python vgg13-mnist-model.py

Posted 2021-05-09

def cnn_model_fn(features, labels, mode):
    # Input Layer
    input_height, input_width = 28, 28
    input_channels = 1
    input_layer = tf.reshape(features["x"], [-1, input_height, input_width, input_channels])

    # Convolutional Layer #1 and Pooling Layer #1
    conv1_1 = tf.layers.conv2d(inputs=input_layer, filters=64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    conv1_2 = tf.layers.conv2d(inputs=conv1_1, filters=64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    pool1 = tf.layers.max_pooling2d(inputs=conv1_2, pool_size=[2, 2], strides=2, padding="same")
    
    # Convolutional Layer #2 and Pooling Layer #2
    conv2_1 = tf.layers.conv2d(inputs=pool1, filters=128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    conv2_2 = tf.layers.conv2d(inputs=conv2_1, filters=128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    pool2 = tf.layers.max_pooling2d(inputs=conv2_2, pool_size=[2, 2], strides=2, padding="same")

    # Convolutional Layer #3 and Pooling Layer #3
    conv3_1 = tf.layers.conv2d(inputs=pool2, filters=256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    conv3_2 = tf.layers.conv2d(inputs=conv3_1, filters=256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    pool3 = tf.layers.max_pooling2d(inputs=conv3_2, pool_size=[2, 2], strides=2, padding="same")

    # Convolutional Layer #4 and Pooling Layer #4
    conv4_1 = tf.layers.conv2d(inputs=pool3, filters=512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    conv4_2 = tf.layers.conv2d(inputs=conv4_1, filters=512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    pool4 = tf.layers.max_pooling2d(inputs=conv4_2, pool_size=[2, 2], strides=2, padding="same")

    # Convolutional Layer #5 and Pooling Layer #5
    conv5_1 = tf.layers.conv2d(inputs=pool4, filters=512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    conv5_2 = tf.layers.conv2d(inputs=conv5_1, filters=512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    pool5 = tf.layers.max_pooling2d(inputs=conv5_2, pool_size=[2, 2], strides=2, padding="same")

    # FC Layers
    pool5_flat = tf.contrib.layers.flatten(pool5)
    FC1 = tf.layers.dense(inputs=pool5_flat, units=4096, activation=tf.nn.relu)
    FC2 = tf.layers.dense(inputs=FC1, units=4096, activation=tf.nn.relu)
    FC3 = tf.layers.dense(inputs=FC2, units=1000, activation=tf.nn.relu)

    """the training argument takes a boolean specifying whether or not the model is currently 
    being run in training mode; dropout will only be performed if training is true. here, 
    we check if the mode passed to our model function cnn_model_fn is train mode. """
    dropout = tf.layers.dropout(inputs=FC3, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
    
    # Logits Layer or the output layer. which will return the raw values for our predictions.
    # Like FC layer, logits layer is another dense layer. We leave the activation function empty 
    # so we can apply the softmax
    logits = tf.layers.dense(inputs=dropout, units=10)
    
    # Then we make predictions based on raw output
    predictions = {
        # Generate predictions (for PREDICT and EVAL mode)
        # the predicted class for each example - a vlaue from 0-9
        "classes": tf.argmax(input=logits, axis=1),
        # to calculate the probablities for each target class we use the softmax
        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
    }
    
    # so now our predictions are compiled in a dict object in python and using that we return an estimator object
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
    
    
    '''Calculate Loss (for both TRAIN and EVAL modes): computes the softmax entropy loss. 
    This function both computes the softmax activation function as well as the resulting loss.'''
    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)

    # Configure the Training Options (for TRAIN mode)
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
        train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())
        
        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)

    # Add evaluation metrics (for EVAL mode)
    eval_metric_ops = {
        "accuracy": tf.metrics.accuracy(labels=labels,
                                        predictions=predictions["classes"])}
    return tf.estimator.EstimatorSpec(mode=mode,
                                      loss=loss,
                                      eval_metric_ops=eval_metric_ops)