wide_resnet.py 文件源码

def __create_wide_residual_network(nb_classes, img_input, include_top, depth=28, width=8, dropout=0.0):
    ''' Creates a Wide Residual Network with specified parameters

    Args:
        nb_classes: Number of output classes
        img_input: Input tensor or layer
        include_top: Flag to include the last dense layer
        depth: Depth of the network. Compute N = (n - 4) / 6.
               For a depth of 16, n = 16, N = (16 - 4) / 6 = 2
               For a depth of 28, n = 28, N = (28 - 4) / 6 = 4
               For a depth of 40, n = 40, N = (40 - 4) / 6 = 6
        width: Width of the network.
        dropout: Adds dropout if value is greater than 0.0

    Returns:a Keras Model
    '''

    N = (depth - 4) // 6

    x = __conv1_block(img_input)
    nb_conv = 4

    for i in range(N):
        x = __conv2_block(x, width, dropout)
        nb_conv += 2

    x = MaxPooling2D((2, 2))(x)

    for i in range(N):
        x = __conv3_block(x, width, dropout)
        nb_conv += 2

    x = MaxPooling2D((2, 2))(x)

    for i in range(N):
        x = ___conv4_block(x, width, dropout)
        nb_conv += 2

    x = AveragePooling2D((8, 8))(x)

    if include_top:
        x = Flatten()(x)
        x = Dense(nb_classes, activation='softmax')(x)

    return x