n", + " period 04 : 3.61\n", + " period 05 : 3.74\n", + " period 06 : 3.33\n", + " period 07 : 3.25\n", + " period 08 : 3.15\n", + " period 09 : 3.25\n", + "Model training finished.\n", + "Final accuracy (on validation data): 0.91\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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j2zmo7RneYTAlNaUcyj5qwQiFEEK0dVK8zUypUBA7JASd3sCm31JNajsmaDhK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\nYFYaYFIaYVYFg92kNESsFxlr/AE/WrpcsHU6YBs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F5atilZWVeOWVV/Daa68NCm4AuOee\ne0I/l5SU4PTp09cNbyIiujaRIKAwX4/CfD1sLR5sP2DFF0cbsaWyFlsqawEASrkEySpZ8J9Sevln\nlQxapWzQ73IpRzpjWdjCu6OjA+vXr8df//pXpKSkDHls1apVePnllyGTybBv3z4sXrw4XE0hIhpR\nzDolli8cgyUleag6bsNZaxvaOnvQ7ulBe2cP7C0efNOQq1wmHhLoyUoptKorj8mQJBNf97YnDb+w\nhffWrVvhcrmwatWq0LEZM2Zg7NixWLhwIUpKSlBWVga5XI4JEyaw101ENMwUcglKiywoLbIMOu7z\n++H29A4K9PbOXrR39lxxrAc1De3wf8PdVZlENCjMk1XSAT/LBgW+Ui5h0A8DLo9KYcdaRwbrHBkj\nrc7+QABub28ozC/9a7si9C8Fvs9//UiRiAVoBob6oN69NNTbv2lUGjzurgi9y9jF5VGJiOg7EwlC\nMGCVMsBw/XMDgQA6u/ouh7ynvyc/8J8nGPgNzk5csF3/Q5A+OQnZJjWyjGpkmzTINqqh1yax5w6G\nNxERDRNBEKBWSKFWSJHxDTuiBQIBdPX4Lg/VXzFc3+rpxTlrKw6dceLQGWfoeQq5BNnGYKBnmdTI\nNmqQkaaKq5n0w4HhTUREEScIAhRyCRRyCUw65ZDHDQYNHI52tHX2oM7hxkV7R/9/3Thd14pTda2h\nc8UiAel6FbJN6mCwmzTIMqoTehMXhjcREcUkQRCQopYjRS3HpDx96Hh3jw/WJjcuOtyo6w/1uiY3\nrE1u7B7wfF2yHNlGTf+wezDU07RJECXAsDvDm4iI4opcJkZ+phb5mdrQMb8/ALvLE+qdX3R0oM7u\nxuGzThw+O3DYXYwsgxpZRk1w2N2kRmaaCtI4W8GT4U1ERHFP1D90nq5XYfr4yztUBofdg0F+sX/4\n/Ux9G05b2y4/VxCQnqbsv5euCU2Si+WNXhjeRESUsLQqGbSj9CgYNWDYvdeHBmcnLto7+ofe3ahz\nuFHf1Imq4/bQeakaeWjIPbu/p25IUcTEsDvDm4iIRhS5VIxR6ckYlZ4cOuYPBNDk8oZ653WOYKAf\nOdeMI+eaLz9XJg4Gev/X17KMwWF3WYSXk2V4ExHRiCcSBJh0Sph0SkwbZwwdb/cEZ7vXXbqP7nCj\npr4dZwcMuwsCkK5XYWx2Ch6cf1NEdnJjeBMREV1DslKGibk6TMzVhY719vlQ7+zERfvgUN99zIYl\nc/KgVjC8iYiIYopUIkauORm55sHD7n5/IGL7pzO8iYiIvieRIEAkjtxEtpG1nhwREVECYHgTERHF\nGYY3ERFRnGF4ExERxRmGNxERUZxheBMREcUZhjcREVGcYXgTERHFGYY3ERFRnGF4ExERxRmGNxER\nUZwRAoFAINqNICIiom+PPW8iIqI4w/AmIiKKMwxvIiKiOMPwJiIiijMMbyIiojjD8CYiIoozIzK8\nn332WZSVlWHZsmU4cuRItJuTsNavX4+ysjIsXboUn3zySbSbk9C6urqwYMECvPvuu9FuSkJ7//33\n8cMf/hD33nsvdu7cGe3mJKTOzk489thjKC8vx7Jly1BZWRntJsUkSbQbEGlfffUVLly4gIqKCpw7\ndw5r165FRUVFtJuVcPbs2YMzZ86goqICLpcLS5YswaJFi6LdrIT18ssvQ6vVRrsZCc3lcuGll17C\n5s2b4fF48Kc//Qlz586NdrMSznvvvYdRo0Zh9erVsNvt+PGPf4yPPvoo2s2KOSMuvKuqqrBgwQIA\nQH5+Ptra2uB2u6FWq6PcssQybdo0FBYWAgCSk5Ph9Xrh8/kgFouj3LLEc+7cOZw9e5ZBEmZVVVW4\n9dZboVaroVar8bvf/S7aTUpIqampOHXqFACgvb0dqampUW5RbBpxw+ZOp3PQxaDT6dDU1BTFFiUm\nsVgMpVIJAHjnnXdQUlLC4A6TdevWYc2aNdFuRsKzWq3o6urCz372MyxfvhxVVVXRblJCuvPOO9HQ\n0ICFCxdixYoV+NWvfhXtJsWkEdfzvhJXhw2vzz77DO+88w42