Basic Learning Loop Explanation:

[Deadsg]

Explanation:

The learning_loop function takes the following parameters:

model: The neural network model you want to train.
data_loader: A DataLoader providing batches of training data.
optimizer: The optimizer (e.g., SGD, Adam) for updating model parameters.
loss_function: The loss function (e.g., CrossEntropyLoss, MSE) for computing the training loss.
num_epochs: The number of times to iterate through the entire dataset.
Within the loop, it first sets the model in training mode (model.train()), then iterates through the data batches. For each batch, it:

Performs a forward pass to get model predictions.
Computes the loss.
Performs backpropagation and updates the model's parameters.
Keeps track of the total loss for the epoch.
After processing all batches, it calculates the average loss for the epoch and prints it.

Optionally, it includes an evaluation phase that runs every few epochs (in this case, every 5 epochs). It sets the model in evaluation mode (model.eval()) to disable dropout and batch normalization layers (if any). It then evaluates the model on a separate validation dataset (not shown here) to monitor its performance.

The function prints the average validation loss (if applicable) and continues with the next epoch.

Once all epochs are completed, it prints "Training complete!"

Please note that you'll need to adapt this code to your specific use case by defining the model, data loaders, optimizer, loss function, and validation data. Additionally, consider saving checkpoints, handling GPU/CPU devices, and other details based on your specific requirements.