Document feedback

Question document fragment

When a question document fragment contains a formula, it is displayed as a space.

Submission type

issue

It's a little complicated...

I'd like to ask someone.

Please select the submission type

Problem type

Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

- Low writing fluency that does not affect understanding.

- Incorrect version numbers, including software package names and version numbers on the UI.

Usability

- Usability:

- Incorrect or missing key steps.

- Missing main function descriptions, keyword explanation, necessary prerequisites, or precautions.

- Ambiguous descriptions, unclear reference, or contradictory context.

- Unclear logic, such as missing classifications, items, and steps.

Correctness

- Correctness:

- Technical principles, function descriptions, supported platforms, parameter types, or exceptions inconsistent with that of software implementation.

- Incorrect schematic or architecture diagrams.

- Incorrect commands or command parameters.

- Incorrect code.

- Commands inconsistent with the functions.

- Wrong screenshots.

- Sample code running error, or running results inconsistent with the expectation.

Risk Warnings

- Risk Warnings:

- Lack of risk warnings for operations that may damage the system or important data.

Content Compliance

- Content Compliance:

- Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions.

- Copyright infringement.

Please select the type of question

Problem description

Describe the bug so that we can quickly locate the problem.

Submit

Document feedback

mindspore.nn.ForwardValueAndGrad

class mindspore.nn.ForwardValueAndGrad(network, weights=None, get_all=False, get_by_list=False, sens_param=False)

Encapsulate training network.

Including the network and a gradient function. The resulting Cell is trained with input ‘*inputs’. The backward graph will be created in the gradient function to calculating gradient.

Parameters

• network (Cell) – The training network.

• weights (ParameterTuple) – The parameters of the training network that need to calculate the gradient. Default: None.

• get_all (bool) – If True, get all the gradients with respect to inputs. Default: False.

• get_by_list (bool) – If True, get all the gradients with respect to Parameter variables. If get_all and get_by_list are both False, get the gradient with respect to first input. If get_all and get_by_list are both True, get the gradients with respect to inputs and Parameter variables at the same time in the form of ((gradients with respect to inputs), (gradients with respect to parameters)). Default: False.

• sens_param (bool) – Whether to append sensitivity (gradient with respect to output) as input. If sens_param is False, a ‘ones_like(outputs)’ sensitivity will be attached automatically. Default: False. If the sens_param is True, a sensitivity (gradient with respect to output) needs to be transferred through the input parameter.

Inputs:

• (*inputs) (Tuple(Tensor…)) - Tuple of inputs with shape $(N,\dots )$.

• (sens) - A sensitivity (gradient with respect to output) as the input of backpropagation. If network has single output, the sens is a tensor. If network has multiple outputs, the sens is the tuple(tensor).

Outputs:

• forward value - The result of network forward running.

• gradients (tuple(tensor)) - The gradients of network parameters and inputs.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, nn, common, ops, ParameterTuple, Parameter
>>>
>>> class Net(nn.Cell):
...    def __init__(self):
...        super(Net, self).__init__()
...        self.weight = Parameter(Tensor(np.ones([2, 2]).astype(np.float32)), name="weight")
...        self.matmul = ops.MatMul()
...
...    def construct(self, x):
...        out = self.matmul(x, self.weight)
...        return out
...
>>> net = Net()
>>> criterion = nn.SoftmaxCrossEntropyWithLogits()
>>> net_with_criterion = nn.WithLossCell(net, criterion)
>>> weight = ParameterTuple(net.trainable_params())
>>> train_network = nn.ForwardValueAndGrad(net_with_criterion, weights=weight, get_all=True, get_by_list=True)
>>> inputs = Tensor(np.ones([1, 2]).astype(np.float32))
>>> labels = Tensor(np.ones([1, 2]).astype(np.float32))
>>> result = train_network(inputs, labels)
>>> print(result)
 (Tensor(shape=[1], dtype=Float32, value= [ 1.38629436e+00]), ((Tensor(shape=[1, 2], dtype=Float32, value=
[[ -1.00000000e+00,  -1.00000000e+00]]), Tensor(shape=[1, 2], dtype=Float32, value=
[[ 0.00000000e+00,  0.00000000e+00]])), (Tensor(shape=[2, 2], dtype=Float32, value=
[[ -5.00000000e-01,  -5.00000000e-01],
 [ -5.00000000e-01,  -5.00000000e-01]]),)))