:1: calling argmax (from tensorflow.python.ops.math_ops) with dimension is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use the `axis` argument instead\n"
]
}
],
"source": [
"y_true_cls = tf.argmax(y_true, dimension=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"HELPER FUNCTION FOR CREATING PRE-PROCESSING\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following helper-functions create the part of the TensorFlow computational graph that pre-processes the input images. Nothing is actually calculated at this point, the function merely adds nodes to the computational graph for TensorFlow.\n",
"\n",
"The pre-processing is different for training and testing of the neural network:\n",
"* For training, the input images are randomly cropped, randomly flipped horizontally, and the hue, contrast and saturation is adjusted with random values. This artificially inflates the size of the training-set by creating random variations of the original input images. Examples of distorted images are shown further below.\n",
"\n",
"* For testing, the input images are cropped around the centre and nothing else is adjusted."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"def pre_process_image(image, training):\n",
" # This function takes a single image as input,\n",
" # and a boolean whether to build the training or testing graph.\n",
" \n",
" if training:\n",
" # For training, add the following to the TensorFlow graph.\n",
"\n",
" # Randomly crop the input image.\n",
" image = tf.random_crop(image, size=[img_size_cropped, img_size_cropped, num_channels])\n",
"\n",
" # Randomly flip the image horizontally.\n",
" image = tf.image.random_flip_left_right(image)\n",
" \n",
" # Randomly adjust hue, contrast and saturation.\n",
" image = tf.image.random_hue(image, max_delta=0.05)\n",
" image = tf.image.random_contrast(image, lower=0.3, upper=1.0)\n",
" image = tf.image.random_brightness(image, max_delta=0.2)\n",
" image = tf.image.random_saturation(image, lower=0.0, upper=2.0)\n",
"\n",
" # Some of these functions may overflow and result in pixel\n",
" # values beyond the [0, 1] range. It is unclear from the\n",
" # documentation of TensorFlow 0.10.0rc0 whether this is\n",
" # intended. A simple solution is to limit the range.\n",
"\n",
" # Limit the image pixels between [0, 1] in case of overflow.\n",
" image = tf.minimum(image, 1.0)\n",
" image = tf.maximum(image, 0.0)\n",
" else:\n",
" # For training, add the following to the TensorFlow graph.\n",
"\n",
" # Crop the input image around the centre so it is the same\n",
" # size as images that are randomly cropped during training.\n",
" image = tf.image.resize_image_with_crop_or_pad(image,\n",
" target_height=img_size_cropped,\n",
" target_width=img_size_cropped)\n",
"\n",
" return image"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function above is called for each image in the input batch using the following function."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"def pre_process(images, training):\n",
" # Use TensorFlow to loop over all the input images and call\n",
" # the function above which takes a single image as input.\n",
" images = tf.map_fn(lambda image: pre_process_image(image, training), images)\n",
"\n",
" return images"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to plot the distorted images, we create the pre-processing graph for TensorFlow, so we may execute it later."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"distorted_images = pre_process(images=x, training=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"HELPER FUNCTION FOR CREATING MAIN PROCESSING\n",
"
\n",
"\n",
"The following helper-function creates the main part of the convolutional neural network. It uses Pretty Tensor which was described in the previous tutorials."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"def main_network(images, training):\n",
" # Wrap the input images as a Pretty Tensor object.\n",
" x_pretty = pt.wrap(images)\n",
"\n",
" # Pretty Tensor uses special numbers to distinguish between\n",
" # the training and testing phases.\n",
" if training:\n",
" phase = pt.Phase.train\n",
" else:\n",
" phase = pt.Phase.infer\n",
"\n",
" # Create the convolutional neural network using Pretty Tensor.\n",
" # It is very similar to the previous tutorials, except\n",
" # the use of so-called batch-normalization in the first layer.\n",
" with pt.defaults_scope(activation_fn=tf.nn.relu, phase=phase):\n",
" y_pred, loss = x_pretty.\\\n",
" conv2d(kernel=5, depth=64, name='layer_conv1', batch_normalize=True).\\\n",
" max_pool(kernel=2, stride=2).\\\n",
" conv2d(kernel=5, depth=64, name='layer_conv2').\\\n",
" max_pool(kernel=2, stride=2).\\\n",
" flatten().\\\n",
" fully_connected(size=256, name='layer_fc1').\\\n",
" fully_connected(size=128, name='layer_fc2').\\\n",
" softmax_classifier(num_classes=num_classes, labels=y_true)\n",
"\n",
" return y_pred, loss"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"HELPER FUNCTION FOR CREATING NEURAL NETWORK\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following helper-function creates the full neural network, which consists of the pre-processing and main-processing defined above.\n",
"\n",
"Note that the neural network is enclosed in the variable-scope named 'network'. This is because we are actually creating two neural networks in the TensorFlow graph. By assigning a variable-scope like this, we can re-use the variables for the two neural networks, so the variables that are optimized for the training-network are re-used for the other network that is used for testing."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"def create_network(training):\n",
" # Wrap the neural network in the scope named 'network'.\n",
" # Create new variables during training, and re-use during testing.\n",
" with tf.variable_scope('network', reuse=not training):\n",
" # Just rename the input placeholder variable for convenience.\n",
" images = x\n",
"\n",
" # Create TensorFlow graph for pre-processing.\n",
" images = pre_process(images=images, training=training)\n",
"\n",
" # Create TensorFlow graph for the main processing.\n",
" y_pred, loss = main_network(images=images, training=training)\n",
"\n",
" return y_pred, loss"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"CREATE NEURAL NETWORK FOR TRAINING PHASE\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First create a TensorFlow variable that keeps track of the number of optimization iterations performed so far. In the previous tutorials this was a Python variable, but in this tutorial we want to save this variable with all the other TensorFlow variables in the checkpoints.\n",
"\n",
"Note that `trainable=False` which means that TensorFlow will not try to optimize this variable."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"global_step = tf.Variable(initial_value=0,\n",
" name='global_step', trainable=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create the neural network to be used for training. The `create_network()` function returns both `y_pred` and `loss`, but we only need the `loss`-function during training."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From C:\\Users\\Reasonable\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\nn\\python\\ops\\cross_entropy.py:68: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"\n",
"Future major versions of TensorFlow will allow gradients to flow\n",
"into the labels input on backprop by default.\n",
"\n",
"See tf.nn.softmax_cross_entropy_with_logits_v2.\n",
"\n"
]
}
],
"source": [
"_, loss = create_network(training=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create an optimizer which will minimize the `loss`-function. Also pass the `global_step` variable to the optimizer so it will be increased by one after each iteration."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss, global_step=global_step)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"CREATE NEURAL NETWORK FOR TEST PHASE / INFERENCE\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now create the neural network for the test-phase. Once again the `create_network()` function returns the predicted class-labels `y_pred` for the input images, as well as the `loss`-function to be used during optimization. During testing we only need `y_pred`."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"y_pred, _ = create_network(training=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We then calculate the predicted class number as an integer. The output of the network `y_pred` is an array with 10 elements. The class number is the index of the largest element in the array."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"y_pred_cls = tf.argmax(y_pred, dimension=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we create a vector of booleans telling us whether the predicted class equals the true class of each image."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"correct_prediction = tf.equal(y_pred_cls, y_true_cls)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The classification accuracy is calculated by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then taking the average of these numbers."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Saver
\n",
"\n",
"In order to save the variables of the neural network, so they can be reloaded quickly without having to train the network again, we now create a so-called Saver-object which is used for storing and retrieving all the variables of the TensorFlow graph. Nothing is actually saved at this point, which will be done further below."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"saver = tf.train.Saver()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Getting the Weights
\n",
"\n",
"Further below, we want to plot the weights of the neural network. When the network is constructed using Pretty Tensor, all the variables of the layers are created indirectly by Pretty Tensor. We therefore have to retrieve the variables from TensorFlow.\n",
"\n",
"We used the names `layer_conv1` and `layer_conv2` for the two convolutional layers. These are also called variable scopes. Pretty Tensor automatically gives names to the variables it creates for each layer, so we can retrieve the weights for a layer using the layer's scope-name and the variable-name.\n",
"\n",
"The implementation is somewhat awkward because we have to use the TensorFlow function `get_variable()` which was designed for another purpose; either creating a new variable or re-using an existing variable. The easiest thing is to make the following helper-function."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"def get_weights_variable(layer_name):\n",
" # Retrieve an existing variable named 'weights' in the scope\n",
" # with the given layer_name.\n",
" # This is awkward because the TensorFlow function was\n",
" # really intended for another purpose.\n",
"\n",
" with tf.variable_scope(\"network/\" + layer_name, reuse=True):\n",
" variable = tf.get_variable('weights')\n",
"\n",
" return variable"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using this helper-function we can retrieve the variables. These are TensorFlow objects. In order to get the contents of the variables, you must do something like: `contents = session.run(weights_conv1)` as demonstrated further below."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"weights_conv1 = get_weights_variable(layer_name='layer_conv1')\n",
"weights_conv2 = get_weights_variable(layer_name='layer_conv2')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Getting the Layer Outputs
"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Similarly we also need to retrieve the outputs of the convolutional layers. The function for doing this is slightly different than the function above for getting the weights. Here we instead retrieve the last tensor that is output by the convolutional layer."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"def get_layer_output(layer_name):\n",
" # The name of the last operation of the convolutional layer.\n",
" # This assumes you are using Relu as the activation-function.\n",
" tensor_name = \"network/\" + layer_name + \"/Relu:0\"\n",
"\n",
" # Get the tensor with this name.\n",
" tensor = tf.get_default_graph().get_tensor_by_name(tensor_name)\n",
"\n",
" return tensor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the output of the convoluational layers so we can plot them later."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"output_conv1 = get_layer_output(layer_name='layer_conv1')\n",
"output_conv2 = get_layer_output(layer_name='layer_conv2')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create TensorFlow Session
\n",
"\n",
"Once the TensorFlow graph has been created, we have to create a TensorFlow session which is used to execute the graph."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"session = tf.Session()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Restore or Initialize Variables
\n",
"\n",
"Training this neural network may take a long time, especially if you do not have a GPU. We therefore save checkpoints during training so we can continue training at another time (e.g. during the night), and also for performing analysis later without having to train the neural network every time we want to use it.\n",
"\n",
"If you want to restart the training of the neural network, you have to delete the checkpoints first.\n",
"\n",
"This is the directory used for the checkpoints."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"save_dir = 'data/checkpoints/'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create the directory if it does not exist."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"if not os.path.exists(save_dir):\n",
" os.makedirs(save_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the base-filename for the checkpoints, TensorFlow will append the iteration number, etc."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"save_path = os.path.join(save_dir, 'data/cifar10_cnn')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First try to restore the latest checkpoint. This may fail and raise an exception e.g. if such a checkpoint does not exist, or if you have changed the TensorFlow graph."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Trying to restore last checkpoint ...\n",
"INFO:tensorflow:Restoring parameters from data/checkpoints/best_validation\n",
"Failed to restore checkpoint. Initializing variables instead.\n"
]
}
],
"source": [
"try:\n",
" print(\"Trying to restore last checkpoint ...\")\n",
"\n",
" # Use TensorFlow to find the latest checkpoint - if any.\n",
" last_chk_path = tf.train.latest_checkpoint(checkpoint_dir=save_dir)\n",
"\n",
" # Try and load the data in the checkpoint.\n",
" saver.restore(session, save_path=last_chk_path)\n",
"\n",
" # If we get to this point, the checkpoint was successfully loaded.\n",
" print(\"Restored checkpoint from:\", last_chk_path)\n",
"except:\n",
" # If the above failed for some reason, simply\n",
" # initialize all the variables for the TensorFlow graph.\n",
" print(\"Failed to restore checkpoint. Initializing variables instead.\")\n",
" session.run(tf.global_variables_initializer())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"HELPER FUNCTION FOR GETTING RANDOM TRAINING BATCH\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 50,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n",
"\n",
"If your computer crashes or becomes very slow because you run out of RAM, then you may try and lower this number, but you may then need to perform more optimization iterations."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"train_batch_size = 64"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function for selecting a random batch of images from the training-set."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"def random_batch():\n",
" # Number of images in the training-set.\n",
" num_images = len(images_train)\n",
"\n",
" # Create a random index.\n",
" idx = np.random.choice(num_images,\n",
" size=train_batch_size,\n",
" replace=False)\n",
"\n",
" # Use the random index to select random images and labels.\n",
" x_batch = images_train[idx, :, :, :]\n",
" y_batch = labels_train[idx, :]\n",
"\n",
" return x_batch, y_batch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"HELPER FUNCTION TO PERFORM OPTIMIZATION\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This function performs a number of optimization iterations so as to gradually improve the variables of the network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples. The progress is printed every 100 iterations. A checkpoint is saved every 1000 iterations and also after the last iteration."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"def optimize(num_iterations):\n",
" # Start-time used for printing time-usage below.\n",
" start_time = time.time()\n",
"\n",
" for i in range(num_iterations):\n",
" # Get a batch of training examples.\n",
" # x_batch now holds a batch of images and\n",
" # y_true_batch are the true labels for those images.\n",
" x_batch, y_true_batch = random_batch()\n",
"\n",
" # Put the batch into a dict with the proper names\n",
" # for placeholder variables in the TensorFlow graph.\n",
" feed_dict_train = {x: x_batch,\n",
" y_true: y_true_batch}\n",
"\n",
" # Run the optimizer using this batch of training data.\n",
" # TensorFlow assigns the variables in feed_dict_train\n",
" # to the placeholder variables and then runs the optimizer.\n",
" # We also want to retrieve the global_step counter.\n",
" i_global, _ = session.run([global_step, optimizer],\n",
" feed_dict=feed_dict_train)\n",
"\n",
" # Print status to screen every 100 iterations (and last).\n",
" if (i_global % 100 == 0) or (i == num_iterations - 1):\n",
" # Calculate the accuracy on the training-batch.\n",
" batch_acc = session.run(accuracy,\n",
" feed_dict=feed_dict_train)\n",
"\n",
" # Print status.\n",
" msg = \"Global Step: {0:>6}, Training Batch Accuracy: {1:>6.1%}\"\n",
" print(msg.format(i_global, batch_acc))\n",
"\n",
" # Save a checkpoint to disk every 1000 iterations (and last).\n",
" if (i_global % 1000 == 0) or (i == num_iterations - 1):\n",
" # Save all variables of the TensorFlow graph to a\n",
" # checkpoint. Append the global_step counter\n",
" # to the filename so we save the last several checkpoints.\n",
" saver.save(session,\n",
" save_path=save_path,\n",
" global_step=global_step)\n",
"\n",
" print(\"Saved checkpoint.\")\n",
"\n",
" # Ending time.\n",
" end_time = time.time()\n",
"\n",
" # Difference between start and end-times.\n",
" time_dif = end_time - start_time\n",
"\n",
" # Print the time-usage.\n",
" print(\"Time usage: \" + str(timedelta(seconds=int(round(time_dif)))))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Helper-Function for plotting Example Errors
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function for plotting examples of images from the test-set that have been mis-classified."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"def plot_example_errors(cls_pred, correct):\n",
" # This function is called from print_test_accuracy() below.\n",
"\n",
" # cls_pred is an array of the predicted class-number for\n",
" # all images in the test-set.\n",
"\n",
" # correct is a boolean array whether the predicted class\n",
" # is equal to the true class for each image in the test-set.\n",
"\n",
" # Negate the boolean array.\n",
" incorrect = (correct == False)\n",
" \n",
" # Get the images from the test-set that have been\n",
" # incorrectly classified.\n",
" images = images_test[incorrect]\n",
" \n",
" # Get the predicted classes for those images.\n",
" cls_pred = cls_pred[incorrect]\n",
"\n",
" # Get the true classes for those images.\n",
" cls_true = cls_test[incorrect]\n",
" \n",
" # Plot the first 9 images.\n",
" plot_images(images=images[0:9],\n",
" cls_true=cls_true[0:9],\n",
" cls_pred=cls_pred[0:9])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Helper-Function to plot Confusion Matrix
"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"def plot_confusion_matrix(cls_pred):\n",
" # This is called from print_test_accuracy() below.\n",
"\n",
" # cls_pred is an array of the predicted class-number for\n",
" # all images in the test-set.\n",
"\n",
" # Get the confusion matrix using sklearn.\n",
" cm = confusion_matrix(y_true=cls_test, # True class for test-set.\n",
" y_pred=cls_pred) # Predicted class.\n",
"\n",
" # Print the confusion matrix as text.\n",
" for i in range(num_classes):\n",
" # Append the class-name to each line.\n",
" class_name = \"({}) {}\".format(i, class_names[i])\n",
" print(cm[i, :], class_name)\n",
"\n",
" # Print the class-numbers for easy reference.\n",
" class_numbers = [\" ({0})\".format(i) for i in range(num_classes)]\n",
" print(\"\".join(class_numbers))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Helper-Function for Calculating Classifications
\n",
"\n",
"This function calculates the predicted classes of images and also returns a boolean array whether the classification of each image is correct.\n",
