Update api docs
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@ -3,13 +3,13 @@ mindspore.Tensor.new_ones
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.. py:method:: mindspore.Tensor.new_ones(size, *, dtype=None)
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返回一个大小为 `size` 的Tensor，填充值为1。默认情况下，返回的Tensor和 `self` 具有相同的数据类型。
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返回一个大小为 `size` 的Tensor，填充值为1。
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参数：
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- **size** (Union[int, tuple, list]) - 定义输出的shape。
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关键字参数：
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- **dtype** (mindspore.dtype, 可选) - 输出的数据类型。默认值：None，使用和 `self` 相同的数据类型。
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- **dtype** (mindspore.dtype, 可选) - 输出的数据类型。默认值：None，返回的Tensor使用和 `self` 相同的数据类型。
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返回：
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Tensor，shape和dtype由输入定义，填充值为1。
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@ -3,13 +3,13 @@ mindspore.Tensor.new_zeros
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.. py:method:: mindspore.Tensor.new_zeros(size, *, dtype=None)
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返回一个大小为 `size` 的Tensor，填充值为0。默认情况下，返回的Tensor和 `self` 具有相同的数据类型。
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返回一个大小为 `size` 的Tensor，填充值为0。
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参数：
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- **size** (Union[int, tuple, list]) - 定义输出的shape。
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关键字参数：
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- **dtype** (mindspore.dtype, 可选) - 输出的数据类型。默认值：None，使用和 `self` 相同的数据类型。
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- **dtype** (mindspore.dtype, 可选) - 输出的数据类型。默认值：None，返回的Tensor使用和 `self` 相同的数据类型。
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返回：
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Tensor，shape和dtype由输入定义，填充值为0。
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@ -3,7 +3,7 @@
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.. py:function:: mindspore.ops.addbmm(x, batch1, batch2, *, beta=1, alpha=1)
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对 `batch1` 和 `batch2` 应用批量矩阵乘法后进行reduced add。矩阵 `x` 和最终的结果相加。
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对 `batch1` 和 `batch2` 应用批量矩阵乘法后进行reduced add， `x` 和最终的结果相加。
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`alpha` 和 `beta` 分别是 `batch1` 和 `batch2` 矩阵乘法和 `x` 的乘数。如果 `beta` 是0，那么 `x` 将会被忽略。
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.. math::
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@ -3,7 +3,7 @@ mindspore.ops.hinge_embedding_loss
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.. py:function:: mindspore.ops.hinge_embedding_loss(inputs, targets, margin=1.0, reduction="mean")
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Hinge Embedding 损失函数。按输入元素计算输出。衡量输入x和标签y（包含1或-1）之间的损失值。通常被用来衡量两个输入之间的相似度。
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Hinge Embedding 损失函数，衡量输入 `inputs` 和标签 `targets` （包含1或-1）之间的损失值。
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mini-batch中的第n个样例的损失函数为：
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@ -3,10 +3,14 @@ mindspore.ops.inner
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.. py:function:: mindspore.ops.inner(x, other)
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计算两个1D Tensor的点积。对于更高维度来说，计算结果为在最后一维上，逐元素乘法的和。
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计算两个1D Tensor的点积。
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对于1D Tensor（没有复数共轭的情况），返回两个向量的点积。
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对于更高的维度，返回最后一个轴上的和积。
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.. note::
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如果 `x` 或 `other` 之一是标量，那么相当于 :code:`mindspore.ops.mul(x, other)`。
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如果 `x` 或 `other` 之一是标量，那么 :func:`mindspore.ops.inner` 相当于 :func:`mindspore.ops.mul`。
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参数：
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- **x** (Tensor) - 第一个输入。
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@ -3,7 +3,7 @@ mindspore.ops.randint_like
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.. py:function:: mindspore.ops.randint_like(x, low, high, *, dtype=None, seed=None)
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返回一个Tensor，其元素为 [ `low` , `high` ) 区间的随机整数。
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返回一个Tensor，其元素为 [ `low` , `high` ) 区间的随机整数，根据 `x` 决定shape和dtype。
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参数：
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- **x** (Tensor) - 输入的Tensor，用来决定输出Tensor的shape和默认的dtype。
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@ -4126,14 +4126,14 @@ class Tensor(Tensor_):
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def new_zeros(self, size, *, dtype=None):
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r"""
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Return a tensor of `size` filled with zeros. By default, the returned tensor has the same dtype as `self`.
