Forward pass

As explained in Deep Learning with R, Third Edition

A forward pass is the propagation of input values through a computational graph in the direction of its outputs.

A forward pass propagates input values through a computational graph from inputs to outputs. In a neural network, it produces predictions by applying operations such as matrix multiplication, bias addition, and activation functions. section 2.4.4 section 2.4

A scalar computation graph

Given an input x, weight w, and bias b, compute an intermediate value with x × w, add b, and compare the resulting prediction y with a target using a loss function. Propagating these values to the loss is the forward pass; calculating parameter derivatives afterward is the backward pass. section 2.4.4

A forward pass occupies the prediction stage of a training loop:

• Draw a batch of inputs and corresponding targets. section 2.4
• Run the model on the inputs to obtain predictions. section 2.4 section 2.4.3
• Compute the loss by comparing predictions with targets, then use the backward pass to calculate gradients and update parameters. section 2.4.3

Figure 2.22 Running a forward pass

Use the computation-graph figure to trace values from inputs through intermediate operations to the loss. section 2.4.4

How Deep Learning with R, Third Edition covers it

How deep it goes

Defines graph-wide value propagation and illustrates it with scalar arithmetic, stopping before detailed implementation of large-network execution.

Examples

Uses scalar multiplication and addition, a two-layer model, and a mini-batch training loop to demonstrate prediction.

What it assumes

Assumes familiarity with basic tensor operations and introduces the forward pass through elementary graph calculations.

What you can do after

Understand how neural networks turn input batches into predictions and how those predictions supply loss values for optimization.

How it connects

Connects computational graphs, neural networks, loss functions, backward passes, gradients, and stochastic gradient descent.

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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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  Deep Learning with PyTorch, Second EditionThe stage of model execution that propagates inputs through the network to produce outputs and loss values.

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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 R, Third Edition. The section links point to the parts of the book it draws on.