setting adam solver

To set the Adam solver in deep learning models, you can do the following:

1. Import the necessary libraries:

import torch.optim as optim

2. Define your model:

model = MyModel()

3. Define your optimizer and set the Adam solver:

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

In this example, we are using the Adam optimizer to update the parameters of the model during training. The learning rate is set to be 0.001, but you can adjust this value to suit your needs.

4. Train your model:

for epoch in range(num_epochs):
    # Forward pass
    outputs = model(inputs)
    loss = loss_function(outputs, labels)

    # Backward and optimize
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

During training, you can use the optimizer to update the model parameters using the backpropagation algorithm. The `zero_grad()` method resets the gradients of the optimizer, and the `step()` method updates the parameters based on the gradients computed during backpropagation.