"
+ ],
+ "text/plain": [
+ " longitude latitude housing_median_age total_rooms total_bedrooms \\\n",
+ "count 17000.0 17000.0 17000.0 17000.0 17000.0 \n",
+ "mean -119.6 35.6 28.6 2643.7 539.4 \n",
+ "std 2.0 2.1 12.6 2179.9 421.5 \n",
+ "min -124.3 32.5 1.0 2.0 1.0 \n",
+ "25% -121.8 33.9 18.0 1462.0 297.0 \n",
+ "50% -118.5 34.2 29.0 2127.0 434.0 \n",
+ "75% -118.0 37.7 37.0 3151.2 648.2 \n",
+ "max -114.3 42.0 52.0 37937.0 6445.0 \n",
+ "\n",
+ " population households median_income median_house_value \n",
+ "count 17000.0 17000.0 17000.0 17000.0 \n",
+ "mean 1429.6 501.2 3.9 207.3 \n",
+ "std 1147.9 384.5 1.9 116.0 \n",
+ "min 3.0 1.0 0.5 15.0 \n",
+ "25% 790.0 282.0 2.6 119.4 \n",
+ "50% 1167.0 409.0 3.5 180.4 \n",
+ "75% 1721.0 605.2 4.8 265.0 \n",
+ "max 35682.0 6082.0 15.0 500.0 "
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 5
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "Lr6wYl2bt2Ep",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Build the First Model\n",
+ "\n",
+ "In this exercise, we'll try to predict `median_house_value`, which will be our label (sometimes also called a target). We'll use `total_rooms` as our input feature.\n",
+ "\n",
+ "**NOTE:** Our data is at the city block level, so this feature represents the total number of rooms in that block.\n",
+ "\n",
+ "To train our model, we'll use the [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor) interface provided by the TensorFlow [Estimator](https://www.tensorflow.org/get_started/estimator) API. This API takes care of a lot of the low-level model plumbing, and exposes convenient methods for performing model training, evaluation, and inference."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "0cpcsieFhsNI",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Step 1: Define Features and Configure Feature Columns"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "EL8-9d4ZJNR7",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "In order to import our training data into TensorFlow, we need to specify what type of data each feature contains. There are two main types of data we'll use in this and future exercises:\n",
+ "\n",
+ "* **Categorical Data**: Data that is textual. In this exercise, our housing data set does not contain any categorical features, but examples you might see would be the home style, the words in a real-estate ad.\n",
+ "\n",
+ "* **Numerical Data**: Data that is a number (integer or float) and that you want to treat as a number. As we will discuss more later sometimes you might want to treat numerical data (e.g., a postal code) as if it were categorical.\n",
+ "\n",
+ "In TensorFlow, we indicate a feature's data type using a construct called a **feature column**. Feature columns store only a description of the feature data; they do not contain the feature data itself.\n",
+ "\n",
+ "To start, we're going to use just one numeric input feature, `total_rooms`. The following code pulls the `total_rooms` data from our `california_housing_dataframe` and defines the feature column using `numeric_column`, which specifies its data is numeric:"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "rhEbFCZ86cDZ",
+ "colab_type": "code",
+ "outputId": "254ca935-1bec-4451-ef0d-9039c2628847",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "# Define the input feature: total_rooms.\n",
+ "my_feature = california_housing_dataframe[[\"total_rooms\"]]\n",
+ "\n",
+ "# Configure a numeric feature column for total_rooms.\n",
+ "feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]\n",
+ "\n",
+ "{key:np.array(value) for key,value in dict(my_feature).items()}\n"
+ ],
+ "execution_count": 0,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'total_rooms': array([3135., 3713., 1066., ..., 8489., 1734., 5151.])}"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 26
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "K_3S8teX7Rd2",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "**NOTE:** The shape of our `total_rooms` data is a one-dimensional array (a list of the total number of rooms for each block). This is the default shape for `numeric_column`, so we don't have to pass it as an argument."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "UMl3qrU5MGV6",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Step 2: Define the Target"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "cw4nrfcB7kyk",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Next, we'll define our target, which is `median_house_value`. Again, we can pull it from our `california_housing_dataframe`:"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "l1NvvNkH8Kbt",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "# Define the label.\n",
+ "targets = california_housing_dataframe[\"median_house_value\"]\n"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "4M-rTFHL2UkA",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Step 3: Configure the LinearRegressor"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "fUfGQUNp7jdL",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Next, we'll configure a linear regression model using LinearRegressor. We'll train this model using the `GradientDescentOptimizer`, which implements Mini-Batch Stochastic Gradient Descent (SGD). The `learning_rate` argument controls the size of the gradient step.\n",
+ "\n",
+ "**NOTE:** To be safe, we also apply [gradient clipping](https://developers.google.com/machine-learning/glossary/#gradient_clipping) to our optimizer via `clip_gradients_by_norm`. Gradient clipping ensures the magnitude of the gradients do not become too large during training, which can cause gradient descent to fail. "
+ ]
+ },
+ {
+ "metadata": {
+ "id": "ubhtW-NGU802",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "# Use gradient descent as the optimizer for training the model.\n",
+ "my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n",
+ "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+ "\n",
+ "# Configure the linear regression model with our feature columns and optimizer.\n",
+ "# Set a learning rate of 0.0000001 for Gradient Descent.\n",
+ "linear_regressor = tf.estimator.LinearRegressor(\n",
+ " feature_columns=feature_columns,\n",
+ " optimizer=my_optimizer\n",
+ ")"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "-0IztwdK2f3F",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Step 4: Define the Input Function"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "S5M5j6xSCHxx",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "To import our California housing data into our `LinearRegressor`, we need to define an input function, which instructs TensorFlow how to preprocess\n",
+ "the data, as well as how to batch, shuffle, and repeat it during model training.\n",
+ "\n",
+ "First, we'll convert our *pandas* feature data into a dict of NumPy arrays. We can then use the TensorFlow [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) to construct a dataset object from our data, and then break\n",
+ "our data into batches of `batch_size`, to be repeated for the specified number of epochs (num_epochs). \n",
+ "\n",
+ "**NOTE:** When the default value of `num_epochs=None` is passed to `repeat()`, the input data will be repeated indefinitely.\n",
+ "\n",
+ "Next, if `shuffle` is set to `True`, we'll shuffle the data so that it's passed to the model randomly during training. The `buffer_size` argument specifies\n",
+ "the size of the dataset from which `shuffle` will randomly sample.\n",
+ "\n",
+ "Finally, our input function constructs an iterator for the dataset and returns the next batch of data to the LinearRegressor."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "RKZ9zNcHJtwc",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+ " \"\"\"Trains a linear regression model of one feature.\n",
+ " \n",
+ " Args:\n",
+ " features: pandas DataFrame of features\n",
+ " targets: pandas DataFrame of targets\n",
+ " batch_size: Size of batches to be passed to the model\n",
+ " shuffle: True or False. Whether to shuffle the data.\n",
+ " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+ " Returns:\n",
+ " Tuple of (features, labels) for next data batch\n",
+ " \"\"\"\n",
+ " \n",
+ " # Convert pandas data into a dict of np arrays.\n",
+ " features = {key:np.array(value) for key,value in dict(features).items()} \n",
+ " \n",
+ " # Construct a dataset, and configure batching/repeating.\n",
+ " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+ " ds = ds.batch(batch_size).repeat(num_epochs)\n",
+ " \n",
+ " # Shuffle the data, if specified.\n",
+ " if shuffle:\n",
+ " ds = ds.shuffle(buffer_size=10000)\n",
+ " \n",
+ " # Return the next batch of data.\n",
+ " features, labels = ds.make_one_shot_iterator().get_next()\n",
+ " return features, labels"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "wwa6UeA1V5F_",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "**NOTE:** We'll continue to use this same input function in later exercises. For more\n",
+ "detailed documentation of input functions and the `Dataset` API, see the [TensorFlow Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets)."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "4YS50CQb2ooO",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Step 5: Train the Model"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "yP92XkzhU803",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "We can now call `train()` on our `linear_regressor` to train the model. We'll wrap `my_input_fn` in a `lambda`\n",
+ "so we can pass in `my_feature` and `target` as arguments (see this [TensorFlow input function tutorial](https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model) for more details), and to start, we'll\n",
+ "train for 100 steps."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "5M-Kt6w8U803",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "_ = linear_regressor.train(\n",
+ " input_fn = lambda:my_input_fn(my_feature, targets),\n",
+ " steps=100\n",
+ ")"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "7Nwxqxlx2sOv",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Step 6: Evaluate the Model"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "KoDaF2dlJQG5",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Let's make predictions on that training data, to see how well our model fit it during training.\n",
+ "\n",
+ "**NOTE:** Training error measures how well your model fits the training data, but it **_does not_** measure how well your model **_generalizes to new data_**. In later exercises, you'll explore how to split your data to evaluate your model's ability to generalize.\n"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "pDIxp6vcU809",
+ "colab_type": "code",
+ "outputId": "5ea6fda6-8ca1-4e09-d8ca-0ad37d2e90bb",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 90
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "# Create an input function for predictions.\n",
+ "# Note: Since we're making just one prediction for each example, we don't \n",
+ "# need to repeat or shuffle the data here.\n",
+ "prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n",
+ "\n",
+ "# Call predict() on the linear_regressor to make predictions.\n",
+ "predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
+ "\n",
+ "# Format predictions as a NumPy array, so we can calculate error metrics.\n",
+ "predictions = np.array([item['predictions'][0] for item in predictions])\n",
+ "\n",
+ "# Print Mean Squared Error and Root Mean Squared Error.\n",
+ "mean_squared_error = metrics.mean_squared_error(predictions, targets)\n",
+ "root_mean_squared_error = math.sqrt(mean_squared_error)\n",
+ "print(\"Mean Squared Error (on training data): %0.3f\" % mean_squared_error)\n",
+ "print(\"Root Mean Squared Error (on training data): %0.3f\" % root_mean_squared_error)\n",
+ "predictions"
+ ],
+ "execution_count": 0,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Mean Squared Error (on training data): 56367.025\n",
+ "Root Mean Squared Error (on training data): 237.417\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0.15674996, 0.18564996, 0.05330002, ..., 0.42444986, 0.0867 ,\n",
+ " 0.25754994], dtype=float32)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 34
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "AKWstXXPzOVz",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Is this a good model? How would you judge how large this error is?\n",
+ "\n",
+ "Mean Squared Error (MSE) can be hard to interpret, so we often look at Root Mean Squared Error (RMSE)\n",
+ "instead. A nice property of RMSE is that it can be interpreted on the same scale as the original targets.\n",
+ "\n",
+ "Let's compare the RMSE to the difference of the min and max of our targets:"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "7UwqGbbxP53O",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "min_house_value = california_housing_dataframe[\"median_house_value\"].min()\n",
+ "max_house_value = california_housing_dataframe[\"median_house_value\"].max()\n",
+ "min_max_difference = max_house_value - min_house_value\n",
+ "\n",
+ "print(\"Min. Median House Value: %0.3f\" % min_house_value)\n",
+ "print(\"Max. Median House Value: %0.3f\" % max_house_value)\n",
+ "print(\"Difference between Min. and Max.: %0.3f\" % min_max_difference)\n",
+ "print(\"Root Mean Squared Error: %0.3f\" % root_mean_squared_error)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "JigJr0C7Pzit",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Our error spans nearly half the range of the target values. Can we do better?\n",
+ "\n",
+ "This is the question that nags at every model developer. Let's develop some basic strategies to reduce model error.\n",
+ "\n",
+ "The first thing we can do is take a look at how well our predictions match our targets, in terms of overall summary statistics."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "941nclxbzqGH",
+ "colab_type": "code",
+ "cellView": "both",
+ "outputId": "381526f4-b195-405d-f1b7-7b48d1dec42e",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 300
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "calibration_data = pd.DataFrame()\n",
+ "calibration_data[\"predictions\"] = pd.Series(predictions)\n",
+ "calibration_data[\"targets\"] = pd.Series(targets)\n",
+ "calibration_data.describe()"
+ ],
+ "execution_count": 0,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
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+ ]
+ },
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+ },
+ "execution_count": 35
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "E2-bf8Hq36y8",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Okay, maybe this information is helpful. How does the mean value compare to the model's RMSE? How about the various quantiles?\n",
+ "\n",
+ "We can also visualize the data and the line we've learned. Recall that linear regression on a single feature can be drawn as a line mapping input *x* to output *y*.\n",
+ "\n",
+ "First, we'll get a uniform random sample of the data so we can make a readable scatter plot."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "SGRIi3mAU81H",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "sample = california_housing_dataframe.sample(n=300)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "N-JwuJBKU81J",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Next, we'll plot the line we've learned, drawing from the model's bias term and feature weight, together with the scatter plot. The line will show up red."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "7G12E76-339G",
+ "colab_type": "code",
+ "cellView": "both",
+ "outputId": "260cea61-17ee-476f-ab63-424a335fe38f",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 361
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "# Get the min and max total_rooms values.\n",
+ "x_0 = sample[\"total_rooms\"].min()\n",
+ "x_1 = sample[\"total_rooms\"].max()\n",
+ "\n",
+ "# Retrieve the final weight and bias generated during training.\n",
+ "weight = linear_regressor.get_variable_value('linear/linear_model/total_rooms/weights')[0]\n",
+ "bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
+ "\n",
+ "# Get the predicted median_house_values for the min and max total_rooms values.\n",
+ "y_0 = weight * x_0 + bias \n",
+ "y_1 = weight * x_1 + bias\n",
+ "\n",
+ "# Plot our regression line from (x_0, y_0) to (x_1, y_1).\n",
+ "plt.plot([x_0, x_1], [y_0, y_1], c='r')\n",
+ "\n",
+ "# Label the graph axes.\n",
+ "plt.ylabel(\"median_house_value\")\n",
+ "plt.xlabel(\"total_rooms\")\n",
+ "\n",
+ "# Plot a scatter plot from our data sample.\n",
+ "plt.scatter(sample[\"total_rooms\"], sample[\"median_house_value\"])\n",
+ "\n",
+ "# Display graph.\n",
+ "plt.show()"
+ ],
+ "execution_count": 0,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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jZWutR85ffdSJxlZfNJO+XGKjloh0EtaUricb5JIScx3XyROR6kD+5JNP4pvf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\nQQAKrF3vt0LhMNZsrsET/98u2cIuAOCV2TVtX00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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "t0lRt4USU81L",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "This initial line looks way off. See if you can look back at the summary stats and see the same information encoded there.\n",
+ "\n",
+ "Together, these initial sanity checks suggest we may be able to find a much better line."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "AZWF67uv0HTG",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Tweak the Model Hyperparameters\n",
+ "For this exercise, we've put all the above code in a single function for convenience. You can call the function with different parameters to see the effect.\n",
+ "\n",
+ "In this function, we'll proceed in 10 evenly divided periods so that we can observe the model improvement at each period.\n",
+ "\n",
+ "For each period, we'll compute and graph training loss. This may help you judge when a model is converged, or if it needs more iterations.\n",
+ "\n",
+ "We'll also plot the feature weight and bias term values learned by the model over time. This is another way to see how things converge."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "wgSMeD5UU81N",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def train_model(learning_rate, steps, batch_size, input_feature=\"total_rooms\"):\n",
+ " \"\"\"Trains a linear regression model of one feature.\n",
+ " \n",
+ " Args:\n",
+ " learning_rate: A `float`, the learning rate.\n",
+ " steps: A non-zero `int`, the total number of training steps. A training step\n",
+ " consists of a forward and backward pass using a single batch.\n",
+ " batch_size: A non-zero `int`, the batch size.\n",
+ " input_feature: A `string` specifying a column from `california_housing_dataframe`\n",
+ " to use as input feature.\n",
+ " \"\"\"\n",
+ " \n",
+ " periods = 10\n",
+ " steps_per_period = steps / periods\n",
+ "\n",
+ " my_feature = input_feature\n",
+ " my_feature_data = california_housing_dataframe[[my_feature]]\n",
+ " my_label = \"median_house_value\"\n",
+ " targets = california_housing_dataframe[my_label]\n",
+ "\n",
+ " # Create feature columns.\n",
+ " feature_columns = [tf.feature_column.numeric_column(my_feature)]\n",
+ " \n",
+ " # Create input functions.\n",
+ " training_input_fn = lambda:my_input_fn(my_feature_data, targets, batch_size=batch_size)\n",
+ " prediction_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n",
+ " \n",
+ " # Create a linear regressor object.\n",
+ " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+ " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+ " linear_regressor = tf.estimator.LinearRegressor(\n",
+ " feature_columns=feature_columns,\n",
+ " optimizer=my_optimizer\n",
+ " )\n",
+ "\n",
+ " # Set up to plot the state of our model's line each period.\n",
+ " plt.figure(figsize=(15, 6))\n",
+ " plt.subplot(1, 2, 1)\n",
+ " plt.title(\"Learned Line by Period\")\n",
+ " plt.ylabel(my_label)\n",
+ " plt.xlabel(my_feature)\n",
+ " sample = california_housing_dataframe.sample(n=300)\n",
+ " plt.scatter(sample[my_feature], sample[my_label])\n",
+ " colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n",
+ "\n",
+ " # Train the model, but do so inside a loop so that we can periodically assess\n",
+ " # loss metrics.\n",
+ " print(\"Training model...\")\n",
+ " print(\"RMSE (on training data):\")\n",
+ " root_mean_squared_errors = []\n",
+ " for period in range (0, periods):\n",
+ " # Train the model, starting from the prior state.\n",
+ " linear_regressor.train(\n",
+ " input_fn=training_input_fn,\n",
+ " steps=steps_per_period\n",
+ " )\n",
+ " # Take a break and compute predictions.\n",
+ " predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
+ " predictions = np.array([item['predictions'][0] for item in predictions])\n",
+ " \n",
+ " # Compute loss.\n",
+ " root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(predictions, targets))\n",
+ " # Occasionally print the current loss.\n",
+ " print(\" period %02d : %0.2f\" % (period, root_mean_squared_error))\n",
+ " # Add the loss metrics from this period to our list.\n",
+ " root_mean_squared_errors.append(root_mean_squared_error)\n",
+ " # Finally, track the weights and biases over time.\n",
+ " # Apply some math to ensure that the data and line are plotted neatly.\n",
+ " y_extents = np.array([0, sample[my_label].max()])\n",
+ " \n",
+ " weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n",
+ " bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
+ "\n",
+ " x_extents = (y_extents - bias) / weight\n",
+ " x_extents = np.maximum(np.minimum(x_extents,\n",
+ " sample[my_feature].max()),\n",
+ " sample[my_feature].min())\n",
+ " y_extents = weight * x_extents + bias\n",
+ " plt.plot(x_extents, y_extents, color=colors[period]) \n",
+ " print(\"Model training finished.\")\n",
+ "\n",
+ " # Output a graph of loss metrics over periods.\n",
+ " plt.subplot(1, 2, 2)\n",
+ " plt.ylabel('RMSE')\n",
+ " plt.xlabel('Periods')\n",
+ " plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+ " plt.tight_layout()\n",
+ " plt.plot(root_mean_squared_errors)\n",
+ "\n",
+ " # Output a table with calibration data.\n",
+ " calibration_data = pd.DataFrame()\n",
+ " calibration_data[\"predictions\"] = pd.Series(predictions)\n",
+ " calibration_data[\"targets\"] = pd.Series(targets)\n",
+ " display.display(calibration_data.describe())\n",
+ "\n",
+ " print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "kg8A4ArBU81Q",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 1: Achieve an RMSE of 180 or Below\n",
