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[Docs] update formulas for math and array operators

This commit is contained in:
hedongdong 2021-02-10 11:25:12 +08:00
parent b036a836a6
commit c67bb608b0
2 changed files with 19 additions and 30 deletions
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44
mindspore/ops/operations/array_ops.py
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@ -3181,11 +3181,9 @@ class ScatterUpdate(_ScatterOp_Dynamic):
Using given values to update tensor value, along with the input indices.
for each `i, ..., j` in `indices.shape`:
.. math::
\begin{array}{l}
\text {for each i, ..., j in indices.shape:} \\
input\_x[indices[i, ..., j], :] = updates[i, ..., j, :]
\end{array}
\text{input_x}[\text{indices}[i, ..., j], :] = \text{updates}[i, ..., j, :]
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, lower priority data type will be converted to
@ -3296,11 +3294,10 @@ class ScatterMax(_ScatterOp):
Using given values to update tensor value through the max operation, along with the input indices.
This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
for each `i, ..., j` in `indices.shape`:
.. math::
\begin{array}{l}
\text {for each i, ..., j in indices.shape:} \\
input\_x[indices[i, ..., j], :] = max(input\_x[indices[i, ..., j], :], updates[i, ..., j, :])
\end{array}
\text{input_x}[\text{indices}[i, ..., j], :]
= max(\text{input_x}[\text{indices}[i, ..., j], :], \text{updates}[i, ..., j, :])
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, lower priority data type will be converted to
@ -3347,11 +3344,10 @@ class ScatterMin(_ScatterOp):
Using given values to update tensor value through the min operation, along with the input indices.
This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
for each `i, ..., j` in `indices.shape`:
.. math::
\begin{array}{l}
\text {for each i, ..., j in indices.shape:} \\
input\_x[indices[i, ..., j], :] = min(input\_x[indices[i, ..., j], :], updates[i, ..., j, :])
\end{array}
\text{input_x}[\text{indices}[i, ..., j], :]
= min(\text{input_x}[\text{indices}[i, ..., j], :], \text{updates}[i, ..., j, :])
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, lower priority data type will be converted to
@ -3392,11 +3388,9 @@ class ScatterAdd(_ScatterOp_Dynamic):
Using given values to update tensor value through the add operation, along with the input indices.
This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
for each `i, ..., j` in `indices.shape`:
.. math::
\begin{array}{l}
\text {for each i, ..., j in indices.shape:} \\
input\_x[indices[i, ..., j], :] \mathrel{+}= updates[i, ..., j, :]
\end{array}
\text{input_x}[\text{indices}[i, ..., j], :] \mathrel{+}= \text{updates}[i, ..., j, :]
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, lower priority data type will be converted to
@ -3443,11 +3437,9 @@ class ScatterSub(_ScatterOp):
Using given values to update tensor value through the subtraction operation, along with the input indices.
This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
for each `i, ..., j` in `indices.shape`:
.. math::
\begin{array}{l}
\text {for each i, ..., j in indices.shape:} \\
input\_x[indices[i, ..., j], :] \mathrel{-}= updates[i, ..., j, :]
\end{array}
\text{input_x}[\text{indices}[i, ..., j], :] \mathrel{-}= \text{updates}[i, ..., j, :]
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, lower priority data type will be converted to
@ -3489,11 +3481,9 @@ class ScatterMul(_ScatterOp):
Using given values to update tensor value through the mul operation, along with the input indices.
This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
for each `i, ..., j` in `indices.shape`:
.. math::
\begin{array}{l}
\text {for each i, ..., j in indices.shape:} \\
input\_x[indices[i, ..., j], :] \mathrel{*}= updates[i, ..., j, :]
\end{array}
\text{input_x}[\text{indices}[i, ..., j], :] \mathrel{*}= \text{updates}[i, ..., j, :]
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, lower priority data type will be converted to
@ -3534,11 +3524,9 @@ class ScatterDiv(_ScatterOp):
Using given values to update tensor value through the div operation, along with the input indices.
This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
for each `i, ..., j` in `indices.shape`:
.. math::
\begin{array}{l}
\text {for each i, ..., j in indices.shape:} \\
input\_x[indices[i, ..., j], :] \mathrel{/}= updates[i, ..., j, :]
\end{array}
\text{input_x}[\text{indices}[i, ..., j], :] \mathrel{/}= \text{updates}[i, ..., j, :]
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, lower priority data type will be converted to

5
mindspore/ops/operations/math_ops.py
View File

@ -767,9 +767,10 @@ class MatMul(PrimitiveWithCheck):
class BatchMatMul(MatMul):
"""
Computes matrix multiplication between two tensors by batch
Computes matrix multiplication between two tensors by batch.
`result[..., :, :] = tensor(a[..., :, :]) * tensor(b[..., :, :])`.
.. math::
\text{output}[..., :, :] = \text{matrix}(a[..., :, :]) * \text{matrix}(b[..., :, :])
The two input tensors must have the same rank and the rank must be not less than `3`.