CNTK_MNIST_Create_CNN_with_Init.py

def create_convolutional_neural_network(input_vars, out_dims, dropout_prob=0.0):

    convolutional_layer_1 = Convolution((5, 5), 32, strides=1, activation=cntk.ops.relu, pad=True, init=gaussian(), init_bias=0.1)(input_vars)
    pooling_layer_1 = MaxPooling((2, 2), strides=(2, 2), pad=True)(convolutional_layer_1)

    convolutional_layer_2 = Convolution((5, 5), 64, strides=1, activation=cntk.ops.relu, pad=True, init=gaussian(), init_bias=0.1)(pooling_layer_1)
    pooling_layer_2 = MaxPooling((2, 2), strides=(2, 2), pad=True)(convolutional_layer_2)

    fully_connected_layer = Dense(1024, activation=cntk.ops.relu, init=gaussian(), init_bias=0.1)(pooling_layer_2)
    dropout_layer = Dropout(dropout_prob)(fully_connected_layer)
    output_layer = Dense(out_dims, activation=None, init=gaussian(), init_bias=0.1)(dropout_layer)

    return output_layer