I want to use JAX as a vehicle for gradient descent; however, I have a moderately large number of parameters and would prefer to pass them as a dictionary f(func, dict) rather than f(func, x1, …xn). So instead of Something more like Is this possible? EDIT: This is my current work around solution: The gist is that now I don’t
Tag: gradient-descent
In PyTorch, how do I update a neural network via the average gradient from a list of losses?
I have a toy reinforcement learning project based on the REINFORCE algorithm (here’s PyTorch’s implementation) that I would like to add batch updates to. In RL, the “target” can only be created after a “prediction” has been made, so standard batching techniques do not apply. As such, I accrue losses for each episode and append them to a list l_losses
SGDRegressor() constantly not increasing validation performance
The model fit of my SGDRegressor wont increase or decrease its performance on the validation set (test) after around 20’000 training records. Even if I try to switch penalty, early_stopping (True/False) or alpha,eta0 to extremely high or low levels, there is no change in the behavior of the “stuck” validation score test. I used StandardScaler and shuffled the data for
Why do we need to call zero_grad() in PyTorch?
Why does zero_grad() need to be called during training? Answer In PyTorch, for every mini-batch during the training phase, we typically want to explicitly set the gradients to zero before starting to do backpropragation (i.e., updating the Weights and biases) because PyTorch accumulates the gradients on subsequent backward passes. This accumulating behaviour is convenient while training RNNs or when we
Logistic Regression Gradient Descent [closed]
Closed. This question needs debugging details. It is not currently accepting answers. Edit the question to include desired behavior, a specific problem or error, and the shortest code necessary to reproduce the problem. This will help others answer the question. Closed 1 year ago. Improve this question I have to do Logistic regression using batch gradient descent. The way I