Computes the theoretical and numeric Jacobian of f.
tf.test.compute_gradient(
f, x, delta=0.001
)
With y = f(x), computes the theoretical and numeric Jacobian dy/dx.
Args |
|---|
f
|
the function.
|
x
|
the arguments for the function as a list or tuple of values convertible
to a Tensor.
|
delta
|
(optional) perturbation used to compute numeric Jacobian.
|
Returns |
|---|
|
A pair of lists, where the first is a list of 2-d numpy arrays representing
the theoretical Jacobians for each argument, and the second list is the
numerical ones. Each 2-d array has "y_size" rows
and "x_size" columns where "x_size" is the number of elements in the
corresponding argument and "y_size" is the number of elements in f(x).
|
Raises |
|---|
ValueError
|
If result is empty but the gradient is nonzero.
|
ValueError
|
If x is not list, but any other type.
|
Example:
@tf.function
def test_func(x):
return x*x
theoretical, numerical = tf.test.compute_gradient(test_func, [1.0])
theoretical, numerical
# ((array([[2.]], dtype=float32),), (array([[2.000004]], dtype=float32),))
TensorFlow 1 version
View source on GitHub