bNgQ7aYkpC1btmDKlCnIysqKdlNG\nhNbWVrz44otoaGjAQw89hB07dkAQhGg3K6H8/e9/R0ZGBl5//XWcPHkSa9eu5VyOqxhx4W00GuF0\nOkO/OxwOGAyGKLYocVVWVuKVV17Ba6+9Bo1GE+3mJKSdO3eirq4OO3fuhM1mg0wmg9lsxsyZM6Pd\ntISj1+sxdepUSCQSZGdnQ6VSoaWlBXq9PtpNSygHDx7E7NmzAQDjxo2Dw+HgLberGHHD5rNmzcLH\nH38MADh+/DiMRiPvd4dBR0cH1q9fj1dffRUpKSnRbk7Cev7557F582a89dZbuP/++7Fy5UoGd5jM\nnj0be/bsgd/vh8vlgsfj4f3YMMjJyUF1dTUAoL6+HiqVisF9FSOu511UVISJEydi2bJlEAQBTz/9\ndLSblJC2bt0Kl8uFVatWhY6tW7cOGRkZUWwV0Y0zmUxYvHgxHnjgAQDAk08+CZFoxPV/wq6srAxr\n167FihUr0NfXh9/85jfRblJM4pagREREcYYfG4mIiOIMw5uIiCjOMLyJiIjiDMObiIgozjC8iYiI\n4gzDmyiBWa1WFBQUoLy8PLRL0+rVq9He3v6tX6O8vBw+n+9bn//ggw9i7969N9JcIvqWGN5ECU6n\n02Hjxo3YuHEjNm3aBKPRiJdffvlbP3/jxo1cJIMoxoy4RVqIRrpp06ahoqICJ0+exLp169DX14fe\n3l78+te/xoQJE1BeXo5x48bhxIkTeOONNzBhwgQcP34cPT09eOqpp2Cz2dDX14e7774by5cvh9fr\nxS9+8Qu4XC7k5OSgu7sbAGC32/HLX/4SQHC/8bKyMtx3333RfOtECYPhTTSC+Hw+fPrppyguLsbj\njz+Ol156CdnZ2UM2gFAqlXjzzTcHPXfjxo1ITk7GH/7wB3R1deGOO+7AnDlzsHv3biQlJaGiogIO\nhwPz588HAHz44YfIy8vDb3/7W3R3d+Ptt9+O+PslSlQMb6IE19LSgvLycgCA3+/HzTffjKVLl+KF\nF17AE088ETrP7XbD7/cDCC4jfKXq6mrce++9AICkpCQUFBTg+PHjOH36NIqLiwEEN/7Jy8sDAMyZ\nMwd/+9vfsGbNGtx2220oKysL6/skGkkY3kQJ7tI974E6OjoglUqHHL9EKpUOOXbl1peBQACCICAQ\nCAxa4/vSB4D8/Hz84x//wL59+/DRRx/hjTfewKZNm77v2yEicMIa0Yik0WhgsViwa9cuAEBtbS1e\nfPHF6z5n8uTJqKysBAB4PB4cP34cEydORH5+Pg4dOgQAaGxsRG1tLQDggw8+wNGjRzFz5kw8/fTT\naGxsRF9fXxjfFdHIwZ430Qi1bt06PPPMM/jzn/+Mvr4+rFmz5rrnl5eX46mnnsKPfvQj9PT0YOXK\nlbBYLLj77ruxfft2LF++HBaLBZMmTQIAjB49Gk8//TRkMhkCgQAeeeQRSCT8k0M0HLirGBERUZzh\nsDkREVGcYXgTERHFGYY3ERFRnGF4ExERxRmGNxERUZxheBMREcUZhjcREVGcYXgTERHFmf8PF+qM\nSiUECjUAAAAASUVORK5CYII=\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "rbQHvsLTjBgM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 178 + }, + "outputId": "7a2330e8-cc97-4797-fa43-92cb8cb56dc7" + }, + "cell_type": "code", + "source": [ + "# Visualizing the above paramater\n", + "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": 19, + "outputs": [ + { + "output_type": "stream", + "text": [ + "['dnn/hiddenlayer_0/bias', 'dnn/hiddenlayer_0/bias/t_0/Adagrad', 'dnn/hiddenlayer_0/kernel', 'dnn/hiddenlayer_0/kernel/t_0/Adagrad', 'dnn/hiddenlayer_1/bias', 'dnn/hiddenlayer_1/bias/t_0/Adagrad', 'dnn/hiddenlayer_1/kernel', 'dnn/hiddenlayer_1/kernel/t_0/Adagrad', 'dnn/logits/bias', 'dnn/logits/bias/t_0/Adagrad', 'dnn/logits/kernel', 'dnn/logits/kernel/t_0/Adagrad', 'global_step']\n", + "weights0 shape: (784, 