Forward Pass

The forward pass computes the network output using the current parameters before loss and gradients are calculated.

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Definition

The forward pass computes the network's output using the current parameters.

Basic path:

input -> layers -> output

Each layer applies its weights, biases, and activation functions.

The forward pass shows what the current network computes.

Training needs an output before it can measure error.

The order is:

forward pass -> output -> loss

Without an output, there is nothing to compare with the target.

The network must first make a prediction before loss can be computed.

The forward pass uses the current weights and biases.

Example:

input: x
parameters: A
output: y1

After training updates the parameters:

input: x
parameters: B
output: y2

The same input can produce a different output after parameter updates.

The forward pass depends on the current parameter setting.

During training, the forward pass produces intermediate values.

Examples:

weighted sums
activations
layer outputs
final output

These values are useful later when computing gradients.

The backward pass needs to know how the output was produced.

Backward computation depends on the path created during the forward pass.

The forward pass only computes.

It does not change parameters.

forward pass:
input -> output

training:
input -> output -> loss -> gradients -> parameter update

The forward pass is evaluation, not adjustment.

The forward pass computes network output.
It uses the current parameters.
It comes before loss computation.
It produces intermediate values used later.
It does not update parameters by itself.
Training adds loss, backward pass, and parameter updates after the forward pass.

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Loss Computation