Integrate mahilleb/PostReleaseMerges into master
This commit is contained in:
Project Philly
Коммит
2e508f95d7
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@ -40,7 +40,7 @@
|
|||
</PropertyGroup>
|
||||
<ItemGroup>
|
||||
<Reference Include="CNTKLibraryManaged-2.0, Version=1.0.0.0, Culture=neutral, processorArchitecture=AMD64">
|
||||
<HintPath>..\packages\CNTK.CPUOnly.2.0-beta9\lib\net45\x64\CNTKLibraryManaged-2.0.dll</HintPath>
|
||||
<HintPath>..\packages\CNTK.CPUOnly.2.0-beta10\lib\net45\x64\CNTKLibraryManaged-2.0.dll</HintPath>
|
||||
<Private>True</Private>
|
||||
</Reference>
|
||||
<Reference Include="System" />
|
||||
|
|
@ -58,12 +58,12 @@
|
|||
<None Include="packages.config" />
|
||||
</ItemGroup>
|
||||
<Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />
|
||||
<Import Project="..\packages\CNTK.CPUOnly.2.0-beta9\build\net45\CNTK.CPUOnly.targets" Condition="Exists('..\packages\CNTK.CPUOnly.2.0-beta9\build\net45\CNTK.CPUOnly.targets')" />
|
||||
<Import Project="..\packages\CNTK.CPUOnly.2.0-beta10\build\net45\CNTK.CPUOnly.targets" Condition="Exists('..\packages\CNTK.CPUOnly.2.0-beta10\build\net45\CNTK.CPUOnly.targets')" />
|
||||
<Target Name="EnsureNuGetPackageBuildImports" BeforeTargets="PrepareForBuild">
|
||||
<PropertyGroup>
|
||||
<ErrorText>This project references NuGet package(s) that are missing on this computer. Use NuGet Package Restore to download them. For more information, see http://go.microsoft.com/fwlink/?LinkID=322105. The missing file is {0}.</ErrorText>
|
||||
</PropertyGroup>
|
||||
<Error Condition="!Exists('..\packages\CNTK.CPUOnly.2.0-beta9\build\net45\CNTK.CPUOnly.targets')" Text="$([System.String]::Format('$(ErrorText)', '..\packages\CNTK.CPUOnly.2.0-beta9\build\net45\CNTK.CPUOnly.targets'))" />
|
||||
<Error Condition="!Exists('..\packages\CNTK.CPUOnly.2.0-beta10\build\net45\CNTK.CPUOnly.targets')" Text="$([System.String]::Format('$(ErrorText)', '..\packages\CNTK.CPUOnly.2.0-beta10\build\net45\CNTK.CPUOnly.targets'))" />
|
||||
</Target>
|
||||
<!-- To modify your build process, add your task inside one of the targets below and uncomment it.
|
||||
Other similar extension points exist, see Microsoft.Common.targets.
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<packages>
|
||||
<package id="CNTK.CPUOnly" version="2.0-beta9" targetFramework="net45" />
|
||||
<package id="CNTK.CPUOnly" version="2.0-beta10" targetFramework="net45" />
|
||||
</packages>
|
||||
|
|
|
|||
|
|
@ -40,7 +40,7 @@
|
|||
</PropertyGroup>
|
||||
<ItemGroup>
|
||||
<Reference Include="CNTKLibraryManaged-2.0, Version=1.0.0.0, Culture=neutral, processorArchitecture=AMD64">
|
||||
<HintPath>..\packages\CNTK.GPU.2.0-beta9\lib\net45\x64\CNTKLibraryManaged-2.0.dll</HintPath>
|
||||
<HintPath>..\packages\CNTK.GPU.2.0-beta10\lib\net45\x64\CNTKLibraryManaged-2.0.dll</HintPath>
|
||||
<Private>True</Private>
|
||||
</Reference>
|
||||
<Reference Include="System" />
|
||||
|
|
@ -62,12 +62,12 @@
|
|||
<None Include="packages.config" />
|
||||
</ItemGroup>
|
||||
<Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />
|
||||
<Import Project="..\packages\CNTK.GPU.2.0-beta9\build\net45\CNTK.GPU.targets" Condition="Exists('..\packages\CNTK.GPU.2.0-beta9\build\net45\CNTK.GPU.targets')" />
|
||||
<Import Project="..\packages\CNTK.GPU.2.0-beta10\build\net45\CNTK.GPU.targets" Condition="Exists('..\packages\CNTK.GPU.2.0-beta10\build\net45\CNTK.GPU.targets')" />
|
||||
<Target Name="EnsureNuGetPackageBuildImports" BeforeTargets="PrepareForBuild">
|
||||
<PropertyGroup>
|
||||
<ErrorText>This project references NuGet package(s) that are missing on this computer. Use NuGet Package Restore to download them. For more information, see http://go.microsoft.com/fwlink/?LinkID=322105. The missing file is {0}.</ErrorText>
|
||||
</PropertyGroup>
|
||||
<Error Condition="!Exists('..\packages\CNTK.GPU.2.0-beta9\build\net45\CNTK.GPU.targets')" Text="$([System.String]::Format('$(ErrorText)', '..\packages\CNTK.GPU.2.0-beta9\build\net45\CNTK.GPU.targets'))" />
|
||||
<Error Condition="!Exists('..\packages\CNTK.GPU.2.0-beta10\build\net45\CNTK.GPU.targets')" Text="$([System.String]::Format('$(ErrorText)', '..\packages\CNTK.GPU.2.0-beta10\build\net45\CNTK.GPU.targets'))" />
|
||||
</Target>
|
||||
<!-- To modify your build process, add your task inside one of the targets below and uncomment it.
