fix docs issues
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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)))`，其中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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@ -42,9 +42,9 @@ mindspore.ops.FFTWithSize
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- **norm** (str，可选) - 表示该操作的规范化方式，可选值：["backward", "forward", "ortho"]。默认值："backward"。
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- "backward"，正向变换不缩放，逆变换按 :math:`1/sqrt(n)` 缩放，其中 `n` 表示输入 `x` 的元素数量。。
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- "ortho"，正向变换与逆变换均按 :math:`1/sqrt(n)` 缩放。
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- "forward"，正向变换按 :math:`1/sqrt(n)` 缩放，逆变换不缩放。
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- "backward"，正向变换不缩放，逆变换按 :math:`1/n` 缩放，其中 `n` 表示输入 `x` 的元素数量。。
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- "ortho"，正向变换与逆变换均按 :math:`1/\sqrt(n)` 缩放。
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- "forward"，正向变换按 :math:`1/n` 缩放，逆变换不缩放。
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- **onesided** (bool，可选) - 控制输入是否减半以避免冗余。默认值：True。
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- **signal_sizes** (list，可选) - 原始信号的大小（RFFT变换之前的信号，不包含batch这一维），只有在IRFFT模式下和设置 `onesided=True` 时需要该参数。默认值： :math:`[]` 。
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@ -6,7 +6,7 @@ mindspore.ops.coo_sqrt
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逐元素返回当前COOTensor的平方根。
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.. math::
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out_{i} = \\sqrt{x_{i}}
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out_{i} = \sqrt{x_{i}}
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参数：
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- **x** (COOTensor) - 输入COOTensor，数据类型为number.Number，其rank需要在[0, 7]范围内.
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@ -6,7 +6,7 @@ mindspore.ops.csr_sqrt
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逐元素返回当前CSRTensor的平方根。
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.. math::
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out_{i} = \\sqrt{x_{i}}
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out_{i} = \sqrt{x_{i}}
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参数：
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- **x** (CSRTensor) - 输入CSRTensor，数据类型为number.Number，其rank需要在[0, 7]范围内.
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@ -20,7 +20,7 @@ mindspore.ops.div
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- **None**: 默认行为。相当于Python中的 `true division` 或NumPy中的 `true_divide` 。
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- **"floor"**: 将除法的结果向下舍入。相当于Python中的 `floor division` 或NumPy中的 `floor_divide` 。
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- **"trunc"**: 将除法的结果舍入到零。相当于C型整数除法。
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- **"trunc"**: 将除法的结果舍入到零。相当于C语言风格的整数除法。
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返回：
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Tensor，输出的shape与广播后的shape相同，数据类型取两个输入中精度较高或数字较高的。
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@ -18,7 +18,7 @@ mindspore.ops.gelu
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当 `approximate` 为 `tanh` ，GELU的定义如下：
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.. math::
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GELU(x_i) = 0.5 * x_i * (1 + tanh[\sqrt{\\frac{2}{pi}}(x + 0.044715 * x_{i}^{3})] )
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GELU(x_i) = 0.5 * x_i * (1 + tanh(\sqrt(2 / \pi) * (x_i + 0.044715 * x_i^3)))
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GELU相关图参见 `GELU <https://en.wikipedia.org/wiki/Activation_function#/media/File:Activation_gelu.png>`_ 。
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@ -857,11 +857,11 @@ class GELU(Cell):
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If approximate is True, The gaussian error linear activation is:
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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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else, it is:
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:math:`x * P(X <= x) = 0.5 * x * (1 + erf(x / sqrt(2)))`, where P(X) ~ N(0, 1).
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:math:`x * P(X <= x) = 0.5 * x * (1 + erf(x / \sqrt(2)))`, where P(X) ~ N(0, 1).
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Inputs:
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- **x** (Tensor) - The input of GELU with data type of float16 or float32.
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@ -1915,7 +1915,7 @@ def diff(a, n=1, axis=-1, prepend=None, append=None):
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prepend/append (Tensor, optional): Values to prepend or append to a along
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`axis` prior to performing the difference. Scalar values are expanded to
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arrays with length 1 in the direction of `axis` and the shape of the input
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array in along all other axes. Otherwise the dimension and shape must
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array in along all other axis. Otherwise the dimension and shape must
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match `a` except along axis. Default: `None`.
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Returns:
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@ -4671,7 +4671,7 @@ def gelu(input_x, approximate='none'):
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When `approximate` argument is `tanh`, GeLU is estimated with:
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.. math::
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GELU(x_i) = 0.5 * x_i * (1 + tanh[\sqrt{\\frac{2}{pi}}(x + 0.044715 * x_{i}^{3})] )
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GELU(x_i) = 0.5 * x_i * (1 + tanh(\sqrt(2 / \pi) * (x_i + 0.044715 * x_i^3)))
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Args:
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input_x (Tensor): The input of the activation function GeLU, the data type is float16, float32 or float64.
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@ -7105,7 +7105,7 @@ class FFTWithSize(Primitive):
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- "backward" has the direct (forward) transforms unscaled and the inverse (backward) transforms
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scaled by 1/n, where n is the input x's element numbers.
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- "ortho" has both direct and inverse transforms are scaled by 1/sqrt(n).
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- "ortho" has both direct and inverse transforms are scaled by 1/\sqrt(n).
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- "forward" has the direct transforms scaled by 1/n and the inverse transforms unscaled.
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onesided (bool, optional): Controls whether the input is halved to avoid redundancy. Default: True.
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