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  • python - Is SGD optimizer in PyTorch actually does Gradient Descent . . .
    I'm working on trying to compare the converge rate of SGD and GD algorithms for the neural networks In PyTorch, we often use SGD optimizer as follows train_dataloader = torch utils data DataLoader(train_dataset, batch_size=64, shuffle=True) optimizer = torch optim SGD(model parameters(), lr=0 001)
  • python - L1 L2 regularization in PyTorch - Stack Overflow
    Yes, pytorch optimizers have a parameter called weight_decay which corresponds to the L2 regularization factor: sgd = torch optim SGD(model parameters(), weight_decay=weight_decay) L1 regularization implementation There is no analogous argument for L1, however this is straightforward to implement manually:
  • pytorch - SGD Optimizer Custom Parameters - Stack Overflow
    The optimizer sgd should have the parameters of SGDmodel: sgd = torch optim SGD(SGDmodel parameters(), lr=0 001, momentum=0 9, weight_decay=0 1) For more details on how pytorch associates gradients and parameters between the loss and the optimizer see this thread
  • python - How to Implement Full Batch Gradient Descent with Nesterov . . .
    The pytorch SGD implementation is actually independent of the batching! It only uses the gradients that were calculated and stored in the parameters grad attribute in the backward pass So the batch size used for calculations and the batch size used for optimization are decoupled You can now either:
  • Why do we need to call zero_grad () in PyTorch? - Stack Overflow
    In PyTorch, for every mini-batch during the training phase, we typically want to explicitly set the gradients to zero before starting to do backpropagation (i e , updating the Weights and biases) because PyTorch accumulates the gradients on subsequent backward passes
  • What is the default batch size of pytorch SGD? - Stack Overflow
    The SGD optimizer in PyTorch is just gradient descent The stocastic part comes from how you usually pass a random subset of your data through the network at a time (i e a mini-batch or batch) The code you posted passes the entire dataset through on each epoch before doing backprop and stepping the optimizer so you're really just doing
  • python - How to change the learning rate of an optimizer at any given . . .
    Is it possible in PyTorch to change the learning rate of the optimizer in the middle of training dynamically (I don't want to define a learning rate schedule beforehand)? So let's say I have an optimizer: optim = torch optim SGD(model parameters(), lr=0 01)
  • How to train a simple linear regression model with SGD in pytorch . . .
    I was trying to train a simple polynomial linear regression model in pytorch with SGD I wrote some self contained (what I thought would be extremely simple code), however, for some reason my model does not train as I thought it should I have 5 points sampled from a sine curve and try to fit it with a polynomial of degree 4
  • PyTorch Optimizer: AdamW and Adam with weight decay
    These methods are same for vanilla SGD, but as soon as we add momentum, or use a more sophisticated optimizer like Adam, L2 regularization (first equation) and weight decay (second equation) become different AdamW follows the second equation for weight decay In Adam weight_decay (float, optional) – weight decay (L2 penalty) (default: 0) In
  • Very simple optim. SGD training loop not working as expected - PyTorch
    I've been reading through the PyTorch documentation and I've been trying to figure out MSELoss and autograd I tried creating a very simple training loop that takes two random tensors and updates the values in each tensor so that the sum all values in tensor1 plus the sum of all values in tensor2 add up to some target number





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