modify the inconsistence of files
This commit is contained in:
parent
38997c0f38
commit
9e86788b84
|
|
@ -13,4 +13,9 @@ mindspore.Tensor.flatten
|
|||
|
||||
异常：
|
||||
- **TypeError** - `order` 不是字符串类型。
|
||||
- **ValueError** - `order` 是字符串类型，但不是'C'或'F'。
|
||||
- **ValueError** - `order` 是字符串类型，但不是'C'或'F'。
|
||||
|
||||
比如：
|
||||
:func:`mindspore.Tensor.reshape`：在不改变数据的情况，改变Tensor的shape。
|
||||
|
||||
:func:`mindspore.Tensor.ravel`：返回一个连续扁平化的Tensor。
|
||||
|
|
@ -19,7 +19,7 @@ mindspore.ops.ApplyRMSProp
|
|||
:math:`m_{t+1}` 为 `moment` ， :math:`m_{t}` 为上一步的 :math:`m_{t+1}` 。
|
||||
:math:`\rho` 为 `decay` 。 :math:`\beta` 为动量项 `momentum` 。
|
||||
:math:`\epsilon` 是避免零为除数的平滑项 `epsilon` 。
|
||||
:math:`\eta` 为 `learning_rate` ， :math:`\nabla Q_{i}(w)` 代表 `grad` 。
|
||||
:math:`\eta` 为 `learning_rate` 。 :math:`\nabla Q_{i}(w)` 代表 `grad` 。
|
||||
|
||||
.. warning::
|
||||
在该算法的稠密实现版本中，"mean_square"和"momemt"即使"grad"为零将仍被更新。但在该稀疏实现版本中，在"grad"为零的迭代"mean_squre"和"moment"将不被更新。
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ mindspore.ops.ScatterMax
|
|||
根据指定更新值和输入索引通过最大值操作更新输入数据的值。
|
||||
该操作在更新完成后输出 `input_x` ，这样方便使用更新后的值。
|
||||
|
||||
对于 `indices.shape` 的每个 `i, ..., j` ：
|
||||
对于 `indices.shape` 的每个 :math:`i, ..., j` ：
|
||||
|
||||
.. math::
|
||||
\text{input_x}[\text{indices}[i, ..., j], :]
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ mindspore.ops.adaptive_avg_pool2d
|
|||
|
||||
2维自适应平均池化。
|
||||
|
||||
对输入Tensor，提供2维的自适应平均池化操作，也就是说，对于输入任何尺寸，指定输出的尺寸都为H * W。但是输入和输出特征的数目不会变化。
|
||||
对输入Tensor，提供2维的自适应平均池化操作。也就是说，对于输入任何尺寸，指定输出的尺寸都为H * W。但是输入和输出特征的数目不会变化。
|
||||
|
||||
输入和输出数据格式可以是"NCHW"和"CHW"。N是批处理大小，C是通道数，H是特征高度，W是特征宽度。
|
||||
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ mindspore.ops.gumbel_softmax
|
|||
|
||||
.. py:function:: mindspore.ops.gumbel_softmax(logits, tau=1, hard=False, dim=-1)
|
||||
|
||||
返回Gumbel-Softmax分布的Tensor，在 `hard = True` 的时候，返回one-hot形式的离散型Tensor，`hard = False` 时返回在dim维进行过softmax的Tensor。
|
||||
返回Gumbel-Softmax分布的Tensor。在 `hard = True` 的时候，返回one-hot形式的离散型Tensor，`hard = False` 时返回在dim维进行过softmax的Tensor。
|
||||
|
||||
参数：
|
||||
- **logits** (Tensor) - 输入，是一个非标准化的对数概率分布。只支持float16和float32。
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@
|
|||
|
||||
.. py:function:: mindspore.ops.sequence_mask(lengths, maxlen=None)
|
||||
|
||||
返回一个表示每个单元的前N个位置的掩码Tensor。内部元素数据类型为bool。
|
||||
返回一个表示每个单元的前N个位置的掩码Tensor，内部元素数据类型为bool。
|
||||
|
||||
如果 `lengths` 的shape为 :math:`(d_1, d_2, ..., d_n)` ，则生成的Tensor掩码拥有数据类型，其shape为 :math:`(d_1, d_2, ..., d_n, maxlen)` ，且mask :math:`[i_1, i_2, ..., i_n, j] = (j < lengths[i_1, i_2, ..., i_n])` 。
|
||||
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ mindspore.ops.split
|
|||
|
||||
根据指定的轴和分割数量对输入Tensor进行分割。
|
||||
|
||||
`input_x` Tensor将被分割为相同shape的子Tensor，且要求 `input_x.shape(axis)` 可被 `output_num` 整除。
|
||||
`input_x` Tensor将被分割为相同shape的子Tensor。要求 `input_x.shape(axis)` 可被 `output_num` 整除。
|
||||
|
||||
参数：
|
||||
- **input_x** (Tensor) - Tensor的shape为 :math:`(x_1, x_2, ..., x_R)` 。
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ mindspore.ops.unique_with_pad
|
|||
对输入一维张量中元素去重，返回一维张量中的唯一元素（使用pad_num填充）和相对索引。
|
||||
|
||||
基本操作与unique相同，但unique_with_pad多了pad操作。
|
||||
unique运算符对张量处理后所返回的元组（ `y` ， `idx` ）， `y` 与 `idx` 的shape通常会有差别，因此，为了解决上述情况，
|
||||
unique运算符对张量处理后所返回的元组（ `y` ， `idx` ）， `y` 与 `idx` 的shape通常会有差别。因此，为了解决上述情况，
|
||||
unique_with_pad操作符将用用户指定的 `pad_num` 填充 `y` 张量，使其具有与张量 `idx` 相同的形状。
|
||||
|
||||
参数：
|
||||
|
|
|
|||
|
|
@ -111,9 +111,11 @@ class Caltech101Dataset(GeneratorDataset):
|
|||
A source dataset that reads and parses Caltech101 dataset.
