"
+ ],
+ "text/plain": [
+ " longitude latitude housing_median_age total_rooms total_bedrooms \\\n",
+ "count 17000.0 17000.0 17000.0 17000.0 17000.0 \n",
+ "mean -119.6 35.6 28.6 2643.7 539.4 \n",
+ "std 2.0 2.1 12.6 2179.9 421.5 \n",
+ "min -124.3 32.5 1.0 2.0 1.0 \n",
+ "25% -121.8 33.9 18.0 1462.0 297.0 \n",
+ "50% -118.5 34.2 29.0 2127.0 434.0 \n",
+ "75% -118.0 37.7 37.0 3151.2 648.2 \n",
+ "max -114.3 42.0 52.0 37937.0 6445.0 \n",
+ "\n",
+ " population households median_income median_house_value \n",
+ "count 17000.0 17000.0 17000.0 17000.0 \n",
+ "mean 1429.6 501.2 3.9 207.3 \n",
+ "std 1147.9 384.5 1.9 116.0 \n",
+ "min 3.0 1.0 0.5 15.0 \n",
+ "25% 790.0 282.0 2.6 119.4 \n",
+ "50% 1167.0 409.0 3.5 180.4 \n",
+ "75% 1721.0 605.2 4.8 265.0 \n",
+ "max 35682.0 6082.0 15.0 500.0 "
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 4
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "Lr6wYl2bt2Ep",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Build the First Model\n",
+ "\n",
+ "In this exercise, we'll try to predict `median_house_value`, which will be our label (sometimes also called a target). We'll use `total_rooms` as our input feature.\n",
+ "\n",
+ "**NOTE:** Our data is at the city block level, so this feature represents the total number of rooms in that block.\n",
+ "\n",
+ "To train our model, we'll use the [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor) interface provided by the TensorFlow [Estimator](https://www.tensorflow.org/get_started/estimator) API. This API takes care of a lot of the low-level model plumbing, and exposes convenient methods for performing model training, evaluation, and inference."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "0cpcsieFhsNI",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Step 1: Define Features and Configure Feature Columns"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "EL8-9d4ZJNR7",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "In order to import our training data into TensorFlow, we need to specify what type of data each feature contains. There are two main types of data we'll use in this and future exercises:\n",
+ "\n",
+ "* **Categorical Data**: Data that is textual. In this exercise, our housing data set does not contain any categorical features, but examples you might see would be the home style, the words in a real-estate ad.\n",
+ "\n",
+ "* **Numerical Data**: Data that is a number (integer or float) and that you want to treat as a number. As we will discuss more later sometimes you might want to treat numerical data (e.g., a postal code) as if it were categorical.\n",
+ "\n",
+ "In TensorFlow, we indicate a feature's data type using a construct called a **feature column**. Feature columns store only a description of the feature data; they do not contain the feature data itself.\n",
+ "\n",
+ "To start, we're going to use just one numeric input feature, `total_rooms`. The following code pulls the `total_rooms` data from our `california_housing_dataframe` and defines the feature column using `numeric_column`, which specifies its data is numeric:"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "rhEbFCZ86cDZ",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "# Define the input feature: total_rooms.\n",
+ "my_feature = california_housing_dataframe[[\"total_rooms\"]]\n",
+ "\n",
+ "# Configure a numeric feature column for total_rooms.\n",
+ "feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "K_3S8teX7Rd2",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "**NOTE:** The shape of our `total_rooms` data is a one-dimensional array (a list of the total number of rooms for each block). This is the default shape for `numeric_column`, so we don't have to pass it as an argument."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "UMl3qrU5MGV6",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Step 2: Define the Target"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "cw4nrfcB7kyk",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Next, we'll define our target, which is `median_house_value`. Again, we can pull it from our `california_housing_dataframe`:"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "l1NvvNkH8Kbt",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "# Define the label.\n",
+ "targets = california_housing_dataframe[\"median_house_value\"]"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "4M-rTFHL2UkA",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Step 3: Configure the LinearRegressor"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "fUfGQUNp7jdL",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Next, we'll configure a linear regression model using LinearRegressor. We'll train this model using the `GradientDescentOptimizer`, which implements Mini-Batch Stochastic Gradient Descent (SGD). The `learning_rate` argument controls the size of the gradient step.\n",
+ "\n",
+ "**NOTE:** To be safe, we also apply [gradient clipping](https://developers.google.com/machine-learning/glossary/#gradient_clipping) to our optimizer via `clip_gradients_by_norm`. Gradient clipping ensures the magnitude of the gradients do not become too large during training, which can cause gradient descent to fail. "
+ ]
+ },
+ {
+ "metadata": {
+ "id": "ubhtW-NGU802",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 134
+ },
+ "outputId": "b3ba2324-3f6b-44eb-ab57-3b9831eafc6e"
+ },
+ "cell_type": "code",
+ "source": [
+ "# Use gradient descent as the optimizer for training the model.\n",
+ "my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n",
+ "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+ "\n",
+ "# Configure the linear regression model with our feature columns and optimizer.\n",
+ "# Set a learning rate of 0.0000001 for Gradient Descent.\n",
+ "linear_regressor = tf.estimator.LinearRegressor(\n",
+ " feature_columns=feature_columns,\n",
+ " optimizer=my_optimizer\n",
+ ")"
+ ],
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+ "For more information, please see:\n",
+ " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+ " * https://github.com/tensorflow/addons\n",
+ "If you depend on functionality not listed there, please file an issue.\n",
+ "\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "-0IztwdK2f3F",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Step 4: Define the Input Function"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "S5M5j6xSCHxx",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "To import our California housing data into our `LinearRegressor`, we need to define an input function, which instructs TensorFlow how to preprocess\n",
+ "the data, as well as how to batch, shuffle, and repeat it during model training.\n",
+ "\n",
+ "First, we'll convert our *pandas* feature data into a dict of NumPy arrays. We can then use the TensorFlow [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) to construct a dataset object from our data, and then break\n",
+ "our data into batches of `batch_size`, to be repeated for the specified number of epochs (num_epochs). \n",
+ "\n",
+ "**NOTE:** When the default value of `num_epochs=None` is passed to `repeat()`, the input data will be repeated indefinitely.\n",
+ "\n",
+ "Next, if `shuffle` is set to `True`, we'll shuffle the data so that it's passed to the model randomly during training. The `buffer_size` argument specifies\n",
+ "the size of the dataset from which `shuffle` will randomly sample.\n",
+ "\n",
+ "Finally, our input function constructs an iterator for the dataset and returns the next batch of data to the LinearRegressor."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "RKZ9zNcHJtwc",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+ " \"\"\"Trains a linear regression model of one feature.\n",
+ " \n",
+ " Args:\n",
+ " features: pandas DataFrame of features\n",
+ " targets: pandas DataFrame of targets\n",
+ " batch_size: Size of batches to be passed to the model\n",
+ " shuffle: True or False. Whether to shuffle the data.\n",
+ " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+ " Returns:\n",
+ " Tuple of (features, labels) for next data batch\n",
+ " \"\"\"\n",
+ " \n",
+ " # Convert pandas data into a dict of np arrays.\n",
+ " features = {key:np.array(value) for key,value in dict(features).items()} \n",
+ " \n",
+ " # Construct a dataset, and configure batching/repeating.\n",
+ " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+ " ds = ds.batch(batch_size).repeat(num_epochs)\n",
+ " \n",
+ " # Shuffle the data, if specified.\n",
+ " if shuffle:\n",
+ " ds = ds.shuffle(buffer_size=10000)\n",
+ " \n",
+ " # Return the next batch of data.\n",
+ " features, labels = ds.make_one_shot_iterator().get_next()\n",
+ " return features, labels"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "wwa6UeA1V5F_",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "**NOTE:** We'll continue to use this same input function in later exercises. For more\n",
+ "detailed documentation of input functions and the `Dataset` API, see the [TensorFlow Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets)."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "4YS50CQb2ooO",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Step 5: Train the Model"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "yP92XkzhU803",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "We can now call `train()` on our `linear_regressor` to train the model. We'll wrap `my_input_fn` in a `lambda`\n",
+ "so we can pass in `my_feature` and `target` as arguments (see this [TensorFlow input function tutorial](https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model) for more details), and to start, we'll\n",
+ "train for 100 steps."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "5M-Kt6w8U803",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "_ = linear_regressor.train(\n",
+ " input_fn = lambda:my_input_fn(my_feature, targets),\n",
+ " steps=100\n",
+ ")"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "7Nwxqxlx2sOv",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Step 6: Evaluate the Model"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "KoDaF2dlJQG5",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Let's make predictions on that training data, to see how well our model fit it during training.\n",
+ "\n",
+ "**NOTE:** Training error measures how well your model fits the training data, but it **_does not_** measure how well your model **_generalizes to new data_**. In later exercises, you'll explore how to split your data to evaluate your model's ability to generalize.\n"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "pDIxp6vcU809",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 50
+ },
+ "outputId": "08ebe62d-fd21-4a08-9b7f-d138f7f9b321"
+ },
+ "cell_type": "code",
+ "source": [
+ "# Create an input function for predictions.\n",
+ "# Note: Since we're making just one prediction for each example, we don't \n",
+ "# need to repeat or shuffle the data here.\n",
+ "prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n",
+ "\n",
+ "# Call predict() on the linear_regressor to make predictions.\n",
+ "predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
+ "\n",
+ "# Format predictions as a NumPy array, so we can calculate error metrics.\n",
+ "predictions = np.array([item['predictions'][0] for item in predictions])\n",
+ "\n",
+ "# Print Mean Squared Error and Root Mean Squared Error.\n",
+ "mean_squared_error = metrics.mean_squared_error(predictions, targets)\n",
+ "root_mean_squared_error = math.sqrt(mean_squared_error)\n",
+ "print(\"Mean Squared Error (on training data): %0.3f\" % mean_squared_error)\n",
+ "print(\"Root Mean Squared Error (on training data): %0.3f\" % root_mean_squared_error)"
+ ],
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Mean Squared Error (on training data): 56367.025\n",
+ "Root Mean Squared Error (on training data): 237.417\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "AKWstXXPzOVz",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Is this a good model? How would you judge how large this error is?\n",
+ "\n",
+ "Mean Squared Error (MSE) can be hard to interpret, so we often look at Root Mean Squared Error (RMSE)\n",
+ "instead. A nice property of RMSE is that it can be interpreted on the same scale as the original targets.\n",
+ "\n",
+ "Let's compare the RMSE to the difference of the min and max of our targets:"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "7UwqGbbxP53O",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 84
+ },
+ "outputId": "4fc7058e-14fb-498f-a661-a354eb558e39"
+ },
+ "cell_type": "code",
+ "source": [
+ "min_house_value = california_housing_dataframe[\"median_house_value\"].min()\n",
+ "max_house_value = california_housing_dataframe[\"median_house_value\"].max()\n",
+ "min_max_difference = max_house_value - min_house_value\n",
+ "\n",
+ "print(\"Min. Median House Value: %0.3f\" % min_house_value)\n",
+ "print(\"Max. Median House Value: %0.3f\" % max_house_value)\n",
+ "print(\"Difference between Min. and Max.: %0.3f\" % min_max_difference)\n",
+ "print(\"Root Mean Squared Error: %0.3f\" % root_mean_squared_error)"
+ ],
+ "execution_count": 11,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Min. Median House Value: 14.999\n",
+ "Max. Median House Value: 500.001\n",
+ "Difference between Min. and Max.: 485.002\n",
+ "Root Mean Squared Error: 237.417\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "JigJr0C7Pzit",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Our error spans nearly half the range of the target values. Can we do better?\n",
+ "\n",
+ "This is the question that nags at every model developer. Let's develop some basic strategies to reduce model error.\n",
+ "\n",
+ "The first thing we can do is take a look at how well our predictions match our targets, in terms of overall summary statistics."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "941nclxbzqGH",
+ "colab_type": "code",
+ "cellView": "both",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 284
+ },
+ "outputId": "bab7079b-b12f-49ad-875b-4be97370480e"
+ },
+ "cell_type": "code",
+ "source": [
+ "calibration_data = pd.DataFrame()\n",
+ "calibration_data[\"predictions\"] = pd.Series(predictions)\n",
+ "calibration_data[\"targets\"] = pd.Series(targets)\n",
+ "calibration_data.describe()"
+ ],
+ "execution_count": 12,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
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+ " predictions targets\n",
+ "count 17000.0 17000.0\n",
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+ "min 0.0 15.0\n",
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+ ]
+ },
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+ "tags": []
+ },
+ "execution_count": 12
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "E2-bf8Hq36y8",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Okay, maybe this information is helpful. How does the mean value compare to the model's RMSE? How about the various quantiles?\n",
+ "\n",
+ "We can also visualize the data and the line we've learned. Recall that linear regression on a single feature can be drawn as a line mapping input *x* to output *y*.\n",
+ "\n",
+ "First, we'll get a uniform random sample of the data so we can make a readable scatter plot."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "SGRIi3mAU81H",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "sample = california_housing_dataframe.sample(n=300)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "N-JwuJBKU81J",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Next, we'll plot the line we've learned, drawing from the model's bias term and feature weight, together with the scatter plot. The line will show up red."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "7G12E76-339G",
+ "colab_type": "code",
+ "cellView": "both",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 361
+ },
+ "outputId": "184abb4c-ebaa-401a-bb56-b0a163967011"
+ },
+ "cell_type": "code",
+ "source": [
+ "# Get the min and max total_rooms values.\n",
+ "x_0 = sample[\"total_rooms\"].min()\n",
+ "x_1 = sample[\"total_rooms\"].max()\n",
+ "\n",
+ "# Retrieve the final weight and bias generated during training.\n",
+ "weight = linear_regressor.get_variable_value('linear/linear_model/total_rooms/weights')[0]\n",
+ "bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
+ "\n",
+ "# Get the predicted median_house_values for the min and max total_rooms values.\n",
+ "y_0 = weight * x_0 + bias \n",
+ "y_1 = weight * x_1 + bias\n",
+ "\n",
+ "# Plot our regression line from (x_0, y_0) to (x_1, y_1).\n",
+ "plt.plot([x_0, x_1], [y_0, y_1], c='r')\n",
+ "\n",
+ "# Label the graph axes.\n",
+ "plt.ylabel(\"median_house_value\")\n",
+ "plt.xlabel(\"total_rooms\")\n",
+ "\n",
+ "# Plot a scatter plot from our data sample.\n",
+ "plt.scatter(sample[\"total_rooms\"], sample[\"median_house_value\"])\n",
+ "\n",
+ "# Display graph.\n",
+ "plt.show()"
+ ],
+ "execution_count": 15,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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Zs0a1RqWraIKy3JTq+FGFiv6g+4NrjiFLdr/41LHRT9FGs46tZIaipCSHa8Mh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\n5XQrn072CcDu2ka43F6sWDhOtjLbqLKBKA5z3ro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+ "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",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 939
+ },
+ "outputId": "65ea0f9f-32a4-4cc7-d2a9-1cd9ff1f9a7c"
+ },
+ "cell_type": "code",
+ "source": [
+ "train_model(\n",
+ " learning_rate=0.00001,\n",
+ " steps=1000,\n",
+ " batch_size=100\n",
+ ")"
+ ],
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 225.63\n",
+ " period 01 : 214.42\n",
+ " period 02 : 204.04\n",
+ " period 03 : 194.62\n",
+ " period 04 : 187.07\n",
+ " period 05 : 180.80\n",
+ " period 06 : 176.56\n",
+ " period 07 : 172.35\n",
+ " period 08 : 169.46\n",
+ " period 09 : 167.93\n",
+ "Model training finished.\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " predictions targets\n",
+ "count 17000.0 17000.0\n",
+ "mean 112.9 207.3\n",
+ "std 93.1 116.0\n",
+ "min 0.1 15.0\n",
+ "25% 62.4 119.4\n",
+ "50% 90.8 180.4\n",
+ "75% 134.6 265.0\n",
+ "max 1619.9 500.0"
+ ],
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "ajVM7rkoYXeL",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below for one possible solution."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "T3zmldDwYy5c",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "train_model(\n",
+ " learning_rate=0.00002,\n",
+ " steps=500,\n",
+ " batch_size=5\n",
+ ")"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "M8H0_D4vYa49",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "This is just one possible configuration; there may be other combinations of settings that also give good results. Note that in general, this exercise isn't about finding the *one best* setting, but to help build your intutions about how tweaking the model configuration affects prediction quality."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "QU5sLyYTqzqL",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Is There a Standard Heuristic for Model Tuning?\n",
+ "\n",
+ "This is a commonly asked question. The short answer is that the effects of different hyperparameters are data dependent. So there are no hard-and-fast rules; you'll need to test on your data.\n",
+ "\n",
+ "That said, here are a few rules of thumb that may help guide you:\n",
+ "\n",
+ " * Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.\n",
+ " * If the training has not converged, try running it for longer.\n",
+ " * If the training error decreases too slowly, increasing the learning rate may help it decrease faster.\n",
+ " * But sometimes the exact opposite may happen if the learning rate is too high.\n",
+ " * If the training error varies wildly, try decreasing the learning rate.\n",
+ " * Lower learning rate plus larger number of steps or larger batch size is often a good combination.\n",
+ " * Very small batch sizes can also cause instability. First try larger values like 100 or 1000, and decrease until you see degradation.\n",
+ "\n",
+ "Again, never go strictly by these rules of thumb, because the effects are data dependent. Always experiment and verify."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "GpV-uF_cBCBU",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 2: Try a Different Feature\n",
+ "\n",
+ "See if you can do any better by replacing the `total_rooms` feature with the `population` feature.\n",
+ "\n",
+ "Don't take more than 5 minutes on this portion."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "YMyOxzb0ZlAH",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 939
+ },
+ "outputId": "a0d7af70-6cd2-4550-bb04-59ae1b4dde83"
+ },
+ "cell_type": "code",
+ "source": [
+ "# YOUR CODE HERE\n",
+ "train_model(\n",
+ " input_feature=\"population\",\n",
+ " learning_rate=0.00002,\n",
+ " steps=2000,\n",
+ " batch_size=200\n",
+ ")"
+ ],
+ "execution_count": 20,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 214.62\n",
+ " period 01 : 196.26\n",
+ " period 02 : 183.70\n",
+ " period 03 : 177.49\n",
+ " period 04 : 176.20\n",
+ " period 05 : 176.05\n",
+ " period 06 : 176.64\n",
+ " period 07 : 177.18\n",
+ " period 08 : 177.46\n",
+ " period 09 : 177.61\n",
+ "Model training finished.\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " predictions targets\n",
+ "count 17000.0 17000.0\n",
+ "mean 143.6 207.3\n",
+ "std 115.3 116.0\n",
+ "min 0.3 15.0\n",
+ "25% 79.3 119.4\n",
+ "50% 117.2 180.4\n",
+ "75% 172.8 265.0\n",
+ "max 3583.2 500.0"
+ ],
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "NqIbXxx222ea"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Train the Neural Network\n",
+ "\n",
+ "Next, we'll train the neural network."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "6k3xYlSg27VB",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def construct_feature_columns(input_features):\n",
+ " \"\"\"Construct the TensorFlow Feature Columns.\n",
+ "\n",
+ " Args:\n",
+ " input_features: The names of the numerical input features to use.\n",
+ " Returns:\n",
+ " A set of feature columns\n",
+ " \"\"\" \n",
+ " return set([tf.feature_column.numeric_column(my_feature)\n",
+ " for my_feature in input_features])"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "De9jwyy4wTUT",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+ " \"\"\"Trains a neural network model.\n",
+ " \n",
+ " Args:\n",
+ " features: pandas DataFrame of features\n",
+ " targets: pandas DataFrame of targets\n",
+ " batch_size: Size of batches to be passed to the model\n",
+ " shuffle: True or False. Whether to shuffle the data.\n",
+ " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+ " Returns:\n",
+ " Tuple of (features, labels) for next data batch\n",
+ " \"\"\"\n",
+ " \n",
+ " # Convert pandas data into a dict of np arrays.\n",
+ " features = {key:np.array(value) for key,value in dict(features).items()} \n",
+ " \n",
+ " # Construct a dataset, and configure batching/repeating.\n",
+ " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+ " ds = ds.batch(batch_size).repeat(num_epochs)\n",
+ " \n",
+ " # Shuffle the data, if specified.\n",
+ " if shuffle:\n",
+ " ds = ds.shuffle(10000)\n",
+ " \n",
+ " # Return the next batch of data.\n",
+ " features, labels = ds.make_one_shot_iterator().get_next()\n",
+ " return features, labels"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "W-51R3yIKxk4",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def train_nn_regression_model(\n",
+ " my_optimizer,\n",
+ " steps,\n",
+ " batch_size,\n",
+ " hidden_units,\n",
+ " training_examples,\n",
+ " training_targets,\n",
+ " validation_examples,\n",
+ " validation_targets):\n",
+ " \"\"\"Trains a neural network regression model.\n",
+ " \n",
+ " In addition to training, this function also prints training progress information,\n",
+ " as well as a plot of the training and validation loss over time.\n",
+ " \n",
+ " Args:\n",
+ " my_optimizer: An instance of `tf.train.Optimizer`, the optimizer to use.\n",
+ " steps: A non-zero `int`, the total number of training steps. A training step\n",
+ " consists of a forward and backward pass using a single batch.\n",
+ " batch_size: A non-zero `int`, the batch size.\n",
+ " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+ " training_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for training.\n",
+ " training_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for training.\n",
+ " validation_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for validation.\n",
+ " validation_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for validation.\n",
+ " \n",
+ " Returns:\n",
+ " A tuple `(estimator, training_losses, validation_losses)`:\n",
+ " estimator: the trained `DNNRegressor` object.\n",
+ " training_losses: a `list` containing the training loss values taken during training.\n",
+ " validation_losses: a `list` containing the validation loss values taken during training.\n",
+ " \"\"\"\n",
+ "\n",
+ " periods = 10\n",
+ " steps_per_period = steps / periods\n",
+ " \n",
+ " # Create a DNNRegressor object.\n",
+ " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+ " dnn_regressor = tf.estimator.DNNRegressor(\n",
+ " feature_columns=construct_feature_columns(training_examples),\n",
+ " hidden_units=hidden_units,\n",
+ " optimizer=my_optimizer\n",
+ " )\n",
+ " \n",
+ " # Create input functions.\n",
+ " training_input_fn = lambda: my_input_fn(training_examples, \n",
+ " training_targets[\"median_house_value\"], \n",
+ " batch_size=batch_size)\n",
+ " predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+ " training_targets[\"median_house_value\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+ " validation_targets[\"median_house_value\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ "\n",
+ " # Train the model, but do so inside a loop so that we can periodically assess\n",
+ " # loss metrics.\n",
+ " print(\"Training model...\")\n",
+ " print(\"RMSE (on training data):\")\n",
+ " training_rmse = []\n",
+ " validation_rmse = []\n",
+ " for period in range (0, periods):\n",
+ " # Train the model, starting from the prior state.\n",
+ " dnn_regressor.train(\n",
+ " input_fn=training_input_fn,\n",
+ " steps=steps_per_period\n",
+ " )\n",
+ " # Take a break and compute predictions.\n",
+ " training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n",
+ " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+ " \n",
+ " validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n",
+ " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+ " \n",
+ " # Compute training and validation loss.\n",
+ " training_root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(training_predictions, training_targets))\n",
+ " validation_root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+ " # Occasionally print the current loss.\n",
+ " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+ " # Add the loss metrics from this period to our list.\n",
+ " training_rmse.append(training_root_mean_squared_error)\n",
+ " validation_rmse.append(validation_root_mean_squared_error)\n",
+ " print(\"Model training finished.\")\n",
+ "\n",
+ " # Output a graph of loss metrics over periods.\n",
+ " plt.ylabel(\"RMSE\")\n",
+ " plt.xlabel(\"Periods\")\n",
+ " plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+ " plt.tight_layout()\n",
+ " plt.plot(training_rmse, label=\"training\")\n",
+ " plt.plot(validation_rmse, label=\"validation\")\n",
+ " plt.legend()\n",
+ "\n",
+ " print(\"Final RMSE (on training data): %0.2f\" % training_root_mean_squared_error)\n",
+ " print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n",
+ "\n",
+ " return dnn_regressor, training_rmse, validation_rmse"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "KueReMZ9Kxk7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 770
+ },
+ "outputId": "6a34fecd-e3b0-4f10-faac-9f54bc3c9be4"
+ },
+ "cell_type": "code",
+ "source": [
+ "_ = train_nn_regression_model(\n",
+ " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n",
+ " steps=5000,\n",
+ " batch_size=70,\n",
+ " hidden_units=[10, 10],\n",
+ " training_examples=training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+ "For more information, please see:\n",
+ " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+ " * https://github.com/tensorflow/addons\n",
+ "If you depend on functionality not listed there, please file an issue.\n",
+ "\n",
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 159.19\n",
+ " period 01 : 148.46\n",
+ " period 02 : 130.44\n",
+ " period 03 : 115.31\n",
+ " period 04 : 108.54\n",
+ " period 05 : 104.21\n",
+ " period 06 : 101.10\n",
+ " period 07 : 102.10\n",
+ " period 08 : 99.01\n",
+ " period 09 : 100.14\n",
+ "Model training finished.\n",
+ "Final RMSE (on training data): 100.14\n",
+ "Final RMSE (on validation data): 101.26\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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IqP1ggNETlh0M8frcQbAxV2L/+WRsPpKAmuaEGJO6IebizUgtVktERKRbDDB6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\nMnrivHRrceAwZ0/ZZNuQEqKx+zR7W6qp7agnqAQxaPVUOSp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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "flxmFt0KKxk9"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Linear Scaling\n",
+ "It can be a good standard practice to normalize the inputs to fall within the range -1, 1. This helps SGD not get stuck taking steps that are too large in one dimension, or too small in another. Fans of numerical optimization may note that there's a connection to the idea of using a preconditioner here."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "Dws5rIQjKxk-",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def linear_scale(series):\n",
+ " min_val = series.min()\n",
+ " max_val = series.max()\n",
+ " scale = (max_val - min_val) / 2.0\n",
+ " return series.apply(lambda x:((x - min_val) / scale) - 1.0)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "MVmuHI76N2Sz"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 1: Normalize the Features Using Linear Scaling\n",
+ "\n",
+ "**Normalize the inputs to the scale -1, 1.**\n",
+ "\n",
+ "**Spend about 5 minutes training and evaluating on the newly normalized data. How well can you do?**\n",
+ "\n",
+ "As a rule of thumb, NN's train best when the input features are roughly on the same scale.\n",
+ "\n",
+ "Sanity check your normalized data. (What would happen if you forgot to normalize one feature?)\n"
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "yD948ZgAM6Cx",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 653
+ },
+ "outputId": "b8de1608-9c4f-4982-a82a-53679ad3bbd0"
+ },
+ "cell_type": "code",
+ "source": [
+ "def normalize_linear_scale(examples_dataframe):\n",
+ " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n",
+ " #\n",
+ " # Your code here: normalize the inputs.\n",
+ " #\n",
+ " features = pd.DataFrame()\n",
+ " features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+ " features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+ " features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n",
+ " features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n",
+ " features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n",
+ " features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n",
+ " features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n",
+ " features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n",
+ " features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n",
+ " return features\n",
+ " \n",
+ " pass\n",
+ "\n",
+ "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n",
+ "normalized_training_examples = normalized_dataframe.head(12000)\n",
+ "normalized_validation_examples = normalized_dataframe.tail(5000)\n",
+ "\n",
+ "_ = train_nn_regression_model(\n",
+ " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n",
+ " steps=5000,\n",
+ " batch_size=70,\n",
+ " hidden_units=[10, 10],\n",
+ " training_examples=normalized_training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=normalized_validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 225.63\n",
+ " period 01 : 195.41\n",
+ " period 02 : 143.06\n",
+ " period 03 : 116.60\n",
+ " period 04 : 112.93\n",
+ " period 05 : 108.92\n",
+ " period 06 : 104.14\n",
+ " period 07 : 98.36\n",
+ " period 08 : 91.80\n",
+ " period 09 : 85.43\n",
+ "Model training finished.\n",
+ "Final RMSE (on training data): 85.43\n",
+ "Final RMSE (on validation data): 88.42\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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6stv/+tfnblakm04FRkREpBKJj49jxYqlV/Xap5+eQGBgrctuf/PN925WrJuuQt5KQERE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\nCkXhZqSfq8uKiou1kSbWRbpWuzblLjMuvagKQPb3uccfw6k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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "jFfc3saSxg6t"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below for one possible solution."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "Ax_IIQVRx4gr"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Since normalization uses min and max, we have to ensure it's done on the entire dataset at once. \n",
+ "\n",
+ "We can do that here because all our data is in a single DataFrame. If we had multiple data sets, a good practice would be to derive the normalization parameters from the training set and apply those identically to the test set."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "D-bJBXrJx-U_",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def normalize_linear_scale(examples_dataframe):\n",
+ " \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n",
+ " processed_features = pd.DataFrame()\n",
+ " processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+ " processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+ " processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n",
+ " processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n",
+ " processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n",
+ " processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n",
+ " processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n",
+ " processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n",
+ " processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n",
+ " return processed_features\n",
+ "\n",
+ "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n",
+ "normalized_training_examples = normalized_dataframe.head(12000)\n",
+ "normalized_validation_examples = normalized_dataframe.tail(5000)\n",
+ "\n",
+ "_ = train_nn_regression_model(\n",
+ " my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.005),\n",
+ " steps=2000,\n",
+ " batch_size=50,\n",
+ " hidden_units=[10, 10],\n",
+ " training_examples=normalized_training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=normalized_validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "MrwtdStNJ6ZQ"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 2: Try a Different Optimizer\n",
+ "\n",
+ "** Use the Adagrad and Adam optimizers and compare performance.**\n",
+ "\n",
+ "The Adagrad optimizer is one alternative. The key insight of Adagrad is that it modifies the learning rate adaptively for each coefficient in a model, monotonically lowering the effective learning rate. This works great for convex problems, but isn't always ideal for the non-convex problem Neural Net training. You can use Adagrad by specifying `AdagradOptimizer` instead of `GradientDescentOptimizer`. Note that you may need to use a larger learning rate with Adagrad.\n",
+ "\n",
+ "For non-convex optimization problems, Adam is sometimes more efficient than Adagrad. To use Adam, invoke the `tf.train.AdamOptimizer` method. This method takes several optional hyperparameters as arguments, but our solution only specifies one of these (`learning_rate`). In a production setting, you should specify and tune the optional hyperparameters carefully."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "61GSlDvF7-7q",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 653
+ },
+ "outputId": "fa83c9ce-555a-48e4-a219-f29d4e3d8b4a"
+ },
+ "cell_type": "code",
+ "source": [
+ "#\n",
+ "# YOUR CODE HERE: Retrain the network using Adagrad and then Adam.\n",
+ "#\n",
+ "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n",
+ " my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.5),\n",
+ " steps=500,\n",
+ " batch_size=100,\n",
+ " hidden_units=[10, 10],\n",
+ " training_examples=normalized_training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=normalized_validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 11,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 92.06\n",
+ " period 01 : 73.85\n",
+ " period 02 : 71.89\n",
+ " period 03 : 70.59\n",
+ " period 04 : 76.92\n",
+ " period 05 : 70.05\n",
+ " period 06 : 69.92\n",
+ " period 07 : 69.55\n",
+ " period 08 : 69.73\n",
+ " period 09 : 69.68\n",
+ "Model training finished.\n",
+ "Final RMSE (on training data): 69.68\n",
+ "Final RMSE (on validation data): 71.13\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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ERMS7FFR6qGM/FU/jN6eCioiIiDcpqPRQx6AyLMhOZNAw8h2FGIbh48pEREQG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\nImbNyaDyj//4j2zfvp0dO3Zw5syZaBdHjPDDH/6Q7du388ADD7Bnz55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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "RWq0xecNKNeG",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Building a Neural Network\n",
+ "\n",
+ "The NN is defined by the [DNNRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNRegressor) class.\n",
+ "\n",
+ "Use **`hidden_units`** to define the structure of the NN. The `hidden_units` argument provides a list of ints, where each int corresponds to a hidden layer and indicates the number of nodes in it. For example, consider the following assignment:\n",
+ "\n",
+ "`hidden_units=[3,10]`\n",
+ "\n",
+ "The preceding assignment specifies a neural net with two hidden layers:\n",
+ "\n",
+ "* The first hidden layer contains 3 nodes.\n",
+ "* The second hidden layer contains 10 nodes.\n",
+ "\n",
+ "If we wanted to add more layers, we'd add more ints to the list. For example, `hidden_units=[10,20,30,40]` would create four layers with ten, twenty, thirty, and forty units, respectively.\n",
+ "\n",
+ "By default, all hidden layers will use ReLu activation and will be fully connected."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "ni0S6zHcTb04",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def construct_feature_columns(input_features):\n",
+ " \"\"\"Construct the TensorFlow Feature Columns.\n",
+ "\n",
+ " Args:\n",
+ " input_features: The names of the numerical input features to use.\n",
+ " Returns:\n",
+ " A set of feature columns\n",
+ " \"\"\" \n",
+ " return set([tf.feature_column.numeric_column(my_feature)\n",
+ " for my_feature in input_features])"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "zvCqgNdzpaFg",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+ " \"\"\"Trains a neural net regression model.\n",
+ " \n",
+ " Args:\n",
+ " features: pandas DataFrame of features\n",
+ " targets: pandas DataFrame of targets\n",
+ " batch_size: Size of batches to be passed to the model\n",
+ " shuffle: True or False. Whether to shuffle the data.\n",
+ " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+ " Returns:\n",
+ " Tuple of (features, labels) for next data batch\n",
+ " \"\"\"\n",
+ " \n",
+ " # Convert pandas data into a dict of np arrays.\n",
+ " features = {key:np.array(value) for key,value in dict(features).items()} \n",
+ " \n",
+ " # Construct a dataset, and configure batching/repeating.\n",
+ " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+ " ds = ds.batch(batch_size).repeat(num_epochs)\n",
+ " \n",
+ " # Shuffle the data, if specified.\n",
+ " if shuffle:\n",
+ " ds = ds.shuffle(10000)\n",
+ " \n",
+ " # Return the next batch of data.\n",
+ " features, labels = ds.make_one_shot_iterator().get_next()\n",
+ " return features, labels"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "U52Ychv9KNeH",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def train_nn_regression_model(\n",
+ " learning_rate,\n",
+ " steps,\n",
+ " batch_size,\n",
+ " hidden_units,\n",
+ " training_examples,\n",
+ " training_targets,\n",
+ " validation_examples,\n",
+ " validation_targets):\n",
+ " \"\"\"Trains a neural network regression model.\n",
+ " \n",
+ " In addition to training, this function also prints training progress information,\n",
+ " as well as a plot of the training and validation loss over time.\n",
+ " \n",
+ " Args:\n",
+ " learning_rate: A `float`, the learning rate.\n",
+ " steps: A non-zero `int`, the total number of training steps. A training step\n",
+ " consists of a forward and backward pass using a single batch.\n",
+ " batch_size: A non-zero `int`, the batch size.\n",
+ " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+ " training_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for training.\n",
+ " training_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for training.\n",
+ " validation_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for validation.\n",
+ " validation_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for validation.\n",
+ " \n",
+ " Returns:\n",
+ " A `DNNRegressor` object trained on the training data.\n",
+ " \"\"\"\n",
+ "\n",
+ " periods = 10\n",
+ " steps_per_period = steps / periods\n",
+ " \n",
+ " # Create a DNNRegressor object.\n",
+ " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+ " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+ " dnn_regressor = tf.estimator.DNNRegressor(\n",
+ " feature_columns=construct_feature_columns(training_examples),\n",
+ " hidden_units=hidden_units,\n",
+ " optimizer=my_optimizer,\n",
+ " )\n",
+ " \n",
+ " # Create input functions.\n",
+ " training_input_fn = lambda: my_input_fn(training_examples, \n",
+ " training_targets[\"median_house_value\"], \n",
+ " batch_size=batch_size)\n",
+ " predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+ " training_targets[\"median_house_value\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+ " validation_targets[\"median_house_value\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ "\n",
+ " # Train the model, but do so inside a loop so that we can periodically assess\n",
+ " # loss metrics.\n",
+ " print(\"Training model...\")\n",
+ " print(\"RMSE (on training data):\")\n",
+ " training_rmse = []\n",
+ " validation_rmse = []\n",
+ " for period in range (0, periods):\n",
+ " # Train the model, starting from the prior state.\n",
+ " dnn_regressor.train(\n",
+ " input_fn=training_input_fn,\n",
+ " steps=steps_per_period\n",
+ " )\n",
+ " # Take a break and compute predictions.\n",
+ " training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n",
+ " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+ " \n",
+ " validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n",
+ " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+ " \n",
+ " # Compute training and validation loss.\n",
+ " training_root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(training_predictions, training_targets))\n",
+ " validation_root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+ " # Occasionally print the current loss.\n",
+ " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+ " # Add the loss metrics from this period to our list.\n",
+ " training_rmse.append(training_root_mean_squared_error)\n",
+ " validation_rmse.append(validation_root_mean_squared_error)\n",
+ " print(\"Model training finished.\")\n",
+ "\n",
+ " # Output a graph of loss metrics over periods.\n",
+ " plt.ylabel(\"RMSE\")\n",
+ " plt.xlabel(\"Periods\")\n",
+ " plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+ " plt.tight_layout()\n",
+ " plt.plot(training_rmse, label=\"training\")\n",
+ " plt.plot(validation_rmse, label=\"validation\")\n",
+ " plt.legend()\n",
+ "\n",
+ " print(\"Final RMSE (on training data): %0.2f\" % training_root_mean_squared_error)\n",
+ " print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n",
+ "\n",
+ " return dnn_regressor"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "2QhdcCy-Y8QR",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 1: Train a NN Model\n",
+ "\n",
+ "**Adjust hyperparameters, aiming to drop RMSE below 110.**\n",
+ "\n",
+ "Run the following block to train a NN model. \n",
+ "\n",
+ "Recall that in the linear regression exercise with many features, an RMSE of 110 or so was pretty good. We'll aim to beat that.\n",
+ "\n",
+ "Your task here is to modify various learning settings to improve accuracy on validation data.\n",
+ "\n",
+ "Overfitting is a real potential hazard for NNs. You can look at the gap between loss on training data and loss on validation data to help judge if your model is starting to overfit. If the gap starts to grow, that is usually a sure sign of overfitting.\n",
+ "\n",
+ "Because of the number of different possible settings, it's strongly recommended that you take notes on each trial to help guide your development process.\n",
+ "\n",
+ "Also, when you get a good setting, try running it multiple times and see how repeatable your result is. NN weights are typically initialized to small random values, so you should see differences from run to run.\n"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "rXmtSW1yKNeK",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 653
+ },
+ "outputId": "4d6cd99f-5c64-4937-a561-0c114270c094"
+ },
+ "cell_type": "code",
+ "source": [
+ "dnn_regressor = train_nn_regression_model(\n",
+ " learning_rate=0.005,\n",
+ " steps=1000,\n",
+ " batch_size=10,\n",
+ " hidden_units=[10, 10],\n",
+ " training_examples=training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 11,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 230.89\n",
+ " period 01 : 156.59\n",
+ " period 02 : 161.09\n",
+ " period 03 : 140.12\n",
+ " period 04 : 129.73\n",
+ " period 05 : 122.65\n",
+ " period 06 : 119.43\n",
+ " period 07 : 122.14\n",
+ " period 08 : 108.27\n",
+ " period 09 : 106.58\n",
+ "Model training finished.\n",
+ "Final RMSE (on training data): 106.58\n",
+ "Final RMSE (on validation data): 107.34\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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iykRERC4fCjAXqE1kAEH+nmzZnU213U788bvy6qZ2IiIiDUcB5gKZTSZ6xIRR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\nERERGXEUYERERGTEGdYAU19fz+LFi3nssccAaG5uZtWqVax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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "O2q5RRCKqYaU",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below to see a possible solution"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "j2Yd5VfrqcC3",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "**NOTE:** This selection of parameters is somewhat arbitrary. Here we've tried combinations that are increasingly complex, combined with training for longer, until the error falls below our objective (training is nondeterministic, so results may fluctuate a bit each time you run the solution). This may not be the best combination; others may attain an even lower RMSE. If your aim is to find the model that can attain the best error, then you'll want to use a more rigorous process, like a parameter search."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "IjkpSqmxqnSM",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "dnn_regressor = train_nn_regression_model(\n",
+ " learning_rate=0.001,\n",
+ " steps=2000,\n",
+ " batch_size=100,\n",
+ " hidden_units=[10, 10],\n",
+ " training_examples=training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "c6diezCSeH4Y",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 2: Evaluate on Test Data\n",
+ "\n",
+ "**Confirm that your validation performance results hold up on test data.**\n",
+ "\n",
+ "Once you have a model you're happy with, evaluate it on test data to compare that to validation performance.\n",
+ "\n",
+ "Reminder, the test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv)."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "icEJIl5Vp51r",
+ "colab_type": "code",
+ "cellView": "both",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "1d60703b-8e50-46f1-f77c-01eba704b278"
+ },
+ "cell_type": "code",
+ "source": [
+ "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+ "\n",
+ "# YOUR CODE HERE\n",
+ "test_examples = preprocess_features(california_housing_test_data)\n",
+ "test_targets = preprocess_targets(california_housing_test_data)\n",
+ "\n",
+ "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n",
+ " test_targets[\"median_house_value\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ "\n",
+ "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n",
+ "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n",
+ "\n",
+ "root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(test_predictions, test_targets))\n",
+ "\n",
+ "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)"
+ ],
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Final RMSE (on test data): 104.89\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "vvT2jDWjrKew",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below to see a possible solution."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "FyDh7Qy6rQb0",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Similar to what the code at the top does, we just need to load the appropriate data file, preprocess it and call predict and mean_squared_error.\n",
+ "\n",
+ "Note that we don't have to randomize the test data, since we will use all records."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "vhb0CtdvrWZx",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+ "\n",
+ "test_examples = preprocess_features(california_housing_test_data)\n",
+ "test_targets = preprocess_targets(california_housing_test_data)\n",
+ "\n",
+ "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n",
+ " test_targets[\"median_house_value\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ "\n",
+ "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n",
+ "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n",
+ "\n",
+ "root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(test_predictions, test_targets))\n",
+ "\n",
+ "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/intro_to_pandas.ipynb b/intro_to_pandas.ipynb
new file mode 100644
index 0000000..1eaf5b1
--- /dev/null
+++ b/intro_to_pandas.ipynb
@@ -0,0 +1,1415 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "intro_to_pandas.ipynb",
+ "version": "0.3.2",
+ "provenance": [],
+ "collapsed_sections": [
+ "JndnmDMp66FL",
+ "YHIWvc9Ms-Ll",
+ "TJffr5_Jwqvd"
+ ],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python2",
+ "display_name": "Python 2"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "JndnmDMp66FL"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Copyright 2017 Google LLC."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "hMqWDc_m6rUC",
+ "cellView": "both",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+ "# you may not use this file except in compliance with the License.\n",
+ "# You may obtain a copy of the License at\n",
+ "#\n",
+ "# https://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing, software\n",
+ "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+ "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+ "# See the License for the specific language governing permissions and\n",
+ "# limitations under the License."
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "rHLcriKWLRe4"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "# Intro to pandas"
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "QvJBqX8_Bctk"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "**Learning Objectives:**\n",
+ " * Gain an introduction to the `DataFrame` and `Series` data structures of the *pandas* library\n",
+ " * Access and manipulate data within a `DataFrame` and `Series`\n",
+ " * Import CSV data into a *pandas* `DataFrame`\n",
+ " * Reindex a `DataFrame` to shuffle data"
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "TIFJ83ZTBctl"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "[*pandas*](http://pandas.pydata.org/) is a column-oriented data analysis API. It's a great tool for handling and analyzing input data, and many ML frameworks support *pandas* data structures as inputs.\n",
+ "Although a comprehensive introduction to the *pandas* API would span many pages, the core concepts are fairly straightforward, and we'll present them below. For a more complete reference, the [*pandas* docs site](http://pandas.pydata.org/pandas-docs/stable/index.html) contains extensive documentation and many tutorials."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "s_JOISVgmn9v"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Basic Concepts\n",
+ "\n",
+ "The following line imports the *pandas* API and prints the API version:"
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "aSRYu62xUi3g",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "9d1d0a4f-0186-47ba-e197-e011c017097f"
+ },
+ "cell_type": "code",
+ "source": [
+ "from __future__ import print_function\n",
+ "\n",
+ "import pandas as pd\n",
+ "pd.__version__"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "u'0.22.0'"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 2
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "daQreKXIUslr"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "The primary data structures in *pandas* are implemented as two classes:\n",
+ "\n",
+ " * **`DataFrame`**, which you can imagine as a relational data table, with rows and named columns.\n",
+ " * **`Series`**, which is a single column. A `DataFrame` contains one or more `Series` and a name for each `Series`.\n",
+ "\n",
+ "The data frame is a commonly used abstraction for data manipulation. Similar implementations exist in [Spark](https://spark.apache.org/) and [R](https://www.r-project.org/about.html)."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "fjnAk1xcU0yc"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "One way to create a `Series` is to construct a `Series` object. For example:"
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "DFZ42Uq7UFDj",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 84
+ },
+ "outputId": "07a27f40-4925-4db3-a320-c1b87e4f9d9c"
+ },
+ "cell_type": "code",
+ "source": [
+ "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])"
+ ],
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "0 San Francisco\n",
+ "1 San Jose\n",
+ "2 Sacramento\n",
+ "dtype: object"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 3
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "U5ouUp1cU6pC"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "`DataFrame` objects can be created by passing a `dict` mapping `string` column names to their respective `Series`. If the `Series` don't match in length, missing values are filled with special [NA/NaN](http://pandas.pydata.org/pandas-docs/stable/missing_data.html) values. Example:"
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "avgr6GfiUh8t",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 136
+ },
+ "outputId": "7a24918e-b424-4092-9184-05e15274bcfe"
+ },
+ "cell_type": "code",
+ "source": [
+ "city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\n",
+ "population = pd.Series([852469, 1015785, 485199])\n",
+ "\n",
+ "pd.DataFrame({ 'City name': city_names, 'Population': population })"
+ ],
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
City name
\n",
+ "
Population
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
0
\n",
+ "
San Francisco
\n",
+ "
852469
\n",
+ "
\n",
+ "
\n",
+ "
1
\n",
+ "
San Jose
\n",
+ "
1015785
\n",
+ "
\n",
+ "
\n",
+ "
2
\n",
+ "
Sacramento
\n",
+ "
485199
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " City name Population\n",
+ "0 San Francisco 852469\n",
+ "1 San Jose 1015785\n",
+ "2 Sacramento 485199"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 4
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "oa5wfZT7VHJl"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "But most of the time, you load an entire file into a `DataFrame`. The following example loads a file with California housing data. Run the following cell to load the data and create feature definitions:"
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "av6RYOraVG1V",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 284
+ },
+ "outputId": "977058b1-71db-4ea7-a9f4-50180b9ccfd3"
+ },
+ "cell_type": "code",
+ "source": [
+ "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+ "california_housing_dataframe.describe()"
+ ],
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "
"
+ ],
+ "text/plain": [
+ " City name Population Area square miles Population density\n",
+ "0 San Francisco 852469 46.87 18187.945381\n",
+ "1 San Jose 1015785 176.53 5754.177760\n",
+ "2 Sacramento 485199 97.92 4955.055147"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 11
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "6qh63m-ayb-c"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Exercise #1\n",
+ "\n",
+ "Modify the `cities` table by adding a new boolean column that is True if and only if *both* of the following are True:\n",
+ "\n",
+ " * The city is named after a saint.\n",
+ " * The city has an area greater than 50 square miles.\n",
+ "\n",
+ "**Note:** Boolean `Series` are combined using the bitwise, rather than the traditional boolean, operators. For example, when performing *logical and*, use `&` instead of `and`.\n",
+ "\n",
+ "**Hint:** \"San\" in Spanish means \"saint.\""
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "zCOn8ftSyddH",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 136
+ },
+ "outputId": "c33194c8-e8c8-47f0-d4c9-374ffeded0db"
+ },
+ "cell_type": "code",
+ "source": [
+ "# Your code here\n",
+ "cities['named saint and area greater than 50'] = cities['Area square miles'] > 50 & cities['City name'].apply(lambda name: name.startswith('San'))\n",
+ "cities"
+ ],
+ "execution_count": 12,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
City name
\n",
+ "
Population
\n",
+ "
Area square miles
\n",
+ "
Population density
\n",
+ "
named saint and area greater than 50
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
0
\n",
+ "
San Francisco
\n",
+ "
852469
\n",
+ "
46.87
\n",
+ "
18187.945381
\n",
+ "
True
\n",
+ "
\n",
+ "
\n",
+ "
1
\n",
+ "
San Jose
\n",
+ "
1015785
\n",
+ "
176.53
\n",
+ "
5754.177760
\n",
+ "
True
\n",
+ "
\n",
+ "
\n",
+ "
2
\n",
+ "
Sacramento
\n",
+ "
485199
\n",
+ "
97.92
\n",
+ "
4955.055147
\n",
+ "
True
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " City name Population Area square miles Population density \\\n",
+ "0 San Francisco 852469 46.87 18187.945381 \n",
+ "1 San Jose 1015785 176.53 5754.177760 \n",
+ "2 Sacramento 485199 97.92 4955.055147 \n",
+ "\n",
+ " named saint and area greater than 50 \n",
+ "0 True \n",
+ "1 True \n",
+ "2 True "
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 12
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "YHIWvc9Ms-Ll"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below for a solution."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "T5OlrqtdtCIb",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "cities['Is wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n",
+ "cities"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "f-xAOJeMiXFB"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Indexes\n",
+ "Both `Series` and `DataFrame` objects also define an `index` property that assigns an identifier value to each `Series` item or `DataFrame` row. \n",
+ "\n",
+ "By default, at construction, *pandas* assigns index values that reflect the ordering of the source data. Once created, the index values are stable; that is, they do not change when data is reordered."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "2684gsWNinq9",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "78a15322-4e06-4682-ff93-0597cda459ed"
+ },
+ "cell_type": "code",
+ "source": [
+ "city_names.index"
+ ],
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "RangeIndex(start=0, stop=3, step=1)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 13
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "F_qPe2TBjfWd",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "31549988-cc31-4b61-965d-c06d1a824858"
+ },
+ "cell_type": "code",
+ "source": [
+ "cities.index"
+ ],
+ "execution_count": 14,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "RangeIndex(start=0, stop=3, step=1)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 14
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "hp2oWY9Slo_h"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Call `DataFrame.reindex` to manually reorder the rows. For example, the following has the same effect as sorting by city name:"
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "sN0zUzSAj-U1",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 136
+ },
+ "outputId": "174703f4-5aaf-41ea-93ab-c54bc115e574"
+ },
+ "cell_type": "code",
+ "source": [
+ "cities.reindex([2, 0, 1])"
+ ],
+ "execution_count": 15,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
City name
\n",
+ "
Population
\n",
+ "
Area square miles
\n",
+ "
Population density
\n",
+ "
named saint and area greater than 50
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
2
\n",
+ "
Sacramento
\n",
+ "
485199
\n",
+ "
97.92
\n",
+ "
4955.055147
\n",
+ "
True
\n",
+ "
\n",
+ "
\n",
+ "
0
\n",
+ "
San Francisco
\n",
+ "
852469
\n",
+ "
46.87
\n",
+ "
18187.945381
\n",
+ "
True
\n",
+ "
\n",
+ "
\n",
+ "
1
\n",
+ "
San Jose
\n",
+ "
1015785
\n",
+ "
176.53
\n",
+ "
5754.177760
\n",
+ "
True
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " City name Population Area square miles Population density \\\n",
+ "2 Sacramento 485199 97.92 4955.055147 \n",
+ "0 San Francisco 852469 46.87 18187.945381 \n",
+ "1 San Jose 1015785 176.53 5754.177760 \n",
+ "\n",
+ " named saint and area greater than 50 \n",
+ "2 True \n",
+ "0 True \n",
+ "1 True "
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 15
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "-GQFz8NZuS06"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Reindexing is a great way to shuffle (randomize) a `DataFrame`. In the example below, we take the index, which is array-like, and pass it to NumPy's `random.permutation` function, which shuffles its values in place. Calling `reindex` with this shuffled array causes the `DataFrame` rows to be shuffled in the same way.\n",
+ "Try running the following cell multiple times!"
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "mF8GC0k8uYhz",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "cities.reindex(np.random.permutation(cities.index))"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "fSso35fQmGKb"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "For more information, see the [Index documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html#index-objects)."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "8UngIdVhz8C0"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Exercise #2\n",
+ "\n",
+ "The `reindex` method allows index values that are not in the original `DataFrame`'s index values. Try it and see what happens if you use such values! Why do you think this is allowed?"
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "PN55GrDX0jzO",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 195
+ },
+ "outputId": "671f9e67-3c53-4fed-8800-82e7ed80e2fc"
+ },
+ "cell_type": "code",
+ "source": [
+ "# Your code here\n",
+ "cities.reindex([4,2,0,3,1])"
+ ],
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
City name
\n",
+ "
Population
\n",
+ "
Area square miles
\n",
+ "
Population density
\n",
+ "
named saint and area greater than 50
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
4
\n",
+ "
NaN
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+ "
NaN
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+ "
NaN
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+ "
NaN
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+ "
NaN
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+ "
\n",
+ "
\n",
+ "
2
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+ "
Sacramento
\n",
+ "
485199.0
\n",
+ "
97.92
\n",
+ "
4955.055147
\n",
+ "
True
\n",
+ "
\n",
+ "
\n",
+ "
0
\n",
+ "
San Francisco
\n",
+ "
852469.0
\n",
+ "
46.87
\n",
+ "
18187.945381
\n",
+ "
True
\n",
+ "
\n",
+ "
\n",
+ "
3
\n",
+ "
NaN
\n",
+ "
NaN
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+ "
NaN
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+ "
NaN
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+ "
NaN
\n",
+ "
\n",
+ "
\n",
+ "
1
\n",
+ "
San Jose
\n",
+ "
1015785.0
\n",
+ "
176.53
\n",
+ "
5754.177760
\n",
+ "
True
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " City name Population Area square miles Population density \\\n",
+ "4 NaN NaN NaN NaN \n",
+ "2 Sacramento 485199.0 97.92 4955.055147 \n",
+ "0 San Francisco 852469.0 46.87 18187.945381 \n",
+ "3 NaN NaN NaN NaN \n",
+ "1 San Jose 1015785.0 176.53 5754.177760 \n",
+ "\n",
+ " named saint and area greater than 50 \n",
+ "4 NaN \n",
+ "2 True \n",
+ "0 True \n",
+ "3 NaN \n",
+ "1 True "
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 16
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "TJffr5_Jwqvd"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below for the solution."
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "8oSvi2QWwuDH"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "If your `reindex` input array includes values not in the original `DataFrame` index values, `reindex` will add new rows for these \"missing\" indices and populate all corresponding columns with `NaN` values:"
+ ]
+ },
+ {
+ "metadata": {
+ "colab_type": "code",
+ "id": "yBdkucKCwy4x",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "cities.reindex([0, 4, 5, 2])"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "colab_type": "text",
+ "id": "2l82PhPbwz7g"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "This behavior is desirable because indexes are often strings pulled from the actual data (see the [*pandas* reindex\n",
+ "documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.reindex.html) for an example\n",
+ "in which the index values are browser names).\n",
+ "\n",
+ "In this case, allowing \"missing\" indices makes it easy to reindex using an external list, as you don't have to worry about\n",
+ "sanitizing the input."
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/logistic_regression.ipynb b/logistic_regression.ipynb
new file mode 100644
index 0000000..e4bf4a1
--- /dev/null
+++ b/logistic_regression.ipynb
@@ -0,0 +1,1620 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "logistic_regression.ipynb",
+ "version": "0.3.2",
+ "provenance": [],
+ "collapsed_sections": [
+ "JndnmDMp66FL",
+ "dPpJUV862FYI",
+ "i2e3TlyL57Qs",
+ "wCugvl0JdWYL"
+ ],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python2",
+ "display_name": "Python 2"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "metadata": {
+ "id": "JndnmDMp66FL",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Copyright 2017 Google LLC."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "hMqWDc_m6rUC",
+ "colab_type": "code",
+ "cellView": "both",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+ "# you may not use this file except in compliance with the License.\n",
+ "# You may obtain a copy of the License at\n",
+ "#\n",
+ "# https://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing, software\n",
+ "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+ "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+ "# See the License for the specific language governing permissions and\n",
+ "# limitations under the License."
