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| """ | |
| Simple example of using pytorch to build a linear regression model. | |
| """ | |
| import torch | |
| from torch.autograd import Variable | |
| from torch import optim | |
| import sys | |
| def build_model(): | |
| # model: y = kx | |
| model = torch.nn.Sequential( | |
| torch.nn.Linear(1, 1, bias=False)) | |
| return model | |
| def train(model, loss, optimizer, x, y): | |
| x = Variable(x) # In: x | |
| y = Variable(y) # Out: y | |
| # zero-out gradients | |
| optimizer.zero_grad() | |
| # forward pass through the model | |
| fx = model.forward(x.view(len(x),1)) | |
| # forward pass through the loss function (mean-squared-error) | |
| error = loss.forward(fx, y) | |
| # backward pass | |
| error.backward() | |
| # take one optimizer step | |
| optimizer.step() | |
| # return the loss | |
| return error.data[0] | |
| def main(): | |
| # set RNG seed | |
| torch.manual_seed(1234) | |
| # create our synthetic (x,y) data | |
| X = torch.linspace(-1,1,100) | |
| Y = 2*X + torch.randn(X.size())*0.33 | |
| # create the model | |
| model = build_model() | |
| # specify the loss function | |
| loss = torch.nn.MSELoss(size_average=True) | |
| # specify the optimizer (here: stochastic gradient descent) | |
| optimizer = optim.SGD(model.parameters(), lr=0.01) | |
| # fix the batch size | |
| batch_size = 10 | |
| # run over the data 100 times - this is the #epochs | |
| for i in range(100): | |
| error = 0. | |
| # compute the number of batches | |
| num_batches = len(X) // batch_size | |
| # run over all batches ... | |
| for k in range(num_batches): | |
| start, end = k * batch_size, (k+1)*batch_size | |
| #... and sum up the error per epoch | |
| error += train(model, | |
| loss, | |
| optimizer, | |
| X[start:end], | |
| Y[start:end]) | |
| print i, error / num_batches | |
| if __name__ == "__main__": | |
| main() |
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