|
||||
|
||||
The columns of the generated dataset depend on the value of `target_type`.
|
||||
When `target_type` is 'category', the columns are :py:obj:`[image, category]`.
|
||||
When `target_type` is 'annotation', the columns are :py:obj:`[image, annotation]`.
|
||||
When `target_type` is 'all', the columns are :py:obj:`[image, category, annotation]`.
|
||||
|
||||
- When `target_type` is 'category', the columns are :py:obj:`[image, category]`.
|
||||
- When `target_type` is 'annotation', the columns are :py:obj:`[image, annotation]`.
|
||||
- When `target_type` is 'all', the columns are :py:obj:`[image, category, annotation]`.
|
||||
|
||||
The tensor of column :py:obj:`image` is of the uint8 type.
|
||||
The tensor of column :py:obj:`category` is of the uint32 type.
|
||||
The tensor of column :py:obj:`annotation` is a 2-dimensional ndarray that stores the contour of the image
|
||||
|
|
@ -3789,8 +3791,9 @@ class SBDataset(GeneratorDataset):
|
|||
A source dataset that reads and parses Semantic Boundaries Dataset.
|
||||
|
||||
The generated dataset has two columns: :py:obj:`[image, task]`.
|
||||
The tensor of column :py:obj:`image` is of the uint8 type.
|
||||
The tensor of column :py:obj:`task` contains 20 images of the uint8 type if `task` is 'Boundaries' otherwise
|
||||
|
||||
- The tensor of column :py:obj:`image` is of the uint8 type.
|
||||
- The tensor of column :py:obj:`task` contains 20 images of the uint8 type if `task` is 'Boundaries' otherwise
|
||||
contains 1 image of the uint8 type.
|
||||
|
||||
Args:
|
||||
|
|
|
|||
|
|
@ -28,7 +28,11 @@ class Poisson(Distribution):
|
|||
r"""
|
||||
Poisson Distribution.
|
||||
A Poisson Distribution is a discrete distribution with the range as the non-negative integers,
|
||||
and the probability mass function as :math:`P(X = k) = \lambda^k \exp(-\lambda) / k!, k = 1, 2, ...`,
|
||||
and the probability mass function as
|
||||
|
||||
.. math::
|
||||
P(X = k) = \lambda^k \exp(-\lambda) / k!, k = 1, 2, ...
|
||||
|
||||
where :math:`\lambda` is the rate of the distribution.
|
||||
|
||||
Args:
|
||||
|
|
|
|||
|
|
@ -134,7 +134,7 @@ def sequence_mask(lengths, maxlen=None):
|
|||
Returns a mask tensor representing the first N positions of each cell.
|
||||
|
||||
If `lengths` has shape (d_1, d_2, ..., d_n), then the resulting tensor mask has type and shape
|
||||
(d_1, d_2, ..., d_n, maxlen), with mask[i_1, i_2, ..., i_n, j] = (j < lengths[i_1, i_2, ..., i_n])
|
||||
(d_1, d_2, ..., d_n, maxlen), with mask[i_1, i_2, ..., i_n, j] = (j < lengths[i_1, i_2, ..., i_n]).
|
||||
|
||||
Args:
|
||||
lengths (Tensor): Tensor to calculate the mask for. All values in this tensor should be
|
||||
|
|
|
|||
|
|
@ -1159,7 +1159,7 @@ def strided_slice(input_x,
|
|||
Raises:
|
||||
TypeError: If `begin_mask`, `end_mask`, `ellipsis_mask`, `new_axis_mask` or
|
||||
`shrink_axis_mask` is not an int.
|
||||
TypeError: If `begin` 、 `end` or `strides` is not tuple[int].
|
||||
TypeError: If `begin`, `end` or `strides` is not tuple[int].
|
||||
ValueError: If `begin_mask`, `end_mask`, `ellipsis_mask`, `new_axis_mask` or
|
||||
`shrink_axis_mask` is less than 0.
|
||||
|
||||
|
|
@ -1229,8 +1229,8 @@ def slice(input_x, begin, size):
|
|||
r"""
|
||||
Slices a tensor in the specified shape.