+ "\n",
+ "Tweak the model hyperparameters to improve loss and better match the target distribution.\n",
+ "If, after 5 minutes or so, you're having trouble beating a RMSE of 180, check the solution for a possible combination."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "UzoZUSdLIolF",
+ "colab_type": "code",
+ "cellView": "both",
+ "outputId": "ac6e3e85-1e9d-46a9-b41e-92ccd3ab8675",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 977
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "train_model(\n",
+ " learning_rate=0.0001,\n",
+ " steps=300,\n",
+ " batch_size=1000\n",
+ ")"
+ ],
+ "execution_count": 0,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 204.04\n",
+ " period 01 : 179.23\n",
+ " period 02 : 167.79\n",
+ " period 03 : 166.39\n",
+ " period 04 : 166.53\n",
+ " period 05 : 166.32\n",
+ " period 06 : 166.39\n",
+ " period 07 : 166.32\n",
+ " period 08 : 166.53\n",
+ " period 09 : 166.53\n",
+ "Model training finished.\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " predictions targets\n",
+ "count 17000.0 17000.0\n",
+ "mean 137.5 207.3\n",
+ "std 113.4 116.0\n",
+ "min 0.1 15.0\n",
+ "25% 76.0 119.4\n",
+ "50% 110.6 180.4\n",
+ "75% 163.9 265.0\n",
+ "max 1973.1 500.0"
+ ],
+ "text/html": [
+ "
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+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "ajVM7rkoYXeL",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below for one possible solution."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "T3zmldDwYy5c",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "train_model(\n",
+ " learning_rate=0.00002,\n",
+ " steps=500,\n",
+ " batch_size=5\n",
+ ")"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "M8H0_D4vYa49",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "This is just one possible configuration; there may be other combinations of settings that also give good results. Note that in general, this exercise isn't about finding the *one best* setting, but to help build your intutions about how tweaking the model configuration affects prediction quality."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "QU5sLyYTqzqL",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Is There a Standard Heuristic for Model Tuning?\n",
+ "\n",
+ "This is a commonly asked question. The short answer is that the effects of different hyperparameters are data dependent. So there are no hard-and-fast rules; you'll need to test on your data.\n",
+ "\n",
+ "That said, here are a few rules of thumb that may help guide you:\n",
+ "\n",
+ " * Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.\n",
+ " * If the training has not converged, try running it for longer.\n",
+ " * If the training error decreases too slowly, increasing the learning rate may help it decrease faster.\n",
+ " * But sometimes the exact opposite may happen if the learning rate is too high.\n",
+ " * If the training error varies wildly, try decreasing the learning rate.\n",
+ " * Lower learning rate plus larger number of steps or larger batch size is often a good combination.\n",
+ " * Very small batch sizes can also cause instability. First try larger values like 100 or 1000, and decrease until you see degradation.\n",
+ "\n",
+ "Again, never go strictly by these rules of thumb, because the effects are data dependent. Always experiment and verify."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "GpV-uF_cBCBU",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 2: Try a Different Feature\n",
+ "\n",
+ "See if you can do any better by replacing the `total_rooms` feature with the `population` feature.\n",
+ "\n",
+ "Don't take more than 5 minutes on this portion."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "YMyOxzb0ZlAH",
+ "colab_type": "code",
+ "outputId": "c8091026-424f-4fbd-a8bd-44d0287dfbc9",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 977
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "train_model(\n",
+ " learning_rate=0.0001,\n",
+ " steps=500,\n",
+ " batch_size=1000,\n",
+ " input_feature=\"population\"\n",
+ ")"
+ ],
+ "execution_count": 0,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 209.51\n",
+ " period 01 : 188.58\n",
+ " period 02 : 177.30\n",
+ " period 03 : 175.95\n",
+ " period 04 : 175.92\n",
+ " period 05 : 175.95\n",
+ " period 06 : 175.92\n",
+ " period 07 : 176.06\n",
+ " period 08 : 175.99\n",
+ " period 09 : 175.92\n",
+ "Model training finished.\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " predictions targets\n",
+ "count 17000.0 17000.0\n",
+ "mean 122.9 207.3\n",
+ "std 98.7 116.0\n",
+ "min 0.3 15.0\n",
+ "25% 67.9 119.4\n",
+ "50% 100.3 180.4\n",
+ "75% 148.0 265.0\n",
+ "max 3068.1 500.0"
+ ],
+ "text/html": [
+ "
"
+ ]
+ },
+ "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": 640
+ },
+ "outputId": "30383cf6-9021-4af9-f19b-df584d9f6f7d"
+ },
+ "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": 28,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 145.53\n",
+ " period 01 : 159.50\n",
+ " period 02 : 116.73\n",
+ " period 03 : 313.37\n",
+ " period 04 : 353.00\n",
+ " period 05 : 316.81\n",
+ " period 06 : 311.05\n",
+ " period 07 : 283.78\n",
+ " period 08 : 265.21\n",
+ " period 09 : 224.52\n",
+ "Model training finished.\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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6KQVVhUzsMpYJncc0+9pEwa7+PB76ALZmNqxKWMtPp9ahV/QGy5qaqLlvQi/G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\nDElIJybIv2e2zS7azMKcv4HbpG57Bj3CenL3pHSCA60vBj90ynXpqjW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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "i4lGvqajDWlw",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## One-Hot Encoding for Discrete Features\n",
+ "\n",
+ "Discrete (i.e. strings, enumerations, integers) features are usually converted into families of binary features before training a logistic regression model.\n",
+ "\n",
+ "For example, suppose we created a synthetic feature that can take any of the values `0`, `1` or `2`, and that we have a few training points:\n",
+ "\n",
+ "| # | feature_value |\n",
+ "|---|---------------|\n",
+ "| 0 | 2 |\n",
+ "| 1 | 0 |\n",
+ "| 2 | 1 |\n",
+ "\n",
+ "For each possible categorical value, we make a new **binary** feature of **real values** that can take one of just two possible values: 1.0 if the example has that value, and 0.0 if not. In the example above, the categorical feature would be converted into three features, and the training points now look like:\n",
+ "\n",
+ "| # | feature_value_0 | feature_value_1 | feature_value_2 |\n",
+ "|---|-----------------|-----------------|-----------------|\n",
+ "| 0 | 0.0 | 0.0 | 1.0 |\n",
+ "| 1 | 1.0 | 0.0 | 0.0 |\n",
+ "| 2 | 0.0 | 1.0 | 0.0 |"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "KnssXowblKm7",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Bucketized (Binned) Features\n",
+ "\n",
+ "Bucketization is also known as binning.\n",
+ "\n",
+ "We can bucketize `population` into the following 3 buckets (for instance):\n",
+ "- `bucket_0` (`< 5000`): corresponding to less populated blocks\n",
+ "- `bucket_1` (`5000 - 25000`): corresponding to mid populated blocks\n",
+ "- `bucket_2` (`> 25000`): corresponding to highly populated blocks\n",
+ "\n",
+ "Given the preceding bucket definitions, the following `population` vector:\n",
+ "\n",
+ " [[10001], [42004], [2500], [18000]]\n",
+ "\n",
+ "becomes the following bucketized feature vector:\n",
+ "\n",
+ " [[1], [2], [0], [1]]\n",
+ "\n",
+ "The feature values are now the bucket indices. Note that these indices are considered to be discrete features. Typically, these will be further converted in one-hot representations as above, but this is done transparently.\n",
+ "\n",
+ "To define feature columns for bucketized features, instead of using `numeric_column`, we can use [`bucketized_column`](https://www.tensorflow.org/api_docs/python/tf/feature_column/bucketized_column), which takes a numeric column as input and transforms it to a bucketized feature using the bucket boundaries specified in the `boundaries` argument. The following code defines bucketized feature columns for `households` and `longitude`; the `get_quantile_based_boundaries` function calculates boundaries based on quantiles, so that each bucket contains an equal number of elements."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "cc9qZrtRy-ED",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def get_quantile_based_boundaries(feature_values, num_buckets):\n",
+ " boundaries = np.arange(1.0, num_buckets) / num_buckets\n",
+ " quantiles = feature_values.quantile(boundaries)\n",
+ " return [quantiles[q] for q in quantiles.keys()]\n",
+ "\n",
+ "# Divide households into 7 buckets.\n",
+ "households = tf.feature_column.numeric_column(\"households\")\n",
+ "bucketized_households = tf.feature_column.bucketized_column(\n",
+ " households, boundaries=get_quantile_based_boundaries(\n",
+ " california_housing_dataframe[\"households\"], 7))\n",
+ "\n",
+ "# Divide longitude into 10 buckets.\n",
+ "longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+ "bucketized_longitude = tf.feature_column.bucketized_column(\n",
+ " longitude, boundaries=get_quantile_based_boundaries(\n",
+ " california_housing_dataframe[\"longitude\"], 10))"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "U-pQDAa0MeN3",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 1: Train the Model on Bucketized Feature Columns\n",
+ "**Bucketize all the real valued features in our example, train the model and see if the results improve.**\n",
+ "\n",
+ "In the preceding code block, two real valued columns (namely `households` and `longitude`) have been transformed into bucketized feature columns. Your task is to bucketize the rest of the columns, then run the code to train the model. There are various heuristics to find the range of the buckets. This exercise uses a quantile-based technique, which chooses the bucket boundaries in such a way that each bucket has the same number of examples."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "YFXV9lyMLedy",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def construct_feature_columns():\n",
+ " \"\"\"Construct the TensorFlow Feature Columns.\n",
+ "\n",
+ " Returns:\n",
+ " A set of feature columns\n",
+ " \"\"\" \n",
+ " households = tf.feature_column.numeric_column(\"households\")\n",
+ " longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+ " latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+ " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+ " median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+ " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+ " \n",
+ " # Divide households into 7 buckets.\n",
+ " bucketized_households = tf.feature_column.bucketized_column(\n",
+ " households, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"households\"], 7))\n",
+ "\n",
+ " # Divide longitude into 10 buckets.\n",
+ " bucketized_longitude = tf.feature_column.bucketized_column(\n",
+ " longitude, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"longitude\"], 10))\n",
+ "\n",
+ " #\n",
+ " # YOUR CODE HERE: bucketize the following columns, following the example above:\n",
+ " #\n",
+ " bucketized_latitude = tf.feature_column.bucketized_column(latitude, boundaries=get_quantile_based_boundaries(training_examples[\"latitude\"], 10))\n",
+ " bucketized_housing_median_age = tf.feature_column.bucketized_column(housing_median_age,boundaries=get_quantile_based_boundaries(training_examples[\"housing_median_age\"], 10))\n",
+ " bucketized_median_income = tf.feature_column.bucketized_column(median_income, boundaries=get_quantile_based_boundaries(training_examples[\"median_income\"], 10))\n",
+ " bucketized_rooms_per_person =tf.feature_column.bucketized_column(rooms_per_person, boundaries=get_quantile_based_boundaries(training_examples[\"rooms_per_person\"], 10))\n",
+ " \n",
+ " \n",
+ " feature_columns = set([\n",
+ " bucketized_longitude,\n",
+ " bucketized_latitude,\n",
+ " bucketized_housing_median_age,\n",
+ " bucketized_households,\n",
+ " bucketized_median_income,\n",
+ " bucketized_rooms_per_person])\n",
+ " \n",
+ " return feature_columns\n"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "0FfUytOTNJhL",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 640
+ },
+ "outputId": "4ebc66b2-8688-4901-b601-d3e3dc857220"
+ },
+ "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": 31,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 170.32\n",
+ " period 01 : 143.98\n",
+ " period 02 : 127.43\n",
+ " period 03 : 116.14\n",
+ " period 04 : 108.01\n",
+ " period 05 : 101.93\n",
+ " period 06 : 97.29\n",
+ " period 07 : 93.64\n",
+ " period 08 : 90.67\n",
+ " period 09 : 88.19\n",
+ "Model training finished.\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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REal1VGBERETqmI0b11Xqfi+99DwpKclXvP0vf3moqiJVORUYERGROiQ1NYW1a9dU6r4P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\nxlGAERERkYzz/wDeZNLXwqN+0QAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "ZTDHHM61NPTw",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below for a solution."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "JQHnUhL_NRwA",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "You may be wondering how to determine how many buckets to use. That is of course data-dependent. Here, we just selected arbitrary values so as to obtain a not-too-large model."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "Ro5civQ3Ngh_",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def construct_feature_columns():\n",
+ " \"\"\"Construct the TensorFlow Feature Columns.\n",
+ "\n",
+ " Returns:\n",
+ " A set of feature columns\n",
+ " \"\"\" \n",
+ " households = tf.feature_column.numeric_column(\"households\")\n",
+ " longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+ " latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+ " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+ " median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+ " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+ " \n",
+ " # Divide households into 7 buckets.\n",
+ " bucketized_households = tf.feature_column.bucketized_column(\n",
+ " households, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"households\"], 7))\n",
+ "\n",
+ " # Divide longitude into 10 buckets.\n",
+ " bucketized_longitude = tf.feature_column.bucketized_column(\n",
+ " longitude, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"longitude\"], 10))\n",
+ " \n",
+ " # Divide latitude into 10 buckets.\n",
+ " bucketized_latitude = tf.feature_column.bucketized_column(\n",
+ " latitude, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"latitude\"], 10))\n",
+ "\n",
+ " # Divide housing_median_age into 7 buckets.\n",
+ " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+ " housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"housing_median_age\"], 7))\n",
+ " \n",
+ " # Divide median_income into 7 buckets.\n",
+ " bucketized_median_income = tf.feature_column.bucketized_column(\n",
+ " median_income, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"median_income\"], 7))\n",
+ " \n",
+ " # Divide rooms_per_person into 7 buckets.\n",
+ " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+ " rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"rooms_per_person\"], 7))\n",
+ " \n",
+ " feature_columns = set([\n",
+ " bucketized_longitude,\n",
+ " bucketized_latitude,\n",
+ " bucketized_housing_median_age,\n",
+ " bucketized_households,\n",
+ " bucketized_median_income,\n",
+ " bucketized_rooms_per_person])\n",
+ " \n",
+ " return feature_columns"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "RNgfYk6OO8Sy",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "_ = train_model(\n",
+ " learning_rate=1.0,\n",
+ " steps=500,\n",
+ " batch_size=100,\n",
+ " feature_columns=construct_feature_columns(),\n",
+ " training_examples=training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "AFJ1qoZPlQcs",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Feature Crosses\n",
+ "\n",
+ "Crossing two (or more) features is a clever way to learn non-linear relations using a linear model. In our problem, if we just use the feature `latitude` for learning, the model might learn that city blocks at a particular latitude (or within a particular range of latitudes since we have bucketized it) are more likely to be expensive than others. Similarly for the feature `longitude`. However, if we cross `longitude` by `latitude`, the crossed feature represents a well defined city block. If the model learns that certain city blocks (within range of latitudes and longitudes) are more likely to be more expensive than others, it is a stronger signal than two features considered individually.\n",
+ "\n",
+ "Currently, the feature columns API only supports discrete features for crosses. To cross two continuous values, like `latitude` or `longitude`, we can bucketize them.\n",
+ "\n",
+ "If we cross the `latitude` and `longitude` features (supposing, for example, that `longitude` was bucketized into `2` buckets, while `latitude` has `3` buckets), we actually get six crossed binary features. Each of these features will get its own separate weight when we train the model."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "-Rk0c1oTYaVH",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 2: Train the Model Using Feature Crosses\n",
+ "\n",
+ "**Add a feature cross of `longitude` and `latitude` to your model, train it, and determine whether the results improve.**\n",
+ "\n",
+ "Refer to the TensorFlow API docs for [`crossed_column()`](https://www.tensorflow.org/api_docs/python/tf/feature_column/crossed_column) to build the feature column for your cross. Use a `hash_bucket_size` of `1000`."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "-eYiVEGeYhUi",
+ "colab_type": "code",
+ "cellView": "both",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def construct_feature_columns():\n",
+ " \"\"\"Construct the TensorFlow Feature Columns.\n",
+ "\n",
+ " Returns:\n",
+ " A set of feature columns\n",
+ " \"\"\" \n",
+ " households = tf.feature_column.numeric_column(\"households\")\n",
+ " longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+ " latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+ " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+ " median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+ " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+ " \n",
+ " # Divide households into 7 buckets.\n",
+ " bucketized_households = tf.feature_column.bucketized_column(\n",
+ " households, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"households\"], 7))\n",
+ "\n",
+ " # Divide longitude into 10 buckets.\n",
+ " bucketized_longitude = tf.feature_column.bucketized_column(\n",
+ " longitude, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"longitude\"], 10))\n",
+ " \n",
+ " # Divide latitude into 10 buckets.\n",
+ " bucketized_latitude = tf.feature_column.bucketized_column(\n",
+ " latitude, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"latitude\"], 10))\n",
+ "\n",
+ " # Divide housing_median_age into 7 buckets.\n",
+ " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+ " housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"housing_median_age\"], 7))\n",
+ " \n",
+ " # Divide median_income into 7 buckets.\n",
+ " bucketized_median_income = tf.feature_column.bucketized_column(\n",
+ " median_income, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"median_income\"], 7))\n",
+ " \n",
+ " # Divide rooms_per_person into 7 buckets.\n",
+ " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+ " rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"rooms_per_person\"], 7))\n",
+ " \n",
+ " # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n",
+ " long_x_lat = tf.feature_column.crossed_column([bucketized_longitude, bucketized_latitude], 1000)\n",
+ " \n",
+ " feature_columns = set([\n",
+ " bucketized_longitude,\n",
+ " bucketized_latitude,\n",
+ " bucketized_housing_median_age,\n",
+ " bucketized_households,\n",
+ " bucketized_median_income,\n",
+ " bucketized_rooms_per_person,\n",
+ " long_x_lat])\n",
+ " \n",
+ " return feature_columns"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "xZuZMp3EShkM",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 640
+ },
+ "outputId": "b4f97380-8835-4641-bcb8-b6abc11c76b6"
+ },
+ "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": 33,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 163.60\n",
+ " period 01 : 135.47\n",
+ " period 02 : 118.48\n",
+ " period 03 : 107.22\n",
+ " period 04 : 99.25\n",
+ " period 05 : 93.33\n",
+ " period 06 : 88.78\n",
+ " period 07 : 85.17\n",
+ " period 08 : 82.21\n",
+ " period 09 : 79.75\n",