|
||||
Other similar extension points exist, see Microsoft.Common.targets.
|
||||
|
|
|
|||
|
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@ -1,4 +1,4 @@
|
|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<packages>
|
||||
<package id="CNTK.GPU" version="2.0-beta9" targetFramework="net45" />
|
||||
<package id="CNTK.GPU" version="2.0-beta10" targetFramework="net45" />
|
||||
</packages>
|
||||
|
|
|
|||
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@ -50,7 +50,7 @@
|
|||
</PropertyGroup>
|
||||
<ItemGroup>
|
||||
<Reference Include="EvalWrapper, Version=0.0.0.0, Culture=neutral, PublicKeyToken=52681d72504348ec, processorArchitecture=AMD64">
|
||||
<HintPath>..\packages\Microsoft.Research.CNTK.CpuEval-mkl.2.0-beta9\lib\net45\x64\EvalWrapper.dll</HintPath>
|
||||
<HintPath>..\packages\Microsoft.Research.CNTK.CpuEval-mkl.2.0-beta10\lib\net45\x64\EvalWrapper.dll</HintPath>
|
||||
<Private>True</Private>
|
||||
</Reference>
|
||||
<Reference Include="System" />
|
||||
|
|
@ -88,11 +88,11 @@
|
|||
</BootstrapperPackage>
|
||||
</ItemGroup>
|
||||
<Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />
|
||||
<Import Project="..\packages\Microsoft.Research.CNTK.CpuEval-mkl.2.0-beta9\build\net45\Microsoft.Research.CNTK.CpuEval-mkl.targets" Condition="Exists('..\packages\Microsoft.Research.CNTK.CpuEval-mkl.2.0-beta9\build\net45\Microsoft.Research.CNTK.CpuEval-mkl.targets')" />
|
||||
<Import Project="..\packages\Microsoft.Research.CNTK.CpuEval-mkl.2.0-beta10\build\net45\Microsoft.Research.CNTK.CpuEval-mkl.targets" Condition="Exists('..\packages\Microsoft.Research.CNTK.CpuEval-mkl.2.0-beta10\build\net45\Microsoft.Research.CNTK.CpuEval-mkl.targets')" />
|
||||
<Target Name="EnsureNuGetPackageBuildImports" BeforeTargets="PrepareForBuild">
|
||||
<PropertyGroup>
|
||||
<ErrorText>This project references NuGet package(s) that are missing on this computer. Use NuGet Package Restore to download them. For more information, see http://go.microsoft.com/fwlink/?LinkID=322105. The missing file is {0}.</ErrorText>
|
||||
</PropertyGroup>
|
||||
<Error Condition="!Exists('..\packages\Microsoft.Research.CNTK.CpuEval-mkl.2.0-beta9\build\net45\Microsoft.Research.CNTK.CpuEval-mkl.targets')" Text="$([System.String]::Format('$(ErrorText)', '..\packages\Microsoft.Research.CNTK.CpuEval-mkl.2.0-beta9\build\net45\Microsoft.Research.CNTK.CpuEval-mkl.targets'))" />
|
||||
<Error Condition="!Exists('..\packages\Microsoft.Research.CNTK.CpuEval-mkl.2.0-beta10\build\net45\Microsoft.Research.CNTK.CpuEval-mkl.targets')" Text="$([System.String]::Format('$(ErrorText)', '..\packages\Microsoft.Research.CNTK.CpuEval-mkl.2.0-beta10\build\net45\Microsoft.Research.CNTK.CpuEval-mkl.targets'))" />
|
||||
</Target>
|
||||
</Project>
|
||||
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|
|||
|
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@ -1,4 +1,4 @@
|
|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<packages>
|
||||
<package id="Microsoft.Research.CNTK.CpuEval-mkl" version="2.0-beta9" targetFramework="net45" />
|
||||
<package id="Microsoft.Research.CNTK.CpuEval-mkl" version="2.0-beta10" targetFramework="net45" />
|
||||
</packages>
|
||||
|
|
|
|||
13
README.md
13
README.md
|
|
@ -46,19 +46,6 @@ Get the Release from the [CNTK Releases page](https://github.com/Microsoft/CNTK/
|
|||
CNTK V 2.0 Beta 8 Runtime packages are now available as [Public Images at Docker Hub](https://hub.docker.com/r/microsoft/cntk/).
