diff --git a/docs/programming_guide/source_zh_cn/dtype.md b/docs/programming_guide/source_zh_cn/dtype.md index 5fc7d208e65aed905222fe79b2ca6530a04f5fdc..1cbdbdee84da420757130e1b5cfb339352ede12a 100644 --- a/docs/programming_guide/source_zh_cn/dtype.md +++ b/docs/programming_guide/source_zh_cn/dtype.md @@ -9,6 +9,8 @@ +   + ## 概述 diff --git a/docs/programming_guide/source_zh_cn/operator.md b/docs/programming_guide/source_zh_cn/operator.md index 588d3a4314412f544695fe619cb26c9116bc60f1..514f1c8b69ba6d7bd8c7a204965d038d3d607d80 100644 --- a/docs/programming_guide/source_zh_cn/operator.md +++ b/docs/programming_guide/source_zh_cn/operator.md @@ -16,7 +16,6 @@ - [求三角函数](#求三角函数) - [向量运算](#向量运算) - [Squeeze](#squeeze) - - [求Sparse2Dense](#求sparse2dense) - [矩阵运算](#矩阵运算) - [矩阵乘法](#矩阵乘法) - [广播机制](#广播机制) @@ -187,7 +186,7 @@ scalar 3 import numpy as np import mindspore from mindspore import Tensor -import mindspore.ops.operations as P + input_x = mindspore.Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32) input_y = 3.0 print(input_x**input_y) @@ -277,30 +276,6 @@ print(output) [1. 1.] [1. 1.]] ``` -#### 求Sparse2Dense - -以下代码实现了对Sparse2Dense示例： -```python -import numpy as np -import mindspore as ms -from mindspore import Tensor -import mindspore.ops.operations as P - -indices = Tensor([[0, 1], [1, 2]]) -values = Tensor([1, 2], dtype=ms.float32) -dense_shape = (3, 4) -out = P.SparseToDense()(indices, values, dense_shape) - -print(out) -``` - -输出如下： -``` -[[0, 1, 0, 0], - [0, 0, 2, 0], - [0, 0, 0, 0]] -``` - ### 矩阵运算 矩阵运算包括矩阵乘法、矩阵范数、矩阵行列式、矩阵求特征值、矩阵分解等运算。 @@ -453,8 +428,6 @@ print(res) #### LossFunction - L1Loss - 以下代码实现了L1 loss function： ```python from mindspore import Tensor @@ -476,8 +449,6 @@ print(res) #### 优化算法 - SGD - 以下代码实现了SGD梯度下降算法的具体实现，输出是result： ```python from mindspore import Tensor diff --git a/docs/programming_guide/source_zh_cn/tensor.md b/docs/programming_guide/source_zh_cn/tensor.md index b7a6196404b7942b7bc978c6d20c01e2b1099bb5..1d8b36f592ecf2a0532130cfeb3c788e1ade492f 100644 --- a/docs/programming_guide/source_zh_cn/tensor.md +++ b/docs/programming_guide/source_zh_cn/tensor.md @@ -12,6 +12,8 @@ +   + ## 概述 diff --git a/tutorials/notebook/programming_guide/dtype.ipynb b/tutorials/notebook/programming_guide/dtype.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3a4d47dcde38c12b1135570924e2bd0c2f623c31 --- /dev/null +++ b/tutorials/notebook/programming_guide/dtype.ipynb @@ -0,0 +1,111 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# dtype" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 概述\n", + "\n", + "MindSpore张量支持不同的数据类型，包含`int8`、`int16`、`int32`、`int64`、`uint8`、`uint16`、`uint32`、`uint64`、`float16`、`float32`、`float64`、`bool_`，与NumPy的数据类型一一对应。\n", + "\n", + "在MindSpore的运算处理流程中，Python中的`int`数会被转换为定义的`int64`类型，`float`数会被转换为定义的`float32`类型。\n", + "\n", + "详细的类型支持情况请参考https://www.mindspore.cn/doc/api_python/zh-CN/master/mindspore/mindspore.html#mindspore.dtype。\n", + "\n", + "以下代码，打印MindSpore的数据类型int32。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Int32\n" + ] + } + ], + "source": [ + "from mindspore import dtype as mstype\n", + "\n", + "data_type = mstype.int32\n", + "print(data_type)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 数据类型转换接口\n", + "\n", + "MindSpore提供了以下几个接口，实现与NumPy数据类型和Python内置的数据类型间的转换。