+ "Model training finished.\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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nVsE0tfUA4NPdX2MYhsmJREREnI8KjBO6NS4We04oKcXH2ZO13+w4IiIiTkcF\nxgm1adqIVvQEzu4Lo1UYERGRc6nAOKlb4ntiO9OYjLJTJGUkmx1HRETEqajAOKnm4X508ojFMCws\n2LsUh+EwO5KIiIjTUIFxYpPio7FnNiXHdobNadvMjiMiIuI0VGCcWJNgH7r5xWE4LHxxYBk2h83s\nSCIiIk5BBcbJTejTBUdGcwrsuaw9ucnsOCIiIk5BBcbJhQR40Ss4HsPuyteHvqfMXmZ2JBEREdOp\nwNQBN/XpiJHekhKjiBXH15kdR0RExHQqMHVAgK8H/Zr0xbBZWXZkJcW2YrMjiYiImEoFpo64Pq4d\nlow2lFPK8iOrzI4jIiJiKhWYOsLXy41BzfthlLmz4sRa8ssKzI4kIiJiGhWYOmRUr9a4ZLbDjo1v\nDv1odhwRERHT1GiB2b9/P0OGDGHevHnnnL927Vrat29fcXrx4sWMHz+eiRMnsmDBgpqMVKd5eVgZ\n2bYfjlJP1qdtJLskx+xIIiIipqixAlNUVMT06dOJi4s75/zS0lLeeecdQkNDK643a9Ys5syZw9y5\nc/nwww/JydEf5osZ2qM5bpkdcGDnqwPfmR1HRETEFDVWYNzd3Xn33XcJCws75/y33nqLW2+9FXd3\ndwCSkpKIjIzEz88PT09PunfvTmJiYk3FqvPc3Vy5vlNfHMU+bEnfSnpRhtmRREREal2NFRir1Yqn\np+c55x05coS9e/cycuTIivMyMzMJCgqqOB0UFERGhv4oV2ZAdDO8sjqBxWDR/mVmxxEREal11tp8\nsOeff54nn3yy0usYhnHJ+wkM9MZqdb1asc4TGupXY/d9tdweP5i3d+1jJzsptObSMrCZ2ZFqRV2Y\nTUOkuTgvzcZ5aTZXptYKzOnTpzl8+DB/+tOfAEhPT2fy5Mk88MADZGZmVlwvPT2d6OjoSu8rO7uo\nxnKGhvqRkZFfY/d/tUS2DMBvQxcKfTbw740LmNbzbrMj1bi6MpuGRnNxXpqN89JsqqaykldrX6MO\nDw/nhx9+YP78+cyfP5+wsDDmzZtHVFQUO3fuJC8vj8LCQhITE+nZs2dtxaqzXF1cmNAjDnteIPvz\n9nM496jZkURERGpNjRWY5ORkpkyZwhdffMFHH33ElClTLvjtIk9PTx555BHuuusu7rzzTu6//378\n/LSsVhU9O4QRXBgFwIK931Tp4zcREZH6wGLUwb96NbnsVteW9ZIOZvLmjg9wDchgavTddAxqZ3ak\nGlPXZtNQaC7OS7NxXppN1TjFR0hSM7q2CaZx2dl9hrQKIyIiDYUKTB1nsVi4Oa4ntjONOV2SRlLm\nLrMjiYiI1DgVmHqgQ4tAWtDw5uKEAAAgAElEQVQDw4DP9y3FYTjMjiQiIlKjVGDqiZvju2HPbEpW\nWSZbTm0zO46IiEiNUoGpJ1pH+NPOLQbDYeGLA8uwOWxmRxIREakxKjD1yKS+XbGnNyfflsuG1M1m\nxxEREakxKjD1yDVhvkT6xWLYXVl88HvK7GVmRxIREakRKjD1zMT4TthPt6TYUciqExvMjiMiIlIj\nVGDqmfAgb3oGxWLYrHx7ZAXFtmKzI4mIiFx1KjD10E3xHXCcbk2ZUcKcXZ/qa9UiIlLvqMDUQ8GN\nPIlv3Bd7bjDJZ/aw8MASHaFXRETqFRWYeurGvm0IyIzDUeTL6pPrWXlyndmRRERErhoVmHrK18uN\nhyb0xHq8N0aZB58fWML29J1mxxIREbkqVGDqsfBAbx68vhe2gz3A7soHu/7DkdxjZscSERG5Yiow\n9dy1zQK4a1AcpQejsTnszE76gIyiM2bHEhERuSIqMA1AbKdwboiKpexoR4psRcza/m8KygvNjiUi\nInLZVGAaiNFxLYhrEkt5aisySs7wdtKHlNvLzY4lIiJyWVRgGgiLxcLtw9vT1jUW25nGHM47ytw9\n83WMGBERqZNUYBoQq6sLU2+MJDg3Fnt+IFvTk1h8aJnZsURERKpNBaaB8fZ04+EJ3fE42QtHsTff\nH1/F2pSNZscSERGpFhWYBigkwItpN8bgONQLo9ydz/Z9QXLmHrNjiYiIVJkKTAPVOsKf3w/vSdn+\n7hgOC+8lf8zx/JNmxxIREakSFZgGrEf7MCb06knpoSjK7GXM3v4BWSXZZscSERG5JBWYBm54r2u4\nrmV3yo53IL88n9nb36fYVmx2LBERkUqpwDRwFouF24ZeS0ef7thOtSCt6DTv7pyLzWEzO5qIiMhF\nqcAIri4u/GFcF8KKe2LPCmNf9kE+2fs5hmGYHU1EROSCVGAEAC8PKw9NjMLrdE8cBY3YdGorS4/+\nYHYsERGRC1KBkQpB/p78cXwPOBKDUerF0iPfszEtwexYIiIi51GBkXO0aOzH/4zuTtn+HmBz4+M9\nC9mbdcDsWCIiIudQgZHzRLcN4Za+3Sk90A2HAe/u/IjUglNmxxIREamgAiMXNLhHMwa170rZoS6U\n2EuZlfQeOaW5ZscSEREBVGCkEjcPupbIoK6Un7iWnNJc3kqaQ4mt1OxYIiIiKjBycS4uFv7n+s5E\nGFHY0ptxoiCF93d9jN1hNzuaiIg0cCowUikPd1f+OCEK36xu2HNC2HVmL/MPfKVjxIiIiKlUYOSS\nAnw9eGhCN1yO98Ao8mNdykZ+OL7a7FgiItKAqcBIlTQL8+W+cdGU7e8JZZ58eWgpW09vNzuWiIg0\nUCowUmVdWgUzZVBXSvb1AIeVj3bP52DOEbNjiYhIA6QCI9XSP7opI7p2pnR/NDaHnbd3fMjpogyz\nY4mISAOjAiPVNn5AG7o36UjZkc4U2YqYvf098ssKzI4lIiINiAqMVJuLxcLdYzrR0r0T5SltyCzJ\n4u0dcyizl5sdTUREGggVGLks7m6uPDC+K40KumDLjOBI3nE+3P0fHIbD7GgiItIAqMDIZfP3ceeh\nidFYU6Nx5AWxPSOZLw5+Y3YsERFpAFRg5IpEhPjwwI1R2A51hxJfVpxYy6oT682OJSIi9ZwKjFyx\nDi0C+e2wSEr2dsdi82DhgcUkZewyO5aIiNRjKjByVcRHNmFsTCeK93UHhwsf7PqEY3knzI4lIiL1\n1GUXmKNHj17FGFIfjOvbitgW7Sg5GEW53cabSR+QWZxldiwREamHKi0wd9555zmnZ8+eXfH/Tz/9\ndM0kkjrLYrFw56iOtPW9lrJjHcgvL2B20vsUlReZHU1EROqZSguMzWY75/TGjRsr/l+/RiwX4mZ1\nYer4roSUd6A8rSWni9J5Z+dHlDtsl76xiIhIFVVaYCwWyzmnf1lafn2ZyM98vdz446QoPDK7YM9q\nzIGcw8zbM1/HiBERkaumWvvAqLRIVYUHevPg+K44jnaFwgASTm/nm8PfmR1LRETqCWtlF+bm5vLT\nTz9VnM7Ly2Pjxo0YhkFeXl6Nh5O67dpmAdw1KpK3l9rw7rKJZcdWEOQVSHxErNnRRESkjqu0wPj7\n+5+z466fnx+zZs2q+H+RS4ntFE5GTge+2FSOV5dNfLr3CwI9AugU3N7saCIiUodVWmDmzp1bWzmk\nHhsd14L0nGI27CvDs+MW/p08l4e730czvwizo4mISB1V6T4wBQUFzJkzp+L0p59+yrhx43jwwQfJ\nzMys6WxST1gsFm4f3p72wa0pOdiVUnsZb+74gOySHLOjiYhIHVVpgXn66ac5c+YMAEeOHOGVV17h\nscceo0+fPvzjH/+olYBSP1hdXbj/xi6Eu7Sm/Hh7ckpzeXPHBxTbSsyOJiIidVClBebEiRM88sgj\nACxfvpwRI0bQp08fbr75Zq3ASLV5e7rx0MQovPKuxXa6OSkFabyXPA+7w252NBERqWMqLTDe3t4V\n/79582Z69+5dcVpfqZbLERLgxbQJ0ZDSCSM3jD1Z+/l03yIdGFFERKql0gJjt9s5c+YMx48fZ9u2\nbcTHxwNQWFhIcXFxrQSU+qd1hD+/H9uF0gNdsRQ3YkPaFpYfW2F2LBERqUMqLTD33HMPo0aNYuzY\nsdx33300atSIkpISbr31Vm644Ybayij1UI/2YUzs34Givd1wsXmz5PByNp9KNDuWiIjUEZV+jbp/\n//6sW7eO0tJSfH19AfD09OTPf/4zffv2rZWAUn8N73UN6TnFrN5Tjnfnzczbs4AAj0a0C2xjdjQR\nEXFyla7ApKamkpGRQV5eHqmpqRX/tW7dmtTU1NrKKPWUxWLhtqHX0rlJC4r3ReNwGLyz8yNOFZ42\nO5qIiDi5SldgBg0aRKtWrQgNDQXO/zHHjz76qNI7379/P/fddx+//e1vmTx5MmlpaTzxxBPYbDas\nVisvvvgioaGhLF68mA8//BAXFxcmTZrExIkTr8JTk7rA1cWFP4zrwvPzykg7XILRZiezkt7nTz2m\n0shDR3sWEZELq7TAzJgxg6+++orCwkJGjx7NmDFjCAoKqtIdFxUVMX36dOLi4irOe/XVV5k0aRKj\nRo3i448/5oMPPmDq1KnMmjWLhQsX4ubmxoQJExg6dCgBAQFX9sykzvDysPLHiV159qMyCk4Wk9Xs\nIG/t+IA/dr8XD1d3s+OJiIgTqvQjpHHjxvH+++/z6quvUlBQwG233cbdd9/NkiVLKCmp/ABk7u7u\nvPvuu4SFhVWc99e//pXhw4cDEBgYSE5ODklJSURGRuLn54enpyfdu3cnMVE7czY0Qf6eTJsQhWtG\nOxyZzTief5IPdn2Mw3CYHU1ERJxQpQXmZ02aNOG+++7j22+/Zfjw4Tz77LOX3InXarXi6el5znne\n3t64urpit9v55JNPGDt2LJmZmees6gQFBZGRkXEZT0XquhaN/fifcV0oO9oJS0EIOzP3sPDAYh0j\nRkREzlPpR0g/y8vLY/HixSxatAi73c7//M//MGbMmMt6QLvdzqOPPkrv3r2Ji4tjyZIl51xelT9W\ngYHeWK2ul/X4VREaqn0vzDI01I9Su8Hbi+34Riaw+uQGWoQ0YUz7IYBm46w0F+el2TgvzebKVFpg\n1q1bx+eff05ycjLDhg3jhRdeoF27dlf0gE888QQtWrRg6tSpAISFhZ3zswTp6elER0dXeh/Z2UVX\nlKEyoaF+ZGTk19j9y6XFtg/lULdW/LjDhk/kZuZuX4S7zZthnftoNk5I7xnnpdk4L82maioreZUW\nmLvvvpuWLVvSvXt3srKy+OCDD865/Pnnn69WkMWLF+Pm5saDDz5YcV5UVBRPPvkkeXl5uLq6kpiY\nyP/+7/9W636l/rl50LVk5pSQ9N9jxHy4+z80DwsjmHCzo4mIiBOwGJV8ZrN582YAsrOzCQwMPOey\nkydPctNNN130jpOTk5kxYwYpKSlYrVbCw8M5c+YMHh4eFQfFa9OmDX/7299YtmwZ7733HhaLhcmT\nJ3P99ddXGromW6tasfMoLbPzwieJnCg+jGf7RFxdXBnfdiz9mvbWb3E5Eb1nnJdm47w0m6qpbAWm\n0gKTkJDAQw89RGlpKUFBQbz99tu0aNGCefPm8c4777BmzZoaCXwpKjANR05BKc9+lEAOKfh32kWp\no5ie4dHc0n48nlYPs+MJes84M83GeWk2VXPZHyH961//Ys6cObRp04Yff/yRp59+GofDQaNGjViw\nYMFVDyryawG+HvxxQhTPf2wnN9GH0Kg9JJzezon8VO7uMpkI38ZmRxQRERNU+jVqFxcX2rQ5+7s0\ngwcPJiUlhdtvv5033niD8HDtiyC1o1mYL0/e3oNrgsJIT4jGJ78dp4vSeTHhdf0ApIhIA1Vpgfn1\nfgZNmjRh6NChNRpI5EKaBPvw8oPXEdepCZl7WuNyrAcYFj7c/Smf7P2ccnu52RFFRKQWVelAdj/T\njpNiJk8PK3eP6cSU4e0pzQwjf3ssfpZg1qdu4uWts8goOmN2RBERqSWV7sQbGRlJcHBwxekzZ84Q\nHByMYRhYLBZWrVpVGxnPo514G6ZfzuZIWh6zv0jmTH4hYV0Ok+91CC+rJ1M6TiIqtIvJSRsWvWec\nl2bjvDSbqrnsnXiXLVt21cOIXA2tmvjz1ztjeHfJbnbudMW/mT/lTXfyzs6PGHzNdYxrMxJXl5o7\nWrOIiJir0gLTtGnT2sohUm2+Xm5Mm9iVbzYc5cu14JrtQ2BkMj+eWMORvOPc1eU2AjwamR1TRERq\nQLX2gRFxNi4WC2PjW/HwzdF4OgLJ2NyTRuUtOZx7lOc3v8rerANmRxQRkRqgAiP1QueWQfztzhja\nNAni1Lb2eGVEUWQr5o3t/+abI9/jMBxmRxQRkatIBUbqjSB/Tx67tTtDel5D1pEm2PbG4e3iy9Ij\n3zM76X3yywrMjigiIleJCozUK1ZXF24d0o57x3WG4gAyE2IIcFzDnqz9vLDlNQ7nHjU7ooiIXAUq\nMFIv9eoYztN39KRpQCBpCZ3wy40ktzSPfyW+xY/H11DJ0QNERKQOUIGReqtJsA9P3t6T3p0bk76v\nKZbDvfFw8WLRwa95N3kuxbZisyOKiMhlUoGRes3D3ZV7xnRiyrB2lGYHkJ0QQ6AlgqSMZF7YMpMT\n+almRxQRkcugAiP1nsViYWD3ZjwxuQdBXo1I3dSFgKJOZBaf4aWtb7A+dZM+UhIRqWNUYKTBOHv0\n3l5Etg4lLbk57idisWLlk72fM3fPfErtZWZHFBGRKlKBkQbl56P33tCvFXlpgeRtjyXQNZxNp7by\nYsLrnC5MNzuiiIhUgQqMNDguFgvXx7fi4d9E42XxJ3VjFMFlHUgrPM2MhJlsPb3d7IgiInIJKjDS\nYHVu9fPRewM4ub0lPqd7YRjw/q5P+Gzfl5Q7bGZHFBGRi1CBkQYtyN+Tx27rzpCezcg8FkRJchwB\n1hDWpGzgX1vf5ExxltkRRUTkAlRgpME75+i9Zb6kbexGqONajuWf4IUtr5GcucfsiCIi8isqMCL/\n9fPReyOC/Dme0JpGWT0ps5fz5o4P+OrQt9gddrMjiojIf6nAiPzC2aP39qB3p8acOhiCY38f/K0B\nfHdsJa9vf5fc0jyzI4qICCowIufxdLdyz9izR+8tyfUhfVMPwlxacSDnMM9veZX92YfMjigi0uCp\nwIhcwDlH7/Xx5djGdgQXdKewvIiZ295h+dEVOAyH2TFFRBosFRiRSrSOOHv03i6tgzm5Owy3o/H4\nWH1ZfHgZb+2YQ0F5odkRRUQaJBUYkUvw9XLjjxOjuKFfK3JP+5C9NYZwa3N2ndnLC5tf42jecbMj\niog0OCowIlXwy6P3err4cHRDR8LLoskpzeWVrW+y6uR6/SCkiEgtUoERqYaKo/dGNOLo9sb4pMXj\n4erBgv1f8f6ujymxlZgdUUSkQVCBEammiqP39mhGxglfCpLiCHOLIDF9BzMSZpJSkGZ2RBGRek8F\nRuQyWF1duHXoz0fv9eTYhi5EOCJJL8rkxYQ32JiWYHZEEZF6TQVG5Ar06hjOU3f0pEmQL4cSmhJ4\nJh5Xiytz98zn4z0LKLOXmx1RRKReUoERuUIRIT48dUdPencKJ/WQH2W7+xDiHs6GtC28tPUN0osy\nzI4oIlLvqMCIXAU/H7138rB2lOR7cHJ9V5q5dCKlII0ZW15ne/pOsyOKiNQrKjAiV4nFYmHQz0fv\n9fPmwMbmhOf3wW7YeTd5LgsPLMbmsJkdU0SkXlCBEbnK/v/ovUEc3eOP68G+BLkHs/LEOl5NfJvs\nkhyzI4qI1HkqMCI1oOLovX1bkZPpwelN3bnGrR1H8o7x/JZX2XVmr9kRRUTqNBUYkRriYrFwfd9W\nPPSbKDytnuxf34qmpbGU2EqZnfQ+bya9r2PGiIhcJhUYkRrWpVUwf7szhtYRjTiYFIjPiQG08GlJ\n8pm9PL/5VT7a/RlZJdlmxxQRqVNUYERqQZC/J4/f1p3BPZpxOtWNQ2s7EmkZQbh3GJtObeWZjS+y\n6ODXFJYXmR1VRKROsJodQKShsLq6cNvQdrS/JoD//HiAzZtK8fbsSbeYUo6SwI/H17AhdTPDWgxk\nQLO+uLu6mR1ZRMRpaQVGpJb17BDG87/vzYQBbTAMCxvWWind2Y/uvv2x4MJXh77lmY3/ZEPqZuwO\nu9lxRUScksUwDMPsENWVkZFfY/cdGupXo/cvl68+zqaguJyvNxxlReJJbHaDZo3dadE1g515CZQ7\nymnsE8641iOIDOmExWIxO+4F1ce51BeajfPSbKomNNTvopepwPyKNirnVZ9nk5lTzKK1h9m46zQA\nHdp40qjNMXbmbMfAoHWjltzQZhRtAlqaG/QC6vNc6jrNxnlpNlWjAlMN2qicV0OYzbFT+SxYdZDd\nR7OxANFdPCFiL3tzzh43pmtIZ8a1GUFjn3Bzg/5CQ5hLXaXZOC/NpmoqKzDaiVfEibRo7Mefbu5G\n8pEzLFh5iG3JBVj3tCamRzty/JLYkbmLnZm7iWsSw+jWQwnwaGR2ZBERU6jAiDihLq2C6dQyiI27\nTvHFmsP8tLkUL48uxPTqwgnXLWxI28yW04kMvKYfQ5sPwNvNy+zIIiK1SgVGxEm5WCz06dKEmA5h\n/Lg1hW9+OsqatTaC/GOJ7VnCvrJNfHdsJetSNjK85SD6N+2Dm756LSINhL5GLeLk3KyujIhtzgv3\nxjEitjl5hXZWrXDF9cBgegcOwMDgi4Pf8MzGF9mUthWH4TA7sohIjdNOvL+iHaucl2Zz1pncEr5Y\ne5ifkk9hAO1aetO4Yxrbsrdgc9iI8GnMuDYj6RzcoVa+eq25OC/NxnlpNlWjbyFVgzYq56XZnOv4\n6XwWrjpE8pEsALp39sHjmkMkZSVhYHBtQGtuaDuKlv7NazSH5uK8NBvnpdlUjQpMNWijcl6azYXt\nPprFgpWHOHY6H6urhdju3hQHJbMnex8A3UIjGdtmBOHeoTXy+JqL89JsnJdmUzX6GrVIPdapZRBP\n/TaQzbtPs2jNYdZvKcTL41piYzpwyiORbRk7ScrcRZ+IXoxqOYRGHv5mRxYRuWIqMCL1gIvFQu/O\njenRPoyV21JYsv4Iq9aVEuDXjfheURywbWRdykY2p21lcPPrGNy8P15WT7Nji4hcNn2E9Cta1nNe\nmk3VFZWUs3Tjcb5POEG5zUFEqBddehSSVPATeWX5+Lr5MKLlYPo17Y3V5cr+HaO5OC/NxnlpNlWj\nfWCqQRuV89Jsqi8rr4Qv1x5h/c60s99YauFLiy6ZJGT9RIm9lGDPIK5vPZzu4VG4WC7vqAqai/PS\nbJyXZlM1KjDVoI3KeWk2l+9kRgELVx1ix6EzAHTv5I9/q+MkZG7Bbti5xjeCcW1H0TGoXbXvW3Nx\nXpqN89JsqkYFphq0UTkvzebK7T2WzYJVBzmSlo+ri4W4bv7Yw/eSlLkDA4MOgdcyrs1Imvs3q/J9\nai7OS7NxXppN1ehbSCICQIcWgTx5e0+27E3n89WHWLc1F0/3ZsT3as8Zn23szT7A3oQD9AyPZmzr\n4YR4BZsdWUTkglRgRBoYi8VCr47hdG8XyqptKSxef5Qf1+UR4NuRAb0jOeTYRMLp7WxL30nfpr0Z\n2XIwfu6+ZscWETmHPkL6FS3rOS/NpmYUl9r4dtMxvtt8gjKbgyYh3nSPKWdH0QYyi8/g4erO0OYD\nGHhNPzytHufdXnNxXpqN89Jsqkb7wFSDNirnpdnUrOz8Ur5ad4S1O1IxDLi2mS/tuuWzJWs9+eUF\n+Ln7MqrlUOIjeuHq4lpxO83FeWk2zkuzqZrKCkyN/hr1/v37GTJkCPPmzQMgLS2NKVOmcOuttzJt\n2jTKysoAWLx4MePHj2fixIksWLCgJiOJyEUE+nnw25Ed+PtdsUS3DeHAyQK+WWKhaeYY+jceQKm9\njM/2f8H0TS+RmL6DOvhvHxGpR2qswBQVFTF9+nTi4uIqzps5cya33norn3zyCS1atGDhwoUUFRUx\na9Ys5syZw9y5c/nwww/JycmpqVgicglNQ3x4cEJXHr+tO60j/Nm2L4fvv/YisnQCvcNjOVOSzXvJ\n83gx4Q32Zx8yO66INFA1VmDc3d159913CQsLqzhv06ZNDB48GICBAwfy008/kZSURGRkJH5+fnh6\netK9e3cSExNrKpaIVFG7awL4y5Qe3HdDF0IaebIuMYsNy0PoY72Z6JBIjuWf4LVtb/Pc6tfZm3UA\nh+EwO7KINCA19i0kq9WK1Xru3RcXF+Pu7g5AcHAwGRkZZGZmEhQUVHGdoKAgMjIyKr3vwEBvrFbX\nSq9zJSr7zE3MpdnUvpFh/gzt04rlG4/x6Xf7+H59FoF+rRk7oBuHHRvZfmo320/tJtw3lMGt4xnY\nKo5GnvrBSGeh94zz0myujGlfo77Y5+dV+Vw9O7voasepoB2rnJdmY65e7UKIbBHA8s3HWb75BPOX\npBMe1Jmbh8RzpGQHiek7+GTHl3y2cwldQzvTNyKWdoFtLvsnCuTK6T3jvDSbqnGaA9l5e3tTUlKC\np6cnp0+fJiwsjLCwMDIzMyuuk56eTnR0dG3GEpEq8vKwckO/1gzs1pSv1h9lzfZUPpifRlhAcwZF\nd8M97BRbMxLYlr6Dbek7CPUKJj4ilt5NeupYMiJyVdXqP4369OnD8uXLAfjuu+/o168fUVFR7Ny5\nk7y8PAoLC0lMTKRnz561GUtEqqmRrwe3D2/P9Lt7MTjmGrILSvly1UkWfW4n+PQwJjadQmzjHuSU\n5vLloaX8Zf0/eC95HvuyDmpfGRG5KmrsODDJycnMmDGDlJQUrFYr4eHhvPTSSzz++OOUlpYSERHB\n888/j5ubG8uWLeO9997DYrEwefJkrr/++krvW8eBaZg0G+cUGurH0RNZbEg+xaptKaSdOfsRb5Ng\nb/pEBeMWmsbm9C2kFZ4+e32tytQavWecl2ZTNTqQXTVoo3Jemo1z+uVcDMNg/4kcVm9PJWFfOja7\ngZvVhZiOobRvD0fKkklMT6LcYcPV4kp0aBfi/7uvjMViMfmZ1D96zzgvzaZqVGCqQRuV89JsnNPF\n5pJXVMb6nWms3pZKek4xANeE+dInKhhLUAqb0rdw6r+rMmFeIfSJ6KVVmatM7xnnpdlUjQpMNWij\ncl6ajXO61FwchsGeY9ms2pbCtv2ZOAwDDzdXYjuHcW07g0MlOytWZawWV6JCu9C3aSzXBmhV5krp\nPeO8NJuqcZpvIYlIw+NisdC5ZRCdWwaRU1DK2h1prNmewprtaazZDq2atGZM1xiMwJNsPL2FrelJ\nbE1PIswrhPimsfRu3BNfdx+zn4aIOBmtwPyKWrHz0myc0+XMxeEw2Hn4DKu3p5J0KBPDAC8PV+I6\nN6ZtOzv7is4eV8Z2zqpMb64NaK1VmWrQe8Z5aTZVo4+QqkEblfPSbJzTlc4lK6+ENUmprE5KJbfg\n7A+8XtusEXFdg7A1OsFPaZs5VZQOQJh3yNlvMGlVpkr0nnFemk3VqMBUgzYq56XZOKerNReb3UHS\nwTOs3p5C8pEsAHw8rcR3bUzrtnZ25yexLeP/V2WiwyLpGxFLW63KXJTeM85Ls6kaFZhq0EblvDQb\n51QTc0nPLmJ1UirrdqSRX1QOQMcWgcR2DaTM9zgb0jZz+r+rMuHeocRHxBLbpAe+blqV+SW9Z5yX\nZlM1KjDVoI3KeWk2zqkm51Juc7DtQAartqWw93gOAP7ebvTt2oSWbW0k525jW8bOX63K9KZtQCut\nyqD3jDPTbKpGBaYatFE5L83GOdXWXNLOFLJ6eyrrd6ZRWGLDAnRuHURcZBCF3kf+uypz9pfsw73D\n6BvRi14NfFVG7xnnpdlUjQpMNWijcl6ajXOq7bmUldvZsjed1dtTOZiSC0Cgnwd9IxvTok05STmJ\nbE/fic2wY3Wx0i00kr5Ne9OmUcsGtyqj94zz0myqRgWmGrRROS/NxjmZOZcT6QWs2p7CT8mnKCmz\nY7FAdNsQekUGku9x+JxVmcbeYcQ3jSW2cQ983LxNyVvb9J5xXppN1ajAVIM2Kuel2TgnZ5hLSZmN\nzXvSWbkthWOnzmYJaeRJv65NaNaqlO3Zv16V6UrfprH1flXGGWYjF6bZVI0KTDVoo3Jemo1zcra5\nHEnLY/X2FDbuPk1ZuQNXFwvd2oUSGxlAlvUQG9I2kV6UCUBjn3D6RsTSq3H3erkq42yzkf+n2VSN\nCkw1aKNyXpqNc3LWuRSV2Php1ylWbU8hJaMQgPBAL66LiqBJi2ISz2xle0YydsOOm4uVbmFdiY+o\nX6syzjob0WyqSgWmGrRROS/Nxjk5+1wMw+BQSh4rt6WwZW86NrsDq6uFnh3C6NUlgAyXA2xI3Ux6\n8dlVmWDPQCJDOtE1pDNtA1rh6uJq8jO4fM4+m4ZMs6kaFZhq0EblvDQb51SX5lJQXM6GnWms3J7K\n6awiAJqG+HBdVBPCm/nH6ggAABhxSURBVBezNXMryZl7KbGX8H/t3Xtsm2fh9vGvDznaTuzEzjnO\nuYe0Tdsd3t9vXbuBtoHE9GMvG9AxVqb3DyS08QeooE2FnQAhdRISh00DxJCmommFHRgIGANBp72i\n4/C2a0bapkmTxjknThzbcY4+vH/YdZKOjZit8eP2+kjROsfxbuu63Vx77vt5HoBiaxHbyrfQ4dlG\ne9kmCq2F2Rx+xnIpm6uNslkfFZgMaFIZl7IxplzMJZFI0O2b4dhbw/y/7kli8QT5VjP/a2sl/7XN\nA/Yp/jl1hk5/FzOLyVO1rSYLm1ytdHja2eFux1lQmuV38e/lYjZXC2WzPiowGdCkMi5lY0y5nksw\nssT/7Rzh9bdG8AeTR15shVZ2trrZ1VqOq2KRMzNn6fR3MTw7mv65Bkc9HZ7kUlO1rdKQ+2ZyPZsr\nmbJZHxWYDGhSGZeyMaYrJZd4IsGZgQAnuic52TPJTOrO2HlWM+0NLnZv8tBQb6Ev0kOn/zS9M33E\nE3EA3IVldHi20eFup7m00TD7Zq6UbK5EymZ9VGAyoEllXMrGmK7EXOKJBANjYU72THLynJ9hf/Is\nJhPQUlfK7jY3W5vsTMQH6PSf5vTUWRZjycJjyytme/lWOtztbCnbRKG1IGvv40rM5kqhbNZHBSYD\nmlTGpWyM6WrIZTwwx8lzfk72TNI7FOTiX5rV5cVcs8nDjhYXS4UTvD11mrcnTxNcCgFgNVvZ4mql\nw72N7e52Sgve/S/jy+FqyCZXKZv1UYHJgCaVcSkbY7racglFljjV6+dkj5+uC9MsR5PLSKX2fHa3\nedjVWo6tLMLp6TN0+k8zEhkDwISJxpJ6Otzb6PC0U1lccdn3zVxt2eQSZbM+KjAZ0KQyLmVjTFdz\nLotLMbouTHPy3CSnzk8xO78MQGG+hR3N5eze5Ka2xkRPqDu1b6afROr4TUWRmx2pTcDNpQ2YTeYP\nfHxXczZGp2zWRwUmA5pUxqVsjEm5JMXicXqHgpzs8XPi3GT6jCaL2cQWr5NdbR42NxUzvNhPp7+L\n09PnWErtm7Hn2ZL7ZjztbC3bRL4l/wMZk7IxLmWzPiowGdCkMi5lY0zK5Z0SiQTDkxFO9Exyssef\nvsEkQEOVg2va3OxocRG2jPD21Gk6/acJL80CkGe2sqWsjQ73Nna423Hk2//jcSgb41I266MCkwFN\nKuNSNsakXP69qeACb/UmNwF3+2aIxZN/7Xqchel9M3klIf45fYbOyS7G5iaA5L6ZplJvct+Mu51K\nW0VG/11lY1zKZn1UYDKgSWVcysaYlEtm5haW6Tw/xYkeP2/3TbG4FAPAXpTHztZyrmnz4KmKc3bm\nLJ2TXfQFB9L7ZiqLPelNwI0l3n+7b0bZGJeyWR8VmAxoUhmXsjEm5fKfW47GOesLcPJccqkpGEnu\nicm3mtnWVMauNjctDUUMRHrp9J/mzPQ5luPJjcKOPDs73Fvp8Gxjs6uNfEveO15f2RiXslkfFZgM\naFIZl7IxJuXywYgnEvSPhtLXmxmdSt5s0mSCttpSdm/ysL2lhKn4MJ3+07ztP83scvICe/nmPLaW\nbWKHZxs7yrdiz7cBysbIlM36qMBkQJPKuJSNMSmXy2Nsei59JeDzwysXz6v12Njd5mZnazmJokBq\nE3AXE3N+ILlvprm0kQ5POze1XUfeQrEh79N0tdPnZn1UYDKgSWVcysaYlMvlF7x48bxzk3RdCBCN\nJS+e53IUsKvNzTVtHpzu5dTF87roD/rS+2Yc+XZanc20OZtpdTZRbau8LNeckczoc7M+KjAZ0KQy\nLmVjTMplYy0sRenqn+bEOT+d5/1EFqIAFBVY6Ghxs7vNTWNdPr3hHvoj/fxz/ByhpZV8bNZiWpxN\ntDmbaHU2U+eoUaHJAn1u1kcFJgOaVMalbIxJuWRPNBanZyiY3gQ8FVq5eN7WBhc37qqlxlVInm2B\nvmA/PTN99M70M70QSL9GoaWQZmdD6ghNMw2OOsPcTftKps/N+qjAZECTyriUjTEpF2NIJBIMTsxy\nsie5Cdg3Ppv+nr0oj831TjZ5nWyud1Jcssz5mX56Z/rpneljYt6ffm6+OY+m0ob0klNjiZe8f3GG\nk7w/+tysjwpMBjSpjEvZGJNyMSZ/cJ7BqXn+0TVG92CA6dBi+nu2QittdU62eJ1s9rooKY3TF0oW\nmp6ZPkYj4+nnWk0WGkq8tLmShaa5tJGCD+hWB1czfW7W570KjHUDxyEiIhvEXVrE1tYKdjeXkUgk\n8AcX6PbN0D0YoNs3w1u9ft7qTR55KSqw0lZXyhbvtXzWewtlLhP94QF6Z/roDfTRF7zA+WA/AGaT\nGa+jLn2EpsXZSJG1KJtvVa5SOgJzCbVi41I2xqRcjOu9spkKLqTLTPfgDBOB+fT3CvMttNaVssXr\nYnO9E4/bii/sSx+h8YWHiCeSZ0KZMFFnr6bVldxD01ralL4Ojbw7fW7WR0tIGdCkMi5lY0zKxbgy\nySYQXqTbF6B7cIZu3wxj03Pp7xXkWWitLWGT18UWr5OaigJ8s4PJQhPoYyDkI5qIpZ9fbatMH6Fp\ndTZTWlDygb+3XKfPzfqowGRAk8q4lI0xKRfjej/ZzMwuci5VZroHZxjxR9Lfy7eaaaktZXO9k81e\nJ/VVRQxHRpJLTjP99AUvsJS65QFARZE7eXQmVWjKi1zv+73lOn1u1kcFJgOaVMalbIxJuRjXB5lN\nKLK0qtAEGJpcKTRWi5mWmhI2p85yaqi2Mb4wlj5t+/zMBRZiC+nnlxW61hyh8RSVX3VXC9bnZn1U\nYDKgSWVcysaYlItxXc5sZueXOTc4w1lfgHO+GQYnZtO3O7BaTDRVXyw0LpprHEwujdMb6Eudut1P\nJLqyRFWa70gdoWmmzdVMVXHFFV9o9LlZHxWYDGhSGZeyMSblYlwbmU1kYZmewSBnU/tofONhLv52\nsZhNNFY72FzvYrPXSXONg2B0OnWEpo+emT7CS6uuW5NnSx+daXU2U2uvuuKuFqzPzfqowGRAk8q4\nlI0xKRfjymY2cwtReoeTS05nfTMMjIWJp37dmE0mGqoc6SWn1tpSZhMz9Ab66EldXC+wOJN+rSJr\nIS2ljXhL6vE6avE66nJ+Y7A+N+ujApMBTSrjUjbGpFyMy0jZzC9GOT8cTJ/l1D8aIhZP/voxmcBb\n6UhvCt5U72QhEU6ftt0708fk/NSa1yvNL8FbUku9o44GRx31jjpKC979l53RGCkbI1OByYAmlXEp\nG2NSLsZl5GwWl2L0jgTp9s1wzhegbzRENJYqNEBdhT29h2az10ncsoAvNIQvPIQvPMxgeJiZxeCa\n17xYaryOOrwGLzVGzsZIVGAyoEllXMrGmJSLceVSNkvLMc6PhOj2BTg3OEPvcIhoLJ7+fq3HRlNV\nCY3VDhqrSqivsDEXm2MwvFJqfKEhgkuhNa/rLCil3lGbXnryltRRkp/9UpNL2WSTCkwGNKmMS9kY\nk3IxrlzOZjkao28klF5yOj8SZGl5pdBYzCZq3bZ0oWmoclDnsTMXi6wqNUP4QsPvWmqSS0+1WSk1\nuZzNRlKByYAmlXEpG2NSLsZ1JWUTi8cZnZpjYCzMhdEwF8ZC+CZmWY6uLTV1FXaaqhw0VCWLTa3H\nRiQ6u2rp6d1LTXLpqXZDSs2VlM3lpJs5iohITrOYzdR57NR57Ny4oxqAaCxZai6MhrgwFubCWJjB\niTADYyvFwGoxU19ho7GqhMaqbdxefQM124uZXZ5ds/Q0GB6i099Fp78r/bMbXWokMyowIiKSk5Ll\nxE59hZ19O5OPRWNxhicjDIyHuTAaon8sjG98lv7RlVKTZzXjrbCnlp628z81N1BdXkx4OcxgqtD4\nwsP43rPU1KU3DDvy7Rv91gUVGBERuYJYLWYaUktIN+2sAWA5GmfYP5teekr+M8z5kZVlpPw8M95K\nB42VDhqrd7C79kaqyooJLYdWlZpksflXpebiqdwqNRtHBUZERK5oeVZzagmpBKgFkpuEByciyUKT\n2lfTNxyid2jl1OyCPAsNlXYaq0toqOrgmrobqSwrJrSULDUDqaUnX3iYU/4uTq0qNa4CZ2rpKXnm\nk9dRq1LzAVOBERGRq06e1UJzTQnNNStX9F1cjjE4MZvaKJwsNj3DQc6tKjWF+RYa05uEd3K9dy+e\n0kJCy+H00tNgeIiB8NC7lhpvSR1bl5qxxUooK3RdcbdJ2Cg6C+kS2hluXMrGmJSLcSmb929xKYZv\nYuXMpwtjYcam5lj9i7OowEpjlSP5VZ08pdtdUrCq1KzsqVl9zyeAPHMeVbYKqm2VVBdXUm2vpNpW\nqWKTorOQRERE/gMF+Rba6py01TnTj80vRvGNJ/fRDIyF6R8Lc2YgwJmBQPo5tkJr6khNCY1Vu/nv\nxpsocxQQTC0/BQnQO+FjNDLOaGScwfDwmv/ummKz6kvFZoUKjIiISAaKCqxs9rrY7HWlH5tbWCk1\nFzcKd10I0HVhpdTYi/JSR2kctLd00Fyxi8qyYsxm8M9PpcvM6q9Li01+uthUrSo4VZQVOq+6YqMl\npEvokKtxKRtjUi7GpWyyK7KwnNxPs2pPjT+4sOY5FrOJyrJiatw26tw2atw2aj02KlxFQIKphel3\nlJrxuUmi8eia11ldbKptlek/53qx0RKSiIjIBrMV5tHeWEZ7Y1n6sdn5ZS6MhQjOR+m+MM2IP8Kw\nP8KIP8I/Vv2s1WKiKlVsat02atwd7K624XEWkSCOf2GasUuKzUhkHN+lR2ws+VQVr12KqrJV5nyx\nARUYERGRDWMvymN7U3ny6Fh78uhYIpFgOrSYLjLDk7PJP09FGJqMrPn5PKuZ6rJiajw2at12atyV\nXFNjw+0sIpFIFpvRyPiacjMyO4ovPLTmdf5Vsam2VeLKoWKjAiMiIpJFJpOJ8tJCyksL6WgpTz8e\nTySYCi6sKjbJf45ORfBNrD2bKd9qpro8ufxU63ZQ467iulobZaWFa4rN6Ow4Y3NXRrFRgRERETEg\ns8mEx1mEx1nErlZ3+vF4PIE/OM/w5Mry03Dqa2B87Z6ngjwLNe6LS1El1Lqr+a96Gy5HAfFEPLl5\neG6C0dlxRiNjjM1NvGuxqS6ufMeZUdksNhtaYCKRCA8++CDBYJDl5WUeeOABPB4Pjz32GACbN2/m\n8ccf38ghiYiI5BSz2USFq5gKVzG7N3nSj8ficSZnFlJHambTpebSe0EBFBVYqCm3pYtNvaea/26w\n47TnrxSbyDijkQlGI2OMRsYZnh1hIDy45nXyLfncUH0dn970vzfkva+2oQXm5ZdfpqmpiYMHDzI+\nPs59992Hx+Ph0KFDdHR0cPDgQV5//XVuvvnmjRyWiIhIzrOYzVSVFVNVVsy1m1eKTTQWZyIwv+ZI\nzYg/8o77QQEUF1jTZ0LVuJ00uGvZ02ijxHZpsVn5WowtbfRbBTa4wLhcLrq7uwEIhUI4nU6Gh4fp\n6OgA4MMf/jDHjx9XgREREfmAWC1malKnaF+36vFoLM749Fyy1EyuLEX1jYToHQ6ueQ1boTV5NpTH\nTq3bRaO7jhubbZQU52/sm1llQwvM7bffzksvvcRtt91GKBTi6aef5hvf+Eb6++Xl5UxOTv7b13G5\nirFaLZdtnO913rlkl7IxJuViXMrGuIyQTXVVKbsueWw5GmNoYhbfWBjfeBjfWIiBf3FfKIBSez63\nXOfl//zPto0bdMqGFphXXnmFmpoannnmGc6ePcsDDzyAw7ES4HqvqRcIzF2uIerCTwambIxJuRiX\nsjEuo2djzzPTXl9Ke31p+rGl5RijU3Nrrl0z7J9laDx02d6LYS5kd+LECfbu3QvAli1bWFxcJBpd\nuZrg+Pg4FRUVGzkkERERWYf8PAsNqTtxG8GGnvvU0NDAqVOnABgeHsZms9HS0sI//pG8/uBrr73G\nvn37NnJIIiIikoM29AjM/v37OXToEPfeey/RaJTHHnsMj8fDI488QjweZ+fOnezZs2cjhyQiIiI5\naEMLjM1m43vf+947Hn/uuec2chgiIiKS44x1XWARERGRdVCBERERkZyjAiMiIiI5RwVGREREco4K\njIiIiOQcFRgRERHJOSowIiIiknNUYERERCTnqMCIiIhIzlGBERERkZxjSiQSiWwPQkRERCQTOgIj\nIiIiOUcFRkRERHKOCoyIiIjkHBUYERERyTkqMCIiIpJzVGBEREQk56jArPLtb3+b/fv3c/fdd9PZ\n2Znt4cgqTzzxBPv37+euu+7itddey/ZwZJWFhQVuvfVWXnrppWwPRVb51a9+xcc//nHuvPNOjh07\nlu3hCBCJRPjiF7/IgQMHuPvuu3njjTeyPaScZs32AIzib3/7GwMDAxw9epTz589z6NAhjh49mu1h\nCfDmm2/S09PD0aNHCQQCfOITn+AjH/lItoclKU8//TSlpaXZHoasEggEeOqpp3jxxReZm5vjBz/4\nAR/60IeyPayr3ssvv0xTUxMHDx5kfHyc++67j1dffTXbw8pZKjApx48f59ZbbwWgpaWFYDDI7Ows\ndrs9yyOT66+/no6ODgBKSkqYn58nFothsViyPDI5f/48vb29+uVoMMePH+eGG27Abrdjt9v55je/\nme0hCeByueju7gYgFArhcrmyPKLcpiWkFL/fv2YylZWVMTk5mcURyUUWi4Xi4mIAXnjhBW666SaV\nF4M4fPgwDz30ULaHIZcYGhpiYWGBL3zhC9xzzz0cP34820MS4Pbbb2dkZITbbruNe++9lwcffDDb\nQ8ppOgLzLnSHBeP54x//yAsvvMBPf/rTbA9FgF/+8pfs2rWL+vr6bA9F/oWZmRmefPJJRkZG+Nzn\nPsef//xnTCZTtod1VXvllVeoqanhmWee4ezZsxw6dEh7x94HFZiUiooK/H5/+t8nJibweDxZHJGs\n9sYbb/DDH/6Qn/zkJzgcjmwPR4Bjx44xODjIsWPHGBsbIz8/n6qqKvbs2ZPtoV31ysvL2b17N1ar\nFa/Xi81mY3p6mvLy8mwP7ap24sQJ9u7dC8CWLVuYmJjQcvj7oCWklBtvvJHf//73AHR1dVFRUaH9\nLwYRDod54okn+NGPfoTT6cz2cCTlu9/9Li+++CI///nP+dSnPsX999+v8mIQe/fu5c033yQejxMI\nBJibm9N+CwNoaGjg1KlTAAwPD2Oz2VRe3gcdgUm55ppr2LZtG3fffTcmk4lHH30020OSlN/+9rcE\nAgG+9KUvpR87fPgwNTU1WRyViHFVVlby0Y9+lE9/+tMAfP3rX8ds1v+vZtv+/fs5dOgQ9957L9Fo\nlMceeyzbQ8pppoQ2e4iIiEiOUSUXERGRnKMCIyIiIjlHBUZERERyjgqMiIiI5BwVGBEREck5KjAi\nclkNDQ2xfft2Dhw4kL4L78GDBwmFQut+jQMHDhCLxdb9/M985jP89a9//U+GKyI5QgVGRC67srIy\njhw5wpEjR3j++eepqKjg6aefXvfPHzlyRBf8EpE1dCE7Edlw119/PUePHuXs2bMcPnyYaDTK8vIy\njzzyCO3t7Rw4cIAtW7Zw5swZnn32Wdrb2+nq6mJpaYmHH36YsbExotEod9xxB/fccw/z8/N8+ctf\nJhAI0NDQwOLiIgDj4+N85StfAWBhYYH9+/fzyU9+MptvXUQ+ICowIrKhYrEYf/jDH7j22mv56le/\nylNPPYXX633Hze2Ki4v52c9+tuZnjxw5QklJCd/5zndYWFjgYx/7GPv27eMvf/kLhYWFHD16lImJ\nCW655RYAfve739Hc3Mzjjz/O4uIiv/jFLzb8/YrI5aECIyKX3fT0NAcOHAAgHo9z3XXXcdddd/H9\n73+fr33ta+nnzc7OEo/HgeTtPS516tQp7rzzTgAKCwvZvn07XV1dnDt3jmuvvRZI3pi1ubkZgH37\n9vHcc8/x0EMPcfPNN7N///7L+j5FZOOowIjIZXdxD8xq4XCYvLy8dzx+UV5e3jseM5lMa/49kUhg\nMplIJBJr7vVzsQS1tLTwm9/8hr///e+8+uqrPPvsszz//PPv9+2IiAFoE6+IZIXD4aCuro7XX38d\ngP7+fp588sn3/JmdO3fyxhtvADA3N0dXVxfbtm2jpaWFkydPAjA6Okp/fz8Av/71r3n77bfZs2cP\njz76KKOjo0Sj0cv4rkRko+gIjIhkzeHDh/nWt77Fj3/8Y6LRKA899NB7Pv/AgQM8/PDDfPazn2Vp\naYn777+furo67rjjDv70pz9xzz33UFdXx44dOwBobW3l0UcfJT8/n0Qiwec//3msVv21J3Il0N2o\nRUREJOdoCUlERERyjgqMiIiI5BwVGBEREck5KjAiIiKSc1RgREREJOeowIiIiEjOUYERERGRnKMC\nIyIiIjnn/wOrfpbT0cmDEwAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "0i7vGo9PTaZl",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below for the solution."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "3tAWu8qSTe2v",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def construct_feature_columns():\n",
+ " \"\"\"Construct the TensorFlow Feature Columns.\n",
+ "\n",
+ " Returns:\n",
+ " A set of feature columns\n",
+ " \"\"\" \n",
+ " households = tf.feature_column.numeric_column(\"households\")\n",
+ " longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+ " latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+ " housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+ " median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+ " rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+ " \n",
+ " # Divide households into 7 buckets.\n",
+ " bucketized_households = tf.feature_column.bucketized_column(\n",
+ " households, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"households\"], 7))\n",
+ "\n",
+ " # Divide longitude into 10 buckets.\n",
+ " bucketized_longitude = tf.feature_column.bucketized_column(\n",
+ " longitude, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"longitude\"], 10))\n",
+ " \n",
+ " # Divide latitude into 10 buckets.\n",
+ " bucketized_latitude = tf.feature_column.bucketized_column(\n",
+ " latitude, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"latitude\"], 10))\n",
+ "\n",
+ " # Divide housing_median_age into 7 buckets.\n",
+ " bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+ " housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"housing_median_age\"], 7))\n",
+ " \n",
+ " # Divide median_income into 7 buckets.\n",
+ " bucketized_median_income = tf.feature_column.bucketized_column(\n",
+ " median_income, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"median_income\"], 7))\n",
+ " \n",
+ " # Divide rooms_per_person into 7 buckets.\n",
+ " bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+ " rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+ " training_examples[\"rooms_per_person\"], 7))\n",
+ " \n",
+ " # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n",
+ " long_x_lat = tf.feature_column.crossed_column(\n",
+ " set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000) \n",
+ " \n",
+ " feature_columns = set([\n",
+ " bucketized_longitude,\n",
+ " bucketized_latitude,\n",
+ " bucketized_housing_median_age,\n",
+ " bucketized_households,\n",
+ " bucketized_median_income,\n",
+ " bucketized_rooms_per_person,\n",
+ " long_x_lat])\n",
+ " \n",
+ " return feature_columns"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "-_vvNYIyTtPC",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "_ = train_model(\n",
+ " learning_rate=1.0,\n",
+ " steps=500,\n",
+ " batch_size=100,\n",
+ " feature_columns=construct_feature_columns(),\n",
+ " training_examples=training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "ymlHJ-vrhLZw",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Optional Challenge: Try Out More Synthetic Features\n",
+ "\n",
