!40453 correct the errors on webpage
Merge pull request !40453 from 宦晓玲/code_docs_0816
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39c5bf5d3a
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@ -16,7 +16,7 @@ mindspore.COOTensor
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[0, 0, 0, 0]]
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.. note::
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这是一个实验特性，在未来可能会发生API的变化。目前COOTensor中相同索引的值不会进行合并。
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这是一个实验特性，在未来可能会发生API的变化。目前COOTensor中相同索引的值不会进行合并。如果索引中包含界外值，则得出未定义结果。
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参数：
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- **indices** (Tensor) - 形状为 `[N, ndims]` 的二维整数张量，其中N和ndims分别表示稀疏张量中 `values` 的数量和COOTensor维度的数量。目前 `ndims` 只能为2。请确保indices的值在所给shape范围内。
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@ -133,7 +133,7 @@ mindspore.set_context
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- **compile_cache_path** (str) - 保存前端图编译缓存的路径。默认值："."。如果目录不存在，系统会自动创建这个目录。缓存会被保存到如下目录： `compile_cache_path/rank_${rank_id}/` 。 `rank_id` 是集群上当前设备的ID。
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- **runtime_num_threads** (int) - 运行时actor和CPU算子核使用的线程池线程数，必须大于0。默认值为30，如果同时运行多个进程，应将该值设置得小一些，以避免线程争用。
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- **disable_format_transform** (bool) - 表示是否取消NCHW到NHWC的自动格式转换功能。当fp16的网络性能不如fp32的时，可以设置 `disable_format_transform` 为True，以尝试提高训练性能。默认值：False。
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- **support_binary** (bool) - 是否支持在图形模式下运行.pyc或.so。如果要支持在图形模式下运行.so或.pyc，可将`support_binary`置为True，并运行一次.py文件，从而将接口源码保存到接口定义.py文件中，因此要保证该文件可写。然后将.py文件编译成.pyc或.so文件，即可在图模式下运行。
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- **support_binary** (bool) - 是否支持在图形模式下运行.pyc或.so。如果要支持在图形模式下运行.so或.pyc，可将 `support_binary` 置为True，并运行一次.py文件，从而将接口源码保存到接口定义.py文件中，因此要保证该文件可写。然后将.py文件编译成.pyc或.so文件，即可在图模式下运行。
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异常：
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- **ValueError** - 输入key不是上下文中的属性。
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@ -11,7 +11,7 @@ mindspore.ops.amax
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- **keep_dims** (bool) - 如果为True，则保留缩小的维度，大小为1。否则移除维度。默认值：False。
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返回：
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Tensor。
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Tensor。
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- 如果 `axis` 为()，且 `keep_dims` 为False，则输出一个0维Tensor，表示输入Tensor中所有元素的最大值。
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- 如果 `axis` 为int，取值为1，并且 `keep_dims` 为False，则输出的shape为 :math:`(x_0, x_2, ..., x_R)` 。
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@ -10,9 +10,6 @@
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.. math::
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(x_1, x_2, ..., \sum_{i=1}^Nx_{mi}, ..., x_R)
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.. note::
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`axis` 的取值范围为 :math:`[-dims, dims - 1]` 。 `dims` 为 `input_x` 的维度长度。
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参数：
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- **input_x** (tuple, list) - 输入为Tensor组成的tuple或list。假设在这个tuple或list中有两个Tensor，即 `x1` 和 `x2` 。要在0轴方向上执行 `Concat` ，除0轴外，其他轴的shape都应相等，即 :math:`x1.shape[1] = x2.shape[1]，x1.shape[2] = x2.shape[2]，...，x1.shape[R] = x2.shape[R]` ，其中 :math:`R` 表示最后一个轴。
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- **axis** (int) - 表示指定的轴，取值范围是 :math:`[-R, R)` 。默认值：0。
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@ -3,17 +3,17 @@ mindspore.ops.csr_softmax
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.. py:function:: mindspore.ops.csr_softmax(logits, dtype)
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计算 CSRTensorMatrix 的 softmax 。
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计算 CSRTensorMatrix 的 softmax 。
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参数：
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- **logits** （CSRTensor） - 输入稀疏的 CSRTensor。
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- **dtype** （dtype） - 输入的数据类型。
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参数：
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- **logits** （CSRTensor） - 输入稀疏的 CSRTensor。
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- **dtype** （dtype） - 输入的数据类型。
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返回：
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- **CSRTensor** （CSRTensor） - 一个 csr_tensor 包含
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- **indptr** - 指示每行中非零值的起始点和结束点。
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- **indices** - 输入中所有非零值的列位置。
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- **values** - 稠密张量的非零值。
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- **shape** - csrtensor 的形状.
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返回：
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- **CSRTensor** （CSRTensor） - 一个 csr_tensor 包含
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- **indptr** - 指示每行中非零值的起始点和结束点。
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- **indices** - 输入中所有非零值的列位置。
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- **values** - 稠密张量的非零值。
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- **shape** - csrtensor 的形状.
