resnet.py 文件源码

def bottleneck(filters, init_strides=(1, 1), is_first_block_of_first_layer=False):
    """Bottleneck architecture for > 34 layer resnet.
    Follows improved proposed scheme in http://arxiv.org/pdf/1603.05027v2.pdf

    Returns:
        A final conv layer of filters * 4
    """
    def f(input):

        if is_first_block_of_first_layer:
            # don't repeat bn->relu since we just did bn->relu->maxpool
            conv_1_1 = Conv2D(filters=filters, kernel_size=(1, 1),
                              strides=init_strides,
                              padding="same",
                              kernel_initializer="he_normal",
                              kernel_regularizer=l2(1e-4))(input)
        else:
            conv_1_1 = _bn_relu_conv(filters=filters, kernel_size=(1, 1),
                                     strides=init_strides)(input)

        conv_3_3 = _bn_relu_conv(filters=filters, kernel_size=(3, 3))(conv_1_1)
        residual = _bn_relu_conv(filters=filters * 4, kernel_size=(1, 1))(conv_3_3)
        return _shortcut(input, residual)

    return f