Linear Regression with PyTorch

import matplotlib.pyplot as plt
with torch.no_grad(): # we don't need gradients in the testing phase
    if torch.cuda.is_available():
        predicted = model(Variable(torch.from_numpy(x_train).cuda())).cpu().data.numpy()
    else:
        predicted = model(Variable(torch.from_numpy(x_train))).data.numpy()
    print(predicted)

plt.clf()
plt.plot(x_train, y_train, 'go', label='True data', alpha=0.5)
plt.plot(x_train, predicted, '--', label='Predictions', alpha=0.5)
plt.legend(loc='best')
plt.show()