+ "So far, we've tried simple bucketized columns and feature crosses, but there are many more combinations that could potentially improve the results. For example, you could cross multiple columns. What happens if you vary the number of buckets? What other synthetic features can you think of? Do they improve the model?"
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/feature_sets.ipynb b/feature_sets.ipynb
new file mode 100644
index 0000000..9fcae40
--- /dev/null
+++ b/feature_sets.ipynb
@@ -0,0 +1,1564 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "feature_sets.ipynb",
+ "version": "0.3.2",
+ "provenance": [],
+ "collapsed_sections": [
+ "JndnmDMp66FL",
+ "IGINhMIJ5Wyt",
+ "pZa8miwu6_tQ"
+ ],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python2",
+ "display_name": "Python 2"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "metadata": {
+ "id": "JndnmDMp66FL",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Copyright 2017 Google LLC."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "hMqWDc_m6rUC",
+ "colab_type": "code",
+ "cellView": "both",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+ "# you may not use this file except in compliance with the License.\n",
+ "# You may obtain a copy of the License at\n",
+ "#\n",
+ "# https://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing, software\n",
+ "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+ "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+ "# See the License for the specific language governing permissions and\n",
+ "# limitations under the License."
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "zbIgBK-oXHO7",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "# Feature Sets"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "bL04rAQwH3pH",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "**Learning Objective:** Create a minimal set of features that performs just as well as a more complex feature set"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "F8Hci6tAH3pH",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "So far, we've thrown all of our features into the model. Models with fewer features use fewer resources and are easier to maintain. Let's see if we can build a model on a minimal set of housing features that will perform equally as well as one that uses all the features in the data set."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "F5ZjVwK_qOyR",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Setup\n",
+ "\n",
+ "As before, let's load and prepare the California housing data."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "SrOYRILAH3pJ",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "from __future__ import print_function\n",
+ "\n",
+ "import math\n",
+ "\n",
+ "from IPython import display\n",
+ "from matplotlib import cm\n",
+ "from matplotlib import gridspec\n",
+ "from matplotlib import pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from sklearn import metrics\n",
+ "import tensorflow as tf\n",
+ "from tensorflow.python.data import Dataset\n",
+ "\n",
+ "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+ "pd.options.display.max_rows = 10\n",
+ "pd.options.display.float_format = '{:.1f}'.format\n",
+ "\n",
+ "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+ "\n",
+ "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+ " np.random.permutation(california_housing_dataframe.index))"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "dGnXo7flH3pM",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def preprocess_features(california_housing_dataframe):\n",
+ " \"\"\"Prepares input features from California housing data set.\n",
+ "\n",
+ " Args:\n",
+ " california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+ " from the California housing data set.\n",
+ " Returns:\n",
+ " A DataFrame that contains the features to be used for the model, including\n",
+ " synthetic features.\n",
+ " \"\"\"\n",
+ " selected_features = california_housing_dataframe[\n",
+ " [\"latitude\",\n",
+ " \"longitude\",\n",
+ " \"housing_median_age\",\n",
+ " \"total_rooms\",\n",
+ " \"total_bedrooms\",\n",
+ " \"population\",\n",
+ " \"households\",\n",
+ " \"median_income\"]]\n",
+ " processed_features = selected_features.copy()\n",
+ " # Create a synthetic feature.\n",
+ " processed_features[\"rooms_per_person\"] = (\n",
+ " california_housing_dataframe[\"total_rooms\"] /\n",
+ " california_housing_dataframe[\"population\"])\n",
+ " return processed_features\n",
+ "\n",
+ "def preprocess_targets(california_housing_dataframe):\n",
+ " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+ "\n",
+ " Args:\n",
+ " california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+ " from the California housing data set.\n",
+ " Returns:\n",
+ " A DataFrame that contains the target feature.\n",
+ " \"\"\"\n",
+ " output_targets = pd.DataFrame()\n",
+ " # Scale the target to be in units of thousands of dollars.\n",
+ " output_targets[\"median_house_value\"] = (\n",
+ " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
+ " return output_targets"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "jLXC8y4AqsIy",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1224
+ },
+ "outputId": "4a00a62d-0a2c-4189-f724-4213165122e8"
+ },
+ "cell_type": "code",
+ "source": [
+ "# Choose the first 12000 (out of 17000) examples for training.\n",
+ "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+ "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+ "\n",
+ "# Choose the last 5000 (out of 17000) examples for validation.\n",
+ "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+ "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+ "\n",
+ "# Double-check that we've done the right thing.\n",
+ "print(\"Training examples summary:\")\n",
+ "display.display(training_examples.describe())\n",
+ "print(\"Validation examples summary:\")\n",
+ "display.display(validation_examples.describe())\n",
+ "\n",
+ "print(\"Training targets summary:\")\n",
+ "display.display(training_targets.describe())\n",
+ "print(\"Validation targets summary:\")\n",
+ "display.display(validation_targets.describe())"
+ ],
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training examples summary:\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " latitude longitude housing_median_age total_rooms total_bedrooms \\\n",
+ "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n",
+ "mean 35.6 -119.6 28.5 2637.3 539.1 \n",
+ "std 2.1 2.0 12.6 2139.4 417.0 \n",
+ "min 32.5 -124.3 2.0 2.0 1.0 \n",
+ "25% 33.9 -121.8 18.0 1456.8 295.0 \n",
+ "50% 34.2 -118.5 29.0 2127.0 433.0 \n",
+ "75% 37.7 -118.0 37.0 3147.2 649.2 \n",
+ "max 42.0 -114.3 52.0 30401.0 4957.0 \n",
+ "\n",
+ " population households median_income rooms_per_person \n",
+ "count 12000.0 12000.0 12000.0 12000.0 \n",
+ "mean 1426.9 500.9 3.9 2.0 \n",
+ "std 1133.9 379.3 1.9 1.1 \n",
+ "min 3.0 1.0 0.5 0.0 \n",
+ "25% 792.0 281.0 2.6 1.5 \n",
+ "50% 1166.0 408.0 3.5 1.9 \n",
+ "75% 1718.2 607.0 4.8 2.3 \n",
+ "max 35682.0 4769.0 15.0 55.2 "
+ ],
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "6N0p91k2iFCP",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "**Try creating some synthetic features that do a better job with latitude.**\n",
+ "\n",
+ "For example, you could have a feature that maps `latitude` to a value of `|latitude - 38|`, and call this `distance_from_san_francisco`.\n",
+ "\n",
+ "Or you could break the space into 10 different buckets. `latitude_32_to_33`, `latitude_33_to_34`, etc., each showing a value of `1.0` if `latitude` is within that bucket range and a value of `0.0` otherwise.\n",
+ "\n",
+ "Use the correlation matrix to help guide development, and then add them to your model if you find something that looks good.\n",
+ "\n",
+ "What's the best validation performance you can get?"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "wduJ2B28yMFl",
+ "colab_type": "code",
+ "cellView": "form",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 640
+ },
+ "outputId": "26cef08b-62bb-4a51-e818-64a10fe4dbed"
+ },
+ "cell_type": "code",
+ "source": [
+ "#\n",
+ "# YOUR CODE HERE: Train on a new data set that includes synthetic features based on latitude.\n",
+ "#First create a seperate feature function which will add a feature for each set of latitudes (Binning) and add 1 if the house exists for the latitude and 0 if the house doesn't exists in the given latitude and longitude\n",
+ "def new_features_for_training(prev_df):\n",
+ " new_df = pd.DataFrame()\n",
+ " new_df[\"median_income\"]=prev_df[\"median_income\"]\n",
+ " latitude_sets = zip(range(32,44),range(33,45))\n",
+ " for i in latitude_sets:\n",
+ " new_df[\"latitude_%d_to_%d\" %i] = prev_df[\"latitude\"].apply(lambda l: 1.0 if l>= i[0] and l < i[1] else 0.0) \n",
+ " return new_df\n",
+ "\n",
+ "new_df_training = new_features_for_training(training_examples)\n",
+ "new_df_validation = new_features_for_training(validation_examples)\n",
+ "\n",
+ "train = train_model(learning_rate = 0.01,\n",
+ " steps = 500,\n",
+ " batch_size=500,\n",
+ " training_examples=new_df_training,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=new_df_validation,\n",
+ " validation_targets=validation_targets)\n",
+ "\n"
+ ],
+ "execution_count": 23,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 226.33\n",
+ " period 01 : 216.09\n",
+ " period 02 : 205.94\n",
+ " period 03 : 195.90\n",
+ " period 04 : 185.97\n",
+ " period 05 : 176.17\n",
+ " period 06 : 166.54\n",
+ " period 07 : 157.10\n",
+ " period 08 : 147.88\n",
+ " period 09 : 138.93\n",
+ "Model training finished.\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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QN/d8wpaz20koSOTu2Hn0CuraTK1vXs2djXCM5KJeko16STZXp0kLmK1bt/LW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\nK80VZFhSCQsK7Y9LGjJk3HhHppB8IJ1KXZKNmiQXdd1pNr1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2jo5G3il5jxM1ZwCYEj+R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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": {
+ "base_uri": "https://localhost:8080/",
+ "height": 2268
+ },
+ "outputId": "f225d61c-0e4a-44f6-e505-2ca13d766e35"
+ },
+ "cell_type": "code",
+ "source": [
+ "_ = train_model(\n",
+ " learning_rate=0.01,\n",
+ " steps=500,\n",
+ " batch_size=5,\n",
+ " training_examples=selected_training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=selected_validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 20,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 226.40\n",
+ " period 01 : 216.24\n",
+ " period 02 : 206.18\n",
+ " period 03 : 196.19\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "error",
+ "ename": "KeyboardInterrupt",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;31m\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0mTraceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mtraining_targets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_targets\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mvalidation_examples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mselected_validation_examples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m validation_targets=validation_targets)\n\u001b[0m",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(learning_rate, steps, batch_size, training_examples, training_targets, validation_examples, validation_targets)\u001b[0m\n\u001b[1;32m 64\u001b[0m linear_regressor.train(\n\u001b[1;32m 65\u001b[0m \u001b[0minput_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_input_fn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_period\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 67\u001b[0m )\n\u001b[1;32m 68\u001b[0m \u001b[0;31m# Take a break and compute predictions.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow_estimator/python/estimator/estimator.pyc\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, input_fn, hooks, steps, max_steps, saving_listeners)\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0msaving_listeners\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_listeners_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msaving_listeners\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaving_listeners\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Loss for final step: %s.'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow_estimator/python/estimator/estimator.pyc\u001b[0m in \u001b[0;36m_train_model\u001b[0;34m(self, input_fn, hooks, saving_listeners)\u001b[0m\n\u001b[1;32m 1181\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_model_distributed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaving_listeners\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1182\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1183\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_model_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaving_listeners\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1185\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_train_model_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaving_listeners\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow_estimator/python/estimator/estimator.pyc\u001b[0m in \u001b[0;36m_train_model_default\u001b[0;34m(self, input_fn, hooks, saving_listeners)\u001b[0m\n\u001b[1;32m 1215\u001b[0m return self._train_with_estimator_spec(estimator_spec, worker_hooks,\n\u001b[1;32m 1216\u001b[0m \u001b[0mhooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglobal_step_tensor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1217\u001b[0;31m saving_listeners)\n\u001b[0m\u001b[1;32m 1218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1219\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_train_model_distributed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaving_listeners\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow_estimator/python/estimator/estimator.pyc\u001b[0m in \u001b[0;36m_train_with_estimator_spec\u001b[0;34m(self, estimator_spec, worker_hooks, hooks, global_step_tensor, saving_listeners)\u001b[0m\n\u001b[1;32m 1406\u001b[0m \u001b[0msave_summaries_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_summary_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1407\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session_config\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1408\u001b[0;31m log_step_count_steps=self._config.log_step_count_steps) as mon_sess:\n\u001b[0m\u001b[1;32m 1409\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1410\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmon_sess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_stop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.pyc\u001b[0m in \u001b[0;36mMonitoredTrainingSession\u001b[0;34m(master, is_chief, checkpoint_dir, scaffold, hooks, chief_only_hooks, save_checkpoint_secs, save_summaries_steps, save_summaries_secs, config, stop_grace_period_secs, log_step_count_steps, max_wait_secs, save_checkpoint_steps, summary_dir)\u001b[0m\n\u001b[1;32m 506\u001b[0m \u001b[0msession_creator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msession_creator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 507\u001b[0m \u001b[0mhooks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mall_hooks\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 508\u001b[0;31m stop_grace_period_secs=stop_grace_period_secs)\n\u001b[0m\u001b[1;32m 509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, session_creator, hooks, stop_grace_period_secs)\u001b[0m\n\u001b[1;32m 932\u001b[0m super(MonitoredSession, self).__init__(\n\u001b[1;32m 933\u001b[0m \u001b[0msession_creator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshould_recover\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 934\u001b[0;31m stop_grace_period_secs=stop_grace_period_secs)\n\u001b[0m\u001b[1;32m 935\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 936\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, session_creator, hooks, should_recover, stop_grace_period_secs)\u001b[0m\n\u001b[1;32m 646\u001b[0m stop_grace_period_secs=stop_grace_period_secs)\n\u001b[1;32m 647\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mshould_recover\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 648\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_RecoverableSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_coordinated_creator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 649\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 650\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_coordinated_creator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, sess_creator)\u001b[0m\n\u001b[1;32m 1120\u001b[0m \"\"\"\n\u001b[1;32m 1121\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sess_creator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess_creator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1122\u001b[0;31m \u001b[0m_WrappedSession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1124\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_create_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.pyc\u001b[0m in \u001b[0;36m_create_session\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1125\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1126\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1127\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sess_creator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1128\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0m_PREEMPTION_ERRORS\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1129\u001b[0m logging.info('An error was raised while a session was being created. '\n",
+ "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.pyc\u001b[0m in \u001b[0;36mcreate_session\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 810\u001b[0m \u001b[0;31m# Inform the hooks that a new session has been created.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 811\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hooks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 812\u001b[0;31m \u001b[0mhook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mafter_create_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf_sess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoord\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 813\u001b[0m return _CoordinatedSession(\n\u001b[1;32m 814\u001b[0m \u001b[0m_HookedSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf_sess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hooks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoord\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/basic_session_run_hooks.pyc\u001b[0m in \u001b[0;36mafter_create_session\u001b[0;34m(self, session, coord)\u001b[0m\n\u001b[1;32m 563\u001b[0m \u001b[0mgraph_def\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_graph_def\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madd_shapes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 564\u001b[0m saver_def=saver_def)\n\u001b[0;32m--> 565\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_summary_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 566\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_summary_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_meta_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeta_graph_def\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 567\u001b[0m \u001b[0;31m# The checkpoint saved here is the state at step \"global_step\".\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/summary/writer/writer.pyc\u001b[0m in \u001b[0;36madd_graph\u001b[0;34m(self, graph, global_step, graph_def)\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0;31m# Serialize the graph with additional info.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 194\u001b[0;31m \u001b[0mtrue_graph_def\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_graph_def\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madd_shapes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 195\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_write_plugin_assets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 196\u001b[0m elif (isinstance(graph, graph_pb2.GraphDef) or\n",
+ "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.pyc\u001b[0m in \u001b[0;36mas_graph_def\u001b[0;34m(self, from_version, add_shapes)\u001b[0m\n\u001b[1;32m 3148\u001b[0m \"\"\"\n\u001b[1;32m 3149\u001b[0m \u001b[0;31m# pylint: enable=line-too-long\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3150\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_as_graph_def\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfrom_version\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_shapes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3151\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.pyc\u001b[0m in \u001b[0;36m_as_graph_def\u001b[0;34m(self, from_version, add_shapes)\u001b[0m\n\u001b[1;32m 3118\u001b[0m \u001b[0mop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nodes_by_name\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3119\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3120\u001b[0;31m node.attr[\"_output_shapes\"].list.shape.extend(\n\u001b[0m\u001b[1;32m 3121\u001b[0m [output.get_shape().as_proto() for output in op.outputs])\n\u001b[1;32m 3122\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_version\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ]
+ }
+ ]
+}
\ 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..8c09a3a
--- /dev/null
+++ b/improving_neural_net_performance.ipynb
@@ -0,0 +1,1780 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "improving_neural_net_performance.ipynb",
+ "version": "0.3.2",
+ "provenance": [],
+ "collapsed_sections": [
+ "JndnmDMp66FL",
+ "jFfc3saSxg6t",
+ "FSPZIiYgyh93",
+ "GhFtWjQRzD2l",
+ "P8BLQ7T71JWd"
+ ],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python2",
+ "display_name": "Python 2"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "JndnmDMp66FL"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Copyright 2017 Google LLC."
+ ]
+ },
+ {
+ "metadata": {
+ "cellView": "both",
+ "colab_type": "code",
+ "id": "hMqWDc_m6rUC",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+ "# you may not use this file except in compliance with the License.\n",
+ "# You may obtain a copy of the License at\n",
+ "#\n",
+ "# https://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing, software\n",
+ "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+ "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+ "# See the License for the specific language governing permissions and\n",
+ "# limitations under the License."