|
||||
See more on CNTK as Docker Images in this [Wiki article](https://github.com/Microsoft/CNTK/wiki/CNTK-Docker-Containers).
|
||||
|
||||
***2017-01-16.* V 2.0 Beta 8 Release**
|
||||
Highlights of this Release:
|
||||
* Support of Python v. 2.7, 3.4, and 3.5. See [binary and source setup](https://github.com/Microsoft/CNTK/wiki/Setup-CNTK-on-your-machine) instructions to find out about how to select Python version.
|
||||
* New Python API features.
|
||||
* New Python example [Feature extraction using a trained model in Python API](https://github.com/Microsoft/CNTK/tree/v2.0.beta8.0/Examples/Image/FeatureExtraction).
|
||||
* Support of [Visual Studio 2015](https://github.com/Microsoft/CNTK/wiki/Setup-Migrate-VS13-to-VS15) for Windows version.
|
||||
* Introduction of [C# API in CNTK Evaluation Library](https://github.com/Microsoft/CNTK/wiki/CNTK-Library-Managed-API) and a new set of [CNTK NuGet Packages](https://github.com/Microsoft/CNTK/wiki/NuGet-Package).
|
||||
* CNTK Runtime packages are now available as [Public Images at Docker Hub](https://github.com/Microsoft/CNTK/wiki/CNTK-Docker-Containers). (**Beta 7** is currently available; Beta 8 Images availability will be announced separately in a few days)
|
||||
* Version 3 of [CNTK Custom MKL Library](https://cntk.ai/mkl/) is available.
|
||||
|
||||
See more in the [Release Notes](https://github.com/Microsoft/CNTK/wiki/CNTK_2_0_beta_8_Release_Notes).
|
||||
Get the Release from the [CNTK Releases page](https://github.com/Microsoft/CNTK/releases).
|
||||
|
||||
See [all news](https://github.com/Microsoft/CNTK/wiki/News).
|
||||
|
||||
# What is The Microsoft Cognitive Toolkit
|
||||
|
|
|
|||
|
|
@ -49,7 +49,7 @@ CNTK_EXAMPLES_PATH="$PWD/Examples"
|
|||
CNTK_TUTORIALS_PATH="$PWD/Tutorials"
|
||||
CNTK_BINARY="$CNTK_BIN_PATH/cntk"
|
||||
CNTK_PY_ENV_FILE="$SCRIPT_DIR/conda-linux-cntk-py$PY_VERSION-environment.yml"
|
||||
CNTK_WHEEL_PATH="cntk/python/cntk-2.0.beta9.0-$PYWHEEL_QUALIFIER-linux_x86_64.whl"
|
||||
CNTK_WHEEL_PATH="cntk/python/cntk-2.0.beta10.0-$PYWHEEL_QUALIFIER-linux_x86_64.whl"
|
||||
|
||||
test -d "$CNTK_BIN_PATH" && test -d "$CNTK_LIB_PATH" && test -d "$CNTK_DEP_LIB_PATH" &&
|
||||
test -d "$CNTK_TUTORIALS_PATH" &&
|
||||
|
|
|
|||
|
|
@ -666,7 +666,7 @@ int wmainWithBS(int argc, wchar_t* argv[]) // called from wmain which is a wrapp
|
|||
|
||||
static void PrintBanner(int argc, wchar_t* argv[], const string& timestamp)
|
||||
{
|
||||
fprintf(stderr, "CNTK 2.0.beta9.0+ (");
|
||||
fprintf(stderr, "CNTK 2.0.beta10.0+ (");
|
||||
#ifdef _GIT_EXIST
|
||||
fprintf(stderr, "%s %.6s, ", _BUILDBRANCH_, _BUILDSHA1_);
|
||||
#endif
|
||||
|
|
|
|||
|
|
@ -144,7 +144,7 @@ opencvVersion="3.1.0"
|
|||
opencvPath="/usr/local/opencv-$opencvVersion/lib"
|
||||
libzipPath="/usr/local/lib"
|
||||
cudaPath="/usr/local/cuda/lib64"
|
||||
cudnnPath="/usr/local/cuda/lib64"
|
||||
cudnnPath="/usr/local/cudnn/cuda/lib64"
|
||||
openblasPath="/usr/local/openblas/lib"
|
||||
kaldiVersion="c024e8aa"
|
||||
kaldiPath="/usr/local/kaldi-$kaldiVersion/src/lib"
|
||||
|
|
|
|||
|
|
@ -130,7 +130,7 @@
|
|||
{
|
||||
"category": ["Image"],
|
||||
"name": "Generative Adversarial Networks (GAN)",
|
||||
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_205_Basic_GAN.ipynb",
|
||||
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_206_Basic_GAN.ipynb",
|
||||
"description": "This tutorial is a basic implementation of GAN networks. This allows us generate realistic looking MNIST images.",
|
||||
"language": ["Python"],
|
||||
"type": ["Tutorial", "Recipe"]
|
||||
|
|
@ -239,6 +239,14 @@
|
|||
"language": ["Python"],
|
||||
"type": ["Tutorial", "Recipe"]
|
||||
},
|
||||
{
|
||||
"category": ["Numeric"],
|
||||