"\n",
"The calculation is done in batches because it might use too much RAM otherwise. If your computer crashes then you can try and lower the batch-size."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"# Split the data-set in batches of this size to limit RAM usage.\n",
"batch_size = 256\n",
"\n",
"def predict_cls(images, labels, cls_true):\n",
" # Number of images.\n",
" num_images = len(images)\n",
"\n",
" # Allocate an array for the predicted classes which\n",
" # will be calculated in batches and filled into this array.\n",
" cls_pred = np.zeros(shape=num_images, dtype=np.int)\n",
"\n",
" # Now calculate the predicted classes for the batches.\n",
" # We will just iterate through all the batches.\n",
" # There might be a more clever and Pythonic way of doing this.\n",
"\n",
" # The starting index for the next batch is denoted i.\n",
" i = 0\n",
"\n",
" while i < num_images:\n",
" # The ending index for the next batch is denoted j.\n",
" j = min(i + batch_size, num_images)\n",
"\n",
" # Create a feed-dict with the images and labels\n",
" # between index i and j.\n",
" feed_dict = {x: images[i:j, :],\n",
" y_true: labels[i:j, :]}\n",
"\n",
" # Calculate the predicted class using TensorFlow.\n",
" cls_pred[i:j] = session.run(y_pred_cls, feed_dict=feed_dict)\n",
"\n",
" # Set the start-index for the next batch to the\n",
" # end-index of the current batch.\n",
" i = j\n",
"\n",
" # Create a boolean array whether each image is correctly classified.\n",
" correct = (cls_true == cls_pred)\n",
"\n",
" return correct, cls_pred"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate the predicted class for the test-set."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"def predict_cls_test():\n",
" return predict_cls(images = images_test,\n",
" labels = labels_test,\n",
" cls_true = cls_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Helper-Function for Classification Accuracy
\n",
"\n",
"This function calculates the classification accuracy given a boolean array whether each image was correctly classified. E.g. `classification_accuracy([True, True, False, False, False]) = 2/5 = 0.4`. The function also returns the number of correct classifications."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"def classification_accuracy(correct):\n",
" # When averaging a boolean array, False means 0 and True means 1.\n",
" # So we are calculating: number of True / len(correct) which is\n",
" # the same as the classification accuracy.\n",
" \n",
" # Return the classification accuracy\n",
" # and the number of correct classifications.\n",
" return correct.mean(), correct.sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Helper-Function for Showing Performance
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function for printing the classification accuracy on the test-set.\n",
"\n",
"It takes a while to compute the classification for all the images in the test-set, that's why the results are re-used by calling the above functions directly from this function, so the classifications don't have to be recalculated by each function."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"def print_test_accuracy(show_example_errors=False,\n",
" show_confusion_matrix=False):\n",
"\n",
" # For all the images in the test-set,\n",
" # calculate the predicted classes and whether they are correct.\n",
" correct, cls_pred = predict_cls_test()\n",
" \n",
" # Classification accuracy and the number of correct classifications.\n",
" acc, num_correct = classification_accuracy(correct)\n",
" \n",
" # Number of images being classified.\n",
" num_images = len(correct)\n",
"\n",
" # Print the accuracy.\n",
" msg = \"Accuracy on Test-Set: {0:.1%} ({1} / {2})\"\n",
" print(msg.format(acc, num_correct, num_images))\n",
"\n",
" # Plot some examples of mis-classifications, if desired.\n",
" if show_example_errors:\n",
" print(\"Example errors:\")\n",
" plot_example_errors(cls_pred=cls_pred, correct=correct)\n",
"\n",
" # Plot the confusion matrix, if desired.\n",
" if show_confusion_matrix:\n",
" print(\"Confusion Matrix:\")\n",
" plot_confusion_matrix(cls_pred=cls_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Helper-Function for plotting Convolutional Weights
"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"def plot_conv_weights(weights, input_channel=0):\n",
" # Assume weights are TensorFlow ops for 4-dim variables\n",
" # e.g. weights_conv1 or weights_conv2.\n",
"\n",
" # Retrieve the values of the weight-variables from TensorFlow.\n",
" # A feed-dict is not necessary because nothing is calculated.\n",
" w = session.run(weights)\n",
"\n",
" # Print statistics for the weights.\n",
" print(\"Min: {0:.5f}, Max: {1:.5f}\".format(w.min(), w.max()))\n",
" print(\"Mean: {0:.5f}, Stdev: {1:.5f}\".format(w.mean(), w.std()))\n",
" \n",
" # Get the lowest and highest values for the weights.\n",
" # This is used to correct the colour intensity across\n",
" # the images so they can be compared with each other.\n",
" w_min = np.min(w)\n",
" w_max = np.max(w)\n",
" abs_max = max(abs(w_min), abs(w_max))\n",
"\n",
" # Number of filters used in the conv. layer.\n",
" num_filters = w.shape[3]\n",
"\n",
" # Number of grids to plot.\n",
" # Rounded-up, square-root of the number of filters.\n",
" num_grids = math.ceil(math.sqrt(num_filters))\n",
" \n",
" # Create figure with a grid of sub-plots.\n",
" fig, axes = plt.subplots(num_grids, num_grids)\n",
"\n",
" # Plot all the filter-weights.\n",
" for i, ax in enumerate(axes.flat):\n",
" # Only plot the valid filter-weights.\n",
" if iHelper-Function for plotting Output of Convolutional Layers"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"def plot_layer_output(layer_output, image):\n",
" # Assume layer_output is a 4-dim tensor\n",
" # e.g. output_conv1 or output_conv2.\n",
"\n",
" # Create a feed-dict which holds the single input image.\n",
" # Note that TensorFlow needs a list of images,\n",
" # so we just create a list with this one image.\n",
" feed_dict = {x: [image]}\n",
" \n",
" # Retrieve the output of the layer after inputting this image.\n",
" values = session.run(layer_output, feed_dict=feed_dict)\n",
"\n",
" # Get the lowest and highest values.\n",
" # This is used to correct the colour intensity across\n",
" # the images so they can be compared with each other.\n",
" values_min = np.min(values)\n",
" values_max = np.max(values)\n",
"\n",
" # Number of image channels output by the conv. layer.\n",
" num_images = values.shape[3]\n",
"\n",
" # Number of grid-cells to plot.\n",
" # Rounded-up, square-root of the number of filters.\n",
" num_grids = math.ceil(math.sqrt(num_images))\n",
" \n",
" # Create figure with a grid of sub-plots.\n",
" fig, axes = plt.subplots(num_grids, num_grids)\n",
"\n",
" # Plot all the filter-weights.\n",
" for i, ax in enumerate(axes.flat):\n",
" # Only plot the valid image-channels.\n",
" if i\n",
"EXAMPLES OF DISTORTED IMAGES\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to artificially inflate the number of images available for training, the neural network uses pre-processing with random distortions of the input images. This should hopefully make the neural network more flexible at recognizing and classifying images.\n",
"\n",
"This is a helper-function for plotting distorted input images."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"def plot_distorted_image(image, cls_true):\n",
" # Repeat the input image 9 times.\n",
" image_duplicates = np.repeat(image[np.newaxis, :, :, :], 9, axis=0)\n",
"\n",
" # Create a feed-dict for TensorFlow.\n",
" feed_dict = {x: image_duplicates}\n",
"\n",
" # Calculate only the pre-processing of the TensorFlow graph\n",
" # which distorts the images in the feed-dict.\n",
" result = session.run(distorted_images, feed_dict=feed_dict)\n",
"\n",
" # Plot the images.\n",
" plot_images(images=result, cls_true=np.repeat(cls_true, 9))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Helper-function for getting an image and its class-number from the test-set."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"def get_test_image(i):\n",
" return images_test[i, :, :, :], cls_test[i]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get an image and its true class from the test-set."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"img, cls = get_test_image(16)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot 9 random distortions of the image. If you re-run this code you will get slightly different results."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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wPMAiv7gInhh1iWtPlRw/p+8vwLsi8NaA6ze/W5UoKqOSRqdpOPCMmaLkfSIl9IeAEDxS\ni1obparrj+6C07P28rMzUBof0MuNxRg1YgjknNoQ51xwOq04nY7ISagTwiHzjcPU8AjqXCcjbTYF\nNVwvDHnDrmQ5CQqDKIulGiK8N25bOYtaWepdttxnNdkNf7Iot0W+6Uq3iiPCbrdrynCaZjjv2ukv\nWLqc1GSj1SLENn+C7zhvmSJmfugGqgBRB+dlocv1bNQlgh3AIBRToHoTnZYlEW/BlzdJtxwI9XrR\n4f8d8+oK9joPPfOQmpXXDAUHUhf67LhQ85ArD4HPCpBTN1YCIaW4piNY/5HMo84dtV/aAcVj4GvA\nhCUgOyGG0KgytUr6pUWNXXItQGqWavcd2lM+aUyeZxka5qI0mqCL3juHUgqOxwMAQq1ASgmHwxGP\nh0fklEGOGp60LAtiiI0kKRkj8r1kJ0zqDgveJG4bJKtlnjQ7wYuLq1HPDrT6Dhu23WupWpZCaOla\nxpMaHtJMmCvdJOQclmURPG+aEGMEAVpUITeX1YFASrchGNu/H2ZGvL0cR3NvOwFXrAxACPchBCVw\nT81CFC+CO2WGe155TgKrbCkJ3Yq50z+GTdAsyza1H4owX5Ow/aeZO7XR25jG8RttK2oFMKy4hvc9\nUQJECLW2/UTK//XqPXofWvDSAqt2sBVlj1j2ENTFlmyYSdL71JUGV1GubrQOLcTTuYuiz/mpuvB5\nyrAZnf2gR60s+F85KS+tIKeCddvw+PiIw+GAUiQ7wSplTHECRWqbxQ88JQJQyw675YRlWbCuq5xI\nzI0ZbxsSkJQ51shnCAEcw7DAuYOwgyvcJp1HDtXl016vZThNEeQ8nJ7IhvXWEsBBq8i4zuwfLfvL\n5P/RKhzPbKenedXfkVrlznnED+S1N1yodlyrlIJUhAeZi9Cu7HAzw+ZDs3i5xa9Xzik01eaILMSL\nQZn0/SP53z0wQYQedFOskIgQYhTLLk6a3y4BFSIppOBAzXXmykrCP2lCheCO2zYpTc/Da+0CR3Zf\nbI/RhS4O22eMxrPd5KZUSpGo77rKRZmxbUkS948nHI9HPB4OWFfJKtjv9/DeodRdc7O8AfWm3SHW\nJhEhpYTT6YSUxcXOOZ3hT2a/GXEb0OyEOjXlCRbX3IcgCd3kGnhvE98iyzQo+mGcr06M+mJkeWZl\nAJSmbMYFRy0EKNLwIWDQOmxnumwUdGXZHBpL9odYBFOcME8z5nkR69TYCWwBmE4KN/6p/W6cT7uH\nNp9X7h6b2P4xHLdDFj0d0oqviPCgDEsLssiH0dmcSmJEwDzL/M3LgmmeG4YMAKQYIZjlQCRxl3PO\nWE8rtiRZT+spYponTJqzLjEKOYhzzq14A9ohOKxLVZpP3crPU4bUF65gNRmblkhiQFO3VhwOBzw+\nHnA4HpBTRohB0/cMBO3Yn7m3Rr1o2QmKBVla3fF40DzHHmlyTm6olApktIIRrUIOdWY90BV5twgV\nzzB33RaInTxXKuOp2qsUScYPWwpUK2l4rgj7/7slyOOGU7VIYJC4FuBSUHPWwhhKmSEp+iEbakaM\nEZvWUSxcwQXCP8wZuQz4ryq6lmxmNzDeq56m76WBXYnYPrsIuDc3s5fSYhQYhWUIkBQhOfe02cE6\n9A4OhGmasewk93yepSiHJTfI55HMv8UEBsZBZeUOnuWXz4PXIX8jHulYq7R7reNzPVWeH0CBFkhI\nhBizkHJ9RJwidjuSWndeKBVB84i997jb77EsO0yar2xKqpX30RF1Wrlmz/dgtpJQUtarlCJArbrM\nMU5ISUuIlU62dbqZBNB3Wg6oNotWOGg68YNbZwGUK4YMVai7SkDDiawYa6lFNlOj/pm7RBefgfde\n526OyOfmhLxtUgGpiCXCJAsdzJgUXtntd8hpAzFaoY5t27ApBizUihZExPkEEiyfldDV5TWLYWuN\nGlM7htdgDyLwAIcYLGY0tlbwVyvSeAhO6L1Qpfb7Oyy7HaZ5RvBWXaOeHY7MkiXWA7IT5lnXVxXu\n6jRpJaR51qItko3GyK1kG3M9X2d4vj3zbGUIlrqBUGKrcx7zsuD+/h7TNCPnjLv7e7x7+07q3Kl7\nO00Tlp2kaxkWZNVp3NkJ4+DYwe2c1jGMTWGu6wmsp0aMGfM8N+u05qwRYm6nlmffAjOWU9vLjamr\nZ9ko4yiauf3swfk4RDBBOeWhXK6W3VGFKF+pQrpGdJL6eLCZQjpXhEp7ULyvpIS0bkinE7aUpLya\nWgNZsangPXbLgvu7OxT1PFLOyBo02ZIqw27q6/9o0IcOPX+i4SJXaxkCaIEvM52k1FnPBrNUOse9\nAsxIkXHePDzNNPK+7aWohZ33+ztlI/ihlJZVsOoQFztRoFOcgJ1UoVoWY6CIMpxVGTpHrczellIP\nsDHDD/f5XTy7ZwZQZJNQBaAWXIyi5PZ3d5iXHWqt8EGA023bUBTz80FwoGlwe7wFRAYrxEQwRRng\nUiUf8XiMsgm2rbnpIaxwzpkxr2A8aaqWAO9W5SalXnbsMmjSMC590KvGlQzTNW4hUQtYWSTX8kv7\nn3TCO7lB2TTsQV0tswbXFdt6QtJiD0mV4ZYyUk7IVWgbzECMEXd3dyil4B0Yh4O+V2tpWn67XET/\nd5HrKrcxYFv26pVOcRdzTYFSuFn/lrkzkt3bYYYOZxnPuCtFKd8nVa0XTdcd8HgVsUAZzNR+BrT4\nS5yawWJE/nmWegdEACmX1PtNidfnnky712dEkoFnY4YasWXJV53nBcuyYJ4XqVBDDnCEOE3Y1Ypp\nmt7PTpiMoB17Er9umhGXAHpppmXZ4f6+IISAdT3BOWG1n9atkXLbom5WiQMRN0V4OJ4kjzaXRuWw\nQau1NEvizB680o1CRPCK15H3CnDTWd1IOdy5YT2WLSDQh+YLq3V4xgvMGdvphPV4QFrX7iLrYbWt\nK446T+Q9XJDc4t1uEa+gZJzWEyrOc0+HGbVC5kOQQDew/dMsw/bq1cllppXtg5QyvEtg5mao2P4a\ntpgefK7nl08TJo0aR6XGNfhL/9iqkxt2DycQTCsUoRZijQZlaUBHr2EMBh+C1EGIkyrijFIw1MAc\nA3lP14bPswzV3RWlqNkJi2QngFw7oR1JFkMjTGuEKWi592jEaXOphglqVWxqd79CjNjt9618F4Ow\nbpvmtY4JVuOJL38vivCIw/GIdTVSbl8IPf3IomEAYUwfuz6xHONlWUAhiOsKHnhiNsrUF6uzmpK+\nUaTM3G7VgNQ1XtcTjgdRhlWjwWnLWFPCUZkIW86CIc2yweZJqmav24Z3j4+tTiKAM5fINsC30eav\nd3YNT6vokXUohis0uc1tMJPKKccXwHvGgkWeQ4iIk1aqirEVdNaPPYvynnmBzC2oYhBL8bWV5YIG\nxEZqnPys3EWl7pRS4LJ8pgVDa8Mlnz4uz1KGTgm5Us1aqA/y0GOlCRqq5KINgAVMxgKSZtraCc5a\niocro1JpoX4rDabmCFLO8JqdYJVsegSzWyGlSDXmdV1VEeYW49Sofpt0S0hvsZMr3S2k1v80ibvj\nQmiuqPNe5k6DWs718lmGC429SYxic1atqApNJ+UsDb7WTRo/rfJ1PJ1w0j4rMUbAkfZhCXDBY7ff\nYdktOB6PStItkunfCLy1VzrHGfLR3ClHDDgjhV8va6BLn7NSC1Lqis57B2JVmjwEXBos19PmuiIM\n6hl097hZk861uWDWjC/DJYlAtaK63vuGB4Votn6/rlbVLxnFyYb2wem9jn//NHmeMiSH3W6nRVzF\nHCagVZ/pjV8GbMYNynDIUHDej2cMCJKjKBEsQuXz35oJHaNmJziJKpWznOOenVBLQbLqN+qKicsu\nTWJMKQpmwe9ZO9caPiGSTKE4GfNfaU86v73HRWcCdAtfN1UdDyUr39Yj+QaFpJTVEpTWASc9tFLK\n7WAKOTbLwTuHZZmx2+1x2q8tZ72WikKlVy/SUOK4DRgAMYOJNO5DkoN7xbqw+VLUDQCujEJSFyD4\nglK8HiCuK0LjHI7BSh9aVfvmVMmgY7Tu2qEECZYCMicjJY6AbuBwp/wAavG1KjWE4D14mqRaDdfz\nmozfJ2ZIjgRHUjPVqwYvOaOEIE1ZYAC6KRY0XHD8/2UqFPM5CO9IaukZAwpAPwk0H9q53m7QBs8K\nMkhNxB44MfpFM/XVKvwm3OhaU7WICNM8C64bIuB7K9bGV+BBi6hiIzCgOKHZ+jIvFVx67mmvJiMb\nYEsbDscTHh8POGkghStLpJJjt+wUaolxwm634HTaYVu3nsJVhYpjOdKtT+8QNW75r8ztHq9WBiyv\nmwGieMyCz6XAl9IKIY+UGPkIaql41jTMDZqQLGRllqFdul1fr3ux9+EcnNYVaHOlShFm7FSrsi2s\nk0rq+qv3eOYiP3Gan52OZ7mFgJrVWpU6WKUQQrMGR/DU/NKzqO0H7lPMcwLX9y00RwTS6sfLEJUm\nN1SlMezDCoLmMYJsNii6CX+BgdB431cozjkZVy3OavpPqo1LfnLwARwjQkArtCuHoFmK1A+psUqQ\nKlKrTANyyFV7L6fU5ouI4Ck0N601MmfNTpkmLMuM0zI3CIR5E0uyZT5crA/WdK9txbZJwKbkepXq\n0Fb3CIufwa/cPa5SK7xWh+qBiQ5fSQAjSCR5TNEcPrfF73vszQzG/uaLiegZYdT0Ctd+oFmnPPPw\nLA2Q+Xytnd3Mt8jzM1AGFW/J8iWX1qvEHuAyP5BZonyuMtjLjTJ1C+3corUTi2H1y7iUZnU4Zw2e\ndMGHiNVJWqDVVDOqhtVNVOhBT6rxYv15xvD8+O81CTlCnGf4IClwxj2zyC0Ykp88uCOt+Za50U76\n5gJWZEFd2JEHqpgSM2tVGl3AagEad61b/7JmvCrr3W4nlWrWDeu6IuUNnj12ux0eHh5wf3+Pu/s7\naUivpPt1XfH4eMDj4yMeHx9xOq5nCuGaxA78cytNxPZr0Tkvyvrg2l3e3htdiqmc97HmbnBwV77n\n12ChZglo2P628RwxGEJkGDAPynDMOjK2wBBJrs8LngDfMTcZg6KTxkH5rDSWU25atwS54QRiPqP9\nzPYPMbqOko3TqsykTbIOZFykCg4I8zxpdsJeSs87QkoZAGNd1QK4qG1no0vUtCOswmWfNLpGPQhA\nlFScpOe0wqmtQggzPkBLkkKbFpFvNS5rgPUyscIYUqMyS6WZPFSZIYNAAlxlzTjSthGshQGqELBj\njNgrnmg1L1PqVJBPPvkEn3/+OV6+fIn7h3vhunmPyhVbSjgejnjz5i2++uorvH71Cu7/8j/YWP+w\nYrjdhxe6WPWMXCoo5W5kOUJ0Ufui96hxyVl7I6lb64XaJnNLkLIMF1cfzUe7QENf+GwPmot+3ga2\nG1/yp9yCZ2NQ9any/AwUmCvbT/YWJRyihmdPASg5UiNMI5DKEIIsDw+subApSZ9jCYB0dzebax4i\n9vs9Hu7vUUvR7ISk7tDW2omaWX/xFO1APLfSr5t/RkSdbK2Hk1FtjAZhJdwINv9SgomZQZnES4i1\ndx20rJKUsW0rTutJinCk1LKYZGMJSG9tRolIejYX6YdCE2kZtwU+hNbT2Spkz9OML7/8Ej/+1R/j\n5ctPcHe3lwIBwbcNkraEN2/e4Hd/93cxa9Oya5Rvw8QZ6N4A0A4bqVUptKspTvDOoZaMda0NM3SO\n4NkD2hiqOgdyFY5931tt79F7XMDLwhEWGZaislZtavi9mVmGFbaqVN8jtUbvvlecAbU8RfPXW3ks\noGt8IjgMWKL+jkBNEUL/Xsr+by0KnLYVaduQto4rCVdQhnGeJtzd37fshMfHdIY/lXo5GmbqG3ZB\napiO+MJ1Bk8AC1QIt8zsaec9grpGbBagpulRHRLstWCCKT/vvGI9RtlI2E4rjsej9LZJSSKA3rWq\n2az30HpgtBvrATQr5WYVSxwR9rs9lmXGl19+gS+//BIPDw9YLPUz9M8ttWC337U2lzFeqTL8ptfJ\nCuBCic8da3feI4aAZZn1INE84ZSQEzfKlbTlqC2gNUJrAnuYj2eeGc7fNwQ+LHVvdKFbUM05oUkx\nSeKExvgsbvAcWg3wXGXIMighBEwaVS5awUIq3Q4YIYTBADstlFrzoUiyRYhyztjWk/Tj3VakTZWa\ntg9Y11UtitwLOxKwWxaNfuXWvc8KgjbFfPkow4viKfeACl2iy9ck6uqaVWiuVG+nOhwmDPjQF2qt\nDM7SU3nbNvk4GK4oLRw2ncNxIkP6AAAgAElEQVT1tCKlTZSZIwAe8H4IxmixXq2mHk0JapDGUvS8\nF7rNtiVMccLLl5+0NDBjNZxh9SQtBh4eHlBKUf7qTUwGEATMVm1e97NzmOYJ87I03m/OGVwFigre\nCQMBsn0K0PC+wAwOjBDNIHrfOmyHLCwwp/cxaEeLYAs1Tw5CcEXOQKUysBZqg3ieKs8+Fm0hLssC\nIt8KH4y9CuwBxZ2iTsq1PGRQf1CrYKz1EU+WnbBtrXmUNCyXOomHwwFJe2PEOGlaoNQw3LSgbEvR\n6/Pav7VjjzrgavKe+rtCfUiANH2yaNy4cNtg9ciijZERnmst2LJAFLWUNt6G/yaz+NOmXDG0Yh3A\n+akv6ZsT5kWyG8xVl0gmYVlmTJNAJaUUOOelSre614J1sdSy1IezxuPzMuOTTz+9Wjf5Uj7sP4k0\nnm8QrHCeZ+11JEVTDC8sIeia6cEOC8RU1noA1tLDa8xgaFPYUvyGakgG+7VD0nBp+KZboC5xye7M\nipR1+/RN/Ox0PCudNc+SieIUy/PaDOiMjGtZCk7TtBQvZFWCAM6yE2zDbNuGdZV+yZadcFL36ng8\nopQiKYBQPEvJwcfTCfO8YJqOSJu4yTKlPdzeAjoffkBV1h7e8fsr5BqEBoKtDYAdHIbtAsM/1E50\np5iS96VVIB/bxJ7VnXMO3kM3FdStUnVInTEwzTOmaUZUfK9XRxG4BsytVYBUTIlSIp7k/gRb0l69\nFsyBUHBocc0SuTapFx5ThyPOI8ymfJqFrn3RCYycrLK9QFdercRSK2IQHHeE1HwoCLWCnXJTz2hs\n3eMARCHafMEBVHvxFc86/6pb7BC2IGpPrdX6p0+0ap6tDC0zYZomTZymxjmySjPO+ebOtFxVrWSC\nQSl1EuzYV0E2TtqSKr8DjoejNpc6IW1StmdmluKf4Dbgy7Jgt9/hdFJlqFZrKWOESTfkmZ/cfSnW\nII974gB+jEKOOtYxSh2wm3by15ZU770HItpCbYT3nAAwArShOwHe+WEutMGQEaI1gBKnXuXI8l69\nVSkBAIgLRxav0w+38lMdkrnEotQzuNIpluDI+QR3mAhtfEWZ9PavcVK4omGCECWk+eUZUmA55iwH\nWdFMJiL4YAkRsuedXnOk4Oml7TsA4olyrcAQ0GN4UBVyffBeDl1fenabc6iuF4B46lb+ToUazGUh\nIqXTnDP9xQAcrEAZff1ZB38IkzOzWg7d8iil4HSSzITHd48CuG/iWnnvEUrA2LC6NbJfFhx3O3XF\nkjasLx3/suIO40RY8MciVDivAnxtYgqkWYeM9v14oDSckGsLUFhDrvNOaEKnqqS1I8nB+zF9rsJp\ngV6Am4tsLtk0d8uw5ROzWDfEHScyBdny4S82mz2L3dNzAfaPRQz3BzowMaa+NXjE9TJdtueDcQ4L\nNzzQ+tGwMj1Kya1dK5EUcrBx7/hy7V4I8ZlCfC+NjtBodwRIOm2D36RSli+aG50SknOtG18j7D9B\nnl2owVj9IXgIB1OsPcMNagiiUAK0gbs+gDtPj5K0H25k7WocJU2jMoVopFopv5UB3XBmdY6NiSQ7\nYcYyLzhNJ8Qof2cb2PvQlPlkm4skIp5zFvKuYl3WlPoapRWskMhS3yAd6kZTj40G8T6vy1wVS9B3\n+jsiQq2uUSRYc8YtU8S65MVW/3Jq3fKcWq2VK6gM2QmGcaIHfeRLb6ZtMB7v7hcwmr984r3D/cM9\nzFy2RIWcc+eMcqc4WTvgMJTRMoOH3NCcjRkoVmBFDrw6ELV1QbV+ywJgVZ0H7tSYs7Uk+qXWscS/\nYf4aICFIIegQkBrToLT191R5dm7yNM8IUapSkOE8SozNRTABi8jK2AhPycpvGaYIWDn5viFa7rDi\nCNZMvlgRBgDeaqh53/ry2uB5jXYtuwW7daecthVu20Be3OiH+3vc3d8LB02r7vTsBOnmdzwcpSvf\n1UqfP1Me3HVItxSHhdtS7mpfuKX22pHmqp5nJ0n7x0pQHppYCdYsPMZ+eIVoBF89fGsFOPcKyqYM\nG51jeJxBYdsjXKcaFPEh4NNPPxVPjbnzP09r605nbT5D61Q4tWZtsMCGYcX61dYAhvXQQGK1zhmt\nmyUP0WN7vXJt3gbQ3dyiVicP2U4m4pGERrtyhiv3yz5Jno8ZxqgVY1Rvq4sqLm7zQSS6oxkHlRku\nD9kJPgz5xKXlFVv0eMwnHkm4jl2rn0aDQq21alkxiSxyZSkTlZK0BKgV3jm8/OQlPv/sc7x4+RL3\n90N2Qq0SiT4c8PbNG3z99dd4/frN1YLr3TWGusZDAGowsEZX2VpISokuszLsNO8ucR3e3w/BqtVn\nSgvGeEu5tOhl0LJQ0GBLBvgME3w/tYzafb7/DPL761SLwXt89vnnAKDKUMrcHQ5HPD56HI9HAFCY\nQsZ/UajCK/2JtRKRWIFKnePeH8W8i3F91FJRnaXVWvfFCjDpuuj9mO1QgzaYwniYUbc0zYNplXPU\nMrQiMs/x7Z6tDL1XrFBz/wxgtdcuCzlYp6vMUkJcshO4DaoNUi5Cu1hPJ5xOwjGUJtVe08M0+0Gr\nVBBRiz6HUhECtSra3vkWna61IoaAaZ4kO+HHv4qXL1/i7u5O2xf6Ztls24bXr19LdsL/93evlnbB\ntQe4xsWMFviqDXowknUuqgSHoEnJpbvCGrQyfMma+LSAWdpQS5GivwqDhBCbZeiGupm1AtVZs3Lj\nJA781TPMyTaueRddGVp3xWsTHwI+++wzAKoMc8a2btjvDy3XuNSiBTEWLLtdI7ADrJ0StViCNmKy\njCHmzhckhaCsQbyV6PcAvAPgCLWqi237tfTCH8655ilbVW2cfb5rhzLI8qUlQ6YEa0b/dIX4HZSh\nVbCVBWcUCHE3LRfRd0wBYl2UUsCFW7K+t2bSY3bCukpdu9MRmxFytUqJkShh4fJB4QKihKcYldrR\nu+ERSc/mZVnwK7/yY/zqr/4YDy9eYLfspDKLPg/IoZaCh4cHWOP5a1WGrRzaGOBS92R83TqT5WJK\nMClRXtIn69B316zAlp+crfVox6zAjEnnNwyN5A2rYg2aiKvc70WCXmYxXDyMQk9sgZ5BT35bStrH\nKt453N3fA1DXtFSkJSFOsY1nylk6Xi477JYF8zwjeK+sj94+FFCrjAjO1a680In6ORe4QRkyGOwZ\nrrpG5DBlaC60ZTiZr9sOLkanz1hAljt+6ZWbWmsBKeXnewmgYHhACTgxPHmwky5azL0IAxjwHqgc\nenJ1LthKwbaldqozJIiS1FQ/nXreKrMWfAxBemoNJzo5bSOgzWiMg+aDx0QT7h/utcvWgm3bME0T\nPvnkE+x2e7EelQLkjB6kqYLLbodPPnmJUjJinJ43PB+BdOzmAgu8UJBVlWBXgFIWK+fUXN7GHRwg\nE5vnLUmzMOunbZkhO+8xzwt2u52Se63DYe+lYlizKWPzABwGpGaURmUTyN4O1Q++9xrEKCeQvGEH\nwLMYHfv9vgUvLYo8BhsZYsgU51vaHZEdTCNE0QOluWQgieKSSlcB3gXFe81c6n/XEjXs66xOZmc7\n6B/0v9TI9bwsICIkv2lJuKfBXd+hO56TcrMYo0T6hnbqduC04UsaOU5ataSfLrZRkqbcSXGGrCW7\nrD7eGAgkRwheyd9jdoJmuLgQ4PZ7zNOM/d0eJUv63m6/AzlN/tfshFI6V9Ksl2W3w2eff36leas9\nIb5eKEMoJlQ0WygpKd5oTFZMw/iC9v5aS8sisgNv3VbklOXQDB7zPGHxCyYtz7Xslk7fMlzJCoKU\njksKviz9c+X2e6QS6BtHKBzalxmGdf1QY/zDCuscNihk8KKmeVYrXHONgx+gL+7FXINS2wgomXqU\nFyNNxgKhtXXJNBqdc3nA5FUBDkViR87gZY56/0aNMqAZVz4EzBpQ8SEgbemMcfKz5Nnd8RwJoVEU\nEzV3GAzhCxmwDnS+V3u4AF8KSuMn9cwTAdt7FWLvHNgRPHdXmNUEd05yIM8IuUPbUXNxOTLiFFFy\n6cEfndTK0ryaGW3yKgteEUMAaXuDa5ROqjZFWFuedzGSrQanxCVOauWVFhFsQTG1Bk/ritPxhNN6\nwqaNuWROpHL13d0eD/cPeHi4x7xYbx1u3e8AGhTx+4rwrGz8Bb+wUXxsJzGg5ZJ+kPH9ZZA67KmW\nvqb7xsbNqyFiUWezzgHf1ogJmSdgP9NwJLVIsqylURETOYjhptzARsFz5wGuFiADBo04iOmiTruT\neEZ6ciD0+fUMHQkFwiI7emPnVAsLnqhFQdCS7VHMXefgG9AuFho8g/V2jPunI6hRJm7AqtcUPCPk\njpYhDeY0M8PViqqRJwPaHVk15rELHvf/NYD2uaPz8UgL3jE3q77Xl0zYkpHaBfTGcKiA8R70cTge\ncTpq4+8qnM/dfo+XL1/i5YsXeLi/x34vrnGIkl4np3pp2SRm0WStSHTGOhgjjUP2idxO37TNX2FJ\nL7xaT9n+lZr754fHYNiRurJtLLVGoTENTAoBXHtDtba3LMhF1NNyXSdEO+dbpRvvlRZj8QDgA7iH\nKr12g3YJ6nufAEe+NZL73pThZd4qj6eLFl3o7pFVo5XB8QFtUFJbrDK4VRewtAv0F25ZbuWayBEu\nsxOmOGYniEjvDcOXSos8tc1CblgAFu00F4qHXh3XJ2Pp9DFnPOdeQcjc4pEi02kywgw4rSuOhwOO\nxyMOxwPWdQVXTaOcZ7x88QJffvEFPvvsM9ztdwJLaHQzp4SMLGC501qYwPm9aMTa8lxhh94YSCH0\nisodg7/JGDxyqkR0YCy90mnahwQsAFNBbONsr+hesaoxFjyxmqctuDVggGaYhOAbHcYN+1I+9sMz\n1eIS4z0M1Coiu2dhIHwvyvDyouYPX56vptFHrKkDfudujAVk7JeX5aK4So5hsYEeUrWmacY8zVqz\nLnRQWLMTLAzfB2y89jlmNCCc/VmvUCxi2+GL7pKOFpmRXwGZIwuOWB3KtdGkBB80qlQMAfcP9/js\n08/wxRdf4IsvfoQXDw/wzgnPVIMyuWS10h3YltqAQZZcekSaAfK97Wej8pCUln9vTbX38NVqxrYf\ndKU7Is0VHmE586bOvSh2DjSkP1p9AYK5vvZ3va+5QWxjJJguFdogI1l7/N3l+zoUguGzLq/xtDF5\nvpsMORnIFpXcObj/9kwRmrXII470IdeGnIbmHeBwtnArs0SrAaVdBO2BMjW3yhrKN2sGg8UAAOBB\nIfZF0II99gxXujm6dB7gqAAtz9sUYcMSS5WWrOvWmACiALdWk1I+TzC9eVnw8sVL/OhHP8KPPv8c\nL1+8wDxNSNuK9XjUKHNB4Qrpr9bpV5J1IvfYqBiKOwV3EXHmCldlLZFuRGCAb1rxz+ud8NHVNIt6\n1Bxm0RmWZ4EIZlYFKYaQb/sZkpWmAaumkMydHRRUx287l9WBAVftynIPui97Ol+vb9CM/9Gq6R+r\n8vQD71nKsJ2oerUx6vge/WL4f63a27ZFAotmm/RKNV1xnndT62TMoidOgFf8cZpmTLOF/TUjhRko\nnQz6/ik0ArE4v++hksdzcho/JmFmrSV56Rp3ZWjluEopzRocaVHS+1j6H5vCkuikHFrTNPWS+wzN\nC99abvh4CBbtdGh9dhq0q2R+hmRKSHaE68U8NaDXsczRDTBM5HoVIRobBIKfmk01DpXtl8HFNYMD\n3L8vltMMoHAfVyvu3K94oZgYrTi0Y4ZjyzJTHjF1S7NbigJ7vI/2DimkpMfiEAZ4ijzbMqwXyuOs\nSxXjLO2puVu5RwCtEdB5dsKQ6WC8MbUkrUKy1Uw00HW0DP2YHqgRzUtX3CLb7RQcNse58jW85EPG\n+8cvtVacTqcWCS7WYTBrwCslbcuQsG5rU2JCrxnS8GppUV5WKMUpN3DbEo6nE96+e4ecM7yjhjPa\n4rd7yVnoWNL7OjcX2FI3bX6F/G/YsW1yeSazAFuJOKuC/oON8g8vFugwaVsDnYPYjIaLvdT+pmpu\ns/eSXaYHlEX2pal834tMDMc9sELK+6SiNRNd7XUHHODgwK7T9ABo/EECYANMDKLLJzIk7+l7+PnK\n0BqCm9s7NBgfSbHnwLuC7krOTXmsggy1MpVE27ITrM6hcQIrpmkCiFqXtBgjYpBoUbNSh4jmuRve\n8Ql5UYauBXyaQtRfX2k0mWvFejq1Q8ms+KJFPDfFAg/HI47HE47GGdRqJ20M9WAcx5UrsK4r3r17\nhxACTqdVs4Zc24xWow5EyKWK5Zl669fWXEpdd4NNuHZ+odISNeZi9yP8NsNBy3AAXqWIVgGAM4vK\nlF9/X/vn3Muyv6u1GSkAGsRCJHxeK+jcaDMD/a1HmgGqDtVXeDWQfLDMMAJqVczSrERqyq81FR2U\n43eV57nJH1CCZiGO1JqGOVmKVupKMCsfrRWXZDTumqTkbdjSNmBTMkExRCyLwzxNWDQ7obcaEAyz\nAi2iaVVTWnZCy3M0zKJ/b0qyMvc1cqUcNGbGpuXS0jZklTSrsNNljqdVsMHUo7ojJndJ2iYSCzCX\nguPxpGmcvRq695JSabxRkFQrSSlj21JbF3bIllwkGqk0nThJ3bwYizSYsvXhrGFZT/0buydepfwM\nrdGYHKQ1I6uDMwtNN0gjs2s6bs6T9L6hre05vRAsjdfI2q3ilNNWD0xDBazB8/QMzwxuliJd3GeD\nEd9z4phJmnH2yMa3yvMtw1rfc49HF3k8tdMmis1K8EuCt5F5bXEaH22IPupmlHQ8yU6Yterxbr/H\nfrfDFCcQ9QhhU4CGL2mmi20e6ZA2ZCdY5GlQiDqMYlrTdVoMzIxNmzVt64atucAdMxTldM4ztIbj\ntViBz17yv8EhEPjh8fFRov9DfUvLdpinCbtlbnUzyTkp9Ks1LaXrYWmZLtMkVZTmaRbWQcpYdjuZ\nawuc2CZTyGbdNrx79w6Hw0FbQ1yftLUPq1CpseEGgYkyK1RBtaDWwVUewDjSjJRpmqT6zbaBtk3n\nqPfTNkXY8s05AB6taRcUH+5VjfpB6pklt9eJUnyfH2VmbS8HRuKBq/n6tDF5tjJs1uEldsiGD+Zm\nCY6KcHR9DZuTahYZ63qS7ARVhCkngNEoNPv9HvcPD7i/u8eyLNL/hKTkuCE/tXKngaT3S4G56s6U\n3xiSH187y3a5Qqm1Yj0e22Fm1mEtGagMByXPeg+nVpVhe81qM/faCjE0THnA6YZFShpptApFyzxh\nnqQdpdf+Ji06PfTPBnOraBMnaxYlpdzAaFQOABr1FiV+PBzw5vVrvH7z5nqVoR3+yqxgOleIlStQ\nBYMvRCCq8iVRjfb3bFVrNBFimeeGE28bUGtqRorNW6kVU62tR4rxEUWnVfTuLA30He7cNeOlufcD\nM8Se6bsAwt9BGdplWX82vK+7umnMThjyHu3mjY7R3K3jEcfDEZtGMZ1z2O13ePFCsxMeHnB3f4fd\nbsE0zcJrs4oU1KNa2XKfvyE7AfSB7ATmbwyTXKNCrKXg+O5RPYAKB8YUAiiGxhvLpeBwWuEOB6Qs\nym/bViQt0FDqh61CkY7pmsj5I56GVDDacPRHSd90QvLPWSz+EZoBpOeG1cmcYsTd3V6K/E6x1d/L\nyl9c1w2HwwFv3r7F11+/wldffS350Tdp+5OG+WENOlEtqIVQ2p7phsMYBI0h4O7uTpXi0va29cgu\nSfoShZxQyoSpTmdFVqxQL8lFJIAKK8OljIQWRLWaAuNTqFd3oRS/l2hyO93VLW74XM4a8euK0CgY\nDWewh8sFm/ZAtqrSh8MBp/WkEePQuGhffvklPv/8c9zd32GeJt2IuSm8ptT0VBkV8qgMR27TJbxA\nF9Gy0Tr8TsfL73FhZuRta66r87pYtfw7OYd1S4Bz2FICgIbB5Vyai9VoWLZebKO16L67uK4oTivy\nuzZ8vy9uZqODUFuHzBWHA7UKRZ988omyEeT9wkbIOJ1WHA6PePP2LV6/eo2vv/4aX3/9tZC7r0w6\nc6Z7SIanj2cVN+sQKOiWYO9FPMwvGM47LHHBvMzYLTuc1lPrS120WEcqGaW4dlhOU0Xfa0G7JUqA\npBeQNWWIphABYe2M66gFVizjiCH0qifKd6DWdMrLWdL8GCBRFwbQ7IQiruuWtoGTtuJ0Og4YYUHw\nHnd3e8lO+PILfPnll3j5yUtErZDR3LCUNLBiEwRYMYHSshNSK/xgPVXMnmUANKCvPTuhwhpnX6NV\nCMgGmadJUqW8R4hB+1NL4nuuBadNsKHHwwGHw6H3jqm9JScwKEM7nhv/i6GAjl6105pMKfYoL53d\n27h3GQKPpJTE4nv9Bl/dfY1pXpBLxf5uDx88Sik4HI548+Y1vv7qK3z11U/x+vVrvHv7tpUQuza5\nTHm7JBr1IEppiqjWCiquWZDyEX1uAWnq7p1DWHzj/9o1Djhg3VbxDDFUsalV61YWBC8HL1iCLBLL\nVCiOpGGYfTH7tr87++OCK6lP9xT5TgEUoyekgZTb6tgNbilXqWVm2QnH0xHraW2VS7Ztay6MI2on\n+xdffoEvv/gSn376KXa7RaqenATIzwMtQiLRvdw8Kw1gzHJpaUQDMFyrtB40vBCA9nBwGrLXU/EK\nxTuPB02Pc94jqjIkR0i5YD0kHI5HvHr9Gl99/RXevH2H0+mk5OqfEaRUtwtch25oI4XDFKSc9s1l\nunjfCLd0/iBj2xLevnuLv/uTnyCXgrdv34pHMc8gR1LF/M1r/PSnP8VXX32Ft2/fSq70NZ56NGCG\nuLDg+4tobTdq1criPZukdZ7r0CO4Srmu4ENrGdAUJrqlLgdnQUroPOSU9eCNiFWDLAiiEJnBqKjl\nPLHDuV4x30H2sCnp9nTPmN5nu8lCRxBLL1l2Qk4X/EA+wwXX04rjSRrAr+uKbd1a/qmlRVmy9jyL\niT3PE5yScdf1hMPh0Aq+MqPRNnIuDVS3I8qKjzJY85Z9c6lZJ/o9DuLFwrC2Btcmzjs8PNw3XpgP\nUheuqEX4eDzi9Zs3eP36Nd68eYvD8dgOp7OofLMW9Hu1IFgjh82Vucif6nDFRTqXfeTw9h7skgPx\neFwBvMK2bXj77h3u7qTCeYwRpRa8e/cOr169EkWoCvw6xaAlfs8iBND2idHTCOjZJ0D72XA+o6pV\nDbhUL4ozRvmdBELjUEyFJLLPGbXSUDF9CLSUihJLwxPbWlC8UDKaVHewkrO9A5wHoDnQtnK+j2iy\nZCccB57XkMRvWJ1FkretZSes23pWAPQyuMEAoJ9pIPfr1xHbtsE5Jz0atvUs17g0RZga3cKwKrNc\nQaQbYnc2qKOMLt1IFheX7/q0oXMO+/3+bPExCFvOeDwc8fWrV/j61Su8ffeIVQNeZxHib5CuG+07\nO2zG0MqQ/jUoxP77LucZBx4EsTqOpyNS1gyXt9ZmVIrEHk8nvHv3Dtu6offruUY+6RDZv1jiPTiB\ntu/k9U6sluix4H7iQTjNWtHsIK1N6r30Od+2hHW1Cui1BefEpe1YoOHFOWVsYRMqTgiKWYfGVbQK\n2+wqHFfUKnUNXHGovvQyYaMB9AR5tmW4nk7imqpCG13mtG09OqwZCtJ6MDeaxVnq3ZAsXwFsKQnt\nQSkPk+ab2oKVumcBRDLgaev0nQ9nJ0hUkUiyVsbir/pA7bkuFaHV6Ls2cUTq3jgF0YFcJUr/7vCI\nV69e49XrNzgej2eKsFtw5jfxWSSv65wxEXIcYGoA/gczHc7moluRhhHpVkRKvbWA1bOzHj0SUV5R\nSqeDXKUy5G4E2M+AjGcrygC0RlEMtI5zREZi1zJ73sucqbVWa0WqDGxCj6tVeKerJlOUUlUP2IUJ\nMPpVldS8hNQsz6ius9UshTalAnvA6951qgwV6pK0PtdpO98HZsjMigu+zxsai39u2zlG2Aq4om+F\nMdoIKNmyiLU3WoTe+6bIgADnJMhhdQpHDpOZ1ykLR85538rBX1oBowd3Gfk8A/6vTIikdDrBelYw\noAdFSrmTnwfstrnHIwSI8+/fVzl8/sZBWWJQqiMflC+VJ8aIM7ThVGnur3OElFJrgl41tVMsGt96\na9xERIkVPbiiiqydRAyQY0jzTjmsqmO4yhLk4N79sCVmWH0BK/AxUHHOCjAM9CsjzI/emdMMpVo9\nXHWoJOvS7hvGWhkeph2oT9zG9BwAmYh+AuDvPPkPfu/LP8jMX/zQN/GLlNscf/xym+MPy7OU4U1u\ncpObfKzytHrYN7nJTW7ykctNGd7kJje5Cb4D6fpDQkSfA/hv9ccfAygAfqI//zozbz+P63zLPfxZ\nAH+Pmf/C932ta5TbHH/8cu1z/HNRhsz8UwB/EACI6M8AeMfMvzW+h5QHwdea2vF7XG5z/PHLtc/x\n9+omE9E/TER/i4j+QwD/M4B/gIheDb//40T0H+n3v0JE/zkR/U9E9DeI6Dee8Pn/NhH9bSL6rwH8\n/uH1f4yIfoeI/iYR/TYRvdTXf0Nf++tE9O8S0f/yc3/oK5PbHH/8ci1z/IvADP8RAP8xM/8hAP/P\nz3jfvw/gzzPzHwbwLwGwwf3HdRLOhIh+HcC/ADnJ/kUAvz78+i8D+FPM/I8C+NsA/i19/S8C+FeZ\n+Y/ixjD7ecptjj9++ejn+OfiJn+L/B/M/D8+4X3/FIA/MBCjPyWiHTP/DoDf+cD7/0kAv83MRwBH\nIvprQMM9Fmb+H/R9/wmAv0REPwIwMfPf0Nf/U73mTf7+5TbHH7989HP8i1CGj8P3Y90mAFiG7wnP\nB2k/RJL8ppPiZiV8f3Kb449fPvo5/oVSaxR0/ZqIfj9JVcZ/bvj1fwPgT9oPRPQHv+Xj/nsA/zwR\nLUT0AsA/o9f4e5AT5o/q+/4EgP+OmX8CIBHRH9bX//jf/xPd5FJuc/zxy8c6xz8Ez/DfBPBfQUL4\n//fw+p8E8McUGP3fAPxrwDdjDWom/1UA/yuAvwIZVJM/AeDfI6K/CcE6/qy+/q8A+ItE9Nchp9vr\nn+eD3aTJbY4/fvno5nJ6vX0AACAASURBVPiq0vGI6J6Z3+n3fxrAZ8z8p37g27rJz1Fuc/zxy/c1\nx78IzPCXSf5ZIvo3IM/9fwL4l3/Qu7nJ9yG3Of745XuZ46uyDG9yk5vc5Jvklpt8k5vc5Ca4KcOb\n3OQmNwFwU4Y3uclNbgLgmQGUuynwJ8sEoFfY7u1EuPdWsN4iWkecyIO8B/kA8h7Oe4DcWQOhD0GX\nbI1rSkYtGVwKUKTxk/XXYGZU0FA9/qKg/1nXG2ol6rmVtZfS9oz3mZ9r2pBKvioi7/3dPX/++WdS\nAt4NPTG0AxrogvXahnjskaw9Llof297VzLoU9u5sXaS0f/+sav1otA83s7SHuGxtae0r6QPNAUap\nVopee38zGG/fvcPpdLqqOZ6mife73fDK+48vHfCG5lw/6wN17Pv8a+uA1pKB32tc39YDyZo4u4f2\np+OaG1t2kPZy7s2lrOH8h9qevn77DscnzPGzlOEnc8S//of+IVQ4FO4KiJnBqaCmCq4FQJVmLd4B\n0wx/9wLzi88xvfwc88NLxLt7uGkGnBdlVLUHytgHgbTP6npCevcK6fVXSG++Rn77BvlwACfps7AV\nYK3AxoTKfag8QztnSc8Whnb+8gHwEewjMnucSsUpF6yVsQEoILBu1r/1d/735wzPRyFffvEj/Lk/\n8+8geo8pancy51sfYzCDCNoNDa0tbMmltYyV3iiQBR8Cwjxj2d9hd3eHaVn0MCRUZmkQxNbfWj6/\nlIJtXXE6POLw7h224wl521rnxZSzdFxLCbkUaXgfArxz0u/aGgxpI2fn5PBNOeN0WnE8rTiuJ6zr\nit/+L//qDzfYP5Dsdzv85m/8seEV6YBITXExYvBYpohlCojewbt+1FQWl1IOSxLDBtaxUprD9y52\nsj6YKxwRQpR2wMt+j2V/h2m3R5gX2Ze6d4kIzkkzeuecNKKyA9k56V0DRskJeTthOx2wHQ9Yjwes\nJ+vEWaThHIC//F/8tSeNy3doCCVNwCscKhNKlQ5YnBicxUZzXm4aIYDmGW5Z4Pd7xP0eftnBxQnk\ngloB2vTFLEt0K621EW3tI6VjmxxbAJGDJ8BXwBe0NoTEaJNExNJWsnXlK2rteHg4BAKCc8hgUOXh\nw68TQfDe45MXD4jeI3qvzcG14Zc2/wIgm4NIDrKsrWOLtoBVs5CIRBnGCXGaEGKUZlPaSQ0sFgNz\nt+dszsk5kPPwPsAFD1ccXHEI3gOQAzRpozHpaljVIpHn4MootYqFGwg+OMQQJHGMCJXlma41g69Z\n4bDelKT2m1lxsgvNGifWvxlcwrHL4dl+HRyyWnsTOTtAfZBWoqUOlp3Te2DofoU0IiNCdd2jc866\nXIpyDCGA44SaE3IKcD4BjsAEVEiDqqfKM5UhkEsFyIMJKJWRC5Azo+YKFIYjIDhCIAcKETTN8PMC\nv+zka5pBPgBu6P945vrIQzARwGYCc1v00n2LW7vBZqFau0jdrEy+TZq4xX2zEVdQrSAqcHDwRHDW\nFYwBhgPhwnS/EnGOsEwTPBE8acc57m1UrT1oaY3DxSqUzmelzSEcAG1Cb4qwWW+ki3647ujadIVI\n0vLRObme/hxAKKHKpiilHXIAaztLdcPRe/JylVaSMQRUZqQUkXO6zlahANBU3/lr1spQlKAqK7J3\n2z5SWEJdXoOainp4rfe1c3A60XbgVVsfIJB22ZM3yPUNbqv63vYa5G+ctn8N2sC+dchzDs57OTiT\nB7kCLk2lP2lEnqcMAWwZ8J5BTvC2VBgpMzgziBneyT5g50EhwsUFft4hzDv4aQEFVYRgsQrZ/P2q\nrrLk15jVyKWAS0HNBUUtkJQzaq5incKh6mCOeCWYwc6Bmd6bSGl/WcCqQIlccxLQ3uuusFGoLVGA\nS0GqBjPUZnXbgWS4m71u7jM51xSh8w4hRlGG0wSnVqFcp4GKAEwZ1q68DA98r4eyg/OMUD1iCMi1\nqOWhrjnpQaxfHVvSrefEuowxIKZ4xcoQ+HB9BBZHue3Jisok3hZGxULti3m08hiklpz0PAeCrg/A\n5tPD+SBWvw+9ja/tYXCz3EvzONQj8UF6KE9BXXeB5uSwk17YzhseWZ+sCIHvYBkW1eoeQAWhVEZS\n7eWguLhzgPNwcUKYF1WEM5wPskCrLvwqFgUXswDRlKFcUDdjziimDFNB2gpyYVQiMGn/1TOjXTHH\n7nDr/GnghCE4Io1BHhqm98PL5DqE2yKUXrfd2mM9pXMRbKhkcZ1RWV0W1ywzwwt9jAjThBBk0Vvg\nxK4FdMut99RFWw/2rqqeAdc+n+QI3nkU2D0WMKuy1XuAHrJsvZRJcC6vVuv16sK+ws/DTh2uqtoH\nmR2jWQ6DH2xDVxkohVsfbTkLSQMwDjU4AB4M6cntY0SIU1sX3vnmvZGtDxbYK20J2yZ9uq3f9TRP\nmKcI7x0cMRxpOJShVql5dc+b3Gen4zVEgUjdJWmU0DRhICB4uCkizgvm3R2mZQ8fJsFq1Nrgqhaf\nWh3FrEImsfQIAOR0kM0pijCnipTFGi3EIK8nkCMwOTDVNtVme3TlJj9VZjhmUYaOcF6RSN5DXSVf\nlTAzckrIOSMnxQCbKyLuZy3SUH5LCbXISSgLOsB51pPfwwexCuPUscI+G7rp1DOoikdaNLpWVZC1\nWwgpZVk7jAG/HOAP+ebsIBMsS76rCpGYBemduWnXK5dPb25lg610Fznn4HTumWs3MRht7mrl7vGi\n47d2MHnvEacZ87JgXhZM04wQJ1lXGBvKd+VcakFKCafTCSllEIAYI6Y5IgaPGKS5fFDYLStkw1Ux\n6WdYNc9WhqSWFEhBSiJUJ8PqHQGTg5s83CRRxDjrAxOh5oIKWdi1ZIk8m9sFUoqMKDVhQNqG4PaV\ni+CUqci1CQTygvsJlikbxJQ1EQ8KkfoEcpWJo6p/MypDjO++KuHKSFtqcETRwAkYCEHA68IVKWes\nWwJrkAIB8N4p1udUEUYJmqgrBOACEqlN4ZXaXV2Z99JwypwLtpSwbZtYq5UFV1aAfrQzzc1WTdu+\nKqD4ErXNTs83Hj5SOSekjT4VAI3ueoH5FasfA55mtVdmdYPRvC2oteeCR4wzlt0ey+4O826PaV7g\ngm9XZBYXG1VMEec6Lp1zwbZtqLUipQ05TZimgDoFxBjUqAGyepAG3XTz7dvlOxRq0IdUTWwK0ZED\nBcAFDx8DfAgCcDKL4ts2VKriYqUNpWTZSEp/gPOA82Dy4vqyA8hMXwciD6YAhkflzmlyhgg2BS1u\nsMUJxWzW6eZ+mgEGEnPDRfoTXrjXVySVGdu2ieVV9Kuqi8kOpAs/FzmxmSVo4VmAdnIOIQZMy4xp\nXhBClOgx0DyBFgzTgEwppWGPLdBlAZtckHNC2hLWbUNKCUUjhKPbfRZ84WEDkK0VtOvaxrtuoeFf\n6DgB3Z+ysUQLljgisKtg7hhuRafGMSB0GO87N5U8QiSQC5iXHXZ399jthWIVpigeXQuU6DVNiTr1\nMHxACB6lBNRS5PPV8xBXWxS1QDoS2JWA3vPm+Du4yX1wzkA2pxZa8IILeOF85e2EAgf2GQUeKdd2\nwhMznAN8iPBxggsAvCpGNjDfw/kIHxf4OIPiBPgVxIAH4B0jOMENKrFiDxAuJAnw60mCOzLB9hBO\nFDmjcxz1tANVBROvT5gZW8rdVcJoN/T3lCIKEWA59EhcKR88pmnGsuywLAtCkCVWckEpuVEpLBhT\nBmJ1HfBAU5ZZ3eOUM1JKWNeEXCuICDFG+ODPXGFx4QawXqOblatYGzBcsl65QhxmlfrPREZ7osYB\nNoUINXo6Tt8PH2b1CHxQb0AUFjmCJwcfJuzv7rG/e8C0LIjTBKcBlrODrNrBJWspxgnLIvM9TROY\n0SLKyzJjWSZhG5SMtJ6QSkEFFHZ7ulUIfBdlOAYchis1KwwSxQMDJSVkPoK3gupWFPZYU8G6JZRS\nhIbjPaalYtYIU3NW20kUEMKMOmeEZYOfT/BbAtMKMCN4wDuJJGX1dqtFkFnBcjhRcIoj6AWaK23h\n/uYcmCK8ws3CLPy9MYJrE20HoIyZBDx0H4E86WkdEGJAjBHO+YZBllIbKdui/sxoh1A7jKjfBw+/\nE+UrCriUCuedWoCdKcDMKMxwtSpsYvfMQFXLkDpNi7leo/EP4BvQgQaud0VXueOBzsv8WLQYQEcj\n9FD0wSOEiBADfPQtYhznBbu7O+zu9ghxUqteLzsAe0waGAULh3AWTDHE2LBrU4bzMmOaJzgC0raB\nAbiUAaVvPRcCeV40uWsqyaarss6gpwc7Rs2MkgoSZaAAhQoyNmR4pEJYU8GWJfoXg8e8zPAhtEiS\ncw7wTpnmanFgbuDovG3ithFQ0waihjS2QR0dXK80Gbn1IpFP5+DIi6uvUedmCdnEXOkmEatPsjqc\nubeGrwEt6g5zNRs4LkowhP+fvbcJtXTr1oOeMeac7/uuvXdVnfrOl3uThtgxCDYkSriGCKaTpgj+\nIOkERLSVZkAbIWojHY2o2FIwXIJ/oMY0BBHUhiKBG0U0iJCGqKCYy/09p6r2Wu87f4aNMcacc+2q\nc0/tc+/5vntr7Qmr9q69117rXe+cc8wxnvGMZwQQEWotwLEDzGhNcOSMfd+1QkHGXDOHEUZjgjWI\nLEyKIM822uFEbF5oCAiBUZuFbM2unQgUAkIv+RxeoIdiTtR+DvXiixo00oqAOQIy/QroIWepFbkU\nQELn445yO8flqGOLnjHu/NK0IK0rlm1DMK9xHLDdMTU/ZFBzvNCCQ0BM1ZgEjkmbBxoTCEBIQFoq\n4pIRk66zVsysfyZl4Ad4hh4eW+mMCMg4QA2kxi8wgAYJFRWCo1UclbBXwZErSm0KlK4rUkoAoFUh\nISDEAIQA4jA2nmEQTXRimnkQ+VFALVuZs/RwfQDpek3uzVqNnz7Nja6mw/RvSBQPwceh4S2N1prO\n74S1+RiE5uEtMmlGL4QA5oDWGvbLDlBWcn5tuOyHZQSzLt4YsaSEZV0R0+D7+QYg1uqClBb1Mntd\newNDkzkpRcQQUI0206QBDaCq5X2B7XrNc7SMmhlD0UP1Vm2hfR3zO6q9ZszQK0h2yhBpSCEghuHV\nXb+mzV0ICGlBWjebX6fRXPM6NfUwokyHLFkYwopFF1Yidc9i2xy6vfD0KHHU91xWpGVBKRlNsy8/\nYgLlKgM3Eg0e8lQQKgWEkMApARwRG1CygFBB1Tm5drLHAI5WchUNZ4huDP0ttaZ4AeHU8YsGQoVk\nMtq1koUZAFegWJVKI81MBXjYhC4+AGJAGgSk2Umjld6yKXwanvrP0O8f2QJ++ne2eWpFtnC1Wklc\nLtVqgi+otSJwwLouwOmEaIehewLukQLmLDTBcuimWg6FRqQ1Tdp0Ltu4djeMgRmCsYaG10P9wGxN\ncJueIU1fjX3x0TOGQay1IVPRGIuoZ3mf7pHu3RMjBEtuLOrwhJSsJh19p3Vv0g7Y7iHCoxHqUUAL\nwxh6ztX5hBrZUy/fjGnBsmRU0kTc5xLrn2kM7ZQVDy0BL1psACoTWgzAuiI8PGA53SEsGwoYy16Q\nLgfykVFLBRGQUsS6qSXnGHs5DcVorrK9KwUgEhZmS9I42bKhnBlUD90gjRADoxZBFa2HbOLerLnc\ns5vPBKlkUOI1cfsm94iNkXkdh50PX6BktCaIZX5LRckFOx3qtZfa6Tf7kXG+XHAc2j1yWVcQAeu6\nArAMZIxj8VtdeAgBRKzCDceBUlUEIB9HjxquKmRlZDVjqGgSEQYQ1rmFTQhMBTc9yf2+PDktcG0q\nAfW4axUUwjiEeoQwQUxisDzMMMWEGNWrZ4MsaIpa+xw+MYYCgK4SmLreRLgf1P1w9iuwQg4QIcaA\nlhYtJ631xzKGY/Q0PAFgQMCQGCDLCr67R3r1GturN1hOdxAK2PeM5bwj7wdaKRDR0z2miLStiEu6\n8hDJSvb0BjEErOopUXEiSAVQcERC28+opSA0IFZGoQoqgFU4diNHDv56IfeUSVZqDU2ba14otzU8\nqcSdeC493AT8LtmdMs+h1qqkWFv0pen/L7uqxOwXrSJQhRnzBpk6NBJi6OGxG1o1XgHNeI2DOwag\nVRNzGAYaNEjBtTroj77PPRohUVzqlk3h0/HUNM5kERHHYxsqV1SjzahKkP7hkyPT7nW0jPEAIcW+\nXhlCf9+r7zWElilikNaMOjdluKHRQ7NqNoiMGnRoSe6PZgxFyB52ApAWz4MjeFkRtxPi3QPSw2vE\nu1fgdQOIkbhoZnhZ0IrKfDGZqsmiLnVIi1Yq9BvosY3WGCPYopcFy8M9mhRwZJR9Rdl3tFxRsoAl\ng7j2CSCbsRmbqCI4pKE0QfUks5924kTsW9wu5u1BK4L89NXhCarxtTUB1YpiAg4cA2JasDJhKRVs\neF+MUelUxFi3FavhxY4z6oOfgOe6tjZsENFsJZm0U8mHhWta4hVjQiiWFPE6WXFy9Qi/+6YS9Ofc\n4iBMh4FttaeG0IdqhppBNOiDhTuoNJM0PIESY0QMWmrndcdkibihFvB9F/mEscLcDelMx4FDNFLV\nOwR6eP2j1SYD48M7qqOZHQbHhLBtSHf3SHcPiKcH0HpS7UAAFAkRAMeolQBm+TsdI0b1DI1IO2yh\nVqfod2bUIiNuKxa8AqeAfFlBlwvq5QCfD3A2JrveLb1gsqtnoEKQa8UBQm6C1iyU9k+oKfLn3pov\najgUcoXjYFBhukEUU7WBAIGR1lV1C5cVTQSn8wWnx8eRSQYQY8S6rlhX5SGyJ1+MRaAeAwzTBWi1\njKKF0kzAfrl0EndsDWlJKMZLLL3CZYTH7Dp4NNFG2m3P8dUgj8SuJUt8aDJyUG1CEzS+dhfcEKZo\nIgxsoh8iHZs37b2PDOKn4rDuAV5d5pOj2daiK+x0HUtLslJfTN8/nmcMZWRpFcRUlYjEBEkL4roi\nLvqguAActbQOAAUlSbNp5HnI6vpkYWKtz28oQmDRYNc9FkCNato2UGAgRAhHHHIGHYDQgeHSyLhw\n+18RQRYVhC1Co/rETi07B3E91bcz/FPPStJuQDrBFgY5iFUaBPUI07ZhO91h3TbFb5LSK47j0OSG\nhTHRpL2WVbOMwZJnTAMLcnC8yzMRWV27yndVE3ttAEoT5FzBfIxrn40haQa0usEsxYRlb3OO50FX\nQfLHhlCjJTGqjd5rbk3/AwCkDlFMCYtJtTE5llzgsTQxQ4IWO6hyFePTZnAeI6Hnl/I0VvHa9ish\nD19Dn2kIgR+CGZpLqlglqa4YRVW0XhakaMo0/jzozWYiSAgAxsnvMvBhCmE6btD/VsM1kqcLnEEh\ngQWIi6pm8F4hfHRZr4Fn6DcNGhJXAXJTVevW81r2XPqdp+ZWxhUvzxdkk77YvPCeiBFSxLKsvQQv\npAQyEdaQIpa2IsTQEzJM3Mm5yTdPl136OCFC0HkJMWJZV5zuKzgw8rGD9x0NwFGreZbXm7l7BqLh\nXakV+3HgMJXsmx2dywLMBunaHMrV8wWabKxNkEuDSDEuKCOGhG1dsSwLokEReb+gWuaZAVW+b8r8\nbWSqQsKTgRvY/uC2Xq8HPYgH5WuuZprLOcnwxuf4Mz/MGDa/WDKvLoLSFOZKQ7O+JWwp754tgkNz\nI61+rVk3TinPFHU+Y2v+5gMUJ4aEqOTLEBVfJPa/6s64i0XWJiiiorDVjV+/Z/o+8mQSbn0IDKTu\noScZSJ3QAMRlwbKuSItyBjmEEWaTJslCCDaXE2E6auj7yahAnDdoIbhVF8UYsW5bL/0DMXJt4P3Q\nsBqE7wLMSy3Y9wOXY8eR1VO9RceQnnw/K1zTnKX9xL3xPSSk3NxIjJR0/rd1xZJUFq2WjJYbmGAU\nKOWiIkZ0/J4ZbKryI8PsXzU3Mb6f6s/tWrxC5krHFMYhhtehf/74AQkUaMVJQ6dBUAhKYDasr5kO\noUv9+M11Y+cW/woc/cQU6RvCYLwGalUVqk19xP8mkJ5MMS4IMfWeF6qAQyryCoUqK6lcmKZwBhhG\nGDdaPZHPgni/zPF0Bc3eoTSQGSWdW0ZcEpZFk1+gQb1yw6eSNiO0VsIs94M0zBlHPwJFQM3aNvQo\nAT3EtmWhpXmXHSCrP+6YJvrrAKpfWUox9ZuMYlVQtzzm4PjpWv/4zlC3N81w+MYKYaQlYds2pCXp\n/q8VLVfApP6jtVwgYVS4WpDuuJBgquo9ru2ePJ44SkSzmMSTazRcmCiCpQGNUUS6yMjnjOdLeJmr\nKs0SE15ZQwDICuCtCdNTt9XVja8cgCmU/UgQYHqSGsEKKgWoFXAqjN2kAELkgOTcJnYvMWiZIBEq\nNERu1P1NuBnu8OL3Yhg3MAid59WzcTbnYkKuDpKDWUuvkpZFuV6lU5gIqjfp8z5ndr2crnPQ0N8K\nAMCkCib6G5f3IsWoOEBiQkzFPFGXeZsMYod0ZCRXSu5VTLc8y/3An4xLHzKeNbKxBlqJTB6b8kPT\nuipfOEZAGsqRlT5XK0IgoCVldMjgObkkXwIZRYrAYhFkx6rRE2rekWie136VzmhBAIG7Uru0ilY+\nv5fR8z1DE3Txtnz9VJmSD+4ZNqnW7EW6fSFcZ4h0Pj51QvtmtFOiVaBkyHFASrGwTb0+YlXPZhE1\niEl19EqMQBVU1gksBBQiVJJJTdu/Ss9g33qY7BgdgCtxzE5VcUNGSoLnoIR5ACbL1VTkFejCnWQn\nPhGsmQ9fnfgTfQCjPNz5ho4oGV4JjORdVHoOseniybhOf1RrDaDGUMWCpX18MN/S6JjblFz0n1+N\nq+ht8iOZjUalUYELJtScUUpGPVSUIzB1JfvWIqJhexy12RPHiNAquAVNtraGuRlbjyhZjZx6pUaf\n6bQpQhAGQZNsgQBhVoV8zp+9k5+dTXYPodWmG0E0uzEWuy7qK4MoDJHQX2K+vVcuumMVA120NxSl\nUeQD7XJGPXaU2lCNEiMcLGHCiERYk4K57djQDqC2jCJAEUIl1jIdM8q+Bx0jIbcEN7pJfExARR+z\ngWEOygft/Stc/KAitAoRa/1oeJBjUX2S/VB86oiLGzQLiw24p+n3zjWLIaCliMXql6NVLg2VZnTA\nv7Zqa8Z17nyyb3TIwNwwwQo+rp0c6qGte/bBlGNcyTzGoEkqsZLMnNUwkmXwa0Eq0f4mIYJAYWSA\nneqknuB1PXGHUJjBramhs54nEM9IqzMTWENuIUKNEXUShfi+8YMqUPTCAW6e3bGQqMvvD9xHXL8O\n1FsF+gboZTjkYbT9X9yFN0PYGlrJqPsF5fKIcrkYNcKwP2JUjpCwgCliCQHbuqKWjAMN5VBScAWh\ngLuMk6Wo+yIY7Cf0MPzWhs+Fe/2iMbP+UjS8IXYPbQS2uibqpFjtBmeaW3Rbhn7IYbAG+u+fXpP/\noklv/uMXy2DEGLC4SkqMOCyR0jyD3CpysWubDOVtNoP9BOJGo7Tx0z7iALE0JHUmwIJlcTZA6H1m\nXPm6VJ2r4ZUnrQcnAsWE6M6VPZ8tDB9c0+v9R3atEO3C2UDmjJnd8EQNs7EPlPf4FH77rvE8Y2gn\nu2dvetN3stKbtABpgaRoMlx6sji1pSvk+gZhxwu4f6ARq4rxhzJaViNYL2fU/Yy6X1CzGsNSgQyg\nUgAtG5BWMBjLGlFlg6CiNu3VUavjSZP3OdnnJx/0ZocvupneoJi5YrfcSJWFzFvAdPipYrULtgqY\nfY1MRnAKeu2t8NQX7Q6jH4a16IHofVmga0i5i9oXY1tXbOuGcmQ4laY11VHMtaB0Ne2rT3qTQ+BQ\nQ+yZXjYjpHNt/YfM29cDhDo84ZqVMWpjpn4naexxAUbXPAvFOTBCTeaY69rxkF31McmYA7am7GJ7\nNnkK7aVNa01cyk+hGYYyFWr6/A6IzxZq8AbgzifXfiUBnBLSuoC2FS1GyFVdoj0mV9utuHMNR0cr\n9x4a0ApaPpD3M/LlEeXyAW2/oB0Haq4oRflORxUUIlCuCFsD0oIUGXK3oqHiqBmoDdJy753hXosy\nRT6+Wbe7TdDD3msAHXY4Aa1xX9xOkcDU46T1RVq1RSd7gb2+zhDLmICSDk+M58lkCGvOyLvWN3tr\nUPf8aq2IIWDbNtzd3ak6tpG8B8m69gb3HQ654UGkfUli1Nab7uF5VOD38LBHKQUgNWYqrWbhrvOK\nMQys7ukw6sU14wpuTWk5tue0QRib4yPq+RPAU0P5rqDkQgwYe7NZ+N0sY6z10iNSddXtXo32PeNZ\nxtBd6fknKr4YEaNqidGSUENEY+9fOkBqJ1+7N8hTHSpg54D3N3FvoGbUfCAfu9Yf7zvakVGOilwE\nR244SkMlAlv70BgIYd2wckKRBelYcGT1DKiOMq1Pe4X2k09l2W5kPD1J/fDwcGZI9CvVReXduRfV\n699MSQwvh+xO4YQvO1zi4bi4l2meifU/yceBbJtSm1UZ9GHF+xAgxYjTaUMpBWcCLuczSi3dgNY5\nfP8kKnobg4iQlgXrtmI7aae6bV17O1dtupSxXy64nC94fHzEvl8ggIbHq/IKl8W0JnkyaPoGuvdD\nUCjNZbTcC8TAhbtUXGsqugFCa2MdXRGqZWiRElEvwROn2gWaP6RWRcX042GG1pYbDa5tZn1PktYX\nk7WEbE55IfcEhkfgBffdEMrkDsNKauxDijcvr4Y9HAX5fCDnipwbcmnItUGIEQigEgFZtC5yiUht\nQVoXxKOg5IpaGqhNG7WH8Bh74+nXGxoeBulCkyu74adzbQ3R3KsQVHyDQ+gFjDOBWtXD/bQengPz\nyCjPw0OgVitKPnDsegiWrNnJai1Ms/EFc9UqCLJQbVkW3N/fQUS7qLXd24pWu35DzOQmpxeAzs/9\nqwc8PDzg1evXOJ2UIxhMQ7S1hpILjn3H4+MjUop4/55Ra8OyLtjWDdu24XQ6YVk0i+xlkt5OgUgl\n2ACNFpxz6p5ntd7bHAuokuG/MEPBHYar1ir4KhKZ8gxMDJnWlaqmGwthkob7nPEDjSHUZeahGKIP\nk2jvJVFWOYLRO0Q3tQAAIABJREFU6LmnKPpK9ECsTadFA6ZOaq6U0xrhyA37UXAcBUdpKMU2bQhA\nnU4QQpfyUWHQgnIU1GyvO5ceD9fVLul2YyjlBQagEQTWIB4+S206RAAi7mFWiLE34dE6Yi+tnIB3\nayqu3iT3MKoH5BYqeTOo4ziwX85d9s1D3pwLjv3AxcJmMq5jMPVr4hNKLThfzlOL0pmMre/XbtQa\nMjNev3mDN1+9wVdffYXT6aTzYb93fcp8HNis2oeIkHNBWhJOdyecTqPhl0iDHKProYfBWm/O3aNz\ngyii1UA5Z9UWMMaAViKRkeyNneBtPwFrLhXsKq3ePAAC7obRowzPZXD4/EToM8PkYdB4AlLjuqge\nYQgAsxEsTSaJAJCAAkABatQqAG5TBtlnwReteYUdtGUTfQioQsgVOErDUapiENCmei51PU4OWP8E\nk49f1KsoRsgURXGtHaH14/DSIIEROW9s2InuoLaYMgkJ0BpZEmxoA8akxpBjNEyodUA+OBRiitTc\n4RHtb0OGsruR6nPvuGNVwdh8HNrY3j3CnLWsblfB1xAjFgiYN6SkGcScs0nOX8A5g0w2DoB6uT0i\nuD2LGELAq9evcH9/j9PdCcu6KtPD7oXSVtQwAUAp2hK25IKQVHFo2zaTYGPUiqu51TI7dYq6cELH\nhtWdqka/6arj0npZZs8d9PWAsWY8qrRDLhgePXuM/YAlhV4+Fx/+QU3kSd1CcLJG8duKYMZQSdIN\nUqwSARXCod9oWGodBnJ2DNHxOwNLIW2c5NY5D6zqNI3YRBYs8cQMigyK3kCIPSGt5UAhIi0K+OYj\naXObWgCoZ7ssAwMJ3a0WhP/zB2vf/oEdGiZzx/o6A42k13wyB8Qpo5iSCfO2CqoVKvseugBHN4BX\nofHIQDsm6WF4h0bsUWrFZdeQ+bLvOPYDe84oOUMEiCLaMlRaJ4Qvy4JtXXHZViUBl2rUH6PzeD+N\nGxwhBDw8PGDZVk1wmKTenLX1UloOrApE0Odx0GgrWE25wl/mGMXYq85qBaRpn2V3x506J6TQBUo2\nkWU1jsGw556XcEgleDQRevbbPX4fH9m7/hL8qd9+cvwgY8jMQIoIy4K4rQjbhpAWULB6VdH0uJIp\ntRieOChhkhpqx1lHnaqfCFeeYRsGUWCldcQQDhDLYDKLkr9nhWw/5JoKBaSUsK6CUgx3dBwCgnXV\nDOTp7oTtdFIP10QhY7o9YwjYKcwCZs0eu9MQmEBwjzD1Zk3OM3OD5rw1H84y7Fn8Bssa+w50ncTW\nk2fDYyO0JshFWweczxfsx46ci3kwrNUL3jnN1md0D2bdLAFTeo9msUw58+eHUF/S4MDYTptGANK0\nn7WbIM/eekKEtD+1GhUZHn53t/SQlBgRxavSxGhPBdIcMrMxRYNuBOdkydAzpe6des8iFwAe72/X\n/GQKr/HFz78vzy/H02bH4HVFPG0IpxPCegItixopAYxtA8cBidTaC0TL+YwX5BccqinfsLvHigNK\n09R5qaY/17QROFyxhBXLIvs/2Q12rluAqMRYSCCOKvNvfEOITuzDwwNev3mDu/t7rHfa3Fp5j+gN\n0G9rjEyvLkhzGkjDZPe8vAxL2zVGfVJFN2SNCIGb/g4ww1enhc4dJpEpUz2HRqDBQVXZqKpCC1kT\nKfoU7vSe2atQg5iwrAvSviAmVarxDGYXi/1M2sWXNagzAD7+1WS8Og2OtWeySIc+OpYkBAnST0yZ\nwHhPxuDKODlzYHAWnWRtVINrIQ/3QD35ghFkEjlhn/r1Xn+UkWj5nPHMvsnUPcJwd0K8u0c43YHX\nFYiLhshNPbWAAGJPh9vNV9h6hEJmEMWe542l3TuoVQvrcz5wZMWH1HUnhBTB4uKfmkbXvhbSS3w8\nGZCihnHKkdONF1krF968eYO3P/kJTg/3WDZrW2mtEMMtGkPDXNxg9WoPGC4kYsKsSrGIRq3oZHY7\n7UWAxuPAA9AJz7MEvxN922wMZ06ZGUPNrZiSsb2OF+j3Bw3Dyj0iWLGuB45d+XI5ZzCFjnsFDrjJ\n4dnK2QwN982+DIqLe4yOsytcxk4ThPYsFwBpvD6gMJklND15NirPqOuZ9sSas1Nc2s3aQjiXEZ0B\n8mRM0cici3jOUfe8BAoz4sMrxG1Fur/D8vAKcTuB0qJq0w0Aq5sciHu5XueSEYGaltCI0TbccLVW\nzRUHNNyqSqM4DhzHjlyyut0wY0veMxVAr2Qx6Xh7OMjPVsjfTjphgQhlXRENSL57uMd2d7I+LEEl\nyQg36jUYzkJ2UAHXJ/uUkHLtQvcKrqWWxoLtggmTWsxQrQm9t4W44owRqftjCpu9qThZZnqA7uh0\nIGZBCGrw2lU5WO6ZyYdXr/D69eubhUJ8dKCgEyo06aD/ecIBtgfB+x0PMeZu8PzZMniBerBdF1no\ny9OVgezJkckweobYHVFA8Wv75qMweCRknxciA881hjFiffsTpG3DcneHdHeHuJ5AJpdFpDfNO5IJ\nD9xnMtvwVLoU9IVf8oQTiRnDWpRwvR8qvSQVwtoDRV+ax5HGSv4O0RpWe3MpS84EJmxLROQT1hRR\ni+IT62lDSKOfa5OmdkA/zM0NN3hkoHpXqiEYhSVaP9zU7+9MtQlWkdCMaOshUK8kmDhjXuzvOFFX\nLbZOe0c+sFsmuYk2hteKB998FmZZEkAxwQrAGhJZw6luiGu12tWAtz95i6+//hpLWn6et/vnOj5p\nK2iEnY0mAzfthR46e+WYAcKtUTeGze43eoLsml/qDtKVetEIeIcn2j3Yaci10ZtM+mQIn+/IPMsY\ncow4/eSnSOuCtK4IywpKCWDz0kjGx7lyUQfzfOgPeuZYOYVX5Erx1n8F1TzCJro5VQwCilXMJ1gI\n4LggphVpXQ3LSr0Chsg6twVGShoyg8hwI0v3twqp1k2P8Gl3/AsfRIRlXZBpANFu2DgGLOuKdds6\n0RqTF0jMiIhoLL0qgJ0pMHkQStAdi90NmlNqinHc9n3Hvl+0z4mplKhqts6Xl115RCG2yIiU4xaD\nhlqelSYoKTvGiK+//ho//elPbxIXnlIPOgYI138/sLgRbvrPQNQLLgY7QKEJT8LUUFDZOxFSfxvj\nJly//zQ801+rOUyijLkh9cagvvfHq3hSbPoYzx7PM4Yh4vT2bQc2lWAdhkjkDL7aB4PfgO7mmmWy\ncCdIg0jUG+gnivceFMUlmAMkWthqgKziRqMhDcWAkJJ5LQvCMpoMdTzByr2ICJXRr6GD7x0Yvt0R\nQsDD69c49h17uiAfB1pVsdaYFqynDZtXHvQQWf+WmQAmsFxn9ABd/iEMA9hQlTtoHfN8eSjR1viE\n+459V4ikdU+QNYlno+NOISDG0YM5sM59YsbpdFIjvyw4jqPz7LbT9oM8iC9hjGTwd+NvLtykZ5kb\nGuoGcVR92PNZzFERlKLagsUx5GaJTglPMsZAl3mz/elQB7emmKElvJhVMlCmsk3rBjEZ6/nxPNDw\nmcYw4PTqtd5D6N1ymFQ/CKY3d0M43OSOOvhBxKp24xgEE6ESoYh21PJaRZcNklZ7mZ56ka1nFDlG\nhGTaakvqWU4N06S76w6wi4kCKDY4Tp4bZVv0wSHg1Zs32C8XJDMetRTAjMl2OmnixMJhAEP+keaT\nGhh+gHZGBJlcv0UE3qCpmfSTGsOKWqomzg7tfldrBZw1gGs80hvRx2jaemkxpXPfYIxtW5FSxLZt\nncqxrmsXpL25MYXCwHUE9NRjc0P4qTOj782phwkTQWLrWLCIoJpIBjEPzUILkQcX1T0/e2U/5FpD\nEEEQ0Soze2fmcc3GAOql7dQf6oF+bnz3zAQKaRN4D3G8dtVvm5ln9wjHeGKI+u03TEmMZxgCpLZR\n+D2dQEQq6ujG0HEiI8BpjazhRGoMoxKwHXl1I+qxlIzThQxs5ekgub0AWQczY7s7qfefIpbDjBGh\n02mcBuUH21NDaP+deITSRRw6edYONDJoxBMnHfub2ngSs/bICOgwSkejmWzely4cEDsdw7EjRgxa\nNlrN8Loowc0O2xfO9ug/ArpXSP1Acyzfsd8GES/MHXmBjiuz0pZ8HgQq8NpK6Xzip22CeynebBCn\nDHaHVPySIOZRTgUbPfx+giN+5mb+QU3kh0967UbpTXxqCKc/EJoM5QSO+gLv6Xj0m8JgNDYhTvFi\nQAASxmXwyBxHb2JtRGDPbAoN3TTHNfyhJ8jsbl83uL61EWPqyZC6WW2ooC/g62yxjHnwn3TwG1cL\nuWvaEXXakku5ed0xBAALEOz3lljROZ8yziLduMauuKwZ7hhDv87ZVRUhtDncm39/c0Ou5qwboP7f\nSb5NYJQmNX6tEag2NG5mjKb2H1C4xGvWSykIx4HjQO9VDaBHey4HpsbT2CDuWDXtjCh56m/TBBJF\nw+3gynE8fQiLRgRTlPd51vD5TeTbEFSYb+L8HP/y8WOQal3dQqpxDv3RT/3ho+nE0JM3HebKN4Ub\nw2jNirR3r/5dAyxTPPl+LggpgItKwN1tfBpO+eIH6f2MMSqx3Yxak5kDOE3yE2NyXWc8vm9TwkR1\n5xgUo8EjrHWqosrlGiJpwkThkdYPy1q1Q2J/HevDkZakmnwpWcmWB4LXFRUOrUwQ922OJ3Pn0dF8\nU9zAabGEJTuFUMnI89WzwFBNgu5tDtJ7SglLWpBTsY6EWgEGAZhHVZDag4goroNqnOMqAMmTtTd5\niXCD6OZ7mGUZtvGzxvNI14B5Zo4FTZ6a3zQMp0udwOnn80OsUqQqn7DlbPqFdgK5Z9EGxiTTo5ai\nYVbH+ujaGMboZTCW2RRIYxDVAQL3k3DCTHwDX4UAtzSoh7wEsnBJQGL6cQ45wFHgyZ8W6UtyJFbG\naunyWdNbDRjEaBYs4AlmIbJWr5Mh7SV15KR6E+Iw7qM3t/cQ3cneRKNtZMfCfuzb+ft1dI/p0xgh\nMKCNvkvMqaDW1DOkhkomxNpfyXuaqKORUsLd3R04BCxpwWXfsV92xaJrRW2HFVcU1DoEY+ca5BHJ\nNbRKqCgDkxaBmMgKz5h1T+p+/h7+QX2Tu4Hri30w/69Cognb6WGTeQmuUNyKFtzXXCC1XBlCmCGs\nzTuaTcYwq8JtCNwTJSEEw4asJ4NtiAbqgiV90m0PEyav0Le4W+FbHNO8DTbAmDt3l/XknRZcby06\n3Tey2zitidYPplko1o0beohEYNXJs+wkuWfA0rui0XQAJpehT548YXsfI4+LbdcpuPBCgNsck5Ho\nQdYMG3iVCTwboX9laueEijpDEDwOH7GoDyIIgXG627CuC/LphMtlx4fHR7x//x7n81n5w8VD6ILa\nqjb4WkThGmCqQtJ9Wav070U0ZEYQVdz3TPU00Z9rDn8AZmgYHEZ2uG8eeWoQ9S/cUDYzairjXkxN\nRL9Xr7BqOzMRq2E2o1mUgK2guou+FgDSAXnXUwympecLXYaTcRUuQQS9IZQZQgeMhy97m6PPqYHX\nwyBOoS8w5vcT2KBMf//0Z90ITvzSHi5Jsz11HRp1eMUUbQD06pNguNOsOuS4ptpi67x2dQDO+dTb\nHCPrOrzzfoNkelIfNn9GpgbmeztFWb36RI2hOinJFI6WToSX1nAWQc4HSmkjgmxi5ZmidKkQEURG\nwsvw/0oAKvrF+r8KIz4/OfbDjCFmbOhjfMg30thEniUsZvyykqlNsNO9vv53TZVwtTJFdc9KKahF\ndQgBBeFTCuAUuwx5ckqFX2vfUJMkVG2dy0h+vdOnG0fjbW+UK4P3iUNuGDn73kKj1ud/3P/mj54p\nNsl+U67uDYe6vJZvsmo48gi9SlbvgacNHIN1x7MQ2Tdw9zjbBNrTXAr287m3P/fhvsAVREH9noxD\nTq72h97KdvU7agwi5YoyTd6kKMldmibAVNxjQTQ8F5COUYqIlkrWhoI8qtJKQTLYq7MEAMsF6AWp\ngwTzEBuEA0IQMLxM88dsIj95hJ/0BGQAnHrTrw1hMR26WgpaKT2RAl/wZrBqKciHqRofagxb1YkI\nzAhLNKrHimVZtawrBMOINMZu5GVaVuta9KHehYA9KSNPTeBI0N/i+K55/cggzt6eeW4DIpmNYeu8\nwt6gyQ64YtjvnJRxb6NOHqMfatoAqOkGASYaR+p0mX4ojw/UvyXPmt7q5ProIRM98QyBrjr/JEIS\nuFdfQaAekvpg1sQYd+9btJ1vUfYAEysFKiWctpNWGuXS8UOd5zocoVJQzBC6M5NSgrJVA0gIIE2K\nStNEGxuxP4hAQlA+4pXD893jByRQ9OEUBfnojYY3NgxhNW/QQuKi+KDJyJghbL36oBS9QXnPyFnT\n8SIqKrqkFeu6YNsStm3phFqCQFpFLaQ3KQjEamS1sZCG595a0E8/PDkZ/Wi86c3idukTRrBN38P/\n3548r3t3hu9O8+oP//9o6ONG0PqfmLx/tgjCQ2NA1Y1dVLaL8lrI3Gk/bow7d9EayDelUt0wCnI9\nengHiFNkOlw0uKEhBNTCyFWbRQFDfUiH6kOKKZwHT45BFYlQKjJnNGv4Vlu1qMMuo2PSGNCMoGPL\nroydYlR1fYfEDCZDUAPpobIbxMb8CRv16fEDEijjrHj6tX+C1tQwOR5ULMzNWb2A2oYhdG+w6QY4\nTJhhP/Sr8pIIIaoc08PDAx7u77CtCcsSEKwjVqtF3fagmS6ECJi0VK0GzvaQXHoGZTaCRDTan9zw\nZukhMAakKk8NXz/0ZNBurqCR0cukeEWJSfcXw4d7GCvjtbw3xnFoOd5hHqQ0JePHGBFMcl5luNYu\n9tCvuY2eyUP9pvRKCJ42yG2eeX6wQcPZZiGz8EDfbP27BxisoqfWgn3fAcFQhIKTqNVDQzDBZR4Q\nSqkNpQhAO5powuRy0fmtxaNCfb4yBgx2oWEImVkV1r1VaU+aRaDFK1hMhYlrr4L5nPGDjKGz19Gd\naOlewEiS1GGESkHrHuHICLeqXct6o5+S1SOce7UKIUZtEH5/f4fXrx7wcH/CkhhMApFqwq8CUAVq\nAwUVf3BKhgq6quiDKlwTtCkLXT3cELqI5K2Oj5MXg+g8J6CeJkcwfa+MgdrrjEdobOuj1au/daOV\nj6wCDbYGalEaRbAqo9V6I59Od1itvWWn20DnzgV+q7cT7eG2MRJoBkNub4gIcs4IQau+GARhnW+y\npKPDTX0/t4aSqzbiOl/U8zKtwa5DaMmSFhjVjOeYW+uJ1F9Xr+FyufS97jari7I6s6DvS6AErSZS\nmC1BaoXUhBYrQq2jhrlrIf5YxlCchzeqOLS1p3ecq73sphvB7o1VK6+x53n/2+x9cF3I1TlH2qY+\nxojT3R1ePTzg9atXeH1/h22NgBSUfFGvs2m6XzgA3PQGkfbxncO1Voq67ETW59dMOtmJ1KkFU8h8\ni6MbQmP849oQih+E8tGf9Q3UjduU/JpVjx2OaE0993xk7MduSjU7spUBEjGWdcH9/T0e7u/NEJ66\nRwioN8lW8+qZxmb44jVO3Pq1d2rNDY7aGs7ns3rXiyo7BQmWRZaemKh17M/j2HE5n3E+n/H4eEZv\nuWDG0IUyYtTeN4BjvB4ZOLl6EPBVocgjheGpd9iKh16A/1/5dmVEoAbBsYXNofdniVaSO/Vf+p7x\nTM9QcTk/UNV7qBNNQr2/kSUsV2GxGiTHg7QQ/8gaPuUywhhXTSZmxLhg2zbc393j/rRhTREMoOQD\n+XJGzoctb4ZwhLCgUUUDGa1Cr1sn2FR3Y0SUbvcmjEKzyS7ccItjTowNGX6/RzK+epZ5undqPIen\nX4qFxT1RNhJszhjQtXDgcrlo0/LLjv3YUa1GfV2VtPv27Vt89eYN7u5OWFLq3qCumaGd6PCHJ+18\nszlAr4M6HnaLgXKrDR/ev8d22sAErRH2bpV2SIlMUmrHjsvlgg8fPuDx8YzL+YJiBQ9uEINJ48VJ\nX9LxYXV0zDt0wWePKWfgcJoXfW1TWw/2Pk2RTCFPtALVolKuFdVoVsE0CkQaotF4Pmc8P0xupW+Y\nJgpMX/EHyzCILY/QtNnNKHZjcnZPsHY3Wl9rujXW2UrbS2uL0VwqGhqKNYVvtVmIqzeoSkOFoAq6\nUdXrHuookRi0UO/gRsO629fn3pUvaEwY4UyZ8jHorMYLm2hL1egynTZTJ2/Qkxm19Z422Q7Cw7QL\nj13bO7hHuK0rXr16ha/evsVP3r7Fq1evkGKEEm+rGTurKvGE3lQAMK5JQ2SXAZvAnZ/lnf19M2qt\neP/+/eTdcRdJ4BC7mk8Iem9Lzrg8nvH4+Ijz+dwPKzhWaEbxONQozsbQ93U1BsdsmK543h4WY3iG\nIgp5sKhKDTMjEhBJ65hTjEjdE9VtS6Ic5CaiikhezvkZ45lhslhG1qkSKsnuoXCrFVKKGr6S0XJG\nOZRKowu/IJeKPBu/ap5Hg2WaADFNqAagVUE+KvZLBgnhOBiMBlTlJUHMfWZCE6BKRRFCFWhbSHf7\nLUFDBIADthO0NQFIydZNtLLbN7+HDDc3HAsW/7b/3BetY4NOs/CQqpQBf3xkCC306sbv0JIs70uS\nc+6RAUBYUsL9wwPevn2Lt2/f4vXr1zitK2qr2C+HPb/0vtmArRevaAF6ttsxLxcHgH/CyUO5pdFa\nw/t370cSpIu0quJM4AAEpS0REUopOF/OOD8+mr5kMfoaRq0/oXM/gfkwHUk44NMRl2ex+0x6Eg4E\nqqJGsRvuZP1tFpzWBeuSEJg6NtmjD6tmqZl6r6XvG8+j1oioBHvHhAwLcmNohlDcMzwOlCOP7GAp\nOGpDrs0MFQBrTA7QuHF2U4gFBwoeeYcI4XI5ENiltioYDUxACIYBgrshLE1QOz4xeI5EAHFQ8m6p\nCKycKQqAC78Ct+s1ALgKgQdGOIXPZmCu+pTMtBn7mZiidRMZgq2eINn3bgi78bTX99CrU2dSUq3L\nZmHbxTvdeQhvQL1hVIo3D1K9h+ghBKS5QulG57i1hvPjuTdfoi5qoWddi9p61Q+t/bLjcr7gsnuL\nVjFGBvocACqy8anhcMSocJkjsZ6/hh+4Blb1tSfVyeCCWtl64hiMlhJSDMZpnJO3nvRrnz3NzzSG\nDWXftVZ4xgYtU+xcwpaz0miy/qyUqvykJmqkBKjuNJhRbM2Nod0AKNcvl4pcGi7nw9QsCEyiWAcJ\nYiDEoJksTYowmhBKayjdPfeqk2rsd7a6R0IrVdP01tODA6MrRN7mXgEwDMxQq2m9iqRXkEyldG4Q\nZ8PYk2STIXQvsHg4XafkxmR4PfmyX3a8j48otSIwa3+UY1ev07KOXutealPsuc6Z6tZD6XVdkZa1\n9+m+5WzykQ/wWfFxsYKHWiuO41CDWCv2fcfj4yPevXuP8+UyDGEPnXSMqpWrd/HfTj+Z5POufipX\nf0UY70FQz1FtRUPOBRfaDeskq1Cxcr+oOGHPPTTnJ3/ePP8Az/C4UpqpuaAeGTUfFhLr15rzCJlF\n1GMDo0E9QUsG2SQYL+wKp/KsH4Eod/wQ0Juv8k5ACowUGSlwF2hwY5jLvNmqlQUxCPZcYqAKsGnp\nEHsGma/VlG9yuHc4Y4KmQl1r7ZnaXjLX7DQ2I1cmLHDfDRPMh2K+teE6jBoZRkCx3lIq9v3Ahw8f\n1JM5p851A6SrFBGRGsJSO1BfOpRj8EjOGsYFxZZCL+j/ud3dn/MQtKI0GS+1Uj5eQ2DtGeMHkWaQ\nL9j33OkxnXVB4ziRj+6l7d8e6XUzB2Dc+mEIh/EU/z2hf++JllK0SkVE+aLrojXpy8JIUVXzazQP\n0Q5yL8H8vvFsas3AB9XQaWg8vMB6ZNTjUM/QeIVNgEZOZWEFuRsZu3zmHrme4chcOraj4dAgHKkw\nNiNHwlIZNQakJghRb1+uFXlO6VtxeWvcSwLLkVFiRCwRklw6DDeKFT4d1yHowAjblUc4jGHr2Jzr\nU9ZSUazcSjHCfMUn6yc/MO0dN4ijJOs4vCEUI1iGMQbvtYvra3OjbCyHWtVIArByztFi4lY9Q03m\nayKLMiE7rh8CKqmj43DGZZLbunIQ/P75lpQnb+CJNlL5t2EQxzPwHf/7xOXqV8PzpWhtuvKLDRKx\ntrHOUawEsBjH8DOZIfQcD4iIfg3A//3Zf/AHf/zdIvKHft4X8bMcL3P85Y+XOf70eJYxfBkv42W8\njC913HBHnJfxMl7GyxjjxRi+jJfxMl4GXozhy3gZL+NlAPgB5XifGkT0NYD/1v77h6Fi3L9m//8l\nETl+L97ne67hLwH4dRH5t37s97rF8TLHX/649Tn+PTGGIvIbAP4YABDRvwLgvYj86/NzyOjnIrfa\naekP9niZ4y9/3Poc/6hhMhH9PUT0vxHRvwPgfwbwdxHRb0+//zNE9O/Z979IRP85Ef1PRPQ3iehP\nfMbr/0tE9LeJ6L8G8Eenn/+DRPQrRPS3iOivEdEb+/mfsJ/9DSL6y0T0v/yef+gbGy9z/OWPW5nj\nnwVm+PcB+Csi8g8A+H9/h+f92wD+NRH54wD+aQB+c/8hm4SrQUS/BOCfhJ5k/xSAX5p+/R8A+PMi\n8vcD+NsA/qL9/JcB/HMi8idxw/UHP8J4meMvf3zxc/x7EiZ/z/g/ROR//Izn/WkAf++kavGWiE4i\n8isAfuUTz/9HAPw1ETkDOBPRfwF03GMTkf/BnvdXAfz7RPRTAIuI/E37+X9k7/kyfvfjZY6//PHF\nz/HPwhh+mL5XEbQxtul7wvNB2k8xxr/rpHjxEn688TLHX/744uf4Z0qtMdD1t4joj5IqL/zj06//\nGwB/zv9DRH/se17uvwfwTxDRRkSvAfyj9h6/Dj1h/qQ9788C+O9E5NcAZCL64/bzP/O7/0Qv4+l4\nmeMvf3ypc/zz4Bn+iwD+K2gK//+Zfv7nAPzDBoz+7wD+eeC7sQZzk/86gP8VwH8Kvak+/iyAf5OI\n/hYU6/hL9vN/FsAvE9HfgJ5u3/xefrCX0cfLHH/544ub45uqTSaiBxF5b9//BQA/EZE//3O+rJfx\nezhe5vhYFnupAAAgAElEQVTLHz/WHP8sMMPfT+MfI6J/Afq5/y8A/8zP9Wpexo8xXub4yx8/yhzf\nlGf4Ml7Gy3gZ3zVeapNfxst4GS8DL8bwZbyMl/EyADwTM/zq9Sv5I7/wNYYAv/bIGI2cMKTBXW3b\nGiu5tL9rvBN5s3bW7lwcwCFo31aOU4Nv6W0f+1eXhxdcCYlLa9bUvkCmhjBPG9j0doLQzl2jH8P4\nFwB+47e/xfvH801x17Ztk9PpbnQam3rOfqyeTp/4zu6p9eGl/ne/cze68ZzpNXxtkDVw6p0L8YnW\nDFP/nNbQF0d/ydH9zf8hInyrzY5uao6/ev1K/vAv/NT+d736ydp91t4EXu8lszZdCzEgxogYE2Ja\nQPyxCRkd6a773Fx31PD2sdpWuJSC0tu/WguO3qSee3MyAL1P8/SG/fXn0US7+P32u3c4n79/Hz/L\nGP6RX/gav/xv/AWQFFDTHifV+lqUok2datUmPCJATAExRJQqeDxnXM7aUJ4ZSJGwbBHracNyd4fl\n7jXWh69w9/ATrPevEeI6faDRqN473pUqvYEUCJCakS/vcbz/Buf3v43zt9/g8uE9ci6ojbQHK2n/\nlWod2yCCGCNSCAC8B0vrvT/+1b/yHz7n9nwR4+7uHn/qT/1pvPv2G7z75hsc+wVA9b5BgLXZJEzG\nDmQtWAmBGSlpm88Utak4s/cc0TEON90oRNrPJsagjb+smXlaVizrhnXbsCwrYkoQaGPxsausX07T\nTXVczsjHBa0WkIgZY+pG1Q0rB338x3/9v/xZ3+Kf+/jFX/ga/+5f/petZaoZmlYBArbTipQS3n04\n4+/86m/gN3/rWxAJHu5PeP36AT/5yVf4+qdf4yc//UW8+foXsN2/AcKK1kg7IO4XlHzogeRtZZto\nHyTRr36qlrzj8dtv8M1v/hp+/Vd/Fb/2d/4//OZv/Abev/uAfFTElHD/cI/ttOGy73g8n9Faxbqu\nOK0LYghg0n17HAVHriDSZmFVBJc943Jk/NX/5D/7rPvyvGyy9StutaDlAzUXlOwNgawJvB/HZE1h\nrMOVdsgbDdolMCgEhBSRlg3rdod1vUNM1sKztW4E/as3nq/N38+vS3qjqlIqSq7IWW/OsRfUCgBs\nrUCDNqRy33ZyIMgu2xsU3WJuqYngyKV3Qpsdut7mR8aNEmvAw6zGrD/MCM6NpWAtJb0bnoYPALuZ\nFACsPbCZA0IIOmccAKIpItB1xEDfWL1xFdDb8c6+Qu8LzMNrHW96W0MEKKVqD/JA3Wj5HSFrKu/3\nVA+PgLQs1m41ARDslzNqA8ALSgHO5ws+fHiPY79on2rWPZfSghATYK9JHMBMCBwQY0KKq7bu5QBA\nvb60EJYlYV0XLEtCqdrUq1q70GCRAkfufbC95akQoTbtsJhz+exOl88yhqTt8VDzgeOyayP2KlDv\nVfsRYzI0/qXB2j/aBmAmUCBwNGO4Lv30Zw5qXMuhLR9L0daA3vpRrKOetxz192jakU3d7oaSG/LR\ncOwVtQqIAkDaMFumhvUN+preWtV7i9+iIQSgzcP3w1oyqhHR+RwVWNINos04EUJgLCkipajeYHcl\nNVRpNAyQdq8bawFh3HyyEMgNYQh6gOlifwLFMM9ASh/kndkmYy4CaHfQyRDe6CSLiBqJSIjdM2wg\nmzQNTak/FwSEoOFxWhJijKi14PH9OzQ6ozXGkRvevXuPb775FpfLBUyEdV1wd3+Ph4dX2O7uEIIa\nRCY1hByAtmzYTnfYtjuktCLEhLgsAATrumI7qTHcs7Y11UO66JpjRgq6pyHokJ320h6dEX8UYygi\nGt/njHxkayoNAAyi0A3KVeNTc7ca1PCQ9WjlGBFSHCc/oFjfcaBBPbzjOFBKhrRmL0cWRmmzeH19\n7h6dBm8BgDaSd+9RBCA2rCEQWiXzNNSwVmuhy0SAEMiwhlscIoLjUOxm4K1XdqWbRDVchBgZafIK\n9UBxXHa6j2L33DefGz8L19xQ9TDW+hs7ftyxwhkLJJt3ZvMo9SsaQ71SwzylaXtaavAT9DZnGIAA\ntQmoNhApJthEwB7UWTtW7ocGIN7jvDQce0Ypglwa9txwHA3n84Fvvn2Hb775FjkXrOuCV69egYhw\nOp10ZwYGSKGzEAMQgh6zteL86oz7V9/g/YdHNAFqzQgxIETuUAugnmGpikmmELAuCcGPxCbdSBpA\ncoV5f994tjHMx4F8ZA2lcoWALU4XiNmnGeCm7hFMp78lS/TkDwAa8nFBqQDoQBXGfhTslx05H4C5\n6jFGxLQgpsVcEjOE0A0VOCEEfTAnwDYVsZ1siREjQ9CArN4qfFEEnXwttbQesTe4W1prKPlArcUM\nScci1AbZP2rEgOgeYYyIUQ9Ex6BEZJp/PQx7w3iI9S82YN7C124Y3ahNhpDcM1QEuF+Xh+khRLQQ\nUUOFSEMrjv82ULM1aNeiRvNneGN/Hw2BGrdGivP7oUEeetLAeRkAmmgv5cuB8+O547qXveDxvOPd\n+zPevTvjm2/f4fHDI0DAq1evEGPE6/oaRKwY8rKA2PY966G5pKR5hVLw4fER+6Ee4PnxvXryPdGq\n196aqHfYBEuKqLUp9GX4CDMjxQQWAYcdT47j33E82xiWXJBzRS0afurGAMA9KuqutZ8yZLgS4N4b\ngcAgC6trKSj1jCYZDQGlEi6XjMt5Ry4ZTISUEtZthTaLdm+SpswSATFhWU7Iy460nhH3i018Q4is\nxjAQatNN0fxaoa3thcjwKxrA060NEZR8oNUCfKeY8RTO9uxiABNZGNvGiWzJFg9vW7+vZvzC8OaI\nqGcQfcOB6Opw/fhK/MANQBC0GBFqVVYBVT347D1bq/YR1SiTeaq3OATmDCigDpAYBgs7M8a+bYbT\n7UfGvmfEVEAcDJdruFwOfPjwAY+Pj9j3HSGGjjWGEM0QJoXBgrFGJsMbU0JpFZf9QLVEDjPQatZ1\nZZhliBEUAlquaE2jx1obIjMgGtl5OE8iIGbNK/wYYTJEDJdraNUTGNQ3gS/0ESXTuKnQkx1NjVMr\nFTVX5D0j54baDuQK5AIcWXC5ZOz7jtYaUkrYTqerm8wcuodJzAALmFZAgFIrtuPAkTMAoJTD9xWI\npDupYhkukYZAQJMR4nUqz40NkYZaDsNfnhhDD6O6J8aIwZMlbGdIuzIwjpaIoFOdAHQAPQTFB2le\nKxZ+Xx2u/vZ2qs48if4zVmpWiJrko8oKdUITe9LEjGTrnueN2kKbDzsUFG3qPx/UM/1aasWRM45S\n0cCI64a7+1fgkPBqz1jvvsGy3eN0/w77ZUcIjPuHB7x+8xp393dIqzIB0pJ6EpNgmDPp/D+8fg0B\nEFNETBEpBVzO7xHMyVqWBevphPUoyEfR5G2p9hlsPYURgjdLANZaP3uOn2kMB0gpQh1ghyUzWoPR\nCsWjGzOI/cABmoYvtTTko6gHwILSgCM3XPaKy6Xgsms4DiLISbAsi1p/OyViDKAQe+YLAKhni6F0\nn9b0TS8AWlGg1R0ONu+wNggElQgSADEwFnK94W5lCASt5v5/5wfAmAI6j1OSw7AlPUOarQ/pHqHT\ncNxIiohScAJ3r7A743YF0+LR63GKjF/TR4RHC5UtamixodYCqhnU2Ggehnmbx0qkCYNb9Qwhw3kh\nBoLh5Z5MadIciEcTQW4NmjhecXr1Bm+//kO4f3iN1oCvfvtbvPnN38SHd++x7zsggmVdcP9wj1ev\n32DbTogp6SNGyxoPGE1YcLo7IS0Ltm1DiAxm4N23v4Wad5SSsdaGu9ywHxXn82GUOY1ABDr3MWry\nDlD6Va0NpdQfKYECi5zMI8RkCP0ZYlZkJOrFvAkLVWxBliqoQhCKCDEhWZhT5EAohFA9M6Qhckzj\nVOEQwH7CcLDwCmAwJESIAadiJx4zUMoFgQ2nCqKvVRSobbWhWqY6CHUDfpNDAJHaDzkden/J1pSG\nN4bvseNwMoXB8xjJim4Mp9duIiA7RRUf4v48sdecPXUmBhhK6XmaSWGAxYy0QSnNXs/pI+opSH+/\nWzWGnaYEi9bYDaEMD54IIQawACEmhGXDeveAu4ev8PDmLR5evQHACGlDTCvOrx6R8wGRhhgj1m3F\ntp2w3d0hLQuC7V0dY4MRKbtkCYwQ3iAXTZymNWE/v8f5fEZphEsGlsdDIwnM6wmK+UtAA1Bzxnk/\ncNkPHD8WtcZuY/8wc8Di7jWRnjQ9FPWLtr9qEFQBKinGF7Y7bHf3SMuG2gjb+cCHxzP2s54IBEG0\nMHndNsS0XBtE8yzUA1HsYLEQKNhJEQJhv7wHWgZaRRBGXAJSI9QqKDVbyNzRLN10N+obXn+d7oGH\no0/gj5nyoutOehgE+3nnMvWX1k1XitJuYKE3QAihQqJXFAmMh6D4FRpYWEN4YYCkU2sIAAyn8ket\nBdS0eqGZJ1Hb5JXcojG0+w+fL4YZQTFKiv48sHILKQiW7Q53D69w9/AGd6/eYNnuQSEBICzbhvtX\nDcu6otYKIk1YLsty5REOQ/jdI0TG6f4eX339NeIS8fj+hPDuHXIlpEs19gmPPISfxcQgbshFscd3\njxc8ns9KIfpMwOvZxrCHPnYxAuoh1NXN9vXfpk3A0OcyASGClw3p7gGnV2+wne4BCtguB5YPjzj2\nHSXnkUleEpZ1RVoXhO4lRiWN9n3HECFEI+zGGG2zVITIqPmMWjIqVSRUlErgvQIoY8MCPTlwm94h\nfccRoN5fT4TZItTb1uwg+Tjp5Jnf8Rv7V2AVSxZfMIxIy6i1IdSGFgQsToUxbMiTNGAImefo+Izt\nDk3MWNayRoVmJly7GdSj13GDxhBw19C+Va6wGsJmUAIhpojTtqGBsZwecP/wGqe7ByzrCRxiP/iY\nI9btpCwPKINAaVZJ2RwTo8Df7+nopZYiSMuC+1evQSEixAVCAY97Qfz2UUPs6bVowrMagD1nvH98\nxLv3jzifL8jlR8IMPSliVu2jD9cN4uQI6D2fFigBnBbEZUPa7rFsD4jrPTidNAUvAZswQlrQagWk\njaxlWpAWpda4V0hutES3XQ/ZWa9xubvDqRVwZOR90VItzihygA8x+sb8IS2sH6zh2xr+sftUyvUP\nroaD7NP/riG/6eeOPU7/n0IyJoLEAOKKECtqqwitorVgfDcLqwE1fjrbgGGQ5GWC5N6lrpdeFlbr\n1YZrIp+9Sb7kcQVwCayarIGJsK0bQAGgiOV0j/u7O6Sk3uAgC2goDSIkaT1K85riZ18PEWKMWNat\nR5tHzkjLe4A0g+2Q1qh71jVUiiZdH88XPF4uOHLuCaLPGT/AM1ReIRlOpHtFrveKGcG5ZI7ttGAE\nhGXDst1h2e6R1nuEtAGkWB8FIC4Ax6hkazNMjgPFlKwq4cmJI9K9k+6lMCEuC9b7e3BkhGUBnRdU\nnLEXAnPpoV53Z/VDqvd6g+PaFn7KWgwvTaYF6c92TNCTZsB1GD2/rIhzxhoaWxLENqO0ZkRgmf5O\nLW0jqLcoDa1dU3KcLExEiCEAKVndsrMO9LnSXgxi3zqW7hcPlauAA2PdVsRlBYcFab3Dtm0g0hrk\nUpT2EoKW1ukhxNNr/272D4FJPct12bCtJ8S4AMRqU3pIr0yQ1hpKrdiPjMu+47LvOPYDtbYrKO/7\nxjONoZ3AmCkXgl4ThwlxshBZMR81ZikRhBPismFZFXTlEAEKI09JVkMsoRtdIjeGoRtCJh7h8RSi\naeZx4EjMAWnZ4PwBEcaRBcQFYsINcnX1E3D/Mmx84g5JgwijSQMJj2e5I+kcQTeaze8pTRjv8NTQ\nBtVDb/0T19IxLjuAK5wiN7BNJ+YrVQcD9pj4jIoxV7Wl8nFYfwvDp6f7MtPvHFd18nLiAA4LwrIg\nBMVqi6nMtCZwGPD7jN8V3ar7L5/6mxFSMjGWlLCuJ2zrpkmYGAFQ9w6boNchHzkrF/LIyFVpN89h\n1j9fqMGzh343/QPMWFPHDF1VRl1fMAEhISyqjME8Uvk9zGG2UlUNV5m8cJ+NlzaXaOHqGpxCSBDl\nNPasYUAICyQBtTRwPPSUkZE0cfPp11PbMKi3Ob7js0/z6uHUYBD4cGI9OnugWSgzeIS6SENjmyf0\nDK/zSNXL8yJ86UHIrGTUs8PQBExysJ4VqRSpfWP1tcMMqu0W7aAO21fN9oz4D82BabVBgiCQQlsU\n2PBhQSkFOR9qDL+TlP/513E9PKSsEFMdiiFgWxecTifcn+6xbSeEmCzylB4256Ke4VEKSq22f58H\ndD3bMwRdVwuQHS8fvfXkGXKw+sIYQSEpZmhHSq0VXEtf+AZM9gSN40BeIjRnMq/fjNST7Iaw6cMS\nOIShkhE4gkhrmB1nHOIN6n6X+vn8pC9pCH6HQ0Cke1RNGprwMFL6x09eiyyM0QULwxO9aqhncx3m\nDU6aVoMWzCBO8Rz86GqtIueMnHM3iG4MlyXZepr0iaa1RJ6CvE0kBIB//JFN765FcwWoAIm4Uvhp\nUlFqsTC5dI7f59xGn2uanKgrJFoEEK0cqjmj7FnXS2CQCJaUcHc64e5Ow/WYEkCE2oCjVFxywW4y\nXqUNpaTnTPPzMcPJGHE/sZ07NmMwMkJlFqsOUNkuMnxIWjOgvPVNwbpbzFHwTPH0cT6R15D5V+Se\nYQM1fwAsmiyJISGlpWOPLlU0IHkFAfxkuckxffCn/veAESwKnQ8nadeGVAZdw8vzvOTrKittmFOM\nWr+arP5cCbqmhEQCgWeBbXPWipIzjuNArRWwDOiSEmIcNc9ezeLJgb5TbnaCLckhRmvyBKgoD7MU\nBoeK6Piv1Y0D0EOoZGQLlWutnff3Oe+p7+P/n7eyoJWMclxwXAzzszA3V/UU1zXh1cMDXr9+jfOH\nD2jlQC4FuWScLxecD6uSsTXy3BToD+AZDqWRRm3AOjLOiO4l2GaghlF0TQDIgE9RVWov6gdkMrbj\n/WbAvi/ikTfp2AdEvUKSBpIKtALUBlT9AyZCIFW7WKJuuhCi4Qpq0pvoaVMaJsN+Q2P+zNMckHmF\nHQLpp7srnJieZVczt4jHwmRpYhCLvbC/NpFqF8aIZVmxrptSqJbFdAz9tfz9Ba2apwETligFx5Eh\n0hByQEkJyeTEkkUlAEy92+TDRJ61Ub6kQaTlaw0AGabvWGyrej9DCJqJJxXepRgADv2e11JQSkat\nizowH0VrzxvSGsp+xv74Hvvlgv1yIJeK0hpyaThKQ2DCw/0d3r59i+NywYf33+K4nLEfF1z2A5c9\nIxdRLJL4KhvwOeMHe4ZsJ26bTtircGkKk6WXzIwbplnfZtp2tWNKs9vtPCZ9X7n6/3RF/mz70hRz\nKBmSD62Brva6HNQ4iyBFJYUuy4ojLUDNaELITZCblgfe5vBE1tMf01XWuBtEwnSAydXp3xMnTfr8\nK2Y3yvRCAECshnA7YT2dsKxrJ9f2yEMMzhDDhnkkRFx/r7kyDdCxRwouDNuuvNRbhED6ILKoSIX1\nWvNdZ6K7paLEauR0QggJcV1BIaEiGNdv4Osj/P2dxoQoP+EcSqso+yOOyyOO8wfs5zMul6MbuCNX\nCAcIVCHp9etXyMcOQcNx7DhyxcW8wjpVruGT9uK7xw/qm6wLzQiwpCGM7YuRzpjAWGIGT8rGWiPs\n2nbVagzVIHLH74ZHeCUB5huVYDihf2byN0WrGTVfUPaL1jBWJwWzVr9UQWBgWxNOpxPKsaMchNqy\nisJWQb5RfL3nxsZPrr4Tq8nz/hIdc7JEF00bQ4MDr0c25ZEQOhZIRGBopci6nbCd7rq8v2rfmUfq\nuLRoRAEZmeNlWQAAMUbDDYOGysuCdV0QmE3hJKOUZkmz9omywdsZRIS0JJRcNcGEUf7YWkGpDf9/\ne98SYuuWpPXFWut/7L0zz7nPutXWq7FtBAfSStM2LThyKIIPpCeCiI562KADURz0SAXFkQOlEcWJ\ntA6cCOpAkYZuRbQRoQeCQltU3Xur6p6Tuff/WI9wEBHrXzvvuXUz76Oq++wdhzyZuV//n/8jVsQX\nX3zhUq6Fza7vsdsf4LoBiYECp5G7+3zRILOmxSfE+Yg0n7DOk6TJ84LpOOH+eMK0rHBaeGVy2O9H\n5LfeRMwRx9MRfDwK5SZvznlz74+PD59GuiY0UaGD89vFWYuCW5YkUaPidpZG19XaObBWFO0iZ+0U\nEJ3CDX88VzSBFm5IV6d6ZOUiT1Gau9cJcZkEYE/aC122fmiwCJLudiNyPGACY10y1hwRc0G+1DQZ\nwEOk5Rzyxlb0Y+N6bc+2gHvtLwaqurEPoiri1CmSE3L0uNth3ElD/1ZIwxlmzDpLgxjwXtSMhPQb\nNkzSeSXtikIyESGlCMwkrVm03RxEzd90QeZUXxCwaF+OnUAaWYUa9H71AcMwYn+4QegHxAKkwgid\n0FwM1/8sJvqoM+b7F1imowQvy4JlWjBNM+7vjnj58h7TPMOFHsNuj36/wzCMcMFjXhd89OIFurt7\nOL8CiBuWbUDOE+7hJ1eTncoeOW+EWNEVRJGWnnbblppQ2bhjznn4rgeFHsUFMAlpE9SyAwHmtvew\nFfs0hesW+C2yosUFaZmQlglxmZHjIgOrovQsxpgl/XUdEDoQO/R9wO6wE3XtEsExah/rxXrCB8AD\ng5tHKipcKUgZSTXxHDVFr1ek2RYduoY8LzMyROvOKsi2uYfOSkavnDMNrO3OlMlJ1XRM2AMAPIDQ\nZREb8AHJJdiQqwv0hTDxEy5Acdt17phRdHiazS/pux79KNL8oR/gi9xL3gsuKzqWn9zHv8ERTYZR\nf5TK9bqumE8nzKcjptOE0/GE03HC/d0R9/dHLGtCPw5gRwhjj7EP6PyA2/kGh9sb7F7eYZ4XxHVV\nlZpSGQ+bHNmn2xN5hgQKHQhZlCzY8KUMJEYBY+NjcxVPNYVikIMPKvLYD8jkUeDqjIs25K6RYI1C\nH/ILK8lCNOpyRGxC7hxnlDWK7tmaEdeEZRFcgTTkpq5HCB67w4iMjCUuoDUCqYD58T2Nr581lRLI\nMUZTxYU+U1jIro6kh1xS4O08NsFdZQdIsUTGTHZdt9FoQqgFFnFSjdNrtslsxTulW3llI9jipYun\nU+l32a4X3EtFA3JOMvoBl1lEMWdYckFOWRJJDUZKkcUohIChl9lEw7hDN+xkNklKAEUE79GphNvn\nWVGML7gsEXd3J7x88QJ3L+9wf3fE6ThhnhdlezC63Q6s11kYBuwPB9zePsPd7T0WrUAb5acKczzh\nJn6yM3TdCLgE6VmMTbVPeH2FKpwHKhvoDnISESh9wvcDEhEy6w3iDUNqizQm2+/Oii8G3vMZ+TbJ\nqMh1QVwX5FWjQhWDXOaEeVkQU4ELCYEZnQpAOOcxlIx+HhHmFTFmULpYT3gG/la8DkCVzGoqvFkj\nBUCrlBTOimBnH2qcUb85RHOEztvMCFYYxD6P6jZ14CKYeJtyl13NQCw1MjER2J9BJHLzPsgwo65T\nSkn5XJjX71kjNB1dUnUldYY5iCBu33cYxxG7/b6KMDjXwTmGJxmzK0K5Wz/4xzbzaRXmii8TUmZM\n04K7uyM++ugl7u7uMc8LchKlmr6UWrAzIddhGHA4HHB7e4vpNGGaZqzrWodAETl04fG45hMxQw83\n3oBSBNwKo6SAGcgssvmb6F3t0hO8yCFoetR1HXzXyQVfZCX3joQTRjJbo0aE7rwH2XAoqVZvfaz2\ns8zwyFjniDjPWOdVyJhLxLpE0SzsCygEBC6Cf/YBfe5VFWeQokvKH0/1LsTaQtjHnzzvNOLCKMjI\nBGTn4FxR3UPaFiyYorjhh5vMlmQFD24mq1DDsGHawsumUmjPFyXc23Vh+1l301Il0rbQIM6QL9UZ\nShVTu7rMGUqc7LMUrrq+w26/x/5wEIzOSRucUJsyuCRkSjLf2kaBAhVeeoxIA7F2Bjnh+6ZcMC8r\npmnBPC8yD0VTdiPjiwwY1UxkHEfsDwfsDwecjidM0wSeJzBEEHocdzpn6dPtaZGhc0C/B7kIT0q0\n5AzkrI5QIkK7cGXa1jaXoFNH2HUdXPAiKglpphYRarY++vq9WusEYYTushVeDLNk0Shc1yzl+dOM\nZYmIq8iEExHgg+AJUNVl5+uMlWEcEDW9vkhAqdqW6jKxRmWbI7S0l2H9oYIfuuwq26BGlxXb1ffW\n6XebSnl9QbuNihlv+7N9M2ySRNTXqbN9IBpSW/dUldjmOxfSbOZCT3GdJuhFE1LWGa4jGPphxLjf\nY3c4IAwDRCRBC2aFkWKU3uQQxKGFADAazrBE4p9oLONDuPYPiwhDztq+CVSMOfQdwmBtlr5Serxz\n2O12uL29xTzNmCfRMJxmGVp1e/sMb775phaLPt2e5AwzE6bs0RHDB4YrCS56ZEtrHzpDzXWcl6Ev\n3dAjaLO10DCK4ImckZkBJyVc5wMKE5AZVLSFymZiaHTQcpwqxYMIkmg75CIOcVGsUHopGd4HMJHM\n3HWu3lTOSwdE3/fo+g5x7S40agBqTsrnv7eO0P5nVulVUxJRAQdpcURT3dOL27QmvUqwwS4X7Xaw\n74SzrX18D7eeaPu9Pm4D7gHAQPR2HIFzcErgpwv0hkQEFzpQSpLBsSqU233QdVK53R0Qhj3gBtiN\nZ46PGaCca+84oOyCLP3iOSU4l7QV129D26CLU85Y44p1XbEuMq8oGx85hCrK4IJHP8ogKcGok5DC\nQ4e+6/HsmbTV5lSwrhHzMiOnBCLgvfe+it/3ta9h3I2POi5Pc4aFcb8U7IPD3kufMWmIi9qy0wDZ\nmvb6IIPiO1WecEqEBQNsYpIMFCf9kMGq0/XkbcRaIhs8ZM6w6AUPEJyOAfBgdtJJopQaGe8sTHoX\ntgldAMQBQ3XU+gF9vyL28YKdoZiVGFp5/epymkNjBOtcCrxCFdYh0p5Dr7QXuzm2ubwGSp5vZ2vx\nfAUJHE1a3FCwzl7BrBQgg1PMM/N5tHlhRs4h9CNSyoBfwVlEF4Sm1KPf7zAebtCNB7gwop4TIklV\nHUeVJjAAACAASURBVMG7XDUiTUmGWQa3p7gilwKCtkcqU4CcdCjlnJBixDxPOJ6OuD8eMc2ztPaF\noHNQxPmZc/beI2s/ek4JADAMA/aHDn0/Ck1HHWWnE/l+8id/Er//p34K+93+UcflSc6wFGBeGYMq\nWXhOyCEALoBcBrltLCOT4AbsnQDl/YhuGOA6mZ0qpGodU6hyPKKCncG8hcJFgVCbxuZs5gm0pa9q\n35WqT2fXPBtor6obzjv4rhPBiJbjWGQbfd8jjwVxlUiSHoF7vN5mi1vjCOnVLsSiwFxYCPZNZCkF\nEx0p6pWKYUPdq0NTpyt5+fapLBt8WJQxDtkrVZMtalQ8e6NZPOidvlBzzmO8ua041Oq9RFNe0uPd\n7S32t8/RDXvAbS5C6EwOcD2ck9ZGoQX7uhBpjIOsVd2U5XunnMTC2j65LJhOJ9zf3eH+eId5nmUx\nDR79bkAoncBYSp9yzTaAbazAMO7Q9TJuIOcMR4SbwwHj0OOb3/oWvvWtb1Zi/qfZkztQmD0cBXSB\n4F1Bjj0odEBO4CyzaqGETXhRrA7DgDCM8P0I5zuAvMJDHt4zwJoyY8OQxNHlGjUSEYpJuauTaqWc\nRFooqqJGVicqqxYT4MF1Lorz0uZVD2BgFarsQORV+pw/k1Lv62KbQ7HfoY7w1dGUkbBzLgBy7Rxy\nzsGTtj7qCs9cBHNyeYs6HQHsKxYp800MMGy2akWUZj9e5RCrknYTFW7kcOmc+iFlotfaXAh49va7\n2O0PWG5usE4yyAnkMOz3ODx7jt3Nc/Tjw4hqO+ZEXhkAW0QueKzJaxXkZcGqX2Zy3yUsy4J5mnC8\nv8fp/l7SW5aB8B31dTFjGKtECjVC9RllvkqTZTx7fgvnHA77PaZpQtd1eO+993C4vX10UPNE2X9C\ncAFd6ND3Dp6BnHaIMZ1VC7lkwIkjCuOIbrdHGHdw3QD4ripRkwvwwYHIgFTUFYD4vGK8gbMFXPtN\nxRnmnBDTijVKBSomqRqTgq+OfV1lTD1HOFUSVRKUaqBVMeuquFxnuDmK6nQ+ISLcXq+jJ3WAE6DH\nNOhF3PcIXQCRqM2sJSlzQDqa2Dl4LtUZmioS0PS9anGltn9alblGiFv8uKlwb61m0Aq1qEo0tK8L\nM+8Dnr35DvJhRlwmaYFbVzCAcX+jUeF4Xtx6aGdFLcUTnYfrJaDhKqCxYlkWRJ1nZM5QHp8xnyZM\ny4yYk/iMLsjALwBGlXLOqyPcYbe/UbrPqB0w4oQPhz3GYcCzZzeIMeljN5V4/xh7kjN0RNgPHXZD\nj3EIcKRvJ4c1BMTQIa0LchE5/dD36Pd7DIdbdMOuRoVyJdu8VtGda3tc7aJ1juHYbSTKVBSncDUc\nN45hXFes84xlXRC1WELewVOAQ3MzkMPWCeFrCC6gvvy8240AWGY7XKARbcH9x/LTV70YTdoKVvVr\ngifailJdJ73smjqBZe6JDZGHlwJN1oqK1CMB50PlojUblTTcKFektw1v+7I1AlqASVvBjB0KR+tB\nuUiTbhyvw91H9GkFM6Efd2cR4XnU3UaGD3DjpoQlgUWHLvWqbpPke8koKVfpL7YWSt0PhKAOcxMD\nFjaK4I7jbo/9YY9RxTy6rlPVe7kOWLUsU5IBYcMwPukefpIz9J7w7DBgv+sxjAHe6SjAYcAyjlim\nkxCekxQfumHQqtQe3bADuYA6TIqERiOuf6s4biXLrcYoAL3geDFr0QQA2JRvBJCNy4I1rkglCR3E\nyzBqmZAnH14rmqFD1w/SY6kHlRzBgzCOQ53vcNmmTgePKzVIBKa9v4r1yFTDXi9KRk5ReWpCjUAX\n9CybJp7hvnLOglUryT0s4SgFy6G4Alfc5gCb+7eqp3tLwQGUgsSbxuLFGjm40CM4D1cGALL4fKaP\nan5mKKY39CCSn5cuSJcIrfVVxkd13uvwN9YiTKqCvUQE33UYxh32+wN2e3GGwzAghG5jmUBhkWJ7\nsDVuPLYO+jRn6Ai3+x67sUPXBwTvEIYBvpfBMWEYEZcFOUu1p+ulrzH0A5wLYOfb8uJ2cVrVsElx\nnE6n8+rwXJHKVSlStt+k562Uv6nvStFEWsMYplWom1WxgNAN6HoZPWrVKoOo+t6j67Rn+pKtiQpe\nnVE+WMFq1mQLjnJLe4kKS84oGiWUnFGcq6mTfTl2oMLwVjizqn8bqrabMzqVY0izre3XJu5RKTyk\nHS2l6JCoS+eSQhyid3AP1v0tU3v1284yueaxqlKkWQGBKhwicIYUWa010ykHWaAwa5qwtjobE9xj\nN+6EBK7OsOuHs2HypWRkzrVf3jRTP55VfLI9LU12hHEI4ii0ouwICM6pk+mQRhVrhczBDaETlRqL\n8uSwbQEgNKVpJLsM/akRht5YrKQ1IiCnjFIy7E911ualWJ8ddGvarjeI36LCqqpsQ4TOMMIt8L80\nqwtNSxJ86A6teqj/1X4R7RzyQVrfQhcQfABgHLSypU1EyCUj5Oa1wUP0dt3GIdU5vtqudL4rRHVw\nLSv5elPVVnwQDgXaxkUyt7tkvw2OutoXZq1DRL3vrJhGdRaNPWaPFxl7V+fqEMk9TDrKYRgH7FT2\nfxj36LpeCjjmCBNVJ9uSW10VFP50e2I7HqHvvGrNOa3Iqax67+C7gL4Sb/WKtW/n+HZz9HAWHdhD\nbUGGnINH2FYZ55AoIiX7fFLyp0QkXLaQu+SssxqsTB/q/OW+74XdHjqdtYF6IJmbnb9AawHsNj1t\nn7WfqkOkTWdQlKY7IVd7pw5to0NZGmQV/WzniY16de6UHRfw5nJRd+khvII2WiE4rRxL65m0djET\nsjcY5OoNvyiz85VLrsRqwwxTSijZYAntUSdWPVTlDjuoNoGr58l57Q7rB+zGEbvdTrLNIFJvxm0E\nRMlcfKEV8Wzm5uPsyXqGIWz9wlZiJFiztpepWrDQtdRB4fUzFBS1gyd8tCbUtujQvuq2aRtEzwWc\nRbmEnfzBWodUYqfbnKGlZ5C0S1JkbQvst1koVjm2mxUlP+XQvHYmq7avxHnSVJM11TnrAGK96LS1\n0dovTa3aimLqMXX9O1edrlSc4OvPbWW4FK4fUa/uutC2WKGuusortJknpOefHIFY9rHU4szVHtqr\njktbTHnl84Bi+I0TXFfEuKq81uYUjSWSc6kLYVEHKjxfEYHpFe/te9W8HEU9x1gnrGNqS/E6qM7p\nxMZzxaPH2BOpNYDz6nA+tpUtErQG/bMDpyEEEykfV52Y+UJuP8mis+17mzLViLFWEx2c6rKJbzXa\njZVFURWYnZOqsfVIh66D80FXGe12AJC5fDJg8tqbimYoUbqrs4ipRnMpJWH823Q6iDO092zQA53h\nSE4XNTiNFlk6V8jGsxbZvp1bixA34MSS4kYIonIIt+lrFn1aT7KlT1Z48d6jKDXjap/fNMGTyZJK\nqVmXBes8i5JUtMKZQleqgp/qLBXF+7IQsmNsh9Q7dP2AYZCosKX8GCmb0pZymwKSUbMea0+fm1zd\nrS7RltrqxcitM+QmykODFGo0+LCdCrB0enOA1nJnYbeE3o0ab31vk5bbmbEgQVNpC7/tBpcZu50y\n6Df8gZlB5XI5hj7IYiFq0X0luJozbC/2mQjrKqu4KMJsghxBF5mz67FRI2LnqqIyYBGemLAIDPOV\n2J9Biv5tzxW7uSpG5HSRNT27UqPW7U/UCnPwF7zgPd5eVSypz8GcYGmuixXzPGOZJ8zqDHOMm09o\nHOGqkWPVISwi9lpyxrgbcQNC1/cYBil4vor7eBYs8TYa9qn2GQZCbdU8LUnUnzZHiOqEzgoi1XE+\n+ANshX9QPBHOmhy4nJNWImNt9alRYrHPKk3qJc/nlJCz8h47uaF9Exn60MEkpKxdS8QhLrN8Qo6w\n2+2w2++w2x8wjgO6YFJbVMm0MUbM84wQAk6nSVquehHu7YdBLt5O+lgNKtkGMdnq7bV1j+osbutx\nzjnXge9oKsJ2HT1MswHU9xtP1RRtiFRSilxdkK1SfU2Tf7h9mkq09BrL+NB1WTEv4gSXeVay9VrZ\nAxVTTAnrsmKZF0zThGkS0rf1HBMB/TDgcOOx2+9xe/sM+/1euIiv2L9SM5WIFCUVN3Wbp9hnIBWp\nq1L3u63kvAVkbZDWOMjtawPGbXIe2N5fzi70DX+IMlw6RRSl1uDMEWq1Umc4mLyXYBSpqmVXWXiN\nDl3QYfIa4j8VZ3jdzDmH/c0Nbm5ucPvsFuMwqNOQhY/1nKSUMA6D6OERIaUkaiejOMJ+kOZ8cEHi\njQpVuWPmiPRaMT4gAG32V6EMR3qRKuanzsvwpax4tBVvAD1/juDgJTXW1PwsqlDI5LLP9uezUhgp\nZ0TrJplmTPMJyzxjXbQTLOcamMj9mLHOC06nE07HE+7v73E6TVjXBSUXUZ7fCY1mtxNHeHt7i3Ec\nP0Z1M8cadXZ2XFeNNCO8zmf60jBDwJCaB/YwQ9VfSnV87YqOGsVtNwia6LDUcDlpJJhSFGzKOGpa\nLW5D7qzM9pRSrWbZSQAg7YMtVqjRjtNowf4qc9IScTz16PzeN+8cDocD9oe9VO76Xi7CpljhS6nO\nzKrCIqtkU+kkKpTJdKgyT6SUCecYzOcKxEbLaTFJKDbNzHDFeJ9tUWXDA+lsG0bzOceWrSpeCzRk\n6jVXA3B+ZzeL1EMzaCJpsUMcoaTEyzIjLmsNWmr3WBbFmWWecTqdcH93j+P9EafTJH3RIAx9j5ub\nA54/f4633nkLb7/zNm5ub9B3HUBASQlwG3Ztn2mO0ARWLKsAv3r/P8meLtSg/1Wox5ySPdlGgKWN\nBB9EhG2BpEaOpeqhpcYJpsYJsolHqkPNOYu0f4yIqxyYlFJ1gt47If2GIOnbOMoMFucbjiPOUjnb\nh6eAr6+LOe+xP+zR9704pwb2ALZzCE09+2GQKmIp0lqnyjQWMZKSen3xdQE0YdWHrV6WBheWlj2O\nlgYVbbs6j+y2gsw2P9kqxvKJ7cvViTZcnKcQcq8mVkyCKyWsMQpuPE3iBDXVtYjMilY5iTDDdDrh\n/niPu5d3uLu7wzzNOrA+4NntLb7y7rv4yle+grfeehPP33yOw2GPYRhQSkZcFpBLgu8rQzwn1URc\nFpX7j8g5VShGRkN8SbL/AKTwUakND6q9aB2fMn2a1BjVEZZzZ9hgQCknZFOf0bS4OkK7gSyNzqJ+\nsi4C2i4aIltrj1fpqK7rMCiO1fd9JX0K7UIihazYR072lR7crJdhzjkMw6DqMqyVYqqcvhbrBVEt\nrABcHZLRrWpK7Dw4bAyDnCFT2LhthzPxXoIB8lD+mESGqmikEV5tsfPtIHmvmpct7WszBrBNpbg6\nQUt9Nhy1LTJ9/OUGW0k0tmBZFsUGJS3OGoSwBi0lJcS4Yp4XnE5H3N3d4eXdS9y9vMPx/ijQSuhw\nOIx4++238Y1vfhNf/9rX8OZbzzHuRhABMa5ajV6V6hWEhwrUfVlUGSeuqwYxeu7p8Y4Q+KyRYbOK\nWwjIbETppmjyoCDSfoK9oPDGORJHmM7SYs65DTG316YtEmwnY3FhHRYzYrcbsdvvsN/vsNuPGMYB\nRCLugESCEToh+eSm2JJVg+0CA0MQoHSGB7JZ+r2ug7V7wKI1Vk7iJqJBrFVfAjwaQJsgoyL4AWbY\nps1EcK+4mAVGdI2Csq9OUvbCNsEwLUR7Y6MdW53qxdrZQt8emFe8FNiKFFWdehFRFK382l1eiqXO\nC+ZZhjSdppOkxvf3OJ1OmE4ndYQBb7zxHD/x1Z/AN77xDXzjm9/Eu++9i7EPMu0yLrqdVTNRwXgL\nU4WyDF9e44oUdRCU9xv09oRD8tm6stVs0nFb0RV5rnPJd0uzuPGO8r5SJbg2pro4Qmvm5zNHeI4T\nyAGXqlWKCWAdcTgMeP7sOZ49v8XhsMMwilAAgcElI0UGUZZwWwfdiBZfqphDzumJh/L1NDr7vmUF\n1OBtxgslJhlnAYLBewwSvmf9nG1p5ELVQW4q5luE6FTU11Idm5bo3aZULs83KTBvW0Ir9QXzl9tf\ndA0OH2eltBL985aWxhU5bQUSm4E8TROO90fc39/jeDyKA1RMMSrsRSSshXfefgdf//rX8PVvfB3v\nvPcu9rcH5HXG6XSPeToKDpgLCovOgKjXZ8Rks1Y0o4xRZvCQR6cdLQCelN09zRmqY6MKGm7prxyQ\nepnjbKUh1B0Xuow2yueMkhNS3mR+NlUTrQpnabw2UD3FhDWKI7TweI0RYKALHXajDIh5483neP78\nGcaxg2CpUsnKydI+GQ8ASjBOm5E+k1IBLrKC8klmTmrzhhvnlM+Xja2QIb8ztXS+DR4RGMWcoWvw\nHe0eoE2eadM4NIe4vV72S66uqmFC4pzPA55XOcULtbPi1WZbWywrfCRZmIm0LutcU1LrKjHGh1Fl\nLAq0Asls0nprrFQo7wO8CxiHEfvdHkM/gACpTE8TTscjlvmkc3VEZT8VIMaMJcaqoUpAdYaFC4ZB\nFPUr6f8Jp/kzYIYF4EYJ5kFFWG6M5qKrN4vhgkqc1nQ0J3GGlYtUCmRsHp9x2iQ8FyxxjWtt2UlJ\nIkLvPfb7Pd54/hxvvPEG3nj+DIfDDkQFKS2IaRVnmAtEycaDyemK0xR3tJJdtKvicm1LW88LEc3z\nH0PkjOKyVXft/G/OketoV0mlG2fYvMd4giLRRJvz1U0xuLZ2soKB1LACtn17mGZTffjqEz9uRkeT\nezJLoGIYftNaty4LlmXGMs+Yprk6QRnXOWNeBLoS7D8h5YycSi2eyn2ZMS8L7u9P+MEPPkJMCV3n\nUdKKdTmJdqnyTHPJWHW427ysWGOqjjWnhHVdwMy4fQbcPnuOcRjQdz38QzmeH2JPdIZaIi4FKhS4\npcHVETZRY6VBaDpsjjAl5GxVYqXMaDsOrOKYcq0QL6tFgTJFyyg0NiKUyCF4GXp9c7jBzeGA3TjA\ne4cUVyyz8pi0m4HhwPAoIOQiq58WSMVpPyDzXpq1NdbNV2iFDhCPqAesfV31M2cOUV+qabVVh8ll\nOIVVXrkHdSeaPahsBJXqspfR+R4/xDpr4cSwzOYvunpDMws+Yk2Dz4IOxfBTjBoVShX5dDzi5d0d\nXr68l2jwNGFZZPi7QGdaTC1CpmftHCuFMU0zPvroJYb+u5inWQn+Ht4L3BKCU+K+Q0wZ87LK+N9l\nxaI0GoPN5nkGSNS233or1aVahoA97gg8vYBSspJXGzDcKo3mEJvqsrVFZesiyVt6bI6wdYoC1Gak\naGTKWJ1iTLJaZZWCqs6qajhpZThnrGvUFHyVkDoruVtvyFzyNj2v+awavTYcxcuyB+mvnltqHJT1\nf5sjsh5ie28rzVQpLaxqWswoPiMlqsdabhiZtVw/jwx3LHVb3GYYjlUJnbdOFYjT3Zzc+Y1QHSEB\nYEuvL3PBswCmcnsVHqoFknWjrKzLIsLJq6bK84K4SBV5Ok24vz/i7v6I0zRjmhesa5R7yuYsM6PY\nPGQLMghYl4icCk7HE3bjDiF40UgNHqELGMYeu3FA33cAOeRcsK4J07xgmmes6qilkDorXc7h2bNn\n2O8PcCDkqLzkR9iTnCEzg3NWkdYiuBsAg8VrtZe3tjjD+6pMU/1KW5SYIpKuQnIiotBk1oQYNWrM\n1oeKsxWHIcPnU5a5qadpBhFhnRd4TwBnFBatL6N9yPzVgpTlfUk/G7wRvm3/L84IDUEZTVjVvODh\nW84KFPVBTW3lmDtgI1DrmFBZuLbRot7zmTMtzm0VZd0vFCBTkYZ8VchhFuDDqQZi6wi5Om9saXWD\nZ16uWVFSOL3m6AySMkxwmRcs84TpNGGepBCyTJPghVrEPM0LlkkZHVHS4aQ0NcHpSw1gagulsg9e\nvrzD++938M5XLcwQPLouYNwNOOx32O1G6XYKATkzpnmuEWhcY+1t7roAEKR1zwfklHBzOFSJr0+z\nJxZQJD+Hc5XS8BBvq6lwdYIbwGrfS/0unMKcIpLRZBb9ihFrTIIzaKjLTfRn5G1A5mYwRxzdBLDD\nPC0IThUsHOC8kK9NfoyZkIo5xIKYijrbXJ1hlX+6YNuqs7RFVGr04JVblLEVSLbXqvNxGyVGZNMi\nAH6gR9d0qljlWCvGbeGDC4E9FIz3MKQGgNJvaIsom/394SSSyzGJ5hW+0pEZ83SqhZIaDRqX0LpL\n5hlpXcE5w5HeV04LWiCUXBCjBDHJusJiFudYNStNZGWL4rk9d94pK6TDbjfo1w59PwDMmJcVp2mu\nrXfGQ7aUejeOVWmp8+HRwf+TI8MUV8ELfYBoiDzg/Fj62zrCbJVicX7JvtZVsAh1iFEd4BozYip1\nZGcpXOcbyMz4potF9kyiyDVjOi0I2irmnKobe4fQOREcDb427ZcCxCRl+tTglqyyTzlfrjNscbaN\nw9xgIu1rjfaEUh0ZOQYVBvt2VJBwBEMIyF1BSHLTMEdRIuK2z7jRRfQy0fDhPAtrBRRStmzLsY6f\ndQ6igW03/mWmwz/UmEElATmhxBV5WbAcj5hOR8ynE5ZJFWdSlCmSpWAMHmPYwQfheMaUpQDy4g5r\nKsj3J1WqScoAyRUmK5zlXraoHcAZsAsLcnIVf5iXGXf3vrbQAtDul22olGkRhC7Ae4e+7zAMPd54\n4w24r9Cjx3c8zRmWgrgu6gw9yItKrbToKF8wWpU3bkozOYvDUydoIXiMK1JcpYfRsMBUUDKD8+YA\nS2bdBm/pcuMQAcG2aF6xCYluQL7zDl1w6HqPvhMVZu+FXxhTxqqrWM5bG5+Qsy/XGdYQSqMxwwwt\nONOMExYRFi6b2g9lUKYaEZaaesu52tSwO22q99u512PuXBbNQb3Ig16qZDikpCTIfB6RemYAMmvH\nSe4M3WVRZm+dIhEu1kVyAUlkAeIMxxmUEziuSPOERR1iihEERvAeQx+qKHI/DPChw7JEeP8C85rg\nXtwhplRxww0zLCjYvgsIrdE/KXHeIn57HUuWmZYInlVARc970WuOGkaDRZt390eE8H3s93t89at3\niDE9+pB85siQnYMLDLggROuSwDmh5CirTFxr5Jf0d3GI2uGREyhneC7wjkB9QC4e3hfQmmUVSVkw\nCHWARUUZWKtRlfBt8aGNimyvd02XV0/wK0mFSqMNQDpPolFuNCq0wsBFRxMPF1M6/+WMq9cwCriI\nujiRRYkVPtTXCrBOBPjgtStIxkF6v1ZSrkTpCrco9BJ0Edv6kO18K+kfGYUAJACewfBwYOlpbsjc\ndZ8v1RUyA2kBwCBieE/og0fuPXIfkIcOHHu4kpG8g3OEvuvQDwPGcUTX93ChQy4FixY0Xrx4iRcv\n73A8naQJIklbXFv0Mke3mWhUFm5pWqw1B9MsbaZhNhchKX1huw6lo2xdV9zd3eN73/s+vvOd7+Jw\nc4t1XfEYe5ozBCPHWFdU1xedhUwik18SKCcgRWBdkJcFaRaSZtJw2+T0PQHBEcjSIOdRGJiXDNCK\nWBYwK+6glakaDTb/tvtRDojx0yx9BrRNMBdkBmJizBRr6CzKOqpQo9GGpYUX7QyBM+qM/Hr+QNsD\nLBGiDZIvEh3q4840MGsEJzxS7z1244i+6zGMonBsnLVlXZByBjXFt64UdB3XdMkKMTWmYDS6eR6e\nAXgGnOkZPvTxF4oecgavE+ClCh96D8cdPI8IKOg8MPYBy7xDTgnkaOvvH0c4HxBTxsu7e7y8P+L9\nDz/Et7/zHXzw4fdxf5pkbrmqFT0AVPR/vdmYhCFVlCelEIxdI9Z6CRi53jXYNdVokfUeNubfuka8\neHGH3/mdbyNlxjRNjzosT+9AKRk5rWDIkB4fREgRpcCVCFcSPCdkznAlgkoE5QjkKI6SZSU6m5Oh\natMxFcS8gAGdoqakz8R1JTeCLRNjW9u3tJiqU5THpc8Btbwv78h41Y1Qgf7t3r1QkxzZjiHRxx3h\ndsz1Zz0/beeOVY7b6jRvq44MaTJtyU71JVWUs2gEmfJGrn34VSvJVSHHpLrsxEnKzLpvDqp849BA\nn5d3krkUlHUC9T3IiQCv6zw89/COETqJ2KNSZIhIR2X06PoBuTDml3d4eXfCh9/7Ab77/of44MPv\n4aOXdxK4aMsRazpskIuxPypmSEKcJ654TLuXteOIrC0TrxBesM9tRFxyZpxOM97/4EPlJi6POi5P\nHgjlAHDOyFzAJQGpE/wQgOOCQBnkiqzIgeB6D4+AzjFKFjZ4lXoKMn+EiRBTwZoSjtOC++NJSudr\nRMpZiblUL95tfWkvemoc4xZyb10x9lqqn9WuMvL3bQf60qNCSymtIHJWudAI+vwMqL6gOjLHLDMo\nqoNC4xTl4HMpgC+qNiNDfwBd5dX38rJN0JPnuErMB51w54MA504rzyiAlHPaKrcDOw92OpvZeqEv\n8DQzM2JaERzgHYQA6gDqPIIbgOAR+k6KWpnr6E7nPEAe67zg7u6I73z3A3z729/B977/AxynWfuF\ncdY1BJgD1G3XykkT4DzMP5pineCK2p3UOkM+v6NBTjyAZiMpZdzd3WOehS70GHsi6Vrwt1wyOAlR\nk1ySWcTeyxhPFCHPBlEa9q5D8ITcBZQsf7bQK4JI7juHNRXMccGyRhxPE17e3+M0LYiFUbiJ9Khd\n8wmtY5S9Kw8OrO12bRDUITGESrqtr2lvdos8L9CUtlQFUpuIS55vIIoHh4iVZ4oCsGNkK2TBilmu\nCjhYYFYKwbuCvteh4zRUDLJodlDbrmyKWpbiSvYyZzkwAxyAYKmT33DEGknqY/CQ/z3gLrNAxpCI\nm5LggeS9OEQvi0QXPELXgZPiu7DJkYSYM2JMePHyDt99/wN89/0P8eLlPdaYtKjmzjBaiWPozHlx\nsycPH5F7c5uoSOoQPx4V6qcbJQfnCy+DsSwrpmlW/PLT7cmRoR4VmU0MDU/BADGc94DiQ84B3hHY\nOxBLtYidRHBOh/GE0IHJwRURS0iFhVqzRqwpiSM0QU+iV7k5fPyAWhn0k/a/6XV9+DoNubezYC8j\niAAAA4tJREFUeIn24A+n5qtZ1F912GtVGToUvKneVqkvR8rhVgZAhi5QNsxJFs6uC6I0ZIo0lZd2\nfmMRyfuKc3C87WCbAZfCgNJ+bMAUw9KzCzSG0lEyULyGh3pPOGnnEkUnFlDdYJDCSIWRC2NZI07H\nCcfjCfOy1C4uGSe0wVUPr6cteDm/gFqe4RbcWHTZQDZnn9FYk30AknWkmLCmtVK2Ps3oKekgEX0A\n4P8++g2/9+1bzPzuj3snfpR2Pcevv13P8avtSc7wale72tVeV7vU4cBXu9rVrnZmV2d4tatd7Wr4\nnLL/ZkT0NoD/oL9+FULk+0B//zlmfhwF/PPtw68A+JCZ/8GXva1LtOs5fv3t0s/xF+IMmfl7AH4G\nAIjobwO4Z+a/176GtF2B+ZGlnav9rrLrOX797dLP8ZeaJhPRHyCi/0lE/wjAfwPwDSL6qHn+F4no\nH+vP7xHRvyKi/0pEv0lEP/+Iz/9bRPTbRPTvAPx08/gfJaLfIKLfIqJfI6Ln+vjP62O/TkR/l4j+\n+xf+R1+YXc/x62+Xco5/FJjhHwLwT5j5jwD4fz/kdf8QwN9h5p8F8BcA2MH9Y3oSzoyIfg7An4Os\nZH8ewM81T/9zAL/MzH8YwG8D+Jv6+K8C+CvM/Av4JDLi1T6LXc/x62+v/Tn+QtLkT7H/zcz/5RGv\n+5MA/mDDMn+TiHbM/BsAfuMVr/8TAH6NmScAExH9G6DiHiMz/2d93T8F8M+I6B0APTP/pj7+L3Sb\nV/v8dj3Hr7+99uf4R+EMj83PJk9hNjY/E54O0r6KJPlJK8U1Svjy7HqOX3977c/xj5Rao6DrD4jo\np0mmSv2Z5ul/D+CX7Bci+plP+bj/BODPEtFIRM8A/CndxoeQFeYX9HV/EcB/ZOYPAEQi+ll9/Bc/\n/190tYd2Pcevv72u5/jHwTP86wD+LaSE/zvN478E4I8rMPq/APxV4JOxBg2T/zWA/wHgX0IOqtlf\nBPD3iei3IFjHr+jjfxnArxLRr0NWtxdf5B92tWrXc/z622t3ji+qHY+Ibpj5Xn/+GwDeYuZf/jHv\n1tW+QLue49ffvqxz/KPADH832Z8mor8G+bv/D4C/9GPdm6t9GXY9x6+/fSnn+KIiw6td7WpX+yS7\n9iZf7WpXuxquzvBqV7va1QBcneHVrna1qwG4OsOrXe1qVwNwdYZXu9rVrgbg6gyvdrWrXQ0A8P8B\nxEcRNWnq5OIAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_distorted_image(img, cls)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Perform Optimizations
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"My laptop computer is a Quad-Core with 2 GHz per core. It has a GPU but it is not fast enough for TensorFlow so it only uses the CPU. It takes about 1 hour to perform 10,000 optimization iterations using the CPU on this PC. For this tutorial I performed 150,000 optimization iterations so that took about 15 hours. I let it run during the night and at various points during the day.\n",
"\n",
"Because we are saving the checkpoints during optimization, and because we are restoring the latest checkpoint when restarting the code, we can stop and continue the optimization later."
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"if False:\n",
" optimize(num_iterations=1000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"RESULTS AND ANALYSIS\n",
"
\n",
"\n",
"After 150,000 optimization iterations, the classification accuracy is about 79-80% on the test-set. Examples of mis-classifications are plotted below. Some of these are difficult to recognize even for humans and others are reasonable mistakes e.g. between a large car and a truck, or between a cat and a dog, while other mistakes seem a bit strange."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy on Test-Set: 10.3% (1031 / 10000)\n",
"Example errors:\n"
]
},
{
"data": {
"image/png": 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U707dh9jfzw2qmZGqZgnEYuzyrOtYmG9vCnhF/El+LQWWoRSOYGYAKWL6LeuD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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confusion Matrix:\n",
"[ 0 0 278 0 0 0 105 390 227 0] (0) airplane\n",
"[ 0 0 119 0 0 0 152 576 153 0] (1) automobile\n",
"[ 0 0 298 0 0 0 65 481 156 0] (2) bird\n",
"[ 0 0 321 0 0 0 67 474 138 0] (3) cat\n",
"[ 0 0 303 0 0 0 77 485 135 0] (4) deer\n",
"[ 0 0 276 0 0 0 61 549 114 0] (5) dog\n",
"[ 0 0 303 0 0 0 59 524 114 0] (6) frog\n",
"[ 0 0 296 0 0 0 158 386 160 0] (7) horse\n",
"[ 0 0 110 0 0 0 153 449 288 0] (8) ship\n",
"[ 0 0 160 0 0 0 276 324 240 0] (9) truck\n",
" (0) (1) (2) (3) (4) (5) (6) (7) (8) (9)\n"
]
}
],
"source": [
"print_test_accuracy(show_example_errors=True,\n",
" show_confusion_matrix=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convolutional Weights
\n",
"\n",
"The following shows some of the weights (or filters) for the first convolutional layer. There are 3 input channels so there are 3 of these sets, which you may plot by changing the `input_channel`.\n",
"\n",
"Note that positive weights are red and negative weights are blue."
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Min: -0.58472, Max: 0.65258\n",
"Mean: 0.00126, Stdev: 0.16380\n"
]
},
{
"data": {
"image/png": 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EklWM+qjev3sms7M//FCKwkBzu8nzaz6Zr3TMTKEbMLIPsz/bJs9qq/EvX45+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\nRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_conv_weights(weights=weights_conv1, input_channel=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot some of the weights (or filters) for the second convolutional layer. These are apparently closer to zero than the weights for the first convolutional layers, see the lower standard deviation."
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Min: -0.14185, Max: 0.14978\n",
"Mean: -0.00008, Stdev: 0.03539\n"
]
},
{
"data": {
"image/png": 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wrljl5UR5ecy14x75N/+4Vx5ltvYvfvXrt5VzgUtLiZQxum373xOy2dmKQ53H\nTUQ502UnJSsJCyNyu7lvwwapS3qd/4lD5y4Izb8qU85nzw4uNpWWzz+nMyNHMt/+p+U5aXWd+qim\ng5PSXI1qaw8HHV93+sb7afFUvh54eOgBoRuqfAJOyEkgFOfmnbKaOyxcbqmpIdqzh7mO/0etkD00\nVlmyWijXjtL2yw5lVjJkCJG62kZ5GULXda6A2Rddq4Rm0EG+zCFbqQVHS0SCGDU/VTOOnJqmcluz\n5nqeT1/v8UgafHMFAAAbQHIFAAAbQHIFAAAbQHIFAAAbCGxDKzqaaPhw5nqzXc5KX66MG1HPaBIR\ndfnbuON2CzcJ+vcXu0JLlkiZevR13Tq5eXVC7nFZit9PdP48900a2yKF6mFh5TkQEY36lG8SxLRq\nbnwQtBojqfCf+DnHddOlLiKHj0XJPlQgNFfX8Vhz1lgba0GRk9LG88Ps8fFS9/mL/EymruKlYSzf\nxM35wxdBx/c/3Hyz2NAaO1TK1uTx70FT58rNq7Lz8sypQ3N+v7fo3lVn3g6hUz9Xe1+X15o7dw6/\ntsvazbjoriYa4T/JfGVNY4QuTk1Wmrk1cxYuZPZLm3u2+Y5vrgAAYANIrgAAYANIrgAAYAMBrbm2\nxyRQ5R18jfWRrz6SwkI+rpbU2mkiOnCQj7628gxxS5eTzvhvYT5dA5Zly7g9eKYcATx48mTh+0lQ\n0XFiYohGj+a+V3fKZihO533M1vVroFu52Rxl7TqWZrI6RSxaIIVK9YKmXJvo9de5XV0dTGiCqCii\nb3+b+06f1gifeYaZwzrPCknBG0pVx8MPC02vqa4WxSvFc2UvhgzlrP6IYxuFZvvMo8JH/2lZLxRK\nqL1EuXuVfYnGe4Qu84c/ZLbTKfuPKNO3yeqe/c0OF50O5Wus39I0kHL8+6+ZPX9+uNB89xC3q+t7\nljbxzRUAAGwAyRUAAGwAyRUAAGwAyRUAAGwgoA2tmhqiXbu4r6lJbgKtWsJXq9tiYoQmd8oUZq+p\nsu5gdns7UYXS/H77Fnkwf9IDfONo7xG5ORc3WxZJWImmgRdtX3lR6GatHsTtc4uFZkYF3+TQTTQI\nho4OolqlYVPlfLlpNqyJH97ev19ey73yOLMbFlrb2LuhoZMOHeLBPvhgohQu+iUzC7LlTWtx8c3R\nrgjNzkhvcblE2zh184qIaI5H2TjMVlu66Sd+WEnXgIHU8jIvGoi+LCcRqL/PiJkzhaSu7ilm33ln\n8PF1J6amlEbt4j9D13nvhz/k76+uk1yi8tpo0pkWfHMFAAAbQHIFAAAbQHIFAAAbQHIFAAAbcJgB\nlEY4HI4qIrJsmqTCAOtGANtzVVvIAAATKUlEQVQaJ9GNE6tlcRIh1m7cKM+f6MaJ9Rv3/ANKrgAA\nAHoGlgUAAMAGkFwBAMAGkFwBAMAGAqrQcsfHm0ZqKvM1h8QKXUwUn7F+pVzmcJeL21eveqm+3mdJ\nfzR3UpJp9O/PfOU+2UosKYnbkaGygqPdlLeooCDfZ9Xiu9vtNg11tISm/V5tSDKzdSNL1H9WXe2l\npiZr7ikRUXS024yPN5gvveYTKVTW8TtuGSYkYX7eA9JbUUG+ujrLYtXe17Y2Kezi72qrKcuc1HaV\nVt5Xt8tlGsqL2BqbInRRzcoYHLdbXkz5XYiI8s+ete5ddTpNI5Z/3j9tGiB0aiHUt2+Vnyv/xx8z\n+woR1ZqmZc8/LMxtRkYazDdkcKfQXesIZXZkQ5W8mFL65q2oIF99/XVjDSi5Gqmp5Nm6lflOx8i5\nPaNu46WmK/Jkf1KlQo5++lPryh+N/v3J8/77zLdmW5rQqVV5g1yy9LGsQ77o6ekOy3YhDcMgz0dK\n2e1OObjrzUjeR/MHP5DXUlukvvCCtSWl8fEGPfYYn6G1ds9AKezkL3HNUY+QJBXy8tecefOCD7Ab\n2vuqDnciEpmzoOMWITl1itvPP2/hu5qURB6lsXDB954QumEePnNMW6fp9wuXIybGunc1NpY8Dz7I\nfEP+/6tCp/4n7zksP1ef9e3LbAs75BIRUWSkQbfeyt+7j440CN1FH+8rPeiI/H0oK4uZOf/wDz2K\nAcsCAABgA0iuAABgAwEtC3RExVLNcL4McOGg1KWP5m1j7j8lz9Jeu8bt0FAh6TV1zeF0wMOXAfbu\nlbpVD/CRz2t2yrXB8eOti0tHfT3Re4f4/3H3aroIPeLlnbJ27B0kNAvGf8bs7S/JPxODIT6eaOpU\nxbkkXwofeICZuhE7Gw6NY/aVepcUBUF7O1FZBb+vaZo/pbuO8NEowzyyM9qwVN5i7dVw62YS+eNS\n6LO7+DLApg1S95vf8D+jzaYtUpScLH1WkplJtIX/3M/f3iV1zz7LzAOnSoQk9yu+WuEUL1ZwxMZq\nPruatmE33dTMbDNfLvm0DR3BNS65z6QD31wBAMAGkFwBAMAGkFwBAMAGkFwBAMAGAtrQqq6WZykX\nPiPP0kZ88AGzM+oPy4spxQiu0NZAQvlaoqLkXPQCI1foHN9ewWzz95q57xmGZXHpiI/poHtzlHOA\n2WOl8MgRZs6aKQ+M095Cbrdad0+JiFzODhqTXcN8S9clCd16ZUPu2DF5LXVv6cCBIINTCPc3Utp5\n5XkuXy50IccUzerV8mKqz8Ld15YWonPnuG/7y7LYYbuhbBzOXSI0bWHyPLk4zB0MDgd1hUUwV8Vd\njwpZWh4/5+yRx5xp8uRMZpvhEVIUBG1tRF4v9x0+Jn9GdTX3VbaPEJq+r73IbEdVz+Yn4ZsrAADY\nAJIrAADYAJIrAADYQEBrrrqR1REHZRVBUSo/IK4rgz65jteWq/XowRBJ12gQKeOpFy4UOvNlvu5z\nNT1daJz1NjcTdzjE4ea1s+W44qUD+Np2Qb6Ma4RSA02RkcHH1412M4wq2/ka6/qZBULn+DYvIjD7\nZwoN5eUx09ksm9UERUwM0ejRzNXllGuSIR18ffPAkuNCk3u3Zf1EBF99RTR/Pve5XHJt8PPIVcx+\nJkaz16FW5lhNaSmFLOLjqtNy5KH7wXm8D0bRITkqnl7jvT96uo7ZU2prTXr77Xbme2v1F0L3D8/x\nXhK6tgG/rudFHuWdv+lRDPjmCgAANoDkCgAANoDkCgAANoDkCgAANhDQhlZGQhOtn6os+D8uDzO7\nfn8fs9etk9c62sE3vRpN67oi1bVG0oFC3jXK5ZJdpCas5g2azQq5SaRu4FlOV5doG7XC/JXU5fND\n5K+9JiVPP80PQPtDNIfKgyDcV059X1nDnZpD6v/xH7zB+Om+sivSp59y22duDjq+7lz6KoRmzee/\nf0KC1G0e+jqzt74jm3bnPv00d+zSdILqJTExROqeUO7YGqHLPbSSO6ZMkRdTT81bTP7VJHL8agbz\nmc/K6o9p0xTHffcJzcV3P2f2tZd7tknUU2JjHTR6NJ8+UhQmG6Grj3bwy4uFJitvI7N1HfZ04Jsr\nAADYAJIrAADYAJIrAADYAJIrAADYgMM0e16B5HA4qojIsmmSCgOsGgFsc5xEN06slsVJhFi7caM8\nf6IbJ9Zv3PMPKLkCAADoGVgWAAAAG0ByBQAAGwioiMCdnGwamUqHI003q/pW3tUnRJPCo5Xz7SUl\nXqqu9lnSfijJ4TAzFF+EZuxwY7LBbF2D+WpNs6arV/N9Vq0PJSS4zX79eBwxjhYpjOD39FKpfHQD\n+3cw21taSr7qastaOkVFuc24OIP5+ifLWM0o/nC7NEMT2nnDIrpyxUu1tdY8fyL9u/rVZfmAw5Tb\nmN5PBtt+9iyzS4moxjQtidUdHm4aaveyJDndgeLjr3utxjbZBa2oyLp3NTHRbaanG8zn9Msx4/UO\nXq0R768UmqoQPiq8utpLTU0WPn+3W+Yqv2bUfFQUM8srZAixyiTtigov1dVdP9aAkquRmUkeZYQL\n1cmb+14h/6U048JFVcqdd8rWZb0lg4jURoiZuXLMy9GZ25mtq+BRx9oQEf3qVw7LFsr79TPo9df5\nHIxR4Wek0DCYOWOh/ADu3sTbtuVMmhR0fN2JizPokUd4rJtny1jVOe+6d/ryZW4//LB1z59I/67O\nWxIndG43t9eulP9ZlMXEMFtTG9VrjMhI8tx2G3f+6EdSOHXqda911CurEO++27p3NT3doLfe4s//\nlmJZofVeGP+s3Xt+o9C86uKVUM8/b8PzP3GCO8+fl0JlHtSadbLd4/jx3J43r2exYlkAAABsAMkV\nAABsAMkVAABsIKA1V+rsFGusY6bLER4nW7/DHQ8+KDSfpfKxFeoGRzBEjBxJmR99xHwtfvn/yITp\nyjqsZhTM8E1y3fJXmqZVvSWGmmmUg8dKQ4dLodKea/dOuUC8dh3vRlVeFdjjvR79k5pp80wlVpeM\n49AhbhcXy2tlZ3Pb6gkl1zpC6aKPr7G+9ppcHzaf3snssjq5PpimBBf+3e9aEOGfiY8XHa4Gb3lK\nyIrqlG5k77wjNM4XzwqflTgbrtItR3j3ssPZMtZ7s5UuaNkPCM14vvdKm61tikZNLSF08hzfWPX5\n5Njs4mPcVtfgiYjGfcJHa7taMFobAAD+10ByBQAAG0ByBQAAG0ByBQAAGwhox+PSlQiatZJvYJ18\nQo68OBDLF9Y3bJDXOj7xJLOdHU1S1Etqaoh2v8H/32jSXH7etm3MPlmcIjQV+y0LS4/DIcuEtmyR\nOqWa4fRrBUKi/o66yqhgKK2JocVv3M58mj1ASrmfF6/kNjcLzdpNfLOhRVOUFgxlZUQrlcko5pSV\nQteS9x6zl8+X19q5kx8st6g4i4iI6qL60b6hfHO3aOUOoTujaJYcWyU0Rw9Jn5W0xqZQwXi+geXS\nfK5un8ZzxEd0u9AUr+Ybo1ZvaMZcyKcRd/Dn5HzySaE7M5vvpI3o+EhoKEsp6tDNWNKAb64AAGAD\nSK4AAGADSK4AAGADAa25NjYSHTumOBcNEbpj/Fw2HT/YIDS7D45hdk2bdaO1Q0Jks5gZEzUHf5VG\nDstXyjXXJXJyuKU0dkbT0Tp+uHkCHRO6iPN8jbXtlTlC81s3b0RjdR/0/hkmbVzXxp1vvy104/vz\nH1zyn+8KzYoj/8LsfY1FwQfYjYQEovvvV5zf2yZ0q1dze8fc40KzciUfA//QQ0EG142wMM3B9VOF\nQpelnMM/qHYmIqLc6Wukk/6517GpNDURqb1QsrKk7uWXuf2ZU65j3mvwRfZVsdZuEDj69iXnrFnM\nd3H+eqHLUz7f+1bKlLh9P2+S5KvrWdrEN1cAALABJFcAALABJFcAALABJFcAALCBgDa00tKIVqnn\nlDWn82fO5PaaTbID/CqliGBjhHVFBFFRRMPVxlLnzgmd4x6+ybVL1kOI7k1WExvdSRNy+IbfRWOx\n0BVP4/arh7YLzfpCfsD7qFkafIDdqa2VG1jf+56QTZ+uOHSd9WfP5vannwYVmkpHB1F9veLMyxO6\n9R9+yB2j/1FoBp/iGyHOugqh6S0uZweNG1rDnWGyi1RcndJpSrOTtFHTWu7/BhUdp09UEy0Yzj+3\ng2aOETp1esfIkaeE5tq10cw2HRZ/z+vXT1SRDFoyT8jeeWcrs2u2yc5ZY5W9dlcP997xzRUAAGwA\nyRUAAGwAyRUAAGwAyRUAAGzAYQZQxuNwOKqIyLJRvQoDrJqvbnOcRDdOrJbFSYRYu3GjPH+iGyfW\nb9zzDyi5AgAA6BlYFgAAABtAcgUAABsIqIjAnZhoGmlp3NnZKYVqW/HISCHxh/GTuFeueKm21mdJ\ni3e302ka6knfqCihqwnvy+wQzX81Ca4O4cv/+GOfVetDcXFus08fQ/FJXWgot3VTBr74gtt+v5fa\n2625p0R/fv7p6czXGe4UOjW28MrL8mLK8/FevUq+hgbrYnW5TCM5mfkanfKRqbFqXlVylvCOXV6/\nn3xtbda8qzExppHEuy5VmH2FLrWNFxF8cU2OtDcMef2CgnzL3tXISLcZHc1/iNp9jogoPa6RO3Q5\nIoJPd/CWlZGvrs6y5x8T4zYTEw3mU14HIiIKD1Fi04zE+MQby+yODi91dl7/cxVQcjXS0sizZw93\n1tVJodfLbU01yWcJvLLj4YdzAgnlazFcLvLk5nLn0KFCtzuVV0LpKi9yx9YInyM52bKF8j59DFq7\n1sN8yhh7IiKKc/Es0OKX/xNMnszts2etu6dEREZ6Onneeov5GjJuEbrWVm73/Zel8mKjeYVOzlKN\nJgiM5GTyPPcc8x3NkhU6fj+3dS30Bs+fwOwcj0eKeomRlESep59mvhfaZYXesq8WMHvKpZeE5te/\nltdPT3dY9q5GRxt01138d9dVMK6deJQ7dDOWMjKYmaOWdQZJYqJBTz7JY1WLAomI+kYp7VA1z3bQ\nXP78r1zp2ecKywIAAGADSK4AAGADAS0L0OXLRMuXM1fb/veELOLOO7njgw+E5pbiA8x2+jXLC73F\nNMXfey855Z9aCzJ41/kXPhwnNB5PkvBZSVQU0W23cV/cPd+VwrvuYmZ0aqqQ3Hcfb9xy8WLQ4XEc\nDrHIFld4Ushab1Kaeej+5FO7ezQ2Sk0QfFHXh6a8w5cB3v+u7NSvTl4dvHCSvNgbb3B7kkbTW/r2\nJVq0iLmWrVwhZBeX8GWA9/dvFJoITcMfK7mp4SztO8I3BCq3yikjlDiW22J8CREVKtMW1LWkIElK\nkv2CfvtbqRs+nP8+d9wt91j+7d+4/bOf9SwGfHMFAAAbQHIFAAAbQHIFAAAbQHIFAAAbCGxDKzVV\nzJrWjZ6enc/7FYz4XLb431j5KLMrr+nGAveOjv4DqWbLbuYbfl7qVhzkG1hr/XJD4OQ0uXHwz9ZN\nK6bycqJ167ivKuFDoXtfOTt40jlBaJZl8IPmb+9qE5pgqGqIpJcODWK+BVnFQtf3l3xDpmzhWqE5\nMpzf1+p35EjrYDBNWcuywq+O0SBaW/4is0+uPiw0dcrRx/rmwD42X0d+fgc5QmuZz/zdeKHLUY5W\nLlok39W2PDk62rEsqPAY5anfoTWP85txTDNk4ujkTdyhqW7YXMfHXl/t3BxseIyILj9l+nnxxwK3\nnEZCz/wrM83vx0qNl09S2NxW3qMY8M0VAABsAMkVAABsAMkVAABsIKDFo9awWCpwK3XWmjLbEeXv\nckesXMdY3J+vse2OsW76a1jNVUrayddwUqc+JXXKb394slxfHWpYFpaWgRnttGNdGXeelwvEJVn8\nvmfrJlBuUw6718i+CMGgqSGhBedkMf64PH7I/o2F8lpqDUF1dZDBKQwOKaajLt5fYvfQA0JXefcT\nzB6zS74D6ijhVSHWFTz07x9GS5YoHUW2bRO6c+f4Pc28Z4juYpbFpaNfRymt8vHP0fBFcq10yDLe\nJ0LXK0MdxqxrURIMzZ1OOl07mPm+88BgoYsYP57ZRwtThGbC3yZyRw8LXvDNFQAAbADJFQAAbADJ\nFQAAbADJFQAAbCCgDa2o2jIatpcfxC7Mlof/HVN5I+Qnn5QtwDffo2x66bqV95akJKLp05lr0P2y\n09Tof+SH9Sedkr/LAc3Bc7tZcUQWCKw9ocSmdFIiIiKfj9sdssNPMCQkEN1zD/e9+cdBQveLX3A7\nralIaB5/nG8ufGXxnM7a5Cx681G+gTVV14TcqRRa6LozKY2dKTw8yOj4j1MbRIkO3iTv++d///dC\nM+X/6bpiWdbc/0+VGco7pWsuvkupGTp4UGoef5zbVj//mMgOGvUtvqFb5pMd7o4d4xtYM5pelRd7\n801uL9Ts0GrAN1cAALABJFcAALABJFcAALABJFcAALABh2ma11f9t9jhqCIii5ee/4cBVo0AtjlO\nohsnVsviJEKs3bhRnj/RjRPrN+75B5RcAQAA9AwsCwAAgA0guQIAgA0guQIAgA0guQIAgA0guQIA\ngA0guQIAgA0guQIAgA0guQIAgA0guQIAgA38F42DbIi7QIC1AAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_conv_weights(weights=weights_conv2, input_channel=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Output of Convolutional Layers
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Helper-function for plotting an image."
