forked from mindspore-Ecosystem/mindspore
58 lines
2.0 KiB
ReStructuredText
58 lines
2.0 KiB
ReStructuredText
mindspore.nn.FixedLossScaleUpdateCell
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=======================================
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.. py:class:: mindspore.nn.FixedLossScaleUpdateCell(loss_scale_value)
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固定梯度放大系数的神经元。
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该类是 :class:`mindspore.nn.FixedLossScaleManager` 的 `get_update_cell` 方法的返回值。训练过程中，类 :class:`mindspore.TrainOneStepWithLossScaleCell` 会调用该Cell。
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**参数：**
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- **loss_scale_value** (float) - 初始梯度放大系数。
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**输入：**
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- **loss_scale** (Tensor) - 训练期间的梯度放大系数，shape为 :math:`()`，在当前类中，该值被忽略。
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- **overflow** (bool) - 是否发生溢出。
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**输出：**
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Bool，即输入 `overflow`。
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**支持平台：**
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``Ascend`` ``GPU``
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**样例：**
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>>> import numpy as np
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>>> from mindspore import Tensor, Parameter, nn, ops
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>>>
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>>> class Net(nn.Cell):
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... def __init__(self, in_features, out_features)：
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... super(Net, self).__init__()
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... self.weight = Parameter(Tensor(np.ones([in_features, out_features]).astype(np.float32)),
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... name='weight')
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... self.matmul = ops.MatMul()
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...
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... def construct(self, x)：
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... output = self.matmul(x, self.weight)
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... return output
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...
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>>> in_features, out_features = 16, 10
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>>> net = Net(in_features, out_features)
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>>> loss = nn.MSELoss()
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>>> optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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>>> net_with_loss = nn.WithLossCell(net, loss)
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>>> manager = nn.FixedLossScaleUpdateCell(loss_scale_value=2**12)
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>>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_sense=manager)
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>>> input = Tensor(np.ones([out_features, in_features]), mindspore.float32)
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>>> labels = Tensor(np.ones([out_features,]), mindspore.float32)
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>>> output = train_network(input, labels)
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.. py:method:: get_loss_scale()
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获取当前梯度放大系数。
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