"name": "Training with Sampled Softmax",
|
||||
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb",
|
||||
"description": "Training with Sampled Softmax",
|
||||
"language": ["Python"],
|
||||
"type": ["Tutorial", "Recipe"]
|
||||
},
|
||||
{
|
||||
"category": ["Speech"],
|
||||
"name": "AN4 Speech DNN",
|
||||
|
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|
|||
|
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@ -10,7 +10,7 @@
|
|||
"\n",
|
||||
"This tutorial is targeted to individuals who are new to CNTK and to machine learning. In this tutorial, you will train a simple yet powerful machine learning model that is widely used in industry for a variety of applications. The model trained below scales to massive data sets in the most expeditious manner by harnessing computational scalability leveraging the computational resources you may have (one or more CPU cores, one or more GPUs, a cluster of CPUs or a cluster of GPUs), transparently via the CNTK library.\n",
|
||||
"\n",
|
||||
"The following notebook users Python APIs. If you are looking for this example in BrainScript, please look [here](https://github.com/Microsoft/CNTK/tree/v2.0.beta9.0/Tutorials/HelloWorld-LogisticRegression). \n",
|
||||
"The following notebook users Python APIs. If you are looking for this example in BrainScript, please look [here](https://github.com/Microsoft/CNTK/tree/v2.0.beta10.0/Tutorials/HelloWorld-LogisticRegression). \n",
|
||||
"\n",
|
||||
"## Introduction\n",
|
||||
"\n",
|
||||
|
|
|
|||
|
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@ -792,7 +792,7 @@
|
|||
"\n",
|
||||
"If you want to try running the tutorial from python command prompt. Please run the [FeedForwardNet.py][] example.\n",
|
||||
"\n",
|
||||
"[FeedForwardNet.py]: https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Tutorials/NumpyInterop/FeedForwardNet.py"
|
||||
"[FeedForwardNet.py]: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/NumpyInterop/FeedForwardNet.py"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@
|
|||
"\n",
|
||||
"CNTK 103 tutorial is divided into two parts:\n",
|
||||
"- Part A: Familiarize with the [MNIST][] database that will be used later in the tutorial\n",
|
||||
"- [Part B](https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb): We will use the feedforward classifier used in CNTK 102 to classify digits in MNIST data set.\n",
|
||||
"- [Part B](https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb): We will use the feedforward classifier used in CNTK 102 to classify digits in MNIST data set.\n",
|
||||
"\n",
|
||||
"[MNIST]: http://yann.lecun.com/exdb/mnist/\n",
|
||||
"\n"
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@
|
|||
"\n",
|
||||
"We assume that you have successfully completed CNTK 103 Part A.\n",
|
||||
"\n",
|
||||
"In this tutorial we will train a fully connected network on MNIST data. This notebook provides the recipe using Python APIs. If you are looking for this example in BrainScript, please look [here](https://github.com/Microsoft/CNTK/tree/v2.0.beta9.0/Examples/Image/GettingStarted)\n",
|
||||
"In this tutorial we will train a fully connected network on MNIST data. This notebook provides the recipe using Python APIs. If you are looking for this example in BrainScript, please look [here](https://github.com/Microsoft/CNTK/tree/v2.0.beta10.0/Examples/Image/GettingStarted)\n",
|
||||
"\n",
|
||||
"## Introduction\n",
|
||||
"\n",
|
||||
|
|
@ -667,7 +667,7 @@
|
|||
"source": [
|
||||
"#### Code link\n",
|
||||
"\n",
|
||||
"If you want to try running the tutorial from Python command prompt please run the [SimpleMNIST.py](https://github.com/Microsoft/CNTK/tree/v2.0.beta9.0/Examples/Image/Classification/MLP/Python) example."
