645c7c386e62d2fb1d50f4621c1a52645a13869f,time_sequence_prediction/train.py,,,#,40

Before Change

        pred = seq(test_input, future = future)
        loss = criterion(pred[:, :-future], test_target)
        print("test loss:", loss.data.numpy()[0])
        y = pred.data.numpy()
        // draw the result
        plt.figure(figsize=(30,10))
        plt.title("Predict future values for time sequences\n(Dashlines are predicted values)", fontsize=30)
        plt.xlabel("x", fontsize=20)

After Change

            return loss
        optimizer.step(closure)
        // begin to predict, no need to track gradient here
        with torch.no_grad():
            future = 1000
            pred = seq(test_input, future=future)
            loss = criterion(pred[:, :-future], test_target)
            print("test loss:", loss.item())
            y = pred.detach().numpy()
        // draw the result
        plt.figure(figsize=(30,10))
        plt.title("Predict future values for time sequences\n(Dashlines are predicted values)", fontsize=30)
        plt.xlabel("x", fontsize=20)
        plt.ylabel("y", fontsize=20)

Instances