diff --git a/api/source_en/api/python/mindspore/mindspore.nn.probability.rst b/api/source_en/api/python/mindspore/mindspore.nn.probability.rst index 9ed8e3699313bcc607274c7616faa39cd49d6f23..2235f574850eceaf0f26a4d6a1b2c4927a2a247e 100644 --- a/api/source_en/api/python/mindspore/mindspore.nn.probability.rst +++ b/api/source_en/api/python/mindspore/mindspore.nn.probability.rst @@ -12,7 +12,14 @@ mindspore.nn.probability.bnn_layers .. automodule:: mindspore.nn.probability.bnn_layers :members: + :exclude-members: ConvReparam , DenseReparam + .. autoclass:: ConvReparam(in_channels, out_channels, kernel_size,stride=1, pad_mode='same', padding=0, dilation=1, group=1, has_bias=False, weight_prior_fn=NormalPrior, weight_posterior_fn=, bias_prior_fn=NormalPrior, bias_posterior_fn=) + :members: + + .. autoclass:: DenseReparam(in_channels, out_channels, activation=None, has_bias=True, weight_prior_fn=NormalPrior, weight_posterior_fn=, bias_prior_fn=NormalPrior, bias_posterior_fn=) + :members: + .. autoclass:: WithBNNLossCell :members: diff --git a/api/source_zh_cn/api/python/mindspore/mindspore.nn.probability.rst b/api/source_zh_cn/api/python/mindspore/mindspore.nn.probability.rst index 729903dfca591c264e4d1b5b093bf3356203c67a..70e8f69bf9f2a3e6d8d6f1be8636cad79b61fb69 100644 --- a/api/source_zh_cn/api/python/mindspore/mindspore.nn.probability.rst +++ b/api/source_zh_cn/api/python/mindspore/mindspore.nn.probability.rst @@ -12,7 +12,14 @@ mindspore.nn.probability.bnn_layers .. automodule:: mindspore.nn.probability.bnn_layers :members: + :exclude-members: ConvReparam , DenseReparam + .. autoclass:: ConvReparam(in_channels, out_channels, kernel_size,stride=1, pad_mode='same', padding=0, dilation=1, group=1, has_bias=False, weight_prior_fn=NormalPrior, weight_posterior_fn=, bias_prior_fn=NormalPrior, bias_posterior_fn=) + :members: + + .. autoclass:: DenseReparam(in_channels, out_channels, activation=None, has_bias=True, weight_prior_fn=NormalPrior, weight_posterior_fn=, bias_prior_fn=NormalPrior, bias_posterior_fn=) + :members: + .. autoclass:: WithBNNLossCell :members: diff --git a/api/source_zh_cn/programming_guide/ops.md b/api/source_zh_cn/programming_guide/ops.md index eb7d4a3352ff0f63bc3b813b30c1dfb1ecd957a7..53bb69f5dfd011be5b4ed47d1f440da45df482a6 100644 --- a/api/source_zh_cn/programming_guide/ops.md +++ b/api/source_zh_cn/programming_guide/ops.md @@ -1,4 +1,4 @@ -# ops模块 +# ops模块 @@ -20,7 +20,7 @@ ops主要包括operations、functional和composite，可通过ops直接获取到 ## mindspore.ops.operations -operations提供了所有的Primitive算子接口，是开放给用户的最低阶算子接口。 +operations提供了所有的Primitive算子接口，是开放给用户的最低阶算子接口。算子支持情况可查询[算子支持列表](https://www.mindspore.cn/docs/zh-CN/master/operator_list.html#mindspore-ops-operations)。 Primitive算子也称为算子原语，它直接封装了底层的Ascend、GPU、AICPU、CPU等多种算子的具体实现，为用户提供基础算子能力。 @@ -47,25 +47,10 @@ output = [ 1. 8. 64.] ## mindspore.ops.functional -为了简化没有属性的算子的调用流程，MindSpore提供了一些算子的functional版本。入参要求参考原算子的输入输出要求。算子支持情况可以查询[算子支持列表](https://www.mindspore.cn/docs/zh-CN/master/operator_list.html#mindspore-ops-operations)。 +为了简化没有属性的算子的调用流程，MindSpore提供了一些算子的functional版本。入参要求参考原算子的输入输出要求。算子支持情况可以查询[算子支持列表](https://www.mindspore.cn/docs/zh-CN/master/operator_list.html#mindspore-ops-functional)。 例如`P.Pow`算子，我们提供了functional版本的`F.tensor_pow`算子。 -使用operations的代码样例如下： - -```python -import numpy as np -import mindspore -from mindspore import Tensor -from mindspore.ops import operations as P - -input_x = mindspore.Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32) -input_y = 3.0 -pow = P.Pow() -output = pow(input_x, input_y) -print("output =", output) -``` - 使用functional的代码样例如下： ```python @@ -138,4 +123,4 @@ tensor [[2.4, 4.2] scalar 3 ``` -此外，高阶函数`GradOperation`提供了根据输入的函数，求这个函数对应的求梯度的函数的方式，详细可以参阅[API文档](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.composite.html#mindspore.ops.composite.GradOperation)。 \ No newline at end of file +此外，高阶函数`GradOperation`提供了根据输入的函数，求这个函数对应的梯度函数的方式，详细可以参阅[API文档](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.composite.html#mindspore.ops.composite.GradOperation)。 \ No newline at end of file diff --git a/api/source_zh_cn/programming_guide/tensor.md b/api/source_zh_cn/programming_guide/tensor.md index 08f9ec6d7f5ed1c450964eb15e5f613831607152..c935dc71bba6a42e0174e03048f7e4d704db9bb4 100644 --- a/api/source_zh_cn/programming_guide/tensor.md +++ b/api/source_zh_cn/programming_guide/tensor.md @@ -55,22 +55,22 @@ print(x, "\n\n", y, "\n\n", z, "\n\n", m, "\n\n", n, "\n\n", p) ``` [[1 2] - [3 4]] + [3 4]] -1 +1 -2 +2 -True +True -[1 2 3] +[1 2 3] [4. 5. 6.] ``` ## 变量张量 -变量张量的值在网络中可以被更新，用来表示需要被更新的参数，MindSpore使用Tensor的子类Parameter构造变量张量，构造时支持传入Tensor或者Initializer。 +变量张量的值在网络中可以被更新，用来表示需要被更新的参数，MindSpore使用Tensor的子类Parameter构造变量张量，构造时支持传入Tensor、Initializer或者Number。 