resnet2d09e.py 文件源码

def res_block(input_tensor, nb_filters=16, block=0, subsample_factor=1):
    subsample = (subsample_factor, subsample_factor)

    x = BatchNormalization(axis=3)(input_tensor)
    x = Activation('relu')(x)
    x = Convolution2D(nb_filters, 3, 3, subsample=subsample, border_mode='same')(x)
    x = BatchNormalization(axis=3)(x)
    x = Activation('relu')(x)
    x = Convolution2D(nb_filters, 3, 3, subsample=(1, 1), border_mode='same')(x)

    if subsample_factor > 1:
        shortcut = Convolution2D(nb_filters, 1, 1, subsample=subsample, border_mode='same')(input_tensor)
    else:
        shortcut = input_tensor

    x = merge([x, shortcut], mode='sum')
    return x