Does anybody have a way to go back from flattening all the weights and putting them into a numpy vector (as done in the code snippet below). I trained the network for 3 days, before my allotted time expired, causing the program to cancel before converging. I have the checkpoints saved as numpy vectors. The model architecture is Resnet18 (for CIFAR10).
gg = np.array() for name, weight in network.named_parameters(): gg.extend(weight.cpu().detach().numpy().flatten()) np.save("name" + str(q)+str(n), gg)
I suppose it is possible to manually split the numpy vector and convert it into a tensor, but any help is appreciated.