setting adam solver
时间: 2024-01-15 21:05:28 浏览: 5
Sure, I can help you with that! Adam is a popular optimization algorithm used in deep learning to update the weights of a neural network during training. Here's an example code snippet in Python using the PyTorch library to set up the Adam optimizer:
```python
import torch
import torch.optim as optim
# Define your neural network architecture
model = ...
# Define your loss function
criterion = ...
# Set up the Adam optimizer
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Training loop
for epoch in range(num_epochs):
for inputs, labels in training_data:
# Forward pass
outputs = model(inputs)
loss = criterion(outputs, labels)
# Backward pass and weight update
optimizer.zero_grad()
loss.backward()
optimizer.step()
```
In this example, `model.parameters()` returns all of the learnable parameters in the neural network, which are then passed to the Adam optimizer along with a learning rate of 0.001. The `zero_grad()` method is used to clear the gradients calculated in the previous iteration, and then `backward()` is called to compute the gradients for all parameters using the current loss. Finally, `step()` is called to update the weights using the calculated gradients.