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "g4T-_IsVbweU",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "# Logistic Regression"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "LEAHZv4rIYHX",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "**Learning Objectives:**\n",
+ " * Reframe the median house value predictor (from the preceding exercises) as a binary classification model\n",
+ " * Compare the effectiveness of logisitic regression vs linear regression for a binary classification problem"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "CnkCZqdIIYHY",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "As in the prior exercises, we're working with the [California housing data set](https://developers.google.com/machine-learning/crash-course/california-housing-data-description), but this time we will turn it into a binary classification problem by predicting whether a city block is a high-cost city block. We'll also revert to the default features, for now."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "9pltCyy2K3dd",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Frame the Problem as Binary Classification\n",
+ "\n",
+ "The target of our dataset is `median_house_value` which is a numeric (continuous-valued) feature. We can create a boolean label by applying a threshold to this continuous value.\n",
+ "\n",
+ "Given features describing a city block, we wish to predict if it is a high-cost city block. To prepare the targets for train and eval data, we define a classification threshold of the 75%-ile for median house value (a value of approximately 265000). All house values above the threshold are labeled `1`, and all others are labeled `0`."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "67IJwZX1Vvjt",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Setup\n",
+ "\n",
+ "Run the cells below to load the data and prepare the input features and targets."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "fOlbcJ4EIYHd",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "from __future__ import print_function\n",
+ "\n",
+ "import math\n",
+ "\n",
+ "from IPython import display\n",
+ "from matplotlib import cm\n",
+ "from matplotlib import gridspec\n",
+ "from matplotlib import pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from sklearn import metrics\n",
+ "import tensorflow as tf\n",
+ "from tensorflow.python.data import Dataset\n",
+ "\n",
+ "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+ "pd.options.display.max_rows = 10\n",
+ "pd.options.display.float_format = '{:.1f}'.format\n",
+ "\n",
+ "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+ "\n",
+ "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+ " np.random.permutation(california_housing_dataframe.index))"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "lTB73MNeIYHf",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Note how the code below is slightly different from the previous exercises. Instead of using `median_house_value` as target, we create a new binary target, `median_house_value_is_high`."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "kPSqspaqIYHg",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def preprocess_features(california_housing_dataframe):\n",
+ " \"\"\"Prepares input features from California housing data set.\n",
+ "\n",
+ " Args:\n",
+ " california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+ " from the California housing data set.\n",
+ " Returns:\n",
+ " A DataFrame that contains the features to be used for the model, including\n",
+ " synthetic features.\n",
+ " \"\"\"\n",
+ " selected_features = california_housing_dataframe[\n",
+ " [\"latitude\",\n",
+ " \"longitude\",\n",
+ " \"housing_median_age\",\n",
+ " \"total_rooms\",\n",
+ " \"total_bedrooms\",\n",
+ " \"population\",\n",
+ " \"households\",\n",
+ " \"median_income\"]]\n",
+ " processed_features = selected_features.copy()\n",
+ " # Create a synthetic feature.\n",
+ " processed_features[\"rooms_per_person\"] = (\n",
+ " california_housing_dataframe[\"total_rooms\"] /\n",
+ " california_housing_dataframe[\"population\"])\n",
+ " return processed_features\n",
+ "\n",
+ "def preprocess_targets(california_housing_dataframe):\n",
+ " \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+ "\n",
+ " Args:\n",
+ " california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+ " from the California housing data set.\n",
+ " Returns:\n",
+ " A DataFrame that contains the target feature.\n",
+ " \"\"\"\n",
+ " output_targets = pd.DataFrame()\n",
+ " # Create a boolean categorical feature representing whether the\n",
+ " # median_house_value is above a set threshold.\n",
+ " output_targets[\"median_house_value_is_high\"] = (\n",
+ " california_housing_dataframe[\"median_house_value\"] > 265000).astype(float)\n",
+ " return output_targets"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "FwOYWmXqWA6D",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1153
+ },
+ "outputId": "7b5b7606-1822-4b7a-9c30-c30aa4702302"
+ },
+ "cell_type": "code",
+ "source": [
+ "# Choose the first 12000 (out of 17000) examples for training.\n",
+ "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+ "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+ "\n",
+ "# Choose the last 5000 (out of 17000) examples for validation.\n",
+ "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+ "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+ "\n",
+ "# Double-check that we've done the right thing.\n",
+ "print(\"Training examples summary:\")\n",
+ "display.display(training_examples.describe())\n",
+ "print(\"Validation examples summary:\")\n",
+ "display.display(validation_examples.describe())\n",
+ "\n",
+ "print(\"Training targets summary:\")\n",
+ "display.display(training_targets.describe())\n",
+ "print(\"Validation targets summary:\")\n",
+ "display.display(validation_targets.describe())"
+ ],
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training examples summary:\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " latitude longitude housing_median_age total_rooms total_bedrooms \\\n",
+ "count 12000.0 12000.0 12000.0 12000.0 12000.0 \n",
+ "mean 35.6 -119.6 28.5 2644.8 538.8 \n",
+ "std 2.1 2.0 12.6 2167.8 419.9 \n",
+ "min 32.5 -124.3 1.0 11.0 3.0 \n",
+ "25% 33.9 -121.8 18.0 1462.0 297.0 \n",
+ "50% 34.2 -118.5 29.0 2137.0 434.0 \n",
+ "75% 37.7 -118.0 37.0 3159.0 649.0 \n",
+ "max 42.0 -114.3 52.0 37937.0 6445.0 \n",
+ "\n",
+ " population households median_income rooms_per_person \n",
+ "count 12000.0 12000.0 12000.0 12000.0 \n",
+ "mean 1430.5 500.7 3.9 2.0 \n",
+ "std 1159.4 382.8 1.9 1.1 \n",
+ "min 8.0 2.0 0.5 0.0 \n",
+ "25% 790.0 282.0 2.6 1.5 \n",
+ "50% 1168.0 408.0 3.6 1.9 \n",
+ "75% 1720.0 605.2 4.8 2.3 \n",
+ "max 35682.0 6082.0 15.0 52.0 "
+ ],
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "uon1LB3A31VN",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## How Would Linear Regression Fare?\n",
+ "To see why logistic regression is effective, let us first train a naive model that uses linear regression. This model will use labels with values in the set `{0, 1}` and will try to predict a continuous value that is as close as possible to `0` or `1`. Furthermore, we wish to interpret the output as a probability, so it would be ideal if the output will be within the range `(0, 1)`. We would then apply a threshold of `0.5` to determine the label.\n",
+ "\n",
+ "Run the cells below to train the linear regression model using [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor)."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "smmUYRDtWOV_",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def construct_feature_columns(input_features):\n",
+ " \"\"\"Construct the TensorFlow Feature Columns.\n",
+ "\n",
+ " Args:\n",
+ " input_features: The names of the numerical input features to use.\n",
+ " Returns:\n",
+ " A set of feature columns\n",
+ " \"\"\"\n",
+ " return set([tf.feature_column.numeric_column(my_feature)\n",
+ " for my_feature in input_features])"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "B5OwSrr1yIKD",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+ " \"\"\"Trains a linear regression model.\n",
+ " \n",
+ " Args:\n",
+ " features: pandas DataFrame of features\n",
+ " targets: pandas DataFrame of targets\n",
+ " batch_size: Size of batches to be passed to the model\n",
+ " shuffle: True or False. Whether to shuffle the data.\n",
+ " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+ " Returns:\n",
+ " Tuple of (features, labels) for next data batch\n",
+ " \"\"\"\n",
+ " \n",
+ " # Convert pandas data into a dict of np arrays.\n",
+ " features = {key:np.array(value) for key,value in dict(features).items()} \n",
+ " \n",
+ " # Construct a dataset, and configure batching/repeating.\n",
+ " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+ " ds = ds.batch(batch_size).repeat(num_epochs)\n",
+ " \n",
+ " # Shuffle the data, if specified.\n",
+ " if shuffle:\n",
+ " ds = ds.shuffle(10000)\n",
+ " \n",
+ " # Return the next batch of data.\n",
+ " features, labels = ds.make_one_shot_iterator().get_next()\n",
+ " return features, labels"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "SE2-hq8PIYHz",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def train_linear_regressor_model(\n",
+ " learning_rate,\n",
+ " steps,\n",
+ " batch_size,\n",
+ " training_examples,\n",
+ " training_targets,\n",
+ " validation_examples,\n",
+ " validation_targets):\n",
+ " \"\"\"Trains a linear regression model.\n",
+ " \n",
+ " In addition to training, this function also prints training progress information,\n",
+ " as well as a plot of the training and validation loss over time.\n",
+ " \n",
+ " Args:\n",
+ " learning_rate: A `float`, the learning rate.\n",
+ " steps: A non-zero `int`, the total number of training steps. A training step\n",
+ " consists of a forward and backward pass using a single batch.\n",
+ " batch_size: A non-zero `int`, the batch size.\n",
+ " training_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for training.\n",
+ " training_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for training.\n",
+ " validation_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for validation.\n",
+ " validation_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for validation.\n",
+ " \n",
+ " Returns:\n",
+ " A `LinearRegressor` object trained on the training data.\n",
+ " \"\"\"\n",
+ "\n",
+ " periods = 10\n",
+ " steps_per_period = steps / periods\n",
+ "\n",
+ " # Create a linear regressor object.\n",
+ " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+ " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+ " linear_regressor = tf.estimator.LinearRegressor(\n",
+ " feature_columns=construct_feature_columns(training_examples),\n",
+ " optimizer=my_optimizer\n",
+ " )\n",
+ " \n",
+ " # Create input functions.\n",
+ " training_input_fn = lambda: my_input_fn(training_examples, \n",
+ " training_targets[\"median_house_value_is_high\"], \n",
+ " batch_size=batch_size)\n",
+ " predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+ " training_targets[\"median_house_value_is_high\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+ " validation_targets[\"median_house_value_is_high\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ "\n",
+ " # Train the model, but do so inside a loop so that we can periodically assess\n",
+ " # loss metrics.\n",
+ " print(\"Training model...\")\n",
+ " print(\"RMSE (on training data):\")\n",
+ " training_rmse = []\n",
+ " validation_rmse = []\n",
+ " for period in range (0, periods):\n",
+ " # Train the model, starting from the prior state.\n",
+ " linear_regressor.train(\n",
+ " input_fn=training_input_fn,\n",
+ " steps=steps_per_period\n",
+ " )\n",
+ " \n",
+ " # Take a break and compute predictions.\n",
+ " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+ " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+ " \n",
+ " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+ " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+ " \n",
+ " # Compute training and validation loss.\n",
+ " training_root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(training_predictions, training_targets))\n",
+ " validation_root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+ " # Occasionally print the current loss.\n",
+ " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+ " # Add the loss metrics from this period to our list.\n",
+ " training_rmse.append(training_root_mean_squared_error)\n",
+ " validation_rmse.append(validation_root_mean_squared_error)\n",
+ " print(\"Model training finished.\")\n",
+ " \n",
+ " # Output a graph of loss metrics over periods.\n",
+ " plt.ylabel(\"RMSE\")\n",
+ " plt.xlabel(\"Periods\")\n",
+ " plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+ " plt.tight_layout()\n",
+ " plt.plot(training_rmse, label=\"training\")\n",
+ " plt.plot(validation_rmse, label=\"validation\")\n",
+ " plt.legend()\n",
+ "\n",
+ " return linear_regressor"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "TDBD8xeeIYH2",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 737
+ },
+ "outputId": "ac7c3eff-25e9-4906-eef9-224f81d9fd56"
+ },
+ "cell_type": "code",
+ "source": [
+ "linear_regressor = train_linear_regressor_model(\n",
+ " learning_rate=0.000001,\n",
+ " steps=200,\n",
+ " batch_size=20,\n",
+ " training_examples=training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+ "For more information, please see:\n",
+ " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+ " * https://github.com/tensorflow/addons\n",
+ "If you depend on functionality not listed there, please file an issue.\n",
+ "\n",
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 0.45\n",
+ " period 01 : 0.45\n",
+ " period 02 : 0.46\n",
+ " period 03 : 0.45\n",
+ " period 04 : 0.45\n",
+ " period 05 : 0.45\n",
+ " period 06 : 0.45\n",
+ " period 07 : 0.44\n",
+ " period 08 : 0.44\n",
+ " period 09 : 0.45\n",
+ "Model training finished.\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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UH1OHc4Nh/d2wt7HgcEoeTTq9qcMRQghxhyTBEZ3iy0OXqG/UMSfSHysLjanD\nuYFWo2ZsiCeVNY0kpRWbOhwhhBB3SBIcYXRFZbXsTbyCm5M144f6mDqcm5JmKiGE6D4kwRFG93l8\nBjq9wrzoQLQa833L+Xk60NfLgaS0Ysqr6k0djhBCiDtgvr82olu4UlTNoZQ8ervbMSbY09Th3FJ0\nmDd6ReFQiizAKYQQXZkkOMKoNu1PR1HgnvGBqNUqU4dzS2OCPdFq1MQn5aLIApxCCNFlSYIjjCY9\np4IT5wsJ6u3IsH5upg6nTeysLRgxwI3c4hrScipMHY4QQojbJAmOMJrP9qUBMH9CECqV+dfeXBUd\n1twRWjobCyFE1yUJjjCKM5dKOHu5lCEBLgz0czZ1OO0yuK8zLo5WHD2bT32DztThCCGEuA2S4IgO\npygKn+1LB+DeCUEmjqb91GoVkUO8qWvQcfycLMAphBBdkSQ4osOdOF9ERm4FowZ50NfLwdTh3JbI\nsOY5cQ4mSzOVEEJ0RZLgiA6l1ytsOpCOWqViXnSAqcO5bR69bBjk14vUzDIKSmtMHY4QQoh2kgRH\ndKjDKXnkFFUTGeqFt6udqcO5I1Hf1eLEJ8ucOEII0dVIgiM6TGOTns0HMtBq1MyN6rq1N1eNHOiB\ntaWGQ6dz0etlThwhhOhKJMERHWbfySsUV9QxaURvXBytTR3OHbOy0DAm2JOSinrOXC4xdThCCCHa\nQRIc0SHqGpr48tAlrCw1zIzoa+pwOowswCmEEF2TJDiiQ3xzPJuKmkamh/fB0dbS1OF0mEAfR7xd\nbTlxvpCq2kZThyOEEKKNJMERd6yqtpGvEzKxt7Fg+mg/U4fToVQqFdFhPjTpFBLO5Js6HCGEEG0k\nCY64Y9uOXKa2volZEX2xsdKaOpwOFxHiiVqlkmYqIYToQiTBEXektLKend9m4+xgxaQRvU0djlE4\n2VsRFuTK5fxKMvMrTR2OEEKINpAER9yRLYcu0dikZ25UABZajanDMZpow5w4UosjhBBdgSQ44rYV\nlNZw4FQOni62RIZ6mTocowoNcsXR1oIjKfk0NulNHY4QQohbMGqHiZUrV3Lq1ClUKhXPP/88YWFh\nN+yzatUqTp48ybp160hISGDZsmX0798fgAEDBrBixQqee+45UlJS6NWrFwCPPvooEydONGboog02\nH8hAp1eYFx2ARt29c2WtRk3EEC+2H83i1MUiRg3yMHVIQgghWmG0BOfo0aNcvnyZ9evXk5aWxvPP\nP8/69euv2+fixYscO3YMCwsLw2OjR49m9erVN5zvmWeeISYmxljhinbKKqgi4Uw+fp72PebHPirU\nm+1Hs4hPzu0x1yyEEF2V0f7HJi6/AAAgAElEQVTsPnz4MFOmTAEgKCiI8vJyqqqqrtvn1Vdf5emn\nnzZWCMKI4valoQD3TghCrVKZOpxO0dvdngBvR5LTiymtrDd1OEIIIVphtBqcoqIiQkJCDNsuLi4U\nFhZib28PQFxcHKNHj6Z37+tH3ly8eJHHH3+c8vJyli5dSmRkJAD//e9/ef/993F1dWXFihW4uLjc\ntGxnZ1u0Ru7w6u7uYNTzm7MzGcWcSismJNCVmNF9UZlRgmPs+zIjMoC1G09xKqOE+yYPMGpZ3U1P\n/syYM7kv5kvuzZ3ptElLFOX7xQrLysqIi4vj/fffJz//+8nT/P39Wbp0KTNmzCArK4uHHnqIHTt2\nMHfuXHr16sXgwYN59913WbNmDb///e9vWlZpaY1Rr8Xd3YHCwp45XFhRFN7bnAzA3HH+FBVV3eKI\nztMZ9yXY1wkLrZrthy8xIdTLrJI7c9aTPzPmTO6L+ZJ703Y3SwSN1kTl4eFBUVGRYbugoAB3d3cA\njhw5QklJCYsXL2bp0qWkpKSwcuVKPD09mTlzJiqVCj8/P9zc3MjPzyciIoLBgwcDMGnSJM6fP2+s\nsMUtJKeXcD67nKFBrvTzdTJ1OJ3O1lrLqIHu5JfWciG73NThCCGEuAmjJTiRkZFs374dgJSUFDw8\nPAzNU7GxsWzdupUNGzawZs0aQkJCeP755/niiy947733ACgsLKS4uBhPT0+efPJJsrKyAEhISDCM\nshKdS68oxO1LQwXcMyHI1OGYjCzAKYQQ5s9oTVQjRowgJCSEhQsXolKpePHFF4mLi8PBwYGpU6e2\neMykSZN49tln2bVrF42NjfzhD3/A0tKSxYsX88tf/hIbGxtsbW3585//bKywRSuOpxaQWVDF2GBP\n+njYmzockxnY1xk3J2uOpRawaGp/rC273/IUQgjR1amUazvHdBPGbrfsiW2jTTo9K/6dQFF5Ha88\nNgYPZ1tTh3SDzrwvX8RnsDk+g0dmDiI6zKdTyuzKeuJnpiuQ+2K+5N60Xaf3wRHdy8HkXPJLaxk/\n1Mcsk5vONi7UCxXSTCWEEOZKEhxxSw2NOr44eAlLrZo5kf6mDscsuDnZMNjfmQvZ5eSVGHfUnhBC\niPaTBEfc0u4TVyitrGfyKF962VuZOhyzEfXdApwHZQFOIYQwO5LgiFbV1jex9chlbKy0zBzb19Th\nmJUR/d2xsdJyMDkXnV4W4BRCCHMiCY5o1fajmVTVNjJjjB921ha3PqAHsbTQMDbYk7KqBlIySkwd\njhBCiGtIgiNuqqKmge3HsnC0s2TqqD6mDscsXW2mks7GQghhXiTBETf11aHL1DfomDPOHytL467t\n1VX5ezng625H4oUiKmsaTB2OEEKI70iC0w65xdX86q2DvPDOQbYfzSS3uJpuOI0QAMXldexJzMbN\nyZoJw2Sel5tRqVREhXqj0yscScm/9QFCCCE6hUzB2g7WllqcHaw4daGIUxeKWL/7Im5O1oQFuRIW\n5MpAP2esLLpHTcfnBzNo0inMjQpAq5E8uDVjh3jx6d40DiTlMmWUryzAKYQQZkASnHZwdrDihYdG\nobGyYO+xyySnFZNyqYTdJ66w+8QVLLRqBvk5ExbkSmiQKx69bEwd8m3JLa7mYHIuvd3siAjxMnU4\nZs/R1pJh/dz49nwhmflV9PVqeVZNIYQQnUcSnNvg4mhNdJgP0WE+NOn0pF0p51RaMclpxSSnN//j\nG/B2tSU0sDnZGeDbCwtt16gJ2bQ/HUWBeeMDUaulNqItIsO8+fZ8IQeScujrNdDU4QghRI8nCc4d\n0mrUDPRzZqCfMwti+lFUXktyegnJacWcuVzCjmNZ7DiWhZWFhmD/72p3Al1xcbQ2degtupRXwfFz\nhQT6ODK8v5upw+kyQgNdcLKz5EhKPvdP6oeFtns0VQohRFclCU4Hc3OyIWZ4b2KG96axScf5rHKS\n0opJSi8m8UIRiReKAPB1tzf03Qnq7YhGbR61O5/tSwfg3vGB0pekHTRqNeNCvdh2JJPEC0WMHuxp\n6pCEEKJHkwTHiCy0GkICXAgJcOEB+pNfWkPyd8lO6uUysgurDLMEDwlwISzIlSGBrjjZWZok3tTL\npaRklBDs78xgfxeTxNCVRYV6s+1IJgeSciXBEUIIE5MEpxN5OtviOcqWKaP6UN+g42xmKcnpxSRd\nLOZYagHHUguA5rlVrnZUDvBy7JR+MIqi8Nm+NADunRBk9PK6I29XO/r1duJMRgnF5XW4OplnM6QQ\nQvQEkuCYiJWlhmH93BjWzw1lqkJucU1zU1ZaEReyy7mUV8kXBy9hb2NBaKALoUGuDAlwxd7GOMsl\nnLxYRFpOBSMHuBPg7WiUMnqCqDBvLl4p59DpXOZEBpg6HCGE6LEkwTEDKpUKHzc7fNzsiB3jR219\nE2culRj67hxOyedwSj4qFQT5OBEa5EpYoCt+nvYd0k9Gr1eI25+OStU8ckrcvvBBHny08zzxybnM\nGuePWvoxCSGESUiCY4ZsrLSMHOjByIEeKIpCVkGVIdlJu1LOxSvlbNqfjpO9JaGBzclOsL8Ltta3\ndzsTzuRzpbCayFAvfNzsOvhqehYbKy3hAz04eDqP85llDOrrbOqQhBCiR5IEx8ypVCr8PB3w83Rg\n9jh/qmobSclort1JTi8mPimX+KRcNGoV/X2/r93xcbNrU+1Ok07PpgPpaDUq5kZJk0pHiArz5uDp\nPOKTcyXBEUIIE5EEp4uxt7FgTLAnY4I90SsKl3IrSUorIjm9mNTMMlIzy/h0TxqujlaEBrkRFujK\n4L7ON10sc/+pHIrK65gy0hc3p64587K5GdCnFx7ONhxPLWDx1AHYWMnHTAghOpt883ZhapWKQB9H\nAn0cuTs6kPLqBk5/N5Py6fQS9iZeYW/iFbQaFQP9nAkLbJ53x9PFFoD6Bh1fHLyElYWG2eP8TXsx\nd2hPVjwXzlzgx4OWoFWb9m2tUqmIDPVm0/50jp7NZ8Kw3iaNRwgheiJJcLoRJztLIkO9iQz1RqfX\nk3alonkYeloxKRklpGSU8PGuC3g42xAW6EpDk56K6gZmj/PH0URz73SEJn0T2y7tpLqxhoteGQxy\n6W/qkIgc4sXm/enEJ+VKgiOEECYgCU43pVGrGdCnFwP69OLeCUGUVtZ/n+xcKmHnt9kA2FlriR3t\nZ+Jo78zZkvNUN9YAkFR0xiwSHBdHa0ICXTidXkJOUbV03hZCiE4mCU4P4exgxfihPowf2rxA6IWs\nMs5cLmWgX6/bHn1lLhLyTgCgUWtILjrDff3vMotlJqJCvTmdXkJ8ci4LYvqZOhwhhOhRzGMBJNGp\ntBo1g/1duHdCEEMCXE0dzh2pbaoluegMnrYejPEdTkldKTnVeaYOC4Dh/d2xs9Zy6HQeTTq9qcMR\nQogeRRIc0aUlFiTTpG9itNcIwnuHAZBUmGLiqJpZaNWMDfGiorqB0+klpg5HCCF6FElwRJd29Lvm\nqXDPYQzzCkGtUpNUdMbEUX0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PhjKmrtAJuea7kVHffDcyytfdngUxQYQEuJh9s4+jrSW/\nfmA4q9af5EBSLk06hUdnDTaL11WIq247wbl06RL+/v4dGIoQcDQ/EbjzpRla069XADZaa5IKz3Bf\n/7lm9WNiZaFh9GBP9p3MYf3ui4ZmqCkjffGVZqh2u7YT8qdm0gm5sUnPnhPZbDHhyKiOYG9jwa8X\nNq9ddTglD51ez09mB6PVSPItzEOr78RHHnnkuu21a9ca/v/73//eOBGJHqumsZbkojN42XrQx763\n0crRqDWEuA6itL6MK1W5RivndsWO8WNIgAv3TQzir7+I5EczBklycweudkJe+dgYxgR7kp5TwUsf\nHGfDnovUN+g6LQ69onAkJY/f/esIn+y+iF6B+yYGsfKxsUSGenep5OYqW2sLfnX/MPr7OnH0bAH/\n/Lx5kU4hzEGrCU5TU9N120eOHDH8X1EU40QkeqyThck06ZsY7TXC6LUqYW7BACQXnTFqObfD09mW\nZ+4fxoyxd7amkLje1U7IzywYioujFV8nZLLivQSS0oqMXvbZSyW8/MFx3t1yhrKqeqaF9+G1xyOY\nMbYvlhZda5TXD9lYaXl6wVAG+fXi2/OFrN10msYmSXKE6bWa4PzwR+bapMacqvVF93B15fBRHTi5\n380Euw5ErVKTVJRi9LKEebnaCXlWRF9KK+v5+6dJrN18mrKq+g4vK7ugir9tOMXrn5zkcn4lY4M9\neeWxsSyc3L9bJa/WllqW3TeUEH9nTl4s4h9xSTQ0dl7tmBAtaVcfHElqhLGU1JVyoSyd/r0CcbVx\nNnp5NlobBvQKIrX0AqV1ZThb9zJ6mcJ83KwT8vwJQUwY3vuOhz2XVDSvGXUo+fuRUffFBOHv1THL\njpgjKwsNT80P461Np0lKK+bNjUk8dW9Yl5uHSHQfrSY45eXlHD582LBdUVHBkSNHUBSFiooKowcn\neo5jec2diztz8r1Qt2BSSy9wuvgs0b0jOq1cYT4MnZBP5vDp3jTW7TjPwdN5PBw76LbmFqqpa2Tr\nkUy+OZ5FY5MeX3c77ovpx5AuMDKqI1hoNfxiXijvfH6axAtF/O3TUyybH4aNlQzYFZ2v1Xedo6Pj\ndR2LHRwceOuttwz/F6IjKIrC0bwTaNVahruHdVq5oW7BfHrhc5KKzkiC04OpVSomDu/N8P5ufLzr\nAkfPFvDH948xfXQf7mrjTMiNTXr2JF5hy8EMquuacHZoHhk1bkjXGhnVESy0an5+9xDe3XKG46kF\nvLHhJE/fNwxba0lyROdq9R23bt26zopD9GBZVVfIqylguHsothadN2urq40zve29OV9ykbqm+g5f\nFkJ0LU72Vjw+dwiRocWs236ObQmZHEst4MFpAwkLannVbr2icPRsPnH7rq4ZpWH+xOY1o7p65+E7\nodWo+dldwWg1Ko6k5LNqfSLP3D8MO+vu0+9ImL9WOxlXVVXxwQcfGLY/+eQT5s6dy1NPPUVRkfFH\nHoie4WrzlDHnvrmZMLdgmhQdZ0vOd3rZwjyFftcJeebYq52QT/F2C52Qz14u5eX/O867X5yhtLKe\nqaP68OrPIpjZDUZGdQSNWs1PZgUTGepFRm4lr3+cSGVNg6nDEj1IqwnO73//e4qLiwHIyMjgjTfe\nYPny5YwbN45XXnmlUwIU3ZtOr+NYfiJ2WluCXQd2evmhZjxcXJiOlUVzTcyLPwonqLcjx1IL+N2/\njrDnRDZZBVX8/dNTvP5xIpfzKhkT7MkrPx3LA1P642BraerQzYpareKRmYOZMMyHzPwqXv84kYpq\nSXJE52i1iSorK4s33ngDgO3btxMbG8u4ceMYN24cX331VacEKLq3c6UXqWyoIrp3hEkWvvRz8MXJ\n0pHTxWfR6XVGWx5CdE2+Hvb89sGR13VCvmqQXy/ui+lHgHf3HRnVEdQqFQ9NH4hWrWbXiWxe++gE\nv35gOL3spUlYGFerNTi2tt+vZnz06FHGjh1r2O4JIwKE8V2d+8YUzVPQ/D4OdQ+murGGjIpMk8Qg\nzNvVTsgrHxtDRIgngT6O/PK+ofz6geGS3LSRSqVi0dT+TB/dh9ziGl773wlKKupMHZbo5lpNcHQ6\nHcXFxWRmZpKYmEhkZCQA1dXV1NbWdkqAovuqa6rnVOFp3GxcCXD0M1kcV2c1TiqUSf/EzTnZW/HY\nnBBeeGgUYUGu8kdeO6lUKhbE9GNWRF/yS2t57aMTFJXL74gwnlYTnMcee4yZM2cyZ84cnnjiCZyc\nnKirq2PRokXcfffdnRWj6KZOFZ6mQd/IaM/hJv2xGNArCEuNJUlFKbIEiRBGpFKpuGd8IHOjAigs\nq+O1/52goLTG1GGJbqrVTg8TJkwgPj6e+vp67O2bJ72ytrbm17/+NVFRUZ0SoOi+juV3/uR+LbHQ\nWBDsMpCThcnk1xTiZedh0niE6M5UKhVzowLQalR8ti+d1z5K5NmFw/B2tTN1aKKbabUGJycnh8LC\nQioqKsjJyTH8CwwMJCcnp7NiFN1QeX0FqSUXCHD0w8PW3dThmPXim0J0R7Mi/Ll/Uj9KK+v5y0eJ\nXCmqNnVIoptptQZn0nOQMXoAACAASURBVKRJBAQE4O7e/AP0w8U2P/zwQ+NGJ7qt4/knUVAIN1Hn\n4h8KcR2EChVJRSlM7TvR1OEI0SNMH+2HVqPmf9+c5y8fneDZhcNva4kMIVrSaoLz2muv8fnnn1Nd\nXc2sWbOYPXs2Li4unRWb6MaO5Z1ArVIz0mOoqUMBwN7SjkAnf9LLL1HZUIWDpXzJCtEZJo/0RaNR\n8eHX5wxJTl8vWQpI3LlWm6jmzp3Lf/7zH/7+979TVVXF4sWL+clPfsKWLVuoq5MhfuL25FTlkVWV\nQ4jrQOwtzafdPcw9GAWF00VnTR2KED3KxGG9eWTmIGrqmnj940TSc2QxZ3HnWk1wrvL29uaJJ55g\n27ZtTJ8+nT/96U/SyVjctqudi0d7jTRxJNeTfjhCmE50mA8/mRNMbUMTf/0kkQvZZaYOSXRxbZo6\ntqKigi+++IK4uDh0Oh0/+9nPmD17trFjE92QXtFzLC8Ra401Q1wHmzqc63jYuuNp68HZkvM06Bqx\n1MjCgEJ0pogQL7QaNf/8PIU31p/il/eFMdDP2dRhiTtQXlVPXkkNA/r06vTpQFpNcOLj4/nss884\nffo006ZN49VXX2XAgAGdFZvohi6WZVBaX8Y473CzTCDC3IL5JnMv50ovGNapEkJ0nvBBHmjUKt7e\nfJq/bTjFk/PDCPGXvp9dSW5xNYkXikg8X0h6zv+3d+fxUVZ3//9fM5OZ7PtMErISQiA7EBYhCIKA\nxQ0UUWIQ+63LvdjSSutC4y3YX++i2Nu7reBtS6vUgpYIRtSK4gaCmpAAgYQkQBICWcg22fdlZn5/\nABFkC5DJNUk+z8eDB5nJdc18JieTvHPOuc5pwgI89/BEwvzdB7SOKwacxx57jJEjRxIfH09dXR0b\nN2684PMvvvjiFR98zZo1HD58GJVKRXJyMnFxcRcd88orr3Do0CE2bdrUe19HRwd33XUXTzzxBIsW\nLWLlypXk5ubi4eEBwKOPPsqsWbP6+hqFDTm3NYOtXD31Q7FnA06OMU8CjhAKiR9j4GeLYnnt/SP8\naWs2P1sUS1yYt9JlicswWyycKG8iq6CGrAIjlXVnFm9Uq1SMDfZgUoQPoX4Dv63JFQPOucvA6+vr\n8fS8sJuwrKzsig+ckZHBqVOnSElJoaioiOTkZFJSUi44prCwkMzMTLTaC/+Sf/3113F3vzDp/fKX\nv2T27NlXfjXCpnWZusmqzsHT3oPRHqFKl3NJoe7BuGidyTHmY7aYUav6NE1NCNHPxo3W8/PFsax7\nL4f1qdn85z0xTAhXfs0scUZ3j4m8k/VkFdRwqLC2d5d4nVbNxDEGJozRExemx8VRuZ76KwYctVrN\nihUr6OzsxMvLi7/85S+EhISwefNmNmzYwKJFiy57blpaGnPnzgUgLCyMxsZGWlpaeldEBnjppZdY\nsWIF69ev772vqKiIwsJC6aEZgnKMeXSYOpgZOM1mg4NapSZGH0l6xX5ONZUR6q7cHllCDHcxod48\nuTiOP72Xzf+9f4R/XxDNpAhZaVwpLe3dZBcZySowcuREHZ3dJgDcnLTMHDeC8eEGokI80Wk1Cld6\nxhUDzh/+8Af+/ve/ExYWxpdffsmqVaswm824u7uzdevWKz6w0WgkOjq697aXlxc1NTW9ASc1NZUp\nU6YQEBBwwXlr167l+eefZ/v27Rfcv3nzZjZu3Ii3tzfPP/+8rMczCGVWnR2e8lV2a4aridNHkV6x\nnxxjngQcIRQWOdKLXz4wnj9sPcyfP8jlMbOZqVF+Spc1bBgb2s/Mpymo4XhpI+azC/76ejoyYYyB\n+HADo/zdUKttb/PZq/bghIWFATBnzhxefPFFnn32WebNm3fNT3T+KsgNDQ2kpqayceNGqqqqeu/f\nvn0748ePJygo6IJzFy5ciIeHB5GRkWzYsIH169ezatWqyz6Xp6cTdnbWTZAGgyxEdS2aOprJqz1G\nqEcQ40LDrfY8/dEuN3vGszHvn+Q1HOVRw/39UJUAec/YqsHQLgaDK97ezrywIY2/fZSHk5M9cyYP\n/T8+lGgbi8XCifJG9uVWkn6kguLz1iQaG+zJTTF+TI0ZQaCPi6KbJPfFFQPOD4sfMWJEn8ONj48P\nRqOx93Z1dXXvlg/p6enU1dWxdOlSurq6KCkpYc2aNVRXV1NaWsru3buprKxEp9Ph5+dHQkJC7+Pc\neuutvPDCC1d87nor705rMLhSU9Ns1ecYar4u+w6TxcwE/Tirfe36s13GeozmSG0++SUn0TvK5MYb\nJe8Z2zSY2sXbScsvl4znf1MO8actWTQ0tjNznL/SZVnNQLZNj8nM8dIGsgqMHCqoobapEwA7jYrY\nUd5MGKNn/Gg9Hi72vecYjS0DUltfXC4I9mkdnHOuJa1Nnz6ddevWkZiYSG5uLj4+Pr3DU/Pnz2f+\n/PnAmcnKv/71r0lOTr7g/HXr1hEQEEBCQgLLly/nmWeeISgoiH379hEebr0eAGEdGZUHUaFiku94\npUvpkzh9FEdq88k25nFr0AylyxFCAKEj3Hj6wQn8z5ZD/P2To/SYzNwaH6h0WYNSe2cPucV1HCyo\nIbuwlrbOHgAc7e2YGu3LhHADMaFeONpfU0ywKVesPCsr64LJvrW1tcyaNQuLxYJKpWL37t2XPTc+\nPp7o6GgSExNRqVSsXr2a1NRUXF1dr3mIa+nSpTz55JM4Ojri5OR01cvThW2pbqvhZFMJkV5jcLcf\n+EsFr0eMPhKOQU6NBBwhbEmwryvPJJ0JOZs/O06PycJtk4OufqKgsaWTrEIjhwqM5J2so8d0ZuqI\nl5s906L9mDBGz5ggD+w0tnkRyLVSWc6fHPMD5eXlVzz5hxOEbYW1u/UGU7euLfj4xGfsOPkFP45K\nZIoV17/p73Z5ef86SpvLWXvzKpy0Tv32uMORvGds02Bul4raVl7+ZxaNLV0snhXGHVNDlC6pX/VX\n21TUtnLweA2HCowUnTefJtDgQvwYPRPCDQT72v58miu5riEqWw0wYvCwWCxkVGWhU2uJ00df/QQb\nEqeP5lRTKbm1x5jsZ9tXfgkx3Izwdmbl0nh+/88stu0uosdkZsF021xfayCdv+jewQIjVectuhcR\n7MGEcAPjw/UYPBwVrtT6Bu/gmhgUiptKMLbXMtk3Hgc7+6ufYEPi9FF8dOJTcox5EnCEsEG+nk48\nm3Qm5GzfW8yBYzV4uNjj5qTF1VmHm5MOVyct7s46XJ10uDmfuT1UhmDO6e4xkXuynkOXWXRvfLie\ncaOVXXRPCRJwhFWd25phyiAMCCOcffF28CK39hg95h7s1PJ2EcLWGDwceTYpnj9/eISSqhZKq69+\ndY+TvR1uzrqrBiE3Zx1O9nY2OXzTu+jecSNHii9cdG9G3AgmjLGtRfeUID+xhdX0mHs4WHUYN50r\nYz1HK13ONVOpVMTpo9hV9g0FDSeI9JKNZoWwRd7uDjy3bBIWi4XObhNNbd00t3bR1NpFU1vX97fb\nztzX3NZNU1sXVXVtXHYS6lkater7wON0LgBpzwak826f/VhrZ73eoastujchXE+Yv7tNLrqnBAk4\nwmpya4/R2tPGrUEz0KgH518RcYYzASfHmCcBRwgbp1KpcNDZ4aCzw6cPc0zMZgst7d29waeprYvm\n1jO3m9u6aGr9/nNVde2UVF29d8jR3u6CniG3sz1B53qG3Jy0vR87OdihvkLvkMVioaSqpXcTy/N7\np0b5uzEh/Mwk4RHeTjbZy6Q0CTjCajJ7h6dsc+fwvghzD8XRzpHsmjzuD18oP0SEGELUZ3tn3Jx1\n0Id9PDu7TGeCT1v394HobBBqbuuisbWr9/PVDY1c/hrlMzRqFS5ne4Z+GIQ6esykZZ++eNG9s/Np\nPF0H15xGJUjAEVbR1t1OTm0+fs6+BLoM3tVGNWoN0d5j2V91iLKWCoJcB+9rEULcGHudBnudI/q+\n9A5ZLLS2d/eGoebeXqLu8z4+02NU09B+yblDjvZ2TI3yZcKYwb/onhLkqyWsIqsmmx5zDzf5xg/6\nXo84fRT7qw6RY8yVgCOE6BO1SoXr2Xk5AXrnqx7f1W3qnRvU1NqFj8EFg4tuyF3xNZDkKyes4tzV\nU5P8BsfWDFcS5T0WjUpDjjFP6VKEEEOUTqvB292B0BFujButJ260QcLNDZKvnuh3te31FDYUE+4x\nCi8HT6XLuWGOdo6Ee4yipLmc+o4GpcsRQgjRBxJwRL/LrMoCBvfk4h+KNUQBkGPMV7gSIYQQfSEB\nR/Qri8VCRuVB7NR2jDfEKl1Ov4n1PhdwZJhKCCEGAwk4ol+VNpdT1VZNrD4KJ+3Q2evE29GTQBd/\njtcX0tHToXQ5QgghrkICjuhXGVVn177xHXxbM1xNrD6KHouJ/LoCpUsRQghxFRJwRL8xmU3srzqE\ns9aJKO+xSpfT7+L0Z4apso25ClcihBDiaiTgiH5ztL6Q5q4WJvqMG5IbUwa5BuBh706u8Sgms0np\ncoQQQlyBBBzRb4bC1gxXolKpiNVH0drTxonGU0qXI4QQ4gok4Ih+0dHTyeGaIxgcvRnpFqx0OVYT\nq5erqYQQYjCQgCP6xeGaI3SZu5nsN/i3ZriSMZ5h2Gt0ZBtzsVxtJz0hhBCKkYAj+sW5rRkmD8Gr\np86nVdsR6TWWmvZaqtqqlS5HCCHEZUjAETesobORY/WFhLqF4OOkV7ocq/v+aioZphJCCFslAUfc\nsP1Vh7BgYYrf0O69OSdaH4EKlczDEUIIGyYBR9ywzMos1Co18b7jlC5lQLhonQnzGElxYwlNXc1K\nlyOEEOISJOCIG3K6pZKyltNEe0fgonVWupwBE6uPwoKFI8ajSpcihBDiEiTgiBuSMcTXvrmcOLlc\nXAghbJoEHHHdzBYzmVVZOGgciPWOVLqcAeXjZMDPyYf8uuN0mbqVLkcIIcQPSMAR162w4QQNnY3E\n+8Si1WiVLmfAxeqj6DZ3c6xeNt8UQghbIwFHXLd9w3R46pw4w9nLxWtkmEoIIWyNBBxxXbpM3Ryq\nzsHT3oMwj1Cly1HESLdgXLTO5NTmYbaYlS5HCCHEeSTgiOuSY8ylw9TJZL8JqFXD89tIrVITq4+i\nuauFU01lSpcjhBDiPMPzN5O4YRmVWcDwHZ46RzbfFEII2yQBR1yz5q4W8uqOEeQawAhnX6XLUVSE\nVzhatR3ZxlylSxFCCHEeCTjimh2oPozZYmbKEN9Ysy/sNTrGeoZT0VpFTVut0uUIIYQ4SwKOuGaZ\nlVmoUDFRAg7w/dVUObUyTCWEELZCAo64JlVtNZxsKiHCKxx3e1ely7EJMd5nA45cLi6EEDZDAo64\nJpkyufgi7vaujHQLprCxmNbuNqXLEUIIgQQccQ0sFguZlQfRaXSMM8QoXY5NidVHYbaYya2VzTeF\nEMIWWDXgrFmzhiVLlpCYmEh2dvYlj3nllVdYtmzZBfd1dHQwd+5cUlNTAaioqGDZsmUkJSXxi1/8\ngq6uLmuWLS6juOkUxo46xhtisNfolC7Hpsjmm0IIYVusFnAyMjI4deoUKSkp/O53v+N3v/vdRccU\nFhaSmZl50f2vv/467u7uvbdfffVVkpKSeOeddwgJCWHbtm3WKltcQe/WDL4yPPVDI5x90Tt4kVd7\njB5zj9LlCCHEsGe1gJOWlsbcuXMBCAsLo7GxkZaWlguOeemll1ixYsUF9xUVFVFYWMisWbN679u3\nbx9z5swBYPbs2aSlpVmrbHEZPeYeDlYdxk3nyhjPMKXLsTkqlYpYQxQdpk4K6k8oXY4QQgx7dtZ6\nYKPRSHR0dO9tLy8vampqcHFxASA1NZUpU6YQEBBwwXlr167l+eefZ/v27b33tbe3o9OdGRLx9vam\npqbmis/t6emEnZ2mv17KJRkMw+sKooyyQ7T1tHPXmDn4+XooXc5lKdkuM8yT2FX6DQWtBcyMmKhY\nHbZquL1nBgtpF9slbXNjrBZwfshisfR+3NDQQGpqKhs3bqSqqqr3/u3btzN+/HiCgoL69DiXU19v\n3StZDAZXamqarfoctuaL498BEOMeY7OvXel20eOLk50jGaWHuTvoDlQqlWK12Bql20ZcmrSL7ZK2\n6bvLBUGrBRwfHx+MRmPv7erqagwGAwDp6enU1dWxdOlSurq6KCkpYc2aNVRXV1NaWsru3buprKxE\np9Ph5+eHk5MTHR0dODg4UFVVhY+Pj7XKFpfQ1t3GEWMeI5x9CXTxV7ocm6VRa4j2jiCzKouylgqC\nXOVrJYQQSrFawJk+fTrr1q0jMTGR3NxcfHx8eoen5s+fz/z58wEoKyvj17/+NcnJyRecv27dOgIC\nAkhISCAhIYGdO3eycOFCPvvsM2bMmGGtssUlZFXn0GMxMcU3XnolriJWH0VmVRbZxlwJOEIIoSCr\nBZz4+Hiio6NJTExEpVKxevVqUlNTcXV1Zd68edf0WMuXL+fZZ58lJSUFf39/7rnnHitVLS7l3NVT\nk/1ka4arifIei0alIceYx52h1/Z9LoQQov9YdQ7OU089dcHtiIiIi44JDAxk06ZNF92/fPny3o99\nfHzYuHFj/xcorqq2vY6ixmLCPUbh6WC7k4tthaOdA2M8w8ivO059R4N8zYQQQiGykrG4osyqQwBM\n8ZOrgvoqtnfRv3yFKxFCiOFLAo64LIvFQkblQbRqOyb4yNYMfRWrjwQg25ircCVCCDF8ScC5Rh09\nnX26VH0oKG0up6qtmlh9FI52jkqXM2h4OXgS6OLP8foi2ns6lC5HCCGGJQk416CkuYyn9qziv754\nmYPV2ZgtZqVLsqqMc1szyM7h1yxOH4XJYiK/7rjSpQghxLAkAecaGBz1xBmiKaw7xRtHNvObtJfZ\nXfYtnaaht/mnyWxif9UhnLVORHmNVbqcQSfWIJtvCiGEkgZsJeOhwNHOgX+LfZhu+1a2Hf6U9MoD\nbD3+ATtOfM6MwGncEpiAm25oLK19tL6A5u4WZgYkoFFbd9uLoSjIJQAPe3dyjUcxmU3yNRRCiAEm\nPTjXwd/Njwcj7uO/E5K5feRcUMGnJ7/k+e9e5J2j26hsrVa6xBsmw1M3RqVSEauPorWnjRONJ5Uu\nRwghhh3pwbkBrjoX7hp1G7eFzCK94gBflu7h29MZfHs6g1h9JHOCbmG0R+igW/23o6eDwzW5GBy9\nGel2+X3BxJXF6aPYW55GtjGPcNmBXQghBpQEnH6g0+iYGTiNmwNuIrsmly9KvibHmE+OMZ8Q1yDm\nhtzCOH30oBmmOFyTS7e5m8l+sjXDjQj3DMNeoyPbmMei0XfJ11IIIQaQBJx+pFapGe8Ty3ifWE40\nnuSLkj1k1+TyxpHNeDt4cWvQDKb5T8Zeo1O61CvqHZ7yleGpG6FV2xHlNZasmhyq2qrxc/ZVuiQh\nhBg2JOBYySj3kfxb7Eiq2mr4qnQv+yr2s7XgAz4u/oyZAdOYGTgdd3vbm5Dc0NnIsfpCQt1CMDh5\nK13OoBerjyKrJofsmjwJOEIIMYBkkrGV+ToZeHDsIn6bkMwdofNQq9R8euorVn23hrfzt1LZWqV0\niRfYX3UICxaZXNxPYvSRqFVqsofp5eKNnc28eeRttuXuwGQ2KV2OEGIYkR6cAeKqc+HO0HnMC76F\nfZUH+LJkD99VZPJdRSYx3pHMDZ7JaI9Ris/TyKg8iEalId43TtE6hgpnrRNh7iMpbCimqat5yCwj\n0BfFjaf4a84mGruaOFB9mP3uOfwk+kG8HDyVLk0IMQxIwBlgOo2OGQHTmO5/EznGPL4o+Zojtfkc\nqT0zIXlO8EzGG2IUmZBc3lJBeUsFcfpoXLTOA/78Q1WsPoqChhMcMR4lwX+y0uUMiG/L9/Hu8e2Y\nLGbuHjUfY3cNaaUHeDHjjzwU+QDjDNFKlyiEGOIk4ChErVIzzhDDOEMMJxpP8mXJHg7X5PJm7tt4\nO3gyO2gG00ZMxsHOfsBqyqzMAmCy34QBe87hIFYfRWrhv8g25g75gNNt7mHr8Q/49vQ+nO2ceCRm\nKRFe4ej1LoQ6jWRrwQdsyHmLWwKnc+/oO9Gq5UeQEMI65KeLDRjlPpJRsSOpbjPyVele0isy2Vbw\nITuKP2dGwJkVkt3t3axag9liJrMqC0c7B2K9I636XMONj5MeP2dfjtYV0GXqQmfjV9Fdr4bORv6W\ns4niphICXfz5t9iH8Xb0As4sfDg94CZC3UN4I/dtvi77lhMNxfwkZim+TgaFKxdCDEWaF1544QWl\ni+hvbW3W3RvK2dneKs/hrHUiRh/JdP+bsNfoKGkuI7/uOF+XfUttRz0GJz2uOpd+f16A4/VFfF3+\nHVN84xnvE2uV57A2a7VLf2jobKSgoYhQ9+Ah+Qu9sKGYVw9toKqthsm+8fx73MMXfK+eaxtXnQvT\nRkyipbuFI7VHSavYj5eDBwEuIxSsfviy5ffMcCdt03fOzpce6ZCAcx2s/Y1nr9ER7hnGLYHT8XTw\noLK1mmP1hewpT6OkqRR3eze8HDz7dULyJye/oKzlNPeF34234+CcBGrLPxB0Gh1pFZno1Drizm7E\nORRYLBb2lKexMfcdus3d3Bd+N/eE3Y7dD4aezm8bjVpDrD4KPycDR4z5HKg+TG17HWM9wy86T1iX\nLb9nhjtpm767XMCRnyY2TKfRMiNgKtP9p5BjzOfLkq85UnuUI7VHCXYNYE7wLUwwxN7whOQuUxeH\nqnPwtPcgzGNk/xQvLjDSLQhXrQs5tXmYLWbUqsG/QkOXqZstx1LZV3kAF60zj8U8dE1bUkz0HU+w\naxBv5r7NvsoDnGwq4ZHopQS6+luxaiHEcCE9ONdhoJO1SqXCz9mHaf6TifIaQ1tPB8fri8iqyWFf\n5UFUqBjh7Hvdf/0eqskhs+oQtwQmEOEV3s/VDxxb/otHpVJR1VZNUeNJor3H4ungoXRJN6Suo57X\nDv+NvLpjhLgG8YsJ/0bAFYLJ5drGWevE1BET6TZ1k1ObT3rlfpztHAl2DVR8yYThwJbfM8OdtE3f\nyRBVP1LyG8/TwYOJvuOY5DsBi8VCUeNJjtTms7c8nfaeDkY4+17zlVcfFO2gut1IUsQiXKw0x2cg\nDIYfCAeqD+OicxnUQfJ4fSHrDv2NmvZapo2YzGMxD+Gsu/KyAldqG7VKTaT3GEJcA8mtPUpWTQ6n\nWyuJ9ApHq9Fa4yWIswbDe2a4krbpOwk4/cgWvvHOTEiO4Gb/qTho7HsnJO8u+xZjRx0GR+8+TUhu\n7mphy/H3CXL1Z/7IOQNQufXYQrtciaeDB1+V7qGlu5WZgQlKl3PNLBYLX5Xu5R/5KfSYTSwZew93\nhd7WpyHSvrSNj5OByX4TKG0uJ6/uGPurDjHSPWjQ93bZMlt/zwxn0jZ9JwGnH9nSN55OoyPccxS3\nBE7H28GTqrYzE5L3lqdxsqkEj6tMSE47nUlu7VHmhswi1D1kgKvvX7bULpeiUWs42VRCUeNJpvjG\n46x1UrqkPusydbEp/12+LN2Dq86Fn45/hPGG2D4PI/W1bRzsHJjiF49apSLHmE965QHsVBpC3UNk\nyMoKbP09M5xJ2/SdTDIe4nQaLdMDbmKa/2SOGPP5omQPebXHyKs9RpBrAHODZjLBJ+6iv7Yzqs7M\n4ZnoM16hyoeXWH0UOcZ8coy53Bo8U+ly+sTYXsuGnH9Q3lLBKPcQHotZZtV1mdQqNXeEziPcYxR/\nz9vCByc+4Vh9IT+OThxWW10IIW6M9OBcB1tO1iqVCt/eCcljae9p752QnF5xAFT0Tkiuaq3mwxOf\nEuU1lhmBU5Uu/YbZcruc42Hvzlcle+k29zB1xCSly7mq/NrjrD/0N+o66pkRMI1HopNw0jpe8+Nc\nT9t4O3pxk99EKluryas7RkblQQJcRmBwlF3u+8tgeM8MV9I2fSc9OMNQqHswj8Uuo6atll1le0k7\nncl7BR+xo/hzbvafSoepE5CtGQaSm86VkW5BFDWepLW7zWaHqSwWC5+f2s2HJz5Fo1KzNGIxCf5T\nBrwOF50z/xH3/9hV9g3bC3fw2qE3mBcyq89zf4QQw5cEnGHA4OTNA2Pu4Y7QeewtS+frsm/5vGQ3\ncGYOzzhDjLIFDjOx+iiKm0rIrT3KFL94pcu5SEdPB5vzt5JVk4OHvTuPxy5jpFuwYvWoVCpuDZpB\nmPtI3sx9h89O7aKg/gQ/iU4atItSCiGsb/CvNib6zEXrzO2hc/htwq9JiriPYNdA5gXfgv0Q3RvJ\nVsXqz6xknG3MU7iSi1W31fD7A6+RVZPDaI9Qnp38c0XDzflC3IJYOfkXTPIdT3HTKV7M/COHqnOU\nLksIYaOkB2cY0mq0TPe/ien+NyldyrA0wtkXvaM3+bXH6Db32MyO2jnGPN7K20J7TwezA2/m3tF3\n2twwkKOdA/8v6kHGeobz7vHt/PXIJmYGTGPR6LtkzRwhxAWkB0eIAaZSqYjTR9Fh6qSw/oTS5WC2\nmNlR/Dl/zv47PeYefhyVyOIxC2wu3JyjUqlI8J/Ms5N/jr+zH3vK0/j9gfVUtVYrXZoQwoZIwBFC\nAd8PU+UqWkd7Tzsbcv7Bx8Wf4+XgyS8nPmGT84IuZYSzL09PWs7N/jdR3lLBS/tfJb1iv9JlCSFs\nhAQcIRQQ5j4SJztHso15WCwWRWqobK3i5f3ryDHmMdZzNM9O+jnBroGK1HK9dBotD0bcx6MxD6FG\nzab8d3krbwsdPR1KlyaEUJhtDP4LMcxo1BqivSPJrDpIWctpglwDBvT5D1Xn8I/8FDpNXcwNvoUF\no+bb7JBUX8T7xBHsGsibuW+TUXmQk40lPBKzdMC/rkII2yE9OEIoJM4w8FdTmS1mPiz6lL8e2YTF\nYuGR6CSbnEx8PfSOXvwq/gnmBt9CdbuR/9m/nt2l3yrWQyaEUJYEHCEUEuk1Bo1KQ84ABZy27jZe\nP7yRnae+Qu/gxVOTfsZE36G1RYdGreHe0XfyxLhHcbBzYGvBB2zI+Qet3W1KlyaEGGAScIRQiKOd\nA2M8wyhtLqe+l3ecngAAGkBJREFUo8Gqz1XeUsHazFfJqztGlPdYnp38cwJcRlj1OZUU7T2WX095\nkjGeo8k25vJixh8pbChWuiwhxACyasBZs2YNS5YsITExkezs7Ese88orr7Bs2TIA2tvb+cUvfsFD\nDz3E/fffz65duwBYuXIld999N8uWLWPZsmXs3r3bmmULMWDizl5NZc1enANVh/if/esxdtQxP+RW\n/jPuJzjZ6BYR/cnD3p3l4x/jrtAf0dDZyJ+y/sKnJ7/EbDErXZoQYgBYbZJxRkYGp06dIiUlhaKi\nIpKTk0lJSbngmMLCQjIzM9FqzyzQtWvXLmJiYnj88ccpLy/nkUceYfbs2QD88pe/7P1YiKEiVh9F\nyvHtZBvzmBmY0K+PbTKb+ODEJ3xZsgd7jY7HYx9m/DDblkOtUnN76BzCPUexMfcdPjqxk+P1Rfw4\nKtGqO6ILIZRntR6ctLQ05s6dC0BYWBiNjY20tLRccMxLL73EihUrem/fcccdPP744wBUVFTg6+tr\nrfKEsAmeDh4EufhzvL6I9n68tLmlq5XXDr/BlyV78HUy8Myk5cMu3JxvtEcov57yJLH6KI7VF7Im\n4w/k1h5TuiwhhBVZLeAYjUY8Pb/fCM/Ly4uampre26mpqUyZMoWAgIsv40xMTOSpp54iOTm5977N\nmzfz8MMPs2LFCurq6qxVthADLlYfhcliIr/ueL88XklzGWv3v8qx+kJi9VE8Peln+DnLHwsuWmf+\nPfbHLA5fQEdPB/93+A22F+7AZDYpXZoQwgoGbB2c8y/VbGhoIDU1lY0bN1JVVXXRsVu2bCE/P5+n\nn36aDz/8kIULF+Lh4UFkZCQbNmxg/fr1rFq16rLP5enphJ2ddS97NRhcrfr44voMxna5xW4KO05+\nwfHm4/woevoNPdaek/v4y8G36TH18EDM3SyKmo9aZRvXEthK2zzgczuTRkbzx7S/8XnJbk62nOQX\n0x7Fx0WvdGmKsJV2EReTtrkxVgs4Pj4+GI3G3tvV1dUYDAYA0tPTqaurY+nSpXR1dVFSUsKaNWtY\nsGAB3t7ejBgxgsjISEwmE3V1dUybNq33cW699VZeeOGFKz53fb11Lwk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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "JjBZ_q7aD9gh",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 1: Can We Calculate LogLoss for These Predictions?\n",
+ "\n",
+ "**Examine the predictions and decide whether or not we can use them to calculate LogLoss.**\n",
+ "\n",
+ "`LinearRegressor` uses the L2 loss, which doesn't do a great job at penalizing misclassifications when the output is interpreted as a probability. For example, there should be a huge difference whether a negative example is classified as positive with a probability of 0.9 vs 0.9999, but L2 loss doesn't strongly differentiate these cases.\n",
+ "\n",
+ "In contrast, `LogLoss` penalizes these \"confidence errors\" much more heavily. Remember, `LogLoss` is defined as:\n",
+ "\n",
+ "$$Log Loss = \\sum_{(x,y)\\in D} -y \\cdot log(y_{pred}) - (1 - y) \\cdot log(1 - y_{pred})$$\n",
+ "\n",
+ "\n",
+ "But first, we'll need to obtain the prediction values. We could use `LinearRegressor.predict` to obtain these.\n",
+ "\n",
+ "Given the predictions and the targets, can we calculate `LogLoss`?"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "dPpJUV862FYI",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below to display the solution."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "kXFQ5uig2RoP",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 347
+ },
+ "outputId": "cd5f03f3-4cd5-481b-e9a3-3ce4af255684"
+ },
+ "cell_type": "code",
+ "source": [
+ "predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+ " validation_targets[\"median_house_value_is_high\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ "\n",
+ "validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+ "validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+ "\n",
+ "_ = plt.hist(validation_predictions)"
+ ],
+ "execution_count": 8,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "rYpy336F9wBg",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 2: Train a Logistic Regression Model and Calculate LogLoss on the Validation Set\n",
+ "\n",
+ "To use logistic regression, simply use [LinearClassifier](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearClassifier) instead of `LinearRegressor`. Complete the code below.\n",
+ "\n",
+ "**NOTE**: When running `train()` and `predict()` on a `LinearClassifier` model, you can access the real-valued predicted probabilities via the `\"probabilities\"` key in the returned dict—e.g., `predictions[\"probabilities\"]`. Sklearn's [log_loss](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html) function is handy for calculating LogLoss using these probabilities.\n"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "JElcb--E9wBm",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def train_linear_classifier_model(\n",
+ " learning_rate,\n",
+ " steps,\n",
+ " batch_size,\n",
+ " training_examples,\n",
+ " training_targets,\n",
+ " validation_examples,\n",
+ " validation_targets):\n",
+ " \"\"\"Trains a linear classification model.\n",
+ " \n",
+ " In addition to training, this function also prints training progress information,\n",
+ " as well as a plot of the training and validation loss over time.\n",
+ " \n",
+ " Args:\n",
+ " learning_rate: A `float`, the learning rate.\n",
+ " steps: A non-zero `int`, the total number of training steps. A training step\n",
+ " consists of a forward and backward pass using a single batch.\n",
+ " batch_size: A non-zero `int`, the batch size.\n",
+ " training_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for training.\n",
+ " training_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for training.\n",
+ " validation_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for validation.\n",
+ " validation_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for validation.\n",
+ " \n",
+ " Returns:\n",
+ " A `LinearClassifier` object trained on the training data.\n",
+ " \"\"\"\n",
+ "\n",
+ " periods = 10\n",
+ " steps_per_period = steps / periods\n",
+ " \n",
+ " # Create a linear classifier object.\n",
+ " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+ " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+ " # YOUR CODE HERE: Construct the linear classifier.\n",
+ " linear_classifier = tf.estimator.LinearClassifier( feature_columns=construct_feature_columns(training_examples), optimizer=my_optimizer )\n",
+ " \n",
+ " \n",
+ " # Create input functions.\n",
+ " training_input_fn = lambda: my_input_fn(training_examples, \n",
+ " training_targets[\"median_house_value_is_high\"], \n",
+ " batch_size=batch_size)\n",
+ " predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+ " training_targets[\"median_house_value_is_high\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+ " validation_targets[\"median_house_value_is_high\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ " \n",
+ " # Train the model, but do so inside a loop so that we can periodically assess\n",
+ " # loss metrics.\n",
+ " print(\"Training model...\")\n",
+ " print(\"LogLoss (on training data):\")\n",
+ " training_log_losses = []\n",
+ " validation_log_losses = []\n",
+ " for period in range (0, periods):\n",
+ " # Train the model, starting from the prior state.\n",
+ " linear_classifier.train(\n",
+ " input_fn=training_input_fn,\n",
+ " steps=steps_per_period\n",
+ " )\n",
+ " # Take a break and compute predictions. \n",
+ " training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n",
+ " training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n",
+ " \n",
+ " validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+ " validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n",
+ " \n",
+ " training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n",
+ " validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n",
+ " # Occasionally print the current loss.\n",
+ " print(\" period %02d : %0.2f\" % (period, training_log_loss))\n",
+ " # Add the loss metrics from this period to our list.\n",
+ " training_log_losses.append(training_log_loss)\n",
+ " validation_log_losses.append(validation_log_loss)\n",
+ " print(\"Model training finished.\")\n",
+ " \n",
+ " # Output a graph of loss metrics over periods.\n",
+ " plt.ylabel(\"LogLoss\")\n",
+ " plt.xlabel(\"Periods\")\n",
+ " plt.title(\"LogLoss vs. Periods\")\n",
+ " plt.tight_layout()\n",
+ " plt.plot(training_log_losses, label=\"training\")\n",
+ " plt.plot(validation_log_losses, label=\"validation\")\n",
+ " plt.legend()\n",
+ "\n",
+ " return linear_classifier"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "VM0wmnFUIYH9",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 619
+ },
+ "outputId": "bf667e19-fe46-4ba3-904b-23dde3b50567"
+ },
+ "cell_type": "code",
+ "source": [
+ "linear_classifier = train_linear_classifier_model(\n",
+ " learning_rate=0.000005,\n",
+ " steps=500,\n",
+ " batch_size=20,\n",
+ " training_examples=training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 11,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "LogLoss (on training data):\n",
+ " period 00 : 0.61\n",
+ " period 01 : 0.59\n",
+ " period 02 : 0.58\n",
+ " period 03 : 0.56\n",
+ " period 04 : 0.55\n",
+ " period 05 : 0.55\n",
+ " period 06 : 0.54\n",
+ " period 07 : 0.54\n",
+ " period 08 : 0.53\n",
+ " period 09 : 0.53\n",
+ "Model training finished.\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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XX/72t7/z2msvEx8fy4cfRqLX63FycuL++2fyzjuvc+7cGWpqarn//hmEhk7ksccWM3jw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\nnz9lkRFCiHhzu9NRZ3sbOdd7nuaBFpoHWnjt4ttROx2VpFG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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "i2e3TlyL57Qs",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below to see the solution.\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "5YxXd2hn6MuF",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def train_linear_classifier_model(\n",
+ " learning_rate,\n",
+ " steps,\n",
+ " batch_size,\n",
+ " training_examples,\n",
+ " training_targets,\n",
+ " validation_examples,\n",
+ " validation_targets):\n",
+ " \"\"\"Trains a linear classification model.\n",
+ " \n",
+ " In addition to training, this function also prints training progress information,\n",
+ " as well as a plot of the training and validation loss over time.\n",
+ " \n",
+ " Args:\n",
+ " learning_rate: A `float`, the learning rate.\n",
+ " steps: A non-zero `int`, the total number of training steps. A training step\n",
+ " consists of a forward and backward pass using a single batch.\n",
+ " batch_size: A non-zero `int`, the batch size.\n",
+ " training_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for training.\n",
+ " training_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for training.\n",
+ " validation_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for validation.\n",
+ " validation_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for validation.\n",
+ " \n",
+ " Returns:\n",
+ " A `LinearClassifier` object trained on the training data.\n",
+ " \"\"\"\n",
+ "\n",
+ " periods = 10\n",
+ " steps_per_period = steps / periods\n",
+ " \n",
+ " # Create a linear classifier object.\n",
+ " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+ " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0) \n",
+ " linear_classifier = tf.estimator.LinearClassifier(\n",
+ " feature_columns=construct_feature_columns(training_examples),\n",
+ " optimizer=my_optimizer\n",
+ " )\n",
+ " \n",
+ " # Create input functions.\n",
+ " training_input_fn = lambda: my_input_fn(training_examples, \n",
+ " training_targets[\"median_house_value_is_high\"], \n",
+ " batch_size=batch_size)\n",
+ " predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+ " training_targets[\"median_house_value_is_high\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ " predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+ " validation_targets[\"median_house_value_is_high\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ " \n",
+ " # Train the model, but do so inside a loop so that we can periodically assess\n",
+ " # loss metrics.\n",
+ " print(\"Training model...\")\n",
+ " print(\"LogLoss (on training data):\")\n",
+ " training_log_losses = []\n",
+ " validation_log_losses = []\n",
+ " for period in range (0, periods):\n",
+ " # Train the model, starting from the prior state.\n",
+ " linear_classifier.train(\n",
+ " input_fn=training_input_fn,\n",
+ " steps=steps_per_period\n",
+ " )\n",
+ " # Take a break and compute predictions. \n",
+ " training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n",
+ " training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n",
+ " \n",
+ " validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+ " validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n",
+ " \n",
+ " training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n",
+ " validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n",
+ " # Occasionally print the current loss.\n",
+ " print(\" period %02d : %0.2f\" % (period, training_log_loss))\n",
+ " # Add the loss metrics from this period to our list.\n",
+ " training_log_losses.append(training_log_loss)\n",
+ " validation_log_losses.append(validation_log_loss)\n",
+ " print(\"Model training finished.\")\n",
+ " \n",
+ " # Output a graph of loss metrics over periods.\n",
+ " plt.ylabel(\"LogLoss\")\n",
+ " plt.xlabel(\"Periods\")\n",
+ " plt.title(\"LogLoss vs. Periods\")\n",
+ " plt.tight_layout()\n",
+ " plt.plot(training_log_losses, label=\"training\")\n",
+ " plt.plot(validation_log_losses, label=\"validation\")\n",
+ " plt.legend()\n",
+ "\n",
+ " return linear_classifier"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "UPM_T1FXsTaL",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "linear_classifier = train_linear_classifier_model(\n",
+ " learning_rate=0.000005,\n",
+ " steps=500,\n",
+ " batch_size=20,\n",
+ " training_examples=training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "i-Xo83_aR6s_",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 3: Calculate Accuracy and plot a ROC Curve for the Validation Set\n",
+ "\n",
+ "A few of the metrics useful for classification are the model [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification), the [ROC curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) and the area under the ROC curve (AUC). We'll examine these metrics.\n",
+ "\n",
+ "`LinearClassifier.evaluate` calculates useful metrics like accuracy and AUC."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "DKSQ87VVIYIA",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 50
+ },
+ "outputId": "d42e4921-4347-41dd-cc50-03bc378a2644"
+ },
+ "cell_type": "code",
+ "source": [
+ "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
+ "\n",
+ "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n",
+ "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])"
+ ],
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "AUC on the validation set: 0.76\n",
+ "Accuracy on the validation set: 0.52\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "47xGS2uNIYIE",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "You may use class probabilities, such as those calculated by `LinearClassifier.predict`,\n",
+ "and Sklearn's [roc_curve](http://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics) to\n",
+ "obtain the true positive and false positive rates needed to plot a ROC curve."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "xaU7ttj8IYIF",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 347
+ },
+ "outputId": "aba17a99-1900-4aaf-f6d0-744e54d5e111"
+ },
+ "cell_type": "code",
+ "source": [
+ "validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+ "# Get just the probabilities for the positive class.\n",
+ "validation_probabilities = np.array([item['probabilities'][1] for item in validation_probabilities])\n",
+ "\n",
+ "false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(\n",
+ " validation_targets, validation_probabilities)\n",
+ "plt.plot(false_positive_rate, true_positive_rate, label=\"our model\")\n",
+ "plt.plot([0, 1], [0, 1], label=\"random classifier\")\n",
+ "_ = plt.legend(loc=2)"
+ ],
+ "execution_count": 14,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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XAhbdWVFDCcvTk8mrPY+v0YeF8bO5KTBB7Viii0gJC6fz5J/3OLZf/eloFZMI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\n9Xy4PRMAXy8j8ya7zmk3NruNHfm72Jb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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "PIdhwfgzIYII",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "**See if you can tune the learning settings of the model trained at Task 2 to improve AUC.**\n",
+ "\n",
+ "Often times, certain metrics improve at the detriment of others, and you'll need to find the settings that achieve a good compromise.\n",
+ "\n",
+ "**Verify if all metrics improve at the same time.**"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "XKIqjsqcCaxO",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 653
+ },
+ "outputId": "b86b23ed-be22-4109-9c24-5e8ca1638059"
+ },
+ "cell_type": "code",
+ "source": [
+ "# TUNE THE SETTINGS BELOW TO IMPROVE AUC\n",
+ "linear_classifier = train_linear_classifier_model(\n",
+ " learning_rate=0.000004,\n",
+ " steps=20000,\n",
+ " batch_size=500,\n",
+ " training_examples=training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=validation_examples,\n",
+ " validation_targets=validation_targets)\n",
+ "\n",
+ "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
+ "\n",
+ "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n",
+ "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])"
+ ],
+ "execution_count": 21,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "LogLoss (on training data):\n",
+ " period 00 : 0.50\n",
+ " period 01 : 0.48\n",
+ " period 02 : 0.47\n",
+ " period 03 : 0.47\n",
+ " period 04 : 0.47\n",
+ " period 05 : 0.47\n",
+ " period 06 : 0.47\n",
+ " period 07 : 0.47\n",
+ " period 08 : 0.47\n",
+ " period 09 : 0.47\n",
+ "Model training finished.\n",
+ "AUC on the validation set: 0.81\n",
+ "Accuracy on the validation set: 0.80\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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Id6ACpwOZvUKI9I0gu/QQlXVVbWrD1cXE+NhwKmvq2ZVdaOMIRUREugYVOB1s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\nI0SMsWjNXDtqNteOmk2/p59j7SeDQ1i7G/ayu2EvGkXNxKT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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "wCugvl0JdWYL",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below for a possible solution."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "VHosS1g2aetf",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "One possible solution that works is to just train for longer, as long as we don't overfit. \n",
+ "\n",
+ "We can do this by increasing the number the steps, the batch size, or both.\n",
+ "\n",
+ "All metrics improve at the same time, so our loss metric is a good proxy\n",
+ "for both AUC and accuracy.\n",
+ "\n",
+ "Notice how it takes many, many more iterations just to squeeze a few more \n",
+ "units of AUC. This commonly happens. But often even this small gain is worth \n",
+ "the costs."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "dWgTEYMddaA-",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "linear_classifier = train_linear_classifier_model(\n",
+ " learning_rate=0.000003,\n",
+ " steps=20000,\n",
+ " batch_size=500,\n",
+ " training_examples=training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=validation_examples,\n",
+ " validation_targets=validation_targets)\n",
+ "\n",
+ "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
+ "\n",
+ "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n",
+ "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/multi_class_classification_of_handwritten_digits.ipynb b/multi_class_classification_of_handwritten_digits.ipynb
new file mode 100644
index 0000000..35ecbd8
--- /dev/null
+++ b/multi_class_classification_of_handwritten_digits.ipynb
@@ -0,0 +1,2625 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "multi-class_classification_of_handwritten_digits.ipynb",
+ "version": "0.3.2",
+ "provenance": [],
+ "collapsed_sections": [
+ "JndnmDMp66FL",
+ "266KQvZoMxMv",
+ "6sfw3LH0Oycm"
+ ],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python2",
+ "display_name": "Python 2"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "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": 224
+ },
+ "outputId": "6727be2d-8928-45d7-c184-feab0321c55a"
+ },
+ "cell_type": "code",
+ "source": [
+ "from __future__ import print_function\n",
+ "\n",
+ "import glob\n",
+ "import math\n",
+ "import os\n",
+ "\n",
+ "from IPython import display\n",
+ "from matplotlib import cm\n",
+ "from matplotlib import gridspec\n",
+ "from matplotlib import pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "from sklearn import metrics\n",
+ "import tensorflow as tf\n",
+ "from tensorflow.python.data import Dataset\n",
+ "\n",
+ "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+ "pd.options.display.max_rows = 10\n",
+ "pd.options.display.float_format = '{:.1f}'.format\n",
+ "\n",
+ "mnist_dataframe = pd.read_csv(\n",
+ " \"https://download.mlcc.google.com/mledu-datasets/mnist_train_small.csv\",\n",
+ " sep=\",\",\n",
+ " header=None)\n",
+ "\n",
+ "# Use just the first 10,000 records for training/validation.\n",
+ "mnist_dataframe = mnist_dataframe.head(10000)\n",
+ "\n",
+ "mnist_dataframe = mnist_dataframe.reindex(np.random.permutation(mnist_dataframe.index))\n",
+ "mnist_dataframe.head()"
+ ],
+ "execution_count": 1,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
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+ "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",
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+ ""
+ ]
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+ "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": 402
+ },
+ "outputId": "dc291380-35e9-4fc5-a9f4-38eb2d578d68"
+ },
+ "cell_type": "code",
+ "source": [
+ "mnist_dataframe.loc[:, 72:72]"
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+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "execute_result",
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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": 313
+ },
+ "outputId": "a81b1223-3014-4c95-8f33-28d6232c7ce1"
+ },
+ "cell_type": "code",
+ "source": [
+ "training_targets, training_examples = parse_labels_and_features(mnist_dataframe[:7500])\n",
+ "training_examples.describe()"
+ ],
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "
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+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 15
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "PDuLd2Hcu8VL",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "84ef3b85-0aa4-4756-9e6d-8098ba95e202"
+ },
+ "cell_type": "code",
+ "source": [
+ "#\n",
+ "# YOUR CODE HERE: Calculate accuracy on the test set.\n",
+ "#\n",
+ "predict_test_input_fn = create_predict_input_fn(\n",
+ " test_examples, test_targets, batch_size=100)\n",
+ "\n",
+ "test_predictions = classifier.predict(input_fn=predict_test_input_fn)\n",
+ "test_predictions = np.array([item['class_ids'][0] for item in test_predictions])\n",
+ " \n",
+ "accuracy = metrics.accuracy_score(test_targets, test_predictions)\n",
+ "print(\"Accuracy on test data: %0.2f\" % accuracy)"
+ ],
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Accuracy on test data: 0.91\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "6sfw3LH0Oycm",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below for a possible solution."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "XatDGFKEO374",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "The code below is almost identical to the original `LinearClassifer` training code, with the exception of the NN-specific configuration, such as the hyperparameter for hidden units."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "kdNTx8jkPQUx",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def train_nn_classification_model(\n",
+ " learning_rate,\n",
+ " steps,\n",
+ " batch_size,\n",
+ " hidden_units,\n",
+ " training_examples,\n",
+ " training_targets,\n",
+ " validation_examples,\n",
+ " validation_targets):\n",
+ " \"\"\"Trains a neural network classification model for the MNIST digits dataset.\n",
+ " \n",
+ " In addition to training, this function also prints training progress information,\n",
+ " a plot of the training and validation loss over time, as well as a confusion\n",
+ " matrix.\n",
+ " \n",
+ " Args:\n",
+ " learning_rate: A `float`, the learning rate to use.\n",
+ " steps: A non-zero `int`, the total number of training steps. A training step\n",
+ " consists of a forward and backward pass using a single batch.\n",
+ " batch_size: A non-zero `int`, the batch size.\n",
+ " hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+ " training_examples: A `DataFrame` containing the training features.\n",
+ " training_targets: A `DataFrame` containing the training labels.\n",
+ " validation_examples: A `DataFrame` containing the validation features.\n",
+ " validation_targets: A `DataFrame` containing the validation labels.\n",
+ " \n",
+ " Returns:\n",
+ " The trained `DNNClassifier` object.\n",
+ " \"\"\"\n",
+ "\n",
+ " periods = 10\n",
+ " # Caution: input pipelines are reset with each call to train. \n",
+ " # If the number of steps is small, your model may never see most of the data. \n",
+ " # So with multiple `.train` calls like this you may want to control the length \n",
+ " # of training with num_epochs passed to the input_fn. Or, you can do a really-big shuffle, \n",
+ " # or since it's in-memory data, shuffle all the data in the `input_fn`.\n",
+ " steps_per_period = steps / periods \n",
+ " # Create the input functions.\n",
+ " predict_training_input_fn = create_predict_input_fn(\n",
+ " training_examples, training_targets, batch_size)\n",
+ " predict_validation_input_fn = create_predict_input_fn(\n",
+ " validation_examples, validation_targets, batch_size)\n",
+ " training_input_fn = create_training_input_fn(\n",
+ " training_examples, training_targets, batch_size)\n",
+ " \n",
+ " # Create the input functions.\n",
+ " predict_training_input_fn = create_predict_input_fn(\n",
+ " training_examples, training_targets, batch_size)\n",
+ " predict_validation_input_fn = create_predict_input_fn(\n",
+ " validation_examples, validation_targets, batch_size)\n",
+ " training_input_fn = create_training_input_fn(\n",
+ " training_examples, training_targets, batch_size)\n",
+ " \n",
+ " # Create feature columns.\n",
+ " feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n",
+ "\n",
+ " # Create a DNNClassifier object.\n",
+ " my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n",
+ " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+ " classifier = tf.estimator.DNNClassifier(\n",
+ " feature_columns=feature_columns,\n",
+ " n_classes=10,\n",
+ " hidden_units=hidden_units,\n",
+ " optimizer=my_optimizer,\n",
+ " config=tf.contrib.learn.RunConfig(keep_checkpoint_max=1)\n",
+ " )\n",
+ "\n",
+ " # Train the model, but do so inside a loop so that we can periodically assess\n",
+ " # loss metrics.\n",
+ " print(\"Training model...\")\n",
+ " print(\"LogLoss error (on validation data):\")\n",
+ " training_errors = []\n",
+ " validation_errors = []\n",
+ " for period in range (0, periods):\n",
+ " # Train the model, starting from the prior state.\n",
+ " classifier.train(\n",
+ " input_fn=training_input_fn,\n",
+ " steps=steps_per_period\n",
+ " )\n",
+ " \n",
+ " # Take a break and compute probabilities.\n",
+ " training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n",
+ " training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n",
+ " training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n",
+ " training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n",
+ " \n",
+ " validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n",
+ " validation_probabilities = np.array([item['probabilities'] for item in validation_predictions]) \n",
+ " validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n",
+ " validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10) \n",
+ " \n",
+ " # Compute training and validation errors.\n",
+ " training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n",
+ " validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n",
+ " # Occasionally print the current loss.\n",
+ " print(\" period %02d : %0.2f\" % (period, validation_log_loss))\n",
+ " # Add the loss metrics from this period to our list.\n",
+ " training_errors.append(training_log_loss)\n",
+ " validation_errors.append(validation_log_loss)\n",
+ " print(\"Model training finished.\")\n",
+ " # Remove event files to save disk space.\n",
+ " _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n",
+ " \n",
+ " # Calculate final predictions (not probabilities, as above).\n",
+ " final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n",
+ " final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n",
+ " \n",
+ " \n",
+ " accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n",
+ " print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n",
+ "\n",
+ " # Output a graph of loss metrics over periods.\n",
+ " plt.ylabel(\"LogLoss\")\n",
+ " plt.xlabel(\"Periods\")\n",
+ " plt.title(\"LogLoss vs. Periods\")\n",
+ " plt.plot(training_errors, label=\"training\")\n",
+ " plt.plot(validation_errors, label=\"validation\")\n",
+ " plt.legend()\n",
+ " plt.show()\n",
+ " \n",
+ " # Output a plot of the confusion matrix.\n",
+ " cm = metrics.confusion_matrix(validation_targets, final_predictions)\n",
+ " # Normalize the confusion matrix by row (i.e by the number of samples\n",
+ " # in each class).\n",
+ " cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
+ " ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n",
+ " ax.set_aspect(1)\n",
+ " plt.title(\"Confusion matrix\")\n",
+ " plt.ylabel(\"True label\")\n",
+ " plt.xlabel(\"Predicted label\")\n",
+ " plt.show()\n",
+ "\n",
+ " return classifier"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "ZfzsTYGPPU8I",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "classifier = train_nn_classification_model(\n",
+ " learning_rate=0.05,\n",
+ " steps=1000,\n",
+ " batch_size=30,\n",
+ " hidden_units=[100, 100],\n",
+ " training_examples=training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "qXvrOgtUR-zD",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Next, we verify the accuracy on the test set."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "scQNpDePSFjt",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "mnist_test_dataframe = pd.read_csv(\n",
+ " \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n",
+ " sep=\",\",\n",
+ " header=None)\n",
+ "\n",
+ "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n",
+ "test_examples.describe()"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "EVaWpWKvSHmu",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "predict_test_input_fn = create_predict_input_fn(\n",
+ " test_examples, test_targets, batch_size=100)\n",
+ "\n",
+ "test_predictions = classifier.predict(input_fn=predict_test_input_fn)\n",
+ "test_predictions = np.array([item['class_ids'][0] for item in test_predictions])\n",
+ " \n",
+ "accuracy = metrics.accuracy_score(test_targets, test_predictions)\n",
+ "print(\"Accuracy on test data: %0.2f\" % accuracy)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "WX2mQBAEcisO",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 3: Visualize the weights of the first hidden layer.\n",
+ "\n",
+ "Let's take a few minutes to dig into our neural network and see what it has learned by accessing the `weights_` attribute of our model.\n",
+ "\n",
+ "The input layer of our model has `784` weights corresponding to the `28×28` pixel input images. The first hidden layer will have `784×N` weights where `N` is the number of nodes in that layer. We can turn those weights back into `28×28` images by *reshaping* each of the `N` `1×784` arrays of weights into `N` arrays of size `28×28`.\n",
+ "\n",
+ "Run the following cell to plot the weights. Note that this cell requires that a `DNNClassifier` called \"classifier\" has already been trained."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "eUC0Z8nbafgG",
+ "colab_type": "code",
+ "cellView": "both",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1171
+ },
+ "outputId": "bd6a44d2-a4ec-4459-9431-a0400186fbcc"
+ },
+ "cell_type": "code",
+ "source": [
+ "print(classifier.get_variable_names())\n",
+ "\n",
+ "weights0 = classifier.get_variable_value(\"dnn/hiddenlayer_0/kernel\")\n",
+ "\n",
+ "print(\"weights0 shape:\", weights0.shape)\n",
+ "\n",
+ "num_nodes = weights0.shape[1]\n",
+ "num_rows = int(math.ceil(num_nodes / 10.0))\n",
+ "fig, axes = plt.subplots(num_rows, 10, figsize=(20, 2 * num_rows))\n",
+ "for coef, ax in zip(weights0.T, axes.ravel()):\n",
+ " # Weights in coef is reshaped from 1x784 to 28x28.\n",
+ " ax.matshow(coef.reshape(28, 28), cmap=plt.cm.pink)\n",
+ " ax.set_xticks(())\n",
+ " ax.set_yticks(())\n",
+ "\n",
+ "plt.show()"
+ ],
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "['dnn/hiddenlayer_0/bias', 'dnn/hiddenlayer_0/bias/t_0/Adagrad', 'dnn/hiddenlayer_0/kernel', 'dnn/hiddenlayer_0/kernel/t_0/Adagrad', 'dnn/hiddenlayer_1/bias', 'dnn/hiddenlayer_1/bias/t_0/Adagrad', 'dnn/hiddenlayer_1/kernel', 'dnn/hiddenlayer_1/kernel/t_0/Adagrad', 'dnn/logits/bias', 'dnn/logits/bias/t_0/Adagrad', 'dnn/logits/kernel', 'dnn/logits/kernel/t_0/Adagrad', 'global_step']\n",
+ "weights0 shape: (784, 100)\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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Hb6E3WcYzzKOGbfmcNSG2qlbkO5mYm7I8ZnnMZej/M3ZtQs+xiQt57akhlp/Z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IIgCEINIh/OCIIgCIIgCIIgCIIg1CDy4YwgCIIgCIIgCIIgCEINIh/OCIIgCIIgCIIgCIIg1CDy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V8cv74SwvOTNTxy061NexXW1nllfBykzHBRl5iFNyWd7J36/oePDCPjp+sSdEx/WmtWCPubzi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kmJjKsODiZ5xbkYwwbmRjJ+svU+yavUvZqM4y9h/FgHO5O8ghScvxoDJgptMqRhQxn/dnwR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\nPs8tzNtWw3vNlxTsaytU92V5RlbGWmxghO9kW4m3CsmMxGdUbD1C6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Eir9hyn9IkDY7DO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\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vZxiGYRiGYRiGYRiGYRoQ/nKGYRiGYRiGYRiGYRimAeEvZxiGYRiGYRiGYRiGYRoQ/nKG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\nuq+BzOtlFORzC3pxeXfHetj3WzfFbwUtvbi8+9wCvGvlnMb7S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8sU3zXiAOqtnp0Ry7Pv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fOPPypjEnUc0rx1y/ayY1P+HK/j3V/Aut6J2OgqpZQDkSW518f1vHbqLsvr+yNkLpN6Qc44\na9EYlndwFcp77xIrY0tzUh5tILUZ+nF3HW9bd0zHHo6OLK/rKJQs29eErGf5qN9Z3piFkDts/RZj\nZ/iyD1netUWQWrn6uujYsS4vXXcKghzB3b2rMjaLh8Deun3vJuyYQxDmWAcvXx3vnv4Hy2v9EWxh\n48j+xqU2/y4ppGTbxR/nMOUpl1J4BKHM2z4Iea5BqF/Pz+TSwZQrmMNsfXHtcmMyWN6eXZDITVwz\nUccvTvFS89jLkG27u2NcXbz7kOUNWwwJ1dN1mF9dIrjMwKU+xqyxr+OOifge0S/5evfpmm90vHDY\nDB13asilQh5tfXWc9wLz182T91hes/dwj9j54rwk7Odr1+072Fe26Iu15uYhWMp6ubiw11QiG586\n48kaaTD/XVoIKUHH2bA8z8/l8/O1JbjWtd+DXCL57xiWR9ccv4HYh60Y9RvLG/o51q3AVnw8G4Oj\n06fruLiUf+esfEhBgok8LeMJHztU9mlRBeuOuTOXgHo0xR61IBUSp+gd/Hp7NMB9S+WHllUxlydc\njmOvaTAWa/q5xZCqBYQZWAiTz5R6DRK5EoPvHjAIcqCiTPIZXPn+JoVILqyrY/+V/4Kvx54d/XXs\nHdBfGZP0dLRr2DX9L3as7cfYz+TGYu337cLn3fvL8Txh6oC1y7d/XZZn64D58N6qHTr2aM/Ps503\nxpmVFY6ZmGAt/HvWCvaaJtMgaX1xPErHWY+45N2DyFNtiQy/tJhfQ2rJbl0Nkri//zjD8sIb4Xmx\ntADPsIHEAlwppW4sOqzjTvPmKWPz7Dqe1crL+M8Dr05inqHn+t4Wvket1RHPFA+OQnYb+m5DludR\nB1LRwkLsPbOf8nNNpbE+EVhDzn23TMe2rnyPFTZuhI6vzv1Fx7XHt2F5e77crON3l0CG+vIOb7Wg\nyE8lKafjdVx3Qg+WFr0Nc3SN/pj/qTW8UkrFn4IsM6TvOGWIVM4IgiAIgiAIgiAIgiBUIPLjjCAI\ngiAIgiAIgiAIQgXyr25N5aUo48mJ4WVGri1RMh+9FWW27aZ1ZHnpt3CshLjjlJbmsbxHv6Jjftjn\nw3X87MRhlpcbA8nK/d2QTNBS3CZTeOfj4mJ0SXbxRulmVjXuGGXljrKoGgNRRmfo4JJ+A9+p55wB\nOraw4F3hH2+Bq4dHK18dv7E2Y3mXF5/Rcd+lxpc1nVgN+U7vr3uxY/Sa7J97UMdpBg4+Vm44N3vn\nI69jfe4Acoo4P9RJRslo305cxlZzCLp+Z0RBnkClVYYuVrTE99KPpGSUyCCUUqrbdz3xPW7iWhm6\nL9SqhvJrO+KeUPyad9a3cISc6vJ8/N1mX/B73VAOYEwS76D0tVqnAHbM2gJd4x1IV3vbJC4dsSF5\nVzdd1XGHr7gkpF4X3PuX1l3SsYkFny4e7ce1tvPBe9i7oqTRUMJXXo777c/xKDVs2Zy7ClBHk9wT\nkCLW8K/G8lp0QrmsU/Xa6p9wDIRMgbow5KZxx6329Xknd2PToR7en8qBlFLq4Wp0indvjrJJzwh+\nbjZ+hvPWtA2O1ejNZXuxByN1nFeE69D98y4s7002xlznfpg7n2zFva4MDFf8BuDvPvkd95KNPx8D\nkZvxGdpMgYvJlpE/s7yJ3wzVMZ2HF09cw/L6k3vQu3stHWfe4nP5nkj83c7KuLwh5eVT101mx0qK\ncD/FEYcAb1fu8hM4GNKKW+vwWamMSSmlEk9BSrJ470wdP/mdOwgWl5ToeOI4rElUMnt5JXeVyY2G\n7IW6iWQauNA51sK69nAl5oNmgdwq4uwyrDPU3atyZQPXm26YH6ijycYl+1jehNWfqrcJdVSq3iGM\nHaOOfzGvMA4MnacqmUCOUns0SpgzH6SwPHq9c6LxnQ0dJry64J7+vO8cHU+d+J6OXQzcSW6ewTw8\nYBFkdcu/+ZzldSBr9ez35uK9V3GJnFMdrCHzJ0C6NnnBRyzv26GQ2oYQh4pwd+5Wcnc/pHqDlhtX\n1kTdVHoO5fu+/HyU4FNZzuP9D1hewSE4gFZpjH1tn/kjWN76iZizkjIwdgzHdnXy39Tdq/sPg3R8\nf9kp9prnadhjHv0I8/uQD/n58g7CtT8xC3Njk4lc+hBMJEpUcrH3LHc4mvYX7pE3uZC7vtOXvx+V\ncb0NqrXw1bG1Jy//Tzkbp2PnUMxFZnZ8f5hyA3uk6mS//eoMl3w9+xMSPJ/3sNdxN3B1eh2Fa0Kl\nVpXNcM+FjOaynPNLcF39avG9CoU6G3m2gzzk7KZLLM+RyK4d6mAPc3TpMZbXYwakFXFbIM+y8uRS\njxML4aLz0e/GlTUVZGHOC2nA9zZUtleQhPXl5TUu460zHnuT3BR8dzNL7txXVIR9fW4mniVd8nlL\ni4SjWD8LX2LN9BkA99im0/l+KOf5Kx3H38A+zMZAFuxcB/dL8nncY1VacmnV9Utwsuv4HaS0/h5c\nJpoah/uNOv/lLDjI8l5kcLmqsaH7LyqZVUqpu0/wPeu+wdplYtDOJDcOz+m+dbiTJiWZSIfsa+AZ\nLC+BOxs514Pc9/IPK3VM5+uanbqz17y4C6l3PeJwGL3lLMsLa4r9SE4q1oLSQn4vmRIpv1cfvCZ6\nO5/LqfQ+/jDmGudQ/pzqXJ/PN4ZI5YwgCIIgCIIgCIIgCEIFIj/OCIIgCIIgCIIgCIIgVCDy44wg\nCIIgCIIgCIIgCEIF8q89Z4pz0afg8UGu03Wwhq6YanPXfrqc5Q1ZBA391T3Ql1mfd2B5kY9gP+iT\nAG1g9j2u3Q79DLpd1kuG6LATjz9lr7Fwhlbw0n5YvPWbP5TlmZtDa/1oyyEdP7j5jOX1njdcx1G/\nQtfm/wG3aKQ9U5JPoSeOmRO39vOu88/aVGNA9bKRK8+zY7V7oweGpzM0f0Wkh4FSSm2ZslXHXUe1\n13F6ZCLLG7kEdqrLxkET7WrPdcR2AbC4o9q7jLvoHfEmi9smU/veEqLVLzOwe3u8CvePiTm0kMe3\n8O/eoT9sD9OIdfr+wxdZnhWxo6W9FALvc0vTFoN4zw9j0noWsY1fx63DR62aouOXN2/r2NGO6409\niaVpNun18OfnBjackew9AAAgAElEQVTnpH9AxxnQvB/4lv/d9qPb6PjQHIyXnHxYG9aswnWVJgfQ\nnySoGj6PXYAzy6veDP0DqjaCPjjl7mOWV5yHXhnZyThG+ycpxb9TNrHmDDXQjCef5vp0Y+PRCp8j\najXvGxI4DH0v0kmvpBnLlrA8+l08WqA3z85p3La8JbF+7fE9ei5ELjrD8pxsYctJdb9uzfDe0Vvu\nsNeYmqK3TO2xsJ7cPOlXltd5DOaK0kLMKV0bNGB5v86FLXtRMbS+n07nPVjoe8ydBkvrUD+u856z\nY7Z6W1SqDP38jUVc+193FPqODB2DPgDuETVY3pKPsE727YG+SWn3nrO8ymT+SjqFde1yFB8Hz5Ix\nb+7bAU11r1K8t70VX3fcyb04gPROqTksnOUdmQW78KRM9LFKSOO92Lo3hE1mrTp476LX6Syvshm+\nE7WoHbXofZZ3eT56tvVa1EEZm5Rs7B9MTPj69CIdn/mdeegTsGMqt4h1uwn75nPnMPd2GdyK5ZUQ\na1QbYnVbcI73N5v7Ae6LLmSMZNzHPujK8dvsNXQt/LLvJB1PXfIxy0u5iHtrkAd6iGz/ZjfLa9EU\newJ3B3zWP77bxvL6NcZ659nSV8emdhYsLzz037X1/4UWX6GXwOPVvJ8KtaP17oBzGXeYj5278egr\nYeuCuXDFhoUsb/zqqTpOvoG+HtUiIlge3Zc+3YS9RHkpeqc1nDaEvSZ15iodT147VscbJm1ied1H\nwNb+9lH0J7k0ln+nL9bjs15dgP5JE1fyeyI/jfSlI/fR5i3HWV6Xh9jbes57RxmbrBuYvy7u4eti\nRHv0zzn7K+a2wABvlmdNegPmRGNu8jTo7UbXELrel5fw/k+070fVmuh5YeOHte/8Ut5vIqQXPmtB\nInr4eLT0YXm5z9GTw8SS96Ck0P42lUxIr5u6NVle/kv8rYwsxPV61mJ5Tarz5y5j8uIQ7u9Kpvzf\n/TPvoY/Lw0dxOu7zYTuWV1KCXpf75mEPZ2bQ04RajjfriLGd95z3KilKRj8a99a4BpEr8CwQ3JPb\ndD88gB5eTjaYD2JT+LNoQyvMoV4dsNfOiIpneREj8ZyxeRLGefvhfI24tRN21PXbon9iUVo+y2vw\neUv1NjGzwnd+Hsm/C+0d5NERey5zg55/zx9j/9p8Cq6xpQ3vz5WXgdeVkHFp6c6t4l+dx+eIeYV7\nycMa13TLxB/Ya5r0RV/TuMdYG1wi+PO2sz/G0sEZWN87zuT9vhKP4TcK317Yqx+8dITldSD9S90a\no99OwSvey68whfw3H85KKamcEQRBEARBEARBEARBqFDkxxlBEARBEARBEARBEIQKpFJ5eXn5/3+a\nIAiCIAiCIAiCIAiC8DaQyhlBEARBEARBEARBEIQKRH6cEQRBEARBEARBEARBqEDkxxlBEARBEARB\nEARBEIQKRH6cEQRBEARBEARBEARBqEDkxxlBEARBEARBEARBEIQKRH6cEQRBEARBEARBEARBqEDk\nxxlBEARBEARBEARBEIQKRH6cEQRBEARBEARBEARBqEDkxxlBEARBEARBEARBEIQKRH6cEQRBEARB\nEARBEARBqEDkxxlBEARBEARBEARBEIQKRH6cEQRBEARBEARBEARBqEDkxxlBEARBEARBEARBEIQK\nRH6cEQRBEARBEARBEARBqEDkxxlBEARBEARBEARBEIQKRH6cEQRBEARBEARBEARBqEDkxxlBEARB\nEARBEARBEIQKRH6cEQRBEARBEARBEARBqEBM/+3g80c7dJx8JpYd8+1bX8fm5m46Pvr1OpZXu12Q\njh+cfKjjiOFNWF5lMxMdP9t2T8dhU7qzvBfn7ujYLdxLxwkHH+nYytOOvSbqb/zdWk39dZxxP4Xl\nNZjck/xXqY4qVTJjeWmP8LdSzsXrOHhkO5Z38rvtOi4uKdFxo6GN+ftdfaHjpp99pYzNkqFDdRyV\nkMCOtatbV8dhAxrqeOvygyxvwpqpOs549kzHSQeesLydly/ruH193CPP09JYXsP6tXTsO6COjvNf\nvtaxQ40q7DWvImN0XPy6CHm1XFjeqpmbdPzB2F46tq/J83b+sE/H5eXlOg7x8WF5gcPDdHzr9ys6\nDvukKcvLeZah49qdRipjcmX5PB2nPE9nx+JTU3Xcpi/uLc8W9VjeipFLdUyvZ+q9Rywv694rHVfr\nGqDjXyb9xfLe6d5CxyW5xTo2d7TQcdLDZPaawN6438xsMK7svKuyvA2T1up4+PJPdXxx7k6WR++r\n3rNwreM232N57u18dexcy48cKWV5T9Ze1HGzaTOVsbm+drGOz/x9gx2j911uYaGOY8n1VUqpdj0j\ndBwfifknLSeH5dUNranj8tIyHZ84x//u0Fn9dbxv4SG8N/m7VZ2c2Gtqe2HufZyUpOM+U/l8/SYT\n36O0EHNg3OmnLC8zN1fHTra2Oq7Vpy7Lo98jcnOkju2srFhevSEYszXChihjkhi3R8dP1vBzScdL\n3vMs/P/2wSzv6Dd4j1ZjW+vYxbcByyssTNRx+v04HT/Yd5fl1esfquML6y/puOVHGKNlb/i97hSE\nMTehxywddw0LY3n3nz/XcY/wcB1vOHuW5XUg8/2pexh/M38dx/LOLj+Nz90a+wOneny+//6Tn3W8\nxuBvGYNzs/CdPTr4sWNRe3B+H7zA+ty9bwuW9+hitI5rBGFMxD5OZHn3yDkM9PTUcdtJ7Vlewl7M\nxY+e4jVxZCwGkdcrpVSTEc10XFKAMZZwmK/NF8i+pUME7hf6d5RSqu0nbXSccgHHdh+7wPI+/nIA\n/qNSJfVPrJmLfdCCQ4f+Me//hrkDB+p4+GI+zn/+FGvIy8xMHU+f8xHL+33uNh1/uWGajtPu8T1v\nfhL2JvZkz3Hil5Msrxa5PvUm4n6J3Yl7yqdPbfaaoswCHdt6YFzeXXqM5bk1wT12YTfmP09nZ5bX\nYfZ4HWel39bxtZ/OsbzGU9vq+OKPf+u40ZjmLC+XzGXG3tsopVRS/F4dl5XwecrM1lLH6Xex1uQn\n8PXOzB77DlUZ92P8JX4dg/thnrJyx1qTm5DF8krzsaeh196qCl7jHsH3ivF77+u4KA3X1L0Nz7Nw\nstbx62fYz1l72bM8S2fkJRx4rOPoB/Esr3bzQB27hOH+i1p3neU5uOP9m02eoYzJ3s8/13HEpNbs\nmKWNh45j9mF9Ch7Yh+VVqoRH0vQXV/Hecw6wvHe+xl7PwR1ryMqPv2N54f543nPwctSxXQDGi3tY\nIHtN3D6MK+vqDjp2C+VrRF4y9p4Zd7DP9erE368gjcwbVXEfxB68xPLII4iKuRGnY3NT/pje7Avs\nsdzc+DOnMTj/3bc69htanx1Lv4l1rUornI/CjAKWd+cPXLsabbAPdQhyY3lZD/EMnnYV7121fQ2W\nd3r9eR0Hk71nUTHGqEuAK3vN8/t4v1dZGNu9v+vN8ugzZ85TPMNZediwPAsXjMUnW/E7hHMN/lyZ\nR+YlC3vMXdnpr1mehRmef9r98IMyRCpnBEEQBEEQBEEQBEEQKpB/rZy5ugr/UtJgaCN2rLwc/0IT\nteaIjj0N/oXV3An/otl6agcdZz/j//rvFOSu44I3b3R8e8kRluc3EFUW2U/xy6WtP/6uqRWvdAlq\nhSoN+utXeLe2ioNf24uK8K9lObG86oP+C2RiIv5Fyy+P57WeQf8VGa9JvcGrVwx/LTc27/2If+Fa\n89kGdqzbnP/D3lfFV3V13y7i7u4hRCAJ7hrcrUDR4lKkQIEiBdoiLS2UFittoVCguEOLWyjuEJzY\nIQlxd+c+/H//Nebc9/v60sPlPqzxNGHPc7LP3mvNtfY5Y8wxXsZPNhyXsZGhIcv7Y+bPMp7463cy\n9grvzPIef4xf2iK+GCnjnETOzti8cLeMh9XBt6kB7fGt5rfDJrPXONvgOo37Beew/9OvWN7Hy/AL\nGv1Bz8KJf7PaLBzfcPsNxi/05jacxaE7CbaMRx0cS72sY3lhw0aId4XL1/FNbfv2/JdtJxf8IvDw\nDH65Sbim+6/vV1VVJOOYE8/ZMcqE6B6Ca9a7A2e7GdviW+GaA8DmeLUd7BMTzTjavxaMrP5jMHZy\nHj9keZam+BUs5SY+e9jIRiyvuTvO79bKszJuNrcLy/tx3HoZz9k2T8ZLhn7N8ob2iBDvEsf/RE0N\ncHVlx7w64Vee4iR8+96gIWfa3duC8VhvOJgM53++yPLswlBTX/35VMaDP+3N8n6cvUXGY8bh2ME9\n+EWY/gIlhBAmFiYy7tYBv5Id+PY4y6OMG/rr9eCF/VheOWHC6f5ErYjad5/l0V//x03De+z77TTL\nq9yBeqtv5oyJJZiZtsH8VxNDU4x3EzI/on+/xfKqqsEA0u3Hvbmdx39NC+sN9ptvS7As3BvyefDr\nx9/K+INZWHdSz4DlaOFny15TngdW03fb5sh48YS1LK9hTfyK1WDOYBmfus/vzT3CqGwVgl8zf1+4\nl+VR1pV9XdRTYwu+bn/x6zTxLlFzNNgjdzdwVgj5EVN0jsAco0wZIYSwMsM9zk3Cr3M1w7xZXosp\nbWVsYIhFacv83Sxv4hqsmcanUQONHmBcOVpzZnBVGcb6zu/BQBi9eCDLc03GOZk6YR8U9SKO5T3d\n/UDGHuH4Fd7MxITl0cXVyBxbyZNrz7I0LatNnwj1xmeytgtix+r7+cl44jfDZGxiy8/nk1WjZfyU\nsLp8yV5TCCHMyDWrJCzAwavHs7w7K/Erf8wOMOvCJ2EflniTM1isfLCGxx4E+zh0OmdqJZ0De6L/\nigEy/nLoapZXMh//9nRBjfLVMD1K0sFYbPYJxqiNM//1f/nHYL9tegfMmbxo7J3NnPkv1vGEBWtZ\nE+sJHcNCCOFEGCOJJ3CdGk3lLCDKkqZ/t+QN/2XbpZWPjB3qgtV3ZSXWRe25mtijHvj0BTvq6fob\nLI8+xzgRBUDk15wp5V8bxxwb4/MZ25qyPEMy/3KfgvlcXFbG8vwb8b2tPuEVivOL2873czah2Kdd\nvYj6kvOKs4KjCMOwfXc8czYM4fuPGqT2GBigLn38yyyWZ2KCv/t0+yH8P3kuvff9SfaaVykpMvZ2\nxNwpiufMKjPCrHhyDePNNoQ/Z+z5Dgz9viPwzOnfi4/Lohw8cxqRtdDQjD+mFybj/jpzIopeUGs8\nni9SLvG1wcQO47s4FfOlSsPKbTonQsaXlmNv5vuMs2PpszR97jCP4vOq96JeMr63EXskyvyrLqtk\nr6lB1DgCQ07cW8fX+safou7FHMbzU0A/Xv+NrTHnvNthTxS5n8/ttkS9YO2PepXzB98v2dX+55un\nmDMKCgoKCgoKCgoKCgoKCgoK7xHqyxkFBQUFBQUFBQUFBQUFBQWF9wj15YyCgoKCgoKCgoKCgoKC\ngoLCe8Q/9pzpvAT654qKbHYsdg+6gIeMhRa+Rg3eY+L+99DbucyGjjU5nrt1PDsOXWkx6TnjWc+T\n5ZWkQiNrSHrLeDaALvzSkm3sNQ0nwlUn8x50bRmWj1heLnFvcmkJvWnaRd7t3dgBurvAluhE/fyX\n2yyv8dwheO9k9Epwb8ZddC4thVtAPS4T1wsSjqKniL+LCzsWfwra54QUfP7hiwewPOea0CHm50NH\nbWbmw/KatoROL/4M3ltr5jBn22IZV1RA9zux4wcy/mw+7+FCu/PnZKGHw81X3JWiaQG0qtl3cb+r\n21azPCPiFkQ1rLRjvBBC1B6APguRX62RcfhE3gvk9S1oV4PbjRH6xMjvoZnXjrOo1+jc36wRXGEO\nn+XayhFje8j49JeHZdxz+UcsL2/xThn/sQ49RLR9R0IbQL9cko176BrhJ+OEbTfpS8TY79H/gzqL\nvL4cy/J6f4Vu/KYW0P0aGnKdeUEGNLEWpE9NRXERyysi2uv4v3D9Zq3h/QK2LUJ/jOYzhN7RmvTi\neJPNa+rlXdDSUveNm5e4M0/bARh3lzfBxabbrK4srzwXHfQDe0H/vu5L3neqEbmvUZHPZDxgEPS8\nT6/zOdaoHVyFrP1xrt0GcB31q6uo871G4v3OrDnH8mxIX4qgpjifWnV5X574lahRx3egxw7tLyGE\nEHX68BqrT+REwwXg7uWn7FjjGuhdRfubOTThDjs++ej3QrXW6Xl5LE+3FZr8Ftfxd0Om8F4UrEcY\n6ang1Bo9OQxM+Np8aQvqc1iQn4z9NGuEvSX03wVZGAfz/viC5UXvj5Tx9atYzweM4X3JfNuhR9GD\nVXtkHJ/O3RNbjeVjSd/Y/wVqoNbpbNAw9MerzEftoK5LQvD+L9RVw4O4rQkhRGUe9jQXbqMfQ7PA\nQJaX9QBjgfaRMCPODla2vAaakXEW6I6avPfbYyyvZTD2Xz4fYJ2gfVuEEMLCF73dinW4Lq62vGdR\nWVaxjJ9ewrhoVI/3frHWuCnqE40/wTwozOP9gHquQP+J6mrcw21TeZ8x2l9o5LrZMs5+/YzlFcSh\nXtNeAoaGViyvJuk1snk59nb+Q+F88vhP7iZI+yLSvg63Vp5neXWGou7a2KBn0pwVfL+RH42ejgf3\no07OnMj7+FlaYj2KOYl+cMZteU+Tpppxqm8knMc6kVdczI61nY/6cflbrButZnBHoCc/Ya/h2xNj\nvegNr6k5j9Czw9wT8zfzJa8/lr4Y79Z+WOPqDcQ9yHvBe6bQZ5L8ONyD4PG8R1h2FNx9Mm6gB6Wv\nP+/J4RYBR5y7m7A/cHPl7lw1R2IsGBjhN3dtP8tK4kClb9jXR+1Jev2SHbPwxHlM/BXzr7SU99/0\nuIX1NOcBrlHQeN7vkO4Do37BHKN9nYQQorAQz11GVrg31GEtYgRfZ5yTsec4/Sdxn10ySXMO2LPE\nX8E+1M6f93WatnmBjK8sR48xnwi+htO+Z+HDMV6MLXmvr1Or0MNl0pahQt+oLMZalUn6FwkhhIU1\nPnPuQxwz8+A1MOMa1sla9f1k7NObu1aWZmOu27xArx4zV/5+f/+AGmZjgXv/thLPdHfWXWGv8aqL\n7w4aTYSj4Y2NvN/X2WV4bgtvg/OrLCpnecUV6I+TeRN7sebtuaNVxm0cK8vCHtzRk/fjLUsrFP8E\nxZxRUFBQUFBQUFBQUFBQUFBQeI9QX84oKCgoKCgoKCgoKCgoKCgovEf8o6wp8TKkI6VpXCZQWQh6\nHLUy0yJ4LOhZUT+CjpVdyCk9vuGwjDMl9nQFL7jldnUlSKj2hPJubAwrwnaLB7PX3F0Je0lKd8y4\nzil1/82+zKsPtxW88QvoZz6+OAe/D+qwvNeXQLNyI/KnjCfcVtrDj9PI9Q1qtRlYh8uQShJhh1av\nJ+hZVSWc/pgeAxlbQQzovQ71uVXf8weg94W3IxQxDZ0yKwbXIOk4KJCfTvtQxs/OcYtnbx9cpzdn\nQYNNy+UWd/Gn8H7JRDpSklPC8hrMhm1wFpFGVRRyiY2RDyh2D+IhcWvnPpXlWdgni3cF3UFYvOVo\n5k7Tehifsa9gxzdjA5fsWNrj3lcTOuC9Vdz+uNVU0IV1Xx2UsZZuTO0cE//ENX9L7PH8Armcw9gC\ntMiSDEihXIL4HDi1FBTrkJqg3VeXc2lachbub3klZFK+mbxehXjgPGLu4h769WzM8qg17ruAjRvo\nvTbunHJMr+fJP0Fh7tSyAcvbvA5yDGtC8bT7jVv62RE5ilN90KWb1Kr1X/OCeqKGWZDzC9DU/3Ur\nIEehsggXjfSBXk+bWpA3ZBVw21Jqs93QF2vG053cfrDPVEi3it9AchF3g1s+nt0GuVftDuOEPrH9\nO1z/LvXqsWO6e5AYNhgD+dmTHXdZXov5sLu2Pw15bdrjFJZHZTSBE3Bdsp/ztWvqFsgtk1+A+u8U\nBJlV4mU+Pkb9BHp5/8aQ8Xw+kEta68+GPLe0FOf3avcllhc8HO/xtgLz9MweTje+s3SrjH2IFyi1\n7BZCiAVT18n4wB0uvdQHcoswpid+PYwdy7qPWl5RAHrzlDWjWd7LLZD4WnlgviRF8/vo0t5Pxs1z\n8HeDRzdkeZSmHbke1zdiBqTjF37kkkCnFMy/luNA0a9hwLXEt7agplTsh6wmZByvgclkbX2iw3hu\n3oGP9RcXsIZ7+aJ+24bzWp50EXMzrJfQK0ytUFO0+9DiYpy7uTmuUc9Z3Vhe5MZIGRsZYa03MOYy\nwLP7se8btXasjMvLubQl9ynkMcFk3fls4AoZL/iCy5CcG+H8zM0hZdm9YzbLc4wENd7KC/I4rZWt\naytIK2Z0hhzjxOKjLC9iaoSMK4h8rziVy/wadQgT7xIeLXG+Plb8PubFYJ/gF4Tr+Xo/l5Q6hmGN\ns6mJcXF91UWWRyWvt/ZB4lzTnUuKinTYV1p6Yl0zNMWzgV0oH+tpkTr8g8hL7QP5vjv3EWp+0ETI\n8PNiM1leYSLOga6ltJ4IwaVbtD2DR0cuRafW4fqGmQP2IgGjeK2oYQgeQPRpWM17R3C516W912Q8\nfM0nMn72C7e7zs7C+Iz4Euv7Vo1ksYLsCcesnyJj9wisNaVZfG/j1hDPQQ2eQFqVeIPLYWwCMMba\nk3NYO5bLfT/+GX83bCRqbfK9OyzPwADXKOsupDEenfh+7cPvJ4p3CbpuMLm0ECIjA/u0uiPwWR7+\nwT9L02ltZJz0F54NDA2tWV7GdTzj5cXhvbXPGt7OkDwFTsLfTbuB/ZFXPS/2GkfSdsHCCfeK2tML\nIYR1LUgE819gfuQ+53W9hLRbMSUS5vRnXPrVYAbkaq+PQBqbmcTbGFS/fSv+CYo5o6CgoKCgoKCg\noKCgoKCgoPAeob6cUVBQUFBQUFBQUFBQUFBQUHiP+EdZU3ESqOdamQt136msBMWsulrbDRzUHRNC\n2/cL4t3GUwid29MKtCPP3lxS5Fsb3bgTXhyQsZkZKIlv33KKZ9BIUIdpJ2qte4U7oQDuXQg5R6gX\np0E1GIj3o/KnU+vOsjxbIhcwc0FcGJ/D8rKTuSxH33BuBcqsZ90Iduz5wUMy3v4T5C1aeQKVQuQT\nypl/F/5+NWqgC7pba1BVjc14p+rSPFDG6s2CvOiniatk3KEZp0bW/Rj0+uJiUKW/rc1tdSoKQM89\ntXK/jNtNaMvyVo5cLuMvD/wq49gLf7K8jJuQ2Az/CnZaO6cvZXnd54Iu7cCH979GcQaol20+5648\nO2bCfYfeN+2c3fEVZALdJ3aQsWcH7sRwcyXuYYsguEhoaXibFqHz/EdT4a5UXYn591wjTfPIwTiy\nIB3Zq4KdWF59MjdPHgfVdeyq4SzPhdB+Hev4ybgonVMSG7eCi5hXN3ymF79Fsjytm5m+YWwParJD\nPU6jLk5GvW1bB/KilERORe5MpDROXhhoLm24S8DObyC/8c7A9QhryGmyzx9iLm39HvWAdsXv2Lw+\ne83sb0bL+NVhSO7sXbhUq4hQhtNIB/82dXjX/rMPQfOuKgMVmTr3CSFE/ivIXKmbxlvN2Ow1vYt4\nV1i0d6OMy8s5VVV3NlLGBoZYJOM0TkQvp2+WMXXY6fbNfJbnexkyhEoyn32acGnG3TU/I68IeWUd\nIOV0b8WlCYWFkHIuGg45afCECJb38Ae4YZy6B5lZx3DuiEUdNPw/xBp5/x537mhVG/eeSuqeJSWx\nvLq+fDzrGx8MRQ18tI3TshMzMeeaNCNz8SKXz1H6tW9jON9QV0khhDAk9Sx8BqRHT9dzqVkxcZUL\na4m9j4UT1s/eXw9hr6koRd1Ivw4pj00gd0nqsLi7jN8Qd5xXv3PpoBVxVqHz7+EV7l5EZaSBXqgp\nO9fz9XPo+O7iXaEkF3XN1MaSHdv3GaSXtdxQa9t+wV1XzE0g97q2HPPSrRl3sRqyDHK/4gzM+2s/\nXWZ5TxIhOZy/8xsZNzoNmZqVD99f3SJrbuflc2TcvQGXtDaZPl3Ge6bPRd5S7lKza9bvMqYU/LYf\ntmB5LgGQXmY/QK1xD27P8pKO/iLeJcqJ5Dz/OV/vTBzN/2NM5fpCCOHRHlKVLPI8EdKdtxugLizU\nEY66ngkhRGEipEI5TyBdoPNK69ZE3Z8KXmGMvIjmY8SzN/Yg5Xnks7/kbRwC+kEiQWXGSYf5vsql\ng5+MvUnrhpznfN2hDmv6Rg2y3pla89qTfh9OasVELnbtm4Msb8DXmGMvd0C+aWDOH1WpPCjmBPLG\nbJjL8k4uxL7exATnFHsKcz5wMHf9SjiPdh5tFkOuFHPyDMt7uhmSuMJSSJ4+3baS5e2aAalV43bY\nh/r24HJSAyL9KiFOPtTRSAghLOwrxbtEwhGMLWtNnbKswPimz7GNJ/C6knAIa4WFD8ZtVRWXS9qF\noS2IJXHAC/Lmf9fACOtnfizmSCGZL6GTuWa2tBjSsNd/QQIaOrI/yysvx3t4t8B++taK7SyvVm/U\nEd0JSHoda/KxHrv9gYytAvF+QQ34fj/j8mvxT1DMGQUFBQUFBQUFBQUFBQUFBYX3CPXljIKCgoKC\ngoKCgoKCgoKCgsJ7hPpyRkFBQUFBQUFBQUFBQUFBQeE94h97zlRXQNNp4c17CVCJP+3vUJzKLVJj\nTkK/1nEp9LJpMddZXvAH0IEZGUE7nJ7I7cvy8tCboDAB2sXbZ1fLuPaonuw1jt7o0ZAcBY13VSHv\nyUH7FrRuAT29sb05yytOhm6OWsR1m9KJ5dUwwrEM0m/BzM2K5Tl68n4s+saLg1Ey/nLmRnasGekp\nMn4h+g7Y1eK9N8zMYAX46sApGceTHgtCCFG3A3R5C4agf8z4bp1ZXk4uxsnmc+jdsuvqXhk/3821\n61VV0F4mnoaNYkAf3kvmxXb0/pn7y8cy/n3eHpb3yQZqsYt7T/sDCSGER0uMhayXsNkuKOHW3CmX\n0I/AO0joFcFj0cPh4Y98ThSWlsq4KekNZO7MdZu1PT1lbBuIHi+l2bw/wv04fI5hs9BLxtKLv9+z\nL9EjgtrQV5VDe6w1iysj2nJjS/SgyriSwPJyczA++gyD/r0ohWtWq4mFaLKmJwLF2ypY1FKb7pjU\nVJb3rq20nZ7fAakAACAASURBVBrjHqRGxrNjD++hB0h4MOxUw7rxfi+bl6MHyHDSe4RqhYUQwt0e\ndaWI9LJwac17eVjVRF5oUuB//H8LN26BSPvC0GtWpLGlzMjH/fIPg97asRG3WG9WhLltYov3q9ef\nf/bs++glsGM36lAnjaW1sbWpeFd4tHmHjPOS89ix2mNgDZp+FZrinlN5/XMMQt+VGqSBm+72CZYX\n0O4DGb99i2v++ha3Fg0aC823gQGuX9JF6J/T7rxgrzF3RZ3z6ImCtW/OVpZHxw4910yNHfrkLrAX\nnjYEdaNVH66tz4vCfsHEEefaqB/vr5FyWSfeJRzro9ePqSNf491I3wvPrpgTVO8uhBBVj9FfhFoo\nGxry372MzFEfM+7gNVrL0LpDUOdzn+E6/f01alZIF96vyYiMdWqDSs9HCCHyXpJeHtWoh8k5vAde\nk87ovde7LsYtnZdCCJEfi54aFqRPTd+urViedU09N2AjOPEN5suA74ayY3RdDB4Me9zYc6dZXt+V\nn8s4+swRGW/5+RjLGzsePQ2K4jHvGw9vyvICHmCvFH8evUb8u6K3xeVlO9hrnEnvMNoLqv3y5Szv\n1XX0QRiyFr0sKir4uth/Ps61mqx92t5KDnXRB+HJDfxdI4sDLM/AjPdn1DcKY7GX9/0wlB0zc0Kd\nSr6A/ZdzS94TKPUK1tOEO9hPuGn2soL0y0y78d/nondtrFHGpF9m5h1qc8ytquk+oyIPddMmhPfU\nO7cePYZo362Wn2j3sufxOTpgT+D1Aa8BtF8OfW6j67QQQrg055be+sTez9E/ZtiqwezYncP3ZDzo\nhwUyfn39HMvbNnuXjHsNj5CxPelNIoQQ19dEyviDH9DXSXf/CMtrvwj7o5ISnYzLMui9JgNCCBHU\nC2tuwm08Szg38dLk9ZPxoTkrZEx7rwkhROsP0deJ9jk9OHcby6smNbnfMry3kSl/HilIwb5b370t\nhRDCOgg9VFyba6zY47BfTjuP+RZzNYbl+YbjWhmQ52C6NxFCCOcQ1GXdhat4v4t8b+zRBedBn6sr\nSzC+S0vesNdYWmNPEzII8yUv4zHLSyH7cJ+e6MvXYHYPlpd2H/trK1vcY7cIf5ZHn3GybuOccqP4\nepycjfWTr5j/A8WcUVBQUFBQUFBQUFBQUFBQUHiPUF/OKCgoKCgoKCgoKCgoKCgoKLxH/KOsKWQU\nZDoPVh9nxyqrQJ3z69pcxlWl0SzP0R3U+IoK0GdTz8eyPFsvUBSNjUFB8qzZl+VlZICS6toIefah\noAgVF3BKlJMrZBH+TSCfcqnDzyH6IKiGoaMJte0Gt8GLOg6ZUHgPSF60tN+0v3Uyrq4AZS07Ko3l\n1RzCLUn1DSo7+Gr1ZHbs1VHY4JbngAaccZfLTNau/E7GS3bNkjG1OBNCiAtLQbdftnWmjPcv4XRD\nalm84zJsH2vUwJCsO2oUe036a8h5jIi13qKB81jehvOggv40erSMhy3gFmpJJ0Djvf4qEn8nj0sV\nXmdAapVbBNnGRxHcgq88q1S8K1xYBfpnSF1Oo+toivETMhXnVJzJKfg2bqCeX12Ba3QnhlMSqbWv\npSekTAlHuWzG19lZxgVxmNvOzTGXg1tzm25LD5yDuxekD4XtuZ28O6F/eoaiDsVe4lTzPEL9f/5C\nJ+MOn3RkeV7tII8pzwDtsP00bhmqtcDVN46sgFSvxyR+jm2DQSelMqLUSB3Lm/gFrHT3rcb7NQng\nFFSKesSWOG4vp3U6N4PUypJYJ1JKdNJxbodcwwhUYOc2uN8mNrwGlh6ADPWvdbCiHLZ6GMtrNLON\njDdOhaymR9smLO9+FObsqI9AO31bWc3ynm29K2Of77jN7L+FZzeM6dwtXCZw9nvQoFt8gHPf8e1h\nlhfgCpp2xAyMA4cQTjsvLsYaVZwLiqyFO5eZ7Zvzh4xb94SMyNQBch0zZ06PptKv9GuQYPVd3Jvl\n2bqA6quLvCjj28e5BbOFKd7vdSzkZzUtjVmeW1dY3hbEom7kPOASw6DRXOakb1AZ8+8/8PWpf2cQ\njdPItTl5/BrLGzCxq4z3/wKZXedm/NwNzbBe0eser7FYdzqB/VMOWWuotKwsm8tp7UM55f9/4Vjb\nj/07PylZxoXEzpbS6YUQYuokrPUTusCSPimLryf0/IaMg3zg5q2nLK8Fsdj1427u/xoVZB+aG5PC\njvUZjXnlHAi5oVMtLvXYMW2RjGNS8B5zNkxgeZaOqJMVFdhvFrzOZnnWxD7VoynmopER5mwnIvEX\nQog1o2GfXZaF+2sdeI/lXTqNejNyDe57zLa7LM+yFvbdRmT+tVrA7WbpHuEV+extArm8xtCcz2F9\nw6MH5NjXf+ay7ZaTcS7u7bD3MTDmvy2/OIR9uUdtyLWe3eX7/C6LsW4kX8SxOsSKWwgh7q25ImO3\nOtgTUelSygX+3lWlGI++AyDPKtPYIfdZiueaZxth3Ryz/SHLMyUtFcwcIKV4c5bv2Xx64W8ZGCCv\n4CWXeseew/rZ5/t+Qp8Ys/4T/J3DV9ix7kvxPJWVhDGcdZNLUWzM8XlNiVTr7Ldcijjw+xkyLiiA\nXLfoNd9H5r/C+Kb3LS8ftev55rPsNXb1Ma9KifzJoQ6XNdFnFSMDjMXos3ytr8jDc0FyFGpw26Hc\nfvrMdjxnGpmiVmQ85GPM3JWv/frGs/PY55tr9hlUNpuehWvt7sFle8Y2WOOsA1APk87ztaEwBuu/\noSmup0NDd5ZH9zu5xL7e3AMtQm6svMhek5AFaWbzpmi3YebK90GurbDnoi1VatTg0ninethD5xPZ\nc/Yjvm/xiEAty76HmmriwPfGXgbcglsLxZxRUFBQUFBQUFBQUFBQUFBQeI9QX84oKCgoKCgoKCgo\nKCgoKCgovEf8o6zpyTpIVKjrhhBCtJ0HqYHuzE0Za10y/AeDx2phAdqgS1vuGGJiAplLeuIlGWc/\n1kiAOoFmW1IC+VIpcQmx9wlmr3nzAp+D0gRt7OuyPKcmoK0+/g2dxymVSwghmoxC9+37f4Ci52zD\nHa1c2oAudXkP3Kk6TeJSioqCMvEu8UCnk3GTeiPZsVO/QsrVrCmurbExp6l1awDqoLVtiIx/Gv8F\ny3tE/ladh8izs+AdzN19IYkxN8d12vrxZzLuu5TTLncuBk1txFJIFXo2asTybqxB93YqHzj24ymW\n1490424wYZKMDQ35GE5Lghwjeguo/K6duLxI62akT9RugLlDZTlCCGFTF3PH2BjnYGDMXTj8BkP+\ntK4f5EHf75/P8vJiCBWUUP/9P+TyuxJCxy1Lx/w79DXee8ACLpE4vRxzMdAbFF4zd+5gVmfoQBmn\nxUbKuDyPS8diY0GL/WDleBk/XM2dvorLcd/CRoNqTuUGQgiR9lyHf3QRekfvmaD/M8s7IURpJii0\nR7bBnUUrfSjaj2vw8WjQo22COU3ShVB8qeznxU1Oia64Bpp/NKG2r9oBR5ENs2ez15x/DGnUWB/c\n45sHuMzHxRbjsUkdyIEqS3nNe30Y9OuIUFC095/l9Ojhg3FTXt0G3dfXx43lGRq8u98d0q7oZOwc\nyv9uzm3Mg8qichlrpZK5hZDPdXOA1MDGhs+x2MtHZVwQA/mElvbbYybkNSmncX+De0PKmfSQ035T\nL2H9rDdxhIwf/fIHy7ObCCcs9xaQEofGcwo5RfPJkKm1rfMhO3Y7FXPzs8k/yrhFMF+3nWPhpDJ2\n00Chb+z6Amu8vRWvP5a+GLc3/kLtGDqXy6yz7oKmTu+xmRunTsf+jlqXSNwt6fokhBBBkyGFozT8\ne2vhZGFkZcJec2U17quJIeSgTxL/YnktyfU1J/ug+h25O041qUvXX2Dd792Yu2759MD7Femw1mhd\nDHNf83VIn+g4orWMr2/nDqB1GkLmaWSE+/nqr4Msz5645fRtib1dVVkVy3tzDffQqQGRgnrydf/G\nNjiClmWhpmc/QR1vuWgKe02YD/ZAB2/g9ctnr2J5Vv6QK5UX4L21rjx+HTH/CnNQJ1OucmlycSL2\n9aM+Q60ws+f7NSuPd+e4JQR3gfMP9OTHiPNZ2jWdjJ2bcrcmB+JO+bYKY5jKr4UQ4vYPkI+E9MPz\nyV2NC2azuXjGKUrFGK4iDjGedTuw1+Tnwx3P0BDjKlezhv+9IVLGoS3hKvP0+iuWFxaGPTTd+1DH\nmv/5W6hfur9uyzhBI/Wr1YTvWfWJykqsfTWMebsDExM8TxSVYH/ZeM4klleHyHhLiyAdGbp2CcvT\n3cA+Mv8F8rQukA6B2DfvmPGLjBMz8Ro694QQoit5Zq3yw73ePZuvi2N/gsubB7FNonIsIYQ4sxd7\nmAHzsFcqiONyyHEbIa9Mvo9a5tGkIctLi4oS7xLeXnieOPPTeXZswDcDZFxvBNaD0+u561bn1njG\nfbkP59todgTLq9EJcsmsJ2ilYenFn6ULyV6WOsNSGf6Z/Vz6lk/c12LJvnbygiEsL/sJvmPYuQ/P\nmEMW8jYY8fvRAqT+rO4yzkviLUDo3tarB+Z27O8PWB7da7cR/zcUc0ZBQUFBQUFBQUFBQUFBQUHh\nPUJ9OaOgoKCgoKCgoKCgoKCgoKDwHqG+nFFQUFBQUFBQUFBQUFBQUFB4j6jx9q2m8QHBw33rZGwT\nyPsZZNxMkrGpI/TL3p24Pi43Xifj1HNxMvYbyrX1qZehf/fsgt4EMVu4XaexHfqBmJC/W6yD3ttY\nY1mltWT+X5SUJLJ/v/gdvUV8B0KHXVFUwfJyoqAV8+4Ey8yEc1xTZkAsJA2ITZhPu1YsL/kuNKK1\nO44X+sbeT2Bxp7XDfJMN3WOPBvgsDWZ3Z3knFu2WsZudnYxDJzRleU8347M41YZ20aEB75Hw5ji0\ntV79/rOu1srHjr2mgOja96+Fnr5Le26369EFWnNHTxyLPsHt4IN6wd4v8R40k0aWXNNv7gI9bzyx\nIY56yi3u/n6Gvhm7b94U+sRLYjd+YMNJdqxHn5Yytq71n+2YhRAi6xHG7U3SR6Ef0ZEKIURZLrTD\npnbQfmbc07E82hPh6mHcd6rxNjHiPV0s6bWMRr8YL2fe4+heDK5tf9K3JmbXI5Znaol6YE8sEGMv\nRrM8rwbQp9vUgj64UGO9SDWsgc15fyZ94NIi6IqpdakQQrQeiH4HmTdQX0/d5zVw1CxoYWNPoScE\n1dwLIYTPANgH5r2CxjrmPLfFpv3EfJ1wH1JycW3onBdCCHMLXPdVB2FDHOjBNd/RyejJMbM37iPv\n4iWELgN9OFqNwniuLud9H3atwRw2NYb2eJhGH1yRB91vcLsxQp8oLERPl4UDprJjUxcPlbFDCPTu\nxZm8d1riMdy3FzHQLA9b9xXLy82CRe6LX9DPJ4Fo5oUQIrw1amj+S9R4r17QPN/bxfsBeTigf4Vd\nA8ydoG6DWV52JuyjL3wNXTe9/kII0Xhccxn/tnivjMcv4xrvfNI7p6oYa+vdyCcsr89y9Bxzc+MW\nwPpA3P1dMt777TF2bOgijKeL69HTJawet6u/dhPnTLdSJeXlLK/nANIDJBqfPzE1g+WFd0O/g3Sy\nx/Im2nUzTU+DyLU4v1bj0YMl9iC/ntsuoZffFysm4oBmMmbfxpy1ro31ZO33e1ke7bMwYeVwGT/9\njY8zV9KfpcEQbiH9bxG5eLGMad8SIXjdHDoW+5lyjRX5r3+gB9Ky3eitpdv3mOWFTcB+4fluvMav\nP9/Lxh9Aj4Wnj7CO0fNr3KMee82b67Br9yX73+y7fI1wagE7X9o/8d4Wvt/wCUOee3v0GXn2G7fc\n9u6I8fzzd/tk3Lcp39fRvmfjN28W+kb0DfQ3M3UwZ8cKYoltOakdz5/qWF7TD9B7kFpGa/t9NeqI\n+0WtuZ9u4NfQtw9qqnMI7ldWLMaFgQnf32Q/xP2ivUcK43nfpavXMEa6DYdVeHUlt7W3q429FN0b\nG1nwPWp+NNYD1xb4TK80c9GuHup8WE/e7+XfYuVQrH39pnZlx2hvLmplXBTP783TWMyDQatgZX/r\nuyMsr/ZHeM5M+1sn4zof9WF5F77EWG3/5WgZX/16p4xbLuD736oq1If7q/FM6NnGj+U9O4P9fv0h\nGHsHfuS9vkYsQ39Meg8NNH15rm1GX7H+K2fKuLqaryWpUZjDQa1HC30jIwN9Zh6vjWTH3Eiv2Lxn\nWLsqC/g56lJQLyxNsVdsMYf3W31zBvt0myCsNdSKWwghorbjM3uGYo8ZdRN72QA33v+Pfi9x6Az6\n/lia8e8HenaDpfmZc3iO6dKJP1cGfthOxklXsbb4RUSwvIoKjGlqt/58C++JE0h61zo7dxZaKOaM\ngoKCgoKCgoKCgoKCgoKCwnuE+nJGQUFBQUFBQUFBQUFBQUFB4T3iH620sx+DmlT8mltp2zcChcip\nrp+Mk69ym6/MO6CzBY0D9evyKm7RFdoeFp3xhE6anVvA8pLjQHvzcgQNyj0cVCeXltwarUYNfAdV\nUgKqcFkpp4yaOoOGWJyCv+vXiNPeipJAhzY0JK95zSl61B7YyhVxUX48y9PKT/SNLkuGyfj70SvY\nsV7Ehvr6S1DEbA/ZszwqfzIzAaUy50kqy7tALHaHNwaV2Mmfy91yfPG6ymJQ4qorIGPIeczf+4+N\noBIv2IXPcevb3Swv7VfQUy1MQD9LzuHU0sI4yDbuPAENtnFwLZYXPAkytIs3YadpoKFRbzy9Ubwr\n6E7h3oxZOYwdy7wHeZBtAKRkayf8zPKoVWb/FXiPvXO2s7wRa2DzeWclrHxtNZah1sS62d0e48Un\nAlTphEgu/Ro7E/aDV17sR96BZyxv0FJQyO+sByUxfBgfRwe/B4V0UHfcNyc3LsMpSUL9qi6FlMK7\nV22Wl/2Ujzl9w6UNatPxHznFvO5LUJN9BuC8hoZzK1ALUkucvCAtsPDh9oNp11ArEx9Cwhk2uAHL\nqyqFXeSy+ZtkPHUQpCTZqby2uZB7v3ASxpJ1LW65mnABEiALF9CZTew5tTQ7HjVx+3eHZUwpsUII\nMXIexsWZXy7IOEYjQbD3J+fRTugVB4i97Re7F7Fju2f9JuOGIZArbT/Dbay/PvCFjN9uhhymuJiv\nDVdW4jMGNAZdfdDnE1lebhYktQYmoEtTu05PNy4dbDgTct+d05fhfDRC50uHUU87DIB0SXcljuUl\nHUONmr4JlPTCJD52bp5ATW7ZD3acphoJZMxW5Ll9rn9Z05PduGZaiRadE7UDQeU+eJLb7fbvAAme\nO5HTvtXIE/KjITWjlO2W/duyPLrm3YvD9Y39HbK4YI10sJY/ZEOn1p2VcWU1P4eKKqythma41nTN\nFYLLwg9sxft9sWkayytOxh4p+Szq/LWXXDbpR/YODbjC7V/DsTHk0o4N+HXxyYWs084bc6cwk8vZ\np1phf7frM0i37sbEsLw2UaDgF5ZCntDDjduwU9lGw06Q0AT1gkzv5XEu06AS0mAnvL7m8Losz9EF\n4yX2yiEZt13Yk+VlPNTJOPkc7k1sKl/fGtSDFfTi3fNkvGDgMpa3YPUE8S5hUxPryesjT9kxWs+o\njXyLIc1YXtZt7IPqjoYkIT+GS/kLY7EPfLERMga31r4sz8YfEqDiQp2M72y7JeMiMg6EEKLVCEgk\nflqGfWmPhnzfUpfYNydfwXtbOfOxVJEPea5tCOr38+33WJ5LGJ7H8mIhN9FsUUX8JYzhMD5k/jWG\nLYd854/P97FjY9d9JGNLy2AZ56bx58UQW8g/bxMpU70pLVheNqmTTk1R/84u/pXlvSSy6uLPcayA\n3LeSQj4nHF1Q01stwpgwMuLPaenXUUcK4jCmpmyax/Ky4yBhtg9AHSpIecPyui9FcVw/DnJNG3Mu\n8/Mge+2g1kLvKCW1KPQTft0f/oi9OH0OrDWG7ylr7MJ9zcymMh8+IJ2bo91AYSL+rl2QK8srr8R6\nbGSJtboWkTLdieXPGi1sUf9ruuL9fJz5ftq9I+TnXaqx+TGy4nvP4jyMJdpyo7yc15fHayCFc2iI\n86thxLkwqdcxF537KlmTgoKCgoKCgoKCgoKCgoKCwv9XUF/OKCgoKCgoKCgoKCgoKCgoKLxH/KOs\nyT3CT8bGtpyGXl0GmtGrrTdkTKUOQnAnEPvboIGVEZqSELyD/r0HoMXaWVqyvBW/w7Xm+KENMs6+\nD4lSJaECCiFEUiXez8ofdCQTB+56UHvghzJ+fhCSC4Mm/DLVMMR3WsbGoM+X5HEXAHot7u2DhCFi\nfheWZ+vJZTT6RsJ5/O2+XVqyYzdugULKKK8aavvUzaDZ5SZBglKSXsTyJiyC0wd1wjEw4LTxmr2h\nNUiIBG3+6Xm8d/OJnLP3QW9QeqN+PChjrdzm4ml0PR/YPwJ/s2EYy3tGaO2NgkBJtwri0ozEs7hG\n6YTyN7R7BMvbOwvj8eOtW4U+0WhODxnHH77Fjnn1BE20NBtU80+3zGZ5Oz9F5/pAd9DB63h5sby0\nR/i8TxIxZ+sb8u7ygcNwDxMvgFJoFwLaoIEx//73s1Q4IKVGQsJh4sSpmyUZhTL2rk+clnw53bHP\nRNABqauWLp5LFm0sMNebj8N5v/jlEssrKQLdtXYHoXe8rcTE6qmhOu/6C9IXj+sYgw38/Vne0b2R\nMu7RFTITx/qc1k/dAB5cwbx6c4I7WVHq78c9u8mY0jBpjhBC+JjjnMpSUQNKU3k9yC0ulvFbwuJ9\ndiuJ5VF6KnWUi/iQ02pLM/D+JkSKcvExlzVNGjJcvCsMWj1HxleW/caOjVr/mYwNDTEes7K4LNjM\nDFTsep8QN4dy7sIUsQjz3tQU9/fp7v0sb+z8b2U8tQ8cKwauBJ28KC2dvaa6GvK+8grEBhr6LZWW\nuTQHHb84USN1Jo58VlaotcXG3AWl2xyMsYR9cBRqOqgxy/NtwddJfYNKH000tY3S1GPiMXAHdGvD\n8q7exLgb0AXreOYdTlmnDoDUcSfnCXfxsvCENDG3CGM9xBPjxaExdz6kMin/TNyT+3FcdrZowWgZ\nn9+EupemcbMZsxz0+n5E/lRZzF0rC4hU6/FDSICo9FAIIR7s4fJNfcLCC2u/nROvp/d3YK+YaoH1\nyaN7EMt7S2RdHfugnnY15/sPx4a4B0tHrZWxdh/56hjGNHUKovOlIIPL9ZsPggzn2S7sS2ys+Xs7\nzMU5+bWELiX+CneI8WqOfd6GzUtk3GtYBMszM8NYzIjB3/3+2A8s74cxy2W89MgIoW/cXoXxWG88\nlyuVF2A//5I4YZm7c5kJnRfUSTL7Ht8LmBPZmQ3Z6znW5/OqKAXj29IdtaK4DOfTfCB3dNn4NWRx\ndlao/5NWrmR5yydPlnFIfaylhub8WePWBbhTWl/HHqnVx1wOSVsDmNohjzo2CiFE9R7u4KZPlGah\nXvlppCMJf0LaE/yhn4z3f8nlfR8ugfS+3nS0E6hhwOUw1AnL0gM1IGxAfZbX1BXzJeMW9rK5xNHQ\n2Iw/Y2a8gXTn+Wa4XWUW8DkbEIa1cOvvaLlQ/OshljdtEmSTZkSyuHLGJpY3dw2kgwNmYN239OBy\n9Tvrroh3iezHWJNKk/lnrjcT1zN6M+p6ZQlfGx5EY+1p2wvruoERlwpd2XBKxnRMV5Zx9yffOqi9\n1JFWd0snYyr3EkII7z54Lnr8DOcT1J8/B26aA+euHu3hUvdA4x5pfh2tL6qI9rvVBL5OGJljX2pd\nE/XFVLNOvD6N9wvvK/4vKOaMgoKCgoKCgoKCgoKCgoKCwnuE+nJGQUFBQUFBQUFBQUFBQUFB4T1C\nfTmjoKCgoKCgoKCgoKCgoKCg8B7xjz1nTJ2gkbL19mbHLi5Fz492C2GZfH7JnyyvwQDogG/sRa8M\nJxuuo7t9HX0uapMeGI51eY+JZW8/ljG1q7QmvWQcGnDtqEPNEBmnkd4EpalcT5eTDn0h7Yny7CTv\nHxLQEQKxzMRrMi7U2OoZ3kWfBj9fnFPmXd5vwbkJ9JR2do2EvmEfjn4Oz7dx/XdJObR9zQIDZXzn\nJrc2TomGDrHNQvRI0O07yfIazxsr45g30EHf+24by6s1AZ/TrwOu9YPT0BQnHOTn0PCzoTLOiIU+\nWqtHHUl6/cSeIf2LwvhYajQT/QNMzKANPL+E93NoT/o+9H2Gvg0pibw/xLA1s8S7gu4YLB8tvPjc\nOboIut0hP0yV8ZkvuEX2kFXoB2Rmhv4Vz7eeYnkmdugvRS0fG88dyvLevkXfqKakz8DD1cdl/Nc9\nbvlIe4tkxOL6Zebz/hXNa0Oz7NwU9aA4PZvlxZ3G/U3adV3G7cdxTfbbKmhEy0vQT6KokPeJ8u3G\n+xHoG9b+0MWaOXEN6phgWGUWJ5DrwR1xhY8Tt0T+X1DduRDcErl2TdRvz178MxbvhIb+/H1o3D0c\nMCea1uGvMbHBGEklfZjqdA1leVZZ+LzUPtsphX+G8xdQl1qHwUa8opB/prNHcI/btaonY+PHvGdI\nLpmnop7QK3bP/E7GoQE+7Fj00XMy/vFn1JFZUwezvBvfbJOxH+kZ9cf3R1neoNHou+LaAvPt3nVe\nG7f/uFDGzi1wTun30QuE2tAKIURFBeZS657QhRcn8bn4kNicd0jGsd8Pn2V5U31o7xysF9TGWAgh\nZvdFX55vDy6Q8Z3vef+ne4dgpT1i40ahb/z1N2rqR9P7sGMWpJ+FywP0j3n0iNsrx5A+bSmnccy9\nWwDL27UM9vCNasK683FCAsvrPb6jjD/oh3WxPBN16sxe3nOgTWsM8PNRWD+rNZ7otP+Xqx32S90+\n7cry3vwFLbyFL9Ya3SE+5owM8H4GxCJ107K9LG/SV3zd0CfMiI6/qqqYHaM138IbnyPtMrerp/vI\nP46el/GUuXzOFpOxP7wt9g5PDj1keeGDYCvrqUNtLM/E+fn3CmGvybiCcXD2EWrwsgO890teHsas\nkxPGSkC7fizv0KyvZNw8CLX72M6LLG98Y/RyMCF9JdeO+4bludryvn76RsOp6C9y5Qd+jsGtsC+1\nc8S85+knTQAAIABJREFUzH+ewfIyk7GuB/XBOqS1+b217m8Zt/8CNSsvic/FfNJTKYXYkQf64pqZ\nu3Lr6/FTcR9oX84WQXz9TMnBudLebieOXWN51Hq4eRf0U9Ht5/0wak9Dr6Skk9gTVZdXsTyXDn7i\nXcE5GLbvzcbx9djCDfft9WXUry5DeL+OhEPPZZxGev4kk+slhBBdJqEh4Dej1sm4cz2+2J8nz3vN\naqEnGO152sKqNntNWQGeU5vMQx/SrdNWs7w6LnjdJ4vR484mgPddjd2B+hCfhPOZNn8IyyvQ4TMe\n2Iq11cGKj7EwzbO4vuHWCvbhN1fyNTlu+WkZh7TGmM7Q9BDstxjr6fHl+E6gVx0XlhfSCGvhmTX4\nzAO+G8byag3Cfj7rJdYnJyesY9peeeV5eB43Jj3lTv9ygeVtP4o9V0IGakrr2nxc0PWUzt+8F7wO\nVZdibOVEYX+gff609bYT/wTFnFFQUFBQUFBQUFBQUFBQUFB4j1BfzigoKCgoKCgoKCgoKCgoKCi8\nR/yjrKmyCPZY+W84banTV6BxRe+PlHGrKVxOUBAP+k/d5qBva+l2Fwj9zNEaFLi8m5yqWkosPyty\nQVuyDQNdyl3jgRv3N2hVRmawufLtzCl1lpagvZWWQpLk0rQmy3t9CzKQZ8dw3q4u3Mor5jXeo2Uv\nWBsamvLLTq0C3wWOrsDnH7dxMTuWMx+WkK1ntpfx+VWcst6WWGTT6+QzUMfynu+DjZwXsaz0aMct\n/YozQVk0sMc98SRSCvuGbuw1z3fhc3j3wlhKv8HpqMf3XZbxgImgbB9dy+U79sSmPZHY91qZcdv4\nnBewYmwwGxKnsuIslndl+Q4Z99RYJ/5bGFnDgu63ddx+cPZPsOB7tRuUPTc7TpuLI/TKoLGgSjq1\n5DTJokTYf9b7FGOiqqqQ5R1fAKtSOi8rqzC3UzR01HahoBuvOoLP0asJt6SklskvjoPCq7V9bTcK\nc9iE0Hkr8vic8myB97/17T4ZGxrw76dP/gZKdUjEWKFvnFyFMdhjbg92LP0ZpCB5xIK6wYdc6vhm\nP8ZdFaFQJp/hkovnL17LuOOnsBxPOcvzqLSiY4O64j/B0ILXLGNbjMd2CyG9ufEdp6RTyVOH8ZBp\nPD/3nOUNXzJQxr8t2C1jr9ecIkwpvn+dh0Vz57r8vPOeccmhPmFhAnlQ4Dg+brMeQwIT6AHpILVI\nFkIIv5pYK9Iv6mTcq1NzlmcbiM8fsxUyn1B/Lqd6Qmwea9yAVXpoK9TJpLMv2WueVUM+0XHJJLzX\n5oMsj8rbfvr8Dxn30FjBl5E5+5ZQgKuruXRw0mBYABsYwPb1+kt+ft6O/N7rG3SfcWb7ZXaseWOs\nVwH9UbNurTrG8iZPhvXruSM3ZNxTs3Z1bg/ZGJWlPkvi+6qsW8SyvhrXMDcPtZdKHYQQYukvu2Tc\nuzH+Tt1QLq2i7xfcBZRtKtkQQggDM1DAy7Jw7/KItbcQQniFQt7RIgJU+GYV3BLd3JnT8vUJOxdI\nPVKecUlIeg5qT/ZNSNizNJa4H/4wR8YWpyC5sA/lNHRzS3xeah1bJ5xLVkxNsRc9umuNjHdERsq4\nj2a9q0OkCkfPYu+1uDyd5SWehiWx/VDsKcvLudzXi8yd+0SW2LEFl/is/XizjOv64h5qZTiBQ/Ws\nDdUgLwb1OqQN/9vZUVgXnZvhHmj30eaemM9UWqKVO7Scg+eDmAMYM05EPi2EEO5tsc8VbfEe9vYt\ncN55Gpt4MjfLczF3Gs2OYGmRy7EPcGqCz9Q6jsvd6F7q5XWs29oWCrbnUPNt60ASnnomjuXpdMTS\nuo3QK/LTcA7WXrz+0WcG09aYV4VZiSyP7meaDYHl8Y0fIlnehq9Q8+asnyjj4T3ns7yEOHz+yfPx\nDPPwMNoivDzOra8NTFD/Yh9gz9x3ZjeWl3IW7+06GutxDtnHCSFEBZGbh8/EXunPhbtYXk1XXJch\nU7A3tK/N69Dl786Jd4nnGyDraj63PTtWlAJpZ+w+PPuWV/HneRvSWqLHLDyDUZtuIYQoeo336/cN\nWiMUpfFnKyHInpfIBU0c8axWWcjtvBdP2yDjURERMra0Mmd5ZyPRtiTmBPalp+7fZ3m25HmRthZg\nLQgEb7MRe1cn43Yayb9ZT97WQAvFnFFQUFBQUFBQUFBQUFBQUFB4j1BfzigoKCgoKCgoKCgoKCgo\nKCi8R/yjrCn9kk7G2i7fr/agizN1RzLQUA0pnTvvMSiaNnW4Wwftcr7qu50y/qgtl0k9TQQNzrsP\nqLm04370GS77KM+CRMCbdMl/vIY7Yzi2Aq0x7TIkAdmFXM5BuzaHdMT7ubfk3Z19ckHFSvoLlG1j\na+6a4drGT7xLxKWBSnZk7o/sWFAd0PHid4Dm3np0K5Y3tft0Ga/Y8qmMH/5+i+U1J/RNA0NIHy4t\n49ea+kh0WQI6bSahHFvEcknMsxc6Gcc8xv3p9GVfljepHbr7lxE6+IgfeAdwyg7/+xtQBdst7Mny\nSjJBjzYxAWX07+WcDmluwu+rPnFsX6SMP10zjh0rz4eEpyIHdNfQ6ZySGL0dTjdF6ZiLxlamLI9K\niq58AzcuVzcHltdxLiialUWgbl5YB2mVlr5tZwEqX0vSDX3/Fe5AQiVZxdRRrCunV//1C+7b6HWT\nZVyjBq9Dt7+Dc86xO3Blm0RkCUIIkf/ihfh/hQ2zf2f/jggDjbdeP3xO6qIhhBBerqidF6+Cntu9\nP5+zzsmovdd/gmyjXh9+DSNMCdWdzAlzD9DEH5/m7hAV2Rhn9o1AYTYzNmZ5bYeCAn5wzQkZa2nZ\nYYnhMrY0Nf2veVRy2LUxztu9ey2W92LfI/GuUNMPciUTE+4+YOYM2V2HuvhMgitRhKkdqLXUMez3\n4ydY3vImn8j43B1QrAtKuFRo6EA4t2z6Ay55fq44v9/OcTp0RDjOz+sAXhMfm8zyJv2Mc4g7BClZ\nRT6XDl67DKegOkPhclFUxOVKV68jL/IKxq/2XnclFPB3gQ/m9Zbxq93cccepBfYCD3ZBujBiAa8X\nk0fC1aY3qXV5T7mDw/NnOhl7JaKODlzEXXbu/gqZRf2P8H42CUSic5bvR54QSXhHIu9zbOrJ8q79\ngfpP5Zwe9lyOnUKkiLakXrf+vDfLy3mFvRh1gsq6w8dP6t9Yqz2/5tfv3yIjDvuPt1Xc1u5WNGQW\ngwd3wjlUcRerigp83lmb4QZaksGv89pJX8p4/s6lMl49einL694Ssi4q0979908yznzCHaOmToID\n3JYFcDCL3Xeb5RW8QX1ZNRxyZi+NBLBeB8jyevXCfujXr/awvFru2LuHdwHtPrDrByyvpITL7/QN\nYyLbprEQfJ+W+xB7WUNzvsb7DSb1ltzi0mxeKw2JbIU6qFQWc1kEre3GxpgjhoaYE1Ti9D9AfRQe\nmBNv33LXwc5L0BYi9jD2PvT5RgguYWzdC+Pq3OHrLM/QEnvP7PuQ4Wfmchl4YMdg8a7g4Inz010+\nw44VJaCGhn6EtSHlfCTLO3Ac//68I+abiyOX6DsTt9+C13hOmNCFrxkv3kBmTPe1dB8a+sFo9pr4\nW3DWs/DCHkgroystxHq1b/4BGWvbCdD9zPHP8Wyrle1aBeB1UcexRnpc4fX5dQZfW/QNKknSumW+\nJg661PEqMZPLyOs6QwJURNwfPdrzFiFFMbh3BTpIM+l3D0IIYd8IdYruOwoSML4NNHLfRcvwnJT/\nHOdn5mrJ8nIewlEpoDue58c34jLH/Kd4D1p7tG1JDMjaWrMhnm3zdVzSZePnLP4JijmjoKCgoKCg\noKCgoKCgoKCg8B6hvpxRUFBQUFBQUFBQUFBQUFBQeI/4R1lTyMeQFJXk8m7wLsPg/pF8+56MM65w\n5xzq2mBVC/SsYzsvsbzhi0GjnNizq/hvaFgTtKiKfNDK3Guiu3V6NZcL1B8FqmpBwVMZO7TgtF+H\ncFCnjCxAEwwNdGd511ag0/q5/aAhdxYc7i1BE63MBU0tcERLlleUxulO+sb8LVNkvHL8z+xYN+KS\nsvWTTTJu68ClFF+uwjU88i0o8EYat5sy4qD1lrh0BLflHfidm8GdIOECqLudvwLl8c3fUew1/UeP\nlPGTtaDob5vB7/cT4j4zezakTJ5tuWNUQTLobGG9QYndMIlfo1IiqxnQHR3ai8s4na1WB/4Z9Ykp\nv+D6Z794w44ZW0JKQuUdZflcFpadBgqgB6GAewTz+ZbjjDFtSWSJqRqqIXUjoJRPShr3DeBz5/xV\ndEAfMCBCxt0bchcJt65wGsm8Aarvs8tcItFtOGoUpaef/OI4y+u6oLuM682E/OfA3AM8ry13GtE3\nOo6PkHHYOe6kkFMI2m05cZva/iOXBFaQzvj+LqBeZ0alsrw9V6/KePlGSFO++XQTy5s2ZYCMXVqC\nhkldLoLSuVNLNZGR2gRCZmVgxpeUJ39CctEqBJRRE019eVuB9ysi86oFcRARQoha/VBTC+IwvpOO\n8XGR8A6pv+GTUTNLSrg84etPUDuWbJ8pYxtH7sKxYvg8GScRp7hUjbtZ/FHQiGds+VzGS4cuYnkX\nzoE2PmPuUBnfOoq1eWzHjuw1cUTaSJ1KHBryObt/DtwMqMPRq5QUlvfxz1NlvGIYZLDziWONEPwz\nUneh3GLuzJh0GM4J/v/ZROxf4eUuSKqsrDXOCYQi3WhUUxnvXsHnIpUitu2LPJtanLJu4WsrYyML\n1GsDQ07FDmiB/Q1zpbCHDC443I+9ZsUUrO9tZkDKmnKR15fbMXB7CSJOYsF1+ByrPR73JOUSxndJ\nFnfQuLkTEo46TbDuaOVFJeWcGq9POPnDMWxK1zHs2KefwJ3FpzPyMh6/Ynk/TVwl42mbISkqquSO\ncmPnY95bWUEe0qcXd/20DUE9NLLCPnJ6L7x3y2AuL/l29ngZ1yBjgspVhBCi0TDM4UYC68CmyVyu\n3qsjJHEnF8FFcuSkXiyvkEjHvds1k3HUth0sz9IXkgv77vpfIxP/wj15k82fNSJm4TNbeGEe3d3F\nJV/GZ7CmWPnjfE0d+dwuSsQ+wYzIL4rfcNeVvGTMF9862Je+fo49g4NXOHtN9ktI+AqJU21Ab157\ndecgM7avj3rbxp47yZzYi7xyIs9qHMCd2BJv6GRMJa++ftw1SSvd0ifOf7Fexh2XTGbHkh9hT7l1\nCuRKvWZ3Z3m2F3A/Ds/9TcYtBjVleV3d4QD3+C/sMSJmcqfeVkSWc34jnCSbtMd9S43nzrSu4agV\nJemQnH07g++bBrWApM2LyBf9GnAnxdqDUDee7cXYsarJ5UpVJaj3HRajtUJxOpem3VnB3fX0Defm\neDbTOk9R99V2n0Eq6veE5yWQtZvWs0JdLsszdcP9dq0DV9LMm1xGGdAO8t/KSuxF467BbVi7fzC+\nhXXWoyeknVqJedxNrHGPojDnO37M20I8voyWB/TZr6YLl7aHTkLtzbiFZ5ei1/yzxx/B3q7zCu4E\nJoRizigoKCgoKCgoKCgoKCgoKCi8V6gvZxQUFBQUFBQUFBQUFBQUFBTeI9SXMwoKCgoKCgoKCgoK\nCgoKCgrvEf/YcyYjCprl6tJKdqyiEJorhzDoGmsY8e97bGpCi3d2xWkZd27XiOWdI/a7QcTer94s\nrpEtzoVNY3UFNLdVVdCbBXXnNoB3N0ILSXV9Lu241jr7MTT0NjWhGc+K4n10zIhlMtWb2dXm1lgZ\nD6GjrT0N+rXoHddYnv/QdyCoJ4j5DX0+vjmyhR2rqoI+te/HxIaOu00KE1vYwTX095exZS2um9y3\nHDbmY9dPlPGJ1adYXq9g6LId6mL8bJiI/gSFGrvY4eW436ZO0OZ2b8zt1ltHQ7Ps3ylCxuG2YSzv\nVsqfMk4j/VS0PRLm9B4rY0PSLyCwgT/LM7HjfTT0ifT70EWmafo6uUf4yZjawgWPiWB5jWZAG39i\nCfoGDfo+lOVVkblenAI7Ue8+XCdv7w5L5rIyaE77rYBNZEUZ13HXT8K9SXgIPaarJ+/RkHkLmlO3\nTtBXZ+zgPTm2rkcPiNm/TpKxpRm/F3kxsMGj1uH1A7m1n319rtHWN3avPibjvoPasWMZt3Gttm3C\n2Bw9ntfA0wfRSyafzJFYTe+qBZ+PkvHjXagBC36YyPJOrYPmupMrtNyVRK9tHeTEXpPzALWyLAfn\nkHOP9735+xl0tWE+0GI39uX9n6gWvnsb9DRwJWNbCCFSL2IepCThntJeKEIIUVLx7rT1q0aid8Tk\n9aPZsVGdUOcz72OtyjHh9+bD4dBr5zxG75e6n/L+T/M+QG+ZsKLmMq7txW0eR26ApfP9DdDGU7vO\nQ7dusdes/hOa/p3Tv5Zxai7XRneoD31+VKxOxucfcvtpl/mwCaV9OK4s5T28ZqxHf43iFIz5Y1/v\nY3nDh71bK+3g4fXJv7gQnfbTqjbAsW7dmrE8S3+sf7Q/145F+1le917oT+DeCr3Jks4/ZXnFCbge\npqQfxu51qAcfzeH22yE2qGcFOtTHC5fusbzRfdEVLy8FfQyePua9aTy7QZ//+Db2MKVvuLV0KZlj\nRqTvWebLdJZ3+Sk+I69k/x7U1pj2gBBCiMKXWGsq2+PzFr8pYHkdmmEde7kbveyqNP05Ut+g586f\nPyNPa4cet+uRjH89i9q6+CtYu1YW8T48RqS3jC4SPSU6LJnB8p7sghV2eQb2vK3CarO8g6RfR79v\n0C/lj0+3s7x2nbAP/3zAXBn3bMT3534fvNs9KoW2j+HTzegtEzoBvUdKNTXezBlj4civuO7hPrwH\niGsg9uzWZP6aOvHeNPT5IjPzbxlXFKCvYvRxbhnNrIJJv82s+Mcsr+g1xqO5O9Yuej5CCNEiCLXC\nNgznfe0K78dILbebN8Xa6tmD90HU7ebnoU+0mIf68nDdbnaM2kSbGOGx89C3vDfg7B3rZBx9GpbW\ndw7zWmZB7KnDu2Nfn3mH92PU3UMPINovpTQVtczNn68zUXvwjOTaCs+IE0b3ZnnOzbAGl+dhTJz6\n6TzLM3PBnqpYh/tuHeDA8soy0Uvl3mr0ZLWx49bP77KHlxBC5D5H/a7I4301m41BjX29H3XdvjHv\nU2cTgn1H/BWsLy1H8/5cGVGoda8vY445t/RmefQ5NeEWvivwCHCVce0w3vvFrT7W98Is7Bvjd/I5\n0GIO9my0z2LcHs0cI3GAK/6uoaZe0dpekoy15uHDaJZH58F/gmLOKCgoKCgoKCgoKCgoKCgoKLxH\nqC9nFBQUFBQUFBQUFBQUFBQUFN4j/pFX407sbR+uPsKO1Z4GivWLn0CX9u7HpQ+pl0EnquUF6tOb\nWE7zbtIR1Om4W3hNdTWncBWngibkWQ+ygIICUJUqSjlttSIH1Kyw6bDcLs7lFLjoE6AdUTqSd3cu\nh/FqBmrWwTmwMKzQUFVrGOK7LxMTp//4/0IIUZhErNI4O0wvsKgJSuGBWcvZsUE/fCHjvzaBjtdt\nOJdcHCM00aFfwXp3/1I+LowMDfHeLSEz+XzgQJZn4wm6YHk56MLUkq7l0ObsNcbWoDIe2gNbvMnr\nRrO8xV+C1j82BVTGjm3asDxKP378DNS7WsXcHnfsCNja2QSBrufbuCfL2zRxtoyDWvNz+re4fwwS\nAl06p41/2AWyn6xUSBLij3OrSRN7SH3qN8c8TXlwh+Xd2IP5TOl75TlcZmbUHlTskgzQRJP/IvQ9\nAy4XMCeSwIM3YcU6f9wklpcfDcmKtQfOodUCTiGvlw6ZQeoVUFhD2gSyvIJXoLibuYC+bOFrw/LK\niRX8u8CwOX1l/OYkpzmGD0a9NTqIeWTuwSU7/afDfjLxBGQHVp78s2TdgazGM8xTxk9232d5veei\nJlYR6WBAq0F4r4xI9hr7YMi/jiyAPWRhKb9+HcJR1y8TiVP9Qn5/kp7iXB1t8Hl/WriT5U2Yh3My\nJjJCKgcRQoi2HRuKd4Vpv8ImNP02l4TUnY7amPYE1/nlEU6lDR2O8yt4jvoXs4dLXtee3CZjS0vI\nKEdv5PU5+RXo9fUmD5PxiQWQaI7q04m9JvrECRm3Hoh55NmiCctbNPAzGU+dP0TGjRrwtb7OmD4y\nXjVyId7PgdO327jhs/8+b46MPxrNbVXtQl3Fu8Tx7yG1bVqff5b4WIxHa3NIaKnNuxBCxJ9F7aT0\n5kHTerC8okSs8W8uwWaU0tyFECJgFGpA2jWdjOv7+f3H9xJCiJc3Yf8Z3h3z7VE8X8cimkGacj4K\nlO3x8wexPGrBHVofa4tNMJeeep6BrKaMSGzCJnLp14PPdeJd4cicFTJ+qON/x9cZMvMDIzCvWoZw\nW/suSzCmjY0xVg0MuDTW9Tneo2Yc9hWWzlzy+TIZY6c5scw+tRO2yPkayfbE70fI2JPISUtLuV29\ndy+cu4MD9qHPj/3Bz+Ea1tZD8yCFeq3ZO5SQukllYb4DuezU2ppbRusbjvUw1y0z+HpnFwq5wqXV\n2KPS+ysEl5xTmat/RC2WRy2zr6yBfKT9Ai5vSb8B+bihKR6VqATeZwC/TiVEmmJii/Fj4WrH8t4k\nYI/17AXeL2I8l+ibu0DSknUbzyt9ZvFaWUjkjG+rIKdKOMLt4Kl1sb7x6jfsN00c+dyx8oNcy8cJ\n88WgBt8fXvjiBxnTvUR4K16fQz8cKeM3z7D2PTnNZaJNx2FMl/8GuX6TGdNlfO/nn9hrqKzT0BT7\nsJe3YlgerXm2dTAWaVsOIYQwtsae904sZDytXbiMLvIq9vhjyDONpSWvV96JfL+ub5gSO3e6bxZC\niId/Yw9XrxXO68ZeLpkOcMP+0M4CnzNPxy2yX53A+9lbYmxaDeQyzYtfojVJ2CjI3p3CsScqeMMl\n9elPsMZVV1bjgIaSUlkCeWQViV3a8LYntUfD7vrkQkjfWk2LYHmFCXgGo3vUt295r5Dmg/g+SwvF\nnFFQUFBQUFBQUFBQUFBQUFB4j1BfzigoKCgoKCgoKCgoKCgoKCi8R/yjrOn5NrgrWfrasmO6g09k\nHDgBnd1f/MopV03mga4ZdwaURDdP7pJSEANqN+1iXJzF5U9lWaCSlZdD+pB4DhQmA2ND9hqXCNCT\nYo/C6cSpiSfLa/gZ6K1PNkGuU5icyfJqeIKK1+5j0MtraCh63k0jZJydDIp70EftWd61bw7KuFaT\nEULfiLr2QsYVVVXs2BcD4CCw7PA2Gdeowb+3C77wUsbpN+CyY2pszPJ6zgA1tMNLdNb/dfMxlud8\nBrS3bdtOynhYb1ybPev/Yq9pQ+jI0zdPlfGZL7m06rdza2Ucdwj03kl+nGpengXa5Is3oIyamnJa\nYuS5uzJOOwDKWo0a3JGDUvn0jfZzIElIjeR0dcc6oJ67EOeXFCIVEUIIj3APGXt2BXXzwjfccaDf\ntxNkXFqE61KUwuWCCcdAmXXvgPl8/SnG27CvOWX+zSlIecZ/BO+OnUsOsryxq+H4lP0S9OK31Zwa\nSF2DSgnN1H8IlyIaNwe9MG476KOmGmppdTmfH/rGntVwJ2itodfT+vE6I0PGNqc4nda+rst/fI2B\nxikv4CO4kCT+iflbZ2A9lmfrCUcHOzvIKkpLQRMtzS5mrzEyR41u0h6U96JY7vRjYIZa3N0E3fOf\nPuFyIBdbrC92DTGP6qX5sTwzJ0LzvoWxWa5x7rCz4nVJn0g4gbXPyo/T1ffNRu0ZtgbuJ5s1TkQ1\nU3HN3bph/mrvIXVBe/DjDhn7j+DuKRYukLRlxIFe3mZuRxmbWfK6Fncca6GlN13f+RzrSlwPXBti\nXhXE/M3y1o79Usbjlg8l58YdSMrLUaNmbV8l4w3jFrC8Jo8hR/BbPljoG41C8P4+H3B5gncV5mZF\nEcZW8ikuRWw6FrR5OjYrCrnEOYu4iDSdOVPGmRmXeN4D1Gz7cMyDul64Pyc2nmOv6TgQ5xB3Cef3\n6agBLK8wGXKOKd9CFlCmmdtxT7G+t5sPB5aiN1xO5dUc+6rKAsi9qJOnEELkFHKXJ32CuofUy+J1\nzdoX4y7u060yrhnGnUAyX6C+nvoFTiBTtqxneb714QIaU7BXxo9+5O4sVsQpsEUPSPhMHbHWXNjO\n586Jb7AHorKPt9XXWd7vW+Da1asxZFY27lzSOnfbNBlf/hrr+8o/ufzp4abfZVxrAM41ZiuXvlq6\nY/yZm/N9sz7w6iruQZMJLdmx+L2QhHZeCGnBm3OxLK8sE+O4USvM52tH+DOJK1lrgpuhBrw5w+d2\n8WvMF8eG2DtlpmONc8/nMt7sR6jXbm0xP4yNufQtvD/Gqm0tHKsq4664Ti0wVql7WNKRFyyvmMgt\na4+G7KM0vYjlmbm8O1lTwEisE5HfnmXHDM2wXwgejLWrTCMj92sBt8K3byFF2T71S5b39i3WQjqv\ncov457X2xFjttmy0jFN1OL/m0/m6k5eHZ8mLX2F+NBzIHcx0p7Gnek6kad0W8ucMawfstbuNxLqQ\nfZPvzz/8HJL3nOfY1x7feYjl1W8JyY8P30LqHdHRiezf1CWrugx75Wb9G7M8W9L+IYOsfZZefL8U\n2BUfgDo9Z93VPLvUI7L87Xgeq9UD18LAmO+dzN0gbTS3g+ysLIOPEd1O3G9n4uDsFM6/ozA3xzFf\nL8gwjcz41yhO9eHilXZTJ+OBKz9iea+P8xqrhWLOKCgoKCgoKCgoKCgoKCgoKLxHqC9nFBQUFBQU\nFBQUFBQUFBQUFN4j1JczCgoKCgoKCgoKCgoKCgoKCu8RNd5q/Z0IkuIOy/jNyVfsmLkn9Fxe7aBV\njd59meVV5EIL6dLBT8bGVqYsrzQdumSq1za24XnUxi5kCnqavDkHzSq1OxZCiJyH6J1ALc9yn3Bb\nQY/O0P4Xp+J8nEO57WvWK2hdy3OgmSzL5vaIxqTvQd5T9K2h1l1CCBEyFdaTbm69hL7x8m/oJvev\nO8GORYSFyvhCFLS9Q2f0Znn0niT+jX4Rdu68F9FbYllG9aRJ2dySrfXHsLV+fQB2akEToV000Fja\nf+4AAAAgAElEQVSOH1mI8Rjm6yPjjDxuo1tAbCrtiD1b68+5DfO26b/IuNtI9A5atWwHy/tyI/Tb\nlm6w2iwv4D1YnDxbydjMTL/9Z659t0zGLu24xZtdAHpJGBpaybisJIPlpV3VydipMTScqX+/Znmh\nQz6UcV7uAxk/WHOV5YUMh8b42Cpo5qmNZXBdP/YaqlO1b4BrZFOT2+1m3IHlnnUAjll78Ouadht1\nyb05tMyGhlxb/eIPnF/gsAgZ3/qO9yvybgNrvrBeHwt949qKpTI2IhaLQgiRFIM6VXcwauqqBVtZ\nHu1tRC1Z+/fj9sruEfgsJlaYp1rLwYoCzG3vRuj5lJVEtPq8nZZIPosaeOgk+ic42fDeBx/Mgt18\nwjHo5I1NuU5Xl4JaXNMbn8+rD7fQvLYRf6vVFNiOxu99wvJMiW18i1kLhT7xaTf0PaBW80II0WcB\n6vfqGb/JePyUviwvsOtAGWeloHeElUMAy6O9vwwMsBbe+XY7y4snFrn9vxsv44xn6AtVoukZdeEo\n+nE1qYW/Sy1phRDCrQu01+7BuOY/T5jP8roMRU238sF4O/UD72nVcXyEjH/7Bn27mtTilrcdv0QP\nOAeHFkLfeHQYFqrX/7zLjnWZhh5fd7bhOrWY3Ibl3f4V9y60F/rxvD7P+0S1Wgi9eU4i7F59Q3kv\nndRU9FkrJba81RWom1orbWrZ61IX63l6FLeVzbyJmkotu1P/5j3MAnti7S8qwpyN2/uA5bl3wpi5\nTualfyDvSZL1Bja/PVeuFPrE0zObZJxxVdMfwQvrkBHZb/p05b1pdMdw7xOfoD+Cb2M/lkf3c3V6\njZHxkyNbWJ6ZK9bg6OO4Bx2XzpJxanQk/yD/ZRf++1e8r10tUvsz8rHvadOWf6aHd7AuXn2OGrBs\n03SWV01sl8vzsF/b9QPvEbhg9wYZv4ueM7H3dsk4en8UO+YUhD27S2u+96GoQfaL51ehp0jjnvVZ\nXiGxQV/6224Zz+3H94f0OcKI1MSbpzAPQgP4+RiY4BxonxVbYgcuhBBVpegtY18Hx4rTeX+m0jT8\n25LU1Nu/3WB51K7YuyPmZf5z3i/TyBrzoPGYWUKfyMhA7yVzcz92LFN3T8a0r9r9TfxzhA9HX5eU\nM9hjmLny3oDXLmOMVJI+mrSfkBBCBIbi/jSYMFHGaTr0lqqh6fNWTvoI2fviWuYl8TrpWgtW9kfm\nrJCxuz3vsVZrFMafkQX2fEYm5iwvak2kjG3JnrdS079MF41+LEPW875Y+sCdTegDZ+bK99H2Yag/\n6TdRb82c+f2JOYt+PPZWeA+bunwelCSSvk7NUFfO/HyB5dmS57igMNxT60BcJ/s6fC9WEI9nTvrM\nXRibw/Ksa+E9zJzxd5xqNmB5JiaoB1VV2Hc/23uA55G9Z2Ec+lPlpPF12yUI16LJ+DlCC8WcUVBQ\nUFBQUFBQUFBQUFBQUHiPUF/OKCgoKCgoKCgoKCgoKCgoKLxH/KOsidLUijO4LOXFDthA1f8U9K43\n57m9XfiHo2Ucf/uojKs00p5KYhNHafZuGhpjcRqo2YZmoBpG73kk4wazOrPXVFUQenAlKHCFCdz2\n1TYQ9EkDA9DPXm29yfKsg0CDCugC27T83McsrzQLf9fMAXSp2z9y6VfoYNDeajXldlv6wItIyCIy\nNdTfrWdBH5s1D/bFGTd4HqXmGVuCGnls8VGWZ0ms1vqvAlXL0JDT3h5tAS0/aBjsXp9vgX37gyec\nGt57ASQSjl6QPz3fd5jl+faGvCUvDhIO5xBuP1uQhbH613LQyUdvXMHynh3cI2PdPUiA6vQNZ3mJ\nxIqx8wr+Hv8WmZmRMs56rmPH7u2F/MTeCpTq9DxOo6PoNK/rfz1GraaDp0BOkHSW2ze+uoXrV38A\nKIB5zyCncmzswV5jVxM2c/FHQXWNi0pgeQ2GgN7qHt5cxlEbuCVx8ATILIrSYGO5dxmXKzUOAD3V\nygPSm4DBTVlecRZowD7B3AZcH7i++msZVxVwuqpHL8gnn+7FPXBw4FKhX/48JeM63rDadNFQeun9\nH7EUnyXvFac62xPK9cNfUOvqT8a9P7b0OHtNi06oBylRoNkG9g1lea+OQm5UROw+A5tym8I7l1A7\ne8zrLuNLP3J6a60AUF+9ekPyVJLG6eDrl4Imv+E8t7r9t8jLw1rzcDW3uWz42TAZJ92B5OX6Hr6G\nDPwe8oKnmyEhaDB1NMv7cx5kIJbEojewD7d+Ls/DtaWyUysLUKcDxnB6v60j6mFBAaSlWhtdIyLn\nSIxFPW0ynkuNvGpD0vXwD8hNwoeNYnlP9qL2W/mDAm7uYsXyfp23U8ZfH/s/7L1lfJRX1/a9iXsC\nMRJChAQCBAIBgktwdyvFoUVKKQWKtKXQFkoNqhQpRYq0SNHiFHcnOISEuLt7ni/vvY+15rmvPh+u\n4Zf3w/p/WjBrJuec5z733jOzjnVwmYUxWDES98SYxVzSkExke9ceYt6rqKxkeYOmYa9hQ+yMHWrz\nfUtJEeYmt9qYe2vU+M+W77m5uHdeHsIYTn6UxPKC38IclvUYf8ewHN6OSEeptCDpBN+zeY+APall\nTazbyZd4WX8ukWo/TSB2qZZcit5pFuSWhjKu/5aoe5ClbPqUrw1DB2NtaDgKssLts79geU/jIfca\n3Brn8thdfh8s3rZAx6l3sTdJucTXLr9RkLdl3se1cmyE/eX1Ldwie8BKSBHLy4mUxZZL6pMjsHes\nQaSmNcxMWd6GRbDM7t0c933LhXx/WVyM6xb9N97vgYN8j9rMB+N51E8/KWND5XzxR/k+w5JIDRwD\nYTsduT2c5fmOxNpjWRPzXuRmLsez9sE6mfMyA68dwNsh0MeodMa3P+x/qWxNKaW8WmE9pvefOZEe\nKqVUnTAca3kprreJGZf7Jl3EvZn3Ap/B/CfyuTz1BvbrDkSmYW7LpdNpNzHWQ8bMUcZk/9y5Om7Q\ng3s8lxGp0MOLuL4dp3GZaN0m2OPTNcnSkktW7v2ItYFaeNs78b979xvkXY/A/nzWb9ifX12xgT3H\nux/2FeZEem44n6bfwL1Tp18DHVeWcjt0KomrLMP6QSWxSinVcUE3HVNZf0kGb5fhUB/jNLDLZGVs\n1k+ZomND2batM+5Fn+EYw9kveAsFl+bY99P3wiYtpVQ82asEz8J+oiCBt6pwD8Lngajj2BNWluK+\n9B/UnT2HSsKfbsP84tWfS+UrK3BNMu5iL+vWzpvlWdli7rm7Cu1BQhe9wfKKi3EvRv6OucfGh+/j\nS9JxXdvMWqQMkcoZQRAEQRAEQRAEQRCEakS+nBEEQRAEQRAEQRAEQahG/lXWlJKCUqCzy0+wx7os\nQjlvfgy6HxelFrA86t7j1Q0d5bMjuUNMrfoo33y5B53/fYby8m3a5TxiE8ow678Fd5P8eC7nqCKl\nZFTW5Ei6wCulVPgalJr2WPGRjpNjeFm8sydKXwsLUepbVsy7QCecRkliBZFteZESOKWUKkyGVKt+\nuwnK2FCnn+eRXK5Uvy7Kz5q8C4nWiU+4Y1HIcJzfNV+glHj67GEsz7U1yjpNzVBaWl5ayPJyI1Ey\nuvN7SCZ6t4Y8Zvvp8+w5H3yNcrtMUn6WGZXB8v66DglBy3qQT3Qa3obl/bRqF157JcoD/1jFS+gd\nSCf80EA4ioQueIflRZyCxKHpoJnKmDw4uFbHkRci2GPNJ2M8ZtxBGXV2BJev1ApCiSJ1rDjzOZeF\n9ftiFl4vGmV52U+4u5lHGM6tiQnOUWE6roeFgSORuRVkDK/239AxdXVTSqnAqSiFjyed+f179WJ5\n+bkokS1KR3mwoxcvSSzMRrn/Hx+ju3rvkR1Z3raNmPNWHz+ujM0fs3Bu4zL4uKUypPaBKL00dECy\n9XPS8YV/IA2zMucSiT6zMUcXkjLRMgM5VQ1TlJqWEYe1R6R038uZl3xbkPJr6oz02EByZ2+NOSCo\nN8pgH5/k5eDUYa1VT8htqFOXUko92AFnFerEZnguY9Mx9pfs5d30/1uSEjBfxezj76OAyKtC5kPm\nU5TLHbJ+m49y686NICOpP5k7BGQ+xLjNfYL3RF1BDGk0FfdI/CWMj81r+bxGx1gUkQTO2PAhy9u3\nAK5GE9fBHSJ813qWF0skn12XQcpUUpLI8hLOwEnGnLiHuLfhbk2pd1HyHNR7mjI2T/+Bm5aJBZeF\nPDmAOadeB0gio65wCVDIVEguK0qwN6lVj8tRchKwT6DuSiYGTiEOjhj7+flwvMiLQ9l4jsE87DcA\nLoFmZpgrok6cZnk1g+G0UZiIPYetF59fHmy8qWMqUyw02FfZ+mAeMjHH+Us5H83ySrJwb3ddsUIZ\nk2lhYTqe0KMre8zSDWuSe2dfHb/YxmUuOYXYmwz6Fm56VFahlFK/TFul4zIicxk1lUuE6TguycO8\ne2wlHAO7Tu3MnpN8CmM9Kw9ziLuvC8trNAFOWnFXIIvY+jOXl8/5eaqO7V1wPHnpfPwmHMe9SN0C\nPb353tgpGHuHRj3eUsbmwtKlOqayOqWUSjyOdagGkUXUHcbzIrbiugaMw/7myRbuxObgChev3DTc\nB9FpXJrRrh/2vMm3Ic0oLsNePnhiK/acB7/jb/m0hhSsZlPuMkmdnEoyMf5satuzvAribmlijrni\n4rdc7hvUC5+Tsu9jLrfx4VJnC0fMt00H8/3rf0vMEziLFSTwucLMGnuTWg19dXzOQH7eZwUcMisr\nS0nM94dU+ufoCFlTatw5lmfvgnmYtlZIf4Xr5OzLJWLUFfHR73hPvsO5ZNvGBvdV0gPsZYvT+Gdg\nquRxDsHnrbjDXL6XT9wUm7wHic/m97azvK7tcbxt5/C12hgkJ/2t41urubyx4QjiiErWTMM9pUsT\nnJuqKqyL5uZ8Pkt9gnu2bghcMCsq+OdFc3OMYwuLmiQP4yI9na939JsNKyvI4XMSuPN0EblejgE4\nvsQzvK2GRS3sZSuK8J5yH/F5w5I4XFFHWntv7uJ158dLOh7w7bfKEKmcEQRBEARBEARBEARBqEbk\nyxlBEARBEARBEARBEIRqRL6cEQRBEARBEARBEARBqEbM/u3BF+uhy7MwsHijgi5qD+Ya6sXSrO2g\nuSrIhPVYZTm3pIw6cFnHNYgO+/bqiyyv4Sho3mr38NMxtYizrct1lqZ2xNqRHPeDNdzOsMPHsBm8\n//tGHXt292d5uTmwuU04hf4f7ga233V6Ug09/i7VkSqlVPZjoiHn7qRGwWcE9Kh24byHQ/0+0DA/\n3gErSqrDVkqptSthJx1E7HtTbsSzvPq9h+u4pATa16jdx1je0TPQaPYKRZ8Fj744Zyunc0v0Z79B\nZ3vhDqx3R5LeDkopNZHYxzadM0jHJ5duZXnWxPIzPxI2hZ61+DkK9IROtOlsvN6pJd+wvDYLwtTr\nYu/WUzpu24D3LDIxxf1SVYax1XAq10MfXQn7N6+e6DcR2JH3R8iMxfimfWasDfTQVZUY0/d/gN7T\nlehqawVzrXV5MbHZDoUO1NqV2+imP4E2vigeuv2KCm4reOxT6GNrkh4kDjaPWF6zuegLkJkHba9b\nm7osb0H7Wep10mIELAEbkV5TSikmTj68F1rfvj15r6SM57gmIX6YA22I5ahSSsUceKpjW09cu4JE\n/nd/Ib11PnpvrI4D/TCXP43ivap6zkNfk2e/o69JWi63QMwg59o3Gtf7TiTvfTBz5TgdF6dDA2xV\ny4bl0R42/uPRV4DP0EqFb7yhXhffT4P15oCWLdlj7Zeg11Ra7DUd0zVSKaXGL8E8mfMM94STezDL\ns3PG+StuTWxv/+LjuxaxrP9x6lc67t+vvY6bevM+TCFvYn7o0zhUx2Zm/F7svxw2xFlZeE+mlrxP\nS1QqxqXZCvTUcfXh/YpMSL+F0kz0OIqMusXyHBpyfbqxSSK9UcxM+O9UzxLRJ6eeCdYkOv6UUur4\navTiaxEKG1faY0EppRKPYZ/gFIL+HY71+XuMu3VWx1nh6AES8CY2Blc3cQvWslzo/d06YQ+S9zyT\n5VFLVrcOGAsJx3gPs8bjsB7f3oT+bQFt67G83Oe8p9n/4Nqez6n0fRibj9dhvi4v4H0pkkgfl7wo\nnIsm77VnebSfwU+TcP/WMLB9nfz1mzq+vx73gVtrvu+zsMD1jb+JvjV1yL6ihkGvobRs9Oigc2hN\ne34vVlRgbtzwHXppfXVoE8vLTMbevbQU8wvtL6aUUpevYR/VZwL6vGXd4dcs/QrZ5/VQRqfhLPTN\nS7+TwB5LTMQ48/Jx03HUNm6lXViC6590Fte+8RS+D7JwQM8nM3OsixY/8N6SlWQv5d4Ca1c+sbTO\nf8X7TNI9SGEMriO1nVdKqfrTsG7YeqF308vf7rC8pDSyL/XEXOHlwXsCpRFbZwsrzD1lWcUsr6Ko\nTL0uSnPwt2jvOqWUuvgn7pesAuw3rC14T0Jq7X5kCcZ3h/H8gxHtEVbmgTUy5i/eJ+qfm+iP+dZq\n7DHo3vXBWt6TrtX76HuTGoV7p6EVv89jr+Kzac3GuOfN7fh7KkqlVul8zaTQHnWlpeihN5X0j1JK\nqQ3v/KrjtsZ1Q1dKKfXoZ1wrw/1cIzIn2tbBuC3O4H126P497wXGvv8wbp1OLbIf/YHeOma2fP20\nqYO+aN6t0Jvm5Rn00XMhnzuUUqogEXNqflU2jtuDf46hfXLNLDHf2gfwfcurv7GfbjEf+1/HhrwH\n3LUN6CUTNhCfs2qY8Lm3vIJ/D2CIVM4IgiAIgiAIgiAIgiBUI/LljCAIgiAIgiAIgiAIQjXyr1ba\nL2+gzCiLWHoqxW3dygtQKhc0YSj/AzVQnkRlLtbWXP5E5QrUJu3xhiMsL3AqyqKsrVFmlvwUpb4Z\nd7h1Z3IEyo68Q1HOa+5kxfJenEDZkm9rXx3fO8ftUnsuQElTDintLTUo5Yu8BfvMNjNh2WtqySVi\n8cdg7dVm1iJlbI4sWKBjVz9eRn34JKRd42ZD4mT4XqhVmoUT5ED1+3Ar7durUPKflomyMlcnLjWj\nZZ2RxAKxdk+UTjv4uLPn0Ov9aDNsq188jWV5/Ve8+b8+59SSH1he6/dhZ+nkAtvEV+e5hfIvq2Gn\n5+aI90ElA0op9eMmHNOOa9eUMTk4b56Onybwst82xIbelJTnJxjYC7efgXuHjltLFy4diTwGi79a\ntfF+G0zswvJq1MA4Tn8G29ekEyhpLC7lFnsNx6NkvpyU2KZejGF5FuSY7lzE/TdgKZewPf8V5dsN\nZ6I0euu8nSxvyk+QLGYRqVbimSiW59UX57JBh4nK2Fz5ElatTs34+E69CunQM3KNw8Zzu+9CIvOi\n0pIcg9Jpty6Y6/IiUB595hwvnX5FbJT9a0OGRuV8vm392HOeXcKc1eE9jIvsZ9xWMOIs8pqNxT1v\nKO08sg6yOGpX796d/11rIt0qIXPUd4u3sLw+IRhng1atUsYkPhLW8zF7uLzoGZF/teoLy8uajd1Y\n3vzxkETuvHJSx0kR3A4yiYxPx8YoZXdtzqWN+clJOj70DeSLbVrCbta9Kz+X7gEoFT+0EFaOXT/i\n1sC29rgeBQW4t1eO/5Hl9WwGmVktZ5Qhv4jm85WdFdbdfivf1/Hc/pNZ3szp2EuEjDF+/farB5Dq\nZtzix1iWA4kElVeZ2fKSdSs3lEEf+wbS3dwiLr8c9Da0IFRedPYwl9+1awUJsn19lFU7N/fQcVEK\nlyWWF2IePbMJcsjhX49mede/gWyDrg0VlVxy1+ttWFJHHIZMICs/n+UlZGJO6dEP0stb5x+yvD5z\nsV/ybfqGMiY73oEdcP0gLjsoTkG5ul0AbEwznvIydJ9+KD23ISXvhtK0tJu4txv0hSzx/q987jl9\nEfPrtF8wpsvyMaZKsrhsfOOnu3Q8bib2Yb5duD24uTmkUdE3sTcuiOXWxYf3YRyMmtlXx9t+PMTy\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3nSf30oRekBL/ceYCe05YEPYTpWS/9duyXSyvfwesme49sDaXpPP55ckevKeKCkiu067H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\nDuy8gwZGU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tyNrvUfDqEPh7Z7eAkhxIsj2FPmf6K2vC69MC9EK710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\nvT6D+dTpKZ1fdI3/dXvynzFywPiOO017JdnVQS+Y0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4zV3mFCCPF2H3rs1B6F+aVHp6Yk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\nDHa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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "kL8MEhNgrx9N",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "The first hidden layer of the neural network should be modeling some pretty low level features, so visualizing the weights will probably just show some fuzzy blobs or possibly a few parts of digits. You may also see some neurons that are essentially noise -- these are either unconverged or they are being ignored by higher layers.\n",
+ "\n",
+ "It can be interesting to stop training at different numbers of iterations and see the effect.\n",
+ "\n",
+ "**Train the classifier for 10, 100 and respectively 1000 steps. Then run this visualization again.**\n",
+ "\n",
+ "What differences do you see visually for the different levels of convergence?"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "l2AHvnWGi4Qp",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 970
+ },
+ "outputId": "687e6798-b290-4861-d257-453bf4ed0817"
+ },
+ "cell_type": "code",
+ "source": [
+ "# Training for 10, 100 and respectively 1000 steps \n",
+ "classifier = train_nn_classification_model(\n",
+ " learning_rate=0.05,\n",
+ " steps=1000,\n",
+ " batch_size=30,\n",
+ " hidden_units=[10, 100],\n",
+ " training_examples=training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 18,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "LogLoss error (on validation data):\n",
+ " period 00 : 6.01\n",
+ " period 01 : 4.63\n",
+ " period 02 : 3.90\n",
+ " period 03 : 4.24\n",
+ " period 04 : 3.61\n",
+ " period 05 : 3.74\n",
+ " period 06 : 3.33\n",
+ " period 07 : 3.25\n",
+ " period 08 : 3.15\n",
+ " period 09 : 3.25\n",
+ "Model training finished.\n",
+ "Final accuracy (on validation data): 0.91\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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j2zmo7RneYTAlNaUcyj5qwQiFEEK0dVK8zUypUBA7JASd3sCm31JNajsmaDhK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\nSiUECjUAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "
"
+ ],
+ "text/plain": [
+ " median_house_value\n",
+ "count 5000.0\n",
+ "mean 229.5\n",
+ "std 122.5\n",
+ "min 15.0\n",
+ "25% 130.4\n",
+ "50% 213.0\n",
+ "75% 303.2\n",
+ "max 500.0"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 9
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "z3TZV1pgfZ1n",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 1: Examine the Data\n",
+ "Okay, let's look at the data above. We have `9` input features that we can use.\n",
+ "\n",
+ "Take a quick skim over the table of values. Everything look okay? See how many issues you can spot. Don't worry if you don't have a background in statistics; common sense will get you far.\n",
+ "\n",
+ "After you've had a chance to look over the data yourself, check the solution for some additional thoughts on how to verify data."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "4Xp9NhOCYSuz",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below for the solution."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "gqeRmK57YWpy",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Let's check our data against some baseline expectations:\n",
+ "\n",
+ "* For some values, like `median_house_value`, we can check to see if these values fall within reasonable ranges (keeping in mind this was 1990 data — not today!).\n",
+ "\n",
+ "* For other values, like `latitude` and `longitude`, we can do a quick check to see if these line up with expected values from a quick Google search.\n",
+ "\n",
+ "If you look closely, you may see some oddities:\n",
+ "\n",
+ "* `median_income` is on a scale from about 3 to 15. It's not at all clear what this scale refers to—looks like maybe some log scale? It's not documented anywhere; all we can assume is that higher values correspond to higher income.\n",
+ "\n",
+ "* The maximum `median_house_value` is 500,001. This looks like an artificial cap of some kind.\n",
+ "\n",
+ "* Our `rooms_per_person` feature is generally on a sane scale, with a 75th percentile value of about 2. But there are some very large values, like 18 or 55, which may show some amount of corruption in the data.\n",
+ "\n",
+ "We'll use these features as given for now. But hopefully these kinds of examples can help to build a little intuition about how to check data that comes to you from an unknown source."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "fXliy7FYZZRm",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 2: Plot Latitude/Longitude vs. Median House Value"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "aJIWKBdfsDjg",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Let's take a close look at two features in particular: **`latitude`** and **`longitude`**. These are geographical coordinates of the city block in question.\n",
+ "\n",
+ "This might make a nice visualization — let's plot `latitude` and `longitude`, and use color to show the `median_house_value`."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "5_LD23bJ06TW",
+ "colab_type": "code",
+ "cellView": "both",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 498
+ },
+ "outputId": "e29e64e8-d295-4bef-8f56-ae49b068dc1d"
+ },
+ "cell_type": "code",
+ "source": [
+ "plt.figure(figsize=(13, 8))\n",
+ "\n",
+ "ax = plt.subplot(1, 2, 1)\n",
+ "ax.set_title(\"Validation Data\")\n",
+ "\n",
+ "ax.set_autoscaley_on(False)\n",
+ "ax.set_ylim([32, 43])\n",
+ "ax.set_autoscalex_on(False)\n",
+ "ax.set_xlim([-126, -112])\n",
+ "plt.scatter(validation_examples[\"longitude\"],\n",
+ " validation_examples[\"latitude\"],\n",
+ " cmap=\"coolwarm\",\n",
+ " c=validation_targets[\"median_house_value\"] / validation_targets[\"median_house_value\"].max())\n",
+ "\n",
+ "ax = plt.subplot(1,2,2)\n",
+ "ax.set_title(\"Training Data\")\n",
+ "\n",
+ "ax.set_autoscaley_on(False)\n",
+ "ax.set_ylim([32, 43])\n",
+ "ax.set_autoscalex_on(False)\n",
+ "ax.set_xlim([-126, -112])\n",
+ "plt.scatter(training_examples[\"longitude\"],\n",
+ " training_examples[\"latitude\"],\n",
+ " cmap=\"coolwarm\",\n",
+ " c=training_targets[\"median_house_value\"] / training_targets[\"median_house_value\"].max())\n",
+ "_ = plt.plot()"
+ ],
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
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TIKCfnQcdHliVoCvR99ieQx5pt5OrLjTYsDVOOqOZN7uIYOD09lRfs8Bi6/4M\njtd3Ha01mbTDrAkWoUE2+eZj2SapdIb9TQavbgFbpxlbazJmeN+v99brqqiptNmwLU4i4VFbE2DZ\nolImjZM0oUIIIc6+gG0wf86pDar5vmb3wfx723YfSLO/3mXi6KHPKgghJAjI8fKGTE4AAKCBF9+K\nsfqVGA1HsxXQo891sGRhCWUVYZrafcqLFZfMCQ44B+B4isMGf/shi/tWpemKZ+/jZlzG1yo+urhv\njeO5kwze2Orlnw3ovp3WGsM08TyfN7b5NNansC2YMsbiK5/IXkspxdJFZSxdNPjeASGEEOJUbduX\n4dVNKZrbPIojBrOn2Fw5N3xGRuh9nc1ql4/nQywh+9uEOFkSBPTT0JJ/A20yDfGuvj+3tLs8vKqd\nojIHO5RdU//mOw63Lg8zbsTQv9LRwy3++VMm67dnaGn3qa0Oc95UG6NfhTlmmMmCmT6vv+PhHVPH\n9VSsTsaltCJCIp7piQtwXNi23+V/Huvg1qWy7l8IIcTZ8/buNA88FSPee6SOz55DLp1RzQ2LT3/B\nvmUqRg+z2BYbOBtQW2MxbYK0c0KcLAkC+gkFFTAwF7/WGv+YHrjW2VSdPUFAY5vPE6+k+NLHB5/q\ndD3N06+n2HXIJZWBkdUGV1wQYN7M4KDvAfjQxTaTRxls2OWxdb+P64EyugOAtEMwaBEI2fieTzKe\nW0Fu2ZMitsiiWA5cEUIIcZa8vCHVLwDI0sBbW9NcvSBEccTM+76TsWRBhMNNDl2xvnY6YMHShWUn\nNRMvhMiSIKCfqWMtjjQPHGXwHA83zzyk7+cGBgcaPBrbPGor81d2v1mVZNOuvk1NTW0+B454fOZ6\nxdja4/8qpo41mTrWJJbwuft3LumUB1pTXB7CtrPvNUwDx82dzYgnNR1RX4IAIYQQZ4XWmsaW/Mtx\nuuKarfsc5s86/SBg5qQg/99N5byyPklLh0txxOCimWFWXFFBc3P0tK8vRKGRIKCfaxeFaOvy2bbf\n7V2DX1YEdS3xvK83rdxKzfUgnc5/qu++epete50Bj3fENC9vyHD7NUP7VYSDCu37ZFIOruuRSjqE\nIjYl5dm1/246NwgYXmUybJCgRAghhDhdSinCAciT8A7TgKrSMzcINWlMgEljZOmPEGeCBAH9WJbi\n0x8uYv8Rlz11LuUlBrMnW/yf+1Js35O7Y9gwDYKRUM5jI6sNRg/P3+HeU+fiDHJm19G2wQ/zOtY7\nezJ0tMbxut/i4eNkXHzPp6g0jNdv2ZJScMl5EZkmFUIIcVZNmxCgoTU14PHxIy0mjZGsPUL8JZIg\nII8JIy0mjOz7au768jj+8zckBvNsAAAgAElEQVR17D6QwvOgssKmI2WT9vo6/OEAXHZBANPI3+Eu\njgzeEQ8Hc59zXM26XZqWLogEYO5UKOs+dv2VjaneAKC/VCLDonNN9tsm7V0+ZcWKOefYfHRJKS0t\nsZP5+EIIIcRJuf6KCB1Rn637MmS6J73HjTD5+NIiyd8vxF8oCQKO4Wt456DJ4VYTT0N1ic+SC03+\n5mM1Oa+ra3T58+YMbV0+JRHF/FkBpo4bfLRj3owAL6/P0NiWu25SAbMm9v0aOuM+D78EDW19r9m8\nD1Zc5DN9nMHR1vzrLj1PU1Ou+PBluRuTh1r5ZhzNpr2ajAMzxkNliewhEEIIMTS2pfibG0o4cMRl\nT12G6nKT2ecEMJTiaKvL27sdQkHF/Fknl05bCHH2SBBwjNVbbHY39H0t9W0mR6M+y2ZnR+V7+Non\nFndo6/BpaIaumEcm43PulPyZfixLcdOSEI++mKK+OduRj4Rg7nSbyy7oe8/qTbkBAEA0CS+9DVPH\naIpCio48+59MA2oqTq3j/s4+nz9t1HR0Txj8eQucP9ln6VwlIzhCCCGGbPxIi/HdM+laa373fJy1\nW9Mk09nnV69Ncf3lYc6bevyseEKIs0+CgH4Otyr2Ng5c09/YBpv2Wyyc6nLgSIY/PBfNObrcsAw6\nopq6RpdPXaeYMTH/pqUpY2z+8RMWm3Y6RBOa2VMsKkv77qe1pq4pf9maOmB3vWb6RJv65oHrgSaO\nsph0CqclxpI+z63XRPtteUhl4M3tmmEVcP5kCQKEEEKcvFc3pnl1Qzon8XZzu88jq5NMHR8YsBRW\nCPHukjUf3VwPNh0KUlxsUlpiEAmDZfVVXS1Rg4yj+dXjXTkBAIDv+nieRzwFr25KH/c+pqG4cHqA\nKy4M5gQAvdfKn1yot4wfvizCpDE2dsDEsi0sy6Sq3OJjSyOnNGq/fhc5AUAPrWFn3XEKI4QQQhzH\n1r2ZPCfvQFuXz2ubBm4iFkK8uyQI6PbG3hBH232aGhO0NiexLEUkbOB178K1DHh1fYK2uElpZTFl\nVSUUlUUwjOxXqL1sVdfSPvRMP8dSSjGyKv9zlSUwdYxiw06ne7mQgVIKZRh0JRQvbRjkPPUTSDuD\nd/QTUkcLIYQ4RamBx+70Sg6STlsI8e6RIAA42qF4fWOc3Ts7aWxIUH84zo6t7XR1OYRDinTaZXSV\nx6b9iqLSCIFQADtoEy4KUVpVjGEYaJ2t0IrCp/eVXnZutsPfX9CGi2dkj01/c4uDM/C4AbbsdWjr\nPPkAZOyw/KckA3RETz2gEUIIUdhqq/K3h5YJU8bJamQh3mvyrxB4aaNLW2vukEUm43OkLsbEyWUE\nTJ+KsENH3Byw5MayLcIlIZLRFAqYPeX08iHXVhrcsdTnje3QHoOwDedNhrHDspVpa2f+7ECJFOyu\n85hflrvEKJH0ee71GIk0TB1nM31i7masc0aDZWhcP/dz+Z5PR5dPc4dPTbnEikIIIU7O4nlhdh9y\naWrPbbdmTbaZNu74B36tfSfOaxvjdHR5VJaZXHphMefPiJzN4gpRcCQIAI62ZpfSBEIWppkd1Xcy\n2Ww/ra0pTFOx+7CPp/Ovubcsk5Jik4Xnhbhybijva05GScRg6YX5nysOKzqiA0fubROGVyre2uaw\n57Df/ZjHph2ttLRlP99zr8Psc4L87Y1lmGb2s/haoX0Px9EY3WccaF/jej5oONqmqSk/7Y8khBCi\nwAyvNPnbG4p54a0UR5o8bFtxzjiLFQvDx33f6je7eHhVO+nuWe8D9bBtT4pbr6vgsrklx32vEGLo\nJAgA8DWRkiCW3TeKbgct0kmHRNzBDtrsaghQWp4dIU+lXFynb2Sjoszgyx8tpziS/7Tgodh12GfD\nHuiIQVEIZo2H8ycPHIGfNcnicNPAhZYTR5n8+R2fzXtyl/A4aQvIBgGeDxt3pPnjqzGWX1LMG29n\niCY0lvJxHT1glqMoDGOHS/YGIYQQp2ZEjcXtHyo+8Qu7+b7mxTWx3gCgRyqjefHNGIsuKO4dsBJC\nnB4JAgAzaGP5uR14pRSBkEU6mSGdSVNRXoxp+ti2jW2bxKIZHCfb4b7kXPu0AoAtB3yeehNS/Sq9\nA43Z9J2XnpsbCCxdECSW1Gze5RBNZM8HGD/S5NzJFo+/NnANvxWwsB0LJ9O3cXjTjgzrdsZJZMCy\nDdAKJ+1iBczejc4AM8cblBbJUiAhhBDvjqOtDnWNeTa+AXVHM7R3eVSVS9dFiDNB/iUBgYAJyYGP\nG4ZBIGTjpF0M5eK6EAgYGKZBKGyB9pg5XlFdDvevStPalT3Ma9ZEk4XnDtw/kI/WmrU7cwMAyKYK\n3bgHFkzX2FbfdQylWDw3SFOnItPgo5WiPWnwxvb811dKYdq5QUBDq4vZL2YxLYPisjCm7xIIGhSF\nYNo4k2Xz5a+HEEKId09R2CQcUiRTA5e9hoMGoaAMTAlxpkgvD7CP8y1oHzSKRMIjGfcwTYVpmkTC\nBrdcbhFL+Pz+T07vaYig2d/g0xXXrLi4b5Ow72vW7tTsO+KjyW70XTBD4fnQ3JH/3u0xqGvWTByh\n8DW8vV9xqEWxvwE6YgEMKxs5JNPZ/5SRLW+eT3HMH3ODE8/1iXYkqK4K8/VbA9i2wpCTgoUQQpyi\nLfs9tu7XpB3N8ArFotkGRaETd+BLi02mTQixcfvAkbmpE4KnnYFPCNFHggDAdx0810RrjWn1jeB7\nrkcimsQO2qTSHo7jk0n7hCMmJWEYN9zg/z7RPwDI0ho27HK5/HyTSMjA15rfv+Sz5UBfZ3z7QZ+9\nR+DmxQZBG5J58ilbJpREstdbtc5kZ31P5WdSXJKdwejq7EvmbygDj9woQGuNm8k9Q0DlWU/pe5pk\nymPjXk17DErCmoumKoK2BANCCCGG7rm1Lq++7eN1N0c7Dml2Hfb55DKLsiEsMb3tukpiiWZ2H8w2\njErBOeOC3HZt5dksthAFp+CDgCPNLjt2tNPRla2tDMPACpgEIwFM08RxPAzLJBQO0dGexOuu1cbW\naLTWA1Kf9eiKw57DPrMnG2w7oHMCgB576mHDLhhfC5v2DrzG2GFQU2aw+4hiZ/3AznggaBEKW6SS\n3dmNbEXKzwYNkK04J4ww8VImqYwiEjLYss/NWfffn+P6PLuu7z6b9mo+slAzukZGXoQQQpxYe9Tn\nre19AUCPhlZ4aaPP9YtO3J5UV1j809/Vsm5rgoYmh9HDbS6YGRnSElshxNAVdBDguJr/90hHbwAA\n4Ps+mZSPYZj4tgY0ShlEwhaGoTANmDjc5/JZPkpBKKCIJvIfthXtXtO4t37wkxFf2OgTCBhYZjZ7\nT08HfnQ1rLgo+/OBJgXkr/xs2+wNAsYMUyycFWD7QQ90dl3/5ReV8dq6DrYecPE9CB5O4AxyuPCx\nFWxrF7ywEe5YOmjxhRBCiF5v7/NJpPM/d7g5/6BZPoahmHdu0RkqlRAin4IOAv68IUFdY/4eseu6\nmHYQw1QYJvjaoLI6zKUzPOZP68nCo5gy2qC5I//Jumu2+sybphlk4L37PgrVc+Kwymb7KQ6kGV1h\nUBqx2VvvsXV3hvZOME1FqChAIND3a+sJGoI2zJtuMHOCxcwJVvdzmv9+tJM/b0j2jcoYNobh4fu5\nlbFhQKR44BkHh5uhpdOnukxmA4QQQhyfeZzBelNSewrxF6Wgg4CW9vyddwDf9bO5iLXOHiCGYtwI\nm/nTckf1r1lo8/Y+j1jimAsY0NwJ63Z4zBxvsGGXxs0zCNKT71ip7L18DV2pAM+vTbJ5j0s8BalM\n9jUOkE67lJSFCYVt0Brb9Jg4QjF/hsnsSblpStdtd3h5XerYW2JaBrg+PXFAOKiIlEYwrYFpTj2f\nQWcOhBBCiP4uOMfgz+/4dB3bJgLjayUIEOIvSUEHAdUV2U5vMBxAGYpMysHvGTJXABqUIhDM5s+v\nLh0YNNiWoqrcJJbqe04p1bu0Jp6CiSMN5s/QvLVd4/S7hGEojH7DJkoptNYYpoFlGzS1Z5cc9T8Y\nRfuQiGcIBE3SSQfDVASKQpjWwAhj+4HBeu+KBbPDjKw2sC2YNzPIQy9qDhwd+MraChheIRW3EEKI\nE4uEDBZfaPDcW7nLgiaPUiy+8NTP0xFCnHkFHQRUVAQpq1a9JwWHi33SyQyJrmS2I28YhIsswiET\n388ur8mnpkxR1zRwuYxpwIQR2Q708nkm08f6bD2g2dcArTGFYagB6/AV2U6/ZZu4jk++W7oZj+bG\nKKlEBu37HK1XdMSqKIn4jK3pe50/+EQHaQeunNu3/OeSmZrWLoj2y8oWCsCCGcjpjEIIIYZs3jSL\niSN81u3wybgwpkYxZ7IhbYkQf2EKNghIO5rn13m9AQBkMwOFIkG0r3EdD9PKdsZNO5vvPzPIwPol\n55rsa/DpiOU+Pm2sYtKovuuPqzUYVwvbDsIjr2nybvZV2Udd5zg9eCBcHMAOWsQ6kmjf5+D+dlbZ\nVXz8UpeK7hPaJ4wy2bQ7f6HbYtl9CD1ByORRBrcu9lm7K5vZqDgM50+CscNlL4AQQoiTU11msHz+\nwPZDa9heb3CgySTtQHmRZvY4j6qS/INs/bPdCSHOrIINAtZu92iLDnxcKYUdzHb6i4qDVFQGjz1q\na4CR1Qa3LrF59W2PxlafgK2YNEpx9dz8X++kEdkzANw8/XyFwnE8XMfvLk82r78yFH73pgLTyh5Y\nZpomqlIRbU/gZnxiKc0Tb1l88koXpeCSOQGee8slnsi9kRUwiaYtXt7sc+FURUn34Su1lQbXLTjB\nhxVCCCFO0Zu7TTbtt9Ddg2ANHXC41WDZeQ7DyvpaW9eDI10msYzC14qw5VNd7FMWOlGLLIQYqoIN\nAvIdztXDMBTFpWGCQYtg0CKdyVY6w8sGr3zGDDO4dcnxR80dV2Mo2HJQ4Xr5hzW0r4l2JLEtmDjK\npClq4ZM9wMzzfFzHw7D67mPbJqGwTTrpYJnZEf43d5pcPM3DMhXDhoVoOJrBdX1QYFsmgZCFUorn\n13m8vgWmj1V8eJElmRuEEEKcNfEU7KjvCwB6RFMGmw6YLJ2TnbnWGg60W8QyfW1dNGOS6DCYUOFS\nHJRAQIgzoWCDgMmjFK9szj8abwcsikuDeJ7GcTwScY/qMsXYKpfB8vUfz646j5c3Ohxp8bFMKC0y\n8Twb0xwYNISDmvPPz6b5XP22iVZ9dzRNA9M0sG1FOtMzU6AIhQN4nk9JiUU6ozjQAheT/WAjqy3a\n88x4aK3RGmJJWLtTE7A9PnRxwf51EEIIcZbtPWqSzORvQ1uife1hV0oRy/M6z1e0xA2Kg8dfLiuE\nGJqCXfA9qsakpMTGDpqY/TL0GIaitDxIIGBhWQb1dVEyGY8jzS4P/AkOHD25EYj6Zo/fPJVg844E\nLS0pWjs9Dh31SMZTA3L1A0wba3DtJUHiGZOGtvzXtG2DSROKKCvLdto9z6esIoJpKiJhRTQFv/6T\nwR/XKmZPsSk6Jv2/1rr35OMeO+t8PF9GV4QQQpwdIXvwNsYy+p5LOIMfkJkZZBZdCHHyCnLod18j\nPL0WHN8kGDTRAY3vabTvEykOYFlGNoe+Uihl4Lk+lm3SmdC88jaMv3po99Fa8z+PdXH0aF+etHQy\nQzASJBQJEu2IU1pR3Ls5d3gFXDorOxXaGjeZON7CMCAW8zja7PZukHJdjR0wGV4TAlI0JR2S8QxN\nzQbDasJoFA3t2f/2HvGorjQpSmlSKZ/2qI/WmmPjj1gSMg6Eg6f//QohhBAAR1p9Gls1E0YoJtXC\nhv0ebbGBqUJHVvY1SrY5tGBBCHF6Ci4I8H340yboiOfm5zcthWVZ2LbZmwrUdbMdZjfTl0XocCtE\nExrTyJ6yGwoMPiqxdkuKuiMDz09PJ9LYAQutNUVGknGjI1SXwbyp2U74jqMBHNtmWHX22tWVNuVl\nLjv3pNAaAoHsBI5hGlSWB4l2OXS1J+lsS1FWFsLrN1OaciDZkX19JGgQcTJE4wPLWlECwcDJfZdC\nCCFEPrGEzx9edtnboHHdbNs2fZzBvOnwxk5FZ7JnIYIGz2P3wQy2r5g3XVEZgZa4T8o9drGCpiKc\n59RNIcQpKbggYMdhaOrI33HPLs8xu3/WpJLZw8O0YfSOwmuteeA5l6NtPqYBY4crls+3GV45cGXV\nxu0DT+vtkYylCEYC+K7LRy/te7wjYVDfaXPsVGh5mcWI4TaNTU7vMiDoy+FvmApPa3zPJx7Pv14y\nkVZEIhbRuJPzuKHgvMkGhuRgE0IIcQY88orLzrq+UftkGjbs8gkHHD62EF7eavDOfognPDLde9wO\nNGg64rDsIsWYcpcjnRbx7qVBtuFTVeRTEZGZACHOlIILAhKD98t7O/q+n11n72Q8lKFw0hn8iJ3N\n0OP6HGzsG4nYflDTHnX4/A0BAnZuJ/pI82An9oLnefiuj3PMSEdTfGDmhB5lpSZamRQV2X1lRpOM\nOyjDoKjEJhZzsGyrt1I9VsoxmD/dYH+jTzwJ5cXZAOCScwvur4IQQoizoKXTZ++R/J31nXU+1yzQ\nNDQ5tHfkPqeBTXs1i2b5FIUNJle7xDMK14eSYHYGXghx5hRcz2/6WPjzNk08NbCjrVR2BgAgk3bR\nvo9lG8SSDqaVpqw8QDQxcJS9sU3z5laPy87Lfp1aa576c4KOuMIKWKCzswx+92ZcrTWqu6M/subY\nX8HgoxzhsIV3bGq1qJM9R8DXaE/T0Z6kqqZ40GsoBZfOsbjukuwUrW0x4NRiIYQQ4lS1delBD9dM\npMHxoKkj//PxFPzyiQwfWWQzcZQp6UCFOIsKLq4uCsGcCWCogRWLaWaX/biOh+t42EGLjuYYvq/x\nXJ+wctBe/gqpLdo38v70a0mefi2F6yuUyh70ZVomRr9hDKWyJyouvqhvJ25ju2LPYUVdg8/hoz5t\nHX7v/gQA55hK1XV96g9HsYMmyjCIRzPEo2kS3ct9smlAc8s7sgoqisFQioCtJAAQQghxRo2uUZQU\n5X+uqkQRsCBo539ea019s8tDz6c40OCRcSQIEOJsKbiZAIAr50BlCew8rElmwPcVnTGfWMLD9XxS\niQzptEs6kT1RTHWvu/e1ZrC0ZaVF2Q6+72s27hi4GRjAMAw8x8M04IJZET58WYTa6uyvoLFT8cKW\nIIl+h6NkMuB4muFVoNCknb7nXNenqTGBFbDx/ey8gutmDxOL2B4zJyr2Nii6En3lLYtoFs3Qcvy6\nEEKIsyYSMpg9weC1LbnLUi0TLpxqoJTCMjyUMrEsI5uAw8129n3Px0k6tOsA//m4Q02VzdTRsGK+\nIQdaCnGGFWQQADBnYva/LM2WvQ73PtyV97U9h3pNGGEQT+sB+wqqy2DBDIP12zPsOuTSkv8yKEMx\neVyAGxYXc8743FycWw5ZOQFAj2RSM6o4wzm1Lg+8bJLxLTxPE406ZByfQCB7mnA07eA6HkqBh8Gy\nCyCZ1uxoCHCkOU3A1GQyHpt3Q0MLXDTNxDKlQhVCCHHmLZlrsn13nLrGDK4LxcUml1wYYf6MIL6G\n9rhJUXEAw1DdZ9doEvEMmZSLYZq4jotpGXTG4a2d4GufDy8cmFpUCHHqCjYIONb0CTaVFTZt7cdk\nzjEN7KBNRQksX2AzcZTPK5s9jrTo3uxASy60+M3TSbbv79kvYGLZBr7n5xwIFrA0E4d5VJcP7Hx3\nJvKvzPK1Ip0B0wDfyXDkqIPvZ0fzTcvAsrJLeiw7O5oSigR7R0vCQVh6UYBX1iV44jWPzn6pQTfu\n1tx2tUlZUcGtCBNCCHGGeR5sPZhd8z95pObBP3aw+0Cm9/mODp/X1kWZM8Wkri2IHeobCFNKYVmK\nUNgiEU13z2x7vXv0AHbWZQe2wkEZvBLiTJEgAHBczdE2n6vmh3j2DUUi4aE1GJaBZZlUlBh8+NIA\nrgfVZYrPXm/T1qmxLEVNucFTr6X6BQBZSikM08gJAjpa4zz4SCtPvXCUZVdUc8tHRvY+FxhkfSRA\nOKBpj/ocadK4/dZHeq6Hb2uCIQvDMLCDFqGwTXGob9mS72teWJ8bAAAcbtY895bHX10pQYAQQohT\nt78Rnlmnae3KtjsvbARlVDJldvawzXhXiqb6Dlo7ff70ZgK7LJT3OrZtEgiZxDIOSqmcPW3RJLRF\nNaMkCBDijCnoIEBrzfNrXTbv8WjtglAAxo0MEgloosns9OSUsSbXXl7O/Y938IcXHVIZqCqF886x\nuPqi7Ne3rz5/Xn6lFIZh4DoOyViCzqY2ADq7XB5d1ci40WEWzq0AYGyVR0O7wbF7DiqKfKbUejzx\nuiaVOfYO4Do+ppVNZWpZ2fJksxBlO/fb9jk0tOb//AePZjcOy+ZgIYQQp8L14PHXfY62ZJekAli2\nSThi42QUkUiAUCiAHbQ4vLeFzTszTJ7ukS8vSU+b6bs+hqF6z8EBKAlDLOayanuGsmKD+eeGsSxp\nu4Q4HQUdBLz6tseLG73e8wFSmWzHeOoYxRdv6hupuP/JKFv2943ot3bB6nUu2/elqSrRtHd6aJ0/\n005tucv6t+rRfm6GA8eFN9Z19AYBs8e6RJOKvUct0q4CNFXFmkvOSWMY0NA2eIYEz9XZDES2wvPA\n9frKmnaP8z4/m5BUqlEhhBCnYuMen/qGVG8AAH0Z9krKgoSCAVJpTUlpmHBRgGTK5Uh9nPJhZQPa\nTM/ziXUmegOAcHEQ3d2c+W6Gnz3QQbp7xe7zr8e55ZpSpk7I3V8nhBi6gg4C3tnXFwD0t++I5s0t\nDnvrPRrbfI626ezyIKNv5EIDh45qduzJ7hJWpsIO2DmVmmlCSTA9IADokUj1VZpKwaJpDnPGuuxv\nMSgKwIThHj0DIdZxVu2YZncGI626X9tXhtmTA1SVZgOXY42uUXJKsBBCiFO2cWcmJwDo4ToeqYRD\nUVG2jUmloagkhOsmicU9ipIpApFwznsSsTS+p1EGREpCmKZBIOBTZDq8vSX3YIH6Jpf/faaLb/9d\n9dn7cEJ8wBX0gvBoIn/n3PHgydcybNzl0dCi8f3sacL91/dDX+pQAO1pPDe3IvQ8iHklg95/9IiB\n6yJLIprZYz0m1fYFAAATavN31pUBwZCNaSpCYZNwxCKe8vG6Aw/bUlxyrjEgJ3NlKVxxnmRaEEII\nceoGO50espt7K4o1kUi2q1E9rAjLzo49jq92mTzCp7JYY+CTiKdJJjKEi4OUV5cQKsqO8F822yDW\nEct7/cONLmu3JM/wJxKicBT0TEBFkaIjmi8Q0KTzrL/Xmpw19P4xI/za09Cvs60MRXOXYsq0Gg7W\nRUGDk0yjtWbMyBDXLxs25LJecb7BvkaPuqZ+11cQClnZA8mUoqUxSihikwla3P9Mhk9fk61EF8yw\nGFbus2G3TyKlqSxRLJxlUFla0DGgEEKI01Rdpth7OP9ztqmoKFV0JsC0oKwkwCGVbUvPGWsyd3o2\ngHj8Dc3mfQbFZZGc95uG5q0tGTrSAQJhyCSdAfeIxgcPQoQQx1fQQcD5U03qml2OGcBHMfg6+p4g\nQGuN7x5T+ajc2YEeSR0mXJx9baQkzOgaxec/UUtleWDIZbVMxV9dbvJ/n1GkMxoUBAJm7xkG2c3A\nikQsQ1FRgJ0HMrRFbWpqsu+fONJg4kjp9AshhDhz5kyxWLvNJd+q12lTQr1LWYsiFpYNtp19YFil\nSTLtc6ARpo2GhjZFU/8VP1oTj7l0ZHxQFuFiE8MwScX7DuqJhBSzp+bPNCSEOLGCDgLmTbdwXVi3\n06OlQxMJZTvL2/Z5OAMHHHIoUxEMB0inMvTEDIaZv5Pten21o8agNabw9ckvxakoUUwZbbC3Mc+h\nYolsgQ3DoPVoFDMY4MnXPKZOHPBSIYQQ4oyYPt7msvN9/rw50zugphRMnhBi+jkRsk2kwrYNPM8j\nnXZBKVa+6oMy6Upkz8EZXa05fzIk04rDTR5NrQ5ev8QWSikCIZt0sm+f3dxZIYZXFXQ3RojTUvD/\nehaea7Fglkk8mU0RaluK36R9Nu8ZuNEpWwlZvT/7vk9VbRntTV2MHmbSGjPxjhkN0b4mk8xdW5RI\nal5dH+XmaypPurxLL/B56i040JTt8HuuTyqZobMt0fsa19cYWrP3iE9Diyu/ZCGEEGfN9ZcFGTU6\nyKZdLmgYPzZEeZmF1tCVMNA6O3CVSrp43TPoh4+kKK0oArKZ6g42ZZcJ3bEM7vnf3ACgh2Ea1FQH\nKY/4zJ4S5OqFRe/ehxTiA0j6h4ChFCXdSxG1hgtn/f/s3XmMZMd94PlvRLwr77qrurv6Yl88xEsU\nKZEUrVuibMuybHg8NmyPbQwMrBfCLLAL24vFYoHB/GN4F2vYwIwxM4Zn1iNjNLZnLMuSrNOSKIki\nJd5Uk+y7u/qouyrvfEdE7B8vK6uqq4rdzUMS1fEBCDYzqzJfZTUjXkT8jiI9aVlaNSwvdgcdev1A\nbar+k9c0Ftxz9wi/9RHBP3434ZtPJ2wslJClKTrLtrxnku4ccvRqSiH8s0cM/+ufNAlCj7ibofXW\nmEiTabQR/Ncvd/i1D+5cASjTliePpyyuGsaGJA/c7uMpVzHIcRzHuX73HYSJkZDFliLRkm4MjbZk\nuZUvAKy1XJ5ZL1OXJlvnxZkFOHXJEnh5mezt/MIHy9x37FW6azqOc93cImCDJIOvvxwxW1eEZcHu\nMkxPF5m50KLV1ltqGq91BU6Mh7WGkSGfQlkg4nwVEAQKiLBG025srmBweP/rq23sk9Fpbb35tybP\nVUiNJU0yzs8pGm1FdZsNk9klzae+2OXi/Ppg+90XUn79oxHjw65ykOM4jnN9hIB9Q5rpqubbJ0PO\nLSnWChBaa1mab7O0sH5ibU3e9V5563ONBRabcHhaMjO/dX6bHBHcc9jdtjjOG8Vlim7w9PmA2brH\nxvZZBsWuPeUdu+oqJUk1xKnl6RMWIRWFQkChEKBUnrhbGSpv+p67jka8667Xd4z5f/5OFXtVyVJr\nLVma5ddqwWhLt2d44dFyE6cAACAASURBVNz2r/H3j8WbFgAAM/OGz3wzfl3X5jiO49xc0szy2HMp\nn388QSUt3nWox/6RlKX5FqdeXuTUK1e1rheQxJvDbn0PDkzAhx/wufMWxcY0u7Ga4GMP5+WwHcd5\nY1zXkrrX6/GzP/uz/O7v/i4PPvggv/d7v4fWmvHxcf7oj/6IILj+Kjc/zubq2+9+SyUplT3arc3H\nl2shQpWCpd62LG7TkAugUPQ5vD9ESTh6IOJj76ttaof+WpQixXve7vGV7/YGCck60+sNzQQYrWk3\nu9TbEtj8szXbhjOXt+Y9AJy5rGl1DeWCWyM6jnNjbpb5wll3aUHz6a+mzG3obL9nTPMrHw747vfa\nLC9uzosLQo9qrUDvqkXAkT2wazSfd37joyGnL2pOXdKUCoIHbvMIfLcAcJw30nXd5f27f/fvqNVq\nAPzJn/wJv/qrv8pf/dVfsX//fv7mb/7mTb3AH6ZtQuv7BMXC5ptoISEs5DX6J4cspQj8HSJoykXJ\n7/3LSf7335niFz889IYNZKWCHOz65/kJ679OIQRxLyVLMuYXtpY6ijN2rICUpDs/5ziO82pulvnC\nWff5xzcvAAAuLVo+/52U4dEyU9MjVIeKVGoRIxP5fxcrBUZqknIEo1V44Bh84uHNc+OhacVH3hnw\n7rt8twBwnDfBNRcBp0+f5tSpU7z3ve8F4IknnuADH/gAAO973/t4/PHH39QL/GEaKW2/CihHMD5k\nKZZ8glARRh6lcojvewgst+2FobJk/9T2r3tgkn6i0xtrdDigPFxiaKLK8ESV2liFsJjvsq31D7DW\nslTf+nONVAV7xrf/9QsB7Z5rwOI4zo25meYLJ7fSMJy7sn0S7/lZgybvZl+pRQyNlpiYqlAseSgl\neOjOkP/lFwX/888JPvqAdEUpHOeH7JqLgD/8wz/kD/7gDwb/3e12B8e5o6OjLCwsvHlX90N2x3RC\nKdx8PCmF5a4D8La9lsCXhJFPEHqDHIHpMcst/Zv/n3mnYN/Exu+FW3bBR9/5+ga2Rsswu5ihN3Rj\nsdby1ClJVAhQKj8R8AOPUrWIH3mI/mAqhKSwTQ6yFIJj+7ePBjMIvv3C9qFCjuM4O7mZ5gsnF6d2\nS8PNNWkGcwsZzWZKkhi6nYzF+Q6ddkIQSpablnrrxirlGWP5/Le7/N9/2eBf/4c6f/a3TZ55qXPt\nb3QcZ4tXzQn4u7/7O+655x727t277fPWXv//vOPjlRu7sh+B8XGYGLM8fRpW2xD6cGyP4NZpAVQQ\nfsL3XtIs1C2eglrB8p67PSYmosH3/95By3MnU+ZWDHsnFLcd8HZMKr6W+eWUv/gfSxw/1aPTs+zb\n5fPBByt89JEaz59KuLK8deCTUhAVQlqNDp6flzS9744S4+N5cvLJixkX5g27RgTVqkCqFGss1uYn\nAELmYUUrLfFj8Tv7cbiG18pd+4/GW/na38putvliJzfbtY+OWvbvWuH8la0lP6USmP7cAgzmwlYz\nxfMVz522PH/asn+X5KcfjLjtwLVLf/77v17in7633jV4cdVwYXaJ3/3lUe69rXjD1//j4mb7e/Pj\n4q187W+EV10EfP3rX2dmZoavf/3rzM7OEgQBxWKRXq9HFEXMzc0xMTHxai8xsLDQfEMu+Ifh3umr\nH6mwsNDkzmkYjTSf/lrK5VnNZWM5dQaO7VP82qMRfj/kZ89w/g+kLC6+tmsw1vL//mWd0xfXB9YL\nV1L+y2eXwaR0Mh9rwRiTT679vAAh87KlVlvwLe9+e4n7jxkuXGzyme/CuTnQRiCEpRxaRsbLeFKQ\nxBmNZjwozayk+ZH/zsbHKz/ya3it3LX/aLzVr/2t7GadLzZ6q//9e63X/s7bBPPL0N1QWE4p0Ei8\nbTbBpMwbh1lrMFZw5pLm//tCm996VDBc3jlAYWFV88Tz7S2PtzqWf/jGKtNjb80T7Jv1782P2lv9\n2t8Ir7oI+OM//uPBn//0T/+UPXv28Mwzz/DFL36Rj3/843zpS1/ikUceeUMu5MedsXBh2eOfnoGV\nVobph+akGbx4RvPZb8X8wnujN+z9nnsl4czF7ZqMwZMv9Hj/gz5GazZWCc0XAxajTb+tumD3ZIgU\nhi89A6eviA1fK2j2BFKCHypKlYBSNWTuchNrLW876PoEOI5z/dx8cfO671aPobLgWy9knLlkSY1A\neXKQm7adLNOkmR3kATTa8ORL8JH74fQlzYU5y2hVcMctEtWvpvfSmZTODhWs55ddHpvj3Kgb7rrx\nyU9+kt///d/n05/+NLt37+bnf/7n34zr+rEyuwpfOV6k3lWUhuG2aonVlZjTJ1dZO+E+MaOx1m4J\n/Vltar7xVI9mxzBclbz3vohS4do32HNL2Q79EqHeMuwdt9jtxjzLoC275ymWm5bvnIkwoeToIWi2\nNLPzyeC6jQFrNPW6xvMEI2NFSLrcfdiVB3Uc5/W5GeeLm9WhacX3T4Lw4VpFYPNTbEsQSMbGiySJ\nodmIqbct/+nzMScv2kG1Pt8T7Jn0efsRyeiQ7IetynzDa0OeXDFyScWOc6OuexHwyU9+cvDnv/iL\nv3hTLubHkbXw2HGod9dv3JWSjI4ViOOMmfMtAHqxxZj8CHTNy+cSPvWFNssbqvM8dTzhN3+uzL6p\nV4993D3hIQRsF0Y7XFU8c9KiTb7jb6xFkCcBS5WHBAkp8HyPF85klBckE2MBpZJHoaDwfcGFi+vb\nKbVagNfW9GKDkIJO5vPF78Mn3v3aPjPHcW5uN+t8cTNLtWXmOvK+rc1z0Ky2jI0X8X2F7ysCX3Ly\nUpteotAmQ0jA5k3Izl9OWGpFHNmtGBopkJn13jhxJ0Frw+23XDufwHGczdx27zVcXlXM17d/rlZb\nL7szOSI3dTK01vK5xzqbFgAAc8uGz36ze833vfNwwOG9Wwe1MIB33RXSaBt0ZvKdEMsgP0BnBgSE\nxQAvUCSpYHk55dSZNssrecOWasUjCvNr9TxBoeARhpK4m6H72y9nZyHTN1a1wXEcx7lJ2e03rdae\nNMb0u9xbglAwMVUkitb3If1AUaoWKFdCqsMFfF+BEIQFD2Msaao5cVGgbV7wQoh8o6tYDvmp+0o8\n+uAbF47rODcLtwi4hm6680ck+zf9hRAeunPzDfvckubsDh15j59J+d7xZNvn1ggh+O2fL/P22wIq\nJYHvwf4pj1/6YIl7joUsN7aPf7TWgsnDkpQnSdOMpJeSZbCwmNJoaoyxVKv54Fup5NWLfF+CsGht\n8HyVNwzbmpLgOI7jOFv4nmD36PbP9e/92T0Kdx8LmdpVIYq2bnJ5Xj7fKiUplEIgP+UOQo9eJ8n/\n66qoH6EU+/cWkdKFAznOjbrhnICbze6hjJdmobvNPbvJNHccVDz4Np/bDm7+KM2r7orAf/tqj04M\n77l3c/TkCy81+daTy3R7hgN7C/z6T09ggDixVMsS2c85WKzv/OLa5KcBaZwhhKC50qYyVMTzBL4v\n6CUQ9zSVimKoFqxfr7EkGZRKgjSG77xoeN/b19/TcRzHcXbyU3cJFuqW1db6Y3ZD7P7MAiw1E0Yn\nw21v2j1PDMJgpRSEkU+aaoJA0euYfE7dZuprtFxSsOO8Fm4RcA3FwHJkNzx/Lq+2sybyDO+5zzBZ\nLWz7fbvGFJ5i5yYqqeW7L2Y8fJc/qI7wt5+b5a8/e4U4yUe5x55Y4clnVvk//tUhhir5rom1lpfO\nZSyuaozO6/pv7UMg8gpBNt/ZV75Hp51QrkYkab5jkySWJEuxI3lIU6ed4YceNtZIKcg0PPYipMby\n6P1uEeA4juO8ur0Tkt/4sOHJlyw/OGdZbdktm2GdnqXY7lGqbJ07PU9SrQbU6/mumxAiD1ewebhQ\nXhHPIOXmE/qJEQm4hYDj3CgXDnQd3n0r3D0dM17OGCpopodTHrylx2RVs1CHLz0Nf/e44KvPQr1f\nwlgIwUht549XSMHCquXCbD5wLa8mfPbL84MFwJpXTnf49N/PAnmC1H/8+y5//vc92p18dyXPC9g8\n+AmRv7/nKaQQKCUxmSYIBCAwBipVj147JY01q/WEdkejpMBai9xQ1u34OUucutwAx3EcZ2fWWk7O\nZJyeyXjkTsFodefT8ImyJvA2PylEvvsfBJIoyuegLNMEkUeWZiglWFls0270aDW6ZFker7p7FB65\nx+UDOM5r4U4CroMQcGwq5dhUuunxVy7BF58SdOK1nXLBiUuWjz1gmR6HB++K+MzXt+vqK5FS4iko\n9zdDvvH4CvXG9kH4J87kK4vPfyfm+NmtRwtGW4RYL09qLYMKQaafHyCVZGwsHyiNhTgxGG0xVlOv\n5+8rFHi+wuj1RUWjA/Mrlr0T7jTAcRzH2WpmLuNvv9bj/BWNsTBUFlQrCmv9bU6q4fAey3JsuLic\nl9Nb27hao5QgSTK6rYTUE8Rxitkw9Vlj6XUS7n+b4tF3eoNGnY7j3Bh3EvAaWQuPv7RxAZCrdwTf\nfjl/7EPvjPjAAwUCXwyqGUgl8cM8GffALsnESH8QfJX3WhsbT13cuRui7cdc2n6zMN+XYAVGW6y1\nlCt+Xm2h/zXNZkaS5XkGAGEoMamhWPTotNcXO4UAht/ajUwdx3GcN1i7B197Dv76MfjLr1guLuYb\nTACrLcvMbIYntm5sTY/BfUclhcAipUBuE9IadzMay/kGWpZZsFtnSGtguKgZrbrbGMd5rdz/Pa/R\nfB1mV7Z/7soS9NJ8Z+MX3l/kD36zwvSUTxD6hFGAlJLpccHHH1lPCn7PQ8MMVbc/mDl2SwmA5FWq\n9Vjym3+d6rzmcuizMYOqWI5YXOyRZYZuN2N4yGdsrEBmBIWiwlpLWAiwFvSG0qCHdkO54P6aOI7j\nOLnlJvyXr+UbYScuCawKqI2WKZTW5zRroRIZjk4LShHUSnDnLYJf+aBCScHhyQxfbY0XiuOMuSut\nzaFEOxSn2KkCn+M418eFA71Gov/PdiGP4qoqZpOjPr//G3njriuLhpGK5O23eoNW6ADDtYCf+8gk\nf/3ZK3R76+E4tx0p8csf3wXAnjG5Y2t0IQRSSoLIw/e9/CKMQQhBuRZhrKTZzOj1NOWKolzyWFqM\nSdO8z4Dn5X8VklhjLURBvgD42IPumNVxHMdZ99gPYLGxeW6QUlAsh3kpz7WJ0Vh+4yMeaWaRkk1z\n3p4Rw/23JBy/5LHaUYCl006Zu9La1An41eQJwY7jvFZuEfAajdfyhKRLS1uf2z0K4VUlkIUQ3HXI\n565DO7/mJz46ye1HS3zjO8t0Y8Mt+4p85H1jBH4+0L3vvoDzs5rlxvoAKYBCyadSDel2EtrtLM83\n8CRCSWojxU3vkaYWKQSLCzFKClotQ6eTUSoFeQxmN6MQwG8+ClPDCsdxHMfZaHabeQ9AeYqwENDr\n5NV9OokkzeyOMfu37s44MpUxV5esNjSf+kKHON3mC7fJMJYSPv6ISwh2nNfDLQJeIyHg3bdbvvAU\nNDrrA9xo2fJTd1gyDT84nzfcun0fFK9zrDp2qMyxQ+VNj2ltefZURqcHv/yhkKdfzlhYMWgr6dqQ\n0fESQgisLdJqxFw4t0K5WiAIt//1riynJImmWvVYXs1o1XssXKqTpilSSuxohW5XwvBr/ngcx3Gc\nn1Di1TbgN9ywdzKPf3zS8LGHdt5QUhJ2Dxt2DwvefbfHN5/NNjWqnJ5QXJwHsyEeVsj8hPvF85K3\nH3k9P4nj3NzcIuAG9VJBkkE5shycgl9/v+WpU5Z2D2pFuO8wnJ2D//EdWG7li4PvvGR5+2F49x03\n/n4vnc/43HdS5pbzgbVUgPtv9fjEeyP+89cjSmp9cBVCUKlFTEyVWZxvEUbVbV8zzQy+L+j0bH/x\nYPthTYIs09RXm/zlV2v8wrstbzvowoEcx3GcddNjsFDf+niWanrdFCkFXuChlOD5M4Z33g4TQ9c+\nWf7ogyFH93k8dzIj03DLbsmlFY+WFhhjiHspQgqiKM89uLCAWwQ4zuvgFgHX6QcXJN8/49FLQCrJ\n1CgcmdRIYZkaExwcSwn9vCPiPz4lSLVAKTDG0uoJvnPcMl6DY9PX/55xYvnMN1OWNoT/tLvwzWcz\neloh1PaDarEcoi+3yJIMY/KkYT/wUEpirSVLNWkKQSCw1tDrpIPKRdZaslhjgM89mZdyiwK3EHAc\nx3Fy73kbzK9aLi2tzw3GGNIkIyoGg2o/Whs6PcmffSbj0QcsD9x27VuOQ3sUh/asz21zT+T/llJS\nKIabvnabpsOO49wAtwi4Dv/0TMZXnvNQKt9tz2LDqbZlblkwMhQSp3D8ss/0UMITL1kM+QIAQEpL\nlhkyI3hpxm67CEgzy+yyoVoU1Mrr56xP/GDzAmCNsXD2UkZtYvvrzQdGSxxn6CxvtR53U/zQw/O9\nwWltEmfE3WTwfWtlTK2xtBtdvOEyT520PPwaTjAcx3Gcn0ylAvza++HZ05aZRcvpi4aVVgZCDMJQ\nszQvMmGMoRsLvva05q5D6oY3lY5Ow3Nn1suPrhHA4T1v0A/kODcptwi4hl4CT54wjI/6BKFAAElq\naLYMq6sp2JTJCZ9mW3JqIcSQsLFmkOh37I17Kb1kayDll7+X8NQrmqW6JQzgyB7JJ94TUC1JOvHO\n16WEJcs0nrf1NKDbSfsNv/KGYVbnrduTXoa1FqXW+wUkccpaLSOxoaxRa7VLbbhML9ny8o7jOM5N\nzlNQ8A2vnNO0ewIp+3ORAOUJlK9I+xtRQkK9Dd9/OePhO70NjS37gag7lAAFOLwb7jsCT5+CtT6W\nSlqmRy0LyzATwfj4m/qjOs5PLLcIuIaXLwqKJS9vvtUXBgqvJkliTbOZsWtSEfqSOBUUC4p6urmg\nv5R5QdHlhmFja4ZvP5/yle9lgx2OOIEXzxriNOF3Ph5xYJdCimzLDgjA9ITg0moPWSn2Xz/X7aYs\nzrUZ3NhfVchUZ3ZwSiGEQEjIEo3y8l4B1lisNQgpsVimx17Pp+c4juP8JHrqpR6f/nJMN85LUfuB\nR6EUIsibVHq+xAsUOlsva/21pw2PH0+ZGMrnnqWGxJLnGLz/XsFYbetGmRDwkXfArfvg5RlodQwX\n5g2nrwjOXBF84znDU6da/Ow7LZ5y8UGOcyPcIuAamqm/aQGwRilBpayYm89oti1RCKQ772hIBZdm\nY1rdiHIh/5rnTultb/DPXDacuaQ5tk9ybL/kpXObewOMVOCRuzySVPMX/1inVA2RUhL3MhbnmnS7\nCaVyAcjzATa5qtRaVIxoxm2M1ijPQypJGqcUqwHVYn4U6ziO4zhrnjsR86kvdEj65TwtlribYIyh\nXC1iTd7FXimJ8gSmP4V1k7yR5kozn4ekMkgpWW3lOQa//VFDMdw632YGVuIAWfIoRXCoalluQJJA\nu53x4rmEgm/4yP2urLXj3AjXaeMaSoWddxbWInGkEoN76yzb2sxLSlBKEceGVy4LLi5L2rGg2dm+\nIYo2cHkp31359Y+E/NQ9HtPjgolhwT1HFL/+aMjUqGLflMf/9sseftxg5swSly+sksSaYqkw2NnP\n4zI3vI8QGGMHjylPIZUkLAYEYd5oTABRIRycYDiO4zjOmm8/Fw8WABulcUaWrXfxtcYONsbWQmM3\n2jg1za/Cd49vfU1t4J9eKXJh2aPeBm0FYSiZGJGM1Cy1Wt4n5+QVN1c5zo1yJwHXUA6379ALsHeo\nyeqywpOSNDOEytKLN3+9EBCGalDF4JWFIqdXJFjDxG7FUqOxpQ+K78H+Sdn/s+BnH/J5+bzgwqyh\nWhJMbeiSWCpI/tWvVOjGhn/732PmVvJBV2uNTjRZpgkLfv4eIl+wQD74WmuRUhBGPr1OQqlSQCmJ\n9BRRKaTRETxxAt517Pq6NzqO4zg/+RZWdp4XsyTD9/t5Z6zPM1Lm85YQ67kAV09+S82tc83XTxS4\nuGiJ+zlyUlpKBcFoDapFQatjKBQk3bYP6C3f7zjOztwi4BpuGU+ZWVKs9ja3APaVZvdoxrDf4lS7\nytFdPdptxWLdI03NoO15EEiklKSpJoxUP5HX4nmSQjli7z7LhfPNTa99ZFqydzIfROPU8p8/3+Pk\nhfXQoW89l/FLHwg5sGv96FNJQaeV0Gub/iC7/npGGyanh2is9gbHshtZ29+xkfngHBYCfF8hJTx9\nWnHHvoxK4fV/lo7jOM5bX7kgWFjZ/rmo4A+aifkePHDvMHFsWFhMmLm0udKEvKrGZ5oJ5lYFEzWL\nEGAMXJjP8+XWGAPNtkVJwUhVUAwtxkrCUOIWAY5zY1w40DV4Eu6cWqFWSFDSoIShEqUcGO3ghwHV\nqkfgGQqBZayqEQKCQBFFHlHkDXY/0lhz4MBa8648RtL3YGQkYO+EIvRhqAzvuFXxqx9ar4X82W/F\nvHJ+c+7A7LLhM9/sbQrzWa5rFlbzO/yrTxbSRKOUpDa89U7eWEuvEwMWa8APPKyxmH48ZzcRHJ9x\nf00cx3Gc3J1Hgm0fj4o+I+MlwijfXywVPYJAUql4HDxQ4MD+cFCYQik4erRMpZw/ICUsdEL++xMB\nn3nSZ7EhuLAkNy0ANur0LAhIsrwfgedJ6h0XEuQ4N8KdBFwHzxccnuygDVgr8NSGajvSp1bM6yNP\nDRnGK4aF5tXJSZZKRVEbWr+5T1JLIRIgJYcPRuzeZakW4YFjEG4YX09f3H5nY2bOcnJGc3Rf/ius\nliWV4vZ5BsqTSCVQngRhMdoOFidxJ84TuDyJlIKJXWUunUuJIoXvy/zkwEUDOY7jOH0femfEworh\nyePpoPpPoegzvmcIqSRB6GF0wq5Jj9n5jGbHsH+3x+6pCOlJrlzuMTFRoFIJKESKl0+0KBR8fF9h\ngcuriq+9KDi6e+edfW3AE4ZM56cDvhJ87QeK99+hqRXdpOU418MtAq5DEIWQGvKcps2DS2p8xqsZ\nSliKAYwPW1a6oHW+I68UhIHA9yOszeP1rbV4niVNQWeG52fWE3BPX7H8zAOWfeP9Ov7bJF+tXUVj\nww1/MZIcO+Dx/eNbv0Gq9XhMKQRpprESjNF0W3mgpRCCciXA9z1GxysUiwHGWAJPc9veneM/Hcdx\nnJuLEIK776gwG0s6rQQ/kBRK65tcYajYNVGiUlGUQs3zJyyvnEnZP+1RKvrcflvAWsxQGCr27yvR\nbG+eWxebgluMRQqLsVt3+H0FK3WD7yvCfg+f5Qa8eFHx8NFsy9c7jrOVi/O4DuNDRbbdDrcW6SmE\nyP/84jmYrwtKRUmlLKhWBOWS3FRi1Nq8cZfJ8pdcXU1YWWqxNN+ivtphpWn5zvH1agq7xrb/FQ2V\n4Y6Dm9dwB/eWCCN/UNBHCAgLPuVqkYUrTdJUE8f54GiModvuISR4vspv+PtHuFEpP4qQUnBwKj+h\ncBzHcZw1Y1VLGEqqw4VNCwDIQ3s8X7HWi3LPVF6cYuZyRrdrEGJth79fpW7b+v4CrGD/+NbTAIFl\n73Cbdk8grKUYSTwPKhWPiwtu08pxrpc7CbgO5aLHcEFS71o2Di/aSqQQXF60fO8HkswKpMwoRJax\nMX9Lz4AkNVhj8X1JmkEh0Jw/U19/vmtY6rVQskA3VhRCeM+9PpfmNY3O+usoCQ/c7lMI89e/vCw4\ndUXwwgXL0FiZNNVkicYL8kRkay1pnNFY7WxayyhPYXV/EPYl3W4K1qIGXYgtd+93A6rjOI6z2dSQ\nZc+o4cLC1tr8hUgigDSD5TpEAdQqkkZLo7Uh8iSd1KI1qMCSZls32aSwTNYMd+/P+CebMFcPSLSk\nFGoOT3bYPxZTKYKKO7RllaaR9PDp7FB623Gcrdwi4DrVCpJSYGjF+X10KQBPSf7mW3B2bn233hho\ndzRqRTAysl5RyNg88TZOLe1ORqmYnwqUygHt1obMJwuL812EKANwZK/Hv/iZAt9+PmWpbihGgrsO\nezxwe76z8rUXFMcvSDKTLwiigiFLNWmq8xwA+p2BlaDTSjctTMLQZ3zUp9WzCKHwPMnlmVVGJ6tI\nmfc+OD9r2TP6Jn6wjuM4zlvSuw5lLDUkni+JCgKtBUlqqfaTfdPM0orBIBiqSnqJpRtbpMjDdzo9\nS6Ascc9wdU+a6VHD9Kill6TceyDG2jwPQMn8lBtguJSwmATsLy1xolOlHitK0dbXchxne24RcAM8\nJRnaEBqzUIeZxe0Hm05XM2y9QQ6AAMJQEgSCbtdQb2iGd8HIeIlOO9lc0tNAo5MRBfmv58Autakc\nqLWWV85rvnvccG5BUygFg7rMUkrK1ZB2M6ax0qFcLRBEHjo1eL7E9xVxL+s3DIOVhua++4Y4N5PS\n7WRYC43VDkHgkaWGS0tv+MfoOI7jvIUZC199TvHKJUmqAQxRTzA57lEtK9J+SL7AkmYCrfOeNkNV\nw0rd0ktFXgJUw1LdkhmBFHn8fymCPSOGh49lm3oKCLHeoHONJy0XmkNMlHoIKYm7mkcf6DfFcRzn\nmlxOwOuw3IJMbz/YGGPR2pJlliSBJGWQGByGEqUEcQqep9izv8bddw+xf39psMPxZ/8geP7U1tpo\nxlr+61dS/tPnE46fzei0EpbmW7QavcHXSCkpVUKshW47Jkmyfgk1RaEUUhmKBouGJDbMXIzZMxWg\nPIUfSOJuRrcdY4xFuLHUcRzH2eCJE5IXLyjSDfNfL7bMLWQIYQc367K/a6+UQEpBsaAYHZEYBErm\nz6/tfxkr0EZw74GM996R4fe3KAPfQ+8QlWpQaCO40B7GmpSj+yUjFXdb4zjXy/3f8jrsHYNStH38\noZSCJBUkaX6EqTWsdVNXShAEguV6vl2SppZ6C4ZGIu65ZwTflyhP8amvZJyf25wU9d0XM549ublv\nABZazZg03ZpAlWWGTrOXhyOx9v6KQmm9DqnOQHoSa8AYQZqkdDt5laEDk6/ts3Ecx3F+Mm0Mgd2o\nF1va/YaVkM91oQ+hD80OgGSkosAKekk+J+oN05ZFcPaqHANPKXo63Nr/Rks6WUQpMqx2QxptH99z\nu1aOcyPcIuB150EThQAAIABJREFUKIZw67Rlu8pBhcLWZKmNg50U0OnknYXjWCOkotGC1abh8JEK\n1oLve/z7z6T8x88b2t38m0/O7LAlYqHbyU8OrLX0uhtKhYq8RKg1ZnC06vkKP1QolS8+jAGtNcOj\nEdZYslRTKxruvsUlWTmO4zjr4h1KVwObknylFJSKgsDP56U0M3jKYkW++YXNT8uv9dpdU2A1LtFN\nfeLMo5WGLMZVNB4SS7udsJyWWGm8UT+h49wc3CLgdfrA3ZZH7jBMDVuqRctQGYpFhbXQbmd0Ohlx\nrDd199XaEoagfJ84ziiEkolRhZQCIRVJBmEgEVKQZoaFhuDf/oPk1MV0EGu5rf5btFsx7ebm8CCj\nDZ1mQqveHTwuEIyMFigUPHS/SlAp0GAs1hiWG5bPfddVB3Icx3HWDZe23xwyxuB7FmPzMCAhLIWw\nHxZkYaSsCXyDIM8r6PY0jUayaX4c2qbRV+hBR0csJ1UW4xr1pIyxCmOg3jIsn7xIqXUJJdymlePc\nCLcIeJ2EgIdug3/xAcP/9NOGOw8CCIzJm4UZk+94xLFByjxXIArzAdEYQa+XMTYWUCkrhmsSIQRJ\nAsMjAaYfCFmqhLQ6GX/+uYyLS+yY82SMYXG2wdzFVYw2GJN/f5bqQVfHbiclSzXWGCanIqb3VQDL\n4lJeOejSTBNjDUHkY7GcvASd2A2sjuM4Tm5XLUVnW8NP282EizNtIL/xVxIKgUFYjfIs1aKmqOLB\nAqFeT9Da0u3mu1vlyHDn/q2vO140ZHrrPKSkYWrEknkFHn7ujyjb5hv8kzrOTza3CHiDXVnZ/g49\nyyyBbygVBIGfZ0t1uxlT4wGT43l8fqmQNzwxJj9GxVoqlRBjYNdkgajgE6d5B+DNCwFLmmQszTWp\nr6w3FDDarH+dGHwpcTdlaChgfKKEtbBaT2m1MoxOQYUMjVWoDBWxxtJoa5bqbhHgOI7j5BaXExbn\nWnTaMWmSEfdSVpfbLC+0mJ3t0utleNJQjAxSwFAxphgKCoGFNGbEq2+awnRmODSp+fBdKePVrfNN\nJbJk6Vrobf6PIH/t0aqlVpaMts6TfOOzfOkZwzbrE8dxtuFKhL4BVruSC8s+nVQiA0G5aGht07DE\nmrxCQpIYLs/GaG1ptPK8gDwUCAJfYqwh7mn8QFGqRkgJhVJebjRNU3zfp1wS7BnJd1pePtsjibcf\n9ayxiKu6Mfa6KWdPr7JYCQgKAVIKqlWfThvGdkX0OglCQJJofGkZG/K3fW3HcRzn5uOpfB7ZlHvW\npw1cudjm4fsL/QhVSzHohwkZ6IoCe+R5TvSODL7HWJDCMF7becMp9POSoFfzPdgfzgIwlV3kK3MB\n57+S8i8/LFx1O8e5BrcIeA20gefOwMVF8iZdQUCxlN8oFwp5PwB/VbNS3xxPb4xhZdUwv5AQ90Ns\nerFheTVjbMRHZ3ljr2LBZ3UlASRC9OMp+0GV3WaMP5I3Cvu1jwQIAf/Xn3WuvsQt1voVAGhtEEaw\nutJjzJfURksAlMqSdjsliDykFAShx3AhpRC4kdRxHMfJvesOn8eeSWh1tz7nKclyXdNsaSplhSdN\n3i9AQ72rCD2DSA3d7vrGlRBw/KKHJ+F9d27e0FpswDOn4HJdUyoIDk2LTVWAjLEcefpT+demVaaj\nZa5kIxy/mHDHXhfs4Divxi0CblCm4b89Bmdn1wchIWImxw17doVAHspTq0hWG2ZQ1ixNDWfPb5/V\nm2UWbewg9l4IQZxohMybrZw/ucitd04gpUD5CmstFkU7huGyYN+Ux4unty/XIOT6dXq+JEvNpq7B\nzUbCcH8RIKXo5y3kJxZBoPjET732z8pxHMf5yVOrKD74QMhnvhlvKt1ZKPmUqxHdTka9ZaiU8yIZ\npv9F9a7iQKXFxWSSMPKpImi1Mnw/v1k/tyB5+ULKlWUoFyAKBF95VtCJ1+NZryxY7r9dUC7l32Oe\neZrlv/wiwaNHODH1fubmygilefxlyR17f5ifiuO89bhFwA16/KXNCwDIE4DnF1NGhrxBaVDflxQL\neXfgLLP0ejuX9YlCSaNpB5V/tLEEYT54+oFHUAg4e6pOZbiQ36RrgxdGPHHS49F7Mx59KGJ2UbN4\n1cmDUmpww++HinIlZMRvcn5O5icY5CE/zWZMpRJibd7gzOaV2xiqCPZPub8ijuM4zmYP3R1wplFG\nSUGWWRotDWK9NPalOcueyTx6P1QZ5Ugzv+pTiWd5oX0rAGGYV8XTJs+Fa3QFf/1NBtXqPGWxUuY5\ncn2NNrx03vKOQzHm+edJ/82/xrZSTlwepzF6FN1KGK+AChTgkgMc59W4O7wbdHFx+8eNgeWVjD2D\n/gCWu/Zn7K5qPv89S8tuH1ITRYIkWz+yTBJDr2dQSvYrKECxElFfbCOVxGhLqRJSKgfMrlrmVgVn\nVorccXeZZicj68ZcuNjBeiFRwcf0a/57gQdCsHdS8Iv3L/HtE0W+/XIRKQXt/k6MlJJ+QSGMsdxz\ni2C7HgiO4zjOzaubCB4/W2RyYv2mf1xbFpczGk1DEHq0e/mpszGGUGkkljS1PL56jE6/grVY62Fj\nLQaL0XawAIB+g01jEL7YdIK9fH6F7v/ze/DM04PH4o5AKYHvS7SBoYLELQIc59W5gLkb9Gq3xEKu\nNw6rFTT3HcjYPWq5Y19eDu1qYSiYnIgAS5pqOt2MRjPFmHw3XkpBluUhRVExQKcGFShK5bx7Yqbh\nyy8EnJr1WekoMkIoVJjcO0alVsAPPMLIp1gOUf3k4E4qGa8ZHr27xa17enh+Pognccbtu1YR/esv\nR5YHjroFgOM4jrPZiYWAbra5IaZSgpEh1c9jE/05yrJYlyhhWFiVjJZT4mTjd62FwK6/xpZkXpsX\nuNik08VuWAAApOXh/qJCkFrFcGVrw07HcTZzi4AbtGd0+8elhIlRReBZioHm1ol4MJi96zZ4751Q\nK4NSEAaCiTGP248WGB2SDNckSknabY0xFjMY8PI+AsZAoRzSbPaoDRUol73+2CnoJFcPdAI/UJsG\nUiEESuW/6qEojzmKAnjHoWSwU2MtvONQh4dvbQF5EzTfnRM5juM4V1ntbH+D7fuSSllijMX3BGma\n0Yx90kzgy4yRSsbt+zLGh9Z26AVef56REjxPUiyHWxYCV29HVS+8uKnEqClVaL3/43lX4tQiBewf\ndacAjnMt7jbvBj10G1xcsJyb35AYDExNeJRL+cA4VU0ZLq7H5wsBuyd9Vm2A1gKl2HC0mXdUHK4J\n6g1Lr7fhKDQzJEnebbhZ75JlhuHhAkEgiWODNptCMAfWknrjeGP1BUEx0LzjQH3wWCnKr9EaS+Dn\n13Noqkc7LXKrq6rgOI7j3KBqWbC4ZDmwV9HtJ/TG+BwY75KKkG6s2D+l0ZllueXhKUEqLErmIT+e\nlycYW2PpdrJBWOyaIh32v/xVrBAIa8n23kLnF38L/4F3kl1O8TyBsIJbxpIdrtBxnDVuEXCDfA/+\n+XvgK88Lzi1IpITRYY+RofW78VasgM3VeuabCiHkYNdjI2Pz04FaNaDb7Q1OA9aqLujM0OuklKsR\n1kq0tnieQEmL2fpyAJvasPcf4f6DLYaL6wuDejcYfO2BCY2vLH7Bsn9ME6eSKLjBD8dxHMf5iTdU\n1LS2nEKDwFApSXZPSNJUoqrgdVOkNUShYKXuoa1ACpiesCy3oBBCnOS9BzINpZJPo5GiraE2HHBo\nLEV5gl5iGalAR46Q/ps/pf78kxB3Sd/+MPgBgbUUIoExgtGqRrl9LMe5JrcIeA2Uglv3S8JquO3z\nQvcgbkFYZqEheGXW5/KKxAhDMQLf2zg65cm3UliklHheXlFooyTOBmXWjDHEscZamB41XFje+v5p\nqun18pv9fBdFUClJwvFxZmKPveEcqx3F989XUErgKcs7dl0CIjqx4IvPeHSfgINTgo89YCkX3pjP\nzXEcx3nrOzqRcHlVUW8ZFhdTokgyOuJRjRI8TzE6HNCutygon7GxNrXFE1yo3kMzDiiFeUiq7+en\n0UJKShF0YqiU823/Tie/mS8WPBIki6sSJcAPDSIA6UnSe9615bqkkihlieOM589JRsqWPaPWNQ1z\nnB24RcBrNFXNuNjwSfXW7YYhu4S3NMMVuZ8vn9lNvKH6TxxDrWIIg7XHLAJIdX7KcOygx/mZlJVm\nXjEh6aXE/a6MOjOkqcFow1hZU/QtvY6hl4BSkihSFAJYWo3pdrN+gpakXJYcPhCCkMxloySZ4YVz\nBdomQIiEyWrGkRf+hnhiHy/XPkg3ya/t1CXDpx+T/PaH3CDqOI7j5FZbmu8900QphedJWu2My1fy\nU+z3vF2SZiVmVzwyBe890CBsNZlrFJDCEvkWYTVYxS27Us4vFPA8i+4YsAJPCSoVj+XllDQzLHU8\nMm3JgAuLikJomRi3m8qGAsSJJdN5/sDFRctSWxGFil3Dhve/LaVa/JF8VI7zY80dmL1GoQf7ainq\nqqo/w2KFQ94FhNUUerMk2eaBylhob2jwm+/UWwqyRxAIlFJ8+AHD3UcM77tf8qGHAw4fyLsRe57E\naM3PTP+ATrPNd16SdHp5edI0NXTaKednOnQ6Wb+iQr5waDY1s7NxP0RIciWZxKsNMTTsUygqxqZH\nOf22f4a/MIM5/tzg2pQSzC5bTl5xKwDHcRwn9+eftxRLAYWih/IlQehRqoQEvuKbz2iW6yY/Mhce\niVbI2hAHRtrUihm+Z+mmCiU1BV8TBNCNAQRpZjHWIoQgCASFwtZ9ym4MrdbmvjtpZmm08hN0o/N/\np6nFIri8ovj6cf/N/kgc5y3JnQS8DnuHM2pRyvxcA2MFVdlkj5pD9hcGQ16HsbDJQlzd9H1pZsEY\npLKUgpTxUpta0OOpi/lAFQVw7IBE27zKT/U+SZIKGj2PyWKL20ZXSVLB3509tql2sjZ5V+BEb66K\nYLTl5NmEVivjvnsqFAJDLxGMjfi02xrlKbrV3cwfeh+3nniWb/vvp9lM8QNFlmouLXkc3e3KhTqO\n4zjgBz5RKJmc8AZlPZeXNSvG0KtnxEke5iOF5dTKKCbqIAtQjWJ6WUCqJaUgJQMkeUPNKMzLe1pD\nP0E4r2rX7W6t8jMcZTTaAisEWkOrYzEGtDaDUNiNrqxIFhuCsaqbxxxnI7cIeJ2qoWEkPIcwWzsC\nGyAzWw9bPGW4d888ntwcZrOr2iHrJZxdGmJ3rU0nk0S+IQoltx4JefJ5zSP7rgCwf6iFFAbL5uSs\nq49I1wghOD/T4+D+iMlRSa2o6aU+1bIikBm+snSH9jAuHmdkNMTzJcuLHSyWyHf1lh3HcZxctSqZ\nGA+oNzTdnkFJGKopSkXJuTTve+N5Ag/NcjeAeJjpGvhSE5OH7GRastLxKYSaNBMUi/1FQL+oRam4\nucLdRuM1y7FyzOMnPOrdfI7NMku3kw4Kaqz1xgHQRlDvuEWA41zNhQO9XlJhg9K2T62kFVbSrc+N\nlBJ8tTXOvua3uad6FoFltlEAaymqLmApRoJySVKaHAMgVJqH9sxufVMLgQ+jQ/m/12ht0BouX+mh\nJISeRQhLqSxRyiAEWOlxoVXFxE2CQJCkGY3lDvNL6db3cRzHcW5KI8Me7eef5baVb/Cg9yQP6G8R\nvPQtOu2EKJIEvmS4JqiVNFJBQ5cQ/fw3AIEg0RIEDJcyAs8gBXhe3mAs8gwPHY1R27TnHC4Z7tqn\nOThh+OcPJYxEMfXVhGYjIcvyr1dKEEXrm1el0LBnZKdaeo5z83InAW8AXZ2GLEFm3cFjRgWY2i4q\nC4bE5GE9SQqVMOO23U2W2z7L7ZBCoNldy2/0pwp1VAqBTGllEWGWUvQSJJZl6fPxdyfM9SapiCYj\nYo537Znn6YVdg0RegF3jlnfdBsWCoNW1zMzCd1+ATiu/kfdVnodgAayl3bIUI58k6xHV53k8vZeX\nXu6ye9owMlriwkqX4xfh4z/cj9RxHMf5MSVOPsexQxGz6h00qBEQM1mbY+TKE1zZ9RBKpIS2w1TF\nEIaSWmQQCLSRCGEQWBo9n+FCShTCaM3SigXaauJYcGxfytEpgzApz5xTLNTzcty7hw3vOpoNGllK\nCb/0sOWlywE/OBvT7AiaPYkXqE29eA5P6S0lr63FFbxwbnpuEfBG8CP0+K3Y9gLoHkgfUxon6UQM\n1ySd/k26wLKrajkxV2OhGWCRgOXsYpnRQouSF3B0WNM1ERZBnCkkGYGCo2NNAmkIPcvp1kEqpZhS\nb5n33N7hhYUxstSSZZrdUwHFQhuAckFw20GICooTl0a4MlPnwP4Ia6GXCpSAY3sT6j3J6krG8VcC\nXrHTeL6m1cgo1zyCSDFUcuFAjuM4Tm7PlM9JdesgHDUhZIZ9TOzyya5cYGxvhUuXYor7JSdnQ3bd\nIsBmdHURTxqkUHgSwsCiDdSbGuFBt2NRSqBNfnd+ZLfh8C7Dalvgq63lqtux4OSsB57PbQcNx6ZS\nLixYXrpkqXcMhcBycNJw74H1sKLnz8JzZ2ClDcUQjuyGR+7IFxSOc7Nxi4A3ipSYyuTgPzMDL14O\n6WxoqGIRXG4EdHr5wLfWDL3RC1htD7G8HGPvkPhSkxlFagRxJgg8jS8MAQkl1SH0QkSvSz0ag0wx\nUUlY6UaAotmDbiIpBOtHn5PDhnMLisNHhykVLUlmafdgeiwhkJbVrsGsLPH9pWkAglCRpIYsSXnk\nnQWEjoHoh/AhOo7jOD/uGtHUlnw0gEXG2RtcoNkNmM+G+PQXljh8sEVZZGgUXRkSKIs1gmohz6Nb\nXDG8/HKbI7cGWJs3r6xE6/OXEDBc3hoWdHlV8eSZYMMcG3J+0ePhIz0+tmv7ENbnzsCXnoZU54uM\nVhfmV6EbWx59x+v8UBznLcitfd8kMyv+pgXAOoGSkCQGnRmstRhtEVJQrfg8fbaMMBoFWCuYa1VA\na3QrryvqkRKpjMrKeWJZxvMse4dbg1c3Gs7MF0g35FMVAkMxSEmNpBsLupmkWsxbtFsE1ULK/loT\nJfKBd2xEEoSSobLh+8926RpXXs1xHMfJ9XbYFDJ4pFGNi91REi25MCuYGoopez1qfpvxYBUh8hv/\n2UXN0qrluRMQFAIW57t5U0xjefmi4sz8zrcn1sILF4Mtc2yjp3h+ZudW98+fXV8AbPTyRWh3t/kG\nx/kJ5xYBb5I02znY0FrodA3NtqHTNRhjsMYSRpJ2T9BKJEpqLAJjoat9xttnuNSs4SdNRnqXUDqh\nWj9HQcQU/fXKRBZoJz4XltbPTZUwvH3fKtbkJwBnL8FqR+Y9DATEqaDoawqBwffgjsOKQwcilNU0\nW3D2fPJmflSO4zjOW4int79jVqRcknvR1mOoqpic8KlMTbCc5mWyI9mj27OcvSx5+ZzgsWcs7S74\ngSJN802oLIP5huRLzwacX9h+Hl3pCJZa29++LLUU2TZFhYyBldbWxwE6seD8wjV+aMf5CeTCgd4k\n4+WMkwsBxm4dxJJ0/WgzTfOKCWGQ11oOAoGne0wUM5a6ZQIf2knAebOLlo4gy9i//CzCGgqNWbLx\nu/DE1qoHrZ6HsXnWQdE2qZYs+8faZLJCuST4/9m78yDLrrvA899zzt3e/nKvrKqsVSWVpNJily0b\n4R1ovAHNAIPDTcMMA8xAxDDRQQdBhAk6Jmamo2GADv5o2vQMHdF09BgDxqZNA8abbNnGi3aVttqz\nqnJf3363c878cbOyKpVZJSHLqirpfCIkpe577777Xry4Z/ud329uSRN6kkaQk+ZlrBmw5+AwocoZ\nGzIY6fPQ85JaM9wxzanjOI7zxjQQZZTN0GLrKnFVt7mUVkkSy9QuxbGDdYRQtPIaNdVDCs3z04JM\nS6wtMuRZW1S2DwKJ2WjKrC3CZ//mMZ9DIwlvvR2aVXj0pObsjCHVgraWNJvRllo5UEyE7ZQIVAgo\nhdCNtz/mKctI7dX5bhznVuIGAd8jQxXDZD1jprV1aTLNDJ1OjqfYmK0Q5Lkl8C1ZVtwMx8o9fC/g\nWPg8s2YPs70yoT5EPWnhyZSlaB/j3bMok6CtR41V3pR8gyeCt3HEO8uCnSA2VSIxILQpQ3aZLPcZ\nq1aZHQjKYZGlYX45x2Y+99bO88LyPpRS5Eja/YQ0l6SZpTlcYveYAnbO1+w4juO8sciwjNSayLSJ\nKeHZDCUNvWCESlUzNqqZGjM0y4LUgEHR0RVI+swsX+l2XM7pb42lWo8QolgJuEwbwbdPwjPTUA0y\nzs5cPeHVo9vJ2TtV3TIQGK5qdiptIwTcNglLre2P7RuDiaHv8ktxnFuQGwR8D90/lVANLUtdRZwJ\nltYtk80+99yboZSl3VecmfOZWQkwxhYVEqVlf7PNsh6hK2tYIxAyYLrtMdWw5CXBYvMoNS9GdlaZ\nLK9ipcft9Xn2tz/BSAkW7BjfUO8m8Cy58VnJmozINUbrKVp2WOjVKQWSlb5gj7/I+eUyF/WejasW\nrHU9tLF4qigytnfMFVhxHMdxCgaLJqAvikmuVPhFus2NhBe7Gjm1UvF3IFNSU2z6PbtY3lwdv1wU\nDIoMdkpJggB6PcNVDyFEMXvf6srLb7BpbTWh3ghoNEIAqqHm2J5rh6+++55iE/ALMzBIBZ6y7BuD\nD771u/9OHOdW5AYB30NSwO0TKbdPQJJZnpvTjNavzKiPNTT1ssYY6MWSZj0kHeT4UlPOuvT9Cutr\nIRaQSoEXEdkErUqsVA5SFQGRyskszIy9lSPrn8LGZcZLS9xVPg/UURI6XpN+LAlMQkl5QB0pLdbC\noysHtl13P5OceGqFWqNIJ3rn3gEQvjZfmuM4jnNT0xshojvl2ZdSMFROECLEIinJAYHMODcreHS6\nvvk85UkiJZBCUKl4KAmVEvT7xXm11mgNWWZQSiKVLJJZ2K2TUjKLuW0yxLMJd0zmVMJrT1pJCR98\nAN7Rg+kly2gdJodfne/EcW5FLtj7NWKsYaS2PaQm9OGefX3ee3gOIST1MAEBkR2A9cBTJBkIDBk+\nMs+o6lWmsz3k1REGlAFLX1SId9+BXVlEAkNee/M9pLC05RhPn/W4sOgDFq3BkzvfLGcv9ZFKMTZe\n5vZ9llrkKqo4juM4BbvDXrfLpBRkNkBrgUCQaJ+SSnlhvgJsdOJtsRfO8xRRSSGlQCnwPYnZ2BiQ\nZ5osM5j8yux/ECl40VuP1TQfeovgzQey6w4ArlavwD0H3ADAcdwg4DWSanvN6oTNimZXtc9wdpHJ\n+gCBQClDXa8yEa3hK4MvYyazc2grqOoWfRMy2x9CJQNyEZLj0ylNENuAPDP0ZJPqYB6sJTcSg8dC\nOsT8uocUBkXGocntG4o7nZTZ2T5KScLQo1oPKQWuWJjjOI5TUOr6ne3UBKS6KIbZz3yUhLv25Rht\nsabI1FMUuDQYU5yr2CBcvP7yhuFeJ0V6xUEpBZVqwMhohVK5CEOSAu484Nonx3ml3CDgNeLJ68yc\nCANKcXdwikOjXayFiu2hpCYiYXejjxeEjMklAtMnI2RYrHE+30O9NY22korXY1WM8IU9v4jOc/q5\nT22wQrV1ifWehwGUlEgVUQo0hyZzmlHGykrMYJAzGOSsrSXMzRdrsf1+jjGWOPdJcxc15jiO4xTu\nm0qu+ZiUkOSCOFWkuShSUVNkwPO8rSFEJrebM/9SFh1/zxMEAfR7GVoXoUAASsmNFQhBuRIQhIq3\nHJUc3ffddWPSDE5ckDw/I9Au/4XzBuN6d6+RciDppoJMv3gGxRISI7KE0WyOmewI6/2A26MOuR8R\nZx5lP6FZ8jBeFWnWWFST1JM5Vvxh+sEw5e4iojrGAI9WXicfm2Ly5OfR+/dQCducPddn/wFBrEMC\nX1KPimqKZ2cta+sZa+tXqitKqfADTZZopLB4SmwUV3Gbgx3HcRw4Oql5fjann3qbLUPRubesrSZg\nfUabgl4SUgszslywGlcolQxpVhTIzDZSZV/OBhQGAm2KaJ9uJyeODVIJSmUPrS0CQakkMFqQZZZ9\nu0N+/F16W4rQf4zHzkienFZ042Ig8chpw9uO5BzZ7do7543BrQS8RoQQDJU9wrwHdmPmg5wyXSIS\ngrVZtAy40BlmKWkwECWkEOwqdRhPzlHxM1IT8LS4l6rfZzy7SLOckHgVAt3DCkVJxORGEPtNQtuj\nk4UgBHeNtjkx7VMpSbTJ8JQlkIbnzu18o/N8D2s1oQ/1yFAvbQ8bchzHcd64fuRNMWmaAQYhLNYa\nFub7LC6mzMzEtHsQZx6ZFsx1KvSTooiXEALlSTy/6Lz3uylpnOL7MIgt3a6m2ylGBsqTxb6B0NvY\nLAy1ahH+k2vxXQ0AppcE3zrtbQ4AANZ6kq8+59Ppv/LvxXFuJW4Q8BoKlGSsWWXX3HcY6p5jJLlE\nrTdHafYFopnn6YzfTq2UE+uAjhrFJ8NXhqpeZ6+apd9LmTeT1OhgylU8BaHMwCsWdHKjMNoyLyag\n2sAawylzO6iAldWc3Ei6rYRmZGgGhiSHoabH3j0heyZDwqC4oQohyJKcCzMxdT/mOpFMjuM4zhtQ\nri1LSzFZalFK4PuKXZNldu8p0+9rFhZTcm3opgFznSr9eGvqTykFxhjy3LKymtIfaLrdHGNASIHn\nSXxfIsTG//uSMFQMEksUCpSEU/OKb54O+OoJQ3fwj2uoTs4qcr39Nf1EcOKi22fgvDG4cKDXmheQ\njhygdu7bhN1FpDWk5RFW9z1AUptA9U2xEdjL8MlQWUygB+xRl8i8JhWvR47Pev0AJoVK3mLgN8it\nYikpE6mEga3gpT3WvXFWGOZCNyBJYiSG4ZJhKLQ8dR4mxgJ2TYR4GxuvRkd8ZuYSZi710LkhSSzn\nZiz377+xX5njOI5zczEaxsbKlCuKsWpM6GnW+wFKFckput2MVttQGpMYrVlcyjfbGigGAVoXq8zG\nwKWZmCyz+J5CSPAChefJzdl+peTGvgCDFwg8JfjGqY3U1XPwlB/xloMphydeXmB/kr2yxxzn9cQN\nAm6E5i5ceG+RAAAgAElEQVTm73w/3qCNMDlZeWhzt1ScewyX+vgSchNQG1xCAtJotCwxUopJVQWE\noMEqXp7SKo+SEWCkz6HGGpmtcKlyJ6fFnURY4kyChTxLWe36/McvBWgDeW544fSAiTGf0ZEAz5NM\nTgScObmCFxQ/jbm1G/g9OY7jODelVizZO26IAoMVPknuIYRmotohSSL6/ZQ4MfT6UApBa4tSdrNT\nr/WVzEAAaWIQUpDnuqghEEmCwOPyU4QoVqmlBKsNIgq2XE+cSZ6Y9tk3unPF4BdrVq4d9z9Sd3sC\nnDcGFw50A5QCH09J8lKdrDK8OQAYZArQ7G+sA2CFQgsfKyUWQa+xhyptlO4h0ewenGYl3E1KGRBo\nLVhPykwv+jxZfR8EHp3YY7WlGR6WrKxpZBCCkJspQEslj7mFlEFczJ4EgWJyssTwWA0Az/1CHMdx\nnBdZ6PmUSxLlKTwliEJJo+aTmhIT9Zh7DhQZhJbXYbCRTOhy59xaS5pqpNoejmOBKPIIQ7/o9G88\nRcmixoC1IOzO+9S6ieLs4ssL5bn/gGaosv08k03DXXvdPjjnjcF18W4AIQSNUkTkeygpwBqMzqmr\nNofqywjyy08k90pY6dHz6gQKov4K4VNfR0rBbO1OWuEkUKRWa/V9lnsel1Z8Aq+Iv5xdsrTXE24/\nENEYKm3bSCWExPMkq6tX1j9Hx6qMjVfwPMHU6Gv2tTiO4zi3gExDrOUO7QlUygIjQs6uNADNUF3Q\n7Rcz66VSURQsCASBL1FSEkaKSsWjUvU3z5Om+WZlYCGK+P8ihail18uYX8pot9Mdr02bl7c3oFqC\nDxzPuGO3plkxDFcNd0/lfOgtGcr1jJw3CBcOdIMoJamXI5J+i8z0EVdNXkirSa0EIRC9FvF6G//A\nLg7qM6jBGnqoQmotwcnHyA49AEFIe+Cx1lMsrIcYayj5hlbs024nvOOBCr1EEgaWXn/7DEet6qE3\ncjVrbdHWQ0oYGVK85143I+I4juNc0Ukkxu7cU7ZAGEjGhnKefW7ArvGQVgeUguEhj1IMcQJhoOh0\nMzxPEkWKNDX0+8UEWJ5bOp2UatXH8yXGCIyFNMlJk2LVetDPaDTCLe8d+YaDY/nL/hwjVfgn97/8\n5zvO640b795AxmjybPDiKugIAQEZOk7JPvHHJP/t0+Tf+RqeTlFJBz0ySaedM7R6kspf/AFrHcFM\nq0KuJcYKet2MCwsSY+DwwSrdxMMi6XYTOu3B5gzLZZ4SlKJiqbU/MGzs1WKiKSmFOI7jOM6myLdY\na4lTuLhgOH0hZ25Zk2VFGk8pYbhWbO71VVEdeKihCPytG4MrFY8wVAghCAKJ54nN2H9rodfLEGJj\nFcAUtQLCyCeM1Ea60CutpxKWo5MZpWCnK37l1jqGFy4Y2jtMoDnOrc6tBNxA/ThnMRkiMT4SS0UN\naPqd4iaIRQY+5X/6U+hnniQ98QTmrvuQ/Q7aK9FdTsj23EH4tT9GPfY1grs/ROhfLrcuWVnXTI5o\n1uMyQkCWGfoDi5QwP9Ni7/46Wkt8H8qR4fAew+lZTbd/5aZajdxNz3Ecx9mq5FkWVi3CxEwNZ3gC\nVnseM4uKaiUgzzS1qqRaLUJTm3UYGfLItSW5KopHIBAbQf+X56akvDJQ0NqSZxrPV3h+Eb4qpcXz\nPOJBTpoadg0bhiqKyVrC1MirV/I3zSyfflhzaqYY7JQjOLrP8KMPFnsgHOf14CUHAYPBgN/4jd9g\nZWWFJEn4lV/5FarVKr//+7+P53mUy2V+53d+h0aj8Vpc7+vGIIXpdpnMXIkDGpiI1HhMRGuAxcNg\nmuOU3nwc0+tiTz+LkJbADpgYLHKmcjvh3R+isnyKNDPUyoZGTZImUK1CvWR4/PkuB/ZHGCPQRlCu\nFKE+y0sD9u2NqFUVU6M55Uiwf0LzzLniJxH5hrv2uGVSx3FeHtdWvHEYA8OlHocnYgJV9N73DkNr\n4PPktCHWEa1uysSIAAyjQ4rAN7S7ckutgKuXwdNUY+0Oefv7OY2mQglQPsSGzRoC/RgOj1s++FbJ\n0tKrNwAA+MzXNE9fVVCzH8NjJy2+0vzIg27+1Hl9eMlf8pe//GWOHTvGL/7iLzIzM8PP//zPU6lU\n+N3f/V0OHTrExz/+cT75yU/yS7/0S6/F9b5uLPbUlgFAQdDRFZq6QyRihDX4uo/wfYJDt5GXa2Se\nj590CbIWC/MWdeiHOHj6s8SxQVUER3etUS3VqJcht7BrVPD8Cz3uvrNKFAmsgWYzZHGhx8JyzuSE\nYqXjUyllVCONrxSjNcM9UxkTTZcmzXGcl8e1FW8c3zltOTx+ZQAAoCQMlTOO7JI8dSFkdiHn6P6i\nk9HNPDxl6cdXzmEtmxuLjbEbm4oVvf6VzrwxFikEUSAYbcDFBUu5BIMYopJCCMvc6qvfTvViy+mZ\nnc978pIl19atBjivCy85CPjgBz+4+ffc3BwTExP4vs/6epHGstVqcejQoe/dFb5OxdnONxCLop9H\nRP4AlSV4duOGGEQkXYt3dIrg3FM0hcf+z/9bnvup3+HcxPeB0Fyal4weChkq51QqitwISoHgwF6P\nlfWc0SbMzBuqVcXQcESuBa2WYajp0R0YyqHhp98+oBy9hl+E4zivC66teOPwVEbgbe8kCwGV0JBn\nKdZaZpcs1VqxqVcbwUhVI4CRalEj4MySR24kSgq8yCMKFcrLabczrLUYbZDKZ6hmKJckeW6ZGres\n9z3WWzlaW3z50oOAXix4bs6jl0pKvuXIRMbQdeoErHct/eQa5xpAnBbZhRznVvey17Q+8pGPMD8/\nz8c//nF83+dnfuZnqNfrNBoNfu3Xfu17eY2vS+I6Ny5JDllOczC7eSxd63L+j77AsT/835j7ziWG\npypUJocZ+tJf8MyxH2Xu6Q7DY1WSXDJcicFKWrqKFxiOjGsePSnZNw4myxF+iO95CCmQ0uJ7lrWu\nolrSbgDgOM53xbUVr3/hdTbfWixSWEolj9Onltk1WcEAkS944LaEREOqBa2+YKRuWGoJLscFCSEo\nlxSt9Xhjtl1iNLTahnIoEBLWWhY/Ap1bTG5p9UCba7eni23B109FdJMrK+/Tyx4PHE7Yf409BKMN\nQaMCrd72x4ZquIQZzuuGsC9OFXMdzz33HL/+67/O8PAwv/qrv8rx48f57d/+bSYnJ/nZn/3Z7+V1\nvu6cmc85u7D9BhSQMOnNoP0KAkuQ9Wh0LxLMnOHE//1Zpv7Xn2T95ALizNN4734ns0+2mXnfT/PF\npwL2TA1x/1HJRLTOSPscj8u3Mjz/NONHR3hyboSJSo9qmDDdGaPdVyAsQ1VLGCoWVwXfd6TH/bfV\ntuV+dhzH+cdwbcXr24mzXUze2TGf/sXViMdPeaz3NKdOLHP49iYHDg9TCiH0LHuGk8v1MTEW5lcV\nM8v+lnMsLw/I8yubhKMQxkZgtS3wPUsY+iwupUgByvd45zHJD79l5znNv/wHy/nF7ccnmvDRd3HN\n9u4vvtjn89+JtxwTAn7snSU+8KBbBnBeH15yJeDEiROMjIwwOTnJnXfeidaab33rWxw/fhyABx98\nkM9+9rMv+UZLS53v/mpvkLGx2qt+/TUJzUjRiotqwAC+NNRUlzwoNs5ZIA6baCR7/LPsevA20laf\nfHGV3omLjL3PQw01OLanxxcf98nyjN3xGZpPfAt16QLD39dk+Et/Rvnun2O03mBXPM1SeJhd+hJt\nswc/VIzUDOsDhbWWSLS4OJNTCv3rXPlr53vxvb9W3LXfGLf6td/KXq22Am7d9uJW//293GsfrcCz\nMwET9a0FuzoDj7lWBCJndXEAQJwIPGkxVjLIBL1EbmaekwJG65r5VYU2RYffWotSCnPV7H6ew/SM\nIQxhfMSj3bH4viSJNcqHkzOGu3d3MFZQCq68Ls1hbrXMTtnQF9YtL5wfMFLdOQveO++xZJnkufOG\nTgyNCtx7SHL8toylpVc3acYb5Xdzs7nVr/3V8JKDgEceeYSZmRk+9rGPsby8TL/f58iRI5w+fZrb\nbruNp59+mv37978qF/NGIgQcGNZ0EkM3KSoihrJPmm1/bhbWaQ0dJhyeJt8/Rfbnn0PYFG/uHOZU\nn5Hqgxw7kLKQwKA2xOH1Z2mvD7jvkX/LcmWI0CRMMEPZjxlincx2GaorhIR0o+pjrWToDDx8T980\ngwDHcW4drq1441ASTi+UaA98hioZShjasc+ltRKVMszNp8SxQQgo13wCX+B5kGbQjeWW9NOBD82q\nYaVddNSzzJJlWwMU0txitMVaQRhI0tSgpMUPihCflTZ8+tESBslIVXP37oy9w3oj3fbOBCDEdcJy\nheAHjyve92ZJrsFX1141cJxb1UsOAj7ykY/wsY99jI9+9KPEccxv/dZv0Ww2+c3f/E1836fRaPCv\n//W/fi2u9XWpFlpqYXEjavWunZc/D8qkS20W/uATHP4nd7LwcIzVKek3H0dIQTmwHKutshqPQGOE\nIdWhdX6GkfsPsXhxiYNmjksvtDl8fJHHuRffF1RLmjT3KQVQ9/rMdyqUohSXwM9xnH8s11a8sRyd\nyJnpRaytlDGmqAg80rRcnMlYmO0wOdWg0VAoJQk2atj4Hiyue1gr2NXMNuraWLK8aAMDZVjpbJ0J\ns7YoTKZNMRDo9AABzaGAXs8SJwYEm9n2FtsenYHkfeGA4YpltKa5tLZ9JWCkqhkqX3sQkG3sXSj5\nELiMoM7r1Ev+tKMo4vd+7/e2Hf/TP/3T78kFvZHJ68wy2FaLS597gvq+YaJGRHl3FUYmyRaWGKSG\nlbzOveOznOjtYf6O97B/9QmC2VnM7AXOd25nVJ2h9f98kbWzR+Hv/g21j/wC2Y//AlIWFR5vb66w\nNKhxYb1CoiWTjczd+BzHedlcW/HGcsdewd99qsP+fSXKZUWWWZ47mfL0U6scu2+MJBd4KmT3hGKQ\nFJtppbD0+hqBIs1g31hGJGLeMdVi3UxweNzwB582gA9cnsa35JkhTzVRPUBKQbkkyTKL70OSQhBs\nTbc9yCSn5n3edjjlvn0pnVjSGlx5TjnQ3LcvY6cmN8vhqZmQC8semYHhquXAaMZtYzss0zvOLW6H\nbT3OjRIFwY5Ll6K9xsz/+YfEiy1MpunOrVIda5CvtCEzXPr9P+IjB05wPptgfinhfOVedFAhHK4i\n8wT19a+z8sXvUPnpf0r35EX0cpfws3+CuHAKay2jpXU8BYHKEVmf/voSf38i4NSiCwtyHMdxdmJ5\nx1sijk7lTNb6lOlS8lN+/Mf3sn9fhJSKsVFFp2cIPMMgKVYDQl/S6WmM8Hj0BckL5yw11eNgOE06\nGFAqB0Qlj6ikiCJFFHkEftFVGR728TyB70vaHcMgtlTKkijc3pXppUVrOlyxvP+eAfdOJexpJhwc\nHvDDxwbsbu6cGeihFyKevuCx1oVuHy4uCR49F3Buxc2KOa8/bhBwE/E9RbUUYZZWsLnGphnxY08x\n/y//d9onzhdPUlB7+3Hs0fvw0x7VeyfoP32JPKiwklRYnE/pdeDry4fI9xwgqISohUWqk03EHXfR\nONTEZBYx6FJ9+FPsqawwWWmDEPRiyR3ZM9xRvsTxsQt86QmYWXU/EcdxHOcKay0LbctoLacUWOpV\nyaF9Pm+/NyDybZEJKIChqmJ1LacWGbQ2KGEIAtCm2B8wPOTzyCmPmVYJqxNsZ5Z6tDVBvxCCMPJo\nNjzqdR8hBKutogOf55Ydp/OB8lUbhM/PpHzpK0t85rNzfPIzC3z8z9Z5+tT2QgALLcHMisJcFZlr\nbVEb4IU5NynmvP64Ht5NJgp8hkaarPz6v2L+n/0SS7/8a6RPPgOA36yw72c/hFetwj0PEP7gu5l4\n614a3/8mOj3Bvt0eCIuf9jgbHeNT4T8jN5JdBwLG3nY70eEpKgd3oaJiWbR+/js0RFHIJ8kEpxcr\n9AdQWZ9hLGjz1oNt/vZxN/vhOI7jXNHPLPEOCXJ8ZamFKUIIPAWeZ/A8ycWFIrtOoAyH8hdQQrC+\nnmKRNJsB35puoq0gUjn3TaxsO6+UguGRiCQ1zC7k5Fe99+rygOnzHRbm+2RZMTgIPcORiSJ8p9XT\n/Oe/7nLqQk6uiwHI2Us5/9/fdllY2fohnpnxuVbJgfXuy9sUbC0stWClU/ztODczNwi4CQVDTaZ+\n5ecpN0KE74ES1O7Yy22//CNU9o8DYKXCjE4xdOwQh9/cwLTWqZQku8Z8jpVeYK5f5tn5GtkP/Bj7\n3rEb2xxheDBNfvAudr33TqKJBrWaYPDw11nuBjx+cZjlbon5bBhlMsLeKlPNHp5vEfGtmULLcRzH\nefUl18mQ6UtDu5OhtcZTAp1DN4aF5YwwMIR5B5n38T2JNpahhsdy26OdFykPh0rpjudNUsPMfP6i\nWXpLPNDkmaHbyViY6zNUznnb4YSRatED/8ojMSut7Uk3Wl3LVx7dWgfAu04//+UkBjo5I/gvX1H8\nyZc9/uRLHp98WHFx+aVf5zg3ipvmvUlVj9/HXf/mf0HPzGDynNKu4c30ZEZIJBojYP6JOUY/+hbW\n7UFIEnxfElfG6XZyohC6OqD3lg8zkS5SXp1jfWgX3s/9c3bPneGJ+3+Ju77173jh8XkulHYBEIni\nBixNjpAwOaRRnQXy6NbOYe44juO8OtR1HhMYTp3s4vkSbXyEhHpVst6Gfj9noXo/t81/lYXag/T7\nIVEkqEaGgQ1JrYdGEQSCTjtDeQLPU/jKEidg7NaeeDzI6HYSolIRJpQkhiGvz/6RK/Obrc7Osf8A\n7Rdl5Jsc0pxe9NgpsWijfO3zACyswxeeUgySjXbawsyq4HOPCT767pyyqzLs3ITcSsBNTAdl/F1j\nlCZHtgwA8lIdz2SofID98E8Sj+0nr4wzHp+jESZ41hIPcqb2+szZ3bT9STydwOIlvLEJ8sYuqrft\nYayW8/xtP0mjUfwMGrLDveWzAOTSp0eV0XoO0v1MHMdxnEKtJPB2bBYsVa9Pzesx6Of0BjA6EuKp\n4vlDtWIg0I4mwKQoafCV4YGpVVayOsv9iIu9JkJYVpb7LC30WFvpUQoMoQdcTheqNb1uwupiD50b\n8vxKZ36ptTUGZ6h+7SFLs7r1Qxye0EzUt3f2A8/QrOU8fCZAXyNe6KnzcnMAcLVWX/D4WdeGOjcn\ntxJwM1OKuDaO1RqpU6yQpGGNwCbIPCPDp7aniUUyIpbwkhb37ioTrl7A8/ayb1zR8kbJEk08yAhy\nje33qI8M00+nkMLA6C7kzCr70uc5Pr5AIHNyFTBfvg1Q5FqQN/be6G/CcRzHuUlIIRgqGZZ7GosC\nBAJNIDNKKuPNR+GhExKlFI2qxVqoVzRCGNa7krXoDoY86HYzvJqkk3h8+xQcnhhjzQzjqaIjbi0M\nBprpmZSx8QpBAEmcszDTQV/V8de5wfeLzn45vNIRtxaO3lFjJaswiC3z830WF4pKxkM1yXveGr3o\nc8EPHUt49Lzh/HJxvkY559DEgHJoiDOfr56OeM+RZFvhsP72fcabevG1H3OcG8kNAm5imReS55ZS\nZwEv7WOkh6lZBrVxAtumH41SyS6RWMOd5jnWyzVKep5PnDzKgw+UqZUNvSRkPlbktVH04ipLn3uM\n0f/+B8jCKtJoqqEmnHmOd5/5HOU3v5ne4buZrR4lVk0kOQoJXnCjvwrHcRznJuLJjLo3IDMeFoEn\ncpQsZsmHGzC5u0KWWxpVi9aWfcMpxnhUqx7zizFhENAMBuwaq7HaGqfdzbgQ1ajVwLxoR208yMgy\nje8rwsij0YxYXe5vPn65P16J4PgRS5pbpIAnZ0osdj3GJ4snTO2rcfFCm6TV5gMPlhltbu8CBT4c\nnMjYNdzDV1uvoxxkjNcF5+c1WpXRwGQ9ox5ZatG2U21qlF/BF+w4rwE3CLiJaSNozD2Dl1+ZRgj7\nq/TSAd2hKQyWXEWUeksMnnmC6Mi9fPPsLg7ct5d6TWGwICH0Ybp0F6PqIVY/8XdMfeAYbV1CDE0A\nmvGn/it4ltapaVaO/xxCChQ57TTg6O4b9/kdx3Gcm5MnPYQo6su8mLES5Xk883yPo4d8RpqCcgSX\nVhS5hqXFAVobxiYz1tqC0aZCKc3ifI8wrFEtK8bGQpaWiul1a9kcBACE4ZWuixDQHC0T+YK9Qxl/\n9U3BaqeoYFwq50zt8TYjWqUSHDhY54H9HsOV7ZuFL+ulltDbOewn8jSPnQkIK0WQ/7nlgL3NjPsO\nJpyek3TirSsEIzXDmw5d+70c50ZygWo3sfLaxS0DACi2K5Xac7CyhLSWKO/TbsHC47P0Rcibu1+i\nWffwlcYaKKscJSyJLNP+gY8ydNsoph8T5W18ZSgvn9u82XnLM/iXTpIYj9iUODgsqEYux5njOI6z\nled5eGrnecSzCwHPvdCl3y8y+jxzKuPJU4L1vkevZ/B8RTywrPV9Ls3laAvDQ5I8t7TWYqJIMjFx\nZWpdyq1Vge3GSoGQUB8qEUU+KI/ptYBLy0VoTqcPi8sZ56YHW67NIphvX3/+s+Rfu9MusEhxpV3M\njeD8qk9fe3zgeM6+MU3oWyLfcmjC8KG3aAJXYsC5SbmVgJuYF3d3PK50RrRwmrg9oFTpczEbx56a\nIfjUX1I7spugM4NpTOCJDGNgLOpS9QZUKzmld+5n4T99gvr/+D/RHwimPvV/bZ5XAOM1gRqVxX4B\nx3Ecx7mGclTm7FyfRiXHV9BPBOcWAh49U8YPLEms6ceWQQKrLRgb16ytpSgJvifwymXKac5qS1Gr\neAwPW5KkaHsqFZ9SSTEYaEplH8+7PAiwVEJDrRlRqYWbqwMASimCwJCmVzb3rrfz4hyl6+U02mqk\nLFjoFnsEXizOJJVaQLpl/7BgoePxln05U2OGQWqQoliFd5ybmRsE3MSsCoDejo9FSRvVBeGXqT3y\nOebOzGG1of5jH8D++R8jVzLEv/x1FnoN3jQ6jTUZIs2xD76X3fFn6D3zOFPP/tGWc4qJKbxDd72s\nfMiO4zjOG1ucKT77nTr1ck6zopld8emnRWdbKUG9JjY65IJcQ7utyTKDlIbmUICHplyVICWVSk49\n9VldKUKA8txgLSgli5l+wJOWI5Oa3sAjFTvn3AxChVKCJNEYYzEG2t18cxAgsEzUr1PoAAh9iRIC\nY+2W9jDVgktLklRu3yenr5o3K7ltdM4twoUD3cRMY2LH41p6hNUS5fYC6WqLYOks8aU22coy0enH\nuX2fJv/AB1n6959gJOpQsV3WzQhKGGbrd9Nf7BCuXtx60koN9a4PI1w6UMdxHOdluNxBXm77nJ6L\nNgcAl+2bCqnWfIaGik68FFCqeExMNvF9hY67ZLkljXNmFyxCetTrxXN7vZwsK8JuTJrwpgMpP/bW\nhPfcnVEKrx2mao3F8yXlir8ZQtQfmI0QIsveoYyR6+wHuGx3w6cWSgappJ9I1roKtCKVO9fMqUdu\n9dy59biVgJuYKTWwSoHWm6VLrBBI3wMpUL0uXsey/O1prLWMHG4iOh2CQ3fQ6Qc0vv9u9tZbWHza\ng4iyP4ySIdNfucQdf/XbqG9/Abu2hChXUcffg5xwqUAdx3Gcl6ccwnBdsNqVmyMCnRu0tpTLknLZ\n48A+j4WllKEhRRgGVKze2NhruHPS8PSsz8JCQqUcYLQhjlO6XY/p6SvZf9o9y1g1Y3yjps2xfYan\nL1heXNTLGovRkKU5UdnH8yVCWDItmZ3p88EHYLLx8jrrQgiaZZ/mVZl9rIWVQc5CZ2ucTz3SHB7d\nudKx49zM3CDgJiZMBmEJdI7VurjJen7xX2OIF9aJ9gyz/PQalaky1oPlp6YJO5a9b76P8ac/z7OL\n/4LbxixpZln3R2nma9R/819gVpfwf+AnbvRHdBzHcW5Rz89IepmH8q50xqUUKGMYHwsQQqAUjAz5\nXJo1NBsCnUkyYzHaMNcfZr1niGNQEqxNmbnQZ/ZSjFISdXW8/1X9/fGGJZCaOFOIjcVra9isHaC1\nxZgiTaj0FULAwqplbsmyu/nKP68QcHwq5vSSYaWnMBYaJc3hsYxgozdlLSy2Jet9xXgjZ6jskms4\nNy83CLiJmVITay1CefCiLAw6SemtxFQ6CeWJCN3XLD22SLI8YPxtLcJ2laWvfQ17ocKlX/gVyrbF\n6Ys1HhxdQdxxlLn/998z9Zv/CpSPNQaLQQi1rQCK4ziO47yYtfDUtCTXW9sMIQRBoKiUroSWBoFA\nSpib6XPoQIlWD5Tv0Yo9ur0eQSBYXRmgc1104K1GCIHQBqkku0dg366t77Nv1PDsBbEZknS5tIA2\nBm2K2gRSFmsFRhcPPvS0ZLENb7vdMLxzVM9LkhJun0g333N61efJmYhMC3xpaPcFy21Z1E6QAbub\nOQ8eSVAu0ta5Cbmf5U1M1yfR5dFtx22es/TQCcbecRfl4TIqioiXE0xsyfuapJ3Q/vTn8ZRkX+tx\nDmbPMtSU7B2O+fyFQ/RkDfmjP4V96iHi3ir9ziKDzhKD7jJpsnNGIsdxHMe5LNOw1t25C6E1xMnW\nsJs8M6ysxHiexVhBHGssYDJNueSRppow8otOvS0688ZY6mV475sU8kUTVO+82yBFsXn48gDAWEu+\nsRogRLE6IKQgywxKCowVnJzz+KtvK7pbM4e+IqeWAp5fDFkfePRSxXrsk6Pw/eJacyO4sOrz14+H\nGAOnZgWPnZGsdr7793acV4NbCbiZCUF84G34Z7+NXJpGKEW23mHlO6epHNpFbTQEY9BpURRsMF9k\nEhosG+JLa4zcPQw6JWBAY+F52uXb2TMesJ5U2TtUo3fhDO3/9kUaP/wOAKzJyeIOQij8oHQjP7nj\nOI5zE1MSAs8SZzuvHntXxe/EsWFxvoc2lulLKZVamSS1BIEhKoWkqUFIgTEGz1N4viKJM8JIsnsy\n5NEz8PBzgqGK5d6Dhsg3/MOzliwxxGlRBEwpiTEWC1fCkwTo3IItViKUEvi+ZLVjeehZwYePXz9L\n0GWvdgUAACAASURBVPXkGmZbHi/elyClIAosaXbl2Hpf8h8+5zHIBCD4hxcsRyYNP3i/3jENqeO8\nVtwg4GbnBWRHvp9Sfw3VXkCVYOrdRzYfjldblIYF/ZkrL+lPL4MSrL+wCFgqJ89StW3C2/czVJL0\n8waJhLN7f4LRf/dz1N77NuRV1UzyrO8GAY7jOM41KQlTo4ZnLm5fDShFgiAojqep4eyZDpaiIz47\nl7DX8xkMNDrXIKDbLZLuD/oZQhSz934oiOOc+TXwPEG3k7PWUUXV4czS61+JtTfaYozG9yVKCYSU\nWL31moQQIARSFtcxvy45vexx2+grGwisDSRxvnPtAfmiw54n6KcWsbGBIc0Fz1xU1EqW7zvqsgo5\nN44LB7oVCEG69x50prcsieZxSuu5c5R3RfgVhRqq0Xz/28FAUIuIl/v0znc5/3/8R+h12LPwdQ40\n1vFNzMCWSXKPo//ze1j+T5/Z8nbWuJuS4ziOc33vuktzcFyj5EYFXyySnEE/Zn4+5sKFHo8+ssJ6\nK6cxVEJtFPzqdovY/3Y7xxpI0yIDntwInPcDhacUvi9ZW+4jAM+XdFoDWq2Y3Gzvukjgv3vQcN8h\ngRQ7d20ERehQo6GQEha6HskrXAwo+VsrB1/NvuiwFAJpss1Kx5edW3RdMOfGcisBtwg9eoDFR09R\nHSnjVSL0IKF16gLJ4irKV5T31qn96IcpHT1A66FHSDsxbGyGSpfWyeZmKR8+SlfH1GnTt3UyI+js\nuZ38r/9my3u5WgGO4zjOSwk8+NG35syuCubWinCdb59WnJuB1ZU+xoAf+lTrIZ6viEoe62sxvW6K\n8hTWQpzkGGPwfUWa5Phh0fnXmUB5ivZ6n+Vlychoibjv0evEyB3aKGNhcV0wNQYnpne+XqmKDQfW\nCHRuybRguSvY07T0E8OTp4tm875DUCtfvx2shpbhsma5t70blb9oYBEEUK4oevHW2J+rQ4Yc50Zw\ng4BbSJZLlv7hyW3HLTD1P7yf8g+9m+5si9JIjf7M2ubjo8d2YdcSBpUxSt1lamnKeng7Ruf0R6YY\n/rF3bTmf55dxHMdxnJdj97Bl93Ax6fSt01CuhpSr2yv6SikplTz6vXQzNCaJc3xPYYzBWEs5CvE8\nRRgq6s2QXjsm7qUk1SJk1Q8Ua0sdGiPb0/ucmQNtLY2KodXb2om/vB8ABFmmsSh6PYMdsXzrOcNX\nn7Z0NkoTfP0EvP1Ow7vvu/5A4K5dMU/PRqwNFCCwttgL0Ltq07GUlnKQM5NuDx0arrn0oc6N5aZ8\nbyHhvcd3PB5NjjP69mOUukuoQXvLAADAClg7tULymf+KCSLGuqcYlUsMB11Ea5l88gAAUnr4Uc3t\nB3Acx3FekfHG9Tu2QgrsRlir0aZI4WkMeW6JSj5RySNLc2p1j3JJkWWaNNX0ujFQhNqkaU6abJ9G\nn1kRfONZ6PQMYmMjsFTgB5KotJECWwAIPE8QJ9DqGr74+JUBAEAvhq8+ZTk1c/3Q2HJgeWD/gLdM\nDTg6ETMaxSSpoZias4S+ZbRhabf1tlSq5dBy30EXeuvcWG4l4BZS/uBPoVtrKJkiGw1skqAvTDP0\n5ruLnMpY/Hh7is/WySVsvszuikcwc5rurjuIpMVLUtLyEMlSj0f6+7ljMue2mrspOY7jOK/MA7dp\nzsxLBunWOUZBkTknSw2eJxESdGrIsqLN8XxJrR4hBOyaDPF9xdLCAGshz3KMiZASsqTYRzDoJfiB\nt1nbRiqx+fcgAaU0pbKH1jAYZGRZsQrheZIwkqQphDLj778jSbIX7SKmSIF64pzlyJ7rf14hYLSq\nGUVzYBj2jxqeuijJjARrqXqGqSnNRA3m1z2SDIaqlvsPGvaNuZUA58Zyg4BbiLCa+vt/CJFftdb4\n9rch4zZkxTH/wD7Ul79B9rm/Q/3h71F98x10HnoMgLlvniMY+wLhL9+N/IeHKY/fjjq6i13Tf89X\nho+z1otQMuHg2PYbouM4juO8lFoJ3nlnzheeDjaPeRsd9MEgK3rNFP8flhRKSbxAUi77KCUJQ4nA\nsrQw4OJ0GyhSYAupiPsp/Y0VgSzV3HvQcGrOI9NiW6FLrS3WQhAopBT0+zlaZzQaPkIopM2Ynrf0\nE4HyJHm2fQLslcTsj9cNP3i3Icvhr78FXz8JcQqlAI7syfnJB8HbOamQ47zmXDjQraS7uHUAACAV\nWVjDbuQqzkWAV68S/MRPEn7yU0z+8k9vPtXzPVaemEV11+j/6V8y0j6JEIJGPMdPhH9Frg0n59y4\n0HEcx3llrIVTCwFhoDb/UUoipcDzFGC50l+XjO+qMDZWplLxiaIiZKc/0FycbqO1Js801XoJow3t\ntaIWzuUsQkrJYlXhJSrdK1WE/2htSVNDr5ezsp6zvlG0S10jWf/EsGBpHf7uEfj01+HLTxShQi/H\n33wHnpkuBgAAgxSeOgd//+jLe73jvBZcj+9Wkvd3Pq48cj/CywasehPFIQnp8G6ysQRZCtn/z99D\ndOwQs3/+MJx8Ft3pE+4eZz6vsCfXNEWbnxh6iL8fvGvn93Acx3Gcl9DuCxZbO88vFhtz2czus5G6\nfxspBTrXqEBRa5SISgHWWurDFUAw6MdYa5lesIzUDDMrO6QM3dgTULyPQClBnlu63Qxj7OZxIQRS\nCfZOVcAaVldT+n3N7hFoVgX/+YvQT66c9/mL8OPvgF1D1/4OBimcndv5sTNzkOXgu96XcxNwP8Nb\nyXXCB3M85r2DzAaHN4/5StOiSe1T/4UR7zxGZ+x92wT4EcpPScb3M/TQJ8AaVL9NdbfPPcmzwG3f\n+8/iOI7jvC69OE/+1a6etY9CQa0qN8KFINeWOLYkCCb3j2x7XRD6pElOqRzR7wyYXzEoaRmuh6y2\nryoeZixJnIOAWi3cOFY8pvWV51lrEcKiVJHdR0iPvXs8KrLP++4x/NlXxZYBAMBKBx5+Gn7qOvNl\n3f61Vwx6cTFIcIMA52bgwoFuJf7OWXtSLflG/n08Ze9jkF0JNqyJFikhuj7MTO0u/HqVxpCiXLaE\nx47S/uJDZF/8W6y1yDzFzxMOpSeu3C0dx3EcZydGE1x6itKzn6d84m8Jz34T0W9RL1vGGzu3IcZa\nPE9ircUYw/paUkxCqaKSb+BLKmVJnu1cwevqYlthudhzMLNUrAbkWU6eabJMk8QZWlu67ZQ0ydH6\nygbky6sRVxPAoF+8pzbQHI5Y7UoW1nf+6DMroK+zda5ZhWbl2o9Vomu/1nFeS24QcCupTmC9rXeP\nzCrOZ1PEslgmjXOJNlBNlznQeYrAtxihSP0yamUWUSoRxm3Kgcb72z8n68ak3Rg8D5UNKJsW8szj\nN+bzOY7jODc/a4nOfINw7hm83jJqsE6wco7S6YeRSYe79mTblgOMseS5xfOLGH4pJWHkcfp0Z0vn\nXilBtbp9mlxrQ5Ze6XlfvaLw3HmNkMXgwhqz5a17vYw41ggBnifxfbUlBMna4lxZZjGm2K/QGly/\nayQ2/7Uz34M79+382N37i3Bdx7kZuJ/irUT5MHQQWxlnRQ9zKR3nycFRZvJdVz1J4MfrHG19jXK6\nRsn0CUoevspRl84iazVEGhO8/8Pw9vchlETVqzAygchikmgIdfKbN+wjOo7jODc31Z7HW5/Zfjzp\nEMw/j68scVLMvue5KWbnU7PZ4YZiL4AfeESRx9ra1tgZX1oEGp0brLVobUgGW1P1XD1w0Nps7C8o\nBheeJ656zBarDIHa3ERcKvnb9iJYC0ZbohA8Ydk3BhPNnT//npGX7si/73545zEYb0IpLM713vvh\nHXdf/3WO81pyUWm3GqmgOs659YjODhUIAcbj80RmQCpLVPJVemqKRrIE43v4/9m78yBNj/rA89/M\n53zvt+7q6vs+dEvoaCQECCOEAYFtbDAz9jjs2ViHY8yGZ8fgWMfaDkf4j1mHY9fY4XBsrD3r8Y7H\nXmAxgwcEBoQsARJIQrfU91HdXff5Xs+VmfvHU0dXV1WrJbWk7lZ+IhR0v8dTWW8X9eQv85e/n5ga\nQScZ4aYNODtvxz/3EtJ1EY5D6gSQzmNa8zinn0NtufEt/uYsy7KsK53TGEesc0hNdubo7jf4riFe\nI6tncfK+OGkXjqTVyjh19ByVesC2HV20OoqZ8SZpZhYO9Upcf+V0JY0z3IXEemMEWpuFHP88EBBC\nLZUIdS6YsTuuICx4dNop8rynggB6alBxFY4D99wA3/wxNM+LUXqrcO8l3BqFgPfeCPfeAJnKy4K+\nShEjy3rL2SDgKlULFY14dRAQ6Bab0+MApG6BQtogcwTFqZO0S104qcHLOhR0A8d3Kf0P/47sO/8A\nx48S3/I+HCU5sfWD7Bo5YoMAy7IsaxUjvfWfc1yqRdjcqzk6uvoepbI8N18ulOWUUhL6mvGxFuNj\nLSbH2hSrBTIt8vQeY9BagcjLXBtjSJOUrv4KhYKg09akiUZlGqUUvp+PLSy4GJ03IVsMDgAQLOwW\n5Kk/vu8iEBQKgk2DEteBGzbldT33b4aBGjx5FDoR1Ctwx558Zf9SdWJDlORnAV6tlKllvdVsEHCV\n2tqV0oglc9HyP6GrY3YlLxIQg1YErUkmi9uJE0EjlQx3HWSf8wyer3AbU8hiQtI1SP2ue4gOvYJP\nSlqugd+LGD30Nn53lmVZ1pUq7duJN3EEJ1lZttoAqp632H3fdRlTDUkzyvcMjGEhNWjxIHA+OQ9C\nwexssnSNudmIKNZ4gYfrOWQL3XyzTGEw+L5HvbcC5JPqjZsKjIzERO2U2ckm/UN57U4hBUHg0mrl\n1/Z8h3rdo1R0aLTyFKFC0cPzHAJPs2e7h+MI+kspheU+Z3RX4f5bX/tnNNfSfO0HihMjhjiBgW64\nfZ/krgN22mVdOexP41XKc+CmoZiR+YzO2WFc1WFzcpS6ngGjIUtxEEybGgXZpt23m24aZEEPMp5A\nYNCdNvNhlb6eAWT/LFJlOEJSV+Nk4TqlDSzLsqx3Ni8g3nwrwfAzyKyDlhKRZWgnRBkJxvDDwx7N\nWILIz9AKkVfmKRTcpTKdnicohvDi8ZVleJTS+AtpPZAHAUIIat3lFa+LIkO56OC5ktSV+L5DuxVR\nKod5/r+E3p6A6emYJFZo5VCrhriuYnpW4Xn5TkW1LBHCUA8Vu3oS3ihjDP/wXcXJ0eWUqZEp+MYT\nmlKouGGHbRlsXRlsEHAVkwI2lmNKzUeR2eqixAJDt5kiDrtxdABjp6CrAFkGSlOKJjnObrSj2dQ1\nh4dGGqh1JmgPbkfGLdzABgOWZVnWSqrcS7RxHzKay2f4nSbO2DDhj79KtOEAp2cfXPUeIQSeJ5DC\n4PsSoVN+/IMxsmx1SdGlRmICWDhQnKUKKcVSx+DFwKJadYlihRf4NOc61BeaiiFAGcHNN5R5/uUW\n8/MpeoMhDCSOVCidBxyvHOowPgIH90u8oTdeL+Xl05pTo6vPTKQZ/OSwtkGAdcWw1YGudtJBFypr\nPpW6IX6tgOdokFA48jTlaAapEzCQJXlDsePRAPPV7YwWd6CQzHTvRVV6GJuJWeN3s2VZlvVOZgxi\n9iRTkcchuY9DYi8z4UbU5t3oco3w3AvsiF9Y863FQPAbH874xK0tnnlyZM0AwHGdPF1IG4xe3DVw\nMBpUZpbOFZSKDlIK4kSB0RiTnws4/5pCCKIUbryujBSglcJxBJ6fnzmIOinGwNQcfPNJzYsn3/hN\nb2zarNvbc659kU5qlvUWs0HA1U4I0oE9GLlyU8cAUX0DWroExEycmIGN2/GSJkIplB9wVm6kQEKc\nuZzztpB6RcbD7Zz29hFpD2MMX3/OdjWxLMuyztOa5LDazmSwBeMXMX6R0WAHh7wb0b1DCGAnx9Z8\naynQuA6AoK/bw3EdXM9FOnn5Ttd3CcL8cK8R4Dj52QEvWD6MrJXB9wWDgyFzczGjZxp0WilRJ8V1\nHLI4BvIKRaiEmZkUITSbN4UEQR5ctOZTGrMxndZyCaM0g2eOvvEgYKBbrNtGoFa0h4OtK4cNAq4B\nWf8uOjvuIi7WSb0CcaHO/MBeGv27cXWKpyNuOvn/UR/qQpe7UGmKOnWK9PQwVTNNlgpwPTyd0nZq\nxNpjTlVpJAFBKDg5bn9MLMuyrNxwu4jwfKRcTtuRErQXcLa0B4BKsFZLXcPOQY3Whv/7aw2m5par\n9kgpcVyHIPTyXQAW6v4vpP50WitTXvt7fZIo5dCL08SdlDRJUZnC9T2kgO6aw/6hJvfvHObs2Qbt\npkIpSFOFlIY4zkiS1WMcmcqDgRWjNoZzU5pTYwqlX30lf/8WydbB1ZN9z4Vb9tj7qXXlsGcCrhFp\n9yZiZ6E6g3AxCALVQWJAZdDdj3Ac0IYsE6QvvUBp8xBpuoUgEJS9CIxG6hQQNLICp2Z8XE/y0pjP\ntv7VZw4sy7Ksd54OBZw1FrSlgJniFrYCXVs3sD/KOD0paceCWtGwc1Bx2w7FD5+LGB5dK0iALM0n\n8ouEFAvnAc5foTecPjHH9NTCfUmAyQzFso/ne3iepKvucuS04URxI0MbfUYnEzqRJpgx7NhZZ+PG\nAkeONFd9/UYH/ubb8Av3QrUIJ0cVDz2hGB43aJNX+bnnBod37V1/+iSE4NP3OUvVgaKF6kB37pf2\nPIB1RbFBwDVCOD5ID6FTXLNyGUPELczemxDGIAA1PgpA4pbxpU8lTNDCIxM+JT0H9BCnLu3Mpys0\nBJ7NYbQsy7JyFy13LyTDOx6gZ8d+3i8zkgw6iaAcmqUuu1Oz66fcGHPh3xebgBmSJMX3PbJUMTe/\nvDDleg6VWpFWI8L3XaQrKZUcZpuS9nTC3r0FTp2Yo6unxPCZBtu3VymVHCpVj8b8yk7E0pGMTsP3\nnoX7bzN86ZGMqbnl58em4es/VHRXBDuG1p/QV0uSf/VBSTta7hOw2BvBsq4Udl/qGiGEQIbV1U9o\njeN7CCnAKIgispPHaQc9jG55N4dmuhkqN2hnDpkWOFoBhpmmS+CB52qq4dorNpZlWdY7jxTrLwy5\nZEz3XocWDi+cgidegXOThvPnv4O960+eL2yopbVBa00QejSmW6hMEbVXlvEMCj4Iges7xFFKoRQy\nPWfItERlhlMn50jilPmZNlKAFIJ2x9DXF573dcFx5VJ34bOT8PhLakUAsKiTwFOHL+3sQDEUdFeF\nDQCsK5LdCbiGyEIdhETHTdApMu0gswhXpQg0ZBnZ0Vdo+T2cPPBzTJs6zXlJlrVwBDTSkIAmUQKt\n1KVaMDhCcePQGr3fLcuyrHekvjBlPPJX7QhoA5vTY8zGJf72u9sYmYbFGp9PHoWP32WoFuG2/QHf\nezLixNmV9xbpCMLSwqFgY1BKk0YpjuegjcBxHbIsAQxSSqQj8AIPx3GAxUpCBiEFrbYmCH2arTZT\nk4p2s4M2ee+BTEEn0oShQ7nikST5hP78ACRKDXON9YOdZsfukFtXPxsEXGNkWF3eEVAZZT3N2ZEG\naQat0Rkm/fcwc+8tBIFk0MCRkzDb9sBxQDpkuo/MCLrKCldkbO1K8exPiWVZlrWgWg5J54aZ8YeW\nc4OMoVePUOuM04q7GZk+P0IQnJ2C7zxr+JmDeVrMgQN1Jpst2q14YaXfp6uvRBh6jJ6dI8syQFIo\n+wgESpmFvgEOpWqIWcgbWpy4K2XIUkVYzHe+00STpBqBIO7EaGUQKqFSr9GJ8wpDZ0/PUCqX0Dpb\namC2qNE2HDonkQ5otXrVv16yK/vW1c9O765ljkurtJ2nTho6iYSagBqgIekYSoGiqw7d5ZRXxitk\nBrqKAb3FjK6Cor9i8OwZJsuyLOsCG5JzDKbDtP06Qgp81caNW3iNSUbjoTXfMzwhiFND4MF022No\nWzdaabQxOAslQjGGsOARdfJmX1oB5OcCVJbvDKhMs1iIX8o8DchxHKQj8sm+EERRRhzlOw1ZmmGU\nJs3ynP84UiSJYmy0zdBmn3LFo9lYDgTycwj5WQbPkyRmuV8BQLUEB697YzfHM+OK8RnDzo2Svr43\ndCnLet1sEHCtMoZs5DBadbin6GIqLrOxz8Nn91AsBziOJEolhVBRdFO0yWsnHz+XcfMuYQMAy7Is\na12qewuFE4/jyUm0H+DEbaRWxG6Zbx7bseZ70iz/L/AgXThqJh258nCiEPkZNlia6BtjUKlamJwL\nVKoRMi8rqrUhiTKCUCwEB4IsM7QaCSrTGG2QSJTOuw0XCy7tdkqWKTzPRWWKWjWk2YhJkgzHWTkt\nEkLgunKpOpHnwifukQz2XPqRSmPgySOC42OCVgSNpmJqOiOONcUQbj8wzwN32IPD1lvPBgHXqOiH\nX6Pz0NeR7QbagNy4merPfZr7tx/m60d2UKoUwHfoCSMCVzNYadFMQ0YnJKzb69CyLMuyQNU2EA3d\ngD95BK/TwABZoYt0ww3URjwm1jhQ21eD0sJZ3N6KodFZ/ZpyqNl3wPCNH2R5zVGTp+MopZHu8sRb\nZRotDW7eeYwsUyRxRrHk0mpE5KcD8gDC8SVID+k4OI5AZYbJsSZpqqhVXYwGP/A5eXiCeneBcr2C\nXqcfQJrB5Cyw9dI/q+88J3nmuIClFmKSUtVBzUa0I8UjT0dIXB64y7vYZSzrsrPVga5B6cs/Jv7K\n/0vJV1QGa1QHqwTz48R/+X8QSsO7+04yNZORKUNVT+FnLYq+puDl7dSLnt0FsCzLsi4u699Fe9/9\ntLe/m/au99LZex+m1s+tOw2eu3ISHbiG23abpSMEt+5QlIKVr5HCcGCT5kMHQ/7XX6twYIsgSzO0\nNnlnYXd53VIIQZZkKJVvKWSpwvVcjDF02nkakOs6aKUpFAuUSgX8wOPM8CxzM23mZ2PSOCONM2Zn\nIowxBKHP7HSHcvmC9dEL6pZOzV/6ZzTXgleGzw8Aco4rKZSWJ/2vnLJV+Ky3nt0JuAZ1vvifKPWW\nlzotCiHwyyHCSUi/+WUKD/wb9iRNzrbqFAoRgWoTa4lwMrYMOgxW7S8jy7Is6xJIB1VfeQbglp1Q\nDA0vnoJmJ2+6deM2w44Ny6/Z1GP46LtSnj3hMNuG0IOKF/HSc7M8/kNFT93l/jsrnBqD1kV6VWql\ncRwnDxQciTGGNM4wWiOlxPM9lFYUiiFaazrtlPHxFhgw2jA83GJoSxdz023kQge0iXNzVLrLRJHK\ne+tccGi4XLz0j+fYqCBK107zcc/b2WhFi/0QbEqQ9daxQcA1yMkiZDFc9bhX8Ilffgn/gZTthTEm\nsiqDpRYOmi45z7H5LrbVOmSpA/7bMHDLsizrmrB3I+zdePHU0sG6YfCWfNX+saca/N1Xp2mdV3rz\nmZfbDA6VaUWrt6YXqwMtHthdrO9vzMIZgsygpQIBjuPgeJK4mSy8B8o1n+ZcQhznO+BzM52la8Vx\nRt2VGJORKb1iI6C7AgcPXHoSRSmAPDFp9eT+/Ov21KQNAKy3nE0HugZd7HCRSRWl00/idOYoZPME\n8Uz+uFYUXSCZ5W8f8fnLb3qMzrxFA7Ysy7LesZQyPPQv8ysCAICpWUXSjrnwnJrWeikNiDXud4v5\n/EobsjRDCIExeTAggKDoEXc0jitxPWfh6gYh8z+F5WBh9V/gOJIgdCiVPTYPCH72XpdS4dKnTrs3\nGvprawdDSZwHQIEHd+y3ObjWW88GAdegLFn7F47RhvnxhM5D36U2eYT7n/tD+P63oTVPe6LBnuo5\nBisRB6/LqFfgiz8MiNM1L2VZlmVZlyRKNVMtxVRL0U700ir+omPDMcOja99sRicStg3kZT5VpsjS\njCzJMCpPn/GDixymNSAWUoQWdwp6Bqt5VaBUUSoHdPWVSWKF60kwAs93kELSbi2OR+C6Dl3dBSr1\nEql5bQkUUsAHbtT0VZd7DQgMQqV4JmHHkOSXP1rh1r02McN669mfumtQu+96/ObLeIWVvxzbEy2i\niSZ01yg5RapkdLSgMHqS67ITRN5BYu1RKsDeLYqZBnzzGZcHb7cdgy3LsqzXbrqtmY+WJ/2N2FD2\nDT2l5fQXzwUpQa/uyYUj85KaazXsko5ECoG+IKjIV/4XmomRlxyVUlAo+fiBS7sV4/oSA/i+S6sZ\nE4QBadKmd6DG3FSTsFrEW+iUuXj9+Y7kB4c9Brtiaq/hXMDGXvjX79e8MmxoxrC1zzDYJTAmRAhB\nX1+BiYnGpV/Qsi4TGwRcg/p+87Mcvf9D9F3XT6G3hFGa+dPzTDw3QXmrz8zDh9l+2xOkUQK1Almp\nC9VuY4DIBACEPmzuN4zPOIANAizLsqyVWhF85XGXqYZAG3AduHVHxt3780lzJ10ZACxqJhB4hkqQ\nBwHbNgbs2ORz9HSy6rXddY+RqTWiA6Do5yk/qxnMQg6+MQYpBUKIpUDCZJru/irN+QilFGHBZWaq\nhco0nVbM/GwbN/SXggDXWU6a6CSCF4Yd7t772gpoOBKu27o6WLGst5MNAq5B0nUpHryFke8+QTKf\ngga34lLeWkBXu4lnTpDMNMnaHeSufZhKnaTYx7yuM6crS9dxXSgGFz/YZVmWZb3zaA1/+z0370a/\nIFPwoyMeUZLygZsM7XVSUwGi1FDJ15wQQvDJB7r4qy9NMTG9vOi0Zcjjur0lxn68OjgAqJUF77vd\n55++n5BkLJ29FTJPAcrSDNdz8TwHrTXzsx2kKylWQwQCKWBseJqN23pJ45R6T5Gx0zM4C6VFjTFo\nrRk/16BWH1z6ukfHPGolyfWbrsx82WMn23zju+OMTSRUKi733tnNXbfV3+5hWVcgGwRco9zP/THO\n/i9T+6e/QWYJxnVJ7vskycGPUTj5q6RZinPbu5FbdkJ7nnZ5E7EooFmusDDfgpu223KhlmVZ1kpP\nHxMrAoDzvTjsct+N6UX7Tl6QwcP+nQX+4N8N8u0fNphraAZ7Xd5/V4WxKc1jzyQka8y3B3oc7r7J\n58vfaZFpges5IMTC2QGFyhRSShxXkiX5n+MopVItkKUaR2iidsr8bAfPd6j3VJg8N0etr0inTKtO\nDAAAIABJREFUmdDTXyJJBI7rMDPVoqunBIDSkueGHQLXsHvwytopf/7lef70/zrF1MzyB/bUc3N8\nZmKIjz8w8DaOzLoS2SDgGtVREnX/p+jc/6kVj0sg/M3/EeeuTciwkC/nRC3E1AnktptxhEEZmGkI\nqqFg34Yr6xecZVmW9fY7ObF+XRGlBaMzUC0LGuvsBgTu6lSYcsnlEz/VteKxzYOSm/b4/PjFlbsB\n5YKgf7DIobOCLDMorZFS4LgSpTQqyxewlMo7CaexyvsIKI3K8nr8szN5A4K4E7FvfxdRJrnxjo0U\nix5Pfv8sWZri+yGlaoGxkVm6eko4Dvi+wCA4NeVecUHAPz40viIAAEgSw0MPT/DAfX0Evq0HYy2z\nQcA1yqxRk3hR+d47kXIajEF0GjhPPkZNO7Q2XU+c+iQpbO0ybNp1ZW51WpZlWW+vSuHiz3/vZY9f\nuCuh6BvaF2TzhC5Uw0vPh//lj1Xorbd5+XhKO9YY6RPWSpyYDjkxbdi8q5ckVnkX4cyQxBlZqmjO\ntZGOzCfuVZ/WfLKQ4qNwXYdKNWBqIqMxF7G9x2Eq7ebo8Zi4FDO0qczkaJNte0pobfBcF98XBL5Y\nyuXvrNME7O2itOHEcHvN50YnEp55YZ47b7VpQdYyGwRcowquIcpW/4Jyspja7CsIRyPGziBffArI\ne4PVRl5ky423A2sfwrIsy7IsgPfsV7w0LFmrCZYjNeWS4OvP+nz0loSGa4hSgyHfAaiF4jUdim1H\nhnp3gTtqBaY7LsPT51e+E/iBh+u5aGXwjcHz8hKfpUpIqeozPxuB0YSFAM8TeL4hDFxEV5H5uRjH\nc3jk8Q4f/0jE+Jjk2LFZbrixl9HRFlrnpUir9ZBqxUVrw2KLgqJ/ZZ2ZkwJ8b+2VfimhXLK9CKyV\nbBBwjeotGTrZhYGAoev0ExR+8pU131O+SLlly7Isy1pUDOG2nRlPHXNZGQgY9myG/m7DS6ccjIFq\nKKmubmJ/SR5/SfPo84ZWnrmDECl+YCiWV7a1X4wphBD4gUuWaYzx8DyPsKCYnmjg+S5+4DIx2mT/\n9VWSJCUoBhhtmJmLKYWa+bbA8xzOjURobRg9M0dQ8BjYkNcElTIvPyox7Oy7snbLhRAc2F1mbGJ6\n1XO7thU5sKf8NozKupLZIOAa5TmwpaaJZcDM2DRuZ5auxglqzZfICgV0p7Pi9abag7P75rdptJZl\nWdbV5t7rDDiGw2cMSQKVIuzYCLWFInNdFcPjhyDNoL8G+zYvT9YvxfiM5uFnDfF56UTGQBxlOK4k\nCJenMPnOwvLKvOtKEiFQShMWPMqVkKidIIC4lTI70yaO8hQijQYER4YljiPo7S+hlEClCsdzSBKF\n4yyvorsO3LwpZlvflVc441c+tZHxyZiXDreWPo2NGwL+zS9ssiVJrVVsEHANcx3om/gJAy8+ijDL\nv6ycoUGSsTFUM88dNH6Iufl94NqtAMuyLOvS1Spw1/XrP/+95wRSSsDw9DH4xLsNpeDSrv3MMVYE\nAOeL2glpklEsB/nq/Br9AoRkRZUgMKhMk6aK2ekOSWIQUqBihV/wmJh1yLIMISRCLlxPCDrNDtXq\nchAQuIYd/VfWgeBF1YrHH35uD4/9aIaTZzp0VV3uf28fQWAPBFur2SDgWqYysuPPrggAAKSUuBu3\nk6UOJizB3tthcOvbNEjLsizratVbUsxEq3PNkxRGpzTbtxU4eaqDEJLhSfjuM4aP3fnq100VtOOL\n9BnoZLSbMVI2qfUUqdRW5htlmcZoQ6YNaZJhBPiBn9f/1wY/9MhUilb5PbG7v8rYZEoWa0pVj+Z8\nvNR1WC80HFvUU1Y4rzKnPjKsOHw6w/cEdx5wqZbfukm4lIJ77+rm3rfsK1pXKxsEXMPk3Bg0ZtZ+\nThrMB34R3JV5lUrDmRkXY2BTd4ZrFw8sy7KsdQxUDPORRrGyadi5SRjsD5FSsGdniZcPN3Fdh+EJ\ngdJm3Ul0ksF3npWcmhC0I4egoImjDKUMznkT8SzLUJnCOJKZiRZSCooLWwy+LxBIzp2axfNcStWQ\nNFXUuko05zoUywGu5wJ5Tn+hFCKEYGayjXQEg5tKeI5hakyitUY6ghefneCm2wbRStNbXGd7AtDa\n8Hf/HPP8UcVCg2K+/3zKh+/yufM6u9tuXVlsEHANM34BHBfU6m1L43hEjz+CGj2LqNYo3PsAp5oV\nXh7xacT5qs5LI4p9gwk7+q7MbU/Lsizr7eU6sHdAM9U0vDzikGTQ6DgIx8NfmLMXQkF33WO+qclU\n3p5mvSDg609Kjo4sP+l5Do4jmZ/tkGQG33dJkozWXAdjyFN9XMncVIuu7gK+LwhDB61ciiWX5nxC\nz0CZYjmf6Lu+pL+7myzTaLW809CcbRO1E7TWTI636O0tUKqXyOKMeleRsTOzNFu9tNuap2KHXYNq\nRVCy6JGfpDxzeOXue7MNDz2ecGC7Q6V4aStrxhhmGxrfE5QKdjXOenPYIOAaZsrdiL6NmNFTq57r\nnBuj9chXl//+2Hc4fte/p9F/09Jjzdjh2TMBtYKmp2zLhlqWZVmrSQHTTYeTEwHnVwryPaiW8sZc\n5bLDfFPTVwdvnZnH6AycHF89sZZSUCh6zE63yZKM1ny0ouOwzjRJnFGrLa+0O66g3l2kOZ8wM9mi\nUAooVQqUqwW0NkSLzQuModOMSKJ04a+GViOlUvHRmWJgsAxCIh1Jq5XfBycbgj/9Yszd10nuvH7l\nbvqRM2sfFm604YkXM37qdn/N58/34xc6fPuHTYZHUzxXsHurz6ceqNHXbads1uVlw8trnHfrB8mq\n/UtVAgyCqKOZ/sGTK184OcLWJ/56VS/3VElOTNktTMuyLGtt7Vjw7LDPhT0DkhQ6cf7nNNUUA8Pt\ne9bP8z87JcjU2hVspJP3JIijZClX/3xaaZRauVil0vzvaZzRaSUImQckSZQSRylgSOJ0KQBgocCQ\nygzDx6eYHZ/l1NFxtDE4riRL811xIQRT8/CVhyOeP7qyTGh2kY3zNHv1vgKvnIj4L1+b5dhwSpJC\nq2N45pWY//OLMyh1ZfUlsK5+Ngi4xslaD53bf5boup8i3nEH7Rs/xPgTL6DT1b+pqhOHqI2/tOrx\ndI2mY5ZlWZYFcHTcJUrXnk4kaR4A1PyYn323YdcGmG9pHn9J88wxTXbexLa/ZpBi7Ynu4oFeL1h/\nUWp8PKLTye9tnVbC2EgDACFFHiQsBAVZpug0OgwMVdmwuY6zcPhNa43jSYzRNGbzxgRRO2FqdJae\n/jIjZ/MzdmmiaDcTohSeeHFlELChd+3PwXPhwPZXX8l/9Mk2zc7qz+DE2ZQfPttZ4x2W9frZvaV3\nAiHJNuwBwKgMs85ShUTjJK1Vj1dCmwpkWZZlrU1d5BZhtGFHd5vrb5IYY/jWk4ZnjhraCzsE33/B\n8MHbBHs2STb3wVC34czUyoUnYwxRJ59sCyEQC7n4xpil1gBh0UdrmJ9P0SrjxJFptDY4jszLgwqQ\njkBliuZMG6Nh5Ow0W7b3M7CpzpkTkxhtCCsF4s7K3YY4VgyUC8xMt5gcmwOcpU3zuebKb/6+2zxO\nnNOcm1z5+M27HbYOvnrH3pnG+h/m2NSV1ZzMuvrZnYB3GOG4uJu3rflcp76JmaGVDcOqoWJ3//qV\nECzLsqx3ts1dGa5cewV/Z3/K9Zvz554+YvjhS8sBAMDELHzjR4Y4zV/zsTs0USdFL0QWaapozkd0\nWinSyVN5HEcu/SelwHEl9d68G26WGUaHZygHKdfvDSmV8rXOIPRQmWZ6bJ5OOwEMrbl8IOVagUIx\nz9VXSUq9u7AwujzY8H0HIyAIQ8bOzZOmy3n/4QX192tlya89GHDvTS67Nkn2b5N84l6Pn//ApTVH\nqF2klGhv3a7bWpfXq/5EdTodfud3foepqSniOOY3fuM3uOeee/id3/kdTp06RalU4gtf+AK1Wu2t\nGK91GRQ++DNk505jZqaWHwxCyvd9iK39MNVUGKC7pLluKOYiu6+WZVmAvVe8k/VWDdv7Uo6MeZx/\nLqC7pLhx8/Ii0qFhc+GxMwBmGvDUIcO7rxeUQrhxa8L3n9cIIFtI4ZFOvgPQaXby3H+TnxPwA4+w\n6ON6y6vs77mjxM6NRQBGxjO+9I0W7WbE3FTzvK8q0NnCtaWgf6jOiVdG6bQSugYqy2cAhCBNNGmc\nNx0TxiDIdxi01mRuie++6PPe/fFSxaNaSfLgvZfYEe0Cd99S5IWjMZ1o5Qe1ZYPL3bcUX9c1Xy9j\nDN94ZJbHn20yN6/oqbvc/a4KHzho/z98rXD+4A/+4A8u9oJ//ud/plAo8Ed/9Efcfffd/PZv/zau\n6xJFEX/+539OkiTMzs6yY8eOi36hdvvqXU0ulYKrdvxrjd3p6cfbewNogyiVcbftpvixX6R0171s\n7FLsHkjZPZCyqSsjeBsXHq61z/1qYcf+9ihdahvVK9TlulfA1Xu/uNp//t7I2DfWFQU/z+kv+Yat\nvSl37ogpnFcM58eHDHOrM04BGOoV7NiQBxB7t7hMzSpGZwXSkTieA8YwMzFPlqqlFCCjDSrLMNpQ\n68l3ArTKuG5rSnGhrGalJNFKcezkedsPAoQjqHYVqHaVAHA9h/mZNirTlKsFjNHEnRQ/8EEIEIIs\nzVCZxgtdiuWA7t4CXb0lZtuSTMOm7teXOnv+Z9/f41IrS6ZnFfMtTeDD/u0Bv/RgjVrlrb0hf/mh\nab700DTTs4p2pJmazXj+UJswkOzeFq4a+9Xmah/75fCqP1E//dM/vfTnkZERBgYGePjhh/nsZz8L\nwKc+9anLMhDrreVu2kb5X/362z0My7KuEfZe8c5jDIw0JPORJNOCwNPctDWhu7jYaRdePiM4N+0g\npaFU0CzN4M8jBWzpX/nYp38q5JNKc+yMplgQ/ON3WkycXaPnjYE0yei0Y4LAY3q8xWNPpHziw8ur\n1QN93lLlHykFSEEap2zctmnpNfk8Pw9CiiWfqXGNXwiQQiIExFFKlmm80KNUKVCrh1QqyxHOuWkH\ndl6enP27bylx8KYiY1MZhUBSr776WYLLLUk0P3i6gb7gnytT8OiP5/nQe2oruihbV6dLDis//elP\nMzo6yl/+5V/yW7/1W/zLv/wLf/zHf0xvby+///u/T71efzPHaVmWZV0F7L3inWN41mG6szxBzRKH\nTiKBjFpo+MbTLifG89KeAFI41Osps7MrJ/O7NsKujasnlK4j2bs1X9F31jlzAGA0TI/PIxB0mjEk\n+SHkxUl9b13gCIORDjrTSAy7b9i49DzkVYAWewfMTreX0pAAhJALKUgGAfiBRxCsnJgn65Q2fb2k\nFGzoe/tycc9NJIxNrV1EZHQyZb6pqFftGYWrnTBrFdxdx8svv8znPvc5kiThs5/9LB/5yEf4i7/4\nCxqNBp///OffzHFalmVZVwl7r7j2tRPNE4dTsjV6Y3WVBVFH8q2nVqfHuA70lVMmZxS+C7s3u3zs\nngKee/FJ9D98fYL/56uTaz4npSRYSI9I2gmVsuQ3f7VnaZK/uS+kvx4wMZ0yPid46OmVaUlpmjFy\naob56fzBvExo/pwjJa6/MNkVgoFNNbQylMsutXph6RrbB+AX3nPt1FqZmUv5t59/mUZr9T9wf4/H\nX/1vBwj8a+f7fad61TDuhRdeoKenhw0bNrB//36UUkgpuf322wG45557+LM/+7NX/UITE403Ptq3\nSV9f5aodvx3728OO/e1xtY/9ana57hVw9d4vrvafv9cy9qmWIFNrr1Q324rjwwpYncaSKdjQLfjk\n3YuTfsXsTHPV6y501w0+f//fBdkaDbekI3EcJ5/0h7BhYDm1p+SDzBKmplIkMFiFB2+D//rdjOmG\nIEsV0xMNola+C2CMIUsVjpuPffF/EQIpoNVISKKMxmy+Q1CtBYSuZmdfzMTE6zsTcKX+3OzfGfKj\n51Yf4jiwK2R+IYq6Usd+Ka72sV8OrxrGPfnkk/z1X/81AJOTk7TbbT7+8Y/z6KOPAvDiiy+yffv2\nyzIYy7Is6+pk7xXvLHnRiLUTCVy53jO519P3thhKbj5QXmrsBXnKTFAMCIrB0qTf9SUfPFikGsBA\nWdBbkivSfgC6K/DT79IMHxvn3MmppQAAwPEcjDZ5nwABSIFcCDCUMiRRniKjNTTn2mztSXnv/pgt\nPddeP51f/fk+bjlQxF+I9cJAcOdNJX75Z/re3oFZl82r7gR8+tOf5nd/93f5zGc+QxRF/N7v/R4H\nDx7k85//PF/60pcoFov8x//4H9+KsVqWZVlXKHuveGcpB4ayb2gmq9N4qqFmQx1OTazeCXClYefA\na58wR4lhuuVS7a6SxinaaIIgQEiBUnqpr0DoS/ZsDimGF08vevGEIiiGZEmG1hqBwPVcpCvzqkNK\nUfALOE7+PRhtlncFFrSaKUePt2jPS2o3C8rFays9plJy+Q//dojjwxEnh2P27AjZNHh1VzGzVnrV\nICAMQ/7kT/5k1eNf+MIX3pQBWZZlWVcfe69459lczxiedRcCAYEjDfVQM1jR9JXg7LRieGp54iww\nHNis2ND92vcCDp3KmG3maT5+6K94TkqBXkhdH+pzKFzCPPXcRJ6uduG1IE8BUrFCK72U1iakWNGL\nACDTguEJGJ7QnJmAX/2wIPCvvYo5OzaH7Ngcvt3DsN4E9mi3ZVmWZVmvWeDCrt6MViyIs3x3YPEM\nrevAx96V8fxpzeiMxHFgW79i1+ByADAypXnpdJ51c9NO6Kmuv5JeKealOtcqZbL4WCGA99zir0r/\nWXPsF5msG2MICy5Ga7Ioo6fHJ1KrIwvHWb7G2Un4/oua+25xSFJIFRSDvPSoZV2pbBBgWZZlWdbr\nVgoMa/Uuchy4ebuG7SvTf4wxfPNJw9NHIV2oQvmjQ3DwgOaGnS5nZj0SJSh6mu29KQXPsH1IsnVQ\ncHJkdRRQKQi2DrocvMFn//ZLK6t5w06Pp19JOb/DMYDWmjRNkY7P3e/byEvPTWKyiB1DIafHzFI1\nJMeVq9KDzkwYvvx9GJ7Iv69KEQqeoRBAXw3u2geFYO2oYGrecHIMBup5B+afHEoIfcGNe3wcW4/f\nepPYIMCyLMuyrLfMS6cMPzq0clU/TuGxF2Ay9giLyyk6402XWzZ1qBXgwfcEfPE7MSNT+RulgF2b\nJP/+l3poNtqvaQy37vP5m681MDiIhUm2VpokThBCYIyh2UzYd30vjz18mk9+UPDgewK+/ZTi8BmD\nlKt3LY6eNSBSlMp7FLQ6Dq4r0drwyjAcOQu/+D5Dpbg8qVfK8J8favPcsfwzEBhUmjI52kJrw1Cf\nw4PvLXLjHpuLb11+NgiwLMuyLOtNFacJaZqiMaSpxHd94nTlRDpTMDal2FpcfqyVOBydDLhtc8SW\nAYf/6VMFnnw5Y66l2dzvsH+bQyF0aL6OSo9JojBG5XUSDWRZhkDgOA5aG2bnMgY3VBjcUMaRgk39\nDh+6UzA8qYiT1dcTQiAdiecJEBC1U4zJAwFjYHQGHn0B7r/N8OyRjCgxTLUcnjm+3JTLIJCeT7Wn\nzOxEg3MTin/4VottG12qpbe+c7B1bbNBgGVZlmVZb5p21CFK4qW/bxmAT7w745+eKNCKVk5s1+pf\nOtfOJ9FCgOsI7rr+4ik/xhhmGuB7UC6sn0ojBGhtYDHFR543FgN9ffnqe1+vx3W7fBodKAaSD9xi\n+JfnNM3O8sulzAMAyK/pBw6Fkk/UTnGc5WDn6DnN84ciRqfyFCnHAS/wKFyQT+WHHq4ryTLNzLzm\n0acjPvKe0kW/b8t6rWwQYFmWZVnWm0IptSIAWNRf19xzfcqhUZ+5ec3MfD75LxfdpQn/kteQEv/M\nUcUPXlCMTIHrwrYBwYfvcuivr07fuW6Hy3NH0rUvJKBeD4mjjN0bDH//iODsZJ7CtKHb4SMH4b/9\nwBAnICSr0oOMNriuREpQmUIrgxe4zM4bZqaXz0goBaqdIh1JEC4HN1IKitUQlRlajQ7PHlV85D2X\n/jlY1qWwQYBlWZZlWZeV0pAo0Nk6k2xgy4DGq/pkyjA1ozh8UtPbpdEXBAFdRXVJVXaOndX8tx8o\nooWYQyVwaNhw+HTELdszPnFfBfe8ij437fV57ki6dtUhA4dfmeH9txd56pWQmeby+4YnYablUClq\nsvVaHgiBWPhPpZo4zlDKYMzab0jjbEUQoDJFay6iq7+CENBMJV99LObj99izAdblc211trAsy7Is\n621jDIzMKmaHh2mePc3ZGYeZuLRmac9FriMY6HW55fp8gutItfRcNczYN7BGAv4anjyklwKAFWMS\nLo88nfGfvjK79NijP2nzxe8qwlJIUAwJigFSnNeNWEjajYSona4IABY1O4JwnUo/QuQr+cYY0jTD\nL7gIAVmaEbXXDorOT4MyxtBpxmSpImonFMsh0nF4+qhcM13Ksl4vuxNgWZZlWdZl0RodZmPjBJ7U\nSJUyFB9j1N/GXHkT9aC14rWRWtmoq+AZZo1L3YuolxwqAWyspRyf8GhEksDT7B7IKPprT4Qb7fUn\nyNKVPHuow+mRBCnhSw+nSEcQBA5xrNAa/KJP0l4OOFLtMDG//vfaVRF0lfLKP3rhSwsBnucghCCJ\nM+JOhuM6BKFL1MkQEtbcDDCQpXmDsqid0JrLDxzoTOP6Dp12TKFe4PvPp9xz4+oGZ2+X6bmM06MZ\nG3odPNeWMr3a2CDAsizLsqw3THcaFNUkcfcgkeODyvCSFkPzR+m4FbTvIYXBGOgon9lk5UFXIfMG\nXL5IKQfQVzY8/EqB2c7ygd1Tkx7v2h4zVFcXfvmF0ptrBwI608QJvHI84eFnFEMbq5RrIb7vkCaK\nxnzEyJl5nCDvFiykoFBwqRXW/36rRfjQbZKXTin+63c0XuDgeQ4YiDoJzbkIyCf33kK34b66w/j0\nyrEXAujECp0ppCPwfAfXk2SpxnElAtDKEEcZT76iuOfGfLfg0Sdb/OTlNp1YM9Tv8aF7qgz0XFqf\nhDdqrqn50ncjjp1t0Imhry54136P+++06UpXExsEWJZlWZb1xjVHyYr15YR+xyUt1ADo6oySOLvw\npGaiJQiclE3FSZSRtLKQubSE0gK04fh4iBGa0VlvRQAA0E4lz5/x2VDrrDon8K59ksNnNJ0LUoLS\nJCNq5w/Wqw613hLdfcsBiOc7dPfmKUsjZ+bRjqarr8rWQcHd1wuOjhimL0gJKhcM79qd//nAVoe0\n06HdBMeRaGMwejkY0UqTCUG9DL/+MwHf+XHK8bOKTBk29Tt0dRd47iRLnY7DIoRFn5nxBpXuIkpp\njMkbmXkLLZn//hsz/PNjjaUdiFeOx7x0NOKzv9THUP+bu1NgjOG/fDPiyPByMDMxa/jmEwmlguDu\nK2inwro4eybAsizLsqw3xBiDkpK1TvCmfglfxFSLIa4jqfkRBVfhOZrQzegOmtT9JkmWp/xMzgoC\nzzDZXHuKMtOWTDRWP7dzSPLgux0qocYYg1aauJMwP5U3EdiyweWmfSGV6tqr1ZVamJf6lIJi6HDw\ngKQYwMfugm0DBs8xuNKwudfw0Tugp5q/TxtDbx7r5BN2vcZuhFZs6IYkMXzyvpDP/VKJ/+VXyvzc\nfQVOToilAGCR57v0DFaRUtJs5DsKvb0BN++STMykPPZUiwu/zOhkxtcfuUj+0mVyZFhx7OzqnRit\n4SeH1j8Ibl157E6AZVmWZVlvkMGIddYVHReCACkMcRqvihOEgKITkWZ1hCOplzO29WjOTK2XYy5W\nTYAX3bTL4YYdgv/8tSbPvtKh2dYIAds3enzmI1XiTOL5azfd8jyJEAaVaYrVkB8dM8RZQrkiOHij\nwACehA0V8FxoRZqvP644MaJpJh6Op9CZXnUIWmcZrU7G07Pw8rGIO28I+Ln7iggheHkY1mt2LKRk\nbrqNzgz1uk+1EnDXgYhvPdak1V67ytCpkUs7RP1GnJvMz1Cs5WLnMqwrjw0CLMuyLMt6gwRIF8zq\nFWKUQoVl3NmT+FoSOyWMXDkRD11F4Gak2mGw26GnnNJVVnRmVwcWtVDRX12vNmdes/9XPl5l+r1F\nnj0c0VV1uHFPvsqfKUPR13TS1YFAEiuSWOG4DjOTDYSo8LUfZHQiQ73m8MF3u3iuYLRpGKoa/u7b\nGcfPLU96HcdBCkmaZktHEwSaTnu5I3AnhkeeitnU73LXDQHhRTJntNIIYejtC9myrcqu/gRHCsJg\n/SQO/zUezu1Eim8/Ok27o7npujL7dr56Q7Jtgw6eA+ka/9T1ik0wuZrYfy3LsizLst4QIQR4hTVL\ngWqRpwmJLKKg25SzmVUlclIlSZXEdwzCyS9y3VBCOVg50/Rdzb4NKfIS5rrddZf331Hm5n0F5MIb\nXAd2DyrSVJEkaqnkpjGG2ZnlJflWIwYEN+4PuG6H4eTJOb74UEwrljRiyStn9YoAYOlzkIJKyWFT\nn2RjL0SdbNVrjIHnj+Yr9vs3w2D32lOxes3nhpv62LajRikw7N2QX+vuW0sM9Ky9hrtvR/jqH8yC\nx5+e43/+wyP8zZdG+eJ/H+cP//cT/OlfnUatt82yYNuQy+4tq4Mo34M79r81B5Oty8MGAZZlWZZl\nvWFuoRvteGjyhXADKCRaekizPBl2TUbWTomy5SlIIwmQUuI7GrnQvKunbHj/3oi9Awkb6yk7+hLe\nu6fD9r7VE+tLdXIMDp/RtNsZnY6i0UhpNhLGRxqMnlnOpx/aWML34PhZuH6Px9YtJaYnmvzjt5rM\ndQTz66TwAAz2OvzWLxbZOrB+pBKn+UTbkfDg3T710vLEWwD1imBog0foGwaqGXfuiKkV8tf4nuTn\nH6jTU1+eiEsJN+8r8Imfql/S59CJFH/75RHGp5Zz+JPU8OiP5vjqQxOv+v5feiDk9v0u3VVJ4MHm\nAckn7g24zQYBVxWbDmRZlmVZ1hsmpcQv95O0Z9FqMTfd4OoUT6/MVa87czzd3MRgOIMWuV9FAAAg\nAElEQVQRLmOdKkU/QwpDwY0QC+cLSqHhlq2XJ8+9HcO3fuIw31menBsDSaaZme4sPSYkbNtRJ4o1\nc/Pw1GHNrQd8Tp9u02om/OTFhH3bXGDtYKQU5tffudnje0/Fa+6ODPUuT7/2b3X5tQ/B00cNUQJD\n3bBnkyHTHbSBYI2Z2ruuL7FvR8j3nmjQjjW7t4bcvK+w6oDxeh7+wQxjk2sf4n32lQY/+9P9F31/\nGEg+86ECtXqZM+fmKRUE8hK/tnXlsEGAZVmWZVmXheN4hOVetErQrSmcpIFco3a/IzQbghlGoy6+\n9pWTVLsibru1RrEcsK06B3Rd9rE9c1ysCACWxywpV0PiToqUgltv788bXxmJrPtMTCh2bEiod5eY\nnmpx+HB+XqCnBlNzK6/lOXDTzjyAuWm3x3U7XV44ujJYGOqTfOCOlRWKAg8O7l99rYspFx0++v5L\nW/m/UDtaI6F/QRxf+uFe3xNUijap5GplgwDLsizLsi4bIQSOG+CUujHJ3Krn8ymmoOjESCH4vXue\nZ24q5m8e3scDH6jS3ffmNJzqXGRDoVjy6NlXZ8vWKo6TBwqOI3AMVKo+rU6KH7ioJCNqZhw/Kbjj\nQJlSqDgzbtAGuitwx36HG3fms3chBL/28Qrf/GGHo6czUmXYPOBy/8GQWvlVZvhvsluvr/KPD00Q\nrTHh37rp0s8VWFc3GwRYlmVZlnX5uSFaBkgds7j+np8VEBghSfFxpeZMz63s9Z/iP5Sf568f282e\nzUNvynC6K+s/19MdMHBBk63F7BbXEcTKQZsEJBhtyGJDOxH8+oMep8Y07Qh2bZIrqvNkytCK4EMH\nC3z0PVdWqsyOLQXe/a463/3+zIrHhwZ8Hvxg79s0KuutZoMAy7Isy7LeFKLUT9Y4h8N51YCEJKJA\nhyKuyJhx+ki9IslAPwfHR1CNEK9y8Zz01+OGrYYXT2tGZ1amr3gu1Ourp0OLufxKGZTwiTsdPM8j\nIUVIQZRKhMhLZp5PG8O3fpTxwgnNXBMqJTiwVfLhu1ycSylr9Bb59X+9kc0bAp55sUknVmwZCvnY\nB3vZOGh3At4pbBBgWZZlWdabQoZlmnE/TjqPT4JGElNgTnaTKUmvGmUmK1FKZhBSsHV7geMvNbmu\nL8J4l3cy6jrw4B2aR1+Cs1MCrWGgbghLHtkFjc6MMSgt0NrgOxqjIUsVnu9RrBYIQp/ZpqbRgkpp\n5Xu/9eOMR55dDnpmGvD9FzTaZDx496tXzzFa5SVUpXvJB31fDykFH/tgHx/7YN+b9jWsK5sNAizL\nsizLetNUqjWmRv9/9u47yu7jOvD8t+oXX36dAxqNRESCQWDOSSSVLVmWzKFlW9JKa61sr9bjMJqR\nZ3zOeNZx7fXujHd0pGN7pdVYtmVLNk1JVCIpUswZRCJy6Ebn8PIvVu0fD0Cj2Q3mBLE+50gk33v9\ne/V+jYNXt+rWvU1m3F4QEqUFKoVyNM6qYCeptRGATDBHMz9MPCIQc8fQvetf0vWjWPPIzpg40bxj\ng00uc+aDqsUsvPfidldfDUgBzTDi2THJRNVCCEEcaxotRaulyGagq2xz9HgAop0i5Pku9fkmGsF3\nHnO54nyHwVKMbbVTgHYeWr6R2a7Dilsv0Xju8hP7iXnFvuOaIBJkbMU5ndP0dHpIv/iS7oNhvFwm\nCDAMwzAM43UjhKDQHGegsZeWVQQ0ubSKpRMiXPrTEQQg0EgpUKvWkOgprLCOdrPtmp1n8ORzMT98\nbIqJ2Xa1mx89HnH1+Q43XbL84eL5eso//bDJ5LxASMFQr8VNlzis6wx49oALQhDHpxr+0gqg2kiZ\nmk7xMx6V2QZCClKlaTVCdh+SFHtLHJx2WNMdUfZi5hvLj7XSgLmapr9raRBweEry6EGfMF2Ylo02\nilwajTA80EB6L97J1zBeLhMEGIZhGIbxurI7B2G6QjGZWfR4XRTorh8CKVEIml4ntvbJTO1FVo+g\n3BxpYYCkc/WSa85WU+64P6R2WuOuagN+8FjMQLdky5rFqTf//MM57rynShS1V+pt12Z0MsfIpMcF\nW/JEydLJeZzA6FiM1oo4StFodApoiIKI6oniR63EYte4j1AWQ0MOk5Mhzebi0qCFbLsJWHucigd2\naqbmoZCrUw0Ewrc4PfsnTB12z/awonPcBAHG68IEAYZhGIZhvK7sfBl7tEXLLSCFJsXCSVt0tQ6e\nqhzU8juoWWXWTt2LwxxKlLGEQM4eQFs2aWlo0TUfejZZFACcFCfw9N50URDwwwcqfPN784tel0QJ\nteka48LCPxzhFs5UmlQQRynNeoglLZI4JYmT9uHgZrTodc3YIZt3WJnxmJhoMDcTnMrr3zQs8V3B\nXE3x9bs1YzMKrTWWpdu7JQXo7c8ueue5IEMrguVOEjRaKfc8FjBfU3QUJNdf4pPLvLmlR42ziwkC\nDMMwDMN4XVnVcey4ST5eOmvXQN3vYaxjC5nKOKoVMdmzhu5oHLwMApCVUeJsF9LJnPq5MDpzU6vg\nec89+FR92depVFGv1olCD/cMJUSDIKI628JxLXr6C8zPNGg1WwgEWi1+n5MBjWUJenqyNGoxrpVy\n7mqL91/VnnL96MmUw6MxadLekRASHKf9nJ8JKJYWDkRLqbGspelQB0civnJHjcnZhfMHj+4I+MQH\ni6wefPHDx4YBYNq8GYZhGIbxulJeHs3yB2JDO89o9ztw7RRR7qTRsZIn3GupOgtdg2USkNQniOsT\naN2e+K7oPfOqd2/H4unNyHhyhldC1AzoLGqy7tIDvWEQMzXWwHEtyl05LLv9Tz/TnmjnC4t7CySn\nNeK1bUG5M0PR13zoWgf7RBOyJ/dEpwIAaBcCisKEKEyYnY3QeiGw6M40yWSXpgLdcW9zUQAAMDmr\nuOOeMxxIMIxlmCDAMAzDMIzXlcp3k+aXb0KVZgt0yVlyIsC3Qxq969l7RLHDvWjh52V7pVzHLZJm\nu8HVxZtt1g4uncb0dQquecfi1fD0zJsG2K7L5Vsk150b019OsYTGsTSWSKnXIjq6s/StKJ2a+Fu2\nRaGcQwAXvKPr1HXiRBM8ryuxZQmm6+0xHptI+fr3WwTB8tWD4jglSRStVjuSKHkB21YGS84DzNdS\nDo3Gy17jwGhMpb789Q3j+Uw6kGEYhmEYr7tg5Tb8o09gNaYRgBIWYbZMs3Mh199Ck7VCtCoSygL3\n1S/k6tzTRF7+1Gt00mq/Vgo++X6fe5+CXQcDlNKsPFHtp/S82v25rMN8FC4Zk+M6CFswMZ1w0zrN\n6p6YegCWhO8+Iak3l+9V4NiCbZf2USxniBNNkkIzWPyaJNEkiQYEf/2vTXYdStEIhBAopUCDPC3V\nR6t2P4LeQkI502TkWJ07x2D9cMBlWz3kiUZjSrX/txydglIvEPEYxmlMEGAYhmEYxutO+wVa669D\n1iaJ5kZRmTzKzSx5XSuAtYMpiRZscI5wf3wZF/jTp12ofaBWCEHGk/zS+wtMTb1wYsOFW3I88qxF\n2ApQiUJIge06ZPIZKtPz3PdEk+svzmJZgsKJIfV3wOHJpdeypebGKwocnfOo1DmVvnN6Y6+TK/pR\nmCJ0cqJ3wMLzUkqUUiilkLI9diEllgWt2Qo/errFybn8w9sjtu+L+NSHClhS0FGUDA/YHBxZmuK0\natCmXDBJHsZLY/6kGIZhGIbxxhACVexD9axZNgDQGg5Pe6zMzbHKHqVXzjAedi6+hOW+7E66m1cJ\nhIBiV4lST5lSd5lcKUfUCoiDkLHplMPHF6fYXLpeMdS9eMldoNm6SlHKgVLtiX6zqWg0FM1mO50n\nTRXVakK9HtNsRIsPCpymHQgsrNrbtqSnBA9vbyEsC8d1cFwHy7F4Zm/EfU8GJ26h4F1XZSnlF9+D\nUr79+PPvTa2Zcsd9Df76X2r8/ffrjE6e+XyE8fZidgIMwzAMw3hD2V6BZquJbS2eZI9WfIa7YtAp\nnWoGKaHLqzNSzTNUrAMS6b38Drqb1mVIGxPUWh6O5wKaqBUSNAIsx8FxBLns4smzY8PPXpHy1AHN\n+Hw7RWhtn2bTkGa+qXl0n0162vxeKQgCTZomzM60cC3Nb3xY8udff4GBaZBSYLk2jmvj0QLLxpIL\na7QW7U7GO/bH3HBxO3A6b73H537B4sdPtJivKcoFyTXv8Nl3XPDlO2OiWNPXKVnXr7jjvgaTMwv3\n+YndIR95Z55Lzj1TSVTj7cIEAYZhGIZhvKGkZXPX9k4Gy00GOiJSJTg65XL/cwWuWFelpfM8WlvD\nxzY10U3NXbv7uf3yaYo5F+lkX/wNnqej5HDx+XnufbhKUG+delwIges5rBty6O9aWlrTseDSDUsT\n8KerkjRt5/s/37o+za/cbOHYAq01mYxFjCCJ04VWxLTTiCxb4uc8hBB0l6BSX0gPWnS/pGSutvix\ngR6b2961UNf0H+6JeXr/wlhHpxVP7gUhchQ7FM1aQJKkNFrwvYeabNvkYlkvb0fF+OliggDDMAzD\nMN5Qx6cVu0dcth9xlzz3zLE80s8RtBKebKxl/2yRJE3YPVHk8g2vfNL6P/18P1IKfvJ4jShSSFvi\neC5r1+T46M1naBJwBrN1wXIBAECYSBxbcHRS84MnIbV9cgVBmiqiICYKErTWpGlKrtBO33EsuHSj\nYPtzElj+1K/rnPmzHxhJeWZf2j4wLNrBzcm0oDRN8X0Xy5bMz9TRSjM2rXh2f8SFG1/f3YD9RwJ+\n8GCNsamYrC+5YFOGW68unjrk/EolqaYZaHIZgfUqr/V2ZoIAwzAMwzDeUJPzEC+fKk+1aeGpmCRO\n2TPVgXQlaSo4VslyiWphv8KmuLYl+PRt/Xz853p56JkWs5WUzrLNlRdkTtXwf6ly3pkr8PiuJk40\ndz4C01U4GSxYlsTPumiVQpIyMODgZSy6yzabhlK2rpHMztvsOhQte93hgeU/+HNHUr7+g4jkRKq/\n1rq9y+BYSCnRCipzDUodOTI5j2atfbbgXx5IOTiZ8L7LrRcMMF6pvYcD/vvXp5mrLvyi9xwKmZpN\n+KUPdr3AT55ZqjT/+pOQXYdSag1NR0Fw4Qabmy99+edEDBMEGIZhGIbxBlvdD77Lkrr6AEorWo0U\ny5ZMtfJsKNZoZLKgFTKsQLb8qt7bsSXXXrS0AdfLsXkoZeeIYra+OHXHlpoNAyn37LDQjk1fnyRJ\nNK1WTLOZIoSgUM5R8OFT71ZkPUlPT56pqXauzzUXujy+K2a2ujjIyPhw0WaP5yZcEgWdmZT+UorS\nmu88FNM67T6e3AWIghgv47YPHwtNtdLAddopT9KSRMrmyb2aZpCyflBxfEpRyMLVF3j43qufUH/v\nJ7VFAcBJD29v8K5ri/R2vvzOxt+6N+ShHQsHmyfmNN9/JAYBt1xqzji8XCYIMAzDMAzjDVXOS7oK\nMaMziyfRWmtUolGpIpN3sW2L0XmfgWLAqq4muUOP0NpyC7wOq77zTcFIxSVMIONoVpZjRqcFO0cs\nqk1BR15z6wUxGa99SPiGcyMe3OswMS9RWlDKKs4dSohSyUjFJZNpj9FxwPMspAyp1xO0hkQ4/NV3\nA379g4vHUMhKPvpOn+88GHJsXKGBgS7JhVt8jjaKBJX2/TqIpnc+wYvrjM8uvyshpSAMIlDt+4kQ\nKFshBPhZ79TK+Z6jiid2hugTlYoefjbmozf7bBh++ZP00x2fXH5Ho9nSPLWrxa1Xv7zrNwPNzkNL\nKxtp4AePBNy4zcZ+pdtEb1MmCDAMwzAM4w13+WbJ39+TIKVsN9DS6lQAICR0drYr4bRii55CwJzq\nwArryNoUqtj7mo5lrGqxe8IjTheCkmNzNscnU8Ko/djYvOav75a8+8KItQOa3pLmZy6OmK4KghgG\nOzVSwB1P+jz/vICUglzOoV5PkFIgpaDRsqk2Unp6Fo9l02qHjatsjoylxAkMD1rcvz9HKz49YBJM\n1hwy+MDy3YNBEIcxWmmk1X7PJFbky7nnTZYFliVJVHvVfrqi+df7Q37jdhv5KoKtjHfmKvTl4suf\nrI/PplQbyz+XKsl/+K8z/MlvvLZ/Ln7amT4BhmEYhmG84c5bK+kva+IwIQpikjBtr1gDhaJ/aqVa\na6i2HLSSkMbIoPqi1w4jzQNPB9z/VEArfOEOulrD4Rl3UQAAgJB0lOxTk3bLkli25K7tCyvYQkBP\nSbOyW2NJaEWC+ebyUyvHsXBdgedZJy4vODK5/CFgIQSrB23WD9uMVVxa8fKTZsdzWFRy6Hkf7OT9\nlJbEsix0mi5ZLdenve6kYxOKfUfPcGjjJTp3/dI+EADDAw6XbH35FZ56ypLM0nPkQPt+BanL/iNL\nu0IbZ2aCAMMwDMMw3nBSCj5wtcNA1+LV5mzeoaOrPYFUSqM1RMrBV/X2hHXfM4j9T4FefgL94DMB\nf/Q3Vf7+By2+8cMWf/g3Fe59IjjjOGqhoBouPx1yHTi9+Ey7qo1k18gZJvq2xrXOFHRo+np9Bgc8\nclmJSjRDPS8+DUuW/5gAKC1OdVBe9LjSpEqdCqTyBR/LFkTh0l2DNFGLmpad1Apf4I1fgg/eVOLy\nC7J4p2X9DPU5fOwDna+oOlAhK8kvH1cAYNmS7z9YfwUjffsy6UCGYRiGYbwpVg9Y/PrPSZ7el7J3\nVBMol0zORoiUnKfIeorD4xJle1zX/BbJfAV59BB696NEj97D5HwnXLUNff55CCE4PpVwx49bNE9b\nEJ6vab59f4uVfRbrhpbmoVuinbxz5qn7YkLAVHX5Up6OBQMdKQcnl07uPVdQzDu0QigUBJW5gO2H\nLc5Z/cI7FYOlhP1TaulOBVDyE1xb0wjS0/oL6IUeBkJTKOdAwtxkleFBh3wRZqrguSC1Yqq6NHe/\nuyzYsubVnQmwLMFnbuvh0EjIrv0BpYLk8gvzL7sS0+muucDim/edeYfi9ahy9NPMBAGGYRiGYbxp\nwkhx9EiF6cmIAI+BNX24noOyNB2liGI2oae2g2phNbMD11DMPErHcz/Ga06gH3iIx/74ixQu38a6\nv/wvPPSstygAOPUeMTy2M1o2CMi6mnImZa61dEoURu10oec7b2jpAdWTNgwkzDZswkQQp5Ak7R2F\nYh5sqx0oxAg6ulyePqzY87cR/Z0ea3oSpuY1x6YFcSLoLikuPkexslsz3BFzcNpFn3bWIO+mbOiP\nKeQs6q2U9HkpPbYj6eguE4UJk8fmUEnKLZcX2bbFZmJOU8zCzLzgq98VzJ1Wjchx4OoL3NdsQr1m\nyGPN0GtTueeKC3z+8d4qUi6kNJ3qhxCnvO/6l9fv4e3OBAGGYRiGYbwpDo8E/LevjDE6sbAandlV\n5YLLV9HZU6AR+JzTH9DZl2HWHkZJh/lN1+JWxsiN76W4qszIPYeo3v8oR77wJwQf/I9nfK8znQ0Q\nAtb3ROwYEzRPy72PIr1ocgzt/Pmcq+gsLv8eO8dc9k+4YAk8C1ytEULjOeJUCozjaBIlcBybIGhh\nWZLJimSi4hIEijhuT+arLYvJecnPXJawZSCi6CuOVyxSJSj4CloN/uGuFtMziiRSiBN5/9A+A1Ao\n+lRm6sydKD/q+xaHjqdcspVTaUjFnORXPpjlvqcipiuKXEZw0SaHrete3S7A60UKwYev9/ine6NT\nnxUgiRO2rtb0d781x/1WZYIAwzAMwzDeFP9w5/SiAACg1QjZ9+wo179rI3EqODrtcc6QQ09ynAln\nGGyXxtBWcuN7ke7CRLD60BMM3tbkTFOb/q4zV6TpyCouX93i6JxDmAgyjkIoxb2zLiePT2qtyXkp\nt1+1fOnL+ZbgwKRLqhdW0Nur1ALfjtFCEqcWllBkPUEQpFiWpJTTaCkIQnAcQXxa2n49EDxxQGKp\nmH2jCa0QekrQkU34yRN1mqcfdUgVTt4mk3XJ5FwsS7YPBNsW0pJoIbnvyQjXafCzN+UX7ku3xUdv\nfoFk+7eYqy/02bzG5kvfrDNX1XiO5t+8J8OWdWfPZ3irMEGAYRiGYRhvuHozZe/h1rLPzU43mJoO\n6ej0iWJopC5dchpfNQisPFGpDw3k169g8ycdnvv/HiWp1Lh4VcSTox5Hxhbnja/olVx/0QunpDgW\nrOuO0VqTJCE6jfk3V1gcnc0TJoI1vSm5F7jEsVmHRC2fQqO1ZqijwXTdoxa0p17lvMXMbEKtpSmd\n2FmQUmDbnOr+C3BwDCanFnYkak0Ai1g7LCoPqiGNEnJ9hXbJ1VQRNGMcd/Hq+PZ9ET9zvcZ6Fbn5\nb7auks2//8SraxpnmCDAMAzDMIw3gVIadYYCNFq1n4/i9gHWJ0c6WDV8HEeHBORpFQYZ3/Quund/\nn/q6DWz81QJHfnCY/Kp+/uc++PYDAYePtxtzrRqwefeVPhn/xSvxKJUSNudR6cJq/4p8Ey9bxrKW\nTpm0hmogidPlzw6c5IiIklND5wVhYtFoaTQC29KkqSRN2gd5tQYpJbatSZL2BVvLFjYSuL5N2AyR\n1sLnisKENFE4jqRWaZEuU1qoWle0Qk0+e/YGAcZrwwQBhmEYhmG84Yp5m7XDPjv3Npc8V+rMki/6\nACglGJnzUMMQiQypglg7zK+5krlv3cO68+bZt/E6evvOQ1gWhRzcdkvuFY0pCmqLAgAArWKiVoVM\nvmvR4/Mtwd5Jj7mWBQg8K8WxNHG6dHLd4QcUZJ3Icsm5DvWmRZxqPFeSKpirxEhp4Zw4jLuwI6AI\nwuWr4Tiug+1ZtGotvKwPGmxb0FMSDPbAQ+PL77J0FC0yvgkADNMnwDAMwzCMN8kHb+6kq2PxeqTr\n2azd1L/QLAywLUlL5KmTJ1IOINGuR7xmM7Wh85k/MELvB659VWPRWpMmyzebUmlEmi6k3qQKdo75\nJyoKtccZphauq7Hk4i2BDr/BmtIsUkBWtohTcG2NJRQD5YhqNaGr0yaKEhxHcvK8q5SCnrIgjs5U\nElPj+R6ZQoYkjnE8m94ej54+j4NTLgNre8gWFnfXEsBFm12sV1Cn/41UbaR860dVvvyP8/zdd6uM\nTp6pK7LxapidAMMwDMMw3hRbN+b4wq8O8f/+S5Xx2RTPdxk+p5ti+bSOshqKecEIq1H69GmLgGIH\n9b2HyHZ1oqT9Klc29Qvm9OgTzcmU1ozNp/TlqvTloRnbTNazpLq9I9BbaCGTEB1HdMoKq0pzpKLE\nyXSf4zM2q3tDomaMbSksyyFNNKWijSXBdSStE+U+g0RiW4IkXVql6GSqj+u5NGst/JxHoWxTKgiO\nHE/BslmztoOxY7MEsSDraS7bZPPuq19+t9430rGJiC//Y4Xx6YXg57EdLW57d5FLtprDv68lEwQY\nhmEYhvGmGej1+Pynevjbh3OkzztYa0lIUs35g02UfN6URWvCH92L97HraMxlkdHyVXteOoGVJNjT\nR0gzBZJSz8Iz0sKyXLTWzNVDLKlPHRLOuQk5J+bgXBmlJQrBls4JOid2kQ0q6FAQZspUejdSaVqs\n6IxxbFjXPcUz0wM4riRKBMW8JAgFJ3t+CQGpkrgZm6S+eCVcKUUSpydeJ7BtSTbnMjkVIhGoNEVI\nQSJsnFyetJUQA0fnBNWmppR76+4E3HlvY1EAAFBrar7zkwYXbfFfUbdhY3kmCDAMwzAM400lBFy5\ntslPDmSRp9KANFprunIRA50hQaII1UJ5nvTgQdz6NPnz16N/eBR3/gjsfZBkZo7qgUmOfGcXsquT\nzvfeRM/PfwDpOiTzVRACu7S4qZSqVRB7H6VwfBeOCkFaxKUequsvRWVL2E4WIQSNMCFKl+4WZN2U\nrmyLqUYOV6YoN8t8z0b85+5G5nL4rXnS6QNsbtZxypfhegIZR4zN2+RyEinFksntyf/WSlMoOEhL\nEsUprUaCYPFrhZBYtkRpQaUl8XybZivF8+xF1z06ofn2Qwm3v3NxmtBbRao0h0aXT/0ZnUjYczhi\ny9rXpvGYYYIAwzAMwzDeAtb0aboK8zxwIEMtcPBsxdbBefJ+O+3F0jHgoeOYdPt29J3fYsV//iwT\nh+a4Nv8M8Wg38eBavI4O+ntz9Fy6mh1/+E9U//5rjP7R/4W2fVQzQLo2+W3nMfibv0I9aDL/6G7k\nmlV4526ldOgwKj9M58gT5FRKcd+jNC/7MI7XPmgcp2coZwRk7AStNUIoGolLxivQKA9RqI1CNo/f\nmkdbNuvrjzPlbObRuTXYjiROBCu6QqbrWSxLo5Smp0tSb0AQpvT0+Agp0bqdBhTkU2amm2Qtl2Y9\nQqWKcncWrcF2LaIYMr5gfi7BtiVBsLi78eFxTZRoXLsdHMxWEn70aIvpuYR8VnLFBRnOWfkmBgkv\nsNBv9gBeWyYIMAzDMAzjLUFKzbbhyrLP+S64c4cJd+wl050l+3ufwDm6j57Dj6NrVayJMcThfcQr\n1jF/4fV0ju9g+Pd/jYn/+6us/sx72f37/0Aw1SSNbSo/eoDqI0+jLQsr46EaLewN64h/99/RNf8s\n0xtvQB99lJycxp2fJEglYn6CfBIQ9W4i9QtLxpcoQRgLZhs+5WxIqn0svw+3No2DAAFxqQfmZ7h7\nZA2eIxCpIutCVy5hfB5sS9DTKbFtST6rqdYBYbXPAKSaMBJkMjbdPVkyGYvJ8QZBK6RvRZm52QDP\ncwFNPts+uxCFMep5Oxdx0u5D4NpwdDzmy/80z+TsQnDzxO6AD99U4Jptb/zZAUsK1q5weLK69ID2\nyj6bjavfmjsYZytTHcgwDMMwjLcE3zlzV1/HtuldtYqhd99E10WXkS2vwDu2D6oVxIkDvTIKcA7t\nJPvco0z1bkV6Hv2/+lEq+ycZ/v1P4F+2GeKEzd/6U2TWoXDrtaz87ldY+a9/Renn3kX9T/+C+fNv\nxQ+mCOeqJP2rqB3YR2N+jj3eeTycv5nqyAyFsR2LxpYqmG54aC1oRjZxPSRMHUYz65lPchztvgRl\nu2DZkM1zbuE478rczzWFZ7h8fYVG2J6O+R5YJ+r+27agkGvfj5N5/+6JObDrWrruYk0AACAASURB\nVEgp6BvIMzjciWVJHMcCAZ4DSoFKNWm8dOeiv1OQOZFR8+37G4sCAGj3JfjBw03i5AUaH7yOPnBD\ngYGexWvUxbzkPdfmlj0PMF9XfPuhhK9+L+Yb9yQ8d+xM1ZSM5zNBgGEYhmEYbwm2ZZFxliYpSCnI\nee3HhZQI24bxw4jJY0teKwB77AjSdlC5AqOFc0lHx+jIQend14KGw3/4Fc696/8hCVNaTpmoeyXe\nB95Lxy+8H/XIIzgqJu4cQNk++8qX0xAlLqr+kF51nMnudzBZdZFRuw5/nAjGqlkqLZ8MDRAS79B2\nXBnh+5KRnouZ3j9DtTDcHp9lsc3aTlHWWe1PolLNyGyWjAeus3iSa9uQzcDJpr8nS3sK0W6m1j5L\nwIk0JFCpotFMmJhOiOMU210cVHkOXHWe1e4orDVHjy+ffz8xk7Jj//LlUl9vgz02v/OJDt5/fY7L\nz/d55+VZfucTnVy0ZWlloIlZxV9/J+GBHYo9RzVP7Vf87Q9T7t+eLHNl4/lMEGAYhmEYxltGIeNS\n8F1cW+JYEt+16ch62NbiCa2oziA4w2p1FCCEZu8xSVOUyA6UseOAjnN6AdDHj6OqdVb/8jVoLBQW\nsXaxrrqatN5EF8o4OQ+KRRK/wBF7PbFwWd3ciZRwpHQJ6uA+js7leHa8g9FKHoAuZuiUM6x0pyFN\nkVox468kuOMugnoEcbRozFLFHJ30yWUlGb993FecFgcI0U6DymehkGv/txDtSqbpiQVvKQRKabRu\nBwHjkwmNRkoap9i2hbQWLljOw7mr21M/AbxQoR3bevMy8HMZi/dfV+CTHyrz0VuL9HYun71+z1OK\n6edlj8UJPLRTEUZvzk7G2cQEAYZhGIZhvGUIIch6Dh25DJ35DKXM0gAAQA2eg3KWrxSjCmWUFqRa\nkLWaWF1diDikEbe7EPdfu5nZr92BM7SC5NChk+9Mavs4l19CqSDxLE2cKdGZi4hwOO6sppjM4KgQ\n15esivdwdf1fuV7fzXqxD4AsTc619jDZfxHZ6nEiZSOFJp2cxKnPYh/ciTytI3FVFaiL4qkGYWKZ\neffJib/rQMZv7wCoExk8Wp/4Gd0OAvIFr33YOEood7YPC2u1MBmersB8feE+rz3DAeChPptz1731\n8+9Hp5c/qD1fh+0Hz3yI22gzQYBhGIZhGGefcjfJirVLHlaOR7z2PILUQRe6yTzzEN65GxHZLEmt\nBq5F8Zx+wkPHkSpm7rO/SfN/fOPET0t0oQxKU4sKNJ0yjqOxZcqE7EMj0bSX4ueGLsIRirJV4QL5\nFFvFdlZ6E5RFlbrXQ4E6Ngk2EVYuT2FyL7I2h1WbA0AjSIqDbFsr2dwfUPDSUz0COPGK57OthR2A\nk4QQKAVSQiZrgRb4GQfbsZASVq/K4fvtC0sBu45Kdo8IUgUfvDHHcP/iVfZyQfL+M+Tfv9XIF5jF\nuqb0zYsyQYBhGIZhGGcdISTx5e8h3HwxabmHNFcg6RsmvPQmosFzONQaQMYN5r7yLQpZhcqV6avv\nZ+V7L6J5fA4rn0VMTaJHjtP48t+QTs+gtcZq1Yn27qLy6E4mJlISJcl7ilDmmN8/QSJc0iimVRpk\ne/ctpArqbgfr2U2hZGPLFCUsFJqBcC+zTZ/oIx9j3/C7EWjSKCb1y0S9m8gODnP+KljXnXDFmibn\ndIeU/ISTAcDzdwaU0sSxao/ztM0RrTWOY5HxJWGzTv1EdZ1SwWJgIMPmzSWyWQtpWzx20OF7Tzl8\n/X6bVuzw2x/v5OduznPNtgzvvirL5z/ZwYWb/Dfot/jqrOpbPlDpKcO5a8wU98WYO2QYhmEYxlnJ\nyXUQbL2a1i230Xrvxwmu+xla/Rs42FqBH9WR/+532PJvP4BIFU7SonV4ioF3nsfoXc/Qdeul7P3b\nR9upNNMzBP98JyCoN1OqA5vouOF86l/7Jo3II1WabH2M/X/+Tbj3+3RmAnjyEXzR4rHiu/DjGmMd\n55G0WoSVFr0TT6KffQpxZA8ZGTE3cC71jmEmOrYQrr2SYPhSkvLKU5/j+HjAnd8fZ9+zo2zubeLa\natnUIKVOHAJG4XkncvsF5PMWA302riu56MISa/pj0iSho9yOFDK+xarhLFs2+NhWilKK6arknh3t\n3YKbL8/xsfcW+eCNBTqKZ88S+i2XWEsCgUIWbr7IelPPNJwtzp7ftGEYhmEYxmmk5TK0dh3P7T5G\nJGxi7TIVlvCjCgOPfZPVf/4JpC0gDknmash8gee+fDe9H7qGsQf30/j6HQsXixPCVDKpB+nL96H8\nhM73rWbnjKCYUZS/8TfMBwL/kXtYc/Mgx6ciCqRkGuMcLG5DpRJ/NiQzNEz8f/53nK4s6Xm3YYuU\nuarNOfIgB9a8hxWHfsze2WHKBYeNfSF/8aX9fPdH4zSa7TyfO743wTtvHaYwONBOPTohTdu7H73d\nDtOzMaWcRmlBnIAlLY6Ph5SKFrFy2LQxz48fi5meTentFliWpKPDpasQs6pf8fReqLdSZmoWf/L1\nmMGOlFsucxnsXnr2Yr6asOtAyIGRiDQV9Hfb3HBpFs998XVkpTSP72iyc3+IlPCOzRnO2+Ajlotw\nXoF8RvKp9wme2JMyPgcZFy7bIinmzBr3S2GCAMMwDMMwzlq2bdPb303QDKhUQzbFz9LX2IXcmgeV\nQKxI/RIjoxGNoy3SBI78139efJFCHnnLzVRDD8uBluhA5z1kPs/UzoQ16yp4nTl6vvxnVD//e2QI\nCC++gb7nHsDJF5ksbiO1XKZ7L2RlvJ/srTeS3v9jUr+MrS1SJcjOHYJSB0c6LmPdX36axm/+Ad9/\nsotv3jmC0guT4vGpiLu+e4TPfKbIWDOPkO0dgDgBEHieIJ+zCEIo5CXFTEwjshFCMD2bUCpaoH3W\nDQQcr8TMWNDb7aK1oNK06S1GDK+QPLk9IlewCGLBM/sSxqZTPnyDx5O7EybnU1xbUKmEHDraPFVp\nRwiBtCTfvr/BxefluP19Pj+6v8LOfQFhrFg54PKea0t0lW2U0nzpH2Z4ZHvr1Ge7//EG11+W42Pv\n73zNfv+WFFy6ZWE6q5TmyT0xU/OKwV7J1jX2axZ0/LQxQYBhGIZhGGc9P+vjZ33QRaL5PFZ9EjTE\nvevQfonezRC/713s/cX/bfEP2jbyZz5EpW8zaIGb1vDSkJrbw1wzS2F+lENzK7js5qtIBjqoXHM9\nOgzI+RH+4e3ITe8gSj1SoXHCmPnSEM01fRSrszSf2U20aQ2em7IncxXbgj2M660UPvMJRv/3P2fL\nf/hf+T+u3oHb6fPv71pLLWyvxE/PxDz86BQDG/N0pJP4qsmkM0Qq2g0DXEfguO2JbSu2KbgBcQxR\npJicSenvtujqcqgph0olpbtTA4IoFkSppKcYU8hbxKkiaLarFU3Oaf76zoBUWySRalcW0hbYLkTt\nMwZaa5RSBIHg4Wda7Dg0Rb0a0qoHAOw9HPHcwZDf+HgvO/cFiwIAaDdVu/fRBu/YlOHc9Uvr/r9a\nE3MpX/9ewJHxdmUgIWDdCotffq9PPmN2B57P3BHDMAzDMH56CEHaMUS0chvR8Da0Xzr1lNPdyYa/\n+0v8X7wdfc0NqFveg/q9PyL5tc8DAq1h3ZFvI3M+ldgl46R0HnqcPQcjqqVVeDLG/eWPcfjBI6wQ\nx4k3XYBVmwEUemyagWP34EZVam4PzQ1XUrfyBAoGijVimUPWKzhxFZEtwH0/5LnqEPX8APrZ3Xz1\nk8f5o9/u4V1Xt8ueds48y4ftf+KS8n6umP0mPxt8lfPDh9ofUYLvLlT8aYQCIWX74HCkiSJNUxXw\nXEkUp6SqfdRY0/4/SyjiRNFsRMSndRXWwqajO0dnX55cyUdIgeu7i1bStdJorUmTlCSFQkcez3dO\nPT8yEfPd+yrsPBAs++tJU3hyd2vZ516tf743PBUAQLuE6v6RlG/d++Y0PnurMzsBhmEYhmG8bTil\nAlv/4HNs310lKA6eelxrTefkdjITe/BLNjOWoNazBmv1mvYLPI9QuuTcBPvydxBHNZr5QfQDT7Gy\n90kOfW8HpaHDNFavIpXD1N0unK19DMt5VLNGS+eY7d3I5t3fIchsBQTNyKJvQ4meXB/7fnSYlR/o\nof/8q/kvlx2m+pO9hM4KOmd2UVl/Ce7hpxjomUPwDM84mzk6qujtlniOIIwkYdBCCoEgbf9TtA/9\nCqDZSsllLRxb4zspQmmmpwJq8y0cb2ECr1JFvRpgWZJM1gGtadZDMsUMcSsmjtodhnOlDK1aiFaa\nJEkpdReYHJk9dZ2jYxHlwsJ1n0+9DiX8p+dTDoymyz53YDQlSjSubdKCTmeCAMMwDMMw3laEFGy5\n94+Z2ngTzfIqhE4pzh+guO8BrMoMTimL3YgIuzYws2Iz59gWOTchET4FOYdtB4j9e0jcPuSxowzW\n97P/q19h+t1rKZ03yVBmJ7HI0DjSonvAwhrZiRgQpIHL1NobSWaOk1k/SF91D+GKPqZya8jIp5jQ\nfazqajJWG+L8m85nzO8m5/TQMX+Q2uYbiXc/Q7HDZWX3RgQWxycSujsklgXzcxHZvItLk8nZPCsH\n26vuSaKoVBIsS9KZTyh4EcdnBLOTVZIowXYXcuaV1sRRSkxKHCV4GQchJQKwXRut2/sJuWK7ERmA\nSjW2v7ixmOdINq7xeGzH0hV/KeCCja99KlAj0CfOTSwVRpo4Nr0Dns/cDsMwDMMw3naE1vQ+9o0T\nLXmB0zrrWq0KXTiM4VIsBJzTWUG26qhCDo0kDGP8VFAa24W7ukw4NUe2O0drLqB7bB/eoUcobNnC\n2B/fQc8vXUMydogVF8QcHnNx3/kuwtIq4l/4X/D+2x8z+vFfZXDmOIUVWfbIPrqtiB31MkOdw7hH\nn6Pa2ctU8RL6amMU84q9mfWEKXSVFMcnYa6Sks1Y2LakXmkxkjr4bsBgv4dOUuJYnUoTmptXdGXg\nqSfniFrtswBRK8LLtlOQVKrQqUZYApDEUYrtWMSBADSu72LZAiklftYjChLQoJLFK/Dnrve57tI8\nO/YFPL1ncVrQFRdmueB16EOwoseit0MyObd0m6G/S5I9O1ofvKFMEGAYhmEYxtuKEBIxuAb93Fw7\ncfy05rzS97EFCKfdiXdNT8SAPU+y5wnSjVfgBzOEs7OIqTEaTz+H7PJp7jqOU84Rph7R+AzVx3cT\n7Zkh3bmX+Se6SY8coyMISZsdJDmfxjtuobr5Rla9d4rkwON412xk/C++ytz5HdQqCteRTAVZhjM+\ntVwvk3OCfXMul2SrjEUdZHwLKRRCauYrMUJCNudQnW+RJprAkzRbMWLfblZMTrBh00b2N4aYrjlU\n6rB9Z/O0u6FP9STQul1dB6XBhjQReL5D1ApBtCf/+VJ7FV+c1q63Vmlfz3Hg0vNy3HJVESkFv/YL\n3fz48Tp7D4UIKTh/vc/lF2Zfl2o9tiW48jyHbz8YLtoRyHhwzYWuqRC0DBMEGIZhGIbxtiOv/QDp\noR0QRQsPWhKvo0DcDFFXXsWANUKfVQdATI+Rad1NdUZT9muo53Ywv7/GxH1TrPuFq6juOkp+RZZ4\nzCWaajL948cBmHt4J/FohczGFdjFDqxV50CS0ohtjvRdxqqLJLNuluCXf50wtmikDsVMhKMj0nIP\nc4HPSNWhFWV4xLmESGeQETQamjBUSClpNFLSVCOEIE01MoW9+xr0DG8k/9RDbFyxjtzIPp5qrCfG\noW+ozNjRORAsOvgrhECh0ArSVCFtgW0L8kWfRj1qB08nU4fS9oq7lPDOSxyUKnLBxgznrFpYcrcs\nwY2XFbjxssIb8Svlum0uhZzgiT0xtYamoyi5bKvNltVnPp/wdvaiQUCr1eLzn/88MzMzhGHIZz/7\nWW644QYA7r//fj71qU/x3HPPve4DNQzDMN66zHeFcbaRPYNw+68jvvVlUCnStnBLBdJEkXauoGJ3\nMFSo4QiFnhyH555FyQKjX3mMSReEbeH1FEnrMcHoDDpOSaOE1tgcjbE6xCkUBIXBMrNHK8SNCJEc\nJ/TzKJUQhD5xdiU9ZU2S2hwrDWNJsC1FKRsxUD9IPbQ5FAwxX0lwPIvZsIALTM4oYiXadfslRGGK\n7YDtSHI5Bz9jESeanh6byQ2X8I9P9vHJtQ+yK1xDlDrkCu30Hy/j4Z6o7KO1Rp0IJDTt3RGtBdJq\nV02ybAt5YvU/TRRx2F5uX79S8qF3Ft+U3+Fytm102LbRTPpfihcNAu655x62bt3Kpz/9aUZHR/nk\nJz/JDTfcQBiGfOlLX6Knp+eNGKdhGIbxFma+K4yzkRzYgP3zv4Le/gBqaoJQ2jjbLoRsmZWZOlpp\n1PHj6Pu/D0pReXIfaT3gZAZ8a7JBfmWZw//yNAA6VUw+PU0w0z4Q233+Orx1g5TjEPmLv8z+uTXI\nVoGu+DjNtJ8kLpJlnLGwAy0sBJo4FWglKR17iqPlG8m5MVMxdHQIWi2FrxSF2jGmvWFsWxBFiihU\ndHRKiiWHckeWoX5NZaxG1o1pDKxDKBvh2GzunmfPbBd+1qN3ZRcgFqXJSKFJ0xTHEYSBwnEltmWR\nKvC9dlWfKIyJTgQAfZ0W777SeyN/ZcZr6EWDgPe85z2n/n1sbIy+vj4AvvjFL3L77bfzp3/6p6/f\n6AzDMIyzgvmuMM5KQpB0nYO4rBPZmIQ0JkESP/0YenwUGnWYmQQgqkdM7ZxecomoFhBX23XoWxMh\nlp+g0/Yhg+zG1WTWFMgNnsdEYQhHFjlaKfP0pEtHZ4rjWkyGHVQaNvl4im6nzoF0JfWWhT+8iqzM\nMKTnGbHKCK0Y6IbCwWeY8AYZUoc54q+h1WqX6azXBX0DBRwbekshxekxeksWz7pZHAmPJ9vwHUlH\nLmXv3oBMzqNVD2nWA7TW2K6N6zkIKejvzzIyUiebc0mihKu22rz/Kp+5Wsr9T8XUGjalguRn39lB\nFLw+Nf+N199LPhNw2223MT4+zhe/+EUOHTrEnj17+NznPmf+YjcMwzBOMd8VxllHCHSuizTXdeoh\nqxKRjhxDTU+2a+VPNZl4YpKkvrQGZTS/uPpNGi5UymkcGsXvXoVYsw5fR2SkZmY2QeHiOBLHFtRD\niRYWxZLNxh3/QveaS9jNZYhykVVOg/rIDKv7i9RCh6yn8MePEmzYSDYXIwJBT7dDqxETRQm2nSXr\npQhhY3d2kcYpji2JI810M0OjBf1dUK+2KHcVsByLNFXEYUzYighdm1wxw+RUwNDKPBsGUi7bLOku\ntTsZdxQsPnCtderzlQo2U8v3BHtdRLHiO/fMsvdQCyFgyzlZbr2uE9syh35fiZccBPzd3/0du3fv\n5rd/+7cZGBjgd3/3d1/WG/X0vDGHQl4vZ/P4zdjfHGbsb46zeew/DV7tdwWc3b9DM/Y3x2s+9p7r\n0Fddw77/+Acc+6tvEMy8jJnuyUpDEqjVqB6YZ3B1ixnLYk51kKYxadI+tOp6cFH5IA/NbGEqLDL/\nxH4KgaD3yvPRQpKrjzPx/cfY8PGNPL7fw1Yh8py1DA1YzCSrSJuKJIFSh0d1roVrCxqBxXQFtqzO\nMTJpIy2BpTTzdc3kVEw5b5Em6kTNf3A8hyRO2o2/ooSgEZIt+FQqIRsuL7D5nBeurflC9z4IFVNz\nKV1li6wvz/i6lyJOFF/447088Wz11GNPPFvn4LGI3/u367Hkyw8EzuY/86+FFw0CduzYQVdXFwMD\nA2zevJlGo8H+/fv5rd/6LQAmJyf52Mc+xte+9rUXvM7UVO21GfGboKencNaO34z9zWHG/uY428d+\nNnutvivg7P2+ONv//JmxL1X6zGeYfGw/wT0PLTwoJdJ3Uc0XDgwGrhggqMPEXY+z+tYN+FEThYXn\nJURRSivS5DKKOJUIAUpZVH/204w7eTqdGXS1gtOcJfKKuEHEqn4FoaC1+lw8mVKPBXEMSaJJEs2a\nQYFlgUxgti6RwkJYDo1GQi5nE4SCaiXg0DGX9ERlnyRu71rYrk0ctLsBp6lCpZpGLeLr329Sqba4\neGN7ujg5p9hzRJHLwoXrLPr7i8vee6U0d/wkYseBhPk6FHOwebXFh67zXvGq/XfvnVkUAJz0wOPz\n3HHXKFdfUnpZ1zvb/8y/Fl40CHj88ccZHR3lC1/4AtPT0yiluPvuu0+dEL/xxhtf0l/qhmEYxk8v\n811h/DSSvseGr/4F09+8i/rjz2BlfLo+8j6yW9ZTufchxr70P6jd98iiPgMAnVs6sTMuSUWho4SZ\nJw7inHMtHU5MX7ek2ZTMzMbkfIeRzBBxpEFC3NFPrZWjKx1H5rOkzXn6btrMwcinrxwyUc9QbUBH\nJj11gDdNIQxTtm6z2DMGqWp39U1SQcaO6ezw6O8WTM1BEivGJ1M83yGJUsJWjJCC1Sscsp5g++4I\nx5FksjbVakKiBN9+MKWQEew4lPLsAUVwoqLq/U+nfOw9Ed35pfftzgcjfvLMQupUtQGP7EyBkI/c\n+Mq6du09dOazBzv3NV52EGC8hCDgtttu4wtf+AK33347QRDwn/7Tfzr1l7phGIZhgPmuMH56Ccui\n5yPvpecj7130ePmGKynfcCXVh59g5HO/jU5S3LxLYbiABqrjMbUdxwE4+q1n2LLtflZdMkTG6eDY\ncQuVgps0UMImSSL6cy0CXAbrT1NWTXZmz2dDr0f2uV2ct0owIc6n4Csm5y2O1SxK+YhsJkOvnKLh\neziORRBqNBrv6D6qa1dRzLa4YGOOINTM1QApEGiU0tQq7Um1EHDxeQ7nDPts3RDz8F1HcLu2EgYJ\nWiuCBP7xxzHVul5USWh8VvO336vz2Q/ai1b3k1Sz8+DiDsIn7T6c0go1Ge/MuwFjMyk/eSZhel6R\n8QTnr7fYtsF5wR0EcybglXnRIMD3ff7sz/7sjM/ffffdr+mADMMwjLOP+a4w3q6Kl1+EGlyDU53A\nyjhUJ1KaY/NEMw0ApG8RVWrtXQRRJchkKeTz2LbEyjiAZkPnLOfIw+wQ5zObHWYOcFpNGn4eZ+UG\nMoRE2sG1U/pKcCS0mKulKKlZn5uia1WRWdWJSjQgyB7fj2cNEqeSJG037dJa43oOpbxNJiuZGm+S\nLzis7Id1K9sB+9phh+FLa/xEaxoFh1ZLo5SiUtfEYYrtWFjWQnB/fCr9/9u78zi7qjLR+7+1pzOf\nU3MlqSSVkHkgJMHIjKIypfHVi4Bc9crVt+37SkOrfdUXh9t6u/3c7n7x029Ptz+ILbytEulGaecJ\nQVAQImEKIQlJyFzzXGc+e++13j9OUkmlqpIUGSrVeb5/wd777POcTW3WevZe61m8tEOxbtmR7mS+\naBjOH/Nq5JDhPPQPa1oa7XH37