Use this feedforward calculation to inspect one deterministic forward pass through explicitly specified layers, weights, biases, and activation functions.

Input vector

Required list input.

Weight matrix

Required list input.

Bias vector

Required list input.

Activation

Required string input.

Run one dense neural-network layer with a selectable activation. The tool evaluates the supplied inputs together and returns the named outputs below; it does not infer omitted operating conditions or change the units shown.^[1]

Layer output

The resulting layer output returned as an object.

• A forward pass does not train or validate a network. Shape conventions, numerical precision, activation choice, normalization, preprocessing, learned parameters, and distribution shift determine meaningful behavior.
• Use finite inputs in the displayed units and preserve more precision than the final presentation requires. Independently verify safety-critical, financial, compliance, or production decisions.

Check tensor shapes and intermediate activations, compare against a trusted framework, and evaluate trained models on held-out and stress-test data.

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