Computing gradients via automatic differentiation and GradientTape
As you already know, optimizing NNs requires computing the gradients of the cost with respect to the NN weights. This is required for optimization algorithms such as stochastic gradient descent (SGD). In addition, gradients have other applications, such as diagnosing the network to find out why an NN model is making a particular prediction for a test example. Therefore, in this section, we will cover how to compute gradients of a computation with respect to some variables.
Computing the gradients of the loss with respect to trainable variables
TensorFlow supports automatic differentiation, which can be thought of as an implementation of the chain rule for computing gradients of nested functions. When we define a series of operations that results in some output or even intermediate tensors, TensorFlow provides a context for calculating gradients of these computed tensors with respect to its dependent nodes in the...