代码样例如下： @@ -83,21 +83,24 @@ from mindspore.common.initializer import initializer x = Tensor(np.arange(2*3).reshape((2, 3))) y = Parameter(x, name="x") z = Parameter(initializer('ones', [1, 2, 3], mstype.float32), name='y') +m = Parameter(2.0, name='m') -print(x, "\n\n", y, "\n\n", z) +print(x, "\n\n", y, "\n\n", z, "\n\n", m) ``` 输出如下： ``` [[0 1 2] - [3 4 5]] + [3 4 5]] -Parameter (name=x, value=[[0 1 2] - [3 4 5]]) +Parameter (name=x, value=[[0 1 2] + [3 4 5]]) Parameter (name=y, value=[[[1. 1. 1.] [1. 1. 1.]]]) + +Parameter (name=m, value=2.0) ``` ## 张量的属性和方法 @@ -152,9 +155,9 @@ print(x_all, "\n\n", x_any, "\n\n", x_array) 输出如下： ``` -False +False -True +True [[ True True] [False False]] @@ -190,9 +193,9 @@ True ``` [[1. 1.] - [1. 1.]] + [1. 1.]] - 1.0 + 1.0 [1 2 3] ``` @@ -270,17 +273,17 @@ True ``` [[[0 1 2] - [3 4 5]]] + [3 4 5]]] [[[0 1] [2 3] - [4 5]]] + [4 5]]] [[[[0 1 2] - [3 4 5]]]] + [3 4 5]]]] [[0 1 2] - [3 4 5]] + [3 4 5]] [[[0 3]] [[1 4]] @@ -315,17 +318,17 @@ True ``` [[0 1 2] - [3 4 5]] + [3 4 5]] [[[0 1 2] [3 4 5]] [[0 1 2] - [3 4 5]]] + [3 4 5]]] [[0 1 2] [3 4 5] [0 1 2] - [3 4 5]] + [3 4 5]] (Tensor(shape=[1, 3], dtype=Int64, [[0 1 2]]), Tensor(shape=[1, 3], dtype=Int64, [[3 4 5]])) ``` @@ -357,7 +360,7 @@ print(x, "\n\n", y) ``` [[0 1 2] - [3 4 5]] + [3 4 5]] [[0 1 2 0 1 2 0 1 2] [3 4 5 3 4 5 3 4 5] diff --git a/docs/source_en/FAQ.md b/docs/source_en/FAQ.md index 681d68614b8840f572c53221c748d1fe4bb01157..6478958c1882e17cafa51e5d0586c4132527e972 100644 --- a/docs/source_en/FAQ.md +++ b/docs/source_en/FAQ.md @@ -1,5 +1,7 @@ # FAQ +`Ascend` `GPU` `CPU` `Environmental Setup` `Model Export` `Model Training` `Beginner` `Intermediate` `Expert` + - [FAQ](#faq) @@ -16,6 +18,7 @@ - [Supported Features](#supported-features) + ## Installation diff --git a/docs/source_en/operator_list.md b/docs/source_en/operator_list.md index b4c760c3175107added8c5376538b050a492c89f..3a79b0a2b9e79d7375608e4ed9a421cf1360a1f4 100644 --- a/docs/source_en/operator_list.md +++ b/docs/source_en/operator_list.md @@ -102,7 +102,7 @@ | [mindspore.ops.operations.Acosh](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Acosh) | Doing | Doing | Doing | nn_ops | [mindspore.ops.operations.FloorMod](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.FloorMod) | Supported | Doing | Doing | nn_ops | [mindspore.ops.operations.Elu](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Elu) | Supported | Doing | Doing | nn_ops -| [mindspore.ops.operations.MirrorPad](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.MirrorPad) | Doing | Doing | Doing | nn_ops +| [mindspore.ops.operations.MirrorPad](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.MirrorPad) | Supported | Supported | Doing | nn_ops | [mindspore.ops.operations.Unpack](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Unpack) | Supported | Doing | Doing | nn_ops | [mindspore.ops.operations.Pack](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Pack) | Supported | Doing | Doing | nn_ops | [mindspore.ops.operations.L2Loss](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.L2Loss) | Supported | Doing | Doing | nn_ops @@ -184,8 +184,8 @@ | [mindspore.ops.operations.Mul](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Mul) | Supported | Supported | Supported | math_ops | [mindspore.ops.operations.Square](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Square) | Supported | Supported | Doing | math_ops | [mindspore.ops.operations.SquareSumAll](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SquareSumAll) | Supported | Doing | Doing | math_ops -| [mindspore.ops.operations.Rsqrt](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Rsqrt) | Supported | Supported | Doing | math_ops -| [mindspore.ops.operations.Sqrt](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Sqrt) | Supported | Supported | Doing | math_ops +| [mindspore.ops.operations.Rsqrt](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Rsqrt) | Supported | Doing | Doing | math_ops +| [mindspore.ops.operations.Sqrt](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Sqrt) | Supported | Doing | Doing | math_ops | [mindspore.ops.operations.Reciprocal](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Reciprocal) | Supported | Supported | Doing | math_ops | [mindspore.ops.operations.Pow](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Pow) | Supported | Supported | Doing | math_ops | [mindspore.ops.operations.Exp](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Exp) | Supported | Supported | Doing | math_ops diff --git a/docs/source_zh_cn/FAQ.md b/docs/source_zh_cn/FAQ.md index 9873b933d666d18b00026515f9cfd14a228f23f5..1698974891db256511ea819d05b944e46ef69ae7 