10)\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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OcnHf0Gr7CdlSonbgI6G1oJuFc84NghNQ/wCX4y2YKaX1vmQpy0X6i3PODfZL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\nqXzG6YtmUjtcf8ntcrbOuu0savfb2x/zynNnNlLdb35/j1d+e+nrXhl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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "_L_Vai8Oj_td", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/sparsity_and_l1_regularization.ipynb b/sparsity_and_l1_regularization.ipynb new file mode 100644 index 0000000..f2b7259 --- /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": 1153 + }, + "outputId": "605b25eb-f5b9-4fca-ee78-bbf2edf1ac8f" + }, + "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 -119.5 28.5 2642.1 539.5 \n", + "std 2.1 2.0 12.6 2145.0 417.3 \n", + "min 32.5 -124.3 1.0 2.0 1.0 \n", + "25% 33.9 -121.8 18.0 1462.8 298.0 \n", + "50% 34.2 -118.5 28.0 2135.0 435.0 \n", + "75% 37.7 -118.0 37.0 3159.2 650.0 \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 1432.6 501.0 3.9 2.0 \n", + "std 1156.3 381.5 1.9 1.3 \n", + "min 3.0 1.0 0.5 0.0 \n", + "25% 792.0 282.0 2.6 1.5 \n", + "50% 1174.0 410.0 3.5 1.9 \n", + "75% 1726.0 606.0 4.7 2.3 \n", + "max 35682.0 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": 586 + }, + "outputId": "8068bf8e-e2c7-427a-ff65-9a13e7be84c8" + }, + "cell_type": "code", + "source": [ + "linear_classifier = train_linear_classifier_model(\n", + " learning_rate=0.1,\n", + " # TWEAK THE REGULARIZATION VALUE BELOW\n", + " regularization_strength=0.2,\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": 10, + "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 : 0.28\n", + " period 03 : 0.27\n", + " period 04 : 0.27\n", + " period 05 : 0.26\n", + " period 06 : 0.26\n", + "Model training finished.\n", + "Model size: 721\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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nn3+elStXsmrVKr766qur3tu2bRsrVqxg1apVbNq0qfcqmr4+MxyE+Dpz2/RQ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\nyC0ZjQZy3AnkuBP48wXZ9Pb1U+Vtp7wq0ExcUdNGdWMHu49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+ "text/plain": [ + "
" + ] + }, + "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 diff --git a/synthetic_features_and_outliers.ipynb b/synthetic_features_and_outliers.ipynb new file mode 100644 index 0000000..6fa157a --- /dev/null +++ b/synthetic_features_and_outliers.ipynb @@ -0,0 +1,1123 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 402 + }, + "outputId": "9fe80b38-7e70-4cbf-b628-9e1419c38c30" + }, + "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": 1, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
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6148-118.233.933.0677.0182.0984.0174.02.688.9
2539-117.633.913.08010.01366.03920.01309.05.5204.8
..............................
