In the training process of a feedforward neural network (either shallow or deep), forward and backward passes play crucial roles. Which of the following statements about forward and backward passes are correct?

I The forward pass computes the output of the network by propagating inputs through the layers.
II During the backward pass, gradients are calculated and propagated from the output layer back to the input layer to update the weights.
III The forward pass adjusts the network's weights and biases to minimize the loss function.
IV The backward pass involves calculating partial derivatives of the loss with respect to each weight and bias in the network.

Question

In the training process of a feedforward neural network (either shallow or deep), forward and backward passes play crucial roles. Which of the following statements about forward and backward passes are correct?

I	The forward pass computes the output of the network by propagating inputs through the layers.
II	During the backward pass, gradients are calculated and propagated from the output layer back to the input layer to update the weights.
III	The forward pass adjusts the network's weights and biases to minimize the loss function.
IV	The backward pass involves calculating partial derivatives of the loss with respect to each weight and bias in the network.

BlackTom AI · Accepted Answer

Let’s break down what each statement claims about forward and backward passes in a feedforward neural network.
Option I: 'The forward pass computes the output of the network by propagating inputs through the layers.' This is correct. During the forward pass, input data is passed through each layer, being transformed by weights, biases, and activation functions, to produce the network’s output.
Option II: 'During the backward pass, gradients are calculated and propagated from the output layer back to the input layer to update the weights.' This is correct. The backward (backpropagation) pass computes gradients of the loss with respect to weights and biases and propagates them backward to inform updates.
Option III: 'The forward pass adjusts the network's weights and biases to minimize the loss function.' This is incorrect. Weight and bias updates are performed during the learning step, which follows the backward pass, typically by an optimizer such as SGD or Adam; the forward pass itself only computes the outputs, not parameter updates.
Option IV: 'The backward pass involves calculating partial derivatives of the loss with respect to each weight and bias in the network.' This is correct. Backpropagation relies on computing partial derivatives (gradients) of the loss with respect to each parameter to guide the updates.

In summary, options I, II, and IV describe accurate aspects of the forward and backward passes, while option III describes an action that happens during/after the backward pass, not during the forward pass itself.

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