100644 --- a/docs/source_zh_cn/FAQ.md +++ b/docs/source_zh_cn/FAQ.md @@ -1,5 +1,7 @@ # FAQ +`Ascend` `GPU` `CPU` `环境准备` `模型导出` `模型训练` `初级` `中级` `高级` + - [FAQ](#faq) @@ -16,6 +18,7 @@ - [特性支持](#特性支持) + ## 安装类 diff --git a/docs/source_zh_cn/operator_list.md b/docs/source_zh_cn/operator_list.md index 6244a7101f59c9705ec876ff3271bc7774026c14..db8e29e3935556c95e4aabc477cc297d2561c8d0 100644 --- a/docs/source_zh_cn/operator_list.md +++ b/docs/source_zh_cn/operator_list.md @@ -102,7 +102,7 @@ | [mindspore.ops.operations.Acosh](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Acosh) | Doing | Doing | Doing | nn_ops | [mindspore.ops.operations.FloorMod](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.FloorMod) | Supported | Doing | Doing | nn_ops | [mindspore.ops.operations.Elu](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Elu) | Supported | Doing | Doing | nn_ops -| [mindspore.ops.operations.MirrorPad](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.MirrorPad) | Doing | Doing | Doing | nn_ops +| [mindspore.ops.operations.MirrorPad](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.MirrorPad) | Supported | Supported | Doing | nn_ops | [mindspore.ops.operations.Unpack](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Unpack) | Supported | Doing | Doing | nn_ops | [mindspore.ops.operations.Pack](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Pack) | Supported| Doing | Doing | nn_ops | [mindspore.ops.operations.L2Loss](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.L2Loss) | Supported | Doing | Doing | nn_ops @@ -184,8 +184,8 @@ | [mindspore.ops.operations.Mul](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Mul) | Supported | Supported | Supported | math_ops | [mindspore.ops.operations.Square](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Square) | Supported | Supported | Doing | math_ops | [mindspore.ops.operations.SquareSumAll](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SquareSumAll) | Supported | Doing | Doing | math_ops -| [mindspore.ops.operations.Rsqrt](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Rsqrt) | Supported | Supported | Doing | math_ops -| [mindspore.ops.operations.Sqrt](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Sqrt) | Supported | Supported | Doing | math_ops +| [mindspore.ops.operations.Rsqrt](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Rsqrt) | Supported | Doing | Doing | math_ops +| [mindspore.ops.operations.Sqrt](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Sqrt) | Supported | Doing | Doing | math_ops | [mindspore.ops.operations.Reciprocal](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Reciprocal) | Supported | Supported | Doing | math_ops | [mindspore.ops.operations.Pow](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Pow) | Supported | Supported | Doing | math_ops | [mindspore.ops.operations.Exp](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Exp) | Supported | Supported | Doing | math_ops diff --git a/install/mindspore_cpu_install.md b/install/mindspore_cpu_install.md index e31b2a72edd0edfc3a4879fc958d6267adc19651..721cad66b18b2db7b7fdc312765fc60c4a5db594 100644 --- a/install/mindspore_cpu_install.md +++ b/install/mindspore_cpu_install.md @@ -97,7 +97,7 @@ | 版本号 | 操作系统 | 可执行文件安装依赖 | 源码编译安装依赖 | | ---------------------- | :------------------ | :----------------------------------------------------------- | :----------------------- | -| MindArmour master | Ubuntu 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- 其他依赖项参见[setup.py](https://gitee.com/mindspore/mindarmour/blob/master/setup.py) | 与可执行文件安装依赖相同 | +| MindArmour master | - Ubuntu 18.04 x86_64
- Ubuntu 18.04 aarch64 | - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- 其他依赖项参见[setup.py](https://gitee.com/mindspore/mindarmour/blob/master/setup.py) | 与可执行文件安装依赖相同 | - 在联网状态下，安装whl包时会自动下载`setup.py`中的依赖项，其余情况需自行安装。 diff --git a/install/mindspore_cpu_install_en.md b/install/mindspore_cpu_install_en.md index 3da8d9eff691e60b198680f084259888b8dffd20..d21f05bdeb3d451ff36588a8346a9753bdf831d6 100644 --- a/install/mindspore_cpu_install_en.md +++ b/install/mindspore_cpu_install_en.md @@ -97,7 +97,7 @@ If you need to conduct AI model security research or enhance the security of the | Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies | | ---- | :--- | :--- | :--- | -| MindArmour master | Ubuntu 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/master/setup.py). | Same as the executable file installation dependencies. | +| MindArmour master | - Ubuntu 18.04 x86_64