+sM+dbPSwweNUpn296QvkHD2pUpntk0jD7m1K4Dl6yZ3sMpp4o8\nphFCCCGEOAVOLMbQtk56XzzA4Ja2kQQAQFdCki0paD9AYqidILCYOwPmOG3UJkJqnDz95TiBXV25\nN0zX06NmUJsMKf7maeK5DoJEHQC2ZYh4mqSVJ51QzPXfoKbBA9elEDqEBixbUQotLNumPx9hMKfI\n5qtzAFJxhzmtKVLpGHVNKebNz7BsSXLUE/74gtk0bPoZgYYF81zQYKofJ/DDkQnFh+WPGaWTiClq\nkuM/mc8koD4zcdfz1y/4oxIAgFDDxtd8Llqe5J1X1OAetQ5YxFPc+PY6Vi4eZ0ySOCFJAoQQQggh\nTkFh63FWw9YQrYvxxr/+HqdtFxhNY6JMPtVK0i4QdYoke3ZRdlOUQxtba1AWKuaRa72Q6N7N+G4c\nR1XH6GPg0vZ/p6nG8K6GV0nHQ4phhI4+Dz8AS0Hf8qvJlmz6CjGKZYuDXZpcXhNqTRAYsrkA17Ox\nHAc/HP1UXrkOjckSkajLUNFm1QVl4l5AqVBdTyAM9MixERcWzRnd4XdsxYULxn/Sv/ICh6g38dCd\n9l497vbBHLz6Rsj/+f6ZfOHOVv7gmlpuekcdX/pEKx94T/PE114c10mXCBVCCCGEEGMF/WOr1hyt\nZ1M30RkRcjvbKM2EzqEYZV/T3qO5IL+bFr+LwL2M0qAiEgd0yEA+TmpWA0O/bafyrjSqbChUXMJy\nAdo7qF07CJlaPFVhR0+aviGFparj/XM+tPda7G8vMHNmklBb9PUV8Ms+Pd1FolEb3zeUKyGeM7rj\n7Qz1Erv2GtRLFqVCyNrFedLNF9C2v59XthRQiSiOW+3kr10aoaVxbKf+hss8ADa/ETKYNWQSiuXz\nbW660jvudXLHzx0ASESr37N0YZylC+PHPY84OZIECCGEEEKckhNPTC11ldn/b8+RurIPx0kTlBTx\nTIroz37Ag4k7+PiyASJeIzG7QtSNYnKDkHHpeGoryt2A/8E/IjQWq3Y9wt5l17DM2wfKIbQ9LB0Q\nagdlaUplQ6WsGRoO6eos0dQYw3MVjm0RYOFGXGK2ZmAwxLEVhcCjUKkQ90IilSHUnl1Yq5eSTFiU\n8gHNlTbqi7t5aeG17NxZwA9DmmsdLlzg8P7rU/T15cb8Vksp1l8e4bpLDLmiIRFVuM6Jr9EFLRZd\nA2MnFc9qsFg+f3SGMJzXPLcNhvKGZAzWLVE0HGeokRhLrpYQQgghxCnwZjSe+CAD+T1dNHjDpP12\nHNsiWeqh/PJWwmKOUMVIhQNEjY+vLdqDegbKKXSygQNPbUMXi9hoBrxmktk38FRAxYpSctMQBnie\nQmtFLhtgKShVFH4lpL2zBJbC9SyMMtgOlMsa11U0NTgE2qFzwCPdtZ3Y3q0ULrmWIAxJpl2aTBfZ\njiy77UXUlw6w9uI6IhGbXFHz9jUu1gkW6HJsRU3SOqkEAGD95R6L5lijUqqGjOKmK0Z/14FuzTd+\nbnh6i+HVPfDsVnjg54bt+8cfTiTGJ0mAEEIIIcQpqFt/zUkdV7O8kVi+j/5CnKgdYP+P/87AKx28\nb+hR8l0DNDsDzAj24BAQjzqE2pD6zKcoJBqp3fRT0naWaDhI8L2HyakkI58MdAAAIABJREFUQ9Fm\nir7NwYEoyoRobbAdhRd1GcxqlIJSSWMphW1bGAOeA7GozdyWCI5T7QbmgigFFSe3cC3l0KY+VsKq\nFHhX/8N0N1yISsTJFPYTT0VxXIfBrGbj66e/LGcsYvGx90T50A0eb1/rcNMVLp+6PcbiuaMHrjz5\niqFnIKBcrFAuVKiUKgznNU9tNmMmLouJSRIghBBCCHEKZt/zx9jp45epTC6fQ9M1F2MP95PzGpnR\n+wKVrbtw0i4N5Q4yA7tIOGV8X1EbyzEn2seK/LPUNMdYduNcnLoUdf5e1PKLKO9vY2BHO6GyaRuI\nExqbQtHguTB/drUkaCqh8GIOtm0deopuiEYsZtTCzCaLec0+rn1o6I2yiGa7aTnwGxIqS9TVXJd+\nHtPdTy45k1luH/3DinS+jXgyQiRi89hzecLw9He4LaW4aJHLTVdEePtaj8gxE4lLFcPre32MNli2\nhXIUxkCl5HOgS9PZL0nAyZIkQAghhBDiFFjRCBc++32wx+9WKc9hxVfuILOwmf5YC62fuJbof/so\nQc5n/j3/lfLBDmpy+4lkuzDDgySCQcJf/hitLaywxFP1NxNtqce1DL4dQ7seA0WPVw9m2NaZxlLg\nB4a6Gpv6FHieTSbjYFkW9dEChAHDQ2WWL01QKGtm1ARkYgGzMiVcOyQeDjMz3E+iPEBjf7XSkVcY\nZOPl/zeeFdBc3svG/GLmD7+ArSs4nsNAX4nfvlI+m5cZreHhJw1ezCOejBKJOigMgR+iFPiVACXr\nhp00SQKEEEIIIU6RV5th1if/cMwcYTvmsfwrH8ZNeJhCluCRHxDrqK4kvPDP78BVPm7aJUjVo4OQ\ndPYg5uXnGf7KfRQHyxw0LQSWhR1xsJVF0D9EYe6FPB97B/v7k4BCWQrfN3ieRa7iYNuKmfUWmZjm\nD2c+xlXWb1mzKkUs5jKUVXh2UI3ZMdTFylwQbMOlui1SHKASwGP2DZSSM2iM5tjcVUdWJ1nkb6Up\nlsOyLILQsPNAcDYvMT9/Adr6bWzHxrIUrueQSMXwPBu/EqC1prlWsoCTJdWBhBBCCCFOg9n//Y+I\nzKhj8JHvERQqxFrqmfWfriC9ZBZ6904CL03/k0/RcOMlNN/6NuzCIKVnniBwklDXTClbQed8ig9u\nAMB//Amia6/mkhU2beUZzI4OoIOQzovfB9bhajmGeFRRKiiUMviBTU1Gk45V+Pzyx4gEJZY67bRH\nS3SVkmgU4VEFeBIqz0r/xZF/Nyh2tMfpzXksmlVE93byy/7V2PiUtEvWZAj9EDfinNJT91AbntpU\nZNd+HwMsmO1yzboYtj3+SYsV2NE2drtSikjcI58rgWLUwmfi+CQJEEIIIYQ4TRo/eAuNV67A3b0R\n5ToQBgSvvIDONDK8bSsLbpyN21hD+OwvKA3nyHYME712PWE0Qax9J8W9+9Fv7AcFevfruL37UPHF\n9OVcFlu9hAf3M7TwtpHvi0QU8ZjCaXSIZzsIapoBi0poY8XjmMEcEatCtNSDHyQxBtoHXBbFqk/x\nC4FHaBS2qo6l35mfwSuDKRwrJN/Vw6P9i8Bo/rP6LjvNAorao1jIEom5mFATBOCcZPWfw7Q2fP27\nw7yyozKy7eXtFV7f6/N/3ZbGHqfqUM8Q5Evjf49tWyil0GdgjsJ/ZJIECCGEEEKcTq3L8FsWol5/\nHsoFzLJ3QtMckut66bn3f6Je2IopVygTIX7F28n8HzcQf/Vpcq9vQfUUUJEIJu/T/1ov9ff+FcN3\n/r+kIhXCwMDvnqL2muspRNN4LjiOhSmXSe55jcW9P6N31bvpSSxgsBAll24g7Q4R+IZuXUelolGW\nReHQUH5joHM4wsO5G3hbdBMBNr8YXgdAuQK7Cmlm0ca11hMMqxp+rG+gXPSplH2a6jM89UKOrbsU\nMxssghBqUxZXXuQyo/44q34BG18tjUoADtuyq8KzL5e4cm1szL66JDj4dHbkqJQDLFuRSMdJZeKE\nWmO0wXPlLcBkSBIghBBCCHG6OS5mxeWjNnl1DTT/2Vco79xMkCtQP28ulgXevq2Eb+xi+I02Op7u\nQOeLAPjZCio7wMUHv8vghdcy9KvnKWzdz/w5P2TP1XePnDf63BM0fPsvabyhmWLfcnL1Cykf7KS3\nfhZpdjMYxOnXKQwKpUAbRbGs6M8qDvTa+P4svlVej8ECZaG1IQgAyyUolNlgv5c8CfyyT3aggNaG\nMDTE4i49gz49g+GhCkSarXsDPnBdlIWzJ+5i7tznT7xvvz9uEpDL+7Tv7SObOzIPIT9UpFKqEI1G\nQEFjzfGTDzGaJAFCCCGEEKfKaCgOgQkgkgInOu5hTiyDtWgNhZ98BzvXA3tfJ9/WxdC+Prqf66KS\nL8PhYS0aBnYNUvzmkyT+/FLyfoLU7Bhm6AAArgqo84aYmdpH+8FuCoN1lAoGO9dP4vWXKK9Yhyrl\n2daeplyr8SIOrguua9GZi9E7qNHaoDWAgzEGE2jC0GAMYDvsKs4gN1wCBkf9jsAPsawj9WWMMSil\nGMrBr1/wj5sEHG+RMXuCfvxPnsqSK4Q4roPWGh1WFwYb6stRSQRYlsWy+dKtnQy5WkIIIYQQp6Kc\nhVwnKqiOszG5HohlIDWL8WbPWrEkzrr1vHrThzHFHLocYsIxh4ENyihy29uYXeygp1JAJ9PUpDWX\nNuwkZlfw7BCz/lLKm95B3vMpffMh7BVbwUpjB6uwdEhv1mJ3dw9LV82svnlwFY6tqM9YDAyNrvAT\nhAaOGlo/3kRby1J4UYfhgeJI5/9oB7tD/MBMuFLwRUs8nttcIjxmgV+l4MJF3pjjCyXNlt2aaDyK\nUgpjDDrUlEtljDYoo3nLihjrL4+M+31ifFIiVAghhBDizTIash0jCQCAQkNxAPK9E34s2trChc98\nn/TFa8ZPAABCaFo3h2idS9rxSTQlcW7+EN6KC8l4RbxDi30pyyJ9zTr6563D3rODaPtO1CWXk8ge\npL/o8st9sxjsL4xEF+pq59x1FenkMR31oxIA19YE5bFrAcSSEQJfUykFGF3tkB+dCDj2uLnPiJUL\nPa66OIpz1FN/x4ar1kZZvWRsR/67vyrga2vkO5RS2I6NF60mDO9YF+HD6+MTVhYS45M3AUIIIYQQ\nb1ZxEBWOneSqgHC4m7B3EGfuApQ19rmrm05x+RMP8ern/5r99z4wZn+0MU7dikYGd3XhJzJ4qxsx\nWjPQspJ6XcS2jvTY/XlLeG3WJVzwZy3MbN9I34VrKLb/hO9uXUR/KUYsUT1WKYge9bC92rGu7jP6\nyPkUMH+WxVXLEjy5qUhnrybQCi/i4rgWg335kWN1qKlojRdxAaiUA17dUWL10ui4bxKUUrz/+hRr\nlkZ4ZXsZA1y0JMKSeWPfAlR8w44J5hDYtk1djc36q5Lj7hfHJ0mAEEIIIcSbpSdeMEsN9aAe/wmF\nbAn7XbcRXXfVuMfN+OTHSRa2s/uRV6gMFrE8h/oVzVzwnhV0Prcd5SbY+6+/Y8b6t1DpHebx7HWk\nvDILa/pYWt8HQG9mITUFzYEF13HJ5XG6/QF+0L+O37cPAJDKVCfbxiLgHOr9+YFhKFsdk1MT19Qm\nAnqGbYyyqMtYZGptyrbLf3lPlNxQmb97OI/va4LQYNsWYVD9rFKKwA8ILAsdhnTu6eOrO+GiZSk+\n84fNE9buX9zqsbh1bMf/aBXfUKyMX/pTKcXb1yWIeDKw5c2QJEAIIYQQ4s3ykph8D4pxOqpD/dhB\niXgMgk0/xW9pxZ01d+xxSlG64HIW3xYQb65BORZ+tkjPS7vo29yLu3Qp1sH9BC86RGY3AJCtRNjc\n00zc8Yl7IXuzDWgs6pIFOsImoo7PmtW1qEqezTsNi5bWkopDMl79SmPAVZoVLQGZuOHCVs3OPo90\nzh0VWiW02DvgYuWKROMe1qGa/MYYwlBTLlbfghiqw4L62ntH3ii8tGWYXz0b59rL02/68iZiihn1\nNvs6xo6Zqs9YXH9F6k2f+3wnqZMQQgghxJvlxSE6tpNrcll4/dWRf3fCEv7PN0x4mvrb38/+rbBj\nw0be+N7v2fuTzRx84gDD+wuU93Sw6LpWen6xicritSOfCY3Nq30zeL53PvpQl05ZkFdJSnYCjcVV\nl9Xy7msSYHk4libU4AdQqUDBt5nTbLF2gcaxYbg0frewULF5o8fFduxR4/IdxyYScdFagwYUpOsz\nR10Ew+PPDE3mao6hlOLK1REio3MTHBuuWB2ZcPKxODF5EyCEEEIIcSrSszGWB5Uc9LZjBnth+2YY\nOGpicBDgHWfki1KKhV//G/Z/+n+Q3/QylaEibjLKBe9bztxrlzHUlkVrxbO5VaM+N1R0cANNLKpQ\nSpH0B2iM9WKF0FOeQ0MyYG1DOwdej9HrpKmvHd1p7s7ZLGyqjrk3TNyh7h4cf0iOZVuEYTjyG2zX\nqU4oOHR4pTLRrOeTd9mqKFFP8dyrZfqHNZmExdrlHpevGr8Mqzg5kgQIIYQQQpwKpSDVDDQTPvoA\nVn54/OMixy9hacWizPvf99L3//wvMokCidm1hJWQ7t/vZc+vdtHzma/SlRs9Cda2IZMwtHdXmFUP\nSwovkIm5dLszSUeKNEcH0U6c1X2/5KXozcDoQvyBPvxkH6J2SDkY+zagUAzpG9Bjth/+oEJhMARh\nABpiyQSlXAFjDI5j8Vdf76JSMcye4bL+6hQzGo8/D2A8a5ZGWLNUSoCeTjIcSAghhBDidJnROv72\nRBK17C0ndYq6T99Dt1nAq/+2i1ce3ExnX5rOT/89B5veOuo4YwzGGEolTVO9IlvQ6GgSFYS4nsWM\nxDCupXFMQH1xP66t4Zi5C+nIkc790KBPoTj6yb3vG9q7AuLx8VfxCoNwZA6ACQ1BEFTLd8Yi2I5F\nf97m9T0V9rT5/PaFAn/37T66eydeMVicPfImQAghhBDiNLFu/Aj821ehp7O6hgBALAEXLMNeccVJ\nnUNZFrPu+gjc9ZGRbS0V+O7GCqpYBGXhde4lteslKqsvZahxHpWsplzWbI6t5GJ3B7NoY0A1otBY\nYQXf8mjK+DSlArqyCQDiXsgFjUfKm+YLhi37KjgOWBZEPIUfGIolqEtAPgv+UTmC1ppSoXRkwTBd\nTQQAHNchVZcimogS+AHFXIlyoUxnT8CGnwzyyTsaT/FKi1MlSYAQQgghxGmiHBfe/xnM5ifgwA5w\nXNTydTBnVXXW7ps0lIcL/vZu7Od/h3E97GK1Tr9unY/9Z/8bNWsmvYMW7eUkpdwqrolsJWoXUSaE\njv10N1zE0jklLKUYKESwlCIZCfHD6gD+Yhn291RrHPmHqp6WjyrNuXi2oiVjeOJFH8tS1RV7ixX8\nyqGDTfWtwGG24xBNVMfsO65DIhMn9EMCP+DVnSVe3l5g9dL4m74e4tRJEiCEEEIIcTrZDmrNdbDm\nutN2ylf/9WlqX3qOeR+7jszqC7A8h9zOdvY/9AT+D79F7s7P45crRCIOFe1xMGimRhUpapvh1GJa\n39aE4xgCbQi0RaAtCr5Nb85hxcwy+9oDhgrjTwxOxQyXLIH7v1cgNzh2YTSoDk0adQnc0cOHLMsi\nmoiQGwwIQ8Pjv8tKEjDFJAkQQgghhDjHRbe/xPI//yD1ly0b2ZZcOIv08rm89P89jz/UQdzJkEk6\ngKIrbCQfZumvxHEdmyWmkyIZ/NAi1Ec6+4G2eKPHY3B44io+rU2GqGfo6J54YTStj8wtsOxqh/9Y\njufguA5+pUJbt39kGJGYEpIECCGEEEKc42Ysb6B23dhx9PHWJuZeu4LCrtdoveStBLbBtTWDeZe8\nXUMhcAgKhnWpfko6wVAhNaYUaK5i43gWx04aPiziVjvr0YiC7Nj9SsFFiz36hhXFwMaORsFAxQ/H\nnDIS9wh8n2hESQIwxSQJEEIIIYQ4x82/diWW7h13X2pxC4lwHp6do2RClJtguFLBTdhkIj5d5Tgu\nIZSKHByaMc4ZDHVpC9sKRr0lANDa8NKOkHJJsWSeR0dvcWxsLQ5OMoPxFUdX7rdsi1KxWglIh5rQ\nD7Esi0g8yvIF41cbEmePlAgVQgghhDjHRTOpCfeFpTJNLQ7JJ39AS/tGkk6OefEeXF0gZvlE7CIG\nUGiccXp+jmUILJflCyMkIkce3etQ45dDsnnYuM2QSMdZvcTDPeoRcutMhyULknQOjH2qb9sWCkO5\nUMYv+0dtV9x2Y+2bug7i9JE3AUIIIYQQ57ggPQt7YB8Woxft0qGmXDOD1gNP0F4eoKFuNiYcJJHf\nQVfsStCKhelujAHleUTcECtQVMLqk3jLMtQkQiKuQRub1Us9nnulSKEEgT/6u3YchLtvruFgh8/O\ngxXmzU6yeLbh35+ZYCExqonAsdIJhefKc+ipJv8FhBBCCCHOdY6HCRX45ZFNQVh9CzB34CU8XaKh\nJcJOfw7lBx8g8drTePEEWR2ndfAFdDaLcj1q40UsG+bW5klFfWZkfOIRg21BIqIZKtkUCnpMAgDV\nMqXDecOCuR43XJ7kqotTWJYi6k4ctj6qapAXd9BaU98YZ9MuCzP+FARxlkgSIIQQQggxDVh+Bbvr\nAFZ/F9ZAD17nbmL9Bw/tdIg01aJrm+n97TbUUB9esZ+mRJ6+xALCYjV5MEYBiqgb0piuoBQcLuxj\nW9XyoTXJ8SfsZhLVp/jHWr1w/ERAa025dNQwIGURT0WpWHF+s8Xi2e3SDZ1KcvWFEEIIIaaBIN0I\nBqz8MFZuEEtXy3oawHgRep7fycyuFwn378PP5okP7KIhViBv12HyeQqBxUAhhq00obFwLQ1UFwgz\nBkINqUjI4jnjf//SuYqIeyQJ6Bv0+def9PHjX/UQ0wPE3SMlRMMwpJAro4MjbxQiMZdUKkLnwUEM\nim0H1agViMXZJXMChBBCCCGmgaBpEWH7VuxSDnVoLI0BTDxFWAno39JB7aoSicYEvTSQmjOD9j6D\nbSfI6TjFShTP0TiWRqnD3X8AhTaGQsliXr3P/CUWGM22fYahPKQSsHSO4sZLjjw7fm1ngQce2Udn\n75En/c0NOaKZDBXtUin7o8qDWpYiU5ugVKhweBzQYN6iPxvSXHOmr5wYjyQBQgghhBDTQSROdl83\nyeULsMoFMAYTTWKMZviZpxnc1Yu7q5PYombyd3yRgUqMSuCDl6THbcG2IeH59AzZ1CVCQnOkU+8H\nCpShohTaKG68xOZdFxuyBUjGwXOOvAEwxvDoL/pHJQAAXb0BK+sKdJcToxIApRSNszIA2I41si/i\nGlKxM3e5xPHJcCAhhBBCiGlizw9eI7/xJcKyT6gVYV8PuSd/y55HNqEHsnT+3QasS68gEoHOoTjx\nSJnAh7bkCvzAQinFvg5o63UoB0cG8isFiZhiuOSwb6i63XUUdWk1KgEA6O7z2bWvNG58uw+UWb4o\nSTwVwXZtIlGXRCaG1od6/sqgrOr5WhsM8bELC4uzRN4ECCGEEEJME07LPF78n/9O08UtxOoTVIZK\ndL5wAF3SEPMgCBhcdyORIIoXsWiI+QRbX6ar+a0MFBRDebh2cQev9TfTqAyhqVYZypcViWg1GciW\nqpV7JlrQNwwNeoLKPlob2noDHNfFcY8kGaViQCFXQqFobqmtTkg+A9dHnDx5EyCEEEIIMU1k3nUl\nJjB0bTzI3p++Tvsz+6oJABCvjYKvKadmkivZhIGhYjzqX/0puSL05xwiDng2NKQCCCtoHZIrW2hj\nc3g9L21GjeYZY2aTxwVzo+PuiyUiGMslnnDJ1ERwvWpXUykFykKbENe1sSzY023xL4/BUGFy1yAM\nDbl8eOTtgnhT5E2AEEIIIcQ00f2NhyfcVzIO7qwUab8TP1+mx15JXOXoufy9aBNS8hVGQ1uljkTM\n4DqKWJglcFPkKxG0VoAh5hqsCd4CQLVD/9531vDA93rpHzxSEcj1bFIN9biuTeBrSoUK9Q0xQg39\nfSVsxyY/eKhUqQbHtdjT7vOzTR63X33iDr3Whg0/6GLT5hyD2YCGOpcrLk7z3usaqkmGmBR5EyCE\nEEIIMU3kXtg84T6dD2lc3ESsazuD0dnV8f/FenRtM3FXMzSsCY1NMfQItY1rhbi6xNz0EBHbB1Vd\nK6AhHpAvc9whPxe0xvhfn1nAdVelWbkkQX1zhrmLZ5NIV2f6WraF47m07R8kFndIJl2iUYvQGIwx\nI4uIaW3Y0x7Q1nvi3/7gI5388Ff9tHdXKJYM+9vKfOeHPXzvZz2Tvo5C3gQIIYQQQkwbuhJMvDOf\noxjOxGuaD14UFcKBLptV6V5i0XqiBUOprHAyBh0GJMmiTBHLcWiI56mYOIODmh9tsyhUbDJxw9IW\nzVsW6pH5Ab98JsszL+Xp7g/IpByWzY+w4sJGwr1jn8RblsJ2HHq7cmTq4qSTLrnhMpWKj6NtIp5N\nPltGWTF6hqGl4Tg/rRDy7EvDRBJRbNtBKUUYhvjlCj96fID3XteA48iz7cmQqyWEEEIIMU3YNemJ\n9zXW4C2eC/EEaqCbSqFMabjAQJgiCBUrG3qwbZgRz1JjZ7EISTl5ADLRgO6ekJd3WwwXLYJQ0Ze1\n+N12mxd3V7uLT2zM8cgvhzjQGVCuQHdfwFOb8vz+pf4JY3I9h0o5IJFwSCVdvIiDCUOMMaTTNs0z\nE8RjinlNx//d+9pKVEwE1/OwbAtlKRzXIRKPUgngt5tyk7+Y5zlJAoQQQgghpomZn/lvE+6bcfVS\nYpkoQxWHmleeoC7lM+RH2F+ciQ4BY8jEAtJuntmxXookGIrOBKB/2OKNjrHdQoPi9bZqtaBnXy4Q\njrPCb1d3Cb/ij91BdYKx49gYrQk1OK5NuRwQakO5okkkXFbOs6lJHv939wxqbMces92yLJyIx4Gu\n47whEeOSJEAIIYQQYppoevfVzLt5Lco+qgunoOGt81n47hVU+vLUdmwhHjOU8hVyRYuhvEXPoMWw\nU8+c+DBRJ6BAioqKExiXsg+vHYwSYuE4CuuY3uFwQdE3FNI/NH5HOwwNxfzYdQO01hitydTH6O4q\n09FZJhZTFAsVlFL091eoy8ANbznx7x4Y1hNO/rVsi3hMurSTJXMChBBCCCGmCTsSZf4tb2Xeuy+i\n/anX8bNl6le1kFnYTKgsSjv2olLPk4nm2Z2OUCpqUOD7ASVfMc/bQ0gDvTQBhmIFdhxM0pv1que3\nIRKx8H1DpVItPTqUC/irB4fB9lBWGXPMjGHPhVjMJQzDkY66MQajq8OB8rkQYyCbDcgPFwh8KBV9\nojGP5pTBPon+e1PdxF1Wz7V527oTvEoQY0gSIIQQQggxTahYirKbJGZC5ly7YtS+YuDiugo1sB+V\niTJcADA4KmTV7C4KqoaMW6DH12ijyBVh47YU5qiBIdVFwhSuC2GoCAJNfrhMdT6yTTQeoZgb/dR/\n+YIoixa7/H4b6GNWGFCWwoxsMuSzPpZjUykFRGMeEfvkav1felGcx5/Lsadt7LCjq9fFaayVLu1k\nybsTIYQQQohpJFj+Nsq4o7aVQ4ed//Aj/FKF0ms78KNpCgcOYtmG2pRmUX2ONbUHwXLwQ5tSYLF5\ntz0qAYAjqwQrpbAsw2B/gcG+/Mh+27bJZKpvDZJxi3UrY3z05lpuWGdx02WKuU3VcygLLFthHTW2\nqFwKUbaFZSmwFBHXsGzOyf1my1J87NY6Vi6K4B3q72eSFrfdkOa/vLtukldQgLwJEEIIIYSYXmpn\nMjTjUsKffBs7Gad0sJeBre2EpRJ+YYDaixbxb7EPcNm2h9jceg8aGx+HmK0JQodB6nBsRVOtYm/n\n6FPbR829LeZ9BnrGVt1ZtzLO5RfWsGhBBr9cHNl+8WLFxYthwxOw+5jzGmMol30sy0IpRSRicdF8\nqJu42NEYMxpc/vSORvoGfXIFQ0uzi2PLImFvlrwJEEIIIYSYZmLLlvPLtV/kjZ+/wsFfvUy+rQ3L\n86ldtZAXmq8nVzOX5H+6nUTCo6M3ZPdwI7EwxxC1+CoKQF3KoA4N31EKXJdRT+7zufK4311fazNv\ntkdNevxnye+5DBbO0tWJwcYQ+CGFXBm/XC0tZDuKW672eNeaN/fb62tcWmd5kgCcInkTIIQQQggx\nDc2cFefBdz6AXRzmioFf0B+fxebUFQAsqo1izZ5HrMulUNDsG4hT483Gj9SOfL61IWTFzJDf7XRp\nGxjdJaxP+OwtFMZ8Z1OtxdVroseNKxGD299usa8z4J9/WCJXOJJo1KYVn7sjhqzrNfUkCRBCCCGE\nmIZmNzm0tKRob4MnvVtHtjc1xZg1K4YVrWBZCmXZaOXS4TfREK2W+bQw1MQNCQ+uX1Vha1tIx6CN\nMdCU0aycHbCoPs6vNpbZ3xVgK5jf4nDTVTFikZN7At86w+Ev/kiq9pyrJAkQQgghhJiGWmoDFi2I\nU1cfoaeniNFQVxclU+MR9TQeZZTy0GG1jKZjVYfjuJahIaFJVOf3Ylmwck7IyjmjVwJbucBjxQUu\ng9lqGc90Uh7f/0ciSYAQQgghxDRkKbi4tcRGHSWZzBzaaoi6hrpYiWJJUy4HDPQVWTzPZVlzBWVB\nTdSMWRBsIkopatMy9v4/IkkChBBCCCGmqVk1mnctGebFAy6V0MVzNLMSwxQqFpt7a8hmfRbPc1kz\np0R98uRq8ovzgyQBQgghhBDTWCrucOVCzZb9eYaK8NLBDEM5i862Ia5a5fC2i2yUkgRAjCZJgBBC\nCCHENGfbiovmVxcQK1cqBCEkYpEpjkqcyyQJEEIIIYT4DyTiKaT7L05EpnkLIYQQQghxnpEkQAgh\nhBBCiPOMJAFCCCGEEEKcZyQJEEIIIYQQ4jwjSYAQQgghhBDnGUkChBBCCCGEOM9IEiCEEEIIIcR5\nRpIAIYQQQgghzjOSBAghhBBCCHGekSRACCGEEEKI84wkAUIIIYQQQpxnJAkQQgghhBDiPOOc6IBi\nscg999xDX18f5XKZO++8k6VLl/K5z32OIAhwHId7772XxsbGsxHNN+0mAAAH/UlEQVSvEEKIc5C0\nFUIIMb2cMAn49a9/zcqVK/nYxz5GW1sbH/3oR1m9ejW33XYb69ev56GHHuLBBx/ks5/97NmIVwgh\nxDlI2gohhJheTpgErF+/fuSfOzo6aG5u5ktf+hKRSASA2tpaXnvttTMXoRBCiHOetBVCCDG9nDAJ\nOOz222+ns7OT++67j3g8DkAYhmzYsIE//uM/PmMBCiGEmD6krRBCiOlBGWPMyR68bds2PvvZz/LD\nH/4QrTWf/exnmT9/PnfdddeZjFEIIcQ0Im2FEEKc+05YHWjLli10dHQAsGzZMsIwpL+/n8997nO0\ntrbK/9SFEEJIWyGEENPMCZOATZs28cADDwDQ29tLoVDgmWeewXVd/uRP/uSMByiEEOLcJ22FEEJM\nLyccDlQqlfjCF75AR0cHpVKJu+66i/vvv59yuUwymQRgwYIFfPnLXz4b8QohhDgHSVshhBDTy6Tm\nBAghhBBCCCGmP1kxWAghhBBCiPOMJAFCCCGEEEKcZ85IEvD73/+eyy67jF//+tcj27Zv384HPvAB\nPvShD3HnnXdSLBYBePbZZ3nPe97DzTffzCOPPHImwpmUycQOYIzh9ttv5x/+4R+mItxRJhP7v/zL\nv3DLLbfwvve9j4ceemiqQh4xmdj/+Z//mVtuuYVbb72Vp556aqpCHjFe7FprvvrVr3LppZeObAvD\nkC984Qt88IMf5LbbbuP73//+VIQ7ysnGDtPjXp0odjj379WJYj/X7tXTSdqKqTGd2wqQ9mKqSHsx\nNc5ke3Hak4D9+/fz4IMPsnbt2lHbv/KVr3DPPffw7W9/m9bWVh599FGCIOBLX/oSX/va13jooYd4\n5plnTnc4kzKZ2A975JFH8H3/bIc6xmRiP3DgAI8++igPP/ww3/nOd/jGN75BNpudosgnH/tPf/pT\nNmzYwNe+9jX+8i//kjAMpyjyiWO///77mTlzJkdPufnNb35DsVjkoYce4pvf/CZf/epX0Vqf7ZBH\nTCb26XKvjhf7Yef6vTpe7OfavXo6SVsxNaZzWwHSXkwVaS+mxpluL057EtDY2Mg//uM/kkqlRm2/\n7777WLVqFQB1dXUMDg7y2muv0drayowZM4jFYvzt3/7t6Q5nUiYTO0B/fz8/+tGPuP322896rMea\nTOwtLS1s2LABx3HwPI9oNEoul5uKsIHJxb5x40auuuoqPM+jrq6OlpYWdu3aNRVhAxPH/qEPfYgP\nfvCDo7bV1tYyPDyM1ppCoUAikcCypm5E3mRiny736nixw/S4V8eL/Vy7V08naSumxnRuK0Dai6ki\n7cXUONPtxWn/i4rFYti2PWb74RJxhUKBH/zgB9xwww20tbXhui6f+MQnuP322/nxj398usOZlMnE\nDnDvvffyqU99atzPnG2Tid2yLBKJBABPP/00tbW1zJw586zGe7TJxN7b20tdXd3IMXV1dfT09Jy1\nWI91otiPtnr1ambNmsU73/lOrr/+ej796U+fjRAnNJnYp9u9eqzpdK8e7Vy7V08naSumxnRuK0Da\ni6ki7cXUONPthXMqwT3yyCNjxnrdfffdXHXVVeMeXygU+PjHP85HP/pRFixYwPbt2+no6GDDhg2U\nSiVuvvlmrrjiCmpra08lrLMS+/PPP49t26xdu5a9e/ee8XiPdqqxH/byyy/z13/919x///1nNN6j\nnWrsjz322Kj9Z7PC7WRjP9amTZvo6Ojgscceo6+vjw9/+MO87W1vw/O8MxHuKKcauzFm2tyrx5pO\n9+pEpuJePZ2krZgef3/nUlsB0l5IezF50l5M7n49pSTg1ltv5dZbbz2pY4Mg4M477+Smm27i5ptv\nBqC+vp4LL7yQWCxGLBZj0aJFHDhw4Kz8oZxq7I8//jhbtmzhtttuo7+/n0qlwpw5c3jve997JsMG\nTj12qE6i+uIXv8h99913Vp/snGrsTU1N7NmzZ+SYrq4umpqazkisx5pM7ON58cUXueyyy3Ach+bm\nZmpqaujq6mLOnDmnMcrxnWrs0+VeHc90uVcnMlX36ukkbcW5//d3rrUVIO2FtBeTJ+3F5O7XU0oC\nJuPrX/86b33rW0f9wDVr1vA3f/M3lMtllFLs27eP2bNnn62QTtp4sd9zzz0j//zoo4/S1tZ2Vv5I\nJmu82MMw5POf/zx///d/f05e78PGi/3SSy/lwQcf5O6772ZgYIDu7m4WLlw4hVGevNbWVn72s58B\nkMvl6OrqorGxcYqjOjnT5V4dz3S5V8czXe7V00naiqkxndsKkPbiXDJd7tfxTJf7dTxv5n497SsG\nP/nkk3zjG99g9+7d1NXV0djYyAMPPMCVV17J7NmzcV0XgEsuuYS77rqLxx9/nH/6p39CKcWtt97K\n+9///tMZzhmN/bDDfyh33333VIU+qdhXr17Nn/7pn7JkyZKRz3/mM58ZmVR1Lsd+11138a1vfYsf\n/ehHKKX45Cc/yWWXXTYlcR8v9r/4i79gx44dvPjii6xdu5Z3vOMd3HHHHXz5y19m586daK358Ic/\nzB/8wR9Mi9g/8pGPTIt7daLYDzuX79XxYl+0aNE5da+eTtJWTI3p3FaAtBfTIXZpL6Ym9jfTXpz2\nJEAIIYQQQghxbpMVg4UQQgghhDjPSBIghBBCCCHEeUaSACGEEEIIIc4zkgQIIYQQQghxnpEkQAgh\nhBBCiPOMJAFCCCGEEEKcZyQJEEIIIYQQ4jwjSYAQQgghhBDnmf8fmcOYFvVGzu4AAAAASUVORK5C\nYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "32_DbjnfXJlC",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Wait a second...this should have given us a nice map of the state of California, with red showing up in expensive areas like the San Francisco and Los Angeles.\n",
+ "\n",
+ "The training set sort of does, compared to a [real map](https://www.google.com/maps/place/California/@37.1870174,-123.7642688,6z/data=!3m1!4b1!4m2!3m1!1s0x808fb9fe5f285e3d:0x8b5109a227086f55), but the validation set clearly doesn't.\n",
+ "\n",
+ "**Go back up and look at the data from Task 1 again.**\n",
+ "\n",
+ "Do you see any other differences in the distributions of features or targets between the training and validation data?"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "pECTKgw5ZvFK",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below for the solution."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "49NC4_KIZxk_",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Looking at the tables of summary stats above, it's easy to wonder how anyone would do a useful data check. What's the right 75th percentile value for total_rooms per city block?\n",
+ "\n",
+ "The key thing to notice is that for any given feature or column, the distribution of values between the train and validation splits should be roughly equal.\n",
+ "\n",
+ "The fact that this is not the case is a real worry, and shows that we likely have a fault in the way that our train and validation split was created."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "025Ky0Dq9ig0",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 3: Return to the Data Importing and Pre-Processing Code, and See if You Spot Any Bugs\n",
+ "If you do, go ahead and fix the bug. Don't spend more than a minute or two looking. If you can't find the bug, check the solution."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "JFsd2eWHAMdy",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "When you've found and fixed the issue, re-run `latitude` / `longitude` plotting cell above and confirm that our sanity checks look better.\n",
+ "\n",
+ "By the way, there's an important lesson here.\n",
+ "\n",
+ "**Debugging in ML is often *data debugging* rather than code debugging.**\n",
+ "\n",
+ "If the data is wrong, even the most advanced ML code can't save things."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "dER2_43pWj1T",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below for the solution."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "BnEVbYJvW2wu",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "The code that randomizes the data (`np.random.permutation`) is commented out, so we're not doing any randomization prior to splitting the data.\n",
+ "\n",
+ "If we don't randomize the data properly before creating training and validation splits, then we may be in trouble if the data is given to us in some sorted order, which appears to be the case here."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "xCdqLpQyAos2",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 4: Train and Evaluate a Model\n",
+ "\n",
+ "**Spend 5 minutes or so trying different hyperparameter settings. Try to get the best validation performance you can.**\n",
+ "\n",
+ "Next, we'll train a linear regressor using all the features in the data set, and see how well we do.\n",
+ "\n",
+ "Let's define the same input function we've used previously for loading the data into a TensorFlow model.\n"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "rzcIPGxxgG0t",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+ " \"\"\"Trains a linear regression model of multiple features.\n",
+ " \n",
+ " Args:\n",
+ " features: pandas DataFrame of features\n",
+ " targets: pandas DataFrame of targets\n",
+ " batch_size: Size of batches to be passed to the model\n",
+ " shuffle: True or False. Whether to shuffle the data.\n",
+ " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+ " Returns:\n",
+ " Tuple of (features, labels) for next data batch\n",
+ " \"\"\"\n",
+ " \n",
+ " # Convert pandas data into a dict of np arrays.\n",
+ " features = {key:np.array(value) for key,value in dict(features).items()} \n",
+ " \n",
+ " # Construct a dataset, and configure batching/repeating.\n",
+ " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+ " ds = ds.batch(batch_size).repeat(num_epochs)\n",
+ " \n",
+ " # Shuffle the data, if specified.\n",
+ " if shuffle:\n",
+ " ds = ds.shuffle(10000)\n",
+ " \n",
+ " # Return the next batch of data.\n",
+ " features, labels = ds.make_one_shot_iterator().get_next()\n",
+ " return features, labels"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "CvrKoBmNgRCO",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Because we're now working with multiple input features, let's modularize our code for configuring feature columns into a separate function. (For now, this code is fairly simple, as all our features are numeric, but we'll build on this code as we use other types of features in future exercises.)"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "wEW5_XYtgZ-H",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def construct_feature_columns(input_features):\n",
+ " \"\"\"Construct the TensorFlow Feature Columns.\n",
+ "\n",
+ " Args:\n",
+ " input_features: The names of the numerical input features to use.\n",
+ " Returns:\n",
+ " A set of feature columns\n",
+ " \"\"\" \n",
+ " return set([tf.feature_column.numeric_column(my_feature)\n",
+ " for my_feature in input_features])"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "D0o2wnnzf8BD",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Next, go ahead and complete the `train_model()` code below to set up the input functions and calculate predictions.\n",
+ "\n",
+ "**NOTE:** It's okay to reference the code from the previous exercises, but make sure to call `predict()` on the appropriate data sets.\n",
+ "\n",
+ "Compare the losses on training data and validation data. With a single raw feature, our best root mean squared error (RMSE) was of about 180.\n",
+ "\n",
+ "See how much better you can do now that we can use multiple features.\n",
+ "\n",
+ "Check the data using some of the methods we've looked at before. These might include:\n",
+ "\n",
+ " * Comparing distributions of predictions and actual target values\n",
+ "\n",
+ " * Creating a scatter plot of predictions vs. target values\n",
+ "\n",
+ " * Creating two scatter plots of validation data using `latitude` and `longitude`:\n",
+ " * One plot mapping color to actual target `median_house_value`\n",
+ " * A second plot mapping color to predicted `median_house_value` for side-by-side comparison."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "UXt0_4ZTEf4V",
+ "colab_type": "code",
+ "cellView": "both",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def train_model(\n",
+ " learning_rate,\n",
+ " steps,\n",
+ " batch_size,\n",
+ " training_examples,\n",
+ " training_targets,\n",
+ " validation_examples,\n",
+ " validation_targets):\n",
+ " \"\"\"Trains a linear regression model of multiple features.\n",
+ " \n",
+ " In addition to training, this function also prints training progress information,\n",
+ " as well as a plot of the training and validation loss over time.\n",
+ " \n",
+ " Args:\n",
+ " learning_rate: A `float`, the learning rate.\n",
+ " steps: A non-zero `int`, the total number of training steps. A training step\n",
+ " consists of a forward and backward pass using a single batch.\n",
+ " batch_size: A non-zero `int`, the batch size.\n",
+ " training_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for training.\n",
+ " training_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for training.\n",
+ " validation_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for validation.\n",
+ " validation_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for validation.\n",
+ " \n",
+ " Returns:\n",
+ " A `LinearRegressor` object trained on the training data.\n",
+ " \"\"\"\n",
+ "\n",
+ " periods = 10\n",
+ " steps_per_period = steps / periods\n",
+ " \n",
+ " # Create a linear regressor object.\n",
+ " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+ " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+ " linear_regressor = tf.estimator.LinearRegressor(\n",
+ " feature_columns=construct_feature_columns(training_examples),\n",
+ " optimizer=my_optimizer\n",
+ " )\n",
+ " \n",
+ " #my_label = \"median_house_value\"\n",
+ " #targets = california_housing_dataframe[my_label].astype('float32')\n",
+ " \n",
+ " \n",
+ " # 1. Create input functions.\n",
+ " training_input_fn = lambda: my_input_fn(training_examples, training_targets[\"median_house_value\"], batch_size=batch_size)# YOUR CODE HERE\n",
+ " predict_training_input_fn = lambda: my_input_fn(training_examples, training_targets[\"median_house_value\"], num_epochs=1, shuffle=False)# YOUR CODE HERE\n",
+ " predict_validation_input_fn = lambda: my_input_fn(validation_examples, validation_targets[\"median_house_value\"], num_epochs=1, shuffle=False)# YOUR CODE HERE\n",
+ " \n",
+ " # Train the model, but do so inside a loop so that we can periodically assess\n",
+ " # loss metrics.\n",
+ " print(\"Training model...\")\n",
+ " print(\"RMSE (on training data):\")\n",
+ " training_rmse = []\n",
+ " validation_rmse = []\n",
+ " for period in range (0, periods):\n",
+ " # Train the model, starting from the prior state.\n",
+ " linear_regressor.train(\n",
+ " input_fn=training_input_fn,\n",
+ " steps=steps_per_period,\n",
+ " )\n",
+ " # 2. Take a break and compute predictions.\n",
+ " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn) # YOUR CODE HERE\n",
+ " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+ " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn) # YOUR CODE HERE\n",
+ " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+ " \n",
+ " # Compute training and validation loss.\n",
+ " training_root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(training_predictions, training_targets))\n",
+ " validation_root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+ " # Occasionally print the current loss.\n",
+ " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+ " # Add the loss metrics from this period to our list.\n",
+ " training_rmse.append(training_root_mean_squared_error)\n",
+ " validation_rmse.append(validation_root_mean_squared_error)\n",
+ " print(\"Model training finished.\")\n",
+ "\n",
+ " # Output a graph of loss metrics over periods.\n",
+ " plt.ylabel(\"RMSE\")\n",
+ " plt.xlabel(\"Periods\")\n",
+ " plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+ " plt.tight_layout()\n",
+ " plt.plot(training_rmse, label=\"training\")\n",
+ " plt.plot(validation_rmse, label=\"validation\")\n",
+ " plt.legend()\n",
+ "\n",
+ " return linear_regressor"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "zFFRmvUGh8wd",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 619
+ },
+ "outputId": "bd01389c-69a6-4db8-f61e-f8b7b0a35bf3"
+ },
+ "cell_type": "code",
+ "source": [
+ "linear_regressor = train_model(\n",
+ " # TWEAK THESE VALUES TO SEE HOW MUCH YOU CAN IMPROVE THE RMSE\n",
+ " learning_rate=0.0001,\n",
+ " steps=100,\n",
+ " batch_size=1,\n",
+ " training_examples=training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 25,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Training model...\n",
+ "RMSE (on training data):\n",
+ " period 00 : 213.82\n",
+ " period 01 : 201.23\n",
+ " period 02 : 190.14\n",
+ " period 03 : 180.50\n",
+ " period 04 : 175.49\n",
+ " period 05 : 168.73\n",
+ " period 06 : 165.49\n",
+ " period 07 : 163.12\n",
+ " period 08 : 161.99\n",
+ " period 09 : 160.95\n",
+ "Model training finished.\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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qKqnKSQMjhBBC1CDJyUls377FoPdOmzYDf/+6d319zpx5VVVWlbPIWwkIIYQQ4s7mzXuP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\nQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "I-La4N9ObC1x",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below for a solution."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "Xyz6n1YHbGef",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "def train_model(\n",
+ " learning_rate,\n",
+ " steps,\n",
+ " batch_size,\n",
+ " training_examples,\n",
+ " training_targets,\n",
+ " validation_examples,\n",
+ " validation_targets):\n",
+ " \"\"\"Trains a linear regression model of multiple features.\n",
+ " \n",
+ " In addition to training, this function also prints training progress information,\n",
+ " as well as a plot of the training and validation loss over time.\n",
+ " \n",
+ " Args:\n",
+ " learning_rate: A `float`, the learning rate.\n",
+ " steps: A non-zero `int`, the total number of training steps. A training step\n",
+ " consists of a forward and backward pass using a single batch.\n",
+ " batch_size: A non-zero `int`, the batch size.\n",
+ " training_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for training.\n",
+ " training_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for training.\n",
+ " validation_examples: A `DataFrame` containing one or more columns from\n",
+ " `california_housing_dataframe` to use as input features for validation.\n",
+ " validation_targets: A `DataFrame` containing exactly one column from\n",
+ " `california_housing_dataframe` to use as target for validation.\n",
+ " \n",
+ " Returns:\n",
+ " A `LinearRegressor` object trained on the training data.\n",
+ " \"\"\"\n",
+ "\n",
+ " periods = 10\n",
+ " steps_per_period = steps / periods\n",
+ " \n",
+ " # Create a linear regressor object.\n",
+ " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+ " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+ " linear_regressor = tf.estimator.LinearRegressor(\n",
+ " feature_columns=construct_feature_columns(training_examples),\n",
+ " optimizer=my_optimizer\n",
+ " )\n",
+ " \n",
+ " # Create input functions.\n",
+ " training_input_fn = lambda: my_input_fn(\n",
+ " training_examples, \n",
+ " training_targets[\"median_house_value\"], \n",
+ " batch_size=batch_size)\n",
+ " predict_training_input_fn = lambda: my_input_fn(\n",
+ " training_examples, \n",
+ " training_targets[\"median_house_value\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ " predict_validation_input_fn = lambda: my_input_fn(\n",
+ " validation_examples, validation_targets[\"median_house_value\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ "\n",
+ " # Train the model, but do so inside a loop so that we can periodically assess\n",
+ " # loss metrics.\n",
+ " print(\"Training model...\")\n",
+ " print(\"RMSE (on training data):\")\n",
+ " training_rmse = []\n",
+ " validation_rmse = []\n",
+ " for period in range (0, periods):\n",
+ " # Train the model, starting from the prior state.\n",
+ " linear_regressor.train(\n",
+ " input_fn=training_input_fn,\n",
+ " steps=steps_per_period,\n",
+ " )\n",
+ " # Take a break and compute predictions.\n",
+ " training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+ " training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+ " \n",
+ " validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+ " validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+ " \n",
+ " \n",
+ " # Compute training and validation loss.\n",
+ " training_root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(training_predictions, training_targets))\n",
+ " validation_root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+ " # Occasionally print the current loss.\n",
+ " print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+ " # Add the loss metrics from this period to our list.\n",
+ " training_rmse.append(training_root_mean_squared_error)\n",
+ " validation_rmse.append(validation_root_mean_squared_error)\n",
+ " print(\"Model training finished.\")\n",
+ "\n",
+ " # Output a graph of loss metrics over periods.\n",
+ " plt.ylabel(\"RMSE\")\n",
+ " plt.xlabel(\"Periods\")\n",
+ " plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+ " plt.tight_layout()\n",
+ " plt.plot(training_rmse, label=\"training\")\n",
+ " plt.plot(validation_rmse, label=\"validation\")\n",
+ " plt.legend()\n",
+ "\n",
+ " return linear_regressor"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "i1imhjFzbWwt",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "linear_regressor = train_model(\n",
+ " learning_rate=0.00003,\n",
+ " steps=500,\n",
+ " batch_size=5,\n",
+ " training_examples=training_examples,\n",
+ " training_targets=training_targets,\n",
+ " validation_examples=validation_examples,\n",
+ " validation_targets=validation_targets)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "65sin-E5NmHN",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Task 5: Evaluate on Test Data\n",
+ "\n",
+ "**In the cell below, load in the test data set and evaluate your model on it.**\n",
+ "\n",
+ "We've done a lot of iteration on our validation data. Let's make sure we haven't overfit to the pecularities of that particular sample.\n",
+ "\n",
+ "Test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv).\n",
+ "\n",
+ "How does your test performance compare to the validation performance? What does this say about the generalization performance of your model?"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "icEJIl5Vp51r",
+ "colab_type": "code",
+ "cellView": "both",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+ "#\n",
+ "# YOUR CODE HERE\n",
+ "#\n",
+ "test_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+ "test_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+ "\n",
+ "predict_test_input_fn = lambda: my_input_fn(test_examples, test_targets[\"median_house_value\"], num_epochs=1, shuffle=False)\n",
+ "test_predictions = linear_regressor.predict(input_fn=predict_test_input_fn) \n",
+ "test_predictions = np.array([item['predictions'][0] for item in test_predictions]) \n",
+ "\n",
+ "test_root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(test_predictions, test_targets))"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "yTghc_5HkJDW",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### Solution\n",
+ "\n",
+ "Click below for the solution."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "_xSYTarykO8U",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+ "\n",
+ "test_examples = preprocess_features(california_housing_test_data)\n",
+ "test_targets = preprocess_targets(california_housing_test_data)\n",
+ "\n",
+ "predict_test_input_fn = lambda: my_input_fn(\n",
+ " test_examples, \n",
+ " test_targets[\"median_house_value\"], \n",
+ " num_epochs=1, \n",
+ " shuffle=False)\n",
+ "\n",
+ "test_predictions = linear_regressor.predict(input_fn=predict_test_input_fn)\n",
+ "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n",
+ "\n",
+ "root_mean_squared_error = math.sqrt(\n",
+ " metrics.mean_squared_error(test_predictions, test_targets))\n",
+ "\n",
+ "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file