blocks.py 文件源码

def gap(network, out_size, batch_norm,
        gap_nonlinearity, out_nonlinearity):

    gap_nonlinearity = getattr(lnn.nonlinearities, gap_nonlinearity)
    out_nonlinearity = getattr(lnn.nonlinearities, out_nonlinearity)

    # output classification layer
    network = lnn.layers.Conv2DLayer(
        network, num_filters=out_size, filter_size=1,
        nonlinearity=gap_nonlinearity, name='Output_Conv')
    if batch_norm:
        network = lnn.layers.batch_norm(network)

    network = lnn.layers.Pool2DLayer(
        network, pool_size=network.output_shape[-2:], ignore_border=False,
        mode='average_exc_pad', name='GlobalAveragePool')
    network = lnn.layers.FlattenLayer(network, name='Flatten')

    network = lnn.layers.NonlinearityLayer(
        network, nonlinearity=out_nonlinearity, name='output')

    return network