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "5326 -118.2 34.6 33.0 2111.0 429.0 \n", + "13863 -122.0 37.3 35.0 2355.0 384.0 \n", + "1885 -117.3 33.7 11.0 1161.0 235.0 \n", + "6148 -118.2 33.9 33.0 677.0 182.0 \n", + "2539 -117.6 33.9 13.0 8010.0 1366.0 \n", + "... ... ... ... ... ... \n", + "8081 -118.4 33.9 39.0 2988.0 605.0 \n", + "9673 -119.5 36.7 32.0 1963.0 508.0 \n", + "969 -117.1 32.7 22.0 2409.0 582.0 \n", + "6681 -118.3 34.1 34.0 2716.0 1114.0 \n", + "12428 -121.6 39.2 36.0 1206.0 197.0 \n", + "\n", + " population households median_income median_house_value \n", + "5326 1067.0 397.0 3.7 111.4 \n", + "13863 1248.0 378.0 6.0 332.5 \n", + "1885 640.0 210.0 2.2 114.6 \n", + "6148 984.0 174.0 2.6 88.9 \n", + "2539 3920.0 1309.0 5.5 204.8 \n", + "... ... ... ... ... \n", + "8081 1466.0 610.0 4.9 341.4 \n", + "9673 2052.0 518.0 1.9 55.8 \n", + "969 1887.0 578.0 1.4 94.2 \n", + "6681 2991.0 1021.0 1.8 187.5 \n", + "12428 537.0 204.0 3.4 79.8 \n", + "\n", + "[17000 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 1 + } + ] + }, + { + "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 939 + }, + "outputId": "b32ddf48-2485-4f7b-80eb-da236916cb12" + }, + "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=0.01,\n", + " steps=500,\n", + " batch_size=5,\n", + " input_feature=\"rooms_per_person\"\n", + ")" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 232.47\n", + " period 01 : 227.45\n", + " period 02 : 222.49\n", + " period 03 : 217.59\n", + " period 04 : 212.75\n", + " period 05 : 207.97\n", + " period 06 : 203.28\n", + " period 07 : 198.79\n", + " period 08 : 194.46\n", + " period 09 : 189.99\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 54.9 207.3\n", + "std 25.8 116.0\n", + "min 11.6 15.0\n", + "25% 44.8 119.4\n", + "50% 54.0 180.4\n", + "75% 61.9 265.0\n", + "max 1230.3 500.0" + ], + "text/html": [ + "
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nc06fPk1oaKi7Qq42RoOBm/q3476x3TGZDLy5ah/LNh3AZre7OzQREZFLpqRE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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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 390 + }, + "outputId": "676dfcfd-c13b-4c28-a161-8a0a9c3c891f" + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE\n", + "plt.figure(figsize=(15, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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AxWQEkN1AFHG43hNzS/Sh2WpDJ54zWSsSL6vKoE+uskMyuF0EEWWDqofmhKCI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\ngfOpyJHeQliSoNfp+oez4vW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" + ] + }, + "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", + "colab": {} + }, + "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": [] + }, + { + "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", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE\n", + "california_housing_dataframe[\"rooms_per_person\"] = california_housing_dataframe[\"rooms_per_person\"].apply(lambda x: min(x, 5))\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "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": { + "base_uri": "https://localhost:8080/", + "height": 939 + }, + "outputId": "4aa09362-c31d-4ff6-a6d3-3e53837d6040" + }, + "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": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 212.83\n", + " period 01 : 189.16\n", + " period 02 : 166.82\n", + " period 03 : 146.53\n", + " period 04 : 129.74\n", + " period 05 : 120.11\n", + " period 06 : 112.77\n", + " period 07 : 110.70\n", + " period 08 : 109.56\n", + " period 09 : 108.51\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 192.7 207.3\n", + "std 50.2 116.0\n", + "min 45.4 15.0\n", + "25% 160.6 119.4\n", + "50% 192.7 180.4\n", + "75% 220.1 265.0\n", + "max 427.0 500.0" + ], + "text/html": [ + "
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XXcbLL7/MpEmTzjrmznuF8vJybrvtNkcBRICff/6ZPXv2MHToUBISEkhLSyM/P5+qqipW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\neOyxx3DXXXfB5XJh69a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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "gySE-UgfSony", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "outputId": "3c27f3d0-d953-4f6a-ceee-f70322308978" + }, + "cell_type": "code", + "source": [ + "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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Kt6+ajwfubMFvdp2tqftS60ilWfXEHigJ8VyBDWDav3MjWtXyPY3U8krpoKaV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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "auQ4WbLmR0Ln", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/validation.ipynb b/validation.ipynb new file mode 100644 index 0000000..5aa5164 --- /dev/null +++ b/validation.ipynb @@ -0,0 +1,1510 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "validation.