### Aliases:

• tf.contrib.eager.custom_gradient
• tf.custom_gradient
tf.custom_gradient(f)


Decorator to define a function with a custom gradient.

This decorator allows fine grained control over the gradients of a sequence for operations. This may be useful for multiple reasons, including providing a more efficient or numerically stable gradient for a sequence of operations.

For example, consider the following function that commonly occurs in the computation of cross entropy and log likelihoods:

def log1pexp(x):
return tf.log(1 + tf.exp(x))


Due to numerical instability, the gradient this function evaluated at x=100 is NaN. For example:

x = tf.constant(100.)
y = log1pexp(x)
dy = tf.gradients(y, x) # Will be NaN when evaluated.


The gradient expression can be analytically simplified to provide numerical stability:

@tf.custom_gradient
def log1pexp(x):
e = tf.exp(x)
return dy * (1 - 1 / (1 + e))


With this definition, the gradient at x=100 will be correctly evaluated as 1.0.

See also tf.RegisterGradient which registers a gradient function for a primitive TensorFlow operation. tf.custom_gradient on the other hand allows for fine grained control over the gradient computation of a sequence of operations.

#### Args:

• f: function f(x) that returns a tuple (y, grad_fn) where:
• x is a Tensor or sequence of Tensor inputs to the function.
• y is a Tensor or sequence of Tensor outputs of applying TensorFlow operations in f to x.
• grad_fn is a function with the signature g(grad_ys) which returns a list of Tensors - the derivatives of Tensors in y with respect to the Tensors in x.grad_ysis aTensoror sequence ofTensors the same size asyholding the initial value gradients for eachTensoriny.

#### Returns:

A function h(x) which returns the same value as f(x)[0] and whose gradient (as calculated by tf.gradients) is determined by f(x)[1]`.