Forward pass

As explained in Deep Learning with PyTorch, Second Edition

The stage of model execution that propagates inputs through the network to produce outputs and loss values.

A forward pass feeds input data through a model to produce outputs. Training then compares those outputs with desired values to calculate a loss. section 5.2 section 5.8

A basic training iteration proceeds as follows:

• Run the forward pass to produce predictions. section 5.8
• Calculate the loss by comparing predictions with measured or desired outputs. section 5.2
• Run the backward pass, update parameters with an optimizer, and clear accumulated gradients before the next iteration. section 5.8

The same pass across two GPUs

With two GPUs and a batch of 20, each device can process 10 inputs independently using a replicated model. Each produces its own loss; after backward passes, gradients must be aggregated before updating shared parameters. section 16.3

Figure 16.4 The distributed training steps—forward pass (1), backward pass (2), and update (3) parameters—but missing a critical step since models have different gradients

The diagram connects the forward pass to the backward pass and parameter update, while highlighting gradient synchronization in distributed training. section 16.3

How Deep Learning with PyTorch, Second Edition covers it

How deep it goes

Explains its place in training and multi-GPU execution, but does not detail layer-level computations.

Examples

Demonstrates noisy-data parameter estimation and independent processing of batch shards across two GPUs.

What it assumes

Assumes input data, desired outputs, and model parameters, while introducing the complete training process from those ingredients.

What you can do after

Apply it via PyTorch as the first step of a training loop before loss calculation and backpropagation.

How it connects

Connects the forward pass to loss functions, backpropagation, optimizers, autograd, and gradient reduction.

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  Deep Learning with R, Third EditionA forward pass is the propagation of input values through a computational graph in the direction of its outputs.
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  Natural Language Processing in Action, Second EditionForward propagation is the computation of a neural network's output by passing inputs through its units in the forward direction.
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  Grokking AI Algorithms, Second EditionA forward pass is the computation that propagates input through a model to produce predictions.
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  Math and Architectures of Deep LearningThe layer-by-layer computation, or forward pass, that produces neural-network activations and outputs from an input.
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  Deep Learning with JAXThe computation that applies a neural network to input data to produce output activations or predictions.

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Related concepts

• neural network layeruses · A neural network layer is a computational stage or component that transforms input representations into outputs within a neural network.
• predicted valueproduces · A predicted value is an estimated outcome or output that a model produces for given input values.
• backpropagationcontrasts with · Backpropagation is a procedure for computing derivatives of an output or loss with respect to inputs and parameters by applying the chain rule backward through a computational graph.
• generationproduces · Generation is the computational production of outputs from supplied inputs or context, often by applying a model to produce predictions or new content.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with PyTorch, Second Edition. The section links point to the parts of the book it draws on.