dd0f68a02596d0e1818b9993c02db5b004cb38e3,chainerrl/links/mlp_bn.py,MLPBN,__init__,#MLPBN#Any#Any#Any#Any#Any#,30

Before Change

        self.normalize_input = normalize_input
        self.normalize_output = normalize_output

        layers = {}

        if normalize_input:
            layers["input_bn"] = L.BatchNormalization(in_size)
            layers["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))
            layers["hidden_layers"] = chainer.ChainList(*hidden_layers)
            layers["output"] = L.Linear(hidden_sizes[-1], out_size)
        else:
            layers["output"] = L.Linear(in_size, out_size)

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

        super().__init__(**layers)

After Change

        self.normalize_output = normalize_output

        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)
            else:
                self.output = L.Linear(in_size, out_size)

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

    def __call__(self, x):
        h = x
        assert (not chainer.config.train) or x.shape[0] > 1
        if self.normalize_input:

Instances