mlp_bn.py 文件源码

def __init__(self, in_size, out_size, hidden_sizes, normalize_input=True,
                 normalize_output=False, nonlinearity=F.relu, last_wscale=1):
        self.in_size = in_size
        self.out_size = out_size
        self.hidden_sizes = hidden_sizes
        self.normalize_input = normalize_input
        self.normalize_output = normalize_output
        self.nonlinearity = nonlinearity

        super().__init__()
        with self.init_scope():
            if normalize_input:
                self.input_bn = L.BatchNormalization(in_size)
                self.input_bn.avg_var[:] = 1

            if hidden_sizes:
                hidden_layers = []
                hidden_layers.append(LinearBN(in_size, hidden_sizes[0]))
                for hin, hout in zip(hidden_sizes, hidden_sizes[1:]):
                    hidden_layers.append(LinearBN(hin, hout))
                self.hidden_layers = chainer.ChainList(*hidden_layers)
                self.output = L.Linear(hidden_sizes[-1], out_size,
                                       initialW=LeCunNormal(last_wscale))
            else:
                self.output = L.Linear(in_size, out_size,
                                       initialW=LeCunNormal(last_wscale))

            if normalize_output:
                self.output_bn = L.BatchNormalization(out_size)
                self.output_bn.avg_var[:] = 1