Table Of Contents
Table Of Contents

numeric_grad

mxnet.test_utils.numeric_grad(executor, location, aux_states=None, eps=0.0001, use_forward_train=True, dtype=<class 'numpy.float32'>)[source]

Calculates a numeric gradient via finite difference method.

Class based on Theano’s theano.gradient.numeric_grad [1]

Parameters:
  • executor (Executor) – Executor that computes the forward pass.
  • location (list of numpy.ndarray or dict of str to numpy.ndarray) – Argument values used as location to compute gradient Maps the name of arguments to the corresponding numpy.ndarray. Value of all the arguments must be provided.
  • aux_states (None or list of numpy.ndarray or dict of str to numpy.ndarray, optional) – Auxiliary states values used as location to compute gradient Maps the name of aux_states to the corresponding numpy.ndarray. Value of all the auxiliary arguments must be provided.
  • eps (float, optional) – Epsilon for the finite-difference method.
  • use_forward_train (bool, optional) – Whether to use is_train=True in testing.
  • dtype (np.float16 or np.float32 or np.float64) – Datatype for mx.nd.array.

References

..[1] https://github.com/Theano/Theano/blob/master/theano/gradient.py