- Ubuntu 18.04 aarch64 | - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/master/setup.py). | Same as the executable file installation dependencies. | - When the network is connected, dependency items in the `setup.py` file are automatically downloaded during .whl package installation. In other cases, you need to manually install dependency items. diff --git a/install/mindspore_d_install.md b/install/mindspore_d_install.md index f355690cddd86d88ba89a4b8b417f14917acdd05..a0d6eb70a9f6d7f7c4452a593695e95c024c135f 100644 --- a/install/mindspore_d_install.md +++ b/install/mindspore_d_install.md @@ -32,7 +32,7 @@ | 版本号 | 操作系统 | 可执行文件安装依赖 | 源码编译安装依赖 | | ---- | :--- | :--- | :--- | -| MindSpore master | - Ubuntu 18.04 aarch64
- Ubuntu 18.04 x86_64
- CentOS 7.6 aarch64
- CentOS 7.6 x86_64
- EulerOS 2.8 aarch64
- EulerOS 2.5 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI处理器配套软件包（对应版本[Atlas Data Center Solution V100R020C10T200](https://support.huawei.com/enterprise/zh/ascend-computing/atlas-data-center-solution-pid-251167910/software/251661816)）
- [gmp](https://gmplib.org/download/gmp/) 6.1.2
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.6/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI处理器配套软件包（对应版本[Atlas Data Center Solution V100R020C10T200](https://support.huawei.com/enterprise/zh/ascend-computing/atlas-data-center-solution-pid-251167910/software/251661816)）
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
- [gmp](https://gmplib.org/download/gmp/) 6.1.2
**安装依赖：**
与可执行文件安装依赖相同 | +| MindSpore master | - Ubuntu 18.04 aarch64
- Ubuntu 18.04 x86_64
- EulerOS 2.8 aarch64
- EulerOS 2.5 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI处理器配套软件包（对应版本[Atlas Data Center Solution V100R020C10T200](https://support.huawei.com/enterprise/zh/ascend-computing/atlas-data-center-solution-pid-251167910/software/251661816)）
- [gmp](https://gmplib.org/download/gmp/) 6.1.2
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.6/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI处理器配套软件包（对应版本[Atlas Data Center Solution V100R020C10T200](https://support.huawei.com/enterprise/zh/ascend-computing/atlas-data-center-solution-pid-251167910/software/251661816)）
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
- [gmp](https://gmplib.org/download/gmp/) 6.1.2
**安装依赖：**
与可执行文件安装依赖相同 | - 确认当前用户有权限访问Ascend 910 AI处理器配套软件包（对应版本[Atlas Data Center Solution V100R020C10T200](https://support.huawei.com/enterprise/zh/ascend-computing/atlas-data-center-solution-pid-251167910/software/251661816)）的安装路径`/usr/local/Ascend`，若无权限，需要root用户将当前用户添加到`/usr/local/Ascend`所在的用户组，具体配置请详见配套软件包的说明文档。 - GCC 7.3.0可以直接通过apt命令安装。 diff --git a/install/mindspore_d_install_en.md b/install/mindspore_d_install_en.md index 4eb1e3ae067791ac98787314d78af52cfc1999f6..827f23bb76e748d6304eabf3444d7974956bc0a1 100644 --- a/install/mindspore_d_install_en.md +++ b/install/mindspore_d_install_en.md @@ -32,7 +32,7 @@ This document describes how to quickly install MindSpore in an Ascend AI process | Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies | | ---- | :--- | :--- | :--- | -| MindSpore master | - Ubuntu 18.04 aarch64
- Ubuntu 18.04 x86_64
- CentOS 7.6 aarch64
- CentOS 7.6 x86_64
- EulerOS 2.8 aarch64
- EulerOS 2.5 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI processor software package(Version:[Atlas Data Center Solution V100R020C10T200](https://support.huawei.com/enterprise/zh/ascend-computing/atlas-data-center-solution-pid-251167910/software/251661816))
- [gmp](https://gmplib.org/download/gmp/) 6.1.2
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.6/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI processor software package(Version:[Atlas Data Center Solution V100R020C10T200](https://support.huawei.com/enterprise/zh/ascend-computing/atlas-data-center-solution-pid-251167910/software/251661816))
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
- [gmp](https://gmplib.org/download/gmp/) 6.1.2
**Installation dependencies:**
same as the executable file installation dependencies. | +| MindSpore master | - Ubuntu 18.04 aarch64
- Ubuntu 18.04 x86_64
- EulerOS 2.8 aarch64
- EulerOS 2.5 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI processor software package(Version:[Atlas Data Center Solution V100R020C10T200](https://support.huawei.com/enterprise/zh/ascend-computing/atlas-data-center-solution-pid-251167910/software/251661816))
- [gmp](https://gmplib.org/download/gmp/) 6.1.2
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.6/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI processor software package(Version:[Atlas Data Center Solution V100R020C10T200](https://support.huawei.com/enterprise/zh/ascend-computing/atlas-data-center-solution-pid-251167910/software/251661816))
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
- [gmp](https://gmplib.org/download/gmp/) 6.1.2