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Return a tensor of `size` filled with zeros.
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Args:
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size (Union[int, tuple, list]): An int, list or tuple of integers defining the output shape.
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Keyword Args:
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dtype (mindspore.dtype, optional): The desired dtype of the output tensor. If None, same dtype as `self`.
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Default: None.
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dtype (mindspore.dtype, optional): The desired dtype of the output tensor. If None, the returned tensor has
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thesame dtype as `self`. Default: None.
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Returns:
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Tensor, the shape and dtype is defined above and filled with zeros.
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@ -4160,14 +4160,14 @@ class Tensor(Tensor_):
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def new_ones(self, size, *, dtype=None):
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r"""
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Return a tensor of `size` filled with ones. By default, the returned tensor has the same dtype as `self`.
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Return a tensor of `size` filled with ones.
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Args:
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size (Union[int, tuple, list]): An int, list or tuple of integers defining the output shape.
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Keyword Args:
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dtype (mindspore.dtype, optional): The desired dtype of the output tensor. Default: if None, same dtype as
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`self`.
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dtype (mindspore.dtype, optional): The desired dtype of the output tensor. If None, the returned
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tensor has the same dtype as `self`. Default: None.
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Returns:
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Tensor, the shape and dtype is defined above and filled with ones.
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@ -2469,9 +2469,7 @@ class GaussianNLLLoss(LossBase):
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class HingeEmbeddingLoss(LossBase):
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r"""
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Hinge Embedding Loss. Compute the output according to the input elements. Measures the loss given an input tensor x
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and a labels tensor y (containing 1 or -1).
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This is usually used for measuring the similarity between two inputs.
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Measures Hinge Embedding Loss given an input Tensor `logits` and a labels Tensor `labels` (containing 1 or -1).
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The loss function for :math:`n`-th sample in the mini-batch is
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@ -387,15 +387,15 @@ def hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, *, dtype
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def where(condition, x, y):
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r"""
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Returns a tensor whose elements are selected from either `x` or `y` depending on `condition`.
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Selects elements from `x` or `y` based on `condition` and returns a tensor.
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.. math::
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output_i = \begin{cases} x_i,\quad &if\ condition_i \\ y_i,\quad &otherwise \end{cases}
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Args:
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condition (Union[Bool Tensor, bool, scalar]): If True, yield `x` otherwise yield `y`.
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x (Union[Tensor, Scalar]): Value (if `x` is a scalar) or values selected at indices where condition is True.
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y (Union[Tensor, Scalar]): Value (if `y` is a scalar) or values selected at indices where condition is False.
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condition (Union[Bool Tensor, bool, scalar]): If True, yield `x`, otherwise yield `y`.
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x (Union[Tensor, Scalar]): When `condition` is True, values to select from.
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y (Union[Tensor, Scalar]): When `condition` is Fasle, values to select from.
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Returns:
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Tensor, elements are selected from `x` and `y`.
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@ -1285,7 +1285,7 @@ def log(x):
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def logdet(x):
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r"""
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Calculates log determinant of a square matrix or batches of square matrices.
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Calculates log determinant of one or a batch of square matrices.
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Args:
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x (Tensor): Input Tensor of any dimension.
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@ -5003,8 +5003,7 @@ def mv(mat, vec):
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def addbmm(x, batch1, batch2, *, beta=1, alpha=1):
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r"""
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Applies batch matrix multiplication to `batch1` and `batch2`, with a reduced add step. The matrix `x` is add to
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final result.
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Applies batch matrix multiplication to `batch1` and `batch2`, with a reduced add step and add `x` to the result.
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The optional values `alpha` and `beta` are the matrix-matrix product between `batch1` and `batch2` and the scale
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factor for the added tensor `x` respectively. If `beta` is 0, then `x` will be ignored.
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@ -5163,7 +5162,7 @@ def addmv(x, mat, vec, beta=1, alpha=1):
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def adjoint(x):
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r"""
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Returns a view of the tensor conjugated and with the last two dimensions transposed.