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@ -9,7 +9,7 @@ mindspore.ops.grad
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1. 对输入求导，此时 `grad_position` 非None，而 `weights` 是None;
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2. 对网络变量求导，此时 `grad_position` 是None，而 `weights` 非None;
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3. 同时对输入和网络变量求导，此时 `grad_position`和 `weights` 都非None。
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3. 同时对输入和网络变量求导，此时 `grad_position` 和 `weights` 都非None。
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参数：
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- **fn** (Union[Cell, Function]) - 待求导的函数或网络。
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@ -16,7 +16,7 @@ mindspore.ops.log_softmax
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- **logits** (Tensor) - shape： :math:`(N, *)` ，其中 :math:`*` 表示任意数量的附加维度，其数据类型为float16或float32。
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- **axis** (int) - 指定进行运算的轴。默认值：-1。
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输出：
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返回：
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Tensor，数据类型和shape与 `logits` 相同。
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异常：
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@ -11,7 +11,7 @@ mindspore.ops.prod
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- **keep_dims** (bool) - 如果为True，则保留缩小的维度，大小为1。否则移除维度。默认值：False。
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返回：
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Tensor。
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Tensor。
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- 如果 `axis` 为()，且 `keep_dims` 为False，则输出一个0维Tensor，表示输入Tensor中所有元素的乘积。
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- 如果 `axis` 为int，取值为1，并且 `keep_dims` 为False，则输出的shape为 :math:`(x_0, x_2, ..., x_R)` 。
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@ -1,7 +1,7 @@
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mindspore.ops.softmax
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=====================
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.. py::: function.ops.softmax(x, axis=-1)
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.. py:function:: mindspore.ops.softmax(x, axis=-1)
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Softmax函数。
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@ -686,7 +686,7 @@ def get_auto_offload():
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Returns:
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bool, Whether the automatic offload feature is enabled.
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Example:
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Examples:
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>>> # Get the global configuration of the automatic offload feature.
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>>> auto_offload = ds.config.get_auto_offload()
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"""
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@ -1061,7 +1061,8 @@ def slice(input_x, begin, size):
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The slice `begin` represents the offset in each dimension of `input_x`,
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The slice `size` represents the size of the output tensor.
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Note that `begin` is zero-based and `size` is one-based.
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Note:
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`begin` is zero-based and `size` is one-based.
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If `size[i]` is -1, all remaining elements in dimension i are included in the slice.
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This is equivalent to setting :math:`size[i] = input_x.shape(i) - begin[i]`.
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@ -2662,11 +2662,6 @@ def equal(x, y):
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r"""
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Computes the equivalence between two tensors element-wise.
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Inputs of `x` and `y` comply with the implicit type conversion rules to make the data types consistent.
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The inputs must be two tensors or one tensor and one scalar.
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When the inputs are two tensors, the shapes of them could be broadcast.
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When the inputs are one tensor and one scalar, the scalar could only be a constant.
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.. math::
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out_{i} =\begin{cases}
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@ -2674,6 +2669,12 @@ def equal(x, y):
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& \text{False, if } x_{i} \ne y_{i}
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\end{cases}
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Note:
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- Inputs of `x` and `y` comply with the implicit type conversion rules to make the data types consistent.
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- The inputs must be two tensors or one tensor and one scalar.
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- When the inputs are two tensors, the shapes of them could be broadcast.
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- When the inputs are one tensor and one scalar, the scalar could only be a constant.
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Args:
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x (Union[Tensor, Number]): The first input is a number or
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a tensor whose data type is number.
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@ -3036,12 +3037,13 @@ def minimum(x, y):
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r"""
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Computes the minimum of input tensors element-wise.
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Inputs of `x` and `y` comply with the implicit type conversion rules to make the data types consistent.
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The inputs must be two tensors or one tensor and one scalar.
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When the inputs are two tensors, dtypes of them cannot be bool at the same time.
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When the inputs are one tensor and one scalar, the scalar could only be a constant.
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Shapes of them are supposed to be broadcast.
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If one of the elements being compared is a NaN, then that element is returned.
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Note:
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- Inputs of `x` and `y` comply with the implicit type conversion rules to make the data types consistent.
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- The inputs must be two tensors or one tensor and one scalar.
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- When the inputs are two tensors, dtypes of them cannot be bool at the same time.
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- When the inputs are one tensor and one scalar, the scalar could only be a constant.
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- Shapes of them are supposed to be broadcast.
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- If one of the elements being compared is a NaN, then that element is returned.
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.. math::
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output_i = min(x_i, y_i)
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@ -347,7 +347,7 @@ def random_poisson(shape, rate, seed=None, dtype=mstype.float32):
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mindspore.dtype.float64, mindspore.dtype.float32 or mindspore.dtype.float16. Default: mindspore.dtype.float32.
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Returns:
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A Tensor whose shape is `mindspore.concat([`shape`, mindspore.shape(`rate`)], axis=0)` and data type is equal to
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A Tensor whose shape is `mindspore.concat(['shape', mindspore.shape('rate')], axis=0)` and data type is equal to
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argument `dtype`.
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Raises:
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@ -634,6 +634,7 @@ class ReduceMean(_Reduce):
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TypeError: If `keep_dims` is not a bool.
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TypeError: If `x` is not a Tensor.
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TypeError: If `axis` is not one of the following: int, tuple or list.
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ValueError: If `axis` is out of range.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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@ -918,6 +919,7 @@ class ReduceMax(_Reduce):
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TypeError: If `keep_dims` is not a bool.
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TypeError: If `x` is not a Tensor.
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TypeError: If `axis` is not one of the following: int, tuple or list.
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ValueError: If `axis` is out of range.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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@ -1004,6 +1006,7 @@ class ReduceMin(_Reduce):
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TypeError: If `keep_dims` is not a bool.
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TypeError: If `x` is not a Tensor.
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TypeError: If `axis` is not one of the following: int, tuple or list.
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ValueError: If `axis` is out of range.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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@ -1126,6 +1129,7 @@ class ReduceProd(_Reduce):
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TypeError: If `keep_dims` is not a bool.
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TypeError: If `x` is not a Tensor.
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TypeError: If `axis` is not one of the following: int, tuple or list.
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ValueError: If `axis` is out of range.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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