forked from huawei/mindspore2022
59 lines
1.9 KiB
ReStructuredText
59 lines
1.9 KiB
ReStructuredText
mindspore.nn.TrainOneStepCell
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=============================
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.. py:class:: mindspore.nn.TrainOneStepCell(network, optimizer, sens=1.0)
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训练网络封装类。
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封装 `network` 和 `optimizer` ，构建一个输入'\*inputs'的用于训练的Cell。
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执行函数 `construct` 中会构建反向图以更新网络参数。支持不同的并行训练模式。
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**参数：**
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- **network** (Cell) - 训练网络。只支持单输出网络。
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- **optimizer** (Union[Cell]) - 用于更新网络参数的优化器。
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- **sens** (numbers.Number) - 反向传播的输入，缩放系数。默认值为1.0。
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**输入：**
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**(\*inputs)** (Tuple(Tensor)) - shape为 :math:`(N, \ldots)` 的Tensor组成的元组。
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**输出：**
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Tensor，损失函数值，其shape通常为 :math:`()` 。
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**异常：**
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**TypeError**：`sens` 不是numbers.Number。
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**支持平台：**
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``Ascend`` ``GPU`` ``CPU``
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**样例：**
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>>> net = Net()
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>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits()
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>>> optim = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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>>> # 1）使用MindSpore提供的WithLossCell
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>>> loss_net = nn.WithLossCell(net, loss_fn)
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>>> train_net = nn.TrainOneStepCell(loss_net, optim)
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>>>
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>>> # 2）用户自定义的WithLossCell
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>>> class MyWithLossCell(Cell):
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... def __init__(self, backbone, loss_fn):
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... super(MyWithLossCell, self).__init__(auto_prefix=False)
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... self._backbone = backbone
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... self._loss_fn = loss_fn
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...
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... def construct(self, x, y, label):
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... out = self._backbone(x, y)
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... return self._loss_fn(out, label)
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...
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... @property
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... def backbone_network(self):
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... return self._backbone
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...
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>>> loss_net = MyWithLossCell(net, loss_fn)
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>>> train_net = nn.TrainOneStepCell(loss_net, optim)
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