python-pytorchHow can I use the Adam Optimizer in PyTorch?

The Adam Optimizer is a popular optimization algorithm used to update the parameters of a neural network. It can be used in PyTorch by first importing the torch.optim library and then instantiating the Adam optimizer.

import torch.optim as optim

# Instantiate the Adam optimizer
optimizer = optim.Adam(model.parameters(), lr=0.001)

The parameters of the model are passed as the first argument, followed by the learning rate, which is set to 0.001 in this example. To update the parameters of the model, the step() method of the optimizer is called.

# Update the parameters
optimizer.step()

The Adam optimizer has several optional parameters that can be set, such as the weight decay and momentum. For more details, please refer to the PyTorch documentation.

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