I’m a pretty new learner in ML/AI. This domain is so amazing but no so easy. In order to learn and understand I’m trying to do my own exercise to learn how Pytorch is working.
To do so I created a test where I want the model to predict the function f(x) = 2x. Pretty simple I guess. But my first attempt to write a python script give me not so bad results.
Here is the script :
import torch " Data for training" x_train = torch.FloatTensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) y_train = torch.FloatTensor([2, 4, 6, 8, 10, 12, 14, 16, 18, 20]) " Data for evaluation of model" x_test = torch.FloatTensor([2, 4, 6, 8, 10, 12, 14, 16, 18, 20]) model = torch.nn.Sequential(torch.nn.Linear(10, 3),torch.nn.ReLU(),torch.nn.Linear(3, 10),) loss_fn = torch.nn.MSELoss(reduction='sum') learning_rate = 1e-4 for t in range(48500): y_pred = model(x_train) loss = loss_fn(y_pred, y_train) if t % 1000 == 999: print(t, loss.item()) model.zero_grad() loss.backward() with torch.no_grad(): for param in model.parameters(): param -= learning_rate * param.grad y_pred_to_validate = model(x_test) print(y_pred_to_validate.detach().numpy()) type or paste code here
And the result displayed is not so bad :
[ 4.138885 8.290704 12.023239 16.034138 19.773151 24.239693 28.20026 31.853294 35.714657 39.374302]
I would like to have your thoughts and your advises are welcome. To be honest the model I created I take it from another example found on the net but I don’t really understand if it’s well suited for this case or not. It’s the same for the number of iteration, I put 48500 because it gave me a good result and I know that overfitting is not really recommended.
I like criticism, it’s the best way to improve myself. Thank you for your time and long life to PyTorch.