**Installation dependencies:**
same as the executable file installation dependencies. | - Confirm that the current user has the right to access the installation path `/usr/local/Ascend `of Ascend 910 AI processor software package(Version:[Atlas Data Center Solution V100R020C10T200](https://support.huawei.com/enterprise/zh/ascend-computing/atlas-data-center-solution-pid-251167910/software/251661816)). If not, the root user needs to add the current user to the user group where `/usr/local/Ascend` is located. For the specific configuration, please refer to the software package instruction document. - GCC 7.3.0 can be installed by using apt command. diff --git a/lite/tutorials/source_zh_cn/use/post_training_quantization.md b/lite/tutorials/source_zh_cn/use/post_training_quantization.md index c09d9b933b64fea4b51663d1a141ab3ba93aa2f2..93ba0889434710d8f6143a0469b970b8e1feb2d7 100644 --- a/lite/tutorials/source_zh_cn/use/post_training_quantization.md +++ b/lite/tutorials/source_zh_cn/use/post_training_quantization.md @@ -32,9 +32,9 @@ | 参数名 | 属性 | 功能描述 | 参数类型 | 默认值 | 取值范围 | | -------- | ------- | ----- | ----- | ----- | ----- | | image_path | 必选 | 存放校准数据集的目录 | String | - | 该目录存放可直接用于执行推理的输入数据。由于目前框架还不支持数据预处理，所有数据必须事先完成所需的转换，使得它们满足推理的输入要求。 | -| batch_count | 可选 | 使用的输入数目 | Integer | 1000 | 大于0 | +| batch_count | 可选 | 使用的输入数目 | Integer | 100 | 大于0 | | method_x | 可选 | 网络层输入输出数据量化算法 | String | KL | KL，MAX_MIN。 KL: 基于[KL散度](http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf)对数据范围作量化校准； MAX_MIN：基于最大值、最小值计算数据的量化参数。 在模型以及数据集比较较简单的情况下，推荐使用MAX_MIN | - +| thread_num | 可选 | 使用校准数据集执行推理流程时的线程数 | Integer | 1 | 大于0 | ## 使用示例 @@ -44,20 +44,20 @@ image_path=/dir/images batch_count=100 method_x=MAX_MIN + thread_num=1 ``` 校准数据集可以选择测试数据集的子集，要求`/dir/images`目录下存放的每个文件均是预处理好的输入数据，每个文件都可以直接用于推理的输入。 -3. 以TensorFlow Lite模型mnist.tflite为例，执行带训练后量化的模型转换命令: +3. 以MindSpore模型为例，执行带训练后量化的模型转换命令: ``` - ./converter_lite --fmk=TFLITE --modelFile=mnist.tflite --outputFile=mnist_quant --quantType=PostTraining --config_file=config.cfg + ./converter_lite --fmk=MS --modelFile=lenet.ms --outputFile=lenet_quant --quantType=PostTraining --config_file=config.cfg ``` -4. 上述命令执行成功后，便可得到量化后的模型mnist_quant.ms，通常量化后的模型大小会下降到FP32模型的1/4。 +4. 上述命令执行成功后，便可得到量化后的模型lenet_quant.ms，通常量化后的模型大小会下降到FP32模型的1/4。 ## 部分模型精度结果 | 模型 | 测试数据集 | method_x | FP32模型精度 | 训练后量化精度 | 说明 | | -------- | ------- | ----- | ----- | ----- | ----- | - | mnist.tflite | [MNIST](http://yann.lecun.com/exdb/mnist/) | MAX_MIN | 97.61% | 97.83% | 校准数据集选择MNIST Test数据集的前100张 | - | mobilenet_v1.tflite | [MNIST](http://yann.lecun.com/exdb/mnist/) | MAX_MIN | 98.36% | 98.40% | 校准数据集选择MNIST Test数据集的前100张 | - - + | [Inception_V3](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz) | [ImageNet](http://image-net.org/) | KL | 77.92% | 77.95% | 校准数据集随机选择ImageNet Validation数据集中的100张 | + | [Mobilenet_V1_1.0_224](https://torage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz) | [ImageNet](http://image-net.org/) | KL | 70.96% | 70.69% | 校准数据集随机选择ImageNet Validation数据集中的100张 | +> 以上所有结果均在x86环境上测得。 diff --git a/resource/faq/FAQ_en.md b/resource/faq/FAQ_en.md index 681d68614b8840f572c53221c748d1fe4bb01157..30335b436d65f797ba361674801d8a513125e8bc 100644 --- a/resource/faq/FAQ_en.md +++ b/resource/faq/FAQ_en.md @@ -16,6 +16,8 @@ - [Supported Features](#supported-features) +This document has been transferred to a [new location](https://www.mindspore.cn/docs/en/master/FAQ.html). This page will be offline later。 + ## Installation diff --git a/resource/faq/FAQ_zh_cn.md b/resource/faq/FAQ_zh_cn.md index 9873b933d666d18b00026515f9cfd14a228f23f5..1e06e6c30f88b04ec5a368a69fa5f30c3e748e1f 100644 --- a/resource/faq/FAQ_zh_cn.md +++ b/resource/faq/FAQ_zh_cn.md @@ -16,6 +16,7 @@ - [特性支持](#特性支持) +此文档已经转移到[新的位置](https://www.mindspore.cn/docs/zh-CN/master/FAQ.html)，此页面后续会下线。 ## 安装类 diff --git a/tutorials/notebook/synchronization_training_and_evaluation.ipynb b/tutorials/notebook/synchronization_training_and_evaluation.ipynb index 486fd4cf2f762b193054db536c9576b5bdc5512f..236ae433c882d620ead10a0247dc321bab8122d3 100644 --- a/tutorials/notebook/synchronization_training_and_evaluation.ipynb +++ b/tutorials/notebook/synchronization_training_and_evaluation.ipynb @@ -230,22 +230,22 @@ "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", "from mindspore.train.callback import Callback\n", "\n", "class EvalCallBack(Callback):\n", - " def __init__(self, model, eval_dataset, eval_per_epoch):\n", + " def __init__(self, model, eval_dataset, eval_per_epoch, epoch_per_eval):\n", " self.model = model\n", " self.eval_dataset = eval_dataset\n", " self.eval_per_epoch = eval_per_epoch\n", + " self.epoch_per_eval = epoch_per_eval\n", " \n", " def epoch_end(self, run_context):\n", " cb_param = run_context.original_args()\n", " cur_epoch = cb_param.cur_epoch_num\n", " if cur_epoch % self.eval_per_epoch == 0:\n", - " acc = self.model.eval(self.eval_dataset,dataset_sink_mode = True)\n", - " epoch_per_eval[\"epoch\"].append(cur_epoch)\n", - " epoch_per_eval[\"acc\"].append(acc[\"Accuracy\"])\n", + " acc = self.model.eval(self.eval_dataset, dataset_sink_mode=True)\n", + " self.epoch_per_eval[\"epoch\"].append(cur_epoch)\n", + " self.epoch_per_eval[\"acc\"].append(acc[\"Accuracy\"])\n", " print(acc)\n" ] }, @@ -351,7 +351,6 @@ } ], "source": [ - "from mindspore.train.serialization import load_checkpoint, load_param_into_net\n", "from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor\n", "from mindspore.train import Model\n", "from mindspore import context\n", @@ -368,21 +367,21 @@ " repeat_size = 1\n", " network = LeNet5()\n", " \n", - " train_data = create_dataset(train_data_path,repeat_size = repeat_size)\n", - " eval_data = create_dataset(eval_data_path,repeat_size = repeat_size)\n", + " train_data = create_dataset(train_data_path, repeat_size=repeat_size)\n", + " eval_data = create_dataset(eval_data_path, repeat_size=repeat_size)\n", " \n", " # define the loss function\n", " net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')\n", " # define the optimizer\n", " net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.01, momentum=0.9)\n", " config_ck = CheckpointConfig(save_checkpoint_steps=eval_per_epoch*1875, keep_checkpoint_max=15)\n", - " ckpoint_cb = ModelCheckpoint(prefix=\"checkpoint_lenet\",directory=ckpt_save_dir, config=config_ck)\n", + " ckpoint_cb = ModelCheckpoint(prefix=\"checkpoint_lenet\", directory=ckpt_save_dir, config=config_ck)\n", " model = Model(network, net_loss, net_opt, metrics={\"Accuracy\": Accuracy()})\n", " \n", - " epoch_per_eval = {\"epoch\":[],\"acc\":[]}\n", - " eval_cb = EvalCallBack(model,eval_data,eval_per_epoch)\n", + " epoch_per_eval = {\"epoch\": [], \"acc\": []}\n", + " eval_cb = EvalCallBack(model, eval_data, eval_per_epoch, epoch_per_eval)\n", " \n", - " model.train(epoch_size, train_data, callbacks=[ckpoint_cb, LossMonitor(375),eval_cb],\n", + " model.train(epoch_size, train_data, callbacks=[ckpoint_cb, LossMonitor(375), eval_cb],\n", " dataset_sink_mode=True)" ] }, @@ -441,11 +440,13 @@ } ], "source": [ + "import matplotlib.pyplot as plt\n", + "\n", "def eval_show(epoch_per_eval):\n", " plt.xlabel(\"epoch number\")\n", " plt.ylabel(\"Model accuracy\")\n", " plt.title(\"Model accuracy variation chart\")\n", - " plt.plot(epoch_per_eval[\"epoch\"],epoch_per_eval[\"acc\"],\"red\")\n", + " plt.plot(epoch_per_eval[\"epoch\"], epoch_per_eval[\"acc\"], \"red\")\n", " plt.show()\n", " \n", "eval_show(epoch_per_eval)" diff --git a/tutorials/source_en/advanced_use/customized_debugging_information.md b/tutorials/source_en/advanced_use/customized_debugging_information.md index 208991c20c35fb5c169882c72e99a59d1a5ae3c8..0e2b71e874e68fd5c967ba61584668a0af117958 100644 --- a/tutorials/source_en/advanced_use/customized_debugging_information.md +++ b/tutorials/source_en/advanced_use/customized_debugging_information.md @@ -31,7 +31,7 @@ For example, you can monitor the loss, save model parameters, dynamically adjust MindSpore provides the callback capabilities to allow users to insert customized operations in a specific phase of training or inference, including: -- Callback functions such as `ModelCheckpoint`, `LossMonitor`, and `SummaryStep` provided by the MindSpore framework +- Callback functions such as `ModelCheckpoint`, `LossMonitor`, and `SummaryCollector` provided by the MindSpore framework - Custom callback functions Usage: Transfer the callback object in the `model.train` method. The callback object can be a list, for example: @@ -39,13 +39,13 @@ Usage: Transfer the callback object in the `model.train` method. The callback ob ```python ckpt_cb = ModelCheckpoint() loss_cb = LossMonitor() -summary_cb = SummaryStep() +summary_cb = SummaryCollector(summary_dir='./summary_dir') model.train(epoch, dataset, callbacks=[ckpt_cb, loss_cb, summary_cb]) ``` `ModelCheckpoint` can save model parameters for retraining or inference. `LossMonitor` can output loss information in logs for users to view. In addition, `LossMonitor` monitors the loss value change during training. When the loss value is `Nan` or `Inf`, the training terminates. -SummaryStep can save the training information to a file for later use. +`SummaryCollector` can save the training information to files for later use. During the training process, the callback list will execute the callback function in the defined order. Therefore, in the definition process, the dependency between callbacks needs to be considered. ### Custom Callback diff --git a/tutorials/source_en/advanced_use/dashboard.md b/tutorials/source_en/advanced_use/dashboard.md index b41b8c70c90dc07af5b2d7e3b036b6b10fc1c5bb..6287ce87cd8322b7dddc18868112d280eb658662 100644 --- a/tutorials/source_en/advanced_use/dashboard.md +++ b/tutorials/source_en/advanced_use/dashboard.md @@ -1,6 +1,6 @@ # Dashboard -`Ascend` `GPU` `Model Optimization` `Intermediate` `Expert` +`Ascend` `GPU` `CPU` `Model Optimization` `Intermediate` `Expert` diff --git a/tutorials/source_en/advanced_use/images/data_chart.png b/tutorials/source_en/advanced_use/images/data_chart.png index 1c8d6995bc15ec28ecfca059b237a0d123dbde4f..f698c682119efc886b46a911d3c61f50ab017879 100644 Binary files a/tutorials/source_en/advanced_use/images/data_chart.png and b/tutorials/source_en/advanced_use/images/data_chart.png differ diff --git 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a/tutorials/source_en/advanced_use/images/tensor_table.png and b/tutorials/source_en/advanced_use/images/tensor_table.png differ diff --git a/tutorials/source_en/advanced_use/lineage_and_scalars_comparision.md b/tutorials/source_en/advanced_use/lineage_and_scalars_comparision.md index 1e79792b6cf3659c3c04e34f6e497cb6f3a9a175..c6a1274665f22082e9cf3ea220caf4389bc18bc9 100644 --- a/tutorials/source_en/advanced_use/lineage_and_scalars_comparision.md +++ b/tutorials/source_en/advanced_use/lineage_and_scalars_comparision.md @@ -1,6 +1,6 @@ # Lineage and Scalars Comparision -`Ascend` `GPU` `Model Optimization` `Intermediate` `Expert` +`Ascend` `GPU` `CPU` `Model Optimization` `Intermediate` `Expert` diff --git a/tutorials/source_en/advanced_use/mindinsight_commands.md b/tutorials/source_en/advanced_use/mindinsight_commands.md index 42b9af599466ce92bd3515c711f7eba24d8b92c0..c65d3d3aea20eb8ea5def821f1d4c8319126c0e3 100644 --- a/tutorials/source_en/advanced_use/mindinsight_commands.md +++ b/tutorials/source_en/advanced_use/mindinsight_commands.md @@ -1,6 +1,6 @@ # MindInsight Commands -`Ascend` `GPU` `Model Optimization` `Intermediate` `Expert` +`Ascend` `GPU` `CPU` `Model Optimization` `Intermediate` `Expert` diff --git a/tutorials/source_en/advanced_use/summary_record.md b/tutorials/source_en/advanced_use/summary_record.md index cebe6c393def9b71faaa6e6966a9f70ee1b202f0..6ce5581ee586f4fc37a60efc981ec5b2f202d2d6 100644 --- a/tutorials/source_en/advanced_use/summary_record.md +++ b/tutorials/source_en/advanced_use/summary_record.md @@ -1,6 +1,6 @@ # Summary Record -`Ascend` `GPU` `Model Optimization` `Intermediate` `Expert` +`Ascend` `GPU` `CPU` `Model Optimization` `Intermediate` `Expert` diff --git a/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md b/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md index d9708232b94726eb3f2af64d2e7abcecc63fac2f..b4d6da9a82d15848681dd01e67121b43be37afe6 100644 --- a/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md +++ b/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md @@ -33,7 +33,7 @@ Callback是回调函数的意思，但它其实不是一个函数而是一个类 MindSpore提供Callback能力，支持用户在训练/推理的特定阶段，插入自定义的操作。包括： -- MindSpore框架提供的`ModelCheckpoint`、`LossMonitor`、`SummaryStep`等Callback函数。 +- MindSpore框架提供的`ModelCheckpoint`、`LossMonitor`、`SummaryCollector`等Callback函数。 - MindSpore支持用户自定义Callback。 使用方法：在`model.train`方法中传入Callback对象，它可以是一个Callback列表，例： @@ -41,13 +41,13 @@ MindSpore提供Callback能力，支持用户在训练/推理的特定阶段， ```python ckpt_cb = ModelCheckpoint() loss_cb = LossMonitor() -summary_cb = SummaryStep() +summary_cb = SummaryCollector(summary_dir='./summary_dir') model.train(epoch, dataset, callbacks=[ckpt_cb, loss_cb, summary_cb]) ``` `ModelCheckpoint`可以保存模型参数，以便进行再训练或推理。 `LossMonitor`可以在日志中输出loss，方便用户查看，同时它还会监控训练过程中的loss值变化情况，当loss值为`Nan`或`Inf`时终止训练。 -SummaryStep可以把训练过程中的信息存储到文件中，以便后续进行查看或可视化展示。 +`SummaryCollector` 可以把训练过程中的信息存储到文件中，以便后续进行查看或可视化展示。 在训练过程中，Callback列表会按照定义的顺序执行Callback函数。因此在定义过程中，需考虑Callback之间的依赖关系。 ### 自定义Callback diff --git a/tutorials/source_zh_cn/advanced_use/dashboard.md b/tutorials/source_zh_cn/advanced_use/dashboard.md index 9f79c106488bff9e67850f62307803e3d84f09b0..817e994f35e0c3afe1e4f05bd34a0eee9254b8f2 100644 --- a/tutorials/source_zh_cn/advanced_use/dashboard.md +++ b/tutorials/source_zh_cn/advanced_use/dashboard.md @@ -1,6 +1,6 @@ # 训练看板 -`Ascend` `GPU` `模型调优` `中级` `高级` +`Ascend` `GPU` `CPU` `模型调优` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/images/data_chart.png b/tutorials/source_zh_cn/advanced_use/images/data_chart.png index 1c8d6995bc15ec28ecfca059b237a0d123dbde4f..f698c682119efc886b46a911d3c61f50ab017879 100644 Binary files a/tutorials/source_zh_cn/advanced_use/images/data_chart.png and b/tutorials/source_zh_cn/advanced_use/images/data_chart.png