|
||||
|
||||
Slice the tensor `input_x` in shape of `size` and starting at the location specified by `begin`,
|
||||
The slice `begin` represents the offset in each dimension of `input_x`,
|
||||
Slice the tensor `input_x` in shape of `size` and starting at the location specified by `begin`.
|
||||
The slice `begin` represents the offset in each dimension of `input_x`.
|
||||
The slice `size` represents the size of the output tensor.
|
||||
|
||||
Note:
|
||||
|
|
@ -1784,7 +1784,8 @@ def scatter_div(input_x, indices, updates):
|
|||
|
||||
Inputs of `input_x` and `updates` comply with the implicit type conversion rules to make the data types consistent.
|
||||
If they have different data types, the lower priority data type will be converted to
|
||||
the relatively highest priority data type.
|
||||
the relatively highest priority data type. A RuntimeError will be reported
|
||||
when `updates` does not support conversion to the data type required by `input_x`.
|
||||
|
||||
Args:
|
||||
input_x (Parameter): The target tensor, with data type of Parameter.
|
||||
|
|
|
|||
|
|
@ -2245,7 +2245,7 @@ def max_pool3d(x, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=Fal
|
|||
|
||||
Typically the input is a Tensor with shape :math:`(N_{in}, C_{in}, D_{in}, H_{in}, W_{in})`, outputs
|
||||
regional maximum in the :math:`(D_{in}, H_{in}, W_{in})`-dimension. Given `kernel_size`
|
||||
:math:`ks = (d_{ker}, h_{ker}, w_{ker})` and `stride` :math:`s = (s_0, s_1, s_2)`, the operation is as follows.
|
||||
:math:`ks = (d_{ker}, h_{ker}, w_{ker})` and `stride` :math:`s = (s_0, s_1, s_2)`, the operation is as follows:
|
||||
|
||||
.. math::
|
||||
\text{output}(N_i, C_j, d, h, w) =
|
||||
|
|
|
|||
|
|
@ -152,7 +152,7 @@ def assign_add(variable, value):
|
|||
def index_add(x, indices, y, axis, use_lock=True, check_index_bound=True):
|
||||
"""
|
||||
Adds tensor `y` to specified axis and indices of Parameter `x`. The axis should be in [0, len(x.dim) - 1],
|
||||
and indices should be in [0, the size of `x` - 1] at the axis dimension.
|
||||
and indices should be in [0, x.shape[axis] - 1] at the axis dimension.
|
||||
|
||||
Args:
|
||||
x (Parameter): The input Parameter to add to.
|
||||
|
|
|
|||
|
|
@ -1004,7 +1004,7 @@ class Unique(Primitive):
|
|||
The shape of Tensor `y` and Tensor `idx` is different in most cases, because Tensor `y` will be duplicated,
|
||||
and the shape of Tensor `idx` is consistent with the input.
|
||||
|
||||
To get the same shape between `idx` and `y`, please ref to 'UniqueWithPad' operator.
|
||||
To get the same shape between `idx` and `y`, please refer to :class:`mindspore.ops.UniqueWithPad`.
|
||||
|
||||
Inputs:
|
||||
- **input_x** (Tensor) - The input tensor.
|
||||
|
|
@ -5982,6 +5982,7 @@ class EmbeddingLookup(PrimitiveWithCheck):
|
|||
raise ValueError(f"For '{self.name}', the dimension of 'input_params' must <= 2, "
|
||||
f"but got {len(params_shp)}.")
|
||||
|
||||
|
||||
class GatherD(Primitive):
|
||||
"""
|
||||
Gathers elements along an axis specified by dim.
|
||||
|
|
@ -6545,7 +6546,7 @@ class TensorScatterAdd(Primitive):
|
|||
Creates a new tensor by adding the values from the positions in `input_x` indicated by
|
||||
`indices`, with values from `updates`. When multiple values are given for the same
|
||||
index, the updated result will be the sum of all values. This operation is almost
|
||||
equivalent to using ScatterNdAdd, except that the updates are applied on output `Tensor`
|
||||
equivalent to using :class:`mindspore.ops.ScatterNdAdd`, except that the updates are applied on output `Tensor`
|
||||
instead of input `Parameter`.
|
||||
|
||||
Refer to :func:`mindspore.ops.tensor_scatter_add` for more detail.
|
||||
|
|
|
|||
|
|
@ -1516,7 +1516,8 @@ def build_searched_strategy(strategy_filename):
|
|||
"""
|
||||
Build strategy of every parameter in network. Used in the case of distributed inference.
|
||||
For details of it, please check:
|
||||
`<https://www.mindspore.cn/tutorials/experts/en/master/parallel/save_load.html>`_.
|
||||
`Saving and Loading Models in Hybrid Parallel Mode
|
||||
<https://www.mindspore.cn/tutorials/experts/en/master/parallel/save_load.html>`_.
|
||||
|
||||
Args:
|
||||
strategy_filename (str): Name of strategy file.
|
||||
|
|
|
|||
Loading…
Reference in New Issue