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "eV16J6oUY-HN"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "# Improving Neural Net Performance"
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "0Rwl1iXIKxkm"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "**Learning Objective:** Improve the performance of a neural network by normalizing features and applying various optimization algorithms\n",
+ "\n",
+ "**NOTE:** The optimization methods described in this exercise are not specific to neural networks; they are effective means to improve most types of models."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "lBPTONWzKxkn"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Setup\n",
+ "\n",
+ "First, we'll load the data."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "VtYVuONUKxko",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "from __future__ import print_function\n",
+ "\n",
+ "import math\n",
+ "\n",
+ "from IPython import display\n",
+ "from matplotlib import cm\n",
+ "from matplotlib import gridspec\n",
+ "from matplotlib import pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from sklearn import metrics\n",
+ "import tensorflow as tf\n",
+ "from tensorflow.python.data import Dataset\n",
+ "\n",
+ "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+ "pd.options.display.max_rows = 10\n",
+ "pd.options.display.float_format = '{:.1f}'.format\n",
+ "\n",
+ "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+ "\n",
+ "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+ " np.random.permutation(california_housing_dataframe.index))"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "B8qC-jTIKxkr",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def preprocess_features(california_housing_dataframe):\n",
+ " \"\"\"Prepares input features from California housing data set.\n",
+ "\n",
+ " Args:\n",
+ " california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+ " from the California housing data set.\n",
+ " Returns:\n",
+ " A DataFrame that contains the features to be used for the model, including\n",
+ " synthetic features.\n",
+ " \"\"\"\n",
+ " selected_features = california_housing_dataframe[\n",
+ " [\"latitude\",\n",
+ " \"longitude\",\n",
+ " \"housing_median_age\",\n",
+ " \"total_rooms\",\n",
+ " \"total_bedrooms\",\n",
+ " \"population\",\n",
+ " \"households\",\n",
+ " \"median_income\"]]\n",
+ " processed_features = selected_features.copy()\n",
+ " # Create a synthetic feature.\n",
+ " processed_features[\"rooms_per_person\"] = (\n",
+ " california_housing_dataframe[\"total_rooms\"] /\n",
+ " california_housing_dataframe[\"population\"])\n",
+ " return processed_features\n",
+ "\n",
+ "def preprocess_targets(california_housing_dataframe):\n",
+ " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+ "\n",
+ " Args:\n",
+ " california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+ " from the California housing data set.\n",
+ " Returns:\n",
+ " A DataFrame that contains the target feature.\n",
+ " \"\"\"\n",
+ " output_targets = pd.DataFrame()\n",
+ " # Scale the target to be in units of thousands of dollars.\n",
+ " output_targets[\"median_house_value\"] = (\n",
+ " california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
+ " return output_targets"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "Ah6LjMIJ2spZ",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1224
+ },
+ "outputId": "b19a9b97-9ca6-4c34-8675-50d27b6e37d2"
+ },
+ "cell_type": "code",
+ "source": [
+ "# Choose the first 12000 (out of 17000) examples for training.\n",
+ "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+ "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+ "\n",
+ "# Choose the last 5000 (out of 17000) examples for validation.\n",
+ "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+ "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+ "\n",
+ "# Double-check that we've done the right thing.\n",
+ "print(\"Training examples summary:\")\n",
+ "display.display(training_examples.describe())\n",
+ "print(\"Validation examples summary:\")\n",
+ "display.display(validation_examples.describe())\n",
+ "\n",
+ "print(\"Training targets summary:\")\n",
+ "display.display(training_targets.describe())\n",
+ "print(\"Validation targets summary:\")\n",
+ "display.display(validation_targets.describe())"
+ ],
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training examples summary:\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " latitude longitude housing_median_age total_rooms total_bedrooms \\\n",
+ "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n",
+ "mean 35.6 -119.6 28.6 2634.2 537.6 \n",
+ "std 2.1 2.0 12.6 2176.3 418.5 \n",
+ "min 32.5 -124.3 1.0 2.0 1.0 \n",
+ "25% 33.9 -121.8 18.0 1452.0 295.0 \n",
+ "50% 34.2 -118.5 29.0 2121.0 434.0 \n",
+ "75% 37.7 -118.0 37.0 3146.0 647.2 \n",
+ "max 42.0 -114.5 52.0 37937.0 5471.0 \n",
+ "\n",
+ " population households median_income rooms_per_person \n",
+ "count 12000.0 12000.0 12000.0 12000.0 \n",
+ "mean 1426.9 499.5 3.9 2.0 \n",
+ "std 1142.5 381.4 1.9 1.2 \n",
+ "min 3.0 1.0 0.5 0.1 \n",
+ "25% 786.0 280.0 2.6 1.5 \n",
+ "50% 1168.0 410.0 3.5 1.9 \n",
+ "75% 1721.0 603.0 4.7 2.3 \n",
+ "max 35682.0 5189.0 15.0 55.2 "
+ ],
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "NqIbXxx222ea"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Train the Neural Network\n",
+ "\n",
+ "Next, we'll train the neural network."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "6k3xYlSg27VB",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def construct_feature_columns(input_features):\n",
+ " \"\"\"Construct the TensorFlow Feature Columns.\n",
+ "\n",
+ " Args:\n",
+ " input_features: The names of the numerical input features to use.\n",
+ " Returns:\n",
+ " A set of feature columns\n",
+ " \"\"\" \n",
+ " return set([tf.feature_column.numeric_column(my_feature)\n",
+ " for my_feature in input_features])"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "De9jwyy4wTUT",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+ " \"\"\"Trains a neural network model.\n",
+ " \n",
+ " Args:\n",
+ " features: pandas DataFrame of features\n",
+ " targets: pandas DataFrame of targets\n",
+ " batch_size: Size of batches to be passed to the model\n",
+ " shuffle: True or False. Whether to shuffle the data.\n",
+ " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+ " Returns:\n",
+ " Tuple of (features, labels) for next data batch\n",
+ " \"\"\"\n",
+ " \n",
+ " # Convert pandas data into a dict of np arrays.\n",
+ " features = {key:np.array(value) for key,value in dict(features).items()} \n",
+ " \n",
+ " # Construct a dataset, and configure batching/repeating.\n",
+ " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+ " ds = ds.batch(batch_size).repeat(num_epochs)\n",
+ " \n",
+ " # Shuffle the data, if specified.\n",
+ " if shuffle:\n",
+ " ds = ds.shuffle(10000)\n",
+ " \n",
+ " # Return the next batch of data.\n",
+ " features, labels = ds.make_one_shot_iterator().get_next()\n",
+ " return features, labels"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "W-51R3yIKxk4",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def train_nn_regression_model(\n",
+ " my_optimizer,\n",
+ " steps,\n",
+ " batch_size,\n",
+ " hidden_units,\n",
+ " training_examples,\n",
+ " training_targets,\n",
+ " validation_examples,\n",
+ " validation_targets):\n",
+ " \"\"\"Trains a neural network regression model.\n",
+ " \n",
+ " In addition to training, this function also prints training progress information,\n",
+ " as well as a plot of the training and validation loss over time.\n",
+ " \n",
+ " Args:\n",
+ " my_optimizer: An instance of `tf.train.Optimizer`, the optimizer to use.\n",
+ " steps: A non-zero `int`, the total number of training steps. A training step\n",
+ " consists of a forward and backward pass using a single batch.\n",
+ " batch_size: A non-zero `int`, the batch size.\n",
+ " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+ " training_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for training.\n",
+ " training_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for training.\n",
+ " validation_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for validation.\n",
+ " validation_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for validation.\n",
+ " \n",
+ " Returns:\n",
+ " A tuple `(estimator, training_losses, validation_losses)`:\n",
+ " estimator: the trained `DNNRegressor` object.\n",
+ " training_losses: a `list` containing the training loss values taken during training.\n",
+ " validation_losses: a `list` containing the validation loss values taken during training.\n",
+ " \"\"\"\n",
+ "\n",
+ " periods = 10\n",
+ " steps_per_period = steps / periods\n",
+ " \n",
+ " # Create a DNNRegressor object.\n",
+ " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+ " dnn_regressor = tf.estimator.DNNRegressor(\n",
+ " feature_columns=construct_feature_columns(training_examples),\n",
+ " hidden_units=hidden_units,\n",
+ " optimizer=my_optimizer\n",
+ " )\n",
+ " \n",
+ " # Create input functions.\n",
+ " training_input_fn = lambda: my_input_fn(training_examples, \n",
+ " training_targets[\"median_house_value\"], \n",
+ " batch_size=batch_size)\n",
+ " predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+ " training_targets[\"median_house_value\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+ " validation_targets[\"median_house_value\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ "\n",
+ " # Train the model, but do so inside a loop so that we can periodically assess\n",
+ " # loss metrics.\n",
+ " print(\"Training model...\")\n",
+ " print(\"RMSE (on training data):\")\n",
+ " training_rmse = []\n",
+ " validation_rmse = []\n",
+ " for period in range (0, periods):\n",
+ " # Train the model, starting from the prior state.\n",
+ " dnn_regressor.train(\n",
+ " input_fn=training_input_fn,\n",
+ " steps=steps_per_period\n",
+ " )\n",
+ " # Take a break and compute predictions.\n",
+ " training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n",
+ " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+ " \n",
+ " validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n",
+ " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+ " \n",
+ " # Compute training and validation loss.\n",
+ " training_root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(training_predictions, training_targets))\n",
+ " validation_root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+ " # Occasionally print the current loss.\n",
+ " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+ " # Add the loss metrics from this period to our list.\n",
+ " training_rmse.append(training_root_mean_squared_error)\n",
+ " validation_rmse.append(validation_root_mean_squared_error)\n",
+ " print(\"Model training finished.\")\n",
+ "\n",
+ " # Output a graph of loss metrics over periods.\n",
+ " plt.ylabel(\"RMSE\")\n",
+ " plt.xlabel(\"Periods\")\n",
+ " plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+ " plt.tight_layout()\n",
+ " plt.plot(training_rmse, label=\"training\")\n",
+ " plt.plot(validation_rmse, label=\"validation\")\n",
+ " plt.legend()\n",
+ "\n",
+ " print(\"Final RMSE (on training data): %0.2f\" % training_root_mean_squared_error)\n",
+ " print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n",
+ "\n",
+ " return dnn_regressor, training_rmse, validation_rmse"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "KueReMZ9Kxk7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 805
+ },
+ "outputId": "dd7f43d6-ad8f-4b8b-ed89-c8fd1a9b81b5"
+ },
+ "cell_type": "code",
+ "source": [
+ "_ = train_nn_regression_model(\n",
+ " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n",
+ " steps=5000,\n",
+ " batch_size=70,\n",
+ " hidden_units=[10, 10],\n",
+ " training_examples=training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+ "For more information, please see:\n",
+ " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+ " * https://github.com/tensorflow/addons\n",
+ "If you depend on functionality not listed there, please file an issue.\n",
+ "\n",
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 150.63\n",
+ " period 01 : 135.21\n",
+ " period 02 : 123.19\n",
+ " period 03 : 115.75\n",
+ " period 04 : 109.58\n",
+ " period 05 : 108.87\n",
+ " period 06 : 108.01\n",
+ " period 07 : 110.06\n",
+ " period 08 : 107.97\n",
+ " period 09 : 109.78\n",
+ "Model training finished.\n",
+ "Final RMSE (on training data): 109.78\n",
+ "Final RMSE (on validation data): 109.73\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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6WPVCYuEN3C5JkSAhERFRx8AC04ZMlAaIGOKKsspaHDqf2uQ24e71ozAHb3MU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\nHT+lfbDD7BJFRMY2wNTX17N8+XIef/zx0WW//vW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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "flxmFt0KKxk9"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Linear Scaling\n",
+ "It can be a good standard practice to normalize the inputs to fall within the range -1, 1. This helps SGD not get stuck taking steps that are too large in one dimension, or too small in another. Fans of numerical optimization may note that there's a connection to the idea of using a preconditioner here."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "Dws5rIQjKxk-",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def linear_scale(series):\n",
+ " min_val = series.min()\n",
+ " max_val = series.max()\n",
+ " scale = (max_val - min_val) / 2.0\n",
+ " return series.apply(lambda x:((x - min_val) / scale) - 1.0)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "MVmuHI76N2Sz"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 1: Normalize the Features Using Linear Scaling\n",
+ "\n",
+ "**Normalize the inputs to the scale -1, 1.**\n",
+ "\n",
+ "**Spend about 5 minutes training and evaluating on the newly normalized data. How well can you do?**\n",
+ "\n",
+ "As a rule of thumb, NN's train best when the input features are roughly on the same scale.\n",
+ "\n",
+ "Sanity check your normalized data. (What would happen if you forgot to normalize one feature?)\n"
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "yD948ZgAM6Cx",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 677
+ },
+ "outputId": "e3e4e31f-4fee-4080-ee1f-9db0bd51091a"
+ },
+ "cell_type": "code",
+ "source": [
+ "def normalize_linear_scale(examples_dataframe):\n",
+ " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n",
+ " #\n",
+ " processed_features = pd.DataFrame()\n",
+ " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+ " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+ " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n",
+ " processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n",
+ " processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n",
+ " processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n",
+ " processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n",
+ " processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n",
+ " processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n",
+ " return processed_features\n",
+ " #\n",
+ " pass\n",
+ "\n",
+ "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n",
+ "normalized_training_examples = normalized_dataframe.head(12000)\n",
+ "normalized_validation_examples = normalized_dataframe.tail(5000)\n",
+ "\n",
+ "_ = train_nn_regression_model(\n",
+ " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n",
+ " steps=5000,\n",
+ " batch_size=70,\n",
+ " hidden_units=[10, 10],\n",
+ " training_examples=normalized_training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=normalized_validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 230.10\n",
+ " period 01 : 206.71\n",
+ " period 02 : 159.93\n",
+ " period 03 : 118.77\n",
+ " period 04 : 115.06\n",
+ " period 05 : 111.25\n",
+ " period 06 : 106.80\n",
+ " period 07 : 101.55\n",
+ " period 08 : 95.33\n",
+ " period 09 : 88.40\n",
+ "Model training finished.\n",
+ "Final RMSE (on training data): 88.40\n",
+ "Final RMSE (on validation data): 88.26\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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REZGbogJTg5hNJiYPacGonk1Iycznw88SmNLsQXxd6rH6zAZWnFqjEiMiInWC\nCkwNYzKZGNe3GZMGNCfzUiELlp9jUqP7CXYP4qv4bXxyfDmlRqmjY4qIiNiVCkwNNeyuxjw4vDW5\n+UW8s/w0o4Pvo7FXA3Ym7eWDw7qStYiI1G4qMDVY346h/GpMO4qKS3nn05MM9JlA83pNOZB6iHe/\nX6grWYuISK2lAlPD3dkmmMcm3IEJeG9lDN1dRtHevzXH0mN467sF5BcXODqiiIjIbacCUwt0aObP\nk/d0wtnJzIerT9KqZDDdgjtxJusc62I3OTqeiIjIbacCU0u0bFSPp+/rgoebE4s3niIg+y58Xerx\ndcJO0gsyHB1PRETktlKBqUXCQrx4dmoXfL1cWPH1OYILO1FcWsya2C8dHU1EROS2UoGpZer7e/Ds\n1C4E+7pxYLcLPhZ/diftJyk32dHRREREbhsVmFoowMeNp+7rjLPVQt7Z5hgYrDq93tGxREREbhsV\nmFrKz9uVwd0akZ1UD19zfb5PO8KZrLOOjiUiInJbqMDUYiN6NMbD1YmME00BWHlqnS41ICIitYIK\nTC3m7urEiB5h5Gd4408Yp7NiOXLxuKNjiYiI3DIVmFpuUNeG+Hq5kHK0MSZMfH56na6VJCIiNZ4K\nTC3n7GRhTO+mFF7ywL8knMTcC+y9cMDRsURERG6JCkwd0KtDCCF+7iQcbojFZOGL2I262KOIiNRo\nKjB1gMVsZlzfZpRedqVeQUvSCzLYkbDL0bFERERumgpMHdG1VSBN63tx/kh9nM0urD/7lS70KCIi\nNZYKTB1hMpmY0C8cip1xz27JpaJcvorb5uhYIiIiN8Vqz52/8sor7N+/n+LiYn71q1/RoUMHnn76\naUpKSggMDOSvf/0rzs7OrFq1io8++giz2cykSZOYOHGiPWPVWW2a+NGuqR9Hjhfjd9cZvorfRt+G\nEXg7ezk6moiISKXYbQZm165dnDx5kiVLlvD+++8zZ84c3njjDSZPnswnn3xCWFgYy5cvJy8vj3nz\n5rFw4UIWLVrERx99RGZmpr1i1XkT+oVDqRVLaksKSwpZf/YrR0cSERGpNLsVmO7du/P6668D4O3t\nTX5+Prt372bQoEEADBgwgG+//ZaDBw/SoUMHvLy8cHV1pUuXLkRHR9srVp0XFuLFnW2CSD0diJel\nHjsSdpOWf9HRsURERCrFbqeQLBYL7u7uACxfvpy+ffuyY8cOnJ2dAfD39yc1NZW0tDT8/Pxs2/n5\n+ZGamlruvn193bFaLfaKTmBg7T6lMm1sB/bPTaU0sSUlwXv4MmEzj0X83NGxKqS2j01NpXGpvjQ2\n1ZfG5tbYdQ0MwKZNm1i+fDlhh5EFAAAgAElEQVQffPABQ4cOtd1/vWvyVORaPRkZebct308FBnqR\nmppjt/1XB05A346hbDlQSv3QQHbE7aV3cC8aeYU6Olq56sLY1EQal+pLY1N9aWwqprySZ9d3IW3f\nvp133nmHBQsW4OXlhbu7OwUFV966m5ycTFBQEEFBQaSlpdm2SUlJISgoyJ6xBBjdqwnOThYunQkH\nYNXpdQ5OJCIiUnF2KzA5OTm88sorvPvuu9SrVw+Anj17smHDBgA2btxInz596NixI4cOHSI7O5vc\n3Fyio6Pp1q2bvWLJf9XzdGFIt0bkJPvgb27A0fQTxGScdnQsERGRCrHbKaS1a9eSkZHBb3/7W9t9\nL7/8MrNmzWLJkiWEhoYyduxYnJycmDlzJtOmTcNkMjF9+nS8vHResCoMvyuMrQcSuHi8KbRMYOXp\ntTzVdQYmk8nR0URERMplMiqy6KSased5w7p2XnL97jiWbjlF4ztPkEosD7ePolNQB0fHuqa6NjY1\nhcal+tLYVF8am4px2BoYqf4GdW2Ar5cLSUcaYcbMqjPrKSktcXQsERGRcqnA1HFOVgtjezelKNcd\n/5LmJOelsuvCPkfHEhERKZcKjNCzQwj1/d2JPxSK1WRlbewmCkuKHB1LRETkulRgBIvZzLi+4RiF\nrvjktyLzchZfn//G0bFERESuSwVGAOjSMoDwUG/OHwnB1ezKhnNbyCuy3wcGioiI3AoVGAHAZDIx\noX84lDjhmtmK/OJ8Np7b6uhYIiIi16QCIzatGvvSvpkfSSeC8LB4sfX8DjIvZzk6loiIyFVUYKSM\nCf3CwbBgSm5JUWkxa2O/dHQkERGRq6jASBmNg73o0TaY1DP++Fj8+DZpH8m5KY6OJSIiUoYKjFxl\nbJ+mWMwWLse1oNQoZdWZDY6OJCIiUoYKjFwlyNedfp1CyUioh58lhO9SD3E2O87RsURERGxUYOSa\nRvdqiouTlexT4QB8fmodNfCyWSIiUkupwMg1+Xg4M6R7I3JSvQg0NyYm8zTH0mMcHUtERARQgZFy\nDL+rMZ5uTqQeC8OEic9Pr6PUKHV0LBERERUYuT43FyujIsLIz/IgkHDOX0okOvmgo2OJiIiowEj5\nBnRpgL+3CwmHGmIxWVh9ZgPFpcWOjiUiInWcCoyUy8lqYUzvZhTnu+Jf1JK0gnS+Sdzj6FgiIlLH\nqcDIDfVsH0KDAA/iDoXgbHZmXewmCoovOzqWiIjUYSowckNms4lx/ZphFLngnduanKJLbInf7uhY\nIiJSh6nASIV0ah5A8wY+xB8Jws3izpdxW8kpvOToWCIiUkepwEiFmEwmJvQPh1IrrumtuVxSyIaz\nmx0dS0RE6igVGKmwlo3qcUe4P4kxAXhZfdie8C0X89MdHUtEROogFRiplPH9wjEZZoykVhQbJXwR\nu9HRkUREpA5SgZFKaRTkSY92waTG+uJrDWTvhQMkXEpydCwREaljVGCk0sb2aYbFbCb/bHMMDFad\nXufoSCIiUseowEilBdZzo3/nBmQkehNgacDhi8c5lRnr6FgiIlKHqMDITRndswkuzlYyYpoCsPLU\nWgzDcHAqERGpK1Rg5KZ4ezgT2b0Rly56EmRuSmz2Ob5PO+roWCIiUkeowMhNi7yzMZ5uTiQfaYQJ\nE6vOrKfUKHV0LBERqQNUYOSmublYGd2zCQU57gQZLbmQm8zupP2OjiUiInWACozckv6dGxDg48r5\nQ/Wxmqysif2SopIiR8cSEZFaTgVGbomT1czYPk0pLnDFr7A1GZcz+Tphp6NjiYhILWfXAhMTE8Pg\nwYNZvHgxAHv37uW+++4jKiqKX/3qV2RlZQHw/vvvM2HCBCZOnMjXX39tz0hiBz3ahtAg0IO4Q0G4\nml3ZeHYL+cX5jo4lIiK1mN0KTF5eHrNnzyYiIsJ230svvcSLL77IokWL6Ny5M0uWLCE+Pp61a9fy\nySef8O677/LSSy9RUlJir1hiB2azifH9wjGKnfG81Jrc4jy+PKciKiIi9mO3AuPs7MyCBQsICgqy\n3efr60tmZiYAWVlZ+Pr6snv3bvr06YOzszN+fn40aNCAU6dO2SuW2EnHcH9aNPQh/mggHhZPNsdv\nJ+tytqNjiYhILWW3AmO1WnF1dS1z3x/+8AemT59OZGQk+/fv5+677yYtLQ0/Pz/bc/z8/EhNTbVX\nLLETk8nEhP7hUGrB+WJrikqLWHt2k6NjiYhILWWtyoPNnj2bt956i65duzJ37lw++eSTq55TkU9z\n9fV1x2q12CMiAIGBXnbbd20WGOjFndGJ7DlaSmhvf3Ym7mFix+HU9wq68caVOIZUPxqX6ktjU31p\nbG5NlRaYEydO0LVrVwB69uzJ6tWr6dGjB7Gx/7uOTnJycpnTTteSkZFnt4yBgV6kpubYbf+13aiI\nxuw9eoGi+OaUBu/mo32fMq391Nuyb41N9aRxqb40NtWXxqZiyit5Vfo26oCAANv6lkOHDhEWFkaP\nHj3YunUrhYWFJCcnk5KSQvPmzasyltxGDQM9iWgfQuq5evhbg4lO+Z647POOjiUiIrWM3WZgDh8+\nzNy5c0lISMBqtbJhwwb+/Oc/M2vWLJycnPDx8WHOnDl4e3szadIkpk6dislk4k9/+hNmsz6epiYb\n26cpe44lkxvbHBol8/npdTza+WFHxxIRkVrEZNTASwjbc9pN03q3x783neTLffGERRwlpSSORzs9\nTGu/Fre0T41N9aRxqb40NtWXxqZiqs0pJKk7RvYMw9XZwsUTTQD4/PRaXehRRERuGxUYsQtvd2eG\n3dmYS+nuhJibE5eTwIGUQ46OJSIitYQKjNjN0Dsb4eXuROLhhphNZlafWU9JqT5lWUREbp0KjNiN\nq7OV0T2bcPmSK0GlrUjNv8jOpD2OjiUiIrWACozYVf/ODQjwcSX++/o4mZ1YG7uJyyWFjo4lIiI1\nnAqM2JXVYubuvs0ovuyMX0Ebsgtz2BK/w9GxRESkhlOBEbu7q20wDQM9Ofd9EG4WN748t5VLRbmO\njiUiIjWYCozYndlkYkL/ZhilVjyz21BQUsDGs1scHUtERGowFRipEh2a+dOyUT3ijvrjZfXm64Sd\npBdkODqWiIjUUCowUiVMJhMT+oeDYcGS2pri0mLWxH7p6FgiIlJDqcBIlWnewIfOLQJIOumLr1MA\nu5P2k5Sb7OhYIiJSA6nASJUa1y8ck8lEUXwLDAxWnV7v6EgiIlIDqcBIlWoQ4EGv9vVJjfMm0BrK\n92lHOJN11tGxRESkhlGBkSo3pndTrBYL2afDAVh5ah018KLoIiLiQCowUuX8fVwZ2KUBmckehFia\ncjorliMXjzs6loiI1CAqMOIQo3o2wc3FQsqxxpgw8fnpdZQapY6OJSIiNYQKjDiEp5sTw+5sTG6m\nGyGmFiTmXmDvhQOOjiUiIjWECow4zNDujfH2cCbhUAMsJgtfxG6kqLTY0bFERKQGUIERh3FxtvCz\nXk24nOdCUEkb0gsy2JGwy9GxRESkBlCBEYfq2zGUwHqunPs+GBezC+vPfkV+cYGjY4mISDWnAiMO\nZbWYubtvM0oKnfAtaMOloly+itvm6FgiIlLNqcCIw93ZJpjGQZ6c/T4AD6sHX8VvI7swx9GxRESk\nGrvpAnP27NnbGEPqMrPJxPj+4RilVtwz21JYUsj6s185OpaIiFRj5RaYhx56qMzt+fPn2/77+eef\nt08iqZPaN/WjdeN6xB3zxcepHjsSdpOWf9HRsUREpJoqt8AUF5d9S+uuXf97h4g++l1uJ9N/Z2Ew\nzJgvtKbEKGH1mQ2OjiUiItVUuQXGZDKVuf3j0vLTx0RuVXioD11bBpJ42gd/pyD2JX9HfE6io2OJ\niEg1VKk1MCotYm93922GyWTiclxLAFadXufgRCIiUh1Zy3swKyuLb7/91nY7OzubXbt2YRgG2dnZ\ndg8ndU9ogAe9O9Rn+/eJhDVuxNH0E8RknKalb7ijo4mISDVSboHx9vYus3DXy8uLefPm2f5bxB7G\n9G7Kt0eSyTrZFJrEs/L0Wp7qOsPRsUREpBopt8AsWrSoqnKI2Ph5uzK4a0PW74mjWXg457JPczD1\nMEOCejo6moiIVBPlroG5dOkSCxcutN3+z3/+w5gxY3jsscdIS0uzdzapw0ZEhOHmYuXCkUaYMbPq\nzHpKSkscHUtERKqJcgvM888/z8WLVz6LIzY2lldffZVnnnmGnj178uKLL1ZJQKmbPN2cGNGjMXnZ\nroSYWpGcl8qW2G9vvKGIiNQJ5RaY+Ph4Zs6cCcCGDRsYNmwYPXv25N57763QDExMTAyDBw9m8eLF\nABQVFTFz5kwmTJjAAw88QFZWFgCrVq1i/PjxTJw4kWXLlt3qa5JaYnDXRvh4OBP/fX2czE4sObSK\nvKJ8R8cSEZFqoNwC4+7ubvvvPXv20KNHD9vtG72lOi8vj9mzZxMREWG7b+nSpfj6+rJ8+XJGjBjB\nvn37yMvLY968eSxcuJBFixbx0UcfkZmZebOvR2oRF2cLP+vdlMJ8Z0KKOpJ1OYcvYjc6OpaIiFQD\n5RaYkpISLl68SFxcHAcOHKBXr14A5Obmkp9f/l/Czs7OLFiwgKCgINt9W7Zs4Wc/+xkA99xzD4MG\nDeLgwYN06NABLy8vXF1d6dKlC9HR0bf6uqSW6HNHfYJ83Tj9nR9B7oFsO79TH24nIiLlF5iHH36Y\nESNGMHr0aB555BF8fHwoKChg8uTJjB07ttwdW61WXF1dy9yXkJDAtm3biIqK4oknniAzM5O0tDT8\n/Pxsz/Hz8yM1NfUWXpLUJlaLmXF9m1FSYsY7swsGBktjVlBqlDo6moiIOFC5b6Pu168fO3bs4PLl\ny3h6egLg6urKU089Re/evSt9MMMwaNq0KTNmzGD+/Pm8++67tG3b9qrn3IivrztWq6XSx6+owEB9\nxk11Mtzfky3fJXLouww6D23L8cyjHMs9Sv+mETfeWKqEfmaqL41N9aWxuTXlFpjExP9N1f/4k3eb\nNWtGYmIioaGhlTpYQEAA3bt3B6B37968+eab9O/fv8yC4JSUFDp16lTufjIy8ip13MoIDPQiNTXH\nbvuXm3PvgObM/mgv579rjHOzk3x84FOauoTj7uTm6Gh1nn5mqi+NTfWlsamY8kpeuQVm4MCBNG3a\nlMDAQODqizl+/PHHlQrSt29ftm/fzvjx4zly5AhNmzalY8eOzJo1i+zsbCwWC9HR0fzhD3+o1H6l\n9gsL8WJEr6Z8sSOWjs27ElO0iy9iNzCpZfmnMkVEpHYqt8DMnTuXzz//nNzcXEaOHMmoUaPKrFcp\nz+HDh5k7dy4JCQlYrVY2bNjA3/72N1588UWWL1+Ou7s7c+fOxdXVlZkzZzJt2jRMJhPTp0/XZQrk\nmqYOa8O2Awkc21uP4B4BbDv/LRH1u9PIq4Gjo4mISBUzGRVYdJKUlMSKFStYvXo1DRo0YMyYMQwZ\nMuSqRbpVxZ7TbprWq74CA71YtfUkC1YfpUXrIs57f0VT7zCe7PobzKZKXVhdbiP9zFRfGpvqS2NT\nMeWdQqrQv/r169fnkUceYd26dURGRvLCCy/c1CJekVvVo20wrRvX4+RxJ5q6tSI2+xy7k/Y7OpaI\niFSxChWY7OxsFi9ezLhx41i8eDG/+tWvWLt2rb2ziVzFZDIxZWgrLGYTFw43wdnszMrTa8krst/C\nbhERqX7KXQOzY8cOPv30Uw4fPszQoUN5+eWXadmyZVVlE7mmBgEeDO3eiHW74+jQojOninaz+swG\n7ml1t6OjiYhIFSm3wPziF7+gSZMmdOnShfT0dD788MMyj7/00kt2DSdyPaN7NWHX0WSO7atHSEQA\n2xN2ERHancZeDR0dTUREqkC5BeaHt0lnZGTg6+tb5rHz58/bL5XIDbg6W7lvUAvmrzyM84UOGL5b\nWHpiJU92fUQLekVE6oBy/6U3m83MnDmT5557jueff57g4GDuvPNOYmJieO2116oqo8g1dW0VSPtm\nfpw56UIT11bEZsexSwt6RUTqhHJnYP7xj3+wcOFCwsPD+eqrr3j++ecpLS3Fx8eHZcuWVVVGkWsy\nmUxMGdKS597fQ+L3jXFuHcvnp9fSMbAdHk7uN96BiIjUWDecgQkPDwdg0KBBJCQkcP/99/PWW28R\nHBxcJQFFyhPs686IHo3JyrTQoLQTl4pyWX1mg6NjiYiInZVbYEwmU5nb9evXZ8iQIXYNJFJZI3qE\nEVjPleP7fPF3CWBHwi7isrVGS0SkNqvUasefFhqR6sDZycKUIS0pLTVhTmiPgcF/YlZQapQ6OpqI\niNhJuWtgDhw4QP/+/W23L168SP/+/TEMA5PJxNatW+0cT6Ri7ggPoEvLQKJjUmndoBXnsk/wbdJe\neoXe5ehoIiJiB+UWmPXr11dVDpFbdt+gFhyOvcj5g41wbhvL56fX0Smwgxb0iojUQuUWmAYNdJVf\nqTn8fVwZ3bMJn359hlZFnYgr3cOqM+u5r9U4R0cTEZHbTJ/4JbVK5J2Nqe/vTkx0PfxdAvgmYTfn\nsuMdHUtERG4zFRipVawWM1OHtsIwzJTGt8PAYMmJlVrQKyJSy6jASK3TJsyXu9oGk3jWjUZOLTmX\nE8+3iXsdHUtERG4jFRiple4Z2BxXZwvnDzbG2ezM56fXcako19GxRETkNlGBkVqpnqcLd/dpRt4l\nK0GXO5JbnMeq03pXnYhIbaECI7XWwK4NaBTkycnvfPF3DmBn4h4t6BURqSVUYKTWspjNRA1tBYaZ\nonNXFvT+54Q+oVdEpDZQgZFarXlDH3rfUZ/keDcaWlsSl3OenYl7HB1LRERukQqM1HoT+ofj4Wol\n/rtGuJhdWHV6PZcKtaBXRKQmU4GRWs/b3Znx/cMpyHPCL7/DlQW9Z9Y5OpaIiNwCFRipE/p2DKVp\nfW/OfO+Hn1MgOxP3EpsV5+hYIiJyk1RgpE4wm0xERbbEhJnCs20wMFgaowW9IiI1lQqM1BlNQrwZ\n0LkBqQnuhFpaEpeTwDeJux0dS0REboIKjNQp4/o2w9vdifgDDbWgV0SkBlOBkTrF3dWJSQObU1jg\njM+l9uQV5/P5aS3oFRGpaVRgpM6JaBdCy0b1OHfY/8qC3qQ9xGadc3QsERGpBBUYqXNMJhNTh7bE\nbLKQd7oVAEtiVmpBr4hIDaICI3VSw0BPhnZvRMYFT+qbWhKfk8COBC3oFRGpKVRgpM76We8m+Hq5\nEPfDJ/SeWU9O4SVHxxIRkQqwa4GJiYlh8ODBLF68uMz927dvp1WrVrbbq1atYvz48UycOJFly5bZ\nM5KIjauzlXsHtaD4shOe2e3J14JeEZEaw24FJi8vj9mzZxMREVHm/suXL/Pee+8RGBhoe968efNY\nuHAhixYt4qOPPiIzM9NesUTK6NYqkHZN/Th/1B9fayDfJu3ljBb0iohUe3YrMM7OzixYsICgoKAy\n97/zzjtMnjwZZ2dnAA4ePEiHDh3w8vLC1dWVLl26EB0dba9YImWYTCamDmmJ1WIh99SVWcGlJ/QJ\nvSIi1Z3dCozVasXV1bXMfbGxsRw/fpzhw4fb7ktLS8PPz89228/Pj9TUVHvFErlKsJ87w+4KIyvF\nk2BaEH8pkR0JuxwdS0REymGtyoO99NJLzJo1q9znGIZxw/34+rpjtVpuV6yrBAZ62W3fcmvsNTYP\n/qw9e4+nEP9dI7y7xvFF7AYGt4nAx9XbLserbfQzU31pbKovjc2tqbICk5yczJkzZ/jd734HQEpK\nClOnTuXRRx8lLS3N9ryUlBQ6depU7r4yMvLsljMw0IvU1By77V9unr3H5p6BzXlj+fe4prcj0yea\nf+5ZRlSbSXY7Xm2hn5nqS2NTfWlsKqa8kldlb6MODg5m06ZNLF26lKVLlxIUFMTixYvp2LEjhw4d\nIjs7m9zcXKKjo+nWrVtVxRKx6dQ8gE7NA0g6EYCvNZBdSfs4k3XW0bFEROQa7FZgDh8+TFRUFCtW\nrODjjz8mKirqmu8ucnV1ZebMmUybNo2HHnqI6dOn4+WlaTVxjMmDW+BstZId0xKAJSdWUlJa4uBU\nIiLyUyajIotOqhl7TrtpWq/6qqqx+WLnWT7bdobG3U6Taj7JxJZj6N+wl92PW1PpZ6b60thUXxqb\niqkWp5BEaorIOxsT4udO/MGGuJhd+OLMBn1Cr4hINaMCI/ITTlYzU4e2xChyweViW/KLC1h5aq2j\nY4mIyI+owIhcQ9smftzZJojkk4HUswSy68I+TmeedXQsERH5LxUYkeu4Z2ALXJ2dyDrRAoAlMSu0\noFdEpJpQgRG5Dl8vF8b2bkpeujcBJS1IuJTEdn1Cr4hItaACI1KOQd0a0jDQ478Lel1ZfWYD2YV6\n54CIiKOpwIiUw2I2M3VoKyh2wSm1DQUlWtArIlIdqMCI3EDLRvXo1SGE1NNB+JgD2X1hP6cyYx0d\nS0SkTlOBEamAif2b4+7yvwW9S2P0Cb0iIo6kAiNSAd4ezozvH05+hjd+Rc1JuJTEtoRvHR1LRKTO\nUoERqaB+HUNpEuJFwqHGuJhd+eLMRrIua0GviIgjqMCIVJDZbCIqshWmYmfMya2vLOg9vcbRsURE\n6iQVGJFKaFrfm/6dG5AeG4yPKZA9F6I5mXHG0bFEROocFRiRShrXrxle7s6kH9eCXhERR1GBEakk\nD1cnJg1oTmGWN/UuNycx9wJfJ+x0dCwRkTpFBUbkJvRsH0KLhj4kHbmyoHfNmS/Jupzt6FgiInWG\nCozITTCZTEQNbYW5xAWSrizoXaFP6BURqTIqMCI3qWGQJ4O7NSTzXDDepkD2JmtBr4hIVVGBEbkF\nY3o3pZ6nC+lHtaBXRKQqqcCI3AI3Fyv3DmpBUY433gX/XdB7/htHxxIRqfVUYERuUffWQbRt4kvy\n0ca4mFxZE/slmZezHB1LRKRWU4ERuUUmk4kpQ1piKXWhNLEVBSWXWXFKn9ArImJPKjAit0F9fw+G\n3dWY7PgQvAhkX/J3nMw47ehYIiK1lgqMyG0yqmcT/L3dSD/aHIAlWtArImI3KjAit4mLk4XJQ1pQ\nfMkHz7xwknKT2aoFvSIidqECI3IbdW4RSMdwf1KPh+FscmVN7EYt6BURsQMVGJHbbPKQljjhSklC\nKy6XFGpBr4iIHajAiNxmgfXcGBURxqXzIXgaVxb0xmSccnQsEZFaRQVGxA6G3RVGsK87F39Y0HtC\nC3pFRG4nFRgRO3CympkytCWluT645zbjQl4KW87vcHQsEZFaQwVGxE7aN/WnW+sgLp5ogrPJlbX6\nhF4RkdtGBUbEju4b1AIXsxtF8S25XFLIZye/cHQkEZFawa4FJiYmhsGDB7N48WIAkpKSePDBB5k6\ndSoPPvggqampAKxatYrx48czceJEli1bZs9IIlXK18uFMb2akpdYH4/SAPanHOREuhb0iojcKrsV\nmLy8PGbPnk1ERITtvtdee41JkyaxePFihgwZwocffkheXh7z5s1j4cKFLFq0iI8++ojMzEx7xRKp\ncoO7NaRBgCfpR1sAsDRmJcWlxQ5OJSJSs9mtwDg7O7NgwQKCgoJs9/3xj38kMjISAF9fXzIzMzl4\n8CAdOnTAy8sLV1dXunTpQnR0tL1iiVQ5q8VMVGQrSvN8cMsJ50JeChvPbaFIJUZE5KZZ7bZjqxWr\ntezu3d3dASgpKeGTTz5h+vTppKWl4efnZ3uOn5+f7dSSSG3RslE9erYPYeexQny6JbAm9kvWxm7C\n382PEPcgQjyCCP7v/4e4B+Hu5OboyCIi1ZrdCsz1lJSU8PTTT9OjRw8iIiJYvXp1mccNw7jhPnx9\n3bFaLfaKSGCgl932LbemJo/Nryd05ODLaRTFdKPPwBLSClI5n32BwxePcfjisTLP9XH1pqF3CKFe\nwTTwDqGhd31CvYPxd/PFZDI56BVcX00el9pOY1N9aWxuTZUXmGeffZawsDBmzJgBQFBQEGlpabbH\nU1JS6NSpU7n7yMjIs1u+wEAvUlNz7LZ/uXm1YWzu7tuMxRtjSDzgR7+OvQhu6Y6HZykXL18kOS+F\nC7kpXMhLITk3laMpJzmSElNmexeLM8HugQS7B/93tiaQEI8gAt0CsJjtV+rLUxvGpbbS2FRfGpuK\nKa/kVWmBWbVqFU5OTjz22GO2+zp27MisWbPIzs7GYrEQHR3NH/7wh6qMJVJl+ndqwM7DFzh8Jp3D\nZ9IBMAF+3q6E+LkR7NeSVn6d6Bfijm89KyWWS6QUpHIhN8VWcBJzk4nLSSizX7PJTKCbPyHuQQT/\n9zRUiEcQQe6BuFldHfBKRUTsy2RU5JzNTTh8+DBz584lISEBq9VKcHAwFy9exMXFBU9PTwDCw8P5\n05/+xPr16/nnP/+JyWRi6tSp/OxnPyt33/ZsrWrF1VdtGZvLRSUcPZtOcno+F9LzSE7P40JGHlmX\nCq96rtViIsjXnWBfN0L83An2cyfI1xVnj8tcKs0gOS+F5LxU28xNfnH+Vfuo5+JD8H9nakLc/7fW\nxtvZ67acjqot41IbaWyqL41NxZQ3A2O3AmNPKjB1U20fm/zLxaRklC01yel5XEjPI//y1ddRcnOx\nEOzrXqbY+PhAiXMOmUUXr5Sa3CsFJ+Py1R9N4GZ1vVJm3IMI9gi0zdr4u/pV6nRUbR+XmkxjU31p\nbCqm2pxCEpHrc3OxEhbiRVhI2R9YwzDIySv6SbHJJzk9j/Oplzh74ep/BH08nQnxbUSwXyta+Lnj\nF2DB4p5HoSWb1PxU2+mouJzznM2OK7Ot1WQh0D2gzLuigj0CCXYPwsXibNevgYhIRanAiFRzJpMJ\nbw9nvD2cadmoXpnHSksNLmYX2GZqktPzbTM3MfGZnIjP/Mm+INDHh2C/+v/f3r0HR3XX/x9/nr1l\ns7dkN9kkJCEkJDThVqAXFQRbtdVRZ4r2RkVQ//g543T8Q6deGGytnTo61Ms4tZ2qtZ3p4NcpSr3U\nUXvxp1WmpbV+oQECgWPEgvcAABiNSURBVJD7fXPZzSa7m+ue3x8bQgKVHy2G3YXXY2aH4ezZk8/p\nh6UvPp/3+XyoDuSy0Z+D0zsJOWOMmWFC8bP1Nr2xfjhnRQN/Tn4q1Jx57NtVhMNbhWmSkU9HiciV\nS1NI59CwXuZS37wzU9Mzs1NSCfrD8bkRnP7hONH41Hnn222WuVqbIn8uefkmFleMGVuU8OyUVH88\nxMjk+X3gtrkWFA+fGbnxO/OxGNpyLV30nclc6puLoykkkauQ3WalLOihLOg57734+BT98+tt5o3g\ndA3Ezjvf7SygJLCU5QEXAb8Vp3cc0zFKwogQNcN0hHtoHWmnZaRtweccFnvqsW93ESVnHv12FxHM\nLcBm0V8/IvLu6W8QkauQy2mnaomdqiW+BcdN0yQyNrmgiPjM01JtfaM090TPvRKFeQGC+WuoKsjB\nkzeJzRVjyhZlZGY4VWsTD9E51rPgU2ce+55fZ5Oalgri1GPfInIRFGBEZI5hGPi9Ofi9OdQt8y94\nbyaZZHDkTL1NYm7kZiCS4ER7mBPt88/OwW4ro9hfw4qAk/xAEocnTtIxRsKIMDg+mFqwLz7AkcGG\nBT8nPyfvvKmoEncxHrtbdTYiMkcBRkQuitViodjvotjv4trqs8eDQS9d3ZG5aai511Dq14VTUnYg\niM9dRnEgl8KAQW7eOJbcGJPWEUamhwklBmgMN9EYblrw81VnIyLzKcCIyCXLcVj/4yPgkbFJ+oZi\n9A3H6Z0Xbk53jtDUeeZMKxDAaimgyH8tKwI2vP5J7O44M45RYoQZGh+8qDqbM2vaFLkKVWcjcgXT\nt1tEFs38KamVlYEF701Nz6QKiYfOH7npHTqz35kFyAPycDtrKCnIIb9gGqcvATkxJiwjjEwP/cc6\nm8LcwNniYdXZiFxRFGBEJC3sNivlQQ/l5zwlNX/hvvlTUb3Dcdp64iS7TVLBxgt4MYxyCvOclAWT\nuPMnsLpiTNuijJlhBsYHCcUbVGcjcgVSgBGRjHKhhfumZ5IMRBILR2tmfz3RNEVqa0zP7KuU3BwL\nwUIrvsAkOd44Zs4YCSKEp4b+Y51NqaeEck8pZd5Syj2lLHEXaSpKJAPpWykiWcNmtbCkwM2SAvd5\n740lphaM2Jx59fTF6eg2APfsqxgD8Odb8QencM0rIo7ODHM60kpTpGXuulbDSom7iHJPKeWzoabc\nswSX3XW5bltE3oYCjIhcETy5dmrK8qgpy1twPJk0GRxJnBdueofjNDfNAK7ZVxCA3FyTwpJpPIEE\nRm6UuJFaz6Z7rJc3+v537roBp38uzJwJNgGnX1NQIpeJAoyIXNEsFoMiv4uicx7/htQO4OdOR/UM\nxuhui5NstQM+oBxI4i+cIT84jsM3xrQ9QmQqtYbN/PqaXFtuKtBoCkpk0elbJSJXrdwcG1VLfOet\nSDw1PUPPYGq379QrRldojNYTdlLFw0uAOmw5UxSUTOD2p0ZrYoamoEQuFwUYEZFz2G1vv65NND5J\nd2iMzoFYKtiExujujtHf7gWKUidZpnH7E+QHJ3B4x5i0hemPDWgKSuS/TAFGROQi+VwOfJWBBWva\nJJMmoUiCrlBqtKYzNEb3QIzuxgRQCFQCSSzOOPlFE7j8cXBGiU0OaQpK5BLoWyEicgksFoOSgIuS\ngIsb6ormjo9PTtM9N1JzdsRmuGP67IftE+R4x8gLjmP3jDFpidAUadEUlMhFUIAREVkEToeN6rI8\nquc9FWWaJuHRiVRNzZn6mtAYvU1xZpJm6iTLNEbuGJ5AHFd+HDM3St/Y+VNQBbNTUGVzoaaUgDNf\nU1By1VCAERG5TAzDIOBzEvA5uba6YO749EySvqE4nXOhJhVw+jsnZs8wMZwxbO5RPAXj2D2jjI6H\nqR9voP7tpqBmQ806+zXkmB5tdilXJAUYEZE0s1ktlBd5KC9auK3CWGKK7tmnoDpna2y6W2NMTM2k\nTrBPYHFFcfpi5ObHMZ0jC6ag9p6AHKuDZb4KqnwVVOVVUOmrwOvwnNsEkayjACMikqE8uXZqK/zU\nVvjnjiVNk8FIYu7R7s7ZgBPqjGPC3BSU1R3FHYiDJ8yp8GlOhU/PXaPQGaAyr4Iq3zKq8ioo8yxR\nobBkHf2JFRHJIhbj7MJ8110TnDs+MTVDz+C8UBMao7M9RiwxBdYpLO4RrN4RXP5RImaYf4+/xb/7\n3wLAZrFR4S2j0ldBVd4yqnwV+J35/6kJIhlBAUZE5AqQY7eetyhfYaGHhlMhWnqjtPZEae2N0n5i\njOmZGQxnHIs7gj1vBCNvlJZkBy0j7dB5AEjt2F05b9qpwluGw+pI1+2JnEcBRkTkCmUYBsUBF8UB\nFxtXlwCpguHugdiCUNPTEsO0TGNxR7G4Izjyo4x6Irw1cZS3Bo4CYDEslHuWUDk77VTpqyCYW6Cn\nniRtFGBERK4iNqtlbpXhD24oA1J7QnX0j54NNT1RhqLjGI5xLJ4IFk8ER94oncleOka7+Wf3awC4\n7S6qfBVzoWaZbym5Nmc6b0+uIgowIiJXudwc23nFwiNjE7T2jtLamxqlaW2KEp+YxHBFsXgiWD0j\nJPJGODbVyLGhRgAMDErcRalQM1skXOIu0mPcsigUYERE5Dx5nhzWr8hh/YpCILUIXyiSoLUnSktv\nlLbeUdrbR5kyEljcqVEam3eEvuQQvbF+Xut9EwCnNYfKuUCTmnryONzpvDW5QijAiIjI/5dhGBT7\nXRT7XbzvnHqa1t5UqGntjdLTOArOsdmppxHGvSM0zjTRGG6au1Ywt2Bu2qnKl3qM22qxpuvWJEsp\nwIiIyLsyv57m5tl6mvHJadr7Rmntna2paYsyFIti8YykQo17hIGZEQYSh3iz/1DqOhYby7zlC9am\nyc/Ju9CPFlGAERGR/x6n4/x6mmhscq6WpqU3SkvDCAlG5kZpkp4IzTNtNI+0zX0mPydvbk2aqrwK\nlnrKsFvtabgjyVQKMCIisqh8bgfragpZV3O2nmYgkpirpWnpjdJ+KsxMTnhupCbsiRCZOMLh0BEA\nLIaV5b4KrgnUUOuvodK3VKsHX+UWtfdPnTrFvffey+c//3l27NhBb28vX//615mZmSEYDPL9738f\nh8PB888/zzPPPIPFYuHuu+/mrrvuWsxmiYhIGhnzVhN+36qz9TQ9g7HZUBOluStKb3QAwz07UuMN\nczrZyumRVv7c+jJ2i50V+VVc46+hNlBDuadUTztdZRYtwMTjcR5++GE2btw4d+zRRx9l+/btfOxj\nH+NHP/oR+/fv55Of/CSPP/44+/fvx263c+edd3LrrbeSn69lrEVErhY2q4WKYi8VxV5Yn6qnmZic\nob1/lJaeKM09IzQeD5FwhLD6hkj6hjiePMXx4VPQDE6rk9rZ0Zlafw3FrqAW2bvCLVqAcTgcPPnk\nkzz55JNzx9544w0eeughAD74wQ/y9NNPU1VVxdq1a/F6vQBcd911HDp0iA996EOL1TQREckCOQ4r\n1yzN55qlqX/QmqZJz2CMxo4IJzsjNLb3krD3Y/ENEfcNUT9zjPqBYwB47V7qAjXUBlZQ668m4PRf\n6EdJFlq0AGOz2bDZFl4+kUjgcKT20igoKGBgYIDBwUECgcDcOYFAgIGBgcVqloiIZCnDMCgLeigL\nevjw9eWY5mp6huKc7AhzoiPMqfZu4rOBJuob5s2pw7zZfxiAgCPAysIaav0ruMZfjdfhSfPdyKVK\nWwWUaZrv6Ph8fr8Lm23x1gwIBr2Ldm25NOqbzKR+yVxXet8UFflYvzJVR2OaJl2hMY42D3Lk9ABH\nW9qIWXux+oYY8oZ5tedfvNrzLwCWuJewoXQla0vqWBmswWXPvextv9L7ZrFd1gDjcrkYHx/H6XTS\n399PUVERRUVFDA4Ozp0TCoVYv379Ba8TDscXrY3BoJeBgdFFu768e+qbzKR+yVxXY984LXDjikJu\nXFGIadbRNxynsSNCY8cQjQPtJOx9WHzD9CT76W3q5c9Nf8PAoNRVxurgCur8K1iet2zRH9m+Gvvm\n3bhQyLusAWbTpk28+OKLbN26lZdeeoktW7awbt067r//fqLRKFarlUOHDrF79+7L2SwREbkCGYbB\nkgI3SwrcfHBDGaa5lr7hOCc7IhzvGOTkYCsJR2rKqcvspjvexUvtf8eClQpPBWuC11AXqKHCW66V\ngjOQYV7MnM27cOzYMfbs2UN3dzc2m43i4mJ+8IMfsGvXLiYmJigtLeV73/sedrudF154gaeeegrD\nMNixYwe33XbbBa+9mKlVqThzqW8yk/olc6lvLsw0TfrDCRo7whzvGODkcDMJez9W3xAW99n/bjbs\nVHqruLb4GuoCK1jiLr7kR7bVNxfnQiMwixZgFpMCzNVJfZOZ1C+ZS33zzpimSWg20DR09HEy3Mx4\nTmqExuI8W7qQY+Sy3FfFupI6av01BHML3vEj2+qbi5MxU0giIiKZyjAMigMuigMublpfhmleRyiS\n4GRHhCOdXZyONJNw9GP6hjgxcpwTI8cByDW8rMhfzvqSOmoDNdrH6TJRgBEREXkb83fg/sC6Ukzz\nRkKRBI3tYY50tXN6pIWJnH7ivmGOhOs5Eq4HwGvxsyK/mg2lK6n1V+O2u9J8J1cmBRgREZGLMD/Q\npEZoNjIwG2gOd7fQEk0Fmqg3zKHhf3No+N9gQr41SG2ghutKV1KTX4XTlpPuW7kiKMCIiIi8C/P3\ndPrA+jJMczMDI+OcaBvkcM9pWkdbmcjpJ+wZ5I3BAd4YPAimQYGthBuWrmV9YR3lXu3h9G6piPcc\nKqzKXOqbzKR+yVzqm/QyTZPBkXGOtYd4q7uJtlgrkzkhDPcIZ2p+7WYuy1zVvG/pWtaX1JFru/wL\n6mUyPYX0DugLn7nUN5lJ/ZK51DeZZzCSoL6tl/rQSVpGm0h6Qhj2ydSbpoHfsoQ1hXVsrlxHmafk\nqt+QUgHmHdAXPnOpbzKT+iVzqW8yVzDopb8/SltflNeaG2kYaiRi6Vo4OpN0U5G7nPctXcv1ZavI\nsTrS2+g00GPUIiIiGcZiMVhemsfy0vcC7yU+Ps2hli7e6DpGe7yZSVc/zRNHaT59lP85ZSHfKGV1\nQS03LV9Pma843c1POwUYERGRDOBy2ti8qpLNqyoxTZPe4TH+2XScY4ONDNNBxNXFq8NdvDr8f7FP\neyl3Luc95Wt4X8VKHLarb3RGAUZERCTDGIZBaYGXewpSozNT00kOt3VwsOMYbbHTTDj7aZ2up7Wt\nnn0tVvLMUlb6a7m5ZgNL84Ppbv5loQAjIiKS4ew2C++pqeQ9NZUADEZj/KPpGEcGTjCY7GDE2cnr\n0U5eP/RXbFN5lOdUcWPpGjYtX4nDtrg7a6eLinjPoaK3zKW+yUzql8ylvslc/82+SZom9R0dvNp+\nhNax0yQc/RiWZOrNGRveZCl1+bXcXL2eysLsGp1REa+IiMgVymIYbFi2jA3LlgEwEo/zStNR6kPH\nCc20M+ro4M1YB28eeRnreD5Lcqq4YclqNtesJNeRvaMzCjAiIiJXkDyXi63r3stW3ksymeREXycH\n2uppjp4m5uinyzhMV99hftfpwDNTSm3eNWypupYVS4JZte6MAoyIiMgVymKxsLp0GatLU6Mzo+Nx\n/tF8hLf6j9NvtBFztnFooo3/PfESlkN+SuyVXFeymi0r6vC6MvvJJtXAnENzxplLfZOZ1C+ZS32T\nuTKhb0zT5ORAO/9sred0tImYJQSzAzDmZA6uyVJqfCt4f+VaVlUEsVou/55NqoERERGRBQzDoK6o\nkrqiSgBGJ2O82nqEQ30N9FrbSXhaOZps5cjplzHqAxTZlrGhZBUbq2sI5rvS23g0AnOeTEjF8vbU\nN5lJ/ZK51DeZK9P7JmkmaRpq50BbPadGThEzBs++N5GLM7GEam8NGytXs7ayCIfduijt0AiMiIiI\nXDSLYaG2sIrawioAopOjvN5xlH/3NtCbbGMyp4UTtHC8/a94j9ewZ+v/uextVIARERGRC/I5vHyk\nZhMfqdnETHKG0+E2Xm0/wsnIKfwF6WmTAoyIiIhcNKvFSm1BNbUF1Wltx+UvKRYRERG5RAowIiIi\nknUUYERERCTrKMCIiIhI1lGAERERkayjACMiIiJZRwFGREREso4CjIiIiGQdBRgRERHJOgowIiIi\nknUUYERERCTrKMCIiIhI1lGAERERkaxjmKZpprsRIiIiIu+ERmBEREQk6yjAiIiISNZRgBEREZGs\nowAjIiIiWUcBRkRERLKOAoyIiIhkHQWYeb773e+ybds27rnnHo4cOZLu5sg8jzzyCNu2beOOO+7g\npZdeSndzZJ7x8XFuueUWfvvb36a7KTLP888/z2233cbtt9/OK6+8ku7mCBCLxfjSl77Ezp07ueee\nezhw4EC6m5TVbOluQKb417/+RXt7O/v27aO5uZndu3ezb9++dDdLgNdff52mpib27dtHOBzmU5/6\nFB/5yEfS3SyZ9cQTT5CXl5fuZsg84XCYxx9/nOeee454PM5PfvITbr755nQ366r3u9/9jqqqKu67\n7z76+/v53Oc+xwsvvJDuZmUtBZhZBw8e5JZbbgGgurqakZERxsbG8Hg8aW6Z3HjjjVx77bUA+Hw+\nEokEMzMzWK3WNLdMmpubOX36tP7nmGEOHjzIxo0b8Xg8eDweHn744XQ3SQC/38/JkycBiEaj+P3+\nNLcou2kKadbg4OCCP0yBQICBgYE0tkjOsFqtuFwuAPbv388HPvABhZcMsWfPHnbt2pXuZsg5urq6\nGB8f54tf/CLbt2/n4MGD6W6SAJ/4xCfo6enh1ltvZceOHXzjG99Id5OymkZg/gPtsJB5/vrXv7J/\n/36efvrpdDdFgN///vesX7+epUuXprsp8jYikQiPPfYYPT09fPazn+Xvf/87hmGku1lXtT/84Q+U\nlpby1FNP0djYyO7du1U7dgkUYGYVFRUxODg49/tQKEQwGExji2S+AwcO8NOf/pRf/OIXeL3edDdH\ngFdeeYXOzk5eeeUV+vr6cDgclJSUsGnTpnQ37apXUFDAhg0bsNlsVFRU4Ha7GR4epqCgIN1Nu6od\nOnSIzZs3A1BXV0coFNJ0+CXQFNKs97///bz44osANDQ0UFRUpPqXDDE6OsojjzzCz372M/Lz89Pd\nHJn14x//mOeee45f//rX3HXXXdx7770KLxli8+bNvP766yS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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "jFfc3saSxg6t"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below for one possible solution."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "Ax_IIQVRx4gr"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Since normalization uses min and max, we have to ensure it's done on the entire dataset at once. \n",
+ "\n",
+ "We can do that here because all our data is in a single DataFrame. If we had multiple data sets, a good practice would be to derive the normalization parameters from the training set and apply those identically to the test set."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "D-bJBXrJx-U_",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def normalize_linear_scale(examples_dataframe):\n",
+ " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n",
+ " processed_features = pd.DataFrame()\n",
+ " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+ " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+ " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n",
+ " processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n",
+ " processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n",
+ " processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n",
+ " processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n",
+ " processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n",
+ " processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n",
+ " return processed_features\n",
+ "\n",
+ "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n",
+ "normalized_training_examples = normalized_dataframe.head(12000)\n",
+ "normalized_validation_examples = normalized_dataframe.tail(5000)\n",
+ "\n",
+ "_ = train_nn_regression_model(\n",
+ " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.005),\n",
+ " steps=2000,\n",
+ " batch_size=50,\n",
+ " hidden_units=[10, 10],\n",
+ " training_examples=normalized_training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=normalized_validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "MrwtdStNJ6ZQ"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 2: Try a Different Optimizer\n",
+ "\n",
+ "** Use the Adagrad and Adam optimizers and compare performance.**\n",
+ "\n",
+ "The Adagrad optimizer is one alternative. The key insight of Adagrad is that it modifies the learning rate adaptively for each coefficient in a model, monotonically lowering the effective learning rate. This works great for convex problems, but isn't always ideal for the non-convex problem Neural Net training. You can use Adagrad by specifying `AdagradOptimizer` instead of `GradientDescentOptimizer`. Note that you may need to use a larger learning rate with Adagrad.\n",
+ "\n",
+ "For non-convex optimization problems, Adam is sometimes more efficient than Adagrad. To use Adam, invoke the `tf.train.AdamOptimizer` method. This method takes several optional hyperparameters as arguments, but our solution only specifies one of these (`learning_rate`). In a production setting, you should specify and tune the optional hyperparameters carefully."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "61GSlDvF7-7q",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 677
