eec207d5743b8121ae9c10e94dbba22f436aa203,tests/links_tests/connection_tests/test_conv_2d_bn_activ.py,TestConv2DBNActiv,setUp,#TestConv2DBNActiv#,38

Before Change

                            dtype=np.float32).reshape((1, 1, 3, 3))
        bn_kwargs = {"decay": 0.8}
        initial_bias = 0
        if self.args_style == "explicit":
            self.l = Conv2DBNActiv(
                self.in_channels, self.out_channels, self.ksize,
                self.stride, self.pad, self.dilate,
                initialW=initialW, initial_bias=initial_bias,
                activ=activ, bn_kwargs=bn_kwargs)
        elif self.args_style == "None":
            self.l = Conv2DBNActiv(
                None, self.out_channels, self.ksize, self.stride, self.pad,
                self.dilate, initialW=initialW, initial_bias=initial_bias,
                activ=activ, bn_kwargs=bn_kwargs)
        elif self.args_style == "omit":
            self.l = Conv2DBNActiv(
                self.out_channels, self.ksize, stride=self.stride,
                pad=self.pad, dilate=self.dilate, initialW=initialW,
                initial_bias=initial_bias, activ=activ, bn_kwargs=bn_kwargs)

    def check_forward(self, x_data):
        x = chainer.Variable(x_data)
        // Make the batch normalization to be the identity function.
        self.l.bn.avg_var[:] = 1

After Change

                            dtype=np.float32).reshape((1, 1, 3, 3))
        bn_kwargs = {"decay": 0.8}
        initial_bias = 0
        comm = create_communicator("naive")
        activ = relu
        self.l = Conv2DBNActiv(
            self.in_channels, self.out_channels, self.ksize, self.stride,
            self.pad, self.dilate, initialW=initialW,

Instances