Forward Propagation
Forward propagation is the process of passing input through every layer of a network to produce an output prediction. It is the computation that happens at inference time.
What is Forward Propagation?
Forward propagation is the process of passing input through every layer of a network to produce an output prediction. It is the computation that happens at inference time.
Forward propagation is the process of passing input through every layer of a network to produce an output prediction. It is the computation that happens at inference time.
Where is it used?
Every ChatGPT response is a forward pass through a transformer; during training, forward propagation produces logits whose loss is then backpropagated.
How to build it
Call `output = model(input_tensor)` in PyTorch — this triggers `forward()` through all submodules; trace shapes at each layer with print statements in a custom module.
Code
A practical example: