resnet.py 文件源码

def bottleneck(nb_filter, init_subsample=(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 nb_filter * 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 = Convolution2D(nb_filter=nb_filter,
                                 nb_row=1, nb_col=1,
                                 subsample=init_subsample,
                                 init="he_normal", border_mode="same",
                                 W_regularizer=l2(0.0001))(input)
        else:
            conv_1_1 = _bn_relu_conv(nb_filter=nb_filter, nb_row=1, nb_col=1,
                                     subsample=init_subsample)(input)

        conv_3_3 = _bn_relu_conv(nb_filter=nb_filter, nb_row=3, nb_col=3)(conv_1_1)
        residual = _bn_relu_conv(nb_filter=nb_filter * 4, nb_row=1, nb_col=1)(conv_3_3)
        return _shortcut(input, residual)

    return f