modify the files to fix the webpage
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@ -22,7 +22,7 @@ mindspore.nn.AdaSumByDeltaWeightWrapCell
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使用本接口时,训练的卡的数量必须是2的幂,并且至少需要16张卡。目前,使用本接口时不支持优化器并行和流水线并行。
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参数:
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- **optimizer** (nn.optimizer) - 必须是单输入的优化器。
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- **optimizer** (Union[Cell]) - 必须是单输入的优化器。
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输入:
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- **grads** (tuple[Tensor]) - `params` 的梯度,形状(shape)与 `params` 相同,与所传优化器的输入一致。
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@ -20,9 +20,9 @@ mindspore.nn.GELU
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参数:
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- **approximate** (bool) - 是否启用approximation,默认值:True。如果approximate的值为True,则高斯误差线性激活函数为:
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:math:`0.5 * x * (1 + tanh(sqrt(2 / pi) * (x + 0.044715 * x^3)))` ,
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:math:`0.5 * x * (1 + tanh(sqrt(2 / pi) * (x + 0.044715 * x^3)))` ,
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否则为: :math:`x * P(X <= x) = 0.5 * x * (1 + erf(x / sqrt(2)))`, where P(X) ~ N(0, 1) 。
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否则为: :math:`x * P(X <= x) = 0.5 * x * (1 + erf(x / sqrt(2)))`,其中P(X) ~ N(0, 1) 。
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输入:
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- **x** (Tensor) - 用于计算GELU的Tensor。数据类型为float16或float32。shape是 :math:`(N,*)` , :math:`*` 表示任意的附加维度数。
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@ -8,7 +8,7 @@ mindspore.nn.WithEvalCell
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参数:
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- **network** (Cell) - 前向网络。
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- **loss_fn** (Cell) - 损失函数。
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- **add_cast_fp32** (bool):是否将数据类型调整为float32。默认值:False。
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- **add_cast_fp32** (bool) - 是否将数据类型调整为float32。默认值:False。
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输入:
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- **data** (Tensor) - shape为 :math:`(N, \ldots)` 的Tensor。
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@ -513,7 +513,7 @@ Parameter Operation Functions
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mindspore.ops.scatter_update
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Differential Functions
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----------------
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----------------------
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.. msplatformautosummary::
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:toctree: ops
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@ -947,7 +947,8 @@ class Cell(Cell_):
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"""
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Compile and run Cell, the input must be consistent with the input defined in construct.
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Note: It is not recommended to call directly.
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Note:
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It is not recommended to call directly.
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Args:
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inputs (tuple): Inputs of the Cell object.
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@ -1080,7 +1081,8 @@ class Cell(Cell_):
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"""
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Defines the computation to be performed. This method must be overridden by all subclasses.
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Note: It is not supported currently that inputs contain both tuple and non-tuple types at same time.
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Note:
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It is not supported currently that inputs contain both tuple and non-tuple types at same time.
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Args:
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inputs (tuple): Tuple of variable parameters.
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