wide_resnet.py 文件源码

def __conv2_block(input, k=1, dropout=0.0):
    init = input

    channel_axis = 1 if K.image_dim_ordering() == 'th' else -1

    # Check if input number of filters is same as 16 * k, else create convolution2d for this input
    if K.image_dim_ordering() == 'th':
        if init._keras_shape[1] != 16 * k:
            init = Conv2D(16 * k, (1, 1), activation='linear', padding='same')(init)
    else:
        if init._keras_shape[-1] != 16 * k:
            init = Conv2D(16 * k, (1, 1), activation='linear', padding='same')(init)

    x = Conv2D(16 * k, (3, 3), padding='same')(input)
    x = BatchNormalization(axis=channel_axis)(x)
    x = Activation('relu')(x)

    if dropout > 0.0:
        x = Dropout(dropout)(x)

    x = Conv2D(16 * k, (3, 3), padding='same')(x)
    x = BatchNormalization(axis=channel_axis)(x)
    x = Activation('relu')(x)

    m = add([init, x])
    return m