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "4Xp9NhOCYSuz", + "pECTKgw5ZvFK", + "dER2_43pWj1T", + "I-La4N9ObC1x", + "yTghc_5HkJDW" + ], + "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": [ + "# Validation" + ] + }, + { + "metadata": { + "id": "WNX0VyBpHpCX", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Use multiple features, instead of a single feature, to further improve the effectiveness of a model\n", + " * Debug issues in model input data\n", + " * Use a test data set to check if a model is overfitting the validation data" + ] + }, + { + "metadata": { + "id": "za0m1T8CHpCY", + "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), to try and predict `median_house_value` at the city block level from 1990 census data." + ] + }, + { + "metadata": { + "id": "r2zgMfWDWF12", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup" + ] + }, + { + "metadata": { + "id": "8jErhkLzWI1B", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "First off, let's load up and prepare our data. This time, we're going to work with multiple features, so we'll modularize the logic for preprocessing the features a bit:" + ] + }, + { + "metadata": { + "id": "PwS5Bhm6HpCZ", + "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": "J2ZyTzX0HpCc", + "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": "sZSIaDiaHpCf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "For the **training set**, we'll choose the first 12000 examples, out of the total of 17000." + ] + }, + { + "metadata": { + "id": "P9wejvw7HpCf", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 284 + }, + "outputId": "4c8287c9-8a6d-4bfc-acb1-bf48af3c7497" + }, + "cell_type": "code", + "source": [ + "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "training_examples.describe()" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "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 34.6 -118.5 27.5 2655.7 547.1 \n", + "std 1.6 1.2 12.1 2258.1 434.3 \n", + "min 32.5 -121.4 1.0 2.0 2.0 \n", + "25% 33.8 -118.9 17.0 1451.8 299.0 \n", + "50% 34.0 -118.2 28.0 2113.5 438.0 \n", + "75% 34.4 -117.8 36.0 3146.0 653.0 \n", + "max 41.8 -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 1476.0 505.4 3.8 1.9 \n", + "std 1174.3 391.7 1.9 1.3 \n", + "min 3.0 2.0 0.5 0.0 \n", + "25% 815.0 283.0 2.5 1.4 \n", + "50% 1207.0 411.0 3.5 1.9 \n", + "75% 1777.0 606.0 4.6 2.3 \n", + "max 35682.0 5189.0 15.0 55.2 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "metadata": { + "id": "JlkgPR-SHpCh", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 284 + }, + "outputId": "76a0aff3-d89a-4712-bce1-5238ecf62c34" + }, + "cell_type": "code", + "source": [ + "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "training_targets.describe()" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value\n", + "count 12000.0\n", + "mean 198.0\n", + "std 111.9\n", + "min 15.0\n", + "25% 117.1\n", + "50% 170.5\n", + "75% 244.4\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "metadata": { + "id": "5l1aA2xOHpCj", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "For the **validation set**, we'll choose the last 5000 examples, out of the total of 17000." + ] + }, + { + "metadata": { + "id": "fLYXLWAiHpCk", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 284 + }, + "outputId": "b2bf3884-38c1-404e-bf8a-eeef7fe086c5" + }, + "cell_type": "code", + "source": [ + "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n", + "validation_examples.describe()" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "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 38.1 -122.2 31.3 2614.8 521.1 \n", + "std 0.9 0.5 13.4 1979.6 388.5 \n", + "min 36.1 -124.3 1.0 8.0 1.0 \n", + "25% 37.5 -122.4 20.0 1481.0 292.0 \n", + "50% 37.8 -122.1 31.0 2164.0 424.0 \n", + "75% 38.4 -121.9 42.0 3161.2 635.0 \n", + "max 42.0 -121.4 52.0 32627.0 6445.0 \n", + "\n", + " population households median_income rooms_per_person \n", + "count 5000.0 5000.0 5000.0 5000.0 \n", + "mean 1318.1 491.2 4.1 2.1 \n", + "std 1073.7 366.5 2.0 0.6 \n", + "min 8.0 1.0 0.5 0.1 \n", + "25% 731.0 278.0 2.7 1.7 \n", + "50% 1074.0 403.0 3.7 2.1 \n", + "75% 1590.2 603.0 5.1 2.4 \n", + "max 28566.0 6082.0 15.0 18.3 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "metadata": { + "id": "oVPcIT3BHpCm", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 284 + }, + "outputId": "c935b031-cde9-49c5-b67c-32ef0c9d295e" + }, + "cell_type": "code", + "source": [ + "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n", + "validation_targets.describe()" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " median_house_value\n", + "count 5000.0\n", + "mean 229.5\n", + "std 122.5\n", + "min 15.0\n", + "25% 130.4\n", + "50% 213.0\n", + "75% 303.2\n", + "max 500.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "metadata": { + "id": "z3TZV1pgfZ1n", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Examine the Data\n", + "Okay, let's look at the data above. We have `9` input features that we can use.\n", + "\n", + "Take a quick skim over the table of values. Everything look okay? See how many issues you can spot. Don't worry if you don't have a background in statistics; common sense will get you far.\n", + "\n", + "After you've had a chance to look over the data yourself, check the solution for some additional thoughts on how to verify data." + ] + }, + { + "metadata": { + "id": "4Xp9NhOCYSuz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "gqeRmK57YWpy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's check our data against some baseline expectations:\n", + "\n", + "* For some values, like `median_house_value`, we can check to see if these values fall within reasonable ranges (keeping in mind this was 1990 data — not today!).\n", + "\n", + "* For other values, like `latitude` and `longitude`, we can do a quick check to see if these line up with expected values from a quick Google search.\n", + "\n", + "If you look closely, you may see some oddities:\n", + "\n", + "* `median_income` is on a scale from about 3 to 15. It's not at all clear what this scale refers to—looks like maybe some log scale? It's not documented anywhere; all we can assume is that higher values correspond to higher income.\n", + "\n", + "* The maximum `median_house_value` is 500,001. This looks like an artificial cap of some kind.\n", + "\n", + "* Our `rooms_per_person` feature is generally on a sane scale, with a 75th percentile value of about 2. But there are some very large values, like 18 or 55, which may show some amount of corruption in the data.\n", + "\n", + "We'll use these features as given for now. But hopefully these kinds of examples can help to build a little intuition about how to check data that comes to you from an unknown source." + ] + }, + { + "metadata": { + "id": "fXliy7FYZZRm", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Plot Latitude/Longitude vs. Median House Value" + ] + }, + { + "metadata": { + "id": "aJIWKBdfsDjg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's take a close look at two features in particular: **`latitude`** and **`longitude`**. These are geographical coordinates of the city block in question.\n", + "\n", + "This might make a nice visualization — let's plot `latitude` and `longitude`, and use color to show the `median_house_value`." + ] + }, + { + "metadata": { + "id": "5_LD23bJ06TW", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 498 + }, + "outputId": "e29e64e8-d295-4bef-8f56-ae49b068dc1d" + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize=(13, 8))\n", + "\n", + "ax = plt.subplot(1, 2, 1)\n", + "ax.set_title(\"Validation Data\")\n", + "\n", + "ax.set_autoscaley_on(False)\n", + "ax.set_ylim([32, 43])\n", + "ax.set_autoscalex_on(False)\n", + "ax.set_xlim([-126, -112])\n", + "plt.scatter(validation_examples[\"longitude\"],\n", + " validation_examples[\"latitude\"],\n", + " cmap=\"coolwarm\",\n", + " c=validation_targets[\"median_house_value\"] / validation_targets[\"median_house_value\"].max())\n", + "\n", + "ax = plt.subplot(1,2,2)\n", + "ax.set_title(\"Training Data\")\n", + "\n", + "ax.set_autoscaley_on(False)\n", + "ax.set_ylim([32, 43])\n", + "ax.set_autoscalex_on(False)\n", + "ax.set_xlim([-126, -112])\n", + "plt.scatter(training_examples[\"longitude\"],\n", + " training_examples[\"latitude\"],\n", + " cmap=\"coolwarm\",\n", + " c=training_targets[\"median_house_value\"] / training_targets[\"median_house_value\"].max())\n", + "_ = plt.plot()" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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TIKCfnQcdHliVoCvR99ieQx5pt5OrLjTYsDVOOqOZN7uIYOD09lRfs8Bi6/4M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\n84fNE9buX9z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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "32_DbjnfXJlC", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Wait a second...this should have given us a nice map of the state of California, with red showing up in expensive areas like the San Francisco and Los Angeles.\n", + "\n", + "The training set sort of does, compared to a [real map](https://www.google.com/maps/place/California/@37.1870174,-123.7642688,6z/data=!3m1!4b1!4m2!3m1!1s0x808fb9fe5f285e3d:0x8b5109a227086f55), but the validation set clearly doesn't.\n", + "\n", + "**Go back up and look at the data from Task 1 again.