ann_creation_helper.py 文件源码

def build_fully_connected_layers_with_batch_norm(the_input, shape, mode, num_previous_fully_connected_layers=0, activation_summaries=[]):
    """
    a function to build the fully connected layers with batch normalization onto the computational
    graph from given specifications.

    shape of the format:
    [num_neurons_layer_1,num_neurons_layer_2,...,num_neurons_layer_n]
    """

    for index, size in enumerate(shape):
        with tf.variable_scope("FC_" + str(num_previous_fully_connected_layers + index + 1)):
            temp_pre_activation = tf.layers.dense(
                inputs=the_input,
                units=size,
                use_bias=False,
                kernel_initializer=layers.xavier_initializer(),
                name="layer")

            temp_batch_normalized = tf.layers.batch_normalization(temp_pre_activation,
                                                                  training=(mode == tf.estimator.ModeKeys.TRAIN),
                                                                  fused=True)

            temp_layer_output = tf.nn.relu(temp_batch_normalized)

            the_input = temp_layer_output

        activation_summaries.append(layers.summarize_activation(temp_layer_output))

    return the_input, activation_summaries