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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 (Union[Cell, Function, MethodType]) – 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 s, 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
>>> import mindspore
>>> from mindspore import Tensor, nn, 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]]),)))