|
||||
"If you want to try running the tutorial from Python command prompt please run the [SimpleMNIST.py](https://github.com/Microsoft/CNTK/tree/v2.0.beta10.0/Examples/Image/Classification/MLP/Python) example."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -41,12 +41,12 @@
|
|||
"In this tutorial we are going to use a (lightly preprocessed) version of the ATIS dataset. You can download the data automatically by running the cells below or by executing the manual instructions.\n",
|
||||
"\n",
|
||||
"#### Fallback manual instructions\n",
|
||||
"Download the ATIS [training](https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Tutorials/SLUHandsOn/atis.train.ctf) \n",
|
||||
"and [test](https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Tutorials/SLUHandsOn/atis.test.ctf) \n",
|
||||
"Download the ATIS [training](https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/SLUHandsOn/atis.train.ctf) \n",
|
||||
"and [test](https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/SLUHandsOn/atis.test.ctf) \n",
|
||||
"files and put them at the same folder as this notebook. If you want to see how the model is \n",
|
||||
"predicting on new sentences you will also need the vocabulary files for \n",
|
||||
"[queries](https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Examples/LanguageUnderstanding/ATIS/BrainScript/query.wl) and\n",
|
||||
"[slots](https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Examples/LanguageUnderstanding/ATIS/BrainScript/slots.wl)"
|
||||
"[queries](https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Examples/LanguageUnderstanding/ATIS/BrainScript/query.wl) and\n",
|
||||
"[slots](https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Examples/LanguageUnderstanding/ATIS/BrainScript/slots.wl)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -88,7 +88,7 @@
|
|||
" print(\"Reusing locally cached:\", item['file'])\n",
|
||||
" else:\n",
|
||||
" print(\"Starting download:\", item['file'])\n",
|
||||
" url = \"https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/%s/%s?raw=true\"%(location, item['file'])\n",
|
||||
" url = \"https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/%s/%s?raw=true\"%(location, item['file'])\n",
|
||||
" download(url, item['file'])\n",
|
||||
" print(\"Download completed\")\n"
|
||||
]
|
||||
|
|
@ -640,7 +640,7 @@
|
|||
"> Note: training with Batch Normalization is currently only supported on GPU.\n",
|
||||
"\n",
|
||||
"So your task will be to insert batch-normalization layers before and after the recurrent LSTM layer.\n",
|
||||
"If you have completed the [hands-on labs on image processing](https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb),\n",
|
||||
"If you have completed the [hands-on labs on image processing](https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb),\n",
|
||||
"you may remember that the [batch-normalization layer](https://www.cntk.ai/pythondocs/layerref.html#batchnormalization-layernormalization-stabilizer) has this form:\n",
|
||||
"```\n",
|
||||
" BatchNormalization()\n",
|
||||
|
|
|
|||
|
|
@ -167,7 +167,7 @@
|
|||
" if os.path.exists(file):\n",
|
||||
" print(\"Reusing locally cached: \", file)\n",
|
||||
" else:\n",
|
||||
" url = \"https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Examples/SequenceToSequence/CMUDict/Data/%s?raw=true\"%file\n",
|
||||
" url = \"https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Examples/SequenceToSequence/CMUDict/Data/%s?raw=true\"%file\n",
|
||||
" print(\"Starting download:\", file)\n",
|
||||
" download(url, file)\n",
|
||||
" print(\"Download completed\")\n"
|
||||
|
|
|
|||
|
|
@ -55,7 +55,7 @@ master_doc = 'index'
|
|||
|
||||
# General information about the project.
|
||||
project = 'Python API for CNTK'
|
||||
copyright = '2016, Microsoft'
|
||||
copyright = '2017, Microsoft'
|
||||
author = 'Microsoft'
|
||||
|
||||
# The version info for the project you're documenting, acts as replacement for
|
||||
|
|
@ -63,9 +63,9 @@ author = 'Microsoft'
|
|||
# built documents.
|
||||
#
|
||||
# The short X.Y version.
|
||||
version = '2.0.beta9.0'
|
||||
version = '2.0.beta10.0'
|
||||
# The full version, including alpha/beta/rc tags.
|
||||
release = '2.0.beta9.0'
|
||||
release = '2.0.beta10.0'
