def predict(self, inputs):
        inputs_1 = tc.mm(inputs,self.weights_0_1) + self.bias1
        outputs_1 = self.relu(inputs_1)

        inputs_2 = tc.mm(outputs_1, self.weights_1_2) + self.bias2
        outputs_2 = inputs_2.softmax(1)

        return outputs_2

    def check_los(self,inputs,clas):
        inputs_1 = tc.mm(inputs, self.weights_0_1) + self.bias1
        outputs_1 = self.relu(inputs_1)

        inputs_2 = tc.mm(outputs_1, self.weights_1_2) + self.bias2
        outputs_2 = inputs_2.softmax(1)

        L = -tc.mean(tc.log(outputs_2[tc.arange(len(outputs_2)), clas]))
        self.test_costs.append(L)

        return outputs_2

    def train(self, inputs, clas):

        inputs_1 = tc.mm(inputs, self.weights_0_1) + self.bias1
        outputs_1 = self.relu(inputs_1)

        inputs_2 = tc.mm(outputs_1, self.weights_1_2) + self.bias2
        outputs_2 = inputs_2.softmax(1)

        with tc.no_grad():
            if self.weights_0_1.grad is not None:
                self.weights_0_1.grad.zero_()

            if self.weights_1_2.grad is not None:
                self.weights_1_2.grad.zero_()

            if self.bias1.grad is not None:
                self.bias1.grad.zero_()

            if self.bias2.grad is not None:
                self.bias2.grad.zero_()