differ diff --git a/tutorials/source_zh_cn/advanced_use/images/data_function.png 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a/tutorials/source_zh_cn/advanced_use/images/tensor_table.png and b/tutorials/source_zh_cn/advanced_use/images/tensor_table.png differ diff --git a/tutorials/source_zh_cn/advanced_use/lineage_and_scalars_comparision.md b/tutorials/source_zh_cn/advanced_use/lineage_and_scalars_comparision.md index d768f30ee4134dc0d73b03304b2bf855965020a7..fae22dbbcf27c6e5ce5b12b62eb4c74aaeee0cba 100644 --- a/tutorials/source_zh_cn/advanced_use/lineage_and_scalars_comparision.md +++ b/tutorials/source_zh_cn/advanced_use/lineage_and_scalars_comparision.md @@ -1,6 +1,6 @@ # 溯源和对比看板 -`Ascend` `GPU` `模型调优` `中级` `高级` +`Ascend` `GPU` `CPU` `模型调优` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/mindinsight_commands.md b/tutorials/source_zh_cn/advanced_use/mindinsight_commands.md index 12905b823e717b992144dff97a4c1e9b0b15cb0b..4f685599e0c553b7fe47e3359cac8333d8da0e7b 100644 --- a/tutorials/source_zh_cn/advanced_use/mindinsight_commands.md +++ b/tutorials/source_zh_cn/advanced_use/mindinsight_commands.md @@ -1,6 +1,6 @@ # MindInsight相关命令 -`Ascend` `GPU` `模型调优` `中级` `高级` +`Ascend` `GPU` `CPU` `模型调优` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/summary_record.md b/tutorials/source_zh_cn/advanced_use/summary_record.md index a1fc1bbcae338e7353b5ca6b43455992feeeffae..e76ff8e730246d65547f08a1bec986b74c4371fe 100644 --- a/tutorials/source_zh_cn/advanced_use/summary_record.md +++ b/tutorials/source_zh_cn/advanced_use/summary_record.md @@ -1,6 +1,6 @@ # Summary数据收集 -`Ascend` `GPU` `模型调优` `中级` `高级` +`Ascend` `GPU` `CPU` `模型调优` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/synchronization_training_and_evaluation.md b/tutorials/source_zh_cn/advanced_use/synchronization_training_and_evaluation.md index 6e6932f1894f5a6caa018e5a7684e738a323f294..431766969810941854ff7754669cf2ccb457eea7 100644 --- a/tutorials/source_zh_cn/advanced_use/synchronization_training_and_evaluation.md +++ b/tutorials/source_zh_cn/advanced_use/synchronization_training_and_evaluation.md @@ -1,4 +1,4 @@ -# 同步训练和验证模型 +# 同步训练和验证模型 @@ -41,25 +41,25 @@ - `model`：即是MindSpore中的`Model`函数。 - `eval_dataset`：验证数据集。 -- `epoch_per_eval`：记录验证模型的精度和相应的epoch数，其数据形式为`{"epoch":[],"acc":[]}`。 +- `epoch_per_eval`：记录验证模型的精度和相应的epoch数，其数据形式为`{"epoch": [], "acc": []}`。 ```python -import matplotlib.pyplot as plt from mindspore.train.callback import Callback class EvalCallBack(Callback): - def __init__(self, model, eval_dataset, eval_per_epoch): + def __init__(self, model, eval_dataset, eval_per_epoch, epoch_per_eval): self.model = model self.eval_dataset = eval_dataset self.eval_per_epoch = eval_per_epoch + self.epoch_per_eval = epoch_per_eval def epoch_end(self, run_context): cb_param = run_context.original_args() cur_epoch = cb_param.cur_epoch_num if cur_epoch % self.eval_per_epoch == 0: - acc = self.model.eval(self.eval_dataset,dataset_sink_mode = True) - epoch_per_eval["epoch"].append(cur_epoch) - epoch_per_eval["acc"].append(acc["Accuracy"]) + acc = self.model.eval(self.eval_dataset, dataset_sink_mode=True) + self.epoch_per_eval["epoch"].append(cur_epoch) + self.epoch_per_eval["acc"].append(acc["Accuracy"]) print(acc) ``` @@ -79,12 +79,10 @@ class EvalCallBack(Callback): - `epoch_per_eval`：定义收集`epoch`数和对应模型精度信息的字典。 ```python -from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor from mindspore.train import Model from mindspore import context from mindspore.nn.metrics import Accuracy -from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits if __name__ == "__main__": context.set_context(mode=context.GRAPH_MODE, device_target="GPU") @@ -98,10 +96,10 @@ if __name__ == "__main__": ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",directory=ckpt_save_dir, config=config_ck) model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) - epoch_per_eval = {"epoch":[],"acc":[]} - eval_cb = EvalCallBack(model,eval_data,eval_per_epoch) + epoch_per_eval = {"epoch": [], "acc": []} + eval_cb = EvalCallBack(model, eval_data, eval_per_epoch, epoch_per_eval) - model.train(epoch_size, train_data, callbacks=[ckpoint_cb, LossMonitor(375),eval_cb], + model.train(epoch_size, train_data, callbacks=[ckpoint_cb, LossMonitor(375), eval_cb], dataset_sink_mode=True) ``` @@ -152,11 +150,13 @@ lenet_ckpt ```python +import matplotlib.pyplot as plt + def eval_show(epoch_per_eval): plt.xlabel("epoch number") plt.ylabel("Model accuracy") plt.title("Model accuracy variation chart") - plt.plot(epoch_per_eval["epoch"],epoch_per_eval["acc"],"red") + plt.plot(epoch_per_eval["epoch"], epoch_per_eval["acc"], "red") plt.show() eval_show(epoch_per_eval)