resnet_152.py 文件源码

def identity_block(input_tensor, kernel_size, filters, stage, block):
    '''The identity_block is the block that has no conv layer at shortcut
    # Arguments
        input_tensor: input tensor
        kernel_size: defualt 3, the kernel size of middle conv layer at main path
        filters: list of integers, the nb_filters of 3 conv layer at main path
        stage: integer, current stage label, used for generating layer names
        block: 'a','b'..., current block label, used for generating layer names
    '''
    eps = 1.1e-5
    nb_filter1, nb_filter2, nb_filter3 = filters
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'
    scale_name_base = 'scale' + str(stage) + block + '_branch'

    x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a', bias=False)(input_tensor)
    x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2a')(x)
    x = Scale(axis=bn_axis, name=scale_name_base + '2a')(x)
    x = Activation('relu', name=conv_name_base + '2a_relu')(x)

    x = ZeroPadding2D((1, 1), name=conv_name_base + '2b_zeropadding')(x)
    x = Convolution2D(nb_filter2, kernel_size, kernel_size,
                      name=conv_name_base + '2b', bias=False)(x)
    x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2b')(x)
    x = Scale(axis=bn_axis, name=scale_name_base + '2b')(x)
    x = Activation('relu', name=conv_name_base + '2b_relu')(x)

    x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c', bias=False)(x)
    x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2c')(x)
    x = Scale(axis=bn_axis, name=scale_name_base + '2c')(x)

    x = merge([x, input_tensor], mode='sum', name='res' + str(stage) + block)
    x = Activation('relu', name='res' + str(stage) + block + '_relu')(x)
    return x