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"def plot_image(image):\n",
" # Create figure with sub-plots.\n",
" fig, axes = plt.subplots(1, 2)\n",
"\n",
" # References to the sub-plots.\n",
" ax0 = axes.flat[0]\n",
" ax1 = axes.flat[1]\n",
"\n",
" # Show raw and smoothened images in sub-plots.\n",
" ax0.imshow(image, interpolation='nearest')\n",
" ax1.imshow(image, interpolation='spline16')\n",
"\n",
" # Set labels.\n",
" ax0.set_xlabel('Raw')\n",
" ax1.set_xlabel('Smooth')\n",
" \n",
" # Ensure the plot is shown correctly with multiple plots\n",
" # in a single Notebook cell.\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot an image from the test-set. The raw pixelated image is used as input to the neural network."
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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CpQ4aLc6goTbMKEIJWPl8No98njGp9Q11xRpitFgGS9LmnDGjmu3YXMF7nQdg\nceDFET2U6AzQW2SXKjOs1kDaGkfX678BdlhmLmuaqg7KOVHShIxnkve4UpAw1syeb203yuVmN3sB\nJiJGCdQfsdTHrEuKX6saFWEF5oG+s9J9y1yxkv1jzVrpPfTOGjd4tUVMsarK3WY0F3I2MM+5TfeN\nKWnpejOgVyC3JdVemwboqQ0Kc4BQ5ipOCRHX9fj+QByOy9IfZi/dBxPVosp72GEsfukM31bZQ2tr\njbT0SICaSjlz6tUjDwbuLePHefPmvXNoBXWwytopKz6b1IFIjWnU2EDWqucyz1xazrllvZTSeHWt\n6pamujI3jxYITtAoKH7G40Ihl0wqjlQan17pnvqd21lYhrc9oKMZTSNl8uCw2ZYPL81DV+AfiYgC\nf7d2Qb/Zzb4f7H3d24L177SMFmVOESlKcbVoZy5OYQHz6pEfB1vuD5G7IcwKib1TomSCFrw1vISS\nyDUdUVOipGxFNrlUPRA7JucsPbABupZCmQp5SjOg55TMO8+ZrI1qWVL4cN5K+GNnVMsG0A/msVeV\nRB9C5SccLeFjTils4N4Gi5YHXv+dZzYwDyY0bz1460HaOHq/NL9W7/DFzdkruUDIhZALPhvNYrMN\nSy/MRREpqGD5+HNEY+GyG6jbhTXlxeIyIsmKu8Q4ewN1TIKgeKbkGLNjyo6sStZsDS1WHMs8aO2C\nyDWYWhLkEZ1qnn5Jdg1ekof+r6jqH4nIR4B/LCK/raq/tt5ARH4J+CWA+7t97vLNbvZdau/r3n50\nd2f8rza97gXAW6ebFv8zQS1HHz2H3vLJ7w4dxz5w1weO0RQS+0azaMaXZHosaULzNPfj1JQpKVPy\n0kYtV7paKk3Q2D3NhbLyzstkXYhalsfsQVbqA1+pj1ABve8Jw4EwNMrFePQWGG0Nm1sO/jokuiEY\nVzz6+vWSG1JnB6smFs1Tl8qdi5O53R7qaVK6RSGWQiiFUJREDVIi1QO3GgcbNAAUN18j4/43kgza\nPPeMFFd12Gsg21kP01wcU/B0IdCFxJR9BXRLa5T1ILncCba/jYeeEZ0gi7UtJKHZZjyqL8FDV9U/\nqo9fEZF/APw88GsX2/wq8KsAH3njjfeMB9zsZt9N9n7v7bfefFNbfnJLV1x+ti2DwtiI4C3QOXTO\nQLwWCB37wKFzDAE6UYIWXElQJspK2rbkiZwsMyWnTKpgbnnRVXWQheQQahpfBfSSKqDnUrnZyvK6\nOVfPPG3vIQQkRnzf4fuBOAx0hyPxUD30rsfHHhcC4hr4tDTN6z/3mVNu8QUsQLoWzppB3VlTaeda\nILRm0VRAtwKsCsiiFdC1LpaRheg3AAAgAElEQVR8oqlYhk8Lem4opRb4dYhYb1JblmMvNYNISoV+\nX/XcxaNOiL5lIxmoTymTiikmOoWm+LLLbMHRJAYsiFsbbtcGJKoech2Rv9MeuojcAU5VH9fn/wbw\nn36L9xDjdpf9lbZhb31oW0X5mR/ZS9LePd3Hqr78x1/arRvP289/SPuKwz/8030wbfzGvnr01TcP\nu3XdR97avP7oT/7sbpvhlX0A93S+0i7tYhR+8rAPi47TfqTuhv1xqd8HXe9e28rgvv5s//l/8id/\nulv35qN9IDbEfdD16dNtUPq1w/57f/QiKA7wlWf7a6my/fyg++PKf7xtCaj5xQRFP8i9be8DKrA0\nvXPRhTltpeedF4YgHDrPXWdVnvcVzHsvdGIepqiJauW8yNyWZN55KcabWyB0AXIrqBGjDWpwds63\nTgugl5QrL2s8s/V0Nq8Y76pSooG5dCa6FYaBcDgQD4ct3VK5c+d87eJzLWVvUQrXGch1NTOoQD8H\nJhsrswD3nPM+n2d7LnUK4kTwqvjiCUUJRYlAIaPJgr1aiuWp1/iAmkBOLepy1vnItQrZSghpo2AS\niskWW1WwlTOF6q1H720JwQaTokximUbL2HYJ6LLy22uXKM2QrXLURG9khw3vZd+Oh/4W8A+qVxKA\n/1ZV/5dv4/NudrPvFvtA97bIilqh5TKswdy88z4KQ3QcouPYOY7RVc98pcOSE5oriE8jeTqTJvPO\nzbNuQlQNuC0gmCuYG8CbtkkuxbzyVBYwtxb35pT7qq3OkmFCsECoxIjrGt0yGNXS6Ja+dhgKVmSE\nc9dwfObP59L/ytXPg83qLaWmTDZQr2eWNWljIYplMLCRkxncXS3MCtnji+IrJ24DXlUUk3p15myc\nRvEoor6yMbUNHTVvv3rRc2WqFHsmEGrqZAyemAMhKz6XGnxthFL7Ls0zX75XSzM1L9003U0WoX3p\n7zCgq+rvA3/+g77/Zjf7brUPcm+30n0wT1Ere9EenbMc8i56hs5z7APH3huQR2Fw0EnBazEwTyM6\njeS6tPTCnGpmy1y63oaMRXs7q5CLKRFaO7NMmoyeKcm8Vcoy0AQM1YWFt5YaCHWxnxUQQ9/484HQ\nD0a1ROtItFSMwgbVm7e+AnNt+dbrqtRKuWiVxZ0bVet6IGgZKDWrJ2c05yoahhX9zAOEnXjLVS81\nKlz3XwzQS40VqM4XcMlokXY+6+FjKaIG/MWWWbHReHjvxIC9gnurYJWW135JtaxSO9czuVaYZuNO\nG7Rupf83u9lLNXEyFxK55mFh3m8Lgg79KhDah5rJAkEyvpiwlqWtWUehGczHiSkt3Pmsdmh7Zq2x\nUpQ562WcMuOYmFogtHrnJtJlNLlVgZr+jKuf5bxVbPpVdksYBktT7FtAdCkmwjmaMFkLNs7LDMxl\nTg1sxUu5lDX8z7ozpdIjTUWxLTlbMVSaEtNkja51pmhsIJtSIZUy1zLNH96ybEqpXP96FsDKY7+Y\nEdA+qMoFaKlCZ2X+2AbqJt+wqmRtAmVXufM2o9PK1DU94ibqvhoQ3yMecWkvFdCdE453212+1u9b\nmf38T2wLVOLjP95t8+UvfXm37mvf2HOoT59tC1IuC5sAfu8P98qEUfcc9/GtvZrjRz/x45vXr//A\nD++2SVcCGmncH+vp2bZw6cmz826bMu0LbB5/fV9Q9cUv7M/Z19/dcublSju4nK4oSur+Zrp2e43p\nYu3DvhDr0O33GdxeoXLiQo1S9wVJTrfnpzx3K93vhFl6oLmaVZWwen7BCX23APlxiBw7z6H2++yk\npiRqrhTLRB7PpHG0PPFpYpqSedopz9x5824tm0WrcKLNDlrFZKot1MbRQLCkqoeOmkeJIFlwxeGr\nty9zmX+3qQpdFxL5bp133ljg+m+lKmo1VQXyKjXbgLpy/6UC+gzqTUisSRFky7fPuaZYThXMx4lx\nnGyQKgWtPUWNarLCopxXHn4D8/pcZ7TfC2jNfP78vH2/JS5iDUFqQLkWSInUPqvO1CxdHWSduBnK\ndfbOWzC0We1U3drr0QaMdSO7b203D/1mN3sBJkJNoQNabnOVjo1eZprFUhQjh5qa2DnFY+qBJSd0\nmsjjSBrNK2+e6JQyKVu7tZysiKVx6NY+DXyoPLhYQ+aca/f4KXMeE2kygJRi3K766o1XMLcc8No9\nyEd87GtruUMtIFrnnBuYz5otFShLbQ1nlMlKW6aUWSNmHpDK1kNvg4Ftl2ZAz6kVQdkyVjA/n0fS\nmGp1rLHQcyyhqi3ODaLnIOsK1FmJkNFomyvecOOytRZDlSotoNTMGqmdksyrbpRNk3fYtsNbpysK\nS7Jm3dGsA7xQMKuD/5Z2A/Sb3ewFmVsp+SECxeHRmqJoDSmOlWoZoiOK4jQjtdxbk7WGS+PIdG6e\neSblbB1yapBz6UJfPTjRyoPXoJ4YUJmXbhz6ecqkKaG54Ch4wWgaba0YDMzn7kGxW5pNdAdi13LO\ne1yIVWmxdSJa8d1NvTC/x9JolEa7FL0O6HWAK2kpfkrTZNTT2cD8fLbXKal1pVM3Q6Ali1ZY3Hjp\n855YQF3XkLr11hvgK83FtjTGUmZqqbT4hbZoxkzJb563e2OmyeZ9LntcB9Kf1ytf2w3Qb3azF2E1\nx1xqME6KccoBpa/t4obOM3SOPjpibd5MMZolT2fKOJHqMlWaJSXzaC1bZUlPbHnnqqZN0ioiG6fb\nytxTUaYK6qnmnTtAvYGH9XAQtOV0h0CIkdj1dMOR/nBHfzjSDUdiTVF0VTZXwXp7qi6B2hWY57zk\nytvz6p3nRre0OEBFS9HaKzQtnvmKapmmxDQmxrqczxPjeWJKhVxankhtOl1lcC24SQXcFcDLVixL\nd1A+X9YF7NtgUJRSq03n/BRt1Mzas2aN2+un7RPXn77w5XLhnb8PaL8B+s1u9gJMMMrFfMSWn6wE\nEYbgGPqqoBisd6WXYt130kgez0znM3msQlmVHkm1pL8BeVXA3QHUHDtjZnxq8x7zgqe6pJqy50Ut\n59oJeAHvkOBx0YSwQj/QH44MxzuG4z2H4z3DcKTrBkKIFnyFJVOlArdqqSJhC6inGZjLsqzAfPbQ\npQK6Njlf481nzryC+TQZ3TKeE+e6TFMmVUAHh/Me7wM+WFxjoVVaAqGRHzYALlB+kd5u6d9I5d5l\nBfz1mqt56Y2/b9dkvbc9KLfn7b2NvrGaAEWXPH5ZBob9YHDdXiqgey+8+up2l5/80Cu77bq0Dab9\n/md/b7fN48f74J33+2KXFnxolq+oHL4z7oOPb35o3yLulY99ZLfuUz++LSQ6Pd0HLb/2+S/s1qXT\nlaDotB2JT7ovgiplf6x/+s4++PhHb+/VIv/4q9t9/sDHP7rb5nylo+21NoHR7xUqnz1L22NN+++Y\n8rVbc/+dTLV2MdV9EVS4yM29bFv3Uq166L5qnAdnXnjnhcEb5dJFR/Dgq2ZHyY1eOTOeTjNn3lIM\nU27At3iAZfYEW8EJtNZ161+9UgsOsQEhVY/dgndQHCswd7hYAb3r6Iae/nBgOBw5HO8YDke6YSCE\nzsBcMcCGmnli8rul6qubl15mnfWcjPtPVTzMSuIXVcqmid7aws2US/Xs08ZDz0w1yDuOifGcGKdM\nyrXVozh8gBhb4HGRyNXKsa9Z6gajTteA28aXRpDImq2ZhdZae72y3kfj6Wl561tve0nsbM+aXO8e\n+FuxqryP+/rmod/sZi/AhFot7yyrpfO1KYUXel+LhoLgmxeaEnkcGc9nzqfTCtDzwpEX8/5QlgIY\nXcC81Ri2IFxrLq01D75gui6ZCqKXYB4EiWswj4QuEvuebhjohwP9cKDrB2LoTOe8grm2CtWUSGmq\ngN7ywsucelgaiK8rWnUR4ppleqUFHvOcBWMDwsqzrzn1thSmqVgGz5SZsmU5ifNEdYgrWK3T4uOu\nfOMa4FwGlQJ1dlX5bhGjbRoDglEt7WIXNVni5vEXrMmztkFqzu5pQ8a6fGoZJNoRLV1N18Cvz+2Z\nN7sB+s1u9iJMrArUOzd3GBpC0zMXojMwl9q4uUwjUwP085nzqWa1pCaBW8G80ggzYNedmbSrVGyX\nGYRYeeo2FmiFC2vP5gRLM/RiNMtMtdgSYpyXGCMhBHwNgKIG4JqLDRQpM6WpplaOtblGa3fXgqSt\nrR2rRWYQXOgWqcFcnd+nNRo5d1nSKm1QWp69zkHilA3+TPtFCS2Vu52Mlo0jCxHSCpsaqF+qlLcC\nMViKuGZXXdpg1KpWlwwbA/RKQc1gva4UVZYGpzrTLZeeuqze8bx2A/Sb3ewFmGDFQ2slxT5Yv8/O\nYRktVcc8p0SeRsbxzDiOjOeR83lkmpKV51c9llYw1JorONsRsPDnzbSCusygvni9a7iQCujiF51x\nH4N55jEQYjCp2pZbXoOUaRqRIhTJFKwL/TQlxnFkGs9M01gDoxV9dS0N1o7HtFNUhELLSKlBZFe3\na/QxppUieERsAY/iFuBsqYkzzdFSBWvbuKrGaHEGmUG9bZ/VgsmulHlQdFL3LQ4qK990eagDgI2d\nTcu9DVBa8+B10VSv+fcG2EYhLnkwlx77GtCZH28e+s1u9mdgIlhTZyeL8l70dN7SE31t2FyyUS3T\n2YB8PJ05n0cDxsmCoI3vRbxN60XxKNp0zXWVk6GbMF3FLDeXpS9wsQo+1m4/LnjChVfug0ecSc3m\nNDGNJ4o4XCqonyh4sgpTyoznidP5zHg+MY2jNTJuYC61zZ5rOubWXFqct/RIUVQM1GVVBt8kr5x4\nXMuHr0VOzkecCyAG7A0cTSjLinm8F0Jwm6Wo4ossgxRKVnOxpViap5XyzyRWPRzTa6EFRssq/RFm\nnZVSB4cmuWAUU64djApK89aX67RAdfPGyyqI+sHtpQJ6Fxwf/9D9Zt1H7veVol/6rc9tXr/99r5V\n2qUaH8Dd3T6QmS46fcQrlanXgmmHK63e7u/vd+s+91v/7+b14/TZ3Ta//Tu/v1v36P64W/djP/Pn\ntsf66EP74+r2mvJvfPTTu3Xx0V6h8kMXAc8PfWiv0vilfdEs7zzZBy1/8NH+/F8GerPfB3XffXba\nv6/sg6dy4Zq4a1Wglxs9Z/HFd8KcGG8e3EpK1Xuit9RFaUU1KVWqxbzycy2SmcbElJrgloG5SEGd\n4F31rutc35zFJX96PgsrSViZ3c3VOap/t7J0axrhfSCEZfHOISglJ6bxZE2KU0H8hLpIxpMzjFPi\ndB55eDhZDGBcPHTzkK09XAhx7jkaOsV5tepSqZrfNbAr2krkQXF48XgXCaEjxoEYB0LscS0P3odl\nllFTNxFHCJ5YG2zH6InBk7WQiqtSucznj1K7Gs3/LQ2hkQa7jb9qVFCZz3ljvRqVZMHeFi8os5de\ndOHI64VYPnlT2r+lfD6I3Tz0m93sBZgI9NHjRYxm8Z7oHdEJTmuOds5L2Xrz0M8NzC0YavpPLY/a\nNGFMj9uoBccqDU9rPkUTkRJmMLcc7JllWB1nE+Bys/fs6+Ja3nYppGki6wNMGfwZXEeRSFFHynAe\nEw+nM88eTpweHmZAh9Yv1RNjoOt6uiHTKzStcUQRp/NxNUGsViKvDvCRGBIlDuRupKvVqkuBU2e9\nRnOxwUHVBpEK6F0F9RBtNPR5FTRmSVmsHb0xEsjhESy5zAY2GrWiWGygLFSLVk7LZICZi6VsybWa\nd82lr3x0bbGORkotgdB2Vrb2fM7KDdBvdrMXYE6EPhigN8/cAqGWJZGL5WjnKS2FQ+OqrL8WEJl3\nXmkAI5PnPHPVSp1ok52tU/8VCMjKQ5/7YM5/XUxmL9HNC2qNknPKZEY0FQoTRc6oBDKBXIQpw/mc\nePZw4tmzB04niwWUUqrgl6eLka7vrDenc/hasNS6OCHNE66BzAboDeS9QuzRLlOGI2mc6A8PdIcj\nXZPwnSYb5LxDi1EmwbsZyENwBG+B0MapLzGIssoZNy/diZrEcePbK5/eslGa6uPsYbeMIqWKdumc\n+z8rSa7C0osHXqMLq7TE5Rq1z2/XtsZFnnPyeQP0m93sBZiIMMSAE8vRN/rFwng0Uaoq92raJPUx\nVW2W2nVozuJz1UdsQF5dcvMbl84+SJVvqu9p3rlzZW6XJjD3NG2xN82KZpMPyKlYuuSUTEwqg7pE\nVkdSIakjqycVYcrCOBVO54mHB/PQz+cz05QAncG8H3pKyTjniLGj1DZKQgNXkxmY+Xzvje6RJRDs\nwDoNFaWkzHgeGY4n+rsTw/lsTa0BGYWSbf/WDGMOFcyznPVMpYUgmxyvqiAUvNTGIFXWfS12qOuq\nLllmFOrEPp91q7mWiKgXwL5QZAvpsk9RXEC9Hu/7oBK/JaCLyN8D/gbwFVX92bruQ8D/AHwS+APg\n31bVrz/3Xm92s+8Ce5H3thMYuoCgBCcEwSoy52KbtFEQbKXwTdekFRA1f83AvOY108BlXaqy0ghs\nhG7lzY06X4N5XYqCFDTbUurAksbEFCfOzlEKiMtkse71U4apwJRgTHBOhXEsPFRAf6jeec4ZcY4u\nRoahR7VYCmfXzw2XRWrzCW/pkjhvmjBz4NQqbHEK6tAG7GLffZomS/Gs2UEpVz7bCWk6ozktCT7C\nkolCo3d05tDnVEUFlYII+OIsS2WuDLWNZ1BvGThijaKbbLDWgdaagy/qkbq5Stuslf3MaVF5+eAM\n+vN56H8f+K+A/2a17peBf6KqvyIiv1xf/8ff6oO64PjhN7aBRf3GPuB5encbJDuf91/Q9ftDfxjT\nfruLzUK3D9SFtK+EfPK1fQu6L35+3yrt3bT9rf/m77692+YrX9u3T/vMz//cbt3xoon2/Rv7wGbs\n9kHdu8/8pd26t7/yld263/y//tHFZ125ceJeyvZaUPSt4z6gKnkbuHzl9X0LuunZXvY4XZlPRrbX\n6dotXnZve98/hL/PC7q3RYQ+eqSW1ntq9aEamNOaMeRcAbV6yHOWX1MsbDzvaqngg7Zqyq3gq8Kq\n3shAvaUw1lrJCjaG7AbmZQHzkBhDwokjF8BZauBUTBL5nArnqXAeCw/nxHnMPJxGHk6jeecpoQo+\neFLfA0oIgTxYlkc7Pw3MQwi4EKwJtfga3GzNICq8KeA9Plg/URByzozTxJgmppRqxWadmZwgT5hO\nefXSaaA+xxIWtG+gjoJWestLIXsb1NStwpR1mtR6xTqxegPvrO+qAXquUgCyAvOWiLiVF9h66O2u\n3XrlF3fXt7r9ZvuWgK6qvyYin7xY/TeBv1qf/9fA/8Zz3PQ3u9l3k73Ie9uJNX4WteIhVx9VMzSN\n71LBvCwpcIsuy+Kd13yL+XUr+Z9zu1dT8Bmo2ovmla8eZ7FWVcgFdWWme4xqyUxjwjlPUOMoMsaV\nj1V+9zRmHk7TspxHTqeRcZpIOQNCjBHvHDl3c2HRHPB0TV/FFhcCElaA7twcyDUvWKqSrOJ9QJwz\nXZqUmHKa6RZdTUEmAcqE8zI3kjahtHVswfZjs4BFU57ag7R56GXl1K9NqN65s8EJ7ylqg7c0qQFZ\nKmENyBtLbqB9Kah79d68eHxe+6Ac+luq+jaAqr4tInuRk5vd7HvTPtC9LQLRuIGF3qjcOSXXpeqE\nl1ZFCXN6SuMCZOlmMxMG9VddilbsNp9v3Zl+M3/f4jvzh1SOt2mSp6oXY0qOlkdtwUFvPDaCo+DU\n4ZKAFAppLuqZY4QzaC9A572fwdsWo1l89bp9DLU5hlEXDYAbxJmOuEMUfFgAPdXFNGBqeX5dnIOS\nHEHU9tEGCs/M07vgcangqmqlYsFRmVMOl/iErs69nU+ZYwC+Hbf35KKW99+889V7Zy//4gJtIX07\n22pbbcOjz+elf8eDoiLyS8AvAbz1xr7jz81u9r1q63v7o2+81hr3mJfeQJelXdmKX1khxWoSLsvr\n2VNXmXPQF6xeuPNrP/g1PMyTfalZMapIEQNGrRovIhRvTaF9N+C7HucjxTm6IsSp4E8j0p2ReMKF\nEy6OhO5MnEzqVkQIMXI49BwOB/phoO97YtfVgqVagbpeggGiNO/cLS0gxFkSIYDLNsAMLZPETn6l\naWoVrbMGH/kcEM0WkPaWPVMA562IKiYlFiHpNOvKmJRBU39kVoL0soDw3KyiDVyVIqL2DJUVjZPb\n59TZ1yVzvmQYLXcA9Y5p69ae+fshEj8ooP+xiHysejAfA/aEbTsw1V8FfhXgJz/1g+93BnGzm71s\n+0D39k99+ge1yaA2EGfuPakLiLcp/uK6YR6ueaON/G0/9xn3jUI3r7w9tvzzFcwvBUe6UDPSwnK2\n71T54KRKqmBOiLjhQLy7ozscid2ACx1FPCkXHk4Tw9MT/dMH+qcPHE5nzqezNa7OGcE49L7vOBwO\nHO+PHO6ODIeBru+X/qO1B6kLfn4ujT9fjWuWs26Nq9VjwceaJijVyzeZgtqtyYMPMD0EyKPNLOoA\nVhBCgViEWBwRT1IhF2tZl3LdbpYSqFTYKi4h4nDOFBa9M15/ztRRnWmxJgmc6wzMLoHptizBz0u6\npREzUq/SFsTfD2h+UED/n4F/H/iV+vg/Pc+bRAv+UlI1XZFOvYh2eb+vAC1lH9w8nfdB0UPYyrxO\nab8N+zgg77q9POwbP7sPZP7lv/gvb17/yEWVK8DX3t4HSl+/EvB89MY2iPjqm/ugYoj78Vo/vA+U\n/uLf+nd264bD9ry+84Xf3W3zsXF/rv/5P9tvl/YRSY6HbaBUr1SKPnnYyx4L+6pTd+GXCPvA9SUD\nOV3Z5gPYB7q3AWi6HWUL5i2lbfGyG9BWIK+BO+qUHrGccNQArEbm2k7m5+si0LbJOid6nWVRP67W\n2yoZqmfuIQRcPxAOd3T3r3C8f0R/vCP2B8R35AKn88jx6YnDk2ccnz7j1NIVx9EaT6vivfHoXQX1\n4Wjyu/1hIHa1EKgCuYG7SQI4X71zWb7TXIRU8+PNg/fV027iYQHvxfqpVsrl3AXKWCOkakJixSUC\njkimw5NoKZggqYBYef5arGtNlUjNY1et/UGb9swcAF0JfTXaZqZu2vVb4HwbDl3Y9WZrtv2bhUqv\n2fOkLf53WJDoDRH5IvCfYDf7/ygi/wHweeBvvY993uxm3xX2Qu/tWgmKFmtPVurzSqTKxU923Ql+\nDeq66TfJjNSWuqjMGlNuy++izQNfBVxtV5YF4jAQUijOOhSV6tZK1+OGA+F4R3//CodXXzNQP9zj\nY48inM8Th2cP9E+ecXjyjNPpxHhqgJ5Qral/3hM7A/VuGBgOB/rDgW6oXnqjX5q3HkINiDZAb4NV\niyXU8+EczhufvkgVeECrAFYGKcToSadnlOlEyVXWd/IEyQTJFdQDU4YwZZxPiKQFxNsMpxHgdp/g\nnKsp6Da4tCs6NxLRVZVoXiiX5uZfuieXzy998wb1cvU9723Pk+Xyt9/jT7/wXHu42c2+S+2F3ttq\n+idSszsoFdg3oF4n3NKAfA3sy09Wl4+k/czXjxW9KwAtxUdaWnrjim6hgpB3JkSlGG8dAi5EXNfj\n+wPxcKQ73tPdPaK/f4X+0SsMh3tC16M4wpiQOCDBGkf3pzPj+UyaJgP0kkGwdL4QiF0k9h1d39MN\nB7phIFbqJXSREOv+5wyX9YxjlTFSAV0E8IIPS8BTHJQykfNIKRNKxgfH1EfSuSNPZ9I4on4iu0SS\nTCIx6YSfMs5PRpvMg6euzrnO14hWrKXMB1mwa5xUmXKuAeayNCWpzanfK6A5D7arFNTLeIi8x3u/\nmd0qRW92sxdgqkqZJhpB7lre9xy/vAiINaCYH6m/6C2PYp+2pltWf9PmUTLnqmup9MrKuxTnKtDa\n51vws8f3PWEwMI8VzLu7R8TDPXG4I/RHfOwAQSXRqZDVQNZHa4SRp2kW5RKquFXlt2PsZlCP/cKl\nh7ji0+eA6GVYd0VQqZi+S4M4MU46akd3ODBM96Q8UTTjgmd86Ejnnul8Yjqf0PNI8RNJJiadCFnw\nIZkCpLTq1O2JbfFpASvWasdRg6yt/d6kcEqJc0qMDdQboM8e+j5krXPd/yIBsPjn7dk6mPp89lIB\nvaTEk6/+yWbd3ZU2ayFsOVVxezU+zXu+9IoeH9MFZTtetHkDeP3Dez77/od+YrfuYz/987t1cr/N\navvhn97zwT/0E/vPurvfqyZ+6MNvbF53V5Qhr1TT8Oy8b89WXt+rRf4Lv/DXNq//4P/7gd02n3/6\nf+/WObdXkLzGaXNR7PU7X/qj3SaPn+05dCf723WS7TW/LDQC8Gz5ftmfmpdmqkqexs0Pz6kuXrGt\nWXhz3AxMrSnw8mH1nw25ukzI7c/rHHbmQF6tHVr431pm74MniMnSSuyIw0A3HOkOd3THe/rjI7rj\nI7rDI8Jwh+sOSOgt0qjm4fuohF7pFFwI5M4aOGubiYhlp/g5aGlNMmJn2S7rjJcZzL3baLjMgKdG\nOahe/q4FXE1DDIHQd/THA7kkVBQXA7HvGE894fSAOz2g4UR2ZwJnfHG4pDg/Vu7erWZJ2x6gs5aO\na3MrqwrNqLXGUxhL4TRlTuPEOCWmVgHcBtx2zKvPVdHV6+XPC82yp2Se124e+s1u9iJMlTxN1bOT\nTWFM89RqFvNc3GLeYWtZvEZvy5polYnoGu5X62bPvNItTffEMXO3LS88hIB6QZ3HxZ44GLfdH4/0\nhzu6wz3d4Z44HPEVzNUFrOa1epQu4EMh9uB8oORkefZzNShzSt8sy9s6H8Uqo9uyXObMFpk9ZJm/\n62rWUb9vmWmmVbjSiXHq/UBXMkUUCd5a6fUd565DYkdxHRMPhOzxE7iQZ2329QypeceyOs9toNG6\nQRPfGosy5sI5Zx6mzHmsHnpqUg7XA531pth8j3k/ugXy98ufww3Qb3azF2Kq1p5tSbmr8F3BdinA\naSDWpGRlWeyTAONMlgyX1X6o4NA88Uq7bFqplSbyZRWaIXhiAcGBD7huqIB+pB+Os4Jh6A/4mq6I\nCyiOPI8fYlkmIeAB8YxFPWsAACAASURBVA4tYaaYBObZgGvqirW4yFrZXeSirxQh4ZLxWDQK27mV\nBuZzWmaxQcA7Qhfp9GAB47mgKSI+goskPGMRwqS4c0L8iDg/R4vXE0RZDoLNyRcb1ApK0sKYs3nm\nKdnjlA3QS1mkedcBzjZazfx8YQf5gsnqsoXwF5rlcrOb3ez5TIvRUNa703rQmCRtuciacAuwu9oV\nZ8ehr9xyXf4000pywaEXzDsvlhqNGqB774kxUsTj8RAivjsQDwP9MFgD6K7Hx844ZRfqB1jed9U8\nNLZIwXvLxdbimYFPKiHR2r65VUXoqoHGAuZtUGOe0dh31NV30jq7sXxylfUpWXm2zlIffadE+9I2\nUNa89axCTIo/Z1yYEBdQ8VtyZRWr0A2QL3trg2YuhZQzU0qMKXGeMufJmlbn3BqUrI6PNTe+mmFU\n2sVO38WIMl/jJeZyObC/l90A/WY3exGm1s0GmifXAnosWQ8z4LS86ualW7XhJW/ePPFmG09tTcOW\nCjgrjZhGtwTvTYTNKyoBQofvB8JwoO8Hur4jhIir9MMCcJUqkurpY01NBV9zshfPHJg97m35v5v1\nW8K6iUZrvrEmFeqXXQtZaR0YVQSn5h1L9daXwbENIEoJunDvLM2kz2PCxxEJJ9QZmGe10v9FFXe1\n3/rPvO4iLXHKyQB9smVKlt2Sszb2aWMbjGY9TLyHtSyoeZRfB0i/ub1UQFfYBTPffboPeIZ+G1h0\nYX+Ykq4F1/bFQCVtT1wa98G8T/3IJ3frjh//4d2602nfPu3ywrzyyiu7LULYH9fhsFcrvGxx564U\nN5XLKC+QzvvirIcr2x3vtsHfj3z8R3fbjPzGft2V+Ge6EpT++pPHm9dffmevMim6r+LyV6KZeXcD\n74Oisrt9318A6YWb6vxgnK+50aWlss2pJ45Nufsq42WrfV15XN670+Tao53zz4sNHM45485F8UFQ\nH5HQ4bqDBUX7ni5GvDeAtUGhSvy6Yvx+o0TEQFtxtZx9FTSs3vkloDcufe6ItPHOLyolmxu76p3c\ntN/rhKPOTubpyGq0a5WcHucjoSgaTZo4pUyII35FIzUQb12GZlCvJ3RdaWtgXxb9m5xIyTx0Wyyz\nJef2GY0yWb7fZUl/O3vrAbF9j8rtsAQVLoOk39xuHvrNbvaCzDrLVCAA89gLM6AbUMii+teWFahv\nfuS6PJl/1LL608pLb2BuDRuMm3diXXvEW4M1fERiX1MWa9aJD+YIqZpOe0rkkAx8S8vK8fP3E+cM\nTFmAvikZuhWguyov62vKpHNuo4fSBorlS61AHaDJ3mJUzLysI8EtGjzTWbLy2CMhFGJIhNgaTBtv\nXhCKylYRsY6/S4HRissvWnPMM1PK5qHnPC9N3OwSzNffsPn/GwpmptdWXrhePL+SAfbN7AboN7vZ\ni7DG3TY2tixLKYWsq4xTEZN13dAuC9DpTBYvQH7pocvssq5oknl/Oje58OJqZ52A+IjEDlcrNkMw\nWsREIgs5WxpiTpaj7Vyus0TdAnDt/VkdyJkPNyBfPHVZfadry2xWglm98IWQWMCcFZBXaYWqXjnn\nB9bn1nfamkwX7/Eh4n1NlXRhFQzd6s3bo2649VIPJGuZufOUM1MuFcjNa8/FKLY2n+IKoM9flUal\nrQv7ry31s5T3Beo3QL/ZzV6ACYL4YJRJNm0QVawjUS7kIqs6o9YEWhbv1ol1C5pJ1gZsC0w0j07X\nXPviTm4GEZyyaH+bepU0DfIG5JXLNnqh0gq1ubFvgdy2/0qRbLJSVmC+BupGFc6gPdM217zz9mlb\nlnntyQpN3qp6zE0np5SlcUep/DqCF0G8hxIpIdPFji72xNgRQkfwEedCbZzhmIW1pJ3OBexBZwXF\npGoqlbUiNBVTacxUBUhk8212MQ9ZhqhV9IA9kC/rZfban89ugH6zm70IE8GFrlYQJmu2XAWfFrEm\nA48FCNdUhMNJ1dQWXehh1lTxCiaUJUtkZh7WoN4c0TYTsHZp4te533U2MYN5IudkPPrcrZ7V8TbP\nU2cgN5pjBfQrsJ7jAZu4wHzC2lbLJiu6o21hYG5A7tTA3BU7XilGXEsD8xnUq56695QQK6AbxdR1\nnQF7jHOl6OKlV4e/BmKtAbQFUA3MK6i3IGkF+isJiNdZ7/ny7YH7vb3092cvFdBd6Bj+f/beLVa2\n7LoOG3OttR9V55x7b99ukt2iRFEkJVLUi34oCeB8KLG/bECB7cSGP2QnCCL/+MOAP2LoJ/70hx8w\nEMQABRuOACFIEBuw4+THiGEYchAFlKJIlCVRlCy+u9ns7vs6p/be6zHzMed67NrV3edSx80WULOx\nu07tW2dX1a46Y8095phjvvSR1b7X3vytk49rY3eiY5JPdEeGtC0xU1yfmPlm+3v37m9dDclsj/X0\n2bbI90FeFwcvdvvtsU5cMsUTRcXjOPV3cOItoovb92SebEf78dN1UffBsC3Mftcr373Z98tu2/16\noG1x81vT2snykLbv+9TXtDv59358/BOFcWyLxt+pICKYbkCKSV5XZDCiqikYnOolfaZnWuli5peZ\nckGsQTjU8mhuS+fcDtpk6ZluSUnATxWF5SogOwRSWQUEIFOKiDHARsnOY4xIeYhEK8GjPHhiDeb5\nHytEsx6+MbraRAPk7Z52DeDa5GN04REwj6AUgBRl7GFT5STURiULgstKH+cKsPd9ztbFS4ZIrXk1\ne05lM5BrLSCkDOryeWYKLVMtlT/H6vbENwXrMukpNcuaW38eaD9n6Oc4x12EMbD9DghBVCA2gSki\nsUHiVNv08x8sNUVEyvptI9m1ATjmERkSwrty4WSOL+dbLTqXDLYuCG1HZqm5Ndl5ShExBZicobNk\n6GXF0PpcOUTWSL8NUVycH09seSEoWK7ywFWGzvXf8puTISERSAEIXua0Bp0AFWvRGbkLV4uoGdj7\nDOq9Zuy54ck6RGM1g1YKBgYRAtpBN58Az4zAMhgj6Sex/pRqvDOoH9+e2vfuRzqOM6Cf4xx3EEQG\npt+BjUdiAkIEGw/Ol/RoWeJ1cbAoQyiJrW3TQVSNmyT75Jx25+x7k6EnpGjEI9xus+M2j0YB3VQy\n9aQcekqpbFxA9QjAMwXEFdLaWGfn60fw6ud6jPpobngYKYRyDOJzHhZw8GAfkELUGoVQWwwqWneA\nwDEBMcIAsGTQOYeh7zEMA4Z+QK/+MtF3MIkAQ0gkYA7Nxn0CfFLKJUGGY2gWz817qFBcM/B3gvjc\nLHU6S9fPKl8CnTrBJ+Jdr1mJ6B8S0TeJ6PPNvr9BRF8jol/R7U/e7unOcY73T9zpd5skQ7f9DqYb\nQK4HTCddiQXUc3aes1zAlE2zdL1f6olo/8Tr/hwt5nFKKpGsYJzJ4Uq9oJFJombqqrOOMepWuXSh\nXxLy4OdVC35DzbRa+BMnqOHiM4Rl3h0l/d8AWyl6RiAGpOCRlgVxnhDnA+J0gzhdIx6uEXTzN9dY\nbq6xHK7hpxuEZQKHAEOMzlkMfYdxGDDuRuzGHcZxh74f4LoeZCwSCD5mr5aEOSQskbEklOw8cgb0\navELNJ/TZqPmdvvfcZa+8rdZ/ds7x20y9H8E4L8H8HNH+/8uM/+tWz3LOc7x/ox/hDv6bpMxMMMe\nbBaYyCCXPUMcmCKY0ibLypy00C3auKM8egY9bpquckJXEruSIEsRNBGAmJAMgUySjJ2zvVbGTFqt\nJJlPF+VIbED8GNQJiQBKcqCUX0TJzmvB9u3a1QtFQ+17odVvMmWpjz5C6RaOESl6JD8jLBPiPMHP\nM7wP1Ytcm4TkjWrrf4K05fsASgmdIQxdh904Yr/bYd7v4ecZiAHRG1AKSIjwajrmg2jP55gzdUIo\nYG6KvmUb7fXY9hOsn+OWS8/1iXW2fru4zYCLf01EH73l8d4xTDdi/92fXu2zX98W71JYdxxe7Ldj\n0fzhsNnHJ6xx01Ht8frptjP1/ndtT9fuYlswnJdtp+jh8Gx1fzjR1XoqY9nvt/a5xq4vmHJDRxsB\n2xF6h7de3+x7/bd+ZbPvm6+9ubr/1rz9Mn73idF4P/SjP7bZ99u//kvb40/r85hoW8y2vC0G21Md\nvkdzAfnExeTx+aHnkHcBd/vdzhk6kxV71i6A3KKgrhN1lCQmbrJU5dKtMWAjlAunDOrrJqMWzNv9\n+l6QkqTiMcoCk2LaZMzUaOBhGnOwcgylXmLl1mUzSDnDB9VzXfhv4ffr8A40Gfk64yyPye+FG/qh\nXILoVUBK4uoYPJJfEJcZYT7AH24U0D2Ctt9La37lthPEyyUw4CPAIcES0DuH3dBjv9thubiQIRgp\nwk+EuMzgyPBaHA4hYokJXgE9qCd80g2ovQebr0TzERWMzmBOdUFrH13BPN++d0XRv0JEfxHA5wD8\nNWZ+6/dxrHOc4/0Uz/3dJjKgfgcDC+oTaFmEeulmmBDV20XQholX+vMyRT7JbVtApNWaW/nUNQeN\n0rWUIL7kyVb+