|
||||
|
||||
# The language for content autogenerated by Sphinx. Refer to documentation
|
||||
# for a list of supported languages.
|
||||
|
|
|
|||
|
|
@ -4,30 +4,30 @@ Examples
|
|||
The best way to learn about the APIs is to look at the
|
||||
following examples in the [CNTK clone root]/Examples directory:
|
||||
|
||||
- `MNIST <https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Examples/Image/Classification/MLP/Python/SimpleMNIST.py>`__:
|
||||
- `MNIST <https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Examples/Image/Classification/MLP/Python/SimpleMNIST.py>`__:
|
||||
A fully connected feed-forward model for classification of MNIST
|
||||
images. (follow the instructions in
|
||||
Examples/Image/DataSets/MNIST/README.md)
|
||||
|
||||
- `TrainResNet_CIFAR10 <https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Examples/Image/Classification/ResNet/Python/TrainResNet_CIFAR10.py>`__:
|
||||
- `TrainResNet_CIFAR10 <https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Examples/Image/Classification/ResNet/Python/TrainResNet_CIFAR10.py>`__:
|
||||
An image classification ResNet model for training on the CIFAR image
|
||||
dataset. (follow the instructions in
|
||||
Examples/Image/DataSets/CIFAR-10/README.md to get the CIFAR dataset
|
||||
and convert it to the CNTK supported format)
|
||||
|
||||
- `SequenceClassification <https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Examples/SequenceClassification/SimpleExample/Python/SequenceClassification.py>`__:
|
||||
- `SequenceClassification <https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Examples/SequenceClassification/SimpleExample/Python/SequenceClassification.py>`__:
|
||||
An LSTM sequence classification model for text data.
|
||||
|
||||
- `Sequence2Sequence <https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Examples/SequenceToSequence/CMUDict/Python/Sequence2Sequence.py>`__:
|
||||
- `Sequence2Sequence <https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Examples/SequenceToSequence/CMUDict/Python/Sequence2Sequence.py>`__:
|
||||
A sequence to sequence grapheme to phoneme translation model that
|
||||
trains on the CMUDict corpus.
|
||||
|
||||
- `NumpyInterop <https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Tutorials/NumpyInterop/FeedForwardNet.py>`__
|
||||
- `NumpyInterop <https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/NumpyInterop/FeedForwardNet.py>`__
|
||||
- NumPy interoperability example showing how to train a simple feed-forward
|
||||
network with training data fed using NumPy arrays.
|
||||
|
||||
- `LanguageUnderstanding <https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Examples/LanguageUnderstanding/ATIS/Python/LanguageUnderstanding.py>`__
|
||||
- `LanguageUnderstanding <https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Examples/LanguageUnderstanding/ATIS/Python/LanguageUnderstanding.py>`__
|
||||
- Language Understanding.
|
||||
|
||||
- `Video <https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Examples/Video/GettingStarted/Python/Conv3D_UCF11.py>`__
|
||||
- `Video <https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Examples/Video/GettingStarted/Python/Conv3D_UCF11.py>`__
|
||||
- Basic 3D convolution networks for deep learning on video tasks.
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
Getting started
|
||||
===============
|
||||
You can optionally try the `tutorials <http://notebooks.azure.com/library/cntkbeta2>`__ with pre-installed CNTK running in Azure hosted environment if you have not insalled the toolkit in your own machine.
|
||||
You can optionally try the `tutorials <http://notebooks.azure.com/library/cntkbeta2>`__ with pre-installed CNTK running in Azure hosted environment if you have not installed the toolkit in your own machine.
|
||||
|
||||
If you have installed CNTK on your machine, after going through the `installation steps <https://github.com/Microsoft/CNTK/wiki/CNTK-Binary-Download-and-Configuration>`__,
|
||||
you can start using CNTK from Python right away (don't forget to ``activate`` your Python environment):
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
.. some aliases
|
||||
.. _CNTK: http://cntk.ai/
|
||||
|
||||
Python API for CNTK (2.0.beta9.0)
|
||||
Python API for CNTK (2.0.beta10.0)
|
||||
=================================
|
||||
|
||||
CNTK_, the Microsoft Cognitive Toolkit, is a system for describing, training,
|
||||
|
|
@ -10,7 +10,7 @@ and executing computational networks. It is also a framework for describing
|
|||
arbitrary learning machines such as deep neural networks (DNNs). CNTK is an
|
||||
implementation of computational networks that supports both CPU and GPU.
|
||||
|
||||
This page describes the Python API for CNTK_ version 2.0.beta9.0. This is an ongoing effort
|
||||
This page describes the Python API for CNTK_ version 2.0.beta10.0. This is an ongoing effort
|
||||
to expose such an API to the CNTK system, thus enabling the use of higher-level
|
||||
tools such as IDEs to facilitate the definition of computational networks, to execute
|
||||
them on sample data in real time. Please give feedback through these `channels`_.
