(self, x, t)
return self.loss
Computes the loss value for an image and label pair.

        Args:
            x (~chainer.Variable): A variable with a batch of images.
            t (~chainer.Variable): A variable with the ground truth
                image-wise label.

        Returns:
            ~chainer.Variable: Loss value.
Computes the loss value for an image and label pair.
Computes the loss value for an image and label pair .
def forward(self, x, t):
        """Computes the loss value for an image and label pair.

        Args:
            x (~chainer.Variable): A variable with a batch of images.
            t (~chainer.Variable): A variable with the ground truth
                image-wise label.

        Returns:
            ~chainer.Variable: Loss value.

        """
        self.y = self.predictor(x)
        self.loss = F.softmax_cross_entropy(
            self.y, t, class_weight=self.class_weight,
            ignore_label=self.ignore_label)

        reporter.report({'loss': self.loss}, self)
        return self.loss
def forward ( self , x , t ) : self . y = self . predictor ( x ) self . loss = F . softmax_cross_entropy ( self . y , t , class_weight = self . class_weight , ignore_label = self . ignore_label ) reporter . report ( { 'loss' : self . loss } , self ) return self . loss