u8gAs6LGWtFp68bGSMaej6fql1owlcJjUkrElAS6ep3UV5cXqyZTz2BeFg8tinJT\nHC3IjnJMVt5cmp0WhGWGnyeE6QA/3egACy+gHlLJ0osKBUKNRBhEskhsQQlwtmbpy35XAP1AjAUM\nv0SE6BFiEjAPrBx6plwq3ZJXpXcC3XJFdZxt83r/Me2y+dxvEd+u7uvvA/g4gM8A+AaAv/12DySi\nnyaizxHR5956/OTbfLpznOM9i2/zu/0UphtlPmc3wvYjbD/A5duuryPXdLBCKYbaepsNrIw1xfuk\nGlqd6LhUEGBk5Z4CWsq+3JpBZzA34oBoi0d51zghmiJtRObWUYumuYBaF5zt1WdpMjryqskj5ap4\nvpE8AlgVabPqJngEvyAsMnlomSb4+VA3BfYwacZ+uIG/vsb87Bmmp09w80S2w9OnmK+vEeYDUlhg\nOAmo9w673Yj9xR77iwvs9nt04wDjHJgMAhNCKYhCJYtVJbllyte5+urM0FGmXWaNbimXlrpaT1N6\n9/i2MnRmfq2+TvpZAP/8HR77WQCfBYBP/8DHb1mrPcc5vjPx7X63f+gHP8lwPQwDpo+wfkQ3LuAY\nQMyIxiIZUWbEEGEMIxkuIGsswyplbGsnEYgIKXKlk4/+gjIjK80xSW1WBMwjc5lkJMcSr3DXdbB9\nD+oHwPVI1iEZCzZdM3ziqKOTUUG8nJ+WA89a9zq8wuT3tjLkynLHNjXnfD7BKYKTUCzBC70Sphv4\nwzX8dA1/uMEy3SDME+IyIy4LwiLnNHjhuxcfscQIH0ViyGSlSN0NgO2QYEGc0DmDcewR4w4JEUwR\nIXn46AG/IHmPCCq9BHnL4+WOwbuSSOUbtMrA21oBNr+H8i/1HLV7bxffFqAT0SvM/A29+6cBfP6d\nHl+frQM9eHm160Of+uHNw17/whdX9y/TlktOJ97kkze3vHo84rYOy/XmMXbY8tnf+4nv3+x71m+b\nhqw7fm2n+Pjt8XcX22PhiBOOxwUAADePv7nZ94XPf26z78mXfnez7xtfXtcrvvjlVzePuf+xj2/2\n/dh/9CObfV967cubffM311dgp1bveIIvP8USbg0YT3Hoz8eZ3ya+7e82EeA6gAHTRbjBi8wuiWQu\nGotoDCIZMLxQzgkwVqoD4i5O1VOSCKAIRJKTEcUZcPu8EL14Hhat1/ExJdgCPFIkzMMfXN+jGwaY\nYQd2HZKxspGVeaM6VaioYXAiSyxZdiu/JF2c8iLV2uVuh3lwg3+yGEVwkuJn8LNm5TeqWnmKMN0g\nHG6EQ58nhGVBXDziEhC8AroPWBZfJgj5xGBjQd0A03uQgjqDYA3QDw4JI9gkKYZGjyUsMPMELAtY\nfdXz1U92zWyprHpCasHz6DSVOP4EaQX6DeVC7WNuH+8K6ET0PwH4CQAvEdFXAfx3AH6CiD6jr+/3\nAPzl53jOc5zjfRF3/d1m0wEWoC7CxBEuBRALoAftCmWWKUZqtyK6aTJgYd/zC2s2vc7XP/tc+Czv\nAepBkioFUuaKsvqyEwF5EITr0fUD+nGEHUag62SxgSw2ZBxs5xrv8tOX/EIHNJRK67LY0EViNZA5\n+pqp5h8ypSNbREoBMXhEP8MvByzTDebDM8w31wjTDeJ0QFwmxHlBXBb4JSDo5n3EMgfM84JpXjD7\ngCUmHbvnYYcAO0SYvgeceLn0vQNZAiwQOWIJCw7LBDsdYOYFZCNA4taSz++q0Hx0btrC9fE/1mx+\nVdZ+G8A+De7vFrdRufyFE7v/we2f4hzneH/GXX63c4cirAUlB+o62DhoZyMXObVh0k1AnCnCGgEN\nrLJYaCYboWk4mHMLfAMOxDCcVTHU0DKsczAlO8/cudOhzf0wwo0j4ATQA6RZhpshz8Za9Zhp+XrU\nrNzIYkRtHaD5WSwNzOp3uVmPqud4LoBGcAxIMSAED7/MWOYJ83TArNl5nCekZZbM3HuE2cMvAX6J\nWJaAefaYDzOmecG0ePiYkMjC9AEuRjhOcJxgaYCxBq6zMJ0FHCGkgGmZMUwHdDcDbL/A+ACyATL7\nNa5S7KaOuyFbtmBMyFDe/LZ+WI10s0npj7P728S5U/Qc57ijSMij2yzYOCTbgW0P7rKJFGDYwMDC\nmgWwHRCCbDZIy3opIurfMkOUMSZVrTkaUCShH9e1s8Jqow5+ECtZl1vf+wHdMIBdh2gMDEP5Zsmu\nrVUfc1vtCbLneaFYyLx9gTcXc1cvrF2MagGUizwyyXCNGJCiRwwBwS/wy4JlnuHnGTFvy4I4S4a+\nzB5+DpiXiHlecDjMmKYFkxelChsLG6OMZjay8JrewRDDOgu2FugsfIwY5gn9zYh+HNFNM9ziYZcA\nYyMo5MlSzfvR27yXmn3tDVa/hfJoud8w52zkA4fZ8Ou3iTOgn+McdxS5SFbMnUjlcqaT8Wg9gcjB\nmg5wA8j79WYsoqn5K5jFo8QwDHEZNLweDi9AwNrsY/I+kkYlqxl3nuvZdR06BfWu7wXQicpot4Q6\nE9RZA2cMrCHYMsDieMtAXimasgA0YF45Zy7pbKto4VbzHlUHH6NMCQqiB/c+IMwL/DQjTALwy+Sx\nzAuWOWCZA6bZY5pmydR9QGQGrIVjBhsD6hxM34FTBBFkWlPvQAz4EDCMI4adAHo/TFg0+w8+IpoE\nMhHE68lSqs9Ba15wnIsf/6Qf3maT6zajdNzzixDf2xF0DExHdb6vYjuy7eJTP766/8Kr2wIclm3j\njH+yHcUGc3QhlLYugR/40Mubfa+88spm35t+29Sz79eFzF2/LeD2/fY5ibdL93RYF2yXE46ShxMj\n+5493b7v/+9Xf3uz7wu/+e/WxzoxW+4HPvTSZt/HfuQHt/s++QObfa997Vur+6+//trmMfFEZ90p\n18TjzCRiO1LPxvW5/k5LqOo4s+zKR4hskMiCTQ90FjA9yEXYEATElwXkF9Ay11mbDDGiikkyc0ra\nQcoANX4uAIpghLXIqDts4bMzmAuQd42NrOt7wDoEQvETTyx8uLUGzuqCcAzorf+Myfx5HkOXs/KC\n5HLTZKz555Ryhp6LoW1DU5ZdqmJHgX1ZAuZpwXKYsBxmzIcJ87QIgM+SrU/zgmUJCFG+beQ60dB3\nDjb0cDFIIZkYxhKscyAA/dBjGAeM44hxHDGNo9I5AUHVSTFapMQgymZgxaW9gfRqq7YF9lO+jDlD\nN6vsXczFzhn6Oc7xngcDhQ/OmuXcWZjYStbsHIwFDDMoBhgXADcDi1OHQNU8pATEALYRbCKSdpPm\numIrG4QBEqsHjCFRu2hm7jKYdx26Xra+79B3nToNOrB2NnOSBUQ6+2WKkSWC1dv8/ALm1W8md7q2\nbeuErFJq5Ij5LHG9L2ZisYB6BfNaeOSs3tGBz95HzLPHdFgw3UyYbg6YDjPmyWNePJZFVC4+Z+dk\nYBkgZ+FitQZmFp2cMaQzVQ26rhPTrlF8XsZpVwF9CYg+rH4/y3QYfATmaznjOo6sDrbfoqNl4Xju\n6DvHGdDPcY47CgF1KX56BuZE8IkQkwHBwJJmuwQYmwAjNIshC4LoncUSNoCthzG+ZMHZLx3UaCS0\ncErKDXOeQZkz7IZi6fpBePO+h+s7uE4oGLYWq45P1kNA4Kad5SlWBFxzykLy68YJnIy8RqJSMMxn\npwB7KYRmykVscVMGy2L4Vd4kmGWiUwiMxUdMs8fhMMt2M2GaFswK5N4HxJgg81sN2BrYlBCzmia/\nKl2QMk3knG1AfcQ4ztKJqlvwHiEvCmxAaotcDgYGwRwB8LGaZat8WWflpFesTSH5Ob6DZ0A/xznu\nIATgBFBFD2HgYTAng6BAacjAEsERYJmkKGcY5GScmokRHDyMX1QH3naHYpWhE5BRF4zq2ihqGypK\nFdc5dEOPfujRDQLsebiDdU411gxKah6WGIxYBiaDGLAMmATAFb6epRrbWBRkvXlqmohwhF9VWll8\nY2LSYdoN1VIqp7mwawGyKNODcqa+KGeuNIsAes7CZWUyJDLROo+5KnTqOL2so7dlEez7Hv0woB8W\ndMMsC+GywPoAE4JcQQFISrbIcQyyHXIpFFMuDpt6TrC+opNpUVwaw4gBSjl7X5VY3zXOgH6Oc9xR\nkDGgxGCyiLAIbDCzhdfuQkC0CxaAA6MHoYdBZzpYl2C6DuydjEY7Gh5dphkZkj965dMhLIuAgNFq\nRLYR6Dq4XsC8H1W5MUiGLkOT1XArqhwvJXCUrDMhSmcpGSQTkayDc1wRgxlgUfVkqWXWpZtWn34M\n7JwlnJkjjwXQq62ALExyPKvdplaAHUYy9cjwMWEJUfTmIYgzYpTsHACIbFHnZA+bvLWLZfn8SOgX\np4A+DAPmYcEwDFiGBcvSwy8ei81DphlJuXPxuDF65dMVqisXo7MEND9fXtBCjAghwIeAEAJiSDK0\nI2pBXJ/ltpD+ngI6GYN+t+6a/J1p20X59M31qLf/5OXv2jzG7L642TfuHm/2Ma8LmTTe3zzmwz+w\n7QodTrghhq9vi3yvv/HV1f0Pnhjh9uajrbfTbrd1c9zv192j07R1d5yXbXHQ9VebfW9dbwu4j4+K\nrPv7298bLy83+1LYFk8/+n3ft9n3pY+uz8Ubj97cPOZHfnjbGfyjf/gzm322WxeSP//539g85rd+\n5d+u7pub7ef/ngURrLGIBoBJSGTgYbFwxBQNQhQAI9WS9wTsjCgvDFk42wGug3EdyDoZ7FzG1Jky\nQo6YQEYFbZqxJ7SUDAHWaIu/ZOddAXOlXLquqFKyn3pxNgyxtLYTAiIZWBvBLhWBCgFgtgLCAPIA\njzIjVSkMud92k6JpfqrOjpykbsCZP28zdLIg42CMEytiGKQmU5dhzbwa2ix1AJKGIWdAzsK0m612\nBJnTB+dZpAbOOs3QpUg6T5qpzwtcv8AusuBW8gkwsHBK1/TDILSN/pyHaFhry2eUwdx7j3ledJvh\nF4+weMQlIiLIFcyqtPrOcc7Qz3GOOwiC2B8L1kozS4DFwhZTSlhCQggCmpYTBgOwI7iOMFgLcoBJ\nvYK6q6Ceuy2jgTGKvsWMXJ6YgJIZgwByrmTn3aBgrpsbBlgFdBhTXBo5aUYYkxYnM1dvkGxSgG+m\nAbHkpSnz4UAB8OzfkpU2RQ2j2WliiOFXPAHoXJ87K3ak01QWOGhXbVLHQ5nrqZa52kQlhjoE44R2\nsp2rW7E10CqBLmRQSwrh0gXQx2GEHwOW3YJl8ViWRQB4WWC9gwkeKRgYAF3XYRxG7PZ77PZ77Pc7\n+Xm3Exlk30umrgtBSgkhBMzzjMM04XCYcLg5YDpMmA8zFjvDG/WpifGEFcbpOAP6Oc5xByGX62LP\nSsYW2sWzwZIMpkDwnpGiAHqyQEcWsRPJiLFGvOK7HtF1INeBCrBHkE060V4VEMq4ZEA3MIU/Nw2Y\nd+OwytBt3+uCIZOUhAWumaYoSliLkwzSwRyGIqKNsOqNDlKJZnZ25GwJXIE82dyhWl0jAb0gyHx5\njKpoybNB8xi8pItFzur1SoBXpVp5D+pTQ/oaLMT0zHYOtu9gOwfTAHkBc1Q+P2fo1tbCaNG/q9LF\new8fQgH2GGPhzIdhwMXFBS6vrnB5eYnLy0vsLy9wcXGBcb/DMAx6ZSTPnVKC9x7TPONwmHB9fYPr\n62vcXN/g5tkNDs9uMNkDvJXnOsy306SfAf0c57iLIMAaC2shlAgJqCdyCEjwnLDoMOPEDEcyiBgk\nWWTnGBYJ1PWIarUL66pneQyqRV93WgK6TzNgYy1sL4DuhqFm5uMIOwwwXQ+yHbIRHKv2UaSKFtaw\nDKlG7XmswykyRQFkK4IUFXxTQqaBYtbAGwNrHZITULfGij0BclNRrNa8Se7nmaYxCqdc+OUYRWGS\nFTB60gs37iwsQYq7JPSu64R2EhuDzHujFl9VVWNSkhmsxqJTnl4ydgNmEp91nVwUfBBaJAYAjBiE\nI9/v97h3/z4ePHiA+/fv4/LqChdXl7i4vMC4U0DvhXYBxDxNFgePw2HCzc0B19fXePbkKZ48foKn\nj5/i+slTHG4OWOYZz/wZ0M9xjvcsCFJQM+qgSMYJ9VL+k6ERiYVHB2vXqO3QOYeuhyhewoLQK/B2\nvUy3DwEwVtv/FcDLJbhqXohA1sL0HdwwwI07dLsd3CibHXaw/QDShSI7CDKghUcH4xhWp/BQTEhG\ns3OTwdhkrSJkQanAGFMsr0koIAV0F+GS6N3ZplWWXjl0LZBq238IHj54LH7RzWNZZDJRUAUL63MY\nVfIwMUyyopAhoaCKVXDnQFboqMRJJxEF2BhhY4RJDIf6Pq3rYF0PaxwAgxi1sSnWKUbMCdYYpBgx\nDAOurq7w8OFDvPjSi3jh4UNc3buHC83Sx92IvmToVl5H4lIQneZFs/RrPHn8BG+98Rbe2r+Jx8OA\nZ0+eYpomvPHkfQjoxhjsjoqNB2yLlL1dE0Yf+cgHN485vP71zb6bm20X5TKtR8S98qlPbh5z/3s/\nsf09M272PXi4Bdc+oQAAIABJREFU7Sidp3WhkU7Yw07Xjzb73vrW1gb3hRceru6fmlf45uvb3/vK\n17+22ed229f/3d//kdV9229HxN1MWwviY2UtAPzhP/JHNvsOh3Uh9gMPH24e81P/1V/a7PvED316\ns6/br1//G4+2w1H+4f/ws6v7//s//fnNY96rEENDC6vj3zLtwmTBcPpZOiUKpIDpjEPnpNFnKIDu\nEZYZbhhhlhkmBlCIQEoqLVwPay4j5awRQ7BhQDfu0O8F0LvdHm7YaXYugM7GKn2hNgLEIOtgEwA2\nIIowthYoDZnCPRtjtIeRC13Bqo4pmba+rkgGNubGoQRWh0OtReJ4IlLNyiuYz/OMeZ6xLDMW1YEn\nkfmAcvGXE8gZ1ZlXQDfZLtg5zdCF6okplmHYRTYJ7a7tOpCx6BJgXQeQER/0mBBDKpYEBKBzHZAS\ndrsRD154AS994CV84IMfxIsvvVQAfXexxzCOqv93Cuhy7mVkXoL34hB5fX2Dx48eYb+/kIzeOXR9\nj8PNDezXbgfV5wz9HOe4k8gFQwZp4U4KeBaMCMCCKMEYRoeE3hkMrsfQ9Rj6DsNgYB2BWMatLcuM\nLviq/NBn4UxZGBJ7AAU2clYmI+1G9BcX6PeySZYuE5So68UQjET6mCDFQAE/gmNRlRirwMVNMdRm\nLtwWV8c60GGdrecGIqNqDm42Y231eGmULrlIGLy6LC4z5nmq2zJj8QtCDJqFS83CdQ4wMqs26SIn\nFyzVY4bsEeXCOmIvd3yi1kBc18G6DkwGrusBIs3QxVcm8+3OWCzjADDjYr/HwxdfxAc/+EF86OWX\n8eIHBND3yp93w4Cu67QYLeoaESJKLSSmhMV73NxMuLi6Qtf35Ty5rsPN9TXsiVnFp+IM6Oc4x50F\nKWhIgTImQkqEpHptaywsgMEwxs5iHHrs+gHj0GEcdKYoBBRDjAhNodFYg2C1+OcsUgyqzoCAeefg\nxhHdfo/x8hLj5RWG/SW6cQ87jDBdD2Ml+5RiIlfqxiQYVomfUfrDtgOmm7mnWUnDCZSo2uOq3jzr\nq1nlg7lNXlQtcQvozUIQgswHFdOtCdOs2zJjXhb4IIXIBBYJpzOwcCALJNgq78sdoEQgY8sVUx6x\nV7xmVBWTwd9ai8452K5XPxuhyGJIpeVfpI1A33Xw8wwCcHFxgYcvPsTDF1+U24cPcXl1hVGzc9f3\nyuXXwSGlQ1Qbu0KIGPcTrLOIKcIHVbdYg3G/k4XrFnGbARffA+DnALwMUQd9lpn/HhE9BPA/A/go\nZBDAnzsPij7HH6S46++2yPgIMUGmxEfGEhNCFM7cGYvBGlw44Gp0uBwHXOwG7HY9xtHAcgfnDKDt\n6uQc5r7HPAxYhgH+MCLME8IyIwYvhUiC8Mh9j243YthfYLy6xO7qPnaXVxj2F+j6EdZ1MhjaWCXO\nc8ETAAhk9crCMEziSp/k/qWmWQgAkAisvjSWK/0i2vYG1JVSCSGUzsnqwijt/4kzNy3t9X6eMU8T\n5sMB83TANGmGHjxCUhtcglAusCCWhq3cDcr58M20JCoDPjqVL0pzVe6otdaVpqM8oIOMwZD6CuYE\ndM5i6DtM+z3CInTrfr/HgwcPcO/+faFZ9ipVHAZ0arMgz6dXCtryK3SX2uRaMRLb7RdcXl1iOtyH\n9x5kDXYXB7g7zNADZPL5LxPRFYBfIqJ/AeC/BPB/MvPfJKK/DuCvA/hvb/Ws5zjH+yPu7LvNgM6e\nBEJi+MBYQoL3Mo3eAHDGYN8R7vUW9/Yd7u0F0Pe7HuNoYSmhyzK7voMbRsy7HabrHZZxh/nmRgck\nZ0CXhi/jrLT37/YYLy5Khj5eXaHfXcD1Q5Od5xpP1V6Tmq7kWzYM09rdqjayjKMDxAqAaqZZ+5O4\nWAlkMI+hra1Qraui4dEzf974nwugT1iWCbNf4GNAYAF0WIKBFfuD3CqLdYs/9OqmdJs2E5s6dZt0\nfV86O611Ky93EKPvOvAombmzBkPfYTeOWKYZQd1Xx3EQueLVJcbdWIqfpSsUUg9LLG29lCDOmSAA\nqdYxDGA7h2E34OLqEj4EWOcwT/PdZeg6X/Eb+vNTIvoNAB8G8J9BxncBwP8I4F/hXb70IQGPDuuC\n55uPtt2Qf/7H1nMtP/bJbafohz667cj83U9ubV6fPF4nVh/95LYoOl69uNnnw1bJf3l/Owf04sj9\nN56wxb1/olB6+PpXN/t+89/++up+zgDaePzWtzb7vvnGtoN1eLjt+Ox4XWikbmvr+9Ir37vZ9/AD\n26L0/mLY7PsTf/w/Xt1/+h/+0e2xPvSBzT6ftt2v7NeFWLfb2hL/5J//M6v7v/Cv/4/NY94p7vK7\nzUB1WQwMHxK8j/AhIQaGJWDoLC4Gi3u7Dg8uety7GHC5V0AfLKwB0tjDjQPsOKLb7THv9hh2O0y7\nPfqbayzTJPM0vUeMQTJ0a6U7ca+AfnGBYX+BfrdHN+7hukH07GoCJjyzUfsAxcKc1SIDORpABzKd\npOdK2v6JYFv6Qn8nd4EiihpEbGcrZ52hDMp5rwA9ePhlUdpFJhYtSrcEpVtE7Smv3zBkKETuvMmy\nzgbQWzB32ahszF2ctXvWutxBqkVuInTOgtDDGoO+c9iNA5bdDov3iNpB3XU9drsddhd7uL4HWSPv\ni6UAy1HOuWEWqqpYUlKpiUR9LIjhOodxv8NVSui6Dn7xcG77t3oqnotDJ6KPAvhDAH4RwIfyMF1m\n/gYRbf/qz3GOPyDx+/1uM1eaxcckgB5Ev8wpwTgjdMvQ4f5+wP2LQTP0HuPYo+8tnJM//C5F4cPH\nWT1YBm0S2mGZRJcc/IIYa4beDz3G3R7jXrZu3MGpsiUrN0CmOBgSUgHhwoCg3raqyAKQeT8zDOff\nV/oAtALzlIR35gilXGQCUW5YKgtHUbpkLxPh0cMihWHvRfMdUxTxJ0GthsXgjBtly8rISukMqT+4\nMq1JsvM8wGJthyC0i1JLUB7eEYxxcM5i6B3CMCDsAkJepFgGxYv3y6jFS+HFY4zS6AWRSxqOoKhz\nWgvlQkiktstaiDbWYBh7gBld1yGGeGIY/em4NaAT0SWAfwzgrzLzk9tOXCeinwbw0wDwoVe2mfY5\nzvGdjrv4br/8Xd8lIK6ceYgJMQpHTBC6Zegs9kOHy12Pq52A+W7s0fcdXG+FPyeC5SRFzL7XTscO\ntuvhhlEy9GUWgIxRMj9j0fW9WL7uRvTjDq6X5iRySrWQWjOSwK/QLZIrK64omLf/z54up6yh1FnR\nGPGnMaKOMUmkgjFFWGsRowVRUKliBXukVHj3rGOPIdQt681zE5FKM43+DDZ6paB2uNxw6MIflaKo\ncQ7O9XDZQngY1UmxoV40Q7fm2OWSxCIBDE4WsWPE2Kk3T5I5sY1Lo3VWARslSy/dYCmfZ/330iim\n9gks2hejBmFggnMdYqr6/XeLWwE6EXWQL/zPM/M/0d2vEdErmsG8AmArkAbAzJ8F8FkA+NQP/fAt\nHQnOcY73Ju7qu/2DP/TDPGtW7mO2QxXAtAT01mDsHHZ9h4uxx37sMA4ycCK78UELZgYM56RDNFMG\n1jpxTtzt4P1SQA8QzbVzXZlE1PVDae8Hqe5Zs2hpcReepYxXoDyeonlv5X8VzNvhDbLpMTf0RhJr\n3jKRKMv9lDUOqAqYpE6CxSVM/MBb2aFVDsgYI1a+nMrjC42TJYv59WaXRet0OLZMauqGQdwnh0Ht\nhPviitiqULJqpy7uBBjAJC5TmmIuGmtHbPl9q/47rW2uXs20h6u9uPUsy/ohfvbonHT/JhZTr1vE\nbVQuBJmE/hvM/Heaf/pnAP4SgL+pt//03Y61hIQvv75u9Hlpt21u+fTH1le4dr/lru/f33LEP/YT\n/+lmn/drfnaZt3yt99sGHmO2+6S9eR3zvB7/Fk5w6PFEY84LL25Hvbmjh732tW3zVLrYNmLRh7fH\nj/O2Qej66bo5p7/YOkp++GPft9n34oe2jIOz24xhOWpKmvz269Xvttz7yRF9Zv2eTNye+91R89Ft\ns5jyHHf43U4MzD6bcIn0T2p34vrXO4uxd9gPDruxw27sMPYOnRpIkRV1i4wCkmHSluS+dEQ6uKGX\nUWgxZ7Ci+c5e3lYbaaxzIAXyDOYVOWTBYOS2/zXdkimVTLVkrTSwvi8P4Xpfj5Bb8R13ZVXI9r9W\nJykFk7PwgHwwGa9nxVhLh25Ate7SdWrBHNXZS1XcicFch2K0oF7oFqfDsftBuPNhqFTL0Mskp66r\nDpS28S3PVLeenzxHNBXlT/keVTfMMmc1/5xH89XbQl8VUF8VLSrdoz4xyFbJt4jbZOh/DMBPAfg1\nIsqDPH8G8mX/X4jovwbwZQD/xa2e8RzneP/EnX23mRmTD1hU1ZISq7JFCodDZ1V77rDrHYbeoeuF\nm6U6z63ZuFitktGOyL7XIcqp0XcfSQsLYChPy6sX2aThVDP01RvJN3rsxBXIm8MIV65bpkVIslXD\nVjJp1OJiHvVmrYf1BsEQghesyubMmsPKUIhGH26iRXLqm56z81S17eWc5AlCqsghq3RL8YVXQB8H\nydR7AXXX9SvP8rZAXN5wXizyz3ruExMMV4vbfP7za89ZeqvXP87vVORSbuvVim4nksS3i9uoXH4B\nm5dQ4o/f+pnOcY73WdzldzsxY1piKYZyYhgCOkPojMHoDMZeQL3vLfpOOHOjl+YVhHNopq6X8NY5\nuIZ3zvJAbvTi9d9QbvWNyhGJkNFfMsP6fHWKvQI4V+BmtBl7lSduN33lxsDAluw2zzvNnu0GFbg4\nG8ogg3lCStUH3lixDyj0jDYpZbvdaMT/JVIEpTxejkrrv3EdnOvR5+HY6k8u9IsMzRb+3BWPdCIU\nUGU5CaKuSQyRH+oM1CSALldU+cw0GXt+vzlbL4t1/VwqtVUXC6wWjkzd3Q7Uz52i5zjHHURixmH2\niJFV2RJhmNERY7DA4AiDM+g7BfPOwjWNJtxI2Yr6pC1QEoSOQMNl5ww6e4hnkytVkiRmIatRf68M\nQcsyP677M3BnLrvccvOcwNHi0WzlVXMmgwXo2IINw1r1PLcWxkaYKFksG/l9MjKso4yGIwIlQjKm\nKaQmXQgEVClPXSJTKKh8DrOXS9dJlt6vePSh4c/Fg74AelnEUrlCQZLzn1JzZZO5pgLE5W0XIC90\nC2GVnZcrmpyZn9hw9PNt4gzo5zjHHURKAugpAd4LBWCQYAwKoPeO0DmCswbWyh97GQYKAXXa/PnS\n5uc8qJmJwUY8ww10Ug8niCuiFj0Ny0CMnHU22aEcjAFuwFwOvL4FmgQxZ65bcF8TM9t3UbfmPxK7\nAcNGMLPh7HO2nIiQKIGNUi6J1XlSjMKiPBgUG8vfXFC1WlB2GdSlEJ2HZ7u+h7N1gpMsJnWhZEq1\nWYnFH379GbW8d+W+M9VElMsi64aqusiiLBDHIF7uPweiv6eAPi8BX/zKG6t9n7i/LYpeXK6LZGxP\naDBP8Eonapary0oAosc9CjrRVmtOHD8cFUDlgevfDcv2Md5vG4SWE8dajkbEmROvte+3RcX97sS4\nvBO/y0eFxYt79zaP+fCHP7zZ9+BEY1GK27F0j9482hevN48Z3LZw2dvtNzbw+lj2xOfhjj/bzSPe\nu0iJcZg8wECIAjoWDGsIowVGC/QO6KwYYRnTFkGP/4Tb2L7vouZo6ZBamZTIgJx/bn4gsCweXAgW\nPXBB0oaaabH8qCDKXG8Lz5wz6EaB0k4iKuAPFGlhLgCSEaDm4++I/DsnksWHWGyEQWBbXpCuWawc\neqMOck4sijtxLyyZet8VMy6T5Yr5reYJShSRKC9aebGMyLQIN5x3oZdWYF4z9M0nytV1vgXzSkHV\ndfi2cc7Qz3GOO4jEjMMS5A8yMUg9tntjMDpgdLJwOZ38hrwpn9KSHPUOo0XUymVXUCx8es4o27/+\nwtXIMUQLLvtKLrvKvFvNxTYL5ebelnbJ0sRcTFVAVIvabDsbi367AfYM2Pn1Mo6AnpSrJj1paltA\nXLj2VhMPaIZuqumWgHonmw6BdkXd0unYPIVQliuAlKJgNxhsjHL7jWbtiHKRiVK5FqBgnj/mFrGL\nGme96XXaukh69Em8W5wB/RznuINICZiWCEsyAq0D4CxhsMDOKYduAfXeai6jj3hV5CxNQTStgbvN\nyNvsOGfotSjaHFAPypqx8xF4Z0xKqHRwCzWVFDguvjZZOHMjH2zBPCJpB+iqaahtHCoF1XqVsa4P\ncvk3ec72fTb8fWGNquTPKqCLFt0VMM+Ui1jaZp92quc6VWKFOSGZIz15YaxaMJYzZagBY2rqoNS+\nz8q7r3j4Bsjl5y0J905xBvRznOMOghmYPaPT6WVkReEydga7zmDXCYfuLJU/+JKKaV7Wmjmh/EQF\nsCoW8DEG6Gs4okRA6ySyzbQbTMnZci6k1sUh//spoK88emIUG1zO/uYK2jEEpBbMj0E9SzBTfQ3l\nNuXO0noFUjpM9Xli0C3bCQNFBmpYqBdrDJyzMkykF0DPGbpxHYxxVWnECuBUFz82plAn1ZY331au\nv9azuYD4KsM+OvF1gW4BPn+gtbkpc/C3iTOgn+McdxAJwBLlj7cz0kTTOwHzfS+3gyN0BrDEIKr5\nb0tlAAoKq2y6/vsGvPUfToIts2bdVY/OyBkvKjByBc9Vxl+eJwN9m53L82TpZFbXCK2igK0DK2Lw\niL6C+hrIGzBfAXr1g2lH1RWZZgZ071c+MUQoU4GkwSp3XiqHXgqjPWzXw1gnnumUB0c32blm6ult\nwNwYAhKKHLM0GjWfWGVa6nLaUlTHG1LN+I1evaXnyNHfU0CPifDWtC7W/eSntgW9jtajzPJq2QZh\nW1zzaVuoC0euiUvYVk5POSsuPmz3nShkhqPi4HKiAPrselsc/Nar39jse/O1V1f307Ltal0O232P\nH22tupfDdhzf4frp6v6PvbwdqffKiX395bY79fr62WbfsQfK0J8oNp8Yq2ewPf/Rr891XLafx+DW\nBfXberD8+whmICQBcoZ0RPbOYNdb7HqLsSMMjuCMEhhtVta+bMp8Ohc4WGXjOXtGBm5eHWpNRbST\n7XH0c5uVt8Ca6u/nxx9RO6nZWOekpjLaTUA7KIAHvyB48TmP3pfMvTxXtkg4olPq4hBLxp+Lqy0/\nX46vGToZQqeTgaSL1qjXihV7hE4sEuoEISeXVPlcl0VQnRGbpq9Ct6ApdhqoNLGhVvRTa8G88mr6\nyTEDK1DH6lwIXaPPgWNpx9vHOUM/xznuIBhAYELHwrVaYzA4i7HJ0PvMoRNDGWv93QzeWYWS4UBp\nk5brzr+1AfEKDOtsul0Q1ll2pVqaeaDNcUo2CaEhBNirz3nSmZh5PmcIEdGHMqhCxsktBXQLoKe0\noh2QmoUqd582cz8l+25a/PPzZ0DXDJ2ZYbV133UdAO2yLUVRydA7/dk6V4zL8hVMHuJ9KjdYNfhQ\nTSCKXLEQI6wce358YeMLpVOLx+saSL3sqsXV4pdzizgD+jnOcUeRuHLh1grlMnYC6oMz6CxBZxWX\nP96WUpFoMvSWJsE6qS9064brPgb1dwFz5obDbjJG5NfXLhg1Q45JOzR1cn3OyoP38DlrXhaxv82A\nrnQLF0BHuS38feRCtcSglrqxLaQ2hdfY2vJm9YmTix6iCuTaPOS6Dk5ljDbrzstIvpyVr6+Yyjlr\n6Kj8mlvJYTvqo62EHMsRuV5erbL19UJa5aLPe9V5BvRznOMOQkC0tog4Q+is0CxjbioyTUE0Azmj\nuKvm+41SsaEj5B+PwR28Llq2oL6mY9ZAvlKYtDLCBtBX2Xl+XFKr25jtbv06I/f55wXBB4SwIPqg\nhUvpoOXyguWNlOJnHsYcok4vCnK84Auwp5jKMeRqQRYIgviSW4hkMbtPDsOIYRgw6KBm546nEikc\nM5dPpQXx9kpEXmP+WRcXhtjglg8E5XYN5nXhXkF0yfq5eYh8KQqoP8f38Azo5zjHHUX+MzYE2Azo\nVsC8txANeitRbJLUVgrBzT8eZ9gZ0Y+BfQXqp2R9+nMB9A3FUvnzk5l5KVIGBV1fR8atQDzv98VR\nsc3MK0/MFcxjQoxJs/wI7wO8X7AsQtksy1JAPRdU88kgiKrFZdtaUjAfBozjDuNOtmEY0Hd9M5Wo\nYbvLVQ3q4pJBPMayGGU1Tfk5JRQNfZu94wjMm59XUka9kqg0iy4qXEuo26O8c7y3gE5i9N/G99zf\ndjSmo6tQ5s1DjuYUSoQTBc/56GHzCavccKL4OJ/o+Az+RJHy6HFvfuv1zWNe+9p23NyTE4XM40Lg\nzdNt4fHL/+4rm31f/crXNvvsiUu1H/nR9Yi+j3//928eY06MyzvuYAWAZyde2+O3Hq3uH663Fr6n\nDG5P7XNHna7M29fwfov8B2sMwRGhs0BntaHIiC96AfQ2mTvaJ3dQs+UmS99sqGBebyuA12OggHhb\nzMzKikq1cKEGWkomNVn5OiNflCf3JZuu0sQK5DmTRgN8rZIl+gjvvYK4xzzrXNFFji+LRlB5ooC5\nUTvernOFPrFdh26owz52uxHjKAMtnHPl+8164piSUC6pea95gYkRIcb1VUOIzQCOBtAzjYQtmDcM\nunrFZNsDAfO2H7cs5kX7jyNwf+c4Z+jnOMddBEE9vwFLMqvCGQF0Z3IxlI7oFBz9qVYaJgPeOlNe\nc60FxHEazFfbKhvPbfhrXnhTDD0J5mEF5BXQM8XiSyab1EBrpejIoN6AefABfglYlgXLtGCeF8zz\njHmescwzliXTLsKjg1loFWuArkMHUhOuTmarjgLi4zhiGEfRnDtXvM5Zi67ZZkuGbXAZG5evKrLG\nPeT3vdLSRzE/gzQwZcfL8nUoBmtlh6qbGvkjZztdLcI2YH78zbhtnAH9HOe4gyBkMFf+3AAdyeaI\nS3Ze5wS1l/zlIGswPwZltLQJmtujDD0fc0PZ8AkAP3U/NWCuapIYCmDnYqdflpWKpQBeKWDGcjy0\nx24kiZknX+YF8zQLmOttBnPvddxezsxzs1DXYRgGycT3e1xcXuDiYo/9fi+j+IZB1CzWinUt65Qk\nRCFrDAAySExFehlKNh5PgHhYZecpycJSLIbzx0jNtCNqW4LEhwYFyHO2vvoiHXFwz8ei32Zi0fcA\n+DkAL0O+K59l5r9HRH8DwH8DIHMMP8PMzzd2/Rzn+A7GXX63c0NRZwXMc1ZuSSZRmSxAJHl0BnVq\n5YoNTdKQ4WsQBzd8edVM6wwfrOqNQAVSlNJboVQkeAW2ubkoZ+YpNhmq8uSVM68NQylGcDwaQrHi\nytWgq0gcNdNfPJZlxjzNmKa5AfMFfvHw2jAElgYh53oMfYdhGLHbDdjtRuz3e1xc7nB5IaB+eXmB\n/W5E3zsYI3x0Uo08MwCTYBIDJgEkKpc8sCPGRllzAshLcTTVekP9PlXt+Gr0XG0hVd5cPoMtTHP9\nMpXPRsn2W2bst8nQA4C/xsy/TERXAH6JiP6F/tvfZea/datnOsc53n9xZ99tIkgB1AC9aTJzMIxu\nAJqC5lbFwBn0laI41iqnnJWjUi01I+e6L28KNhUKjkEhcz41c+aWYlH6oYK5L3x5CF7VKwEpBnCM\nQEqi4eYybbS8/laK6EMtdi6zAPk0TZgPE6ZpxrIs8KqMSUnOlFX72/1uh4v9hQD3xV4y8osdLvY7\n7PaZMx8wDL3MZQUjxYAAoVWMFRA3NolTqomapaPJ0H0t6m6sCmJZ9NrProK4buYY1JtPgIu4Bs16\nfsS06NGfs1fuNhOLvgHgG/rzUyL6DQBbj9VbRG+A77lal8D2vC00Rl6/C8fb1SnM02bfdGpe6FHJ\nzR9XXAHEEx2mKW6LrofrJ5t9b3xrPT/4jVdf3Txmud52baZp+/pff319rC984bc2j/nKl7ZF0e/+\n8Pdu9v25//zPbvZ97GPrLtCnT7fv5xtf2R6fxu381idPHm/2PX28LorCba1+Xbe1SzZuWxg3R/a5\nJ2q1kmGt4vl4x7v8bhvKBlxKtxi1z4V0HJZsdfVq15fb5Wq7DHHIQLsF9OzTktuT1hJFFMCpZ6X5\n95arR5udx5JBxxiEZjkF5jkrX6lXcmZ+lJ0rj56itOkvXjNy5cjnacI0TTgcDpgOE+Z5Vmonghmi\nWrEyqm83jri6usL9+/fx4P59XF1d4vJSAH23GzCOvQyzcLYMluAUETyQYgKZCDIBZGSAttzXxiKg\ndLu277lIJoNfFXk5cflStln4KkNvZ4hS/YylGHqE1O3qvlrln+87/VwcOhF9FMAfAvCLkHmMf4WI\n/iKAz0Eyna104xzn+AMQv9/vtoEOsrCSuDhCAXOqSLoC3tJgtPrjbQnx2pLfFjsFxKnw8TlT59V/\nesiWUkE9Pusot9QAbmqAPDcK+U3Xp2bmMYBjVq9wAXXOk4Uanlyy8ixFXDDPCuLThOlwwFRuZyzz\nUvhyMhadM3DOYRgG7C8ucO/+PTx8+AIePnyI+/eucHm5x243YBg6dF22wG18YJQKAhkQWfFtMQFk\nHcgEwIifMYPqVUSMKyVPBfUg9r+5eJwtF3Hk9VIolgzy8hjOPEvB/+3vESst16byz4Hptx6TTkSX\nAP4xgL/KzE8A/H0AHwfwGUiW87ff5vd+mog+R0Sfe/Z0m9Wd4xzf6biL7/bNs8cyxMKK46KjbKO6\n5pI3GwAoTVGR+W2KlamlYI6yYXDxAq9RcvOahTfdntIck7XVWvAMjRyx8WGpnZ6VYkkpgmMS7rxx\nVyxdosuMRcF7mg44HA44HG5wc3PAzc0NDjc3mpkLmM/zXAugAKw16Ice+/0OV5eXeHD/Hh4+eICH\nD1/Aiw9fwMOHD/Dg/j3cu7rAxX7EODh0TorT4AhOuYg7w+u26DbPk2zThOkgC8o8yb5lnrDMc1Hx\nrEA9d61ypV2ggJ1F5nzk0sXQhLxk8To82tQsvp101ObunBf5W8atMnQi6iBf+J9n5n+iT/Ra8+8/\nC+Cfn/pi73nZAAAgAElEQVRdZv4sgM8CwEc+9snnu344xzn+PcddfrcHy+gNCd2ipko1Kz8C6hbo\nW41EzsQb6eBa6cKoToztG5GNmoyfc5aXy6HMko2XgmfS9v2warMXUM8SxLqfs5a8ZPrK86eouu16\nHK/qFeHLPRbNzpdlwTRPWvzM4LpUIGcGkRhs7cYdLi8vce/ePTy4/wAPHz7Eiy8+xIsPX8ALD+7h