|
||||
|
|
|
|||
|
|
@ -119,7 +119,7 @@ seems to be successful in practice is the Long Short Term Memory (LSTM) network.
|
|||
LSTMs are a type of RNN that are exceedingly useful and in practice are what we commonly
|
||||
use when implementing an RNN. A good explanation of the merits of LSTMs is at
|
||||
http://colah.github.io/posts/2015-08-Understanding-LSTMs. An LSTM is a
|
||||
a differentiable function that takes an input and a state and produces an output
|
||||
differentiable function that takes an input and a state and produces an output
|
||||
and a new state.
|
||||
|
||||
In our example, we will be using an LSTM to do sequence classification. But for even
|
||||
|
|
@ -219,6 +219,6 @@ This list denotes a minibatch of 1 and **minibatches
|
|||
are specified as lists**. The reason for this is because different elements of
|
||||
the minibatch can have different lengths. If all the elements in the
|
||||
minibatch are sequences of the same length then it is acceptable to provide
|
||||
the minibatch as one big tensor of dimnesion :math:`b \times s \times d_1 \times \ldots \times d_k`
|
||||
the minibatch as one big tensor of dimension :math:`b \times s \times d_1 \times \ldots \times d_k`
|
||||
where `b` is the batch size, `s` is the sequence length and :math:`d_i`
|
||||
is the dimension of the i-th static axis of the input variable.
|
||||
|
|
|
|||
|
|
@ -1,56 +1,61 @@
|
|||
Tutorials
|
||||
===============
|
||||
|
||||
#. *Classify cancer using simulated data (Logistic Regression)*
|
||||
CNTK 101: `Logistic Regression`_ with NumPy
|
||||
|
||||
#. *Classify cancer using simulated data (Logistic Regression)*
|
||||
CNTK 101: `Logistic Regression`_ with NumPy
|
||||
|
||||
#. *Classify cancer using simulated data (Feed Forward)*
|
||||
CNTK 102: `Feed Forward network`_ with NumPy
|
||||
|
||||
|
||||
#. *Recognize hand written digits (OCR) with MNIST data*
|
||||
CNTK 103 Part A: `Data preparation <https://github.com/Microsoft/CNTK/tree/v2.0.beta9.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb>`_ , Part B: `Feed Forward classifier`_
|
||||
|
||||
CNTK 103 Part A: `Data preparation <https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb>`_ , Part B: `Feed Forward classifier`_
|
||||
|
||||
#. *Learn how to predict the stock market*
|
||||
CNTK 104: `Time Series basics`_ with finance data
|
||||
|
||||
#. *Compress (using autoencoder) hand written digits from MNIST data with no human input (unsupervised learning)*
|
||||
CNTK 105 Part A: `Data preparation <https://github.com/Microsoft/CNTK/tree/v2.0.beta9.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb>`_ , Part B: `Feed Forward autoencoder`_
|
||||
|
||||
#. *Forecasting using data from an IOT device*
|
||||
CNTK 106: LSTM based forecasting - Part A: `with simulated data <https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Tutorials/CNTK_106A_LSTM_Timeseries_with_Simulated_Data.ipynb>`_
|
||||
#. *Compress (using autoencoder) hand written digits from MNIST data with no human input (unsupervised learning)*
|
||||
CNTK 105 Part A: `Data preparation <https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb>`_ , Part B: `Feed Forward autoencoder`_
|
||||
|
||||
#. *Forecasting using data from an IOT device*
|
||||
CNTK 106: LSTM based forecasting - Part A: `with simulated data <https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_106A_LSTM_Timeseries_with_Simulated_Data.ipynb>`_
|
||||
|
||||
#. *Recognize objects in images from CIFAR-10 data*
|
||||
CNTK 201 Part A: `Data preparation <https://github.com/Microsoft/CNTK/tree/v2.0.beta9.0/Tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb>`_, Part B: `VGG and ResNet classifiers`_
|
||||
|
||||
CNTK 201 Part A: `Data preparation <https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb>`_, Part B: `VGG and ResNet classifiers`_
|
||||
|
||||
#. *Infer meaning from text snippets using LSTMs and word embeddings*
|
||||
CNTK 202: `Language understanding`_ with ATIS3 text data
|
||||
|
||||
|
||||
#. *Train a computer to perform tasks optimally (e.g., win games) in a simulated environment*
|
||||
CNTK 203: `Reinforcement learning basics`_ with OpenAI Gym data
|
||||
|
||||
#. *Translate text from one domain (grapheme) to other (phoneme)*
|
||||
CNTK 204: `Sequence to sequence basics`_ with CMU pronouncing dictionary
|
||||
|
||||
|
||||
#. *Teach a computer to paint like Piccasso or van Gogh*
|
||||
CNTK 205: `Artistic Style Transfer`_
|
||||
|
||||
|
||||
#. *Produce realistic data (MNIST images) with no human input (unsupervised learning)*
|
||||
CNTK 206 Part A: `Data preparation <https://github.com/Microsoft/CNTK/tree/v2.0.beta9.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb>`_ , Part B: `Basic Generative Adversarial Networks (GAN)`_
|
||||
CNTK 206 Part A: `Data preparation <https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb>`_ , Part B: `Basic Generative Adversarial Networks (GAN)`_
|
||||
|
||||
#. *Training with Sampled Softmax*
|
||||
CNTK 207: `Training with Sampled Softmax`_
|
||||
|
||||
For our Japanese users, you can find some of the `tutorials in Japanese`_.
