After setting up your model, loss function, optimizer, and data loader, the core of the training loop begins. The first operational step within each iteration is the forward pass. This is where the model takes the current batch of input data and generates predictions.

Think of the forward pass as the process of information flowing through your neural network, from the input layer, through any hidden layers, to the output layer. Each layer performs its defined computation on the data it receives from the previous layer.

How the Forward Pass Works in PyTorch

In PyTorch, performing the forward pass is remarkably straightforward. If you recall from Chapter 4 ("Building Models with torch.nn"), you define the structure and sequence of operations within your model's forward method when subclassing torch.nn.Module.

PyTorch's nn.Module base class provides a __call__ method. This allows you to treat your model instance as if it were a function. When you call model(inputs), PyTorch implicitly executes the forward method you defined, passing the inputs through the network's layers.

Implementing the Forward Pass

Inside your training loop, after retrieving a batch of data (features and labels) from your DataLoader, you feed the features (inputs) to your model:

# Assume 'model' is an instance of your nn.Module subclass
# Assume 'data_batch' is loaded from the DataLoader
# Assume 'device' is defined (e.g., 'cuda' or 'cpu')

# Unpack the batch (adjust based on your DataLoader structure)
inputs, labels = data_batch
inputs = inputs.to(device) # Move input data to the correct device
labels = labels.to(device) # Move labels to the correct device (needed for loss)

# --- The Forward Pass ---
# Pass inputs through the model
outputs = model(inputs)
# -----------------------

# 'outputs' now contains the model's predictions for the 'inputs' batch.
# The next step will be to calculate the loss using these 'outputs' and 'labels'.

Here's a visual representation of this step:

Data flow during the forward pass: A batch is loaded, inputs are prepared and sent to the appropriate device, then passed through the model to produce outputs.

Understanding the Output

The outputs tensor generated by the forward pass contains the model's predictions for the given input batch. The exact nature of these predictions depends on the model's final layer and the task:

Classification: Often, the output represents raw scores, known as logits, for each class. These logits are typically passed to a loss function like nn.CrossEntropyLoss, which internally applies a Softmax function.
Regression: The output might directly represent the predicted continuous values.
Other Tasks: For tasks like segmentation or object detection, the output structure will be more complex, reflecting the specific requirements of the task.

It's important to ensure that your input data (inputs) and your model (model) reside on the same computational device (CPU or a specific GPU). If they are on different devices, PyTorch will raise a runtime error. Moving the data batch to the designated device using .to(device), as shown in the code snippet, is standard practice to prevent this.

The forward pass computes the model's predictions based on its current parameters (weights and biases). These predictions are then compared against the true labels in the next step: calculating the loss.