\n", + "\n", + " * `dtype_to_nptype`：将MindSpore的数据类型转换为NumPy对应的数据类型。\n", + "\n", + " * `dtype_to_pytype`：将MindSpore的数据类型转换为Python对应的内置数据类型。\n", + "\n", + " * `pytype_to_dtype`：将Python内置的数据类型转换为MindSpore对应的数据类型。\n", + "\n", + "以下代码实现了不同数据类型间的转换，并打印转换后的类型。" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64\n", + "\n" + ] + } + ], + "source": [ + "from mindspore import dtype as mstype\n", + "\n", + "np_type = mstype.dtype_to_nptype(mstype.int32)\n", + "ms_type = mstype.pytype_to_dtype(int)\n", + "py_type = mstype.dtype_to_pytype(mstype.float64)\n", + "\n", + "print(np_type)\n", + "print(ms_type)\n", + "print(py_type)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/notebook/programming_guide/tensor.ipynb b/tutorials/notebook/programming_guide/tensor.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..fc4e13fb3dfae27a4f0996aefcf31a15fd7be21f --- /dev/null +++ b/tutorials/notebook/programming_guide/tensor.ipynb @@ -0,0 +1,149 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tensor" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 概述\n", + "\n", + "张量（Tensor）是MindSpore网络运算中的基本数据结构。张量中的数据类型可参考[dtype](https://www.mindspore.cn/doc/programming_guide/zh-CN/master/dtype.html)。\n", + "\n", + "不同维度的张量分别表示不同的数据，0维张量表示标量，1维张量表示向量，2维张量表示矩阵，3维张量可以表示彩色图像的RGB三通道等等。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> 本文中的所有示例，支持在PyNative模式下运行，暂不支持CPU。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 张量构造\n", + "\n", + "构造张量时，支持传入`Tensor`、`float`、`int`、`bool`、`tuple`、`list`和`NumPy.array`类型。\n", + "\n", + "`Tensor`作为初始值时，可指定dtype，如果没有指定dtype，`int`、`float`、`bool`分别对应`int32`、`float32`、`bool_`，`tuple`和`list`生成的1维`Tensor`数据类型与`tuple`和`list`里存放数据的类型相对应。\n", + "\n", + "代码样例如下：" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 2]\n", + " [3 4]] \n", + "\n", + " 1 \n", + "\n", + " 2 \n", + "\n", + " True \n", + "\n", + " [1 2 3] \n", + "\n", + " [4. 5. 6.]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from mindspore import Tensor\n", + "from mindspore.common import dtype as mstype\n", + "\n", + "x = Tensor(np.array([[1, 2], [3, 4]]), mstype.int32)\n", + "y = Tensor(1.0, mstype.int32)\n", + "z = Tensor(2, mstype.int32)\n", + "m = Tensor(True, mstype.bool_)\n", + "n = Tensor((1, 2, 3), mstype.int16)\n", + "p = Tensor([4.0, 5.0, 6.0], mstype.float64)\n", + "\n", + "print(x, \"\\n\\n\", y, \"\\n\\n\", z, \"\\n\\n\", m, \"\\n\\n\", n, \"\\n\\n\", p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 张量的属性" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 属性\n", + "\n", + "张量的属性包括形状（shape）和数据类型（dtype）。\n", + "\n", + " * 形状：`Tensor`的shape，是一个tuple。\n", + "\n", + " * 数据类型：`Tensor`的dtype，是MindSpore的一个数据类型。\n", + "\n", + "代码样例如下：" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2, 2) Int32\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from mindspore import Tensor\n", + "from mindspore.common import dtype as mstype\n", + "\n", + "x = Tensor(np.array([[1, 2], [3, 4]]), mstype.int32)\n", + "x_shape = x.shape\n", + "x_dtype = x.dtype\n", + "\n", + "print(x_shape, x_dtype)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}