tf.compat.v1.test.compute_gradient_error(
x,
x_shape,
y,
y_shape,
x_init_value=None,
delta=0.001,
init_targets=None,
extra_feed_dict=None
)
Computes the maximum error for dy/dx between the computed Jacobian and the
numerically estimated Jacobian.
This function will modify the tensors passed in as it adds more operations
and hence changing the consumers of the operations of the input tensors.
This function adds operations to the current session. To compute the error
using a particular device, such as a GPU, use the standard methods for
setting a device (e.g. using with sess.graph.device() or setting a device
function in the session constructor).
Args
x
a tensor or list of tensors
x_shape
the dimensions of x as a tuple or an array of ints. If x is a list,
then this is the list of shapes.
y
a tensor
y_shape
the dimensions of y as a tuple or an array of ints.
x_init_value
(optional) a numpy array of the same shape as "x"
representing the initial value of x. If x is a list, this should be a list
of numpy arrays. If this is none, the function will pick a random tensor
as the initial value.
delta
(optional) the amount of perturbation.
init_targets
list of targets to run to initialize model params.
extra_feed_dict
dict that allows fixing specified tensor values
during the Jacobian calculation.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[],[]]