densenet.py 文件源码

def __init__(self, n_class, in_ch, n_layer=12, growth_rate=12,
                 dropout_ratio=0.2, block=3):
        in_chs = [in_ch + n_layer * growth_rate * i
                  for i in moves.range(block + 1)]
        super(DenseBlock, self).__init__()
        self.add_link(
            'conv1', L.Convolution2D(3, in_ch, 3, 1, 1, wscale=np.sqrt(2))
        )
        for i in moves.range(block):
            self.add_link('dense%d' % (i+2),
                          DenseBlock(in_chs[i], growth_rate, n_layer))
            if not i == block - 1:
                self.add_link('trans%d' % (i+2), Transition(in_chs[i+1]))

        self.add_link(
            'bn%d' % (block+1), L.BatchNormalization(in_chs[block])
        )
        self.add_link('fc%d' % (block+2), L.Linear(in_chs[block], n_class))
        self.train = True
        self.dropout_ratio = dropout_ratio
        self.block = block