/TensorFlow 2.4


Computes the gradient function for function f via backpropagation.

input A list of Tensor objects. a list of input tensors of size N + M;
Tout A list of tf.DTypes that has length >= 1. the type list for the input list.
f A function decorated with @Defun. The function we want to compute the gradient for.

The function 'f' must be a numerical function which takes N inputs and produces M outputs. Its gradient function 'g', which is computed by this SymbolicGradient op is a function taking N + M inputs and produces N outputs.

I.e. if we have (y1, y2, ..., y_M) = f(x1, x2, ..., x_N), then, g is (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N, dL/dy1, dL/dy2, ..., dL/dy_M),

where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the loss function). dL/dx_i is the partial derivative of L with respect to x_i.

(Needs some math expert to say the comment above better.)

name A name for the operation (optional).
A list of Tensor objects of type Tout.

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Code samples licensed under the Apache 2.0 License.