**\n", + "\n", + "Do you see any other differences in the distributions of features or targets between the training and validation data?" + ] + }, + { + "metadata": { + "id": "pECTKgw5ZvFK", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "49NC4_KIZxk_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Looking at the tables of summary stats above, it's easy to wonder how anyone would do a useful data check. What's the right 75th percentile value for total_rooms per city block?\n", + "\n", + "The key thing to notice is that for any given feature or column, the distribution of values between the train and validation splits should be roughly equal.\n", + "\n", + "The fact that this is not the case is a real worry, and shows that we likely have a fault in the way that our train and validation split was created." + ] + }, + { + "metadata": { + "id": "025Ky0Dq9ig0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 3: Return to the Data Importing and Pre-Processing Code, and See if You Spot Any Bugs\n", + "If you do, go ahead and fix the bug. Don't spend more than a minute or two looking. If you can't find the bug, check the solution." + ] + }, + { + "metadata": { + "id": "JFsd2eWHAMdy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "When you've found and fixed the issue, re-run `latitude` / `longitude` plotting cell above and confirm that our sanity checks look better.\n", + "\n", + "By the way, there's an important lesson here.\n", + "\n", + "**Debugging in ML is often *data debugging* rather than code debugging.**\n", + "\n", + "If the data is wrong, even the most advanced ML code can't save things." + ] + }, + { + "metadata": { + "id": "dER2_43pWj1T", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "BnEVbYJvW2wu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The code that randomizes the data (`np.random.permutation`) is commented out, so we're not doing any randomization prior to splitting the data.\n", + "\n", + "If we don't randomize the data properly before creating training and validation splits, then we may be in trouble if the data is given to us in some sorted order, which appears to be the case here." + ] + }, + { + "metadata": { + "id": "xCdqLpQyAos2", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 4: Train and Evaluate a Model\n", + "\n", + "**Spend 5 minutes or so trying different hyperparameter settings. Try to get the best validation performance you can.**\n", + "\n", + "Next, we'll train a linear regressor using all the features in the data set, and see how well we do.\n", + "\n", + "Let's define the same input function we've used previously for loading the data into a TensorFlow model.\n" + ] + }, + { + "metadata": { + "id": "rzcIPGxxgG0t", + "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 multiple features.\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": "CvrKoBmNgRCO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Because we're now working with multiple input features, let's modularize our code for configuring feature columns into a separate function. (For now, this code is fairly simple, as all our features are numeric, but we'll build on this code as we use other types of features in future exercises.)" + ] + }, + { + "metadata": { + "id": "wEW5_XYtgZ-H", + "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": "D0o2wnnzf8BD", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, go ahead and complete the `train_model()` code below to set up the input functions and calculate predictions.\n", + "\n", + "**NOTE:** It's okay to reference the code from the previous exercises, but make sure to call `predict()` on the appropriate data sets.\n", + "\n", + "Compare the losses on training data and validation data. With a single raw feature, our best root mean squared error (RMSE) was of about 180.\n", + "\n", + "See how much better you can do now that we can use multiple features.\n", + "\n", + "Check the data using some of the methods we've looked at before. These might include:\n", + "\n", + " * Comparing distributions of predictions and actual target values\n", + "\n", + " * Creating a scatter plot of predictions vs. target values\n", + "\n", + " * Creating two scatter plots of validation data using `latitude` and `longitude`:\n", + " * One plot mapping color to actual target `median_house_value`\n", + " * A second plot mapping color to predicted `median_house_value` for side-by-side comparison." + ] + }, + { + "metadata": { + "id": "UXt0_4ZTEf4V", + "colab_type": "code", + "cellView": "both", + "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 of multiple features.\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", + " #my_label = \"median_house_value\"\n", + " #targets = california_housing_dataframe[my_label].astype('float32')\n", + " \n", + " \n", + " # 1. Create input functions.\n", + " training_input_fn = lambda: my_input_fn(training_examples, training_targets[\"median_house_value\"], batch_size=batch_size)# YOUR CODE HERE\n", + " predict_training_input_fn = lambda: my_input_fn(training_examples, training_targets[\"median_house_value\"], num_epochs=1, shuffle=False)# YOUR CODE HERE\n", + " predict_validation_input_fn = lambda: my_input_fn(validation_examples, validation_targets[\"median_house_value\"], num_epochs=1, shuffle=False)# YOUR CODE HERE\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", + " # 2. Take a break and compute predictions.\n", + " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn) # YOUR CODE HERE\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) # YOUR CODE HERE\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": "zFFRmvUGh8wd", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 619 + }, + "outputId": "bd01389c-69a6-4db8-f61e-f8b7b0a35bf3" + }, + "cell_type": "code", + "source": [ + "linear_regressor = train_model(\n", + " # TWEAK THESE VALUES TO SEE HOW MUCH YOU CAN IMPROVE THE RMSE\n", + " learning_rate=0.0001,\n", + " steps=100,\n", + " batch_size=1,\n", + " training_examples=training_examples,\n", + " training_targets=training_targets,\n", + " validation_examples=validation_examples,\n", + " validation_targets=validation_targets)" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 213.82\n", + " period 01 : 201.23\n", + " period 02 : 190.14\n", + " period 03 : 180.50\n", + " period 04 : 175.49\n", + " period 05 : 168.73\n", + " period 06 : 165.49\n", + " period 07 : 163.12\n", + " period 08 : 161.99\n", + " period 09 : 160.95\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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QQtyVNDACAGu1NWOaDWVC0MPoFB2fx33F2jM/yanWQgghzJI0MOIW7Xxb83K7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\nhikW7DPixdrcPH2DztAtqEC4Cfx+eM2aYTJJDJI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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "I-La4N9ObC1x", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for a solution." + ] + }, + { + "metadata": { + "id": "Xyz6n1YHbGef", + "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 of multiple features.\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(\n", + " training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " batch_size=batch_size)\n", + " predict_training_input_fn = lambda: my_input_fn(\n", + " training_examples, \n", + " training_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + " predict_validation_input_fn = lambda: my_input_fn(\n", + " validation_examples, 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", + " \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": "i1imhjFzbWwt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "linear_regressor = train_model(\n", + " learning_rate=0.00003,\n", + " steps=500,\n", + " batch_size=5,\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": "65sin-E5NmHN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 5: Evaluate on Test Data\n", + "\n", + "**In the cell below, load in the test data set and evaluate your model on it.**\n", + "\n", + "We've done a lot of iteration on our validation data. Let's make sure we haven't overfit to the pecularities of that particular sample.\n", + "\n", + "Test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv).\n", + "\n", + "How does your test performance compare to the validation performance? What does this say about the generalization performance of your model?" + ] + }, + { + "metadata": { + "id": "icEJIl5Vp51r", + "colab_type": "code", + "cellView": "both", + "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", + "# YOUR CODE HERE\n", + "#\n", + "test_examples = preprocess_features(california_housing_dataframe.head(12000))\n", + "test_targets = preprocess_targets(california_housing_dataframe.head(12000))\n", + "\n", + "predict_test_input_fn = lambda: my_input_fn(test_examples, test_targets[\"median_house_value\"], num_epochs=1, shuffle=False)\n", + "test_predictions = linear_regressor.predict(input_fn=predict_test_input_fn) \n", + "test_predictions = np.array([item['predictions'][0] for item in test_predictions]) \n", + "\n", + "test_root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(test_predictions, test_targets))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "yTghc_5HkJDW", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for the solution." + ] + }, + { + "metadata": { + "id": "_xSYTarykO8U", + "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_test_input_fn = lambda: my_input_fn(\n", + " test_examples, \n", + " test_targets[\"median_house_value\"], \n", + " num_epochs=1, \n", + " shuffle=False)\n", + "\n", + "test_predictions = linear_regressor.predict(input_fn=predict_test_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