mindspore.nn.TrainOneStepCell

class mindspore.nn.TrainOneStepCell(network, optimizer, sens=1.0)

Network training package class.

Wraps the network with an optimizer. The resulting Cell is trained with input ‘*inputs’. The backward graph will be created in the construct function to update the parameter. Different parallel modes are available for training.

Parameters

• network (Cell) – The training network. The network only supports single output.

• optimizer (Union[Cell]) – Optimizer for updating the weights.

• sens (numbers.Number) – The scaling number to be filled as the input of backpropagation. Default value is 1.0.

Inputs:

• (*inputs) (Tuple(Tensor)) - Tuple of input tensors with shape \((N, \ldots)\).

Outputs:

Tensor, a tensor means the loss value, the shape of which is usually \(()\).

Raises

TypeError – If sens is not a number.

Supported Platforms:

Ascend GPU CPU

Examples

>>> net = Net()
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits()
>>> optim = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> #1) Using the WithLossCell existing provide
>>> loss_net = nn.WithLossCell(net, loss_fn)
>>> train_net = nn.TrainOneStepCell(loss_net, optim)
>>>
>>> #2) Using user-defined WithLossCell
>>> class MyWithLossCell(Cell):
...    def __init__(self, backbone, loss_fn):
...        super(MyWithLossCell, self).__init__(auto_prefix=False)
...        self._backbone = backbone
...        self._loss_fn = loss_fn
...
...    def construct(self, x, y, label):
...        out = self._backbone(x, y)
...        return self._loss_fn(out, label)
...
...    @property
...    def backbone_network(self):
...        return self._backbone
...
>>> loss_net = MyWithLossCell(net, loss_fn)
>>> train_net = nn.TrainOneStepCell(loss_net, optim)