+ },
+ "outputId": "aefe89df-c727-45e8-e0b6-b243b7886201"
+ },
+ "cell_type": "code",
+ "source": [
+ "#\n",
+ "# YOUR CODE HERE: Retrain the network using Adagrad and then Adam.\n",
+ "#\n",
+ "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n",
+ " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.5),\n",
+ " steps=500,\n",
+ " batch_size=100,\n",
+ " hidden_units=[10, 10],\n",
+ " training_examples=normalized_training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=normalized_validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 93.44\n",
+ " period 01 : 75.53\n",
+ " period 02 : 75.13\n",
+ " period 03 : 73.52\n",
+ " period 04 : 71.01\n",
+ " period 05 : 71.45\n",
+ " period 06 : 70.28\n",
+ " period 07 : 71.60\n",
+ " period 08 : 69.35\n",
+ " period 09 : 68.68\n",
+ "Model training finished.\n",
+ "Final RMSE (on training data): 68.68\n",
+ "Final RMSE (on validation data): 67.68\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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4g4LKSTp0llp7dEd/ivpUREREep+Cykk6dN2fekux+lRERES8REHlJCWFJhBm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\nt7/9LQ0NDezatUv6u27TvAsqVqsVl8sV+d7hcGCxWKI4IjH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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "RWq0xecNKNeG",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Building a Neural Network\n",
+ "\n",
+ "The NN is defined by the [DNNRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNRegressor) class.\n",
+ "\n",
+ "Use **`hidden_units`** to define the structure of the NN. The `hidden_units` argument provides a list of ints, where each int corresponds to a hidden layer and indicates the number of nodes in it. For example, consider the following assignment:\n",
+ "\n",
+ "`hidden_units=[3,10]`\n",
+ "\n",
+ "The preceding assignment specifies a neural net with two hidden layers:\n",
+ "\n",
+ "* The first hidden layer contains 3 nodes.\n",
+ "* The second hidden layer contains 10 nodes.\n",
+ "\n",
+ "If we wanted to add more layers, we'd add more ints to the list. For example, `hidden_units=[10,20,30,40]` would create four layers with ten, twenty, thirty, and forty units, respectively.\n",
+ "\n",
+ "By default, all hidden layers will use ReLu activation and will be fully connected."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "ni0S6zHcTb04",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def construct_feature_columns(input_features):\n",
+ " \"\"\"Construct the TensorFlow Feature Columns.\n",
+ "\n",
+ " Args:\n",
+ " input_features: The names of the numerical input features to use.\n",
+ " Returns:\n",
+ " A set of feature columns\n",
+ " \"\"\" \n",
+ " return set([tf.feature_column.numeric_column(my_feature)\n",
+ " for my_feature in input_features])"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "zvCqgNdzpaFg",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+ " \"\"\"Trains a neural net regression model.\n",
+ " \n",
+ " Args:\n",
+ " features: pandas DataFrame of features\n",
+ " targets: pandas DataFrame of targets\n",
+ " batch_size: Size of batches to be passed to the model\n",
+ " shuffle: True or False. Whether to shuffle the data.\n",
+ " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+ " Returns:\n",
+ " Tuple of (features, labels) for next data batch\n",
+ " \"\"\"\n",
+ " \n",
+ " # Convert pandas data into a dict of np arrays.\n",
+ " features = {key:np.array(value) for key,value in dict(features).items()} \n",
+ " \n",
+ " # Construct a dataset, and configure batching/repeating.\n",
+ " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+ " ds = ds.batch(batch_size).repeat(num_epochs)\n",
+ " \n",
+ " # Shuffle the data, if specified.\n",
+ " if shuffle:\n",
+ " ds = ds.shuffle(10000)\n",
+ " \n",
+ " # Return the next batch of data.\n",
+ " features, labels = ds.make_one_shot_iterator().get_next()\n",
+ " return features, labels"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "U52Ychv9KNeH",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def train_nn_regression_model(\n",
+ " learning_rate,\n",
+ " steps,\n",
+ " batch_size,\n",
+ " hidden_units,\n",
+ " training_examples,\n",
+ " training_targets,\n",
+ " validation_examples,\n",
+ " validation_targets):\n",
+ " \"\"\"Trains a neural network regression model.\n",
+ " \n",
+ " In addition to training, this function also prints training progress information,\n",
+ " as well as a plot of the training and validation loss over time.\n",
+ " \n",
+ " Args:\n",
+ " learning_rate: A `float`, the learning rate.\n",
+ " steps: A non-zero `int`, the total number of training steps. A training step\n",
+ " consists of a forward and backward pass using a single batch.\n",
+ " batch_size: A non-zero `int`, the batch size.\n",
+ " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+ " training_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for training.\n",
+ " training_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for training.\n",
+ " validation_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for validation.\n",
+ " validation_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for validation.\n",
+ " \n",
+ " Returns:\n",
+ " A `DNNRegressor` object trained on the training data.\n",
+ " \"\"\"\n",
+ "\n",
+ " periods = 10\n",
+ " steps_per_period = steps / periods\n",
+ " \n",
+ " # Create a DNNRegressor object.\n",
+ " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+ " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+ " dnn_regressor = tf.estimator.DNNRegressor(\n",
+ " feature_columns=construct_feature_columns(training_examples),\n",
+ " hidden_units=hidden_units,\n",
+ " optimizer=my_optimizer,\n",
+ " )\n",
+ " \n",
+ " # Create input functions.\n",
+ " training_input_fn = lambda: my_input_fn(training_examples, \n",
+ " training_targets[\"median_house_value\"], \n",
+ " batch_size=batch_size)\n",
+ " predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+ " training_targets[\"median_house_value\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+ " validation_targets[\"median_house_value\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ "\n",
+ " # Train the model, but do so inside a loop so that we can periodically assess\n",
+ " # loss metrics.\n",
+ " print(\"Training model...\")\n",
+ " print(\"RMSE (on training data):\")\n",
+ " training_rmse = []\n",
+ " validation_rmse = []\n",
+ " for period in range (0, periods):\n",
+ " # Train the model, starting from the prior state.\n",
+ " dnn_regressor.train(\n",
+ " input_fn=training_input_fn,\n",
+ " steps=steps_per_period\n",
+ " )\n",
+ " # Take a break and compute predictions.\n",
+ " training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n",
+ " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+ " \n",
+ " validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n",
+ " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+ " \n",
+ " # Compute training and validation loss.\n",
+ " training_root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(training_predictions, training_targets))\n",
+ " validation_root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+ " # Occasionally print the current loss.\n",
+ " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+ " # Add the loss metrics from this period to our list.\n",
+ " training_rmse.append(training_root_mean_squared_error)\n",
+ " validation_rmse.append(validation_root_mean_squared_error)\n",
+ " print(\"Model training finished.\")\n",
+ "\n",
+ " # Output a graph of loss metrics over periods.\n",
+ " plt.ylabel(\"RMSE\")\n",
+ " plt.xlabel(\"Periods\")\n",
+ " plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+ " plt.tight_layout()\n",
+ " plt.plot(training_rmse, label=\"training\")\n",
+ " plt.plot(validation_rmse, label=\"validation\")\n",
+ " plt.legend()\n",
+ "\n",
+ " print(\"Final RMSE (on training data): %0.2f\" % training_root_mean_squared_error)\n",
+ " print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n",
+ "\n",
+ " return dnn_regressor"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "2QhdcCy-Y8QR",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 1: Train a NN Model\n",
+ "\n",
+ "**Adjust hyperparameters, aiming to drop RMSE below 110.**\n",
+ "\n",
+ "Run the following block to train a NN model. \n",
+ "\n",
+ "Recall that in the linear regression exercise with many features, an RMSE of 110 or so was pretty good. We'll aim to beat that.\n",
+ "\n",
+ "Your task here is to modify various learning settings to improve accuracy on validation data.\n",
+ "\n",
+ "Overfitting is a real potential hazard for NNs. You can look at the gap between loss on training data and loss on validation data to help judge if your model is starting to overfit. If the gap starts to grow, that is usually a sure sign of overfitting.\n",
+ "\n",
+ "Because of the number of different possible settings, it's strongly recommended that you take notes on each trial to help guide your development process.\n",
+ "\n",
+ "Also, when you get a good setting, try running it multiple times and see how repeatable your result is. NN weights are typically initialized to small random values, so you should see differences from run to run.\n"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "rXmtSW1yKNeK",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 677
+ },
+ "outputId": "9456e61e-73a5-4871-b48d-cc8338607171"
+ },
+ "cell_type": "code",
+ "source": [
+ "dnn_regressor = train_nn_regression_model(\n",
+ " learning_rate=0.001,\n",
+ " steps=5000,\n",
+ " batch_size=10,\n",
+ " hidden_units=[10, 10],\n",
+ " training_examples=training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 8,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 189.98\n",
+ " period 01 : 162.18\n",
+ " period 02 : 158.25\n",
+ " period 03 : 146.24\n",
+ " period 04 : 151.64\n",
+ " period 05 : 134.54\n",
+ " period 06 : 127.59\n",
+ " period 07 : 124.71\n",
+ " period 08 : 121.16\n",
+ " period 09 : 106.83\n",
+ "Model training finished.\n",
+ "Final RMSE (on training data): 106.83\n",
+ "Final RMSE (on validation data): 103.87\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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1exK1DkcIIYQwC1LAXMRab83DA+7GSmfFl0eXk1ViHrcwj4log7ODNev3JJFX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\nfkcFRkRERPyOCoyIiIj4nf8PhRCKpGZkt3wAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "O2q5RRCKqYaU",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below to see a possible solution"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "j2Yd5VfrqcC3",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "**NOTE:** This selection of parameters is somewhat arbitrary. Here we've tried combinations that are increasingly complex, combined with training for longer, until the error falls below our objective (training is nondeterministic, so results may fluctuate a bit each time you run the solution). This may not be the best combination; others may attain an even lower RMSE. If your aim is to find the model that can attain the best error, then you'll want to use a more rigorous process, like a parameter search."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "IjkpSqmxqnSM",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "dnn_regressor = train_nn_regression_model(\n",
+ " learning_rate=0.001,\n",
+ " steps=2000,\n",
+ " batch_size=100,\n",
+ " hidden_units=[10, 10],\n",
+ " training_examples=training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "c6diezCSeH4Y",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 2: Evaluate on Test Data\n",
+ "\n",
+ "**Confirm that your validation performance results hold up on test data.**\n",
+ "\n",
+ "Once you have a model you're happy with, evaluate it on test data to compare that to validation performance.\n",
+ "\n",
+ "Reminder, the test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv)."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "icEJIl5Vp51r",
+ "colab_type": "code",
+ "cellView": "both",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "outputId": "bedbaf6c-3cc0-4748-ea0b-2b0704339c86"
+ },
+ "cell_type": "code",
+ "source": [
+ "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+ "\n",
+ "# YOUR CODE HERE\n",
+ "test_examples = preprocess_features(california_housing_test_data)\n",
+ "test_targets = preprocess_targets(california_housing_test_data)\n",
+ "input_function = lambda: my_input_fn(test_examples, \n",
+ " test_targets[\"median_house_value\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ "predictions = dnn_regressor.predict(input_fn=input_function)\n",
+ "predictions = np.array([item['predictions'][0] for item in predictions])\n",
+ "root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(predictions, targets))\n",
+ "\n",
+ "print(\"Final RMSE: %0.2f\" % root_mean_squared_error)"
+ ],
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Final RMSE: 107.04\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "vvT2jDWjrKew",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below to see a possible solution."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "FyDh7Qy6rQb0",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Similar to what the code at the top does, we just need to load the appropriate data file, preprocess it and call predict and mean_squared_error.\n",
+ "\n",
+ "Note that we don't have to randomize the test data, since we will use all records."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "vhb0CtdvrWZx",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+ "\n",
+ "test_examples = preprocess_features(california_housing_test_data)\n",
+ "test_targets = preprocess_targets(california_housing_test_data)\n",
+ "\n",
+ "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n",
+ " test_targets[\"median_house_value\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ "\n",
+ "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n",
+ "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n",
+ "\n",
+ "root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(test_predictions, test_targets))\n",
+ "\n",
+ "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/logistic_regression.ipynb b/logistic_regression.ipynb
new file mode 100644
index 0000000..ea99620
--- /dev/null
+++ b/logistic_regression.ipynb
@@ -0,0 +1,1607 @@
+{
+ "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": [
+ ""
+ ]
+ },
+ {
+ "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": 1224
+ },
+ "outputId": "f24b25b6-61da-4833-e0cc-013dcba900c8"
+ },
+ "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 2645.6 539.5 \n",
+ "std 2.1 2.0 12.6 2204.2 425.1 \n",
+ "min 32.5 -124.3 2.0 8.0 1.0 \n",
+ "25% 33.9 -121.8 18.0 1459.0 296.0 \n",
+ "50% 34.2 -118.5 29.0 2127.0 433.0 \n",
+ "75% 37.7 -118.0 37.0 3143.2 648.0 \n",
+ "max 42.0 -114.3 52.0 37937.0 5471.0 \n",
+ "\n",
+ " population households median_income rooms_per_person \n",
+ "count 12000.0 12000.0 12000.0 12000.0 \n",
+ "mean 1432.2 501.2 3.9 2.0 \n",
+ "std 1156.5 388.7 1.9 1.2 \n",
+ "min 3.0 1.0 0.5 0.0 \n",
+ "25% 790.0 281.8 2.6 1.5 \n",
+ "50% 1166.0 409.0 3.5 1.9 \n",
+ "75% 1720.0 603.0 4.8 2.3 \n",
+ "max 35682.0 5189.0 15.0 55.2 "
+ ],
+ "text/html": [
+ "
"
+ ]
+ },
+ "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": 640
+ },
+ "outputId": "0460c7f6-1552-4dce-f4c9-7e1a88553d6b"
+ },
+ "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": 10,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 0.45\n",
+ " period 01 : 0.45\n",
+ " period 02 : 0.45\n",
+ " period 03 : 0.44\n",
+ " period 04 : 0.44\n",
+ " period 05 : 0.44\n",
+ " period 06 : 0.44\n",
+ " period 07 : 0.44\n",
+ " period 08 : 0.45\n",
+ " period 09 : 0.44\n",
+ "Model training finished.\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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vMv/aOwghhOiQJPERFlFVX82qY++RU3Ge6MCRzO07q8MkPRdE+V6Y7pJRHyGE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\nEXaVvYtSoeRO0y1Ouea+3GqGRsa4ZXGcPfQ4kl7nxfVzI7n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FgF6vp6ioyFFlC+GWlAoF\ndyxL4LWPynjr0wre+rSCGKM/mbOMZM4KIzTIOQvACTEVBHkFkhmeQWZ4xuVf7ADvHalidMzK+iVx\nKJXS2uNuHBZ8WltbSU1NtX8dHBxMS0uLPfhkZ2eTmZlJVFTUhPc99dRTPPbYY+zatct+zNfXF4DR\n0VFeffVVvvvd7wIwNDTEww8/TF1dHTfffDP/9E//dMma9Hpf1GrHdYWFhspUY3c1HZ7NfTfPYv0N\nSeQWNfLJyTpOnG3mzY/LefPjcmbEBrF8XjTL5kYSMoVC0HR4LtOVPJur0941wMH8BsKCfVl/QxJq\n1eRvgirP5to4bXCz1Wq1/7qjo4Ps7GxeeOEFmpqa7Md37drFvHnziImJuej9o6OjPPLIIyxevJgl\nS5YA8Mgjj3D77bejUCj4+te/zsKFC0lLS/vSGiyWvkm8o4lCQ3W0tHQ77Pzi6k23Z5MaG0RqbBC9\nAzPIO9tCbnEzZyotlFR38MfdhcyIDmTRrDAWphgJ9Lv2hd8cZbo9l+lEns3Ve+3DUoZHxlibGYOl\nvXfSzy/P5spcKhw6LPgYjUZaW1vtXzc3NxMaGgrA4cOHaW9v54EHHmBoaIjq6mq2b99Oc3MzNTU1\nHDhwgMbGRrRaLeHh4WRlZbFlyxbi4uL43ve+Zz/n1772NfuvFy9eTElJySWDjxDTiZ+3huVzI1k+\nN5KuviGOn20h93QTJTUdlNR28uoHJaTE6rludhgZM0Lx95Fp8EI4UlffEAdO1qHXeZE1J8LV5Ygv\n4bDgs3TpUnbs2MGmTZsoKirCaDTau7nWrl3L2rVrAaitrWXLli1s3bp1wvt37NhBVFQUWVlZ7N69\nG41Gww9+8AP775eXl/Pss8/y61//mtHRUfLy8uznFMLTBPhqWTk/ipXzo7B0D3KsuJnc4ibOVFk4\nU2Xh5X1nSU0IZlGKkfnJofh6y0oWQky293NrGBoe494VcWjUk9/FJSaHwz79MjIySE1NZdOmTSgU\nCrZt20Z2djY6nY41a77aBnGvvvoqg4ODbN68GbANln788ccJDw/nnnvuQalUcuONN37hdHkhPI1e\n58WaRTGsWRRDa2c/R4ubyT3dTL65jXxzG2rVWdISg7ludhhzTSF4aWUJCCGuVU//MB/m1RLgp2V5\nurT2uDOF9cLBN9OcI/tFpd/VfcmzsWlq7yP3TBO5Z5qpa7WNPdBqlMxLCiFzVhhpicFoHDj4//Pk\nubgveTZf3a6D5ez+rJL7Viax9rpYh11Hns2VcckYHyGEewkL9uW2pQnctjSB2pYecs80c3Q8COWe\nacbHS8X85FAyZxmZHR/skNkoQkxH/YMjfHCsFn8fDSvnR13+DcKlJPgI4YGiQ/2JDvXnruUJVDf1\n2FuCDhU2cqiwET9vNQtmhpI5K4yZsUGolBKChPgyH+XV0jc4wobrE6XreAqQ4COEB1MoFMSF64gL\n13HPChPl9V0cOdPE0eJmPjnVwCenGgjw1bAwxbZQYlJ0oCy/L8QFBodG2Zdbg6+XmlULol1djrgC\nEnyEEIAtBJmiAjFFBbLpxmRKazts3WHFzXyUV8dHebZpuovGQ1BChE52nBYeb/+JOnr6h7l9aTw+\nXvIjdSqQpySEuIhSqWBmrJ6ZsXruX5NMcVUHR840kXe2hfeP1vD+0RpCAr3JnBVG5iwjMUZ/CUHC\n4wwNj7IvtxovrYrVCy9eeFe4Jwk+QohLUimVpCYEk5oQzDdunklhRTtHzzSRV9rKu4erePdwFeHB\nvvZ9wyJDZPNU4RkO5jfQ2TvEusVxskDoFCLBRwhxxdQq2/T3eUkhDA2Pkm9uI7e4mfyyVnZ/Vsnu\nzyqJDvWztwQZ9b6uLlkIhxgeGePdw1Vo1UpuypTWnqlEgo8Q4qpoNSoWphhZmGJkYGiEk2Wt5J5u\nprCijexPysn+pJz4cB2Zs8JYlGLEEOjt6pKFmDSHChuwdA9y06IYAnzdd088cTEJPkKIa+atVbN4\ndjiLZ4fTNzBMXkkrucVNnK6wUNnYzV/3l5EUFUjmLCOLUowE+nu5umQhrtrI6Bjv5FShVikdulih\ncAwJPkKISeXrrWFZegTL0iPo7hvieIlt89Sz1R2U1XXylw9KmRkbxOrr4kiLC3LqatFCTIYjp5to\n7RzgxowogiTETzkSfIQQDqPz1bJiXhQr5kXR0XNu89Rmiqs7KK7uwBDgzb0rTSxKMcqsMDEljI1Z\n2ZNThUqp4Jbr4lxdjrgKEnyEEE4R5O/F6oUxrF4YQ1vnAIfONLP7EzO/f6uID47VsmlVMomRAa4u\nU4hLOlrcTFN7H9fPjZBxa1OUrEMvhHA6Q6A3/3xbKr/81nUsmBFKWV0nv/i/Yzy/u4i2zgFXlyfE\nFxqzWtmTU4lSoWDdknhXlyOukrT4CCFcxqj35bsb0jhbbeG1D8s4fLqJ4yUt3JwZy7rFsXhr5SNK\nuI8TJa3UtfSyJDUcY5CPq8sRV0lafIQQLjczVs9j/7iQb946Cz9vNXsOVbLlD4c5eKqesTGrq8sT\nAqvVytuHKlAA67NkbM9UJsFHCOEWlAoFS9MieOLbS7h9aTz9gyO88F4x//niUYqrLK4uT3i4gvI2\nqpt6WDTLSIRBViefyiT4CCHcipdWxZ3LE9n+7cUsSQ2nurmHX/3lBDvezKepvc/V5QkPZLVaefuz\nSgDWy9ieKU860IUQbik4wJtv3Tab1Qujee3DUk6UtpJvbmPVgmhuWxqPn7fsjSSc43SVBXN9F/OT\nQ4g2+ru6HHGNpMVHCOHWEiICePSBDB68cw56nRfvH63h0d/n8MGxGkZGx1xdnvAAe8Zbe25bGu/S\nOsTkkBYfIYTbUygULEwxMjfJwAfHann7UCWvflDK/hN13LcyiXSTQRZAFA5RUtPB2ZoO0hINxIfL\nOlPTgbT4CCGmDI1axS2L43jyX5awYn4Uje19/M8b+fxm50lqm3tcXZ6Yht7+rAKQ1p7pRIKPEGLK\nCfDT8o2bZ/If/5xJakIwRZUWtr2Qy0t7i+nsHXJ1eWKaMNd3UlRpYVacnqSoQFeXIyaJdHUJIaas\n6FB/fnzfXArK29n5USkfn6znyOkm1mfFs2ZhtGyAKq6JfWxPVrxL6xCTS4KPEGJKUygUpJsMzI7X\n8/HJet76tII3Dpg5cKKOe1bIBqji6lQ1dnPK3EZydCAzY4NcXY6YRNLVJYSYFtQqJasWRPPEvyzm\npkUxWLoH+f1bRTzx5zzK67tcXZ6YYvbkVAK2sT0SnKcXCT5CiGnFz1vDplXJ/OJb15ExI5Sy2vEN\nUN8uor1LNkAVl1fX0sPxsy0kROhIjQ92dTlikklXlxBiWgrT+/K9DWkUV1l47aNSDhc1kXfWtgHq\nLbIBqriEPTlVANyWlSCtPdOQtPgIIaa1lDg9P/vHRfzzuln4eKt5+1AlW54/zKf5DYxZZQNUMVFj\nex+5Z5qIMfozN8ng6nKEA0jwEUJMe0qFgmXpETzx7cXclhVP/8AIf3r3DP/54lHOVssGqOK8d3Iq\nsVptM7mktWd6kuAjhPAY3lo1d11/bgPUMKqbenjq1RM8k11Ak0U2QPV0LR395BQ2ERniR8bMUFeX\nIxxEOrmFEB7HtgFqKqsWxPDah6XklbRwqqyVVQuiuX1pPL6yAapHeu9wFWNWK+uXxKGU1p5pS1p8\nhBAeKzEygC1fz+A7d6Se3wD1D4f58Hgto2OyAaonae8a4NOCBox6HxbNMrq6HOFADg0+27dvZ+PG\njWzatIn8/PwvfM3TTz/N5s2bJxwbGBhg9erVZGdnA9DQ0MDmzZu5//77+eEPf8jQkG1J+t27d3P3\n3Xdz77338vrrrzvyVoQQ05RCoSBzVhi//NZ13LPCxMjoGH/+ewk/+2Mu+eZWrDIA2iO8d6SakVEr\nty6JQ6WUNoHpzGFPNzc3l6qqKnbu3Mkvf/lLfvnLX170mrKyMo4ePXrR8eeee47AwPP7ovzud7/j\n/vvv59VXXyUuLo433niDvr4+nn32WV588UVefvllXnrpJTo6Ohx1O0KIaU6jVrFucRxP/MsSVsyL\npLG9j9++ns9v/nqK2hbZAHU66+wZ5JNT9RgCvFmSGu7qcoSDOSz45OTksHr1agBMJhOdnZ309Ez8\n8HjyySd56KGHJhwzm82UlZWxYsUK+7EjR46watUqAFauXElOTg6nTp0iLS0NnU6Ht7c3GRkZ5OXl\nOep2hBAeItBPyzfWpvAf/5RJaryeoop2tv0pl//bW0yXbIA6Le3LrWF4ZIx1S+JQq6S1Z7pz2BNu\nbW1Fr9fbvw4ODqalpcX+dXZ2NpmZmURFRU1431NPPcWjjz464Vh/fz9arRYAg8FAS0sLra2tBAef\nX1Hz8+cXQohrEW3058cb5/HDe9IJ0/ty4GQ9W57P4b3DVQyPyPif6aK7b4j9J+rQ67xYlhbh6nKE\nEzhtVteF/eQdHR1kZ2fzwgsv0NTUZD++a9cu5s2bR0xMzBWd50qOX0iv90XtwN2aQ0N1Dju3uDby\nbNzTVHguq40BrMiMY29OJa/uK+b1A2Y+yW/gn9ankpUeMW3XepkKz2Yy7H3vDIPDo3zj1llERgRe\n/g1uwFOejaM4LPgYjUZaW1vtXzc3NxMaalsX4fDhw7S3t/PAAw8wNDREdXU127dvp7m5mZqaGg4c\nOEBjYyNarZbw8HB8fX0ZGBjA29ubpqYmjEbjF55/3rx5l6zJ4sB1OkJDdbS0dDvs/OLqybNxT1Pt\nuVw3M5Q5cUG8/VklHx6v5cn/O0pydCCbViWTEBHg6vIm1VR7Nlerb2CYtw+aCfDVkGEyTIl79pRn\nc60uFQ4dFnyWLl3Kjh072LRpE0VFRRiNRvz9/QFYu3Yta9euBaC2tpYtW7awdevWCe/fsWMHUVFR\nZGVlkZWVxb59+7jjjjt4//33Wb58OXPnzuWnP/0pXV1dqFQq8vLyLjqHEEJMpnMboK6cH8Vf95dx\norSVn790jCWp4Vw3O4xAPy0Bflp0vhoZKzIFfHCslv7BUdavjMdL47jeAOFeHBZ8MjIySE1NZdOm\nTSgUCrZt20Z2djY6nY41a9Z8pXN9//vf5yc/+Qk7d+4kMjKSO++8E41Gw8MPP8w3v/lNFAoF3/3u\nd9HppPlPCOF4YcG+fP/udM5UWdj5YSk5RY3kFDVOeI2/j4YAPy0BvhoC/b0I8NUS4Gc7FujnJSHJ\nxfoHR/j7sRr8vNWsmBd1+TeIaUNh9aBFKhzZPCjNj+5Lno17mi7PZWzMyonSVhraeunqHaKrb4iu\n3iE6e23/7x0Yuew5LgxJ54LR+ZB07mvnhaTp8mwu5d3DVbxxwMxdyxO4bWmCq8u5Yp7wbCaDS7q6\nhBDCEyiVChbMDAW+eG+nkdGxCUHoXDjq7JkYkjp7Bqlv7b3s9fy81eMtSONhaTwcTfy/l7QkXcLg\n0Cj7cqvx8VKzasGXT6YR05MEHyGEcCC1SklwgDfBAd6Xfe25kGQPRud+fUFo+qoh6cJA9EUhydYF\np/WokPTxqXq6+4ZZnxWPr7f8GPQ08sSFEMJNXG1I6uo934L0+ZDU1TtEQ9vlZ7ReGJKiwwKICPYh\nMSKAqFC/aRWKhkdGee9IFV5aFTctktYeTyTBRwghpqBrCkmfC0afD0nF1ee3/9GolcSF6UiICCAh\nUkdiRAChQT5Tdv2ig/kNdPYMcct1sfj7aFxdjnABCT5CCDHNfdWQNGRVcLyogYqGLsobuiiv76Ks\nrtP+Gn8fjS0IRehIjAwgISIAna/WkbcwKUZGx3jvcBVatZKbMmNdXY5wEQk+Qggh7NQqJRGhOnzV\nCpbPjQRgcHiU6qZuKurPB6GC8jYKytvs7wsN8iYhIoDEiAASIwOJDfNH62Zr4xwqbKSta5DVC6MJ\n9HP/oCYcQ4KPEEKIS/LSqEiODiI5Osh+rKtviMrxEFTe0EVFfRe5Z5rJPdMMgFKhINroR2JEAAmR\ntkAUYfBDqXRNF9no2Bjv5FSiVim45bo4l9Qg3IMEHyGEEF9ZgK+WdFMI6aYQwLZfYktH//kg1NBF\nVWMP1U09HDhZD4CXVkVCuM4ehBIiAtDrvJwyXij3dDMtHQOsmB+FXufl8OsJ9yXBRwghxDVTKBQY\n9b4Y9b4sTg0HbGNqalt67F1kFQ3dnK3umDB4OtBfO949ZgtC8eEBkz7FfGzMyp6cSlRKBesWy9ge\nTyfBRwghhEOoVUriw21hZuX4sb6BEaoazweh8vpOTpS2cqLUtum0Agg3+Nq7yBIiAogx+l/TlPpj\nZ5tpaOtjWXoEIYE+135jYkqT4COEEMJpfL3VzIoPZlZ8sP2YpXuQ8npb91h5fScVjd00tDXyWaFt\n/zO1SklcmP/4lHpb65DxCqfUj1mt7DlUiUIBty6RsT1Cgo8QQggX0+u8WDAzdHzrD1vXVEN73/ku\nsvouKhu7Mdd3wXHbe/y81eNT6s93kwV8wUytU6Wt1Lb0sjg1jDC9rzNvS7gpCT5CCCHcilKpICrE\nj6gQP5alRwAwNDxKdVOPfeB0RX0XhRXtFFa0298XEug9IQjFhenYfagSBXDrknjX3IxwOxJ8hBBC\nuD2tRkVSdCBJ0YH2Y919Q1Q0dNuC0PjU+qPFzRwttk2pVyjAaoWFM0OJCvFzVenCzUjwEUIIMSXp\nfLWkmwykmwzA+JT6zgEq6s8HoY6eQe5YnujiSoU7keAjhBBiWlAoFBiDfDAG+XDd7DBXlyPc1PTZ\nclcIIYQQ4jIk+AghhBDCY0jwEUIIIYTHkOAjhBBCCI8hwUcIIYQQHkOCjxBCCCE8hgQfIYQQQngM\nCT5CCCGE8BgSfIQQQgjhMST4CCGEEMJjSPARQgghhMeQ4COEEEIIjyHBRwghhBAeQ2G1Wq2uLkII\nIYQQwhmkxUcIIYQQHkOCjxBCCCE8hgQfIYQQQngMCT5CCCGE8BgSfIQQQgjhMST4CCGEEMJjSPCZ\nBNu3b2fjxo1s2rSJ/Px8V5cjLvCrX/2KjRs3cvfdd/P++++7uhxxgYGBAVavXk12drarSxEX2L17\nN7fffjsbNmzgwIEDri5HAL29vXzve99j8+bNbNq0iYMHD7q6pClN7eoCprrc3FyqqqrYuXMnZrOZ\nrVu3snPnTleXJYDDhw9TWlrKzp07sVgs3HXXXdx0002uLkuMe+655wgMDHR1GeICFouFZ599ljff\nfJO+vj527NjBihUrXF2Wx/vb3/5GQkICDz/8ME1NTfzDP/wDe/fudXVZU5YEn2uUk5PD6tWrATCZ\nTHR2dtLT04O/v7+LKxOLFi0iPT0dgICAAPr7+xkdHUWlUrm4MmE2mykrK5Mfqm4mJyeHJUuW4O/v\nj7+/Pz//+c9dXZIA9Ho9Z8+eBaCrqwu9Xu/iiqY26eq6Rq2trRP+EgYHB9PS0uLCisQ5KpUKX19f\nAN544w2uv/56CT1u4qmnnuLRRx91dRnic2praxkYGOA73/kO999/Pzk5Oa4uSQC33nor9fX1rFmz\nhq9//ev85Cc/cXVJU5q0+Ewy2QHE/XzwwQe88cYb/OlPf3J1KQLYtWsX8+bNIyYmxtWliC/Q0dHB\nM888Q319Pd/4xjfYv38/CoXC1WV5tLfeeovIyEj++Mc/UlxczNatW2Vs3DWQ4HONjEYjra2t9q+b\nm5sJDQ11YUXiQgcPHuT3v/89//u//4tOp3N1OQI4cOAANTU1HDhwgMbGRrRaLeHh4WRlZbm6NI9n\nMBiYP38+arWa2NhY/Pz8aG9vx2AwuLo0j5aXl8eyZcsASElJobm5Wbrtr4F0dV2jpUuXsm/fPgCK\nioowGo0yvsdNdHd386tf/Yo//OEPBAUFubocMe63v/0tb775Jn/961+59957efDBByX0uIlly5Zx\n+PBhxsbGsFgs9PX1yXgSNxAXF8epU6cAqKurw8/PT0LPNZAWn2uUkZFBamoqmzZtQqFQsG3bNleX\nJMa9++67WCwWfvSjH9mPPfXUU0RGRrqwKiHcV1hYGDfffDP33XcfAD/96U9RKuXfx662ceNGtm7d\nyte//nVGRkZ4/PHHXV3SlKawyqAUIYQQQngIifJCCCGE8BgSfIQQQgjhMST4CCGEEMJjSPARQggh\nhMeQ4COEEEIIjyHBRwjhlmpra5kzZw6bN2+270r98MMP09XVdcXn2Lx5M6Ojo1f8+q997WscOXLk\nasoVQkwREnyEEG4rODiYl19+mZdffpnXXnsNo9HIc889d8Xvf/nll2WhNyHEBLKAoRBiyli0aBE7\nd+6kuLiYp556ipGREYaHh/nZz37G7Nmz2bx5MykpKZw5c4aXXnqJ2bNnU1RUxNDQEI899hiNjY2M\njIxwxx13cP/999Pf389DDz2ExWIhLi6OwcFBAJqamvjXf/1XAAYGBti4cSP33HOPK29dCDFJJPgI\nIaaE0dFR/v73v7NgwQL+7d/+jWeffZbY2NiLNm309fXllVdemfDel19+mYCAAJ5++mkGBgZYt24d\ny5cv59ChQ3h7e7Nz506am5tZtWoVAO+99x6JiYn8x3/8B4ODg7z++utOv18hhGNI8BFCuK329nY2\nb94MwNjYGAsXLuTuu+/md7/7Hf/+7/9uf11PTw9jY2OAbRuZzzt16hQbNmwAwNvbmzlz5lBUVERJ\nSQkLFiwAbBsOJyYmArB8+XJeffVVHn30UW644QY2btzo0PsUQjiPBB8hhNs6N8bnQt3d3Wg0mouO\nn6PRaC46plAoJnxttVpRKBRYrdYJe1GdC08mk4l33nmHo0ePsnfvXl566SVee+21a70dIYQbkMHN\nQogpRafTER0dzccffwxARUUFzzzzzCXfM3fuXA4ePAhAX18fRUVFpKamYjKZOHHiBAANDQ1UVFQA\n8Pbbb1NQUEBWVhbbtm2joaGBkZERB96VEMJZpMVHCDHlPPXUU/ziF7/g+eefZ2RkhEcfffSSr9+8\neTOPPfYYDzzwAENDQzz44INER0dzxx138NFHH3H//fcTHR1NWloaAElJSWzbtg2tVovVauVb3/oW\narV8XAoxHcju7EIIIYTwGNLVJYQQQgiPIcFHCCGEEB5Dgo8QQgghPIYEHyGEEEJ4DAk+QgghhPAY\nEnyEEEII4TEk+AghhBDCY0jwEUIIIYTH+P8BllFZD4QDeL4AAAAASUVORK5CYII=\n",
+ "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": "yiVTBuHIcIXx",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 347
+ },
+ "outputId": "ea6b2617-6ea4-41b8-961f-9a2c0028e70c"
+ },
+ "cell_type": "code",
+ "source": [
+ "predict_validation = lambda: my_input_fn(validation_examples, \n",
+ " validation_targets[\"median_house_value_is_high\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ "\n",
+ "validation_predictions = linear_regressor.predict(input_fn=predict_validation)\n",
+ "validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+ "\n",
+ "_ = plt.hist(validation_predictions)"
+ ],
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "kXFQ5uig2RoP",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+ " validation_targets[\"median_house_value_is_high\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ "\n",
+ "validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+ "validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+ "\n",
+ "_ = plt.hist(validation_predictions)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "rYpy336F9wBg",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 2: Train a Logistic Regression Model and Calculate LogLoss on the Validation Set\n",
+ "\n",
+ "To use logistic regression, simply use [LinearClassifier](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearClassifier) instead of `LinearRegressor`. Complete the code below.\n",
+ "\n",
+ "**NOTE**: When running `train()` and `predict()` on a `LinearClassifier` model, you can access the real-valued predicted probabilities via the `\"probabilities\"` key in the returned dict—e.g., `predictions[\"probabilities\"]`. Sklearn's [log_loss](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html) function is handy for calculating LogLoss using these probabilities.\n"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "JElcb--E9wBm",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def train_linear_classifier_model(\n",
+ " learning_rate,\n",
+ " steps,\n",
+ " batch_size,\n",
+ " training_examples,\n",
+ " training_targets,\n",
+ " validation_examples,\n",
+ " validation_targets):\n",
+ " \"\"\"Trains a linear classification model.\n",
+ " \n",
+ " In addition to training, this function also prints training progress information,\n",
+ " as well as a plot of the training and validation loss over time.\n",
+ " \n",
+ " Args:\n",
+ " learning_rate: A `float`, the learning rate.\n",
+ " steps: A non-zero `int`, the total number of training steps. A training step\n",
+ " consists of a forward and backward pass using a single batch.\n",
+ " batch_size: A non-zero `int`, the batch size.\n",
+ " training_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for training.\n",
+ " training_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for training.\n",
+ " validation_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for validation.\n",
+ " validation_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for validation.\n",
+ " \n",
+ " Returns:\n",
+ " A `LinearClassifier` object trained on the training data.\n",
+ " \"\"\"\n",
+ "\n",
+ " periods = 10\n",
+ " steps_per_period = steps / periods\n",
+ " \n",
+ " # Create a linear classifier object.\n",
+ " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+ " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+ " linear_classifier = 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": "VM0wmnFUIYH9",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 640
+ },
+ "outputId": "3905c6bb-1b9d-42b7-fbb7-2586e2de6f15"
+ },
+ "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": 15,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "LogLoss (on training data):\n",
+ " period 00 : 0.60\n",
+ " period 01 : 0.58\n",
+ " period 02 : 0.57\n",
+ " period 03 : 0.55\n",
+ " period 04 : 0.55\n",
+ " period 05 : 0.54\n",
+ " period 06 : 0.54\n",
+ " period 07 : 0.53\n",
+ " period 08 : 0.54\n",
+ " period 09 : 0.53\n",
+ "Model training finished.\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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HV746FUFCTmKFMXs7M/eP7YIBfLA2moKiEusUKSIicoMpzNQhrvYu3NlpCsWl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\nNTyZBwr+xW+/XxakCCGEECKgSTMjhBBCiIAmzYwQQgghApo0M0IIIYQ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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": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
+ "\n",
+ "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n",
+ "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "47xGS2uNIYIE",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "You may use class probabilities, such as those calculated by `LinearClassifier.predict`,\n",
+ "and Sklearn's [roc_curve](http://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics) to\n",
+ "obtain the true positive and false positive rates needed to plot a ROC curve."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "xaU7ttj8IYIF",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+ "# Get just the probabilities for the positive class.\n",
+ "validation_probabilities = np.array([item['probabilities'][1] for item in validation_probabilities])\n",
+ "\n",
+ "false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(\n",
+ " validation_targets, validation_probabilities)\n",
+ "plt.plot(false_positive_rate, true_positive_rate, label=\"our model\")\n",
+ "plt.plot([0, 1], [0, 1], label=\"random classifier\")\n",
+ "_ = plt.legend(loc=2)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "PIdhwfgzIYII",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "**See if you can tune the learning settings of the model trained at Task 2 to improve AUC.**\n",
+ "\n",
+ "Often times, certain metrics improve at the detriment of others, and you'll need to find the settings that achieve a good compromise.\n",
+ "\n",
+ "**Verify if all metrics improve at the same time.**"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "XKIqjsqcCaxO",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 677
+ },
+ "outputId": "2d1e1c73-75e8-4e4d-8448-49dfd03f6506"
+ },
+ "cell_type": "code",
+ "source": [
+ "# TUNE THE SETTINGS BELOW TO IMPROVE AUC\n",
+ "linear_classifier = train_linear_classifier_model(\n",
+ " learning_rate=0.000003,\n",
+ " steps=50000,\n",
+ " batch_size=50,\n",
+ " training_examples=training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=validation_examples,\n",
+ " validation_targets=validation_targets)\n",
+ "\n",
+ "#I hope that it doesn't overfit\n",
+ "\n",
+ "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation)\n",
+ "\n",
+ "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n",
+ "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])"
+ ],
+ "execution_count": 19,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "LogLoss (on training data):\n",
+ " period 00 : 0.50\n",
+ " period 01 : 0.49\n",
+ " period 02 : 0.48\n",
+ " period 03 : 0.48\n",
+ " period 04 : 0.47\n",
+ " period 05 : 0.47\n",
+ " period 06 : 0.47\n",
+ " period 07 : 0.47\n",
+ " period 08 : 0.46\n",
+ " period 09 : 0.46\n",
+ "Model training finished.\n",
+ "AUC on the validation set: 0.80\n",
+ "Accuracy on the validation set: 0.78\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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MGYObmxsdOnTgww8/ZNmyZbz88ss1nsvd3QGNRm3Wz+Pl5Vyn46dEdOC9tafY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\nUjEeuDUOk0dEYfPuPBw4UYrjP1Ug6bY4TB8bAxFPLycKCL/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+ "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..6a9b5fc
--- /dev/null
+++ b/multi_class_classification_of_handwritten_digits.ipynb
@@ -0,0 +1,2094 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "multi-class_classification_of_handwritten_digits.ipynb",
+ "version": "0.3.2",
+ "provenance": [],
+ "collapsed_sections": [
+ "JndnmDMp66FL",
+ "266KQvZoMxMv",
+ "6sfw3LH0Oycm"
+ ],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python2",
+ "display_name": "Python 2"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "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": [
+ ""
+ ]
+ },
+ {
+ "metadata": {
+ "id": "c7HLCm66Cs2p",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "**Learning Objectives:**\n",
+ " * Train both a linear model and a neural network to classify handwritten digits from the classic [MNIST](http://yann.lecun.com/exdb/mnist/) data set\n",
+ " * Compare the performance of the linear and neural network classification models\n",
+ " * Visualize the weights of a neural-network hidden layer"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "HSEh-gNdu8T0",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Our goal is to map each input image to the correct numeric digit. We will create a NN with a few hidden layers and a Softmax layer at the top to select the winning class."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "2NMdE1b-7UIH",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Setup\n",
+ "\n",
+ "First, let's download the data set, import TensorFlow and other utilities, and load the data into a *pandas* `DataFrame`. Note that this data is a sample of the original MNIST training data; we've taken 20000 rows at random."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "4LJ4SD8BWHeh",
+ "colab_type": "code",
+ "cellView": "both",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 236
+ },
+ "outputId": "7959881e-725e-402c-b364-0931bc22b187"
+ },
+ "cell_type": "code",
+ "source": [
+ "from __future__ import print_function\n",
+ "\n",
+ "import glob\n",
+ "import math\n",
+ "import os\n",
+ "\n",
+ "from IPython import display\n",
+ "from matplotlib import cm\n",
+ "from matplotlib import gridspec\n",
+ "from matplotlib import pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "from sklearn import metrics\n",
+ "import tensorflow as tf\n",
+ "from tensorflow.python.data import Dataset\n",
+ "\n",
+ "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+ "pd.options.display.max_rows = 10\n",
+ "pd.options.display.float_format = '{:.1f}'.format\n",
+ "\n",
+ "mnist_dataframe = pd.read_csv(\n",
+ " \"https://download.mlcc.google.com/mledu-datasets/mnist_train_small.csv\",\n",
+ " sep=\",\",\n",
+ " header=None)\n",
+ "\n",
+ "# Use just the first 10,000 records for training/validation.\n",
+ "mnist_dataframe = mnist_dataframe.head(10000)\n",
+ "\n",
+ "mnist_dataframe = mnist_dataframe.reindex(np.random.permutation(mnist_dataframe.index))\n",
+ "mnist_dataframe.head()"
+ ],
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
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+ "metadata": {
+ "id": "kg0-25p2mOi0",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Each row represents one labeled example. Column 0 represents the label that a human rater has assigned for one handwritten digit. For example, if Column 0 contains '6', then a human rater interpreted the handwritten character as the digit '6'. The ten digits 0-9 are each represented, with a unique class label for each possible digit. Thus, this is a multi-class classification problem with 10 classes."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "PQ7vuOwRCsZ1",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ ""
+ ]
+ },
+ {
+ "metadata": {
+ "id": "dghlqJPIu8UM",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Columns 1 through 784 contain the feature values, one per pixel for the 28×28=784 pixel values. The pixel values are on a gray scale in which 0 represents white, 255 represents black, and values between 0 and 255 represent shades of gray. Most of the pixel values are 0; you may want to take a minute to confirm that they aren't all 0. For example, adjust the following text block to print out the values in column 72."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "2ZkrL5MCqiJI",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 424
+ },
+ "outputId": "e2788e98-fe58-44e8-e6ed-609fb99b6014"
+ },
+ "cell_type": "code",
+ "source": [
+ "mnist_dataframe.loc[:, 72:72]"
+ ],
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
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+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "vLNg2VxqhUZ",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Now, let's parse out the labels and features and look at a few examples. Note the use of `loc` which allows us to pull out columns based on original location, since we don't have a header row in this data set."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "JfFWWvMWDFrR",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def parse_labels_and_features(dataset):\n",
+ " \"\"\"Extracts labels and features.\n",
+ " \n",
+ " This is a good place to scale or transform the features if needed.\n",
+ " \n",
+ " Args:\n",
+ " dataset: A Pandas `Dataframe`, containing the label on the first column and\n",
+ " monochrome pixel values on the remaining columns, in row major order.\n",
+ " Returns:\n",
+ " A `tuple` `(labels, features)`:\n",
+ " labels: A Pandas `Series`.\n",
+ " features: A Pandas `DataFrame`.\n",
+ " \"\"\"\n",
+ " labels = dataset[0]\n",
+ "\n",
+ " # DataFrame.loc index ranges are inclusive at both ends.\n",
+ " features = dataset.loc[:,1:784]\n",
+ " # Scale the data to [0, 1] by dividing out the max value, 255.\n",
+ " features = features / 255\n",
+ "\n",
+ " return labels, features"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "mFY_-7vZu8UU",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 350
+ },
+ "outputId": "be81a869-0655-47b9-b0eb-2f58e8a1b9e4"
+ },
+ "cell_type": "code",
+ "source": [
+ "training_targets, training_examples = parse_labels_and_features(mnist_dataframe[:7500])\n",
+ "training_examples.describe()"
+ ],
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "
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