blocks.py 文件源码

def conv(network, batch_norm, num_layers, num_filters, filter_size, pad,
         pool_size, dropout):
    for k in range(num_layers):
        network = lnn.layers.Conv2DLayer(
            network, num_filters=num_filters,
            filter_size=filter_size,
            W=lnn.init.Orthogonal(gain=np.sqrt(2 / (1 + .1 ** 2))),
            pad=pad,
            nonlinearity=lnn.nonlinearities.rectify,
            name='Conv_{}'.format(k))
        if batch_norm:
            network = lnn.layers.batch_norm(network)

    if pool_size:
        network = lnn.layers.MaxPool2DLayer(network, pool_size=pool_size,
                                            name='Pool')
    if dropout > 0.0:
        network = lnn.layers.DropoutLayer(network, p=dropout)

    return network