custom.py 文件源码

def forward(self, inp):
        """
        :param inp: torch.FloatTensor (batch_size x inp_size)

        :return: torch.FloatTensor (batch_size x nb_classes)
        """
        # hidden layers
        for layer in self.layers:
            out = layer(inp)
            if self.act is not None:
                out = getattr(F, self.act)(out)
            if self.dropout > 0:
                out = F.dropout(out, p=self.dropout, training=self.training)
            inp = out

        # output projection
        out = self.output(out)

        return out