\n8mKPYXAC4EhIKdT3k3XrISJm1CUrGTk5ycpJvI4ZVOsQjbKnFka1qUmVLQzIvFTNh3l9lrG9Vmrm\nrKq6hlh+mzMVps/PZEDEzVzT5yMSb6NyIQD/AMBvMPPfafa/ohwkAPxpAJ9/t2P1hvGR3ZrnXpYt\nf9rN64YU7vebx4R4zJ8CMWybT46TllPOfteHp5t9j996Y7PvzW++ttn35K0316/rcIIbf3X7e1/8\n4u9s9n31q+sGoacnmneurh5s9v3kn/qTm32f+fQnNvt++zc/t7p/c6LmsJit++Wjw4mmnhPc3nTk\n8PjSCW7/eLQcAIQTNYx+t1vfX7av4XBcm3hOvvEuv9sEYDCMQQuiFhlXs56bQS3PnJLUAFJatY9m\n5cSKE0cF8zWo58JZM3BBnhGpBfGSlUf1YFlz5bEAd3ZIDAix0gzVITFuOPLqSy6/t/hFlSsK4A2Q\n521ufg5eiqQyWFvA3CrFcnFxgfv37+OFBy/g4Qsv4MWHGczv497VJXa7AdYwUsqc9yQZtQKwDzoh\niQkMo2BuwRTBREhKWyUW9VCpPRRpZV2kSjFUviByrk1DblG+GmrgPC/emsG3UkawaM9N8zuJGUZN\n0OrBuFmY3z1uk6H/MQA/BeDXiOhXdN/PAPgLRPQZfabfA/CXb/WM5zjH+yfu7LttSAA9K1wsVRli\nSqr8aDjmslFScDYVzIGSmWUW5pimqRqLmpIXupUBUo4cBZzCuuiZs3Ffwbz9ubbtx9rlmYuB2Zs8\nc+4+qGrFY24LnrNw4vMyK4B77QZdqsY7W+RG6dg06sMyDCMuLi5w7+oeXnjwAA9feAEvvPAA9+/f\nx9XlBS52I7rOgjUrD8uEebrGsszyXrTbUwrJJJk42bIlLVNEBiKzNgllikPOd0r5CkYXNNZzbS0M\nqM4dbqSovFoYZGGtVPsa1I1+nIYZiRIMZW0QGjBn9b653Rf6NiqXXyjfmHWcNefn+AMdd/ndJjB6\nSuiJVHdOABt1JmRQTCArDoFsIpAiKFkB9GLTUD1BVkXN9gKea1bY/vGzcrjl0p0rdSBbWAN5QyVk\nPXkL6oVuWKlYlL8vk4I0K19EN74si/LlKkPMoN6Auc9eLy2FoYAq80MtnHXoux7DsBON+YVoyy/2\ne+zGAUPfiU0uRDfulxnzdMDh5hrLPCPEoMeF0ilCrzAlbfWPiIkRmBESq7a8nnMpVuvrajzZZaU0\nsNzJAsFQkM78ef3MCt3SXDUWX3TK+brQK0Udcwzmqdlu+T08d4qe4xx3EAbASFEAHQSjFoqS2IrR\nk2kUIDmDFklj1iac+rOtnh4CDqny8s1vlZ85D5+oio2cacfYArWCeu7wbBQdoi/3ONZfVx/yVDh2\noVd8ycQzNz7nzHwW/tz73GnaZOXZrbCcxHyJIYoUcUS0spEVwGMghoRgAhJxUc5I16ouSEUnrpk5\n5HkSRPYZmQTMIyMk9W3JxmRoREdthp5yodbJlYTrxEvGWlgni5DNo+pyw1j5YATIc4JfvNK5+QSb\nOsv2G3D7OAP6Oc5xB0HE6CmiB4m3CBOQGCkBMUYgiiKETJRmFiM/s7HKp+biWcOzHsVJP5eGq02F\nr28HT6ih1FGGnpqmmRWI623N2tf+JW3X6LJkXblvsnRtGtIuUO+zD0peXLLxFRfs4tV7JC0x5M5N\nRgwJ3kfMi8fhMAPMCN5Khh7E72WePYKPetysPtG0mUkXuoTIjJCAEBk+JYQoW11cKuXC6i4Z1G8d\ngAzOIIOuFwsCZx06J7a8Auq2tv4DqjzS+VNqXZDX76JcKpl4qqCuJ4cgjNxtQf09BfQeER+mtXTx\nyZNtA8/FxbpB5WJ/tT3YiS+8P+GQeH00du3R462c+K0339zsu370aLPv2RvbQunrR404v/Xbv7t5\nzJe+vi2Kzoft+45hrSJdTjhDprgtuv7qr35hs++bJ55zcOsi6OXVttj8rSfbAvHrj0+Ms7vZ7rs+\narz6Ux//wc1j7Lb+CT4xXu646H04McZvulmfC3G/+86EAWNAhFMwpwzoEQjRAEGoFtLN2AhOVgql\nRrP0LG0rdbT6HW+LouvZnzrPs7W4zZ7lQdwCQ0OfpJJ1Kz9ewFoz8kamGPyCsOjACgXvkpEXznwN\n3j4XVYvlrHLZ2e9cFzlOdYB1hlEDqCd6EhBfIuZ5weEw43o4gIgQQ8Q8dXDOwIDBySP4Gd5LQ1JK\nSevEMvtTDy8La0oIEfCREWKCVzAPxe9cM2XNmJMO0AhBFDhEQNcnGNthTAlEYtvbOYfOWlgjnjEG\n+vmXMkfDz3MCpWY0YO4DaNVHLdXCz6d0OWfo5zjHHQQB6CnAguCYYJJRQBcQgomwIYCUQkAQCR2Z\nCCSjXPo7US8tAKQC4JzqwIUyeCLbvobcJBSLl3judkwtuLdZuPeICuBVS14dEKdCp+i+DPQFxFMZ\nAJ2z7CyMybx2rge3GXoGOCJG8AnL4jFNM26uJ/TdNQwZhCXgcDOh7yyczcNCRG+eUgCnAEBtda0B\nGYBIzLNCEorFh4QlpvKzj9odGpuFMgkdJuZhsqClFEFE6GOC63oEL6qcPCDakGmAPGfZsqgXK8WU\nIEu/VkY4Ff2+SEjz58dIkYsDJBe3tXePM6Cf4xx3EARGjwgDAwvt+FO6xRgChwAmA0Paak4G0OYS\nI5OGhRrQ4xVhC1Cz8sZyts3oVr4rGcBz235rLFVAPZa2/XxbwVyplsxLlxb9GYdpwmGaMc2LAPvi\nK6BnME9VlSG4Rvqziji5WtVWTb7k6DIoIgAgTNOCzk1w9hoEg+AjrocD+s7BOQNnjAxoJoYhBpGI\nEI0hOGdgrcwSJSPFyxWgh1gydB8kS4+pXknU8xxKg1WKEWQIY0zougG7vUxiSiE3V+WOW6N0i6hY\nZNWR9wYD1fFnw7BmClKpdaSyMMa4nix1mzgD+jnOcQdBADpiGEoiacteKwkIUbK4BFP+zUKaWqT1\n3JZxaAxTx8gViiXzyc3EoCbbjplWOZ7x2QD6KRvY1BpQte6Jy1IBXbnxKZtozYtsCuSzF713kQlm\nX3DdMqjXW26sDJo6gAK6CYQYkj6WkBJjWTxubg464NnCGtLhFqi3JGuicwads3C6GSucdmIgJihn\nzgLmMUqWrna5Maaq008yQSnr2lOKMMYghAjnOozjDof9AeNuB+ectA4xI7kE5yw4JhhnYawROwJj\nIMVeZBmNrm6VP48xNU1ZQWsCopKKJ/puTsUZ0M9xjjsIAqMzaTW8AAlIRECMSAAMS5HMqOJCZtVZ\n3cSlT67YSdUYwjnHzIvH7NPtV2qVAuiNdjzEUAucQX4OK35cATwbbi2VMxfKZSn7Fp0glDPyyXss\nPmIJASE0PHTMvC/KbeumWymXOjWpBXRAcM9YK6C2BMzTgmfPbjD0PaxzBcwNZVAnGCsj/5wzcM6i\n7yz6zjVGXVbOLTKw52w9Ygmxcv+hXu1keipPYWJOsNYKoNsOQz9iHHboux4EkizbB8S+h+scui6i\n6xxs50S3brXkbZoCZ1648xi+IBTPPFU6S5q8JIO/TbyngG6Jcd+tO/6ePtu+0Gdf+r3V/Q/ut92L\ns91t9j1680TR8tV19+XjN761/b1vbB0SX/3ylzf7XjvhRHh9NHbt1cfbsWuPn22Lfk+fboubj4+K\nj9O8LfJG3h7r137ti5t9dDTqDwDu3V+fs+/98Eubx7z44nbfN17fFpIfvbUtGluzvix8+ML2WOE/\n2L7vT3zqk5t9cOuv5jdf3Y72+ze/8Iur+09PFHTfqyASQ67cRSK1sCSAptkYJdlMEudEGNMAusjy\n2FgkJkRWuSNXxUoobflZy52B3a8ULKuMPIP60hhsNdl3GVIx12EVUUE9LwK5jX8OEUvOyLXYGVPm\ne1EaYCt3zk0hdM0Lrwu7CuiqyTZEpRjadTdwrg53Jv13IsnOM19unRYoO4uhd+g7h77vlKKxsNaJ\nMReRnN8k1Mvio6p1WjVOc+Wj5xjMcM4ixghrHbq+Rz/IJCQklnM1eoRhQN/34CEB3JfvgiiXTCl0\nF0+eYi0sVIvUDibc3Ew4HCYs81KsF24T5wz9HOe4gyAIn5vvCF1CtaCVWAqfMcFoByOMAVkLWCcF\nUgZggQQqqoxaKKuNPMUsqmlzXzUDHRc5j8BchkfM8POCZZnh1YgqzArojT1uLqgK1yxALgW8FqQr\nmMeyPzU0kWTvRe2SKqAX6gWZUmBVqRjVoRstJJuV8oc0QzeGYKyMo3Od0C1D3wmo623Xdei6Ds46\nGCumDDFphu6D1gGyHFPqEG1HrRREoYCeBNC7Dn3XwRordIkqhXJNgpAXHJmuBMPIraGlkzTz5yGP\n4Vu0EHzAs2fPcH19g+kwY/FijXCbOAP6Oc5xR5FVhqs+T1Zg5wiGAShKxyjQALoFjBRSkRKYrGbo\nXNwNk2rHU/CqF1daxHuEsKgR1dtx4r48Nhc6S2bebj4X+nRgRUygFMvACsM6tS1bDmSevGTiWsiL\ntcBYMvlmKwqdJjuvtMtxV2RtweRKVpSsl3KGbo1QLp3F0Dn0vdPbTjN1AXVrXVG+ZEDPWvo8dq6l\nXZI6KxIgVgPMsE6lip2Mt8uadbCOx7MWse+rjDZb6mqzUS6IlsVSu23necHh5oDr62s8ffIUT58+\nw83NAfO8IIZzhn6Oc7x3QQAZValwk4nlLsXsaQUDUl8QJgNYAzYGiQCTGOSS+o2QZrIiy+MYwNGD\ngwd7j+Q90rIoBz5XDlwVKll2GJalqFZajbksCh4cAuA9TAywKcEQg3OV0TGYXSkiLjHB+AQKEewj\nIkvDVEppVRjN0sWYRE9eFCS6r/DnJ/8DUKB97SrZdsQCYkhGkWC06OysgfMG3ll0i8XUWfTOous0\nS3dOqRfSYnWED1G82vPVSJEQZkloBHOCISBEJ1YuekzXOTgrHuydsxiGHpySvhaLzslC0ncdrOtg\nrAUDRfMeYmzAfMbhILbCz54+w5PHT/D48RNcP7vBNM8IZ0A/xzneyyAkUzMwAfbG/yRTEyAwRRhm\n8c02pM5/DBsTqAsg24m0kSEOhxnMYwBiAIIH+0W2ZUaaZ8RZqZN5LnRKmGfZMi8e8pQhbcBJEZQS\nXEowYHSWAOMKnUE6yScmYAkJc4hwSwAtAYk8Qlowe+HRgxYXW7VLTA2IK81SfcRTdYQsAC6xBXA6\nAvP676S/nZjAoRaQfSQ4bzBbKZRKe34ukIrqRhqNVLO/0s83U6KUTyLSyWaEIom0mepxFmMv7pBg\nhrVWKJm+R9cP6PpBMnljxJYgyvMK3SM0y+Ew4ea6gvmjR4/x6K3HePb0GabD9P4E9MiMR37drbjn\nEx2Hb607Dv3WYReXH3h5s48ebQue4avr4uarn//1zWNe/73f2+zb9dsn/fTLL272DT+wtqn9v359\ne6xf/zf/72bfoyfb9x3S+kOL2Nrb8omPzKQTc0pOqJy++cZRAfeNbTfp0QxyPdRWA2uxPT/26GX8\nq3/5LzeP+aM//uObfcNuW/T+naOi9C/+3//P5jH/2//6z1b3TxVq36tgAKGIxxnVKzwhW/tx7pJU\nZ8UK6HKObYowcYDpdDQaSJpcQgDphuCBGEDBA95XUJ8mxGlCmCb4acIyTfDzjDBPCPOi/K6YgmVd\noVH7VhLCV4FcwM8q+IEMQmLMIeGwBNDkkcyCkAiTTwC8dlQG5aGjFkFbENf3jtRQLAnrnDyfx2pU\nhuyJordv025VbhN08QwsEsVQC6eZjzeU5aEC2O1rbee36opchokQoRiIHTI3biUT7/sOF/v/v71z\njZUlq+r4b9Wj+5xz73WGYcbJiISHEiNRGAEREtAAfkBCMr6SUYwRQzIhweCLEBISUWOISnQ+KMSQ\nYCCRMCgPBRMYcQRnlBEYYB7ICAwEHBycB/f0s7q6q2ovP6xd3dVdfe49F8/tc/qwf0nfrtpdXb1O\n977/2rX22mvtURSFuWciIYpjq4Pq7wrq0bn6Se6iXIh5NpkwHmeMRmOGwxGDwYB+r09vv8doMGQy\nyW1x2iEII/RA4AhwqkzL0twAOB+eWCff8gLvV03OU7UKi9G5OmJX2aPqECWJJZYy3wBUBVKZa2Tx\nKIjKAikKWBL2CdXEC/w0p5zNcEXp85l7Ife+ZxOmmMiLeF2M2VwECeoFPZmVEBeU5EwrJYpLHzFi\nCa7mrouiWoi4j9RZzhC5KuZNn3lTzC1mv/baL6UznLMIeK8L8ymL7In+x5hnVIDal+3frfWKzfqO\nqv50mW/X50VBK5DCzmXeMhP0nZ0dzp3LyPPciltXzodk+u+gngAFiqqy+P1pU8zHDEcjBsMhg8GA\nQb/PoNdn0OsxHIyYZPnJDFsMBE4rzjlG4wkRdqeS+DDGOi/6PCqvFvfKx6j7UEfFVhpqVUFaoklq\ni40UqCqk9CLuKmJnkRfzC4Ar/YWgtO2qInIlUpVIVRFVNjJXdURgPl4v5EkSewH3Qp4mxKnFfEsU\n4xCmpaNwWDKxhquiFvHSx3EXZWk+6DoWXVdlu+liWWzDqjvFT7p6AZe1gq7z45pb9Xnru4HFKqfm\nuRdnWLwmjU9rpLJtHqGWA0aKkkiEOLJ6p3t7Y0ajjNE4YzweszfO6HS7SJziENLKFiU5hVlZMMlz\nE/LMRuWj0YhhLeT9PoP+gOFgwHAwmrtc3FEJuojsALcDXX/8+1T1TSLyFOAW4Crgc8Cvquqa0jaB\nwMnkKPt2WVacP98jjiK6SUQ3jdlJErpJ5NOq2mg8EuajNqoKJ16G/MSn+cg7xElqoYyCj103Xzpa\n2gWhHjmKohFopGgsaOyvJElElMbELqFEcbHlFYkaPuDER2oknZQ07RCnKVGSECcWRulEKCpFy4LC\nOaazGVmeM/bClU0yqwU6m/mqQ3UR5aYjpC3ZbYFfZZHzRLzDT1YEXZfOZN+HtrZ1zdmV5QvD8vbq\nxUMb73DYb1GWjpmURHlBkuQMRxm7gyG7+z263V0kSigrRz4r2Dtzhk63a3dc2Ah9OpuRTTJG4zHD\n4ZD+oEev36PX26fX69Ef9BkOh2TjEZMsY5pP53d1F+MwI/Qp8GJVHfn6i/8mIh8Bfge4WVVvEZG/\nAl6FFdc9kP3egL//wK1LbTe+/CWt43Y7y2aN/rtdri06315ocuWZdvbA+NyyY3fnqde2julf2fbh\n7uzstNr2rmz70O+870tL+x//5H+0jnms184UqNL+6ld91W6dI5z2YiNH++qtSx3VkJXzxVG3dUxE\nuuYz17Am26VbWfzw5Qf+p3XMW9/2jlbbM59zV6st7i5/Px+99SOtYx568BtL+0V1yeOJI+vbZVHy\n2KPnSZOYvW6HM7tdot0uiXSIfS5vm2PQRf4SdTZZqg6txATd+8klSZE4JhLveFCreCSuMpeOKFLH\nEcYgSYSkEVIlxJoS4ygEilioihhX1WF1DUGvoz86HRIv6BKb39whFM6hVUlROfJpwWgyYTAa0x8O\n6Q8zhtmEbGKCXpalrWat3RdLESrLot4U2eUe39yr++pqyKIsHWvnquucOj9JWh+5zk3TFnNZOb49\nPl+213lRL6KSPJ8xHk8YDIZ0ujtEcUpZKZPpjFGWsXfmDN3dHZI09aN0ZVba4qFxNmYwHNLv9ejt\n77O/b4I+HPQZj0fkk4zZNKeYzVA9oqX/ar2vLm6Z+ocCLwZe4dvfBfw+F+n0gcBJ4ij7dlGaoHc7\nKcXeLlSOThTRTVNIbLJx4ai1OEbzrVaWlRH8ZKfFk9eCLpElmBKp16Y4Iiy8MIpAYiFKIqJOTKQJ\nsTjSSOnEQpHElJ3U6nVWJnriXQW1vzztLAS99plXamGKblYwqxyTWcFokjMYZfQGQ/b7AwbjjPFk\nSj4rmRU+ZNEtR6u0J9ibrpRlWV92ijSPiRr7y2GMTR+6jx+aj+al8a7FBCtL54FVuW9IeaOeXx1J\nU0/LmqhDUTriWUmW5STpCIlSnBO7+GUZZwfnOHP2DDu7O3S6HUsDIH6Vqh+lD4Z9PzLfp7e/z6A/\nYDQaMplkTH05vcOuEoVD+tBFJAY+C/wg8Fbgq0BPdb4O/ZvAEw79qYHACeGo+nZVVpw/32d3p2Ni\nnsSc2dmxjKliqx0tl4eauApW9KKONVeHqwTiCqkLUMQJUieY8tVwLDrFj9BjIBFEY4SYWFKSCIo4\nokhjyk5CWVTzVZvoYoWlhd3FJJ2OuVv8BaRSmJUVUzejqJR8VjKeTBmNMwajkR+djxhmE/LpjFnp\nbBGULsbJgt0VL092rkazLGg7Y2RF1GuBbt8Vrp6xKdXRPBNWLeiLZVFtf7xvk8ZrUs8FuEaMjffR\n60LU87wgiicoEWXlmEynjLOMM8MRZ87usbO3S7fbJe2kRIlFhxVlSZ5PGAwH9Pt9+v0eg0HfLybK\nmOY5ZVFYpag1TqmDOJSgq2oFXC8iVwIfBNqVC1bvnuqvSOQm4CaA71njEgkEjpOj6ttnd3cZZxNU\nlW6nw7SoKJ3iiEBiJLb4bnFKhJUzswnSCnC2klQBtfwtUseJa0LkYhutxz41q/oFQBEksYBGCMki\nesUnqSpTK3JRL9NfCLoP4YttQjRJUiRJAUsy5UpLK2tug5zhOGMwnjAcTxhnOZlPozsrSsr52DWa\nS2otwbW8r4r4wWK+2F72dGvL5dIe0dfbXsAtFtP2ZSHmLUFvaPkqivhKUhGoNv4aL+oOylKZRgVM\ncpxaNsfprCDLc/ayjL3RHju7O3R3unS6lpOGSHDOkU9zRuMRg36f4WDIaDRiMsmY5b5YR2XlBtdd\nxg7ikqJcVLUnIp8AngdcKSKJH8l8P/DQAe95O/B2gOuufvzhLzWBwAb5//btq6+4QvOiJE7N51wq\nVBJZibk4QRJbci6+0o1Ji0M0QtzCL6xqAlz5yT3BISQgDpHYh90pKmq5nmKfijeKzZfuXTBx4kiq\nCldaDpXaBSuIz49i2R3r8EQin26gKpiVjmxaMBzn9IdjeoMx/eGY4XhClk+ZznyCrsbsjSxJcL3X\nFPNlN8t6IVj1qCvSEvK2G6f5yfMJVG0ut6+TYjUnPVs/5pp5IQVtetib6u+99wpl6ePxvaDPZgWT\naU6WTdjZHdPd6dLtdki7HdI0IYojVJVZUZBlY4ajIePRmDzLmeWzReUl7AIcS1znfLsoh4lyuQYo\nfIffBX4a+BPg48AvYtEAvwb8w8XOVTrl0cnyxJU7d1XruG684tkqR61jhlk7A2CcticHO1csT25e\nd7Z993z1tWsmFbX91Tx0vr0Y6Nbblyf0Hl47AdruQOtuH93Kr7ZuGkR03QToOh/bmsVGLE+CRrpm\nAnTNZO26ainrEu5HUWdpv1hj1r/f+ZlW2z1rFntddfXjlvYfe6w9CX5mZ7k04XTS/n0uxFH2bVVf\nFcdB6WVYJUKjBJIU4tTnHtGGrDj7PTVCNJqP0hXL0mg2YsWMKkAs9tm6jlqtydhWdMbqxTyOiFxM\nXDlcqfNEWiboskjvK979EC/CE7V0VA6mRcnY+8z3ByN6gxGDYcY4y5lMCysQoUpFQ0CXHgd8RyvP\nq9uwkO326+uG0s27gNUIlaarxQQeWbFxfnJ/SdBGs+DF3L8298JY2zyeXX0agbKymPzS4szz6ZRJ\nN6eTdeh0UtKOrRxNOpb/RUQoq5J8mpNlYybjnOl05l1kdiGJJLabA1EoDzdOP8wI/TrgXd7XGAF/\nq6r/KCJfBG4RkT8CPg+0wxcCgZPNkfVtBSqx2HIngotiNK4fiaUDrkfoeAnSBNEK0ZhF7ln10U4W\nlVKpCboV3MTOAV6XvLvFYiKJNEISJfL1OzVRL+Zqnp1a4GRuAfi8MTiLsioqN/ebD0YZ/cHYBH2c\nMc5nTIvSu5KaXut1k45tV8syzXiU5vfYHOosT7Eun2016qV5cfHtqyI+3181qu3jX2w0p3YFxF86\nmh+jWPpb52xFbzGz+Yk895FEFuOf+syPlgpYcOqYFTOmuZX2K2elL+5hy6oQQWK70Eh1RIKuqvcC\nP7am/WvAcw/1KYHACeRI+7ZYThYVQaPIPxa5zokSX45MTLzjGLRCnI9iiQTVCI0qC4KZDxcd4otO\nV2o+1Uj8qFOYu28gNolUBY2IYiu4HFWWR0ZjWHY5WLEHJKJSsUlZv+pzOiuZ5DPGk5xR7TefTJlO\nC4rSL5O3P7rxgKa4Lu+vjLNlRU9XdH3Z8744YP1ovrm3cMvIkogvjl54VhYiXre3WKOh4i+otaAL\nzBcxuWqRn0bm6QEiXz0pmYeJJmlCHFucvVVFKiiKwsTcqf3evk6pLzm9tML1QoSVooHAUdCM9FD8\nRKG5XfD1Q6lXhkbmNxeJFiJfv6b4CTjFYRkFnVpRhkh9aTb/kSILsYr8s9bvF8vPrvXCI4f5lRf3\nB0idLMzfBSwq+thS/tmssHwjs5mvG2orQetY86WJy4sIzkFulvVzkuvWj64bzS+Pn9edqX3B8X/v\n0rH1X7Jy4VlyqzdH5Qs1r5f3V06pqHBUJupOESfEZUQ0M1FP05QyLfzK3BjBEn4tSgVaFs66UCHe\nNXag338NQdADgSOgTvakDbGb3+7L8kOaAk77v+pcTOYjbotQWax8lKU3SfNcUr/Xz9PoYixtPuGF\nOMz/dQ1blXlFIVvibylw60LKzfzlta1LLolLEJ+GV6Mhsq1XV7bb0r9e1A9nQ/t9F5t9XHPe+QV4\nMZk9nzQlItYILXTpFJbwy9wuVVXnv3HMC4ULc9fOpfw1cthq0keBiDwKfAO4GminRtwettn+bbYd\nLv072Y0AAAX3SURBVGz/k1T1mk0aUxP69olgm22HI+jbGxX0+YeK3KWqz9n4Bx8R22z/NtsOJ9/+\nk27fxdhm+7fZdjga+9fFtgUCgUBgCwmCHggEAqeE4xL0tx/T5x4V22z/NtsOJ9/+k27fxdhm+7fZ\ndjgC+4/Fhx4IBAKBoye4XAKBQOCUsHFBF5GXisiXROQBEXnDpj//UhGRvxaRR0TkC422q0TkYyLy\nFf/8uAud47gQkSeKyMdF5H4R+U8R+U3ffuLtF5EdEfm0iNzjbf8D3/4UEfmUt/29ItK52Lk2QejX\nm2Ob+zVc3r69UUH3OTPeCvwM8HTgl0Xk6Zu04TvgncBLV9reANymqk8DbvP7J5ES+F1V/WEsi+Br\n/Pe9DfbX1YSeCVwPvFREnoclz7rZ276PVRM6VkK/3jjb3K/hMvbtTY/Qnws8oKpf8zUabwFu2LAN\nl4Sq3g6cX2m+Aatkg3/+2Y0adUhU9Vuq+jm/PQTux4o1nHj71TiomtD7fPtJsT306w2yzf0aLm/f\n3rSgPwF4sLG/rZWOrlXVb4F1LuB7j9meiyIiT8YSUX2KLbFfRGIRuRt4BPgYJ7dSVujXx8Q29mu4\nfH1704K+LiVBCLO5zIjIWeD9wG+p6uC47Tksqlqp6vVYkYnncgnVhDZM6NfHwLb2a7h8fXvTgv5N\n4ImN/QOrwZxwHhaR6wD88yPHbM+BiFWzfz/wblX9gG/eGvsBVLUHfIJGNSH/0knpP6Ffb5jT0K/h\n6Pv2pgX9M8DT/GxuB/gl4EMbtuEo+BBWyQYOWdHmOBBLovwO4H5V/fPGSyfefhG5RqzOJ7KoJnQ/\ni2pCcHJsD/16g2xzv4bL3LfnKT839ABeBnwZ8xm9cdOf/x3Y+x7gW0CBjcReBTwem0X/in++6rjt\nPMD2F2C3bfcCd/vHy7bBfuAZWLWge4EvAL/n258KfBp4APg7oHvctnq7Qr/enO1b26+9/Zetb4eV\nooFAIHBKCCtFA4FA4JQQBD0QCAROCUHQA4FA4JQQBD0QCAROCUHQA4FA4JQQBP0YEJFKRO4WkS+I\nyIfrmNRA4KQjIm/0GQLv9X34Jy7T5zxZRF7R2H+liPzl5fis00QQ9ONhoqrXq+qPYAmSXnPcBgUC\nF0NEng+8HHiWqj4DWxDz4IXf9R3zZOAVFzsosEwQ9OPnTnwSHhE5KyK3icjnROQ+EbnBt79eRF7r\nt28WkX/x2y8Rkb85NssD321cBzymqlMAVX1MVR8Ska+LyJtF5E4RuUtEniUit4rIV0Xk1WCrO0Xk\nLf6u9D4RufFC7cAfAy/0dwG/7du+T0Q+6vOF/+mm//htIAj6MeLzaL+ExTLxHPg5VX0W8CLgz/wy\n59uBF/pjngOc9bksXgDcsVmrA9/F/BPwRBH5soi8TUR+qvHag6r6fKw/vhNbwv484A/96z+P5f5+\nJjayf4vPt3JQ+xuAO/yd7M3+HNcDNwI/CtwoIs38OQGCoB8Xuz515reBq7D0mWBZ+94sIvcC/4yN\n3K8FPgs8W0TOYcnx78SE/YUEQQ9sCLUc3s8GbgIeBd4rIq/0L9eDkvuAT6nqUFUfBXI/R/QC4D1q\nWQYfBv4V+PELtK/jNlXtq2oOfBF40tH/ldtNEPTjYaKWOvNJQIeFD/1XgGuAZ/vXHwZ2VLUAvg78\nOvBJTMRfBPwAltQnENgIXng/oapvAn4D+AX/0tQ/u8Z2vZ+wPsUwF2hfR/O8lT9voEEQ9GNEVfvA\na4HXeRfKFcAjqlqIyItYHoHcDrzOP98BvBq4W0MynsCGEJEfEpGnNZquB75xyLffjrlJYhG5BvhJ\nLBHVQe1D4NzRWf/dQbjCHTOq+nkRuQdLufpu4MMicheWQe6/GofeAbwRuFNVxyKSE9wtgc1yFvgL\n70IpsayAN2GRLxfjg8DzgXuwTImvV9X/FZGD2r8NlP7/xjuxGpuBixCyLQYCgcApIbhcAoFA4JQQ\nBD0QCAROCUHQA4FA4JQQBD0QCAROCUHQA4FA4JQQBD0QCAROCUHQA4FA4JQQBD0QCAROCf8HbH76\nbIYAdYMAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img, cls = get_test_image(16)\n",
"plot_image(img)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the raw image as input to the neural network and plot the output of the first convolutional layer."
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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RRupvpmmiVqu5OjeNQP9+RVHQaDTQ7/cRDodRLBaxurrqqtTlWUA6AjTxdxKQ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\nTgAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_layer_output(output_conv1, image=img)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the same image as input to the neural network, now plot the output of the second convolutional layer."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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EjtJ5gvngxkHjz3r33XclxSfdxXbnTUI6nda+ffvKVscffvihpLjD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\nAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_layer_output(output_conv2, image=img)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Predicted Class Labels
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the predicted class-label and class-number for this image."
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"label_pred, cls_pred = session.run([y_pred, y_pred_cls],\n",
" feed_dict={x: [img]})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the predicted class-label."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.042 0.098 0.132 0.053 0.04 0.008 0.099 0.38 0.119 0.029]\n"
]
}
],
"source": [
"# Set the rounding options for numpy.\n",
"np.set_printoptions(precision=3, suppress=True)\n",
"\n",
"# Print the predicted label.\n",
"print(label_pred[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The predicted class-label is an array of length 10, with each element indicating how confident the neural network is that the image is the given class.\n",
"\n",
"In this case the element with index 3 has a value of 0.493, while the element with index 5 has a value of 0.490. This means the neural network believes the image either shows a class 3 or class 5, which is a cat or a dog, respectively."
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'cat'"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class_names[3]"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'dog'"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class_names[5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Close TensorFlow Session
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are now done using TensorFlow, so we close the session to release its resources."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"# This can be commented out in case you want to modify and experiment\n",
"# with the Notebook without having to restart it.\n",
"session.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"SUMMARY\n",
"
\n",
"\n",
"This tutorial showed how to make a Convolutional Neural Network for classifying images in the CIFAR-10 data-set. The classification accuracy was about 79-80% on the test-set.\n",
"\n",
"The output of the convolutional layers was also plotted, but it was difficult to see how the neural network recognizes and classifies the input images. Better visualization techniques are needed."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"CHALLENGE\n",
"
\n",
"\n",
"These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n",
"\n",
"You may want to backup this Notebook before making any changes.\n",
"\n",
"* Run the optimization for 10,000 iterations and see what the classification accuracy is. This will create a checkpoint that saves all the variables of the TensorFlow graph.\n",
"* Continue running the optimization for another 100,000 iterations and see if the classification accuracy has improved. Then try another 100,000 iterations. Does the accuracy improve and do you think it is worth the extra computational time?\n",
"* Try changing the image distortions in the pre-processing.\n",
"* Try changing the structure of the neural network. You can try making \n",
"the neural network both smaller or bigger. How does it affect the training time and the classification accuracy? Note that the checkpoints cannot be reloaded when you change the structure of the neural network.\n",
"* Try using batch-normalization for the 2nd convolutional layer as well. Also try removing it from both layers.\n",
"* Research some of the [better neural networks](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html) for CIFAR-10 and try to implement them.\n",
"* Explain to a friend how the program works."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"LICENSE (MIT)\n",
"
\n",
"\n",
"Copyright (c) 2016 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
"\n",
"Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
"\n",
"The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
"\n",
"THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}