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Returns the conjugate with the last two dimensions transposed.
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Args:
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x (Tensor): Input tensor.
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@ -7933,11 +7932,15 @@ def matmul(x1, x2):
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def inner(x, other):
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r"""
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Computes the dot product of 1D tensors. For higher dimensions, the result will be the summation of the elemental
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wise production along their last dimension.
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Returns the inner product of two tensors.
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For 1-D tensors (without complex conjugation), returns the ordinary inner product of vectors.
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For higher dimensions, returns a sum product over the last axis.
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Note:
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If either `x` or `other` is a Tensor scalar, the result is equivalent to mindspore.mul(x, other).
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If `x` or `other` is a Tensor scalar, :func:`mindspore.ops.inner` will be the same as
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:func:`mindspore.ops.mul` .
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Args:
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x (Tensor): First input.
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@ -4065,9 +4065,7 @@ def gaussian_nll_loss(x, target, var, full=False, eps=1e-6, reduction='mean'):
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def hinge_embedding_loss(inputs, targets, margin=1.0, reduction='mean'):
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r"""
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Hinge Embedding Loss. Compute the output according to the input elements. Measures the loss given an input tensor x
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and a labels tensor y (containing 1 or -1).
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This is usually used for measuring the similarity between two inputs.
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Measures Hinge Embedding Loss given an input Tensor `logits` and a labels Tensor `labels` (containing 1 or -1).
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The loss function for :math:`n`-th sample in the mini-batch is
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@ -847,8 +847,8 @@ def _generate_shapes(shape):
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@_function_forbid_reuse
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def rand(*size, dtype=None, seed=None):
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r"""
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Returns a new Tensor with given shape and dtype, filled with random numbers from the uniform distribution on the
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interval :math:`[0, 1)`.
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Returns a new tensor that fills numbers from the uniform distribution over an interval :math:`[0, 1)`
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based on the given shape and dtype.
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Args:
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size (Union[int, tuple(int), list(int)]): Shape of the new tensor, e.g. :math:`(2, 3)` or :math:`2`.
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@ -890,8 +890,8 @@ def rand(*size, dtype=None, seed=None):
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@_function_forbid_reuse
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def rand_like(x, seed=None, *, dtype=None):
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r"""
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Returns a new Tensor with the shape and dtype as `x`, filled with random numbers from the uniform distribution on
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the interval :math:`[0, 1)`.
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Returns a new tensor that fills numbers from the uniform distribution over an interval :math:`[0, 1)`
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based on the given shape and dtype.
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Args:
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x (Tensor): Input Tensor to specify the output shape and its default dtype.
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@ -961,9 +961,9 @@ def randn(*size, dtype=None, seed=None):
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Examples:
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>>> import mindspore.ops as ops
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>>> print(ops.randn((2,3)))
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[[ 0.30639967 -0.42438635 -0.20454668]
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[-0.4287376 1.3054721 0.64747655]]
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>>> print(ops.randn((2, 2)))
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[[ 0.30639967 -0.42438635]
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[-0.4287376 1.3054721 ]]
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"""
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if dtype is None:
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dtype = mstype.float32
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@ -1025,7 +1025,7 @@ def randn_like(x, seed=None, *, dtype=None):
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@_function_forbid_reuse
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def randint(low, high, size, seed=None, *, dtype=None):
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r"""
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Return a Tensor whose elements are random integers from low (inclusive) to high (exclusive).
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Returns a Tensor whose elements are random integers in the range of [ `low` , `high` ) .
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Args:
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low (int): Start value of interval.
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@ -1077,8 +1077,8 @@ def randint(low, high, size, seed=None, *, dtype=None):
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@_function_forbid_reuse
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def randint_like(x, low, high, seed=None, *, dtype=None):
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r"""
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Returns a tensor with the same shape as Tensor `x` filled with random integers generated uniformly between
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low (inclusive) and high (exclusive).
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Returns a tensor with the same shape as Tensor `x` whose elements are random integers in the range
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of [ `low` , `high` ) .
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Args:
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x (Tensor): Input Tensor to specify the output shape and its default dtype.
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