|
||||
|
||||
.. _`Logistic Regression`: https://github.com/Microsoft/CNTK/tree/v2.0.beta9.0/Tutorials/CNTK_101_LogisticRegression.ipynb
|
||||
.. _`Feed Forward network`: https://github.com/Microsoft/CNTK/tree/v2.0.beta9.0/Tutorials/CNTK_102_FeedForward.ipynb
|
||||
.. _`Data preparation`: https://github.com/Microsoft/CNTK/tree/v2.0.beta9.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb
|
||||
.. _`Feed Forward classifier`: https://github.com/Microsoft/CNTK/tree/v2.0.beta9.0/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
|
||||
.. _`Time Series basics`: https://github.com/Microsoft/CNTK/tree/v2.0.beta9.0/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
|
||||
.. _`Feed Forward autoencoder`: https://github.com/Microsoft/CNTK/tree/v2.0.beta9.0/Tutorials/CNTK_105_Basic_Autoencoder_for_Dimensionality_Reduction.ipynb
|
||||
.. _`Basic LSTM based time series`: https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Tutorials/CNTK_106A_LSTM_Timeseries_with_Simulated_Data.ipynb
|
||||
.. _`data preparation`: https://github.com/Microsoft/CNTK/tree/v2.0.beta9.0/Tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb
|
||||
.. _`VGG and ResNet classifiers`: https://github.com/Microsoft/CNTK/tree/v2.0.beta9.0/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb
|
||||
.. _`Language understanding`: https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Tutorials/CNTK_202_Language_Understanding.ipynb
|
||||
.. _`Reinforcement learning basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Tutorials/CNTK_203_Reinforcement_Learning_Basics.ipynb
|
||||
.. _`Sequence to sequence basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Tutorials/CNTK_204_Sequence_To_Sequence.ipynb
|
||||
.. _`Artistic Style Transfer`: https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Tutorials/CNTK_205_Artistic_Style_Transfer.ipynb
|
||||
.. _`Basic Generative Adversarial Networks (GAN)`: https://github.com/Microsoft/CNTK/blob/v2.0.beta9.0/Tutorials/CNTK_206_Basic_GAN.ipynb
|
||||
.. _`Logistic Regression`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_101_LogisticRegression.ipynb
|
||||
.. _`Feed Forward network`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_102_FeedForward.ipynb
|
||||
.. _`Data preparation`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb
|
||||
.. _`Feed Forward classifier`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
|
||||
.. _`Time Series basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
|
||||
.. _`Feed Forward autoencoder`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_105_Basic_Autoencoder_for_Dimensionality_Reduction.ipynb
|
||||
.. _`Basic LSTM based time series`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_106A_LSTM_Timeseries_with_Simulated_Data.ipynb
|
||||
.. _`data preparation`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb
|
||||
.. _`VGG and ResNet classifiers`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb
|
||||
.. _`Language understanding`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_202_Language_Understanding.ipynb
|
||||
.. _`Reinforcement learning basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_203_Reinforcement_Learning_Basics.ipynb
|
||||
.. _`Sequence to sequence basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_204_Sequence_To_Sequence.ipynb
|
||||
.. _`Artistic Style Transfer`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_205_Artistic_Style_Transfer.ipynb
|
||||
.. _`Basic Generative Adversarial Networks (GAN)`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_206_Basic_GAN.ipynb
|
||||
.. _`Training with Sampled Softmax`: https://github.com/Microsoft/CNTK/blob/v2.0.beta10.0/Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb
|
||||
|
||||
.. _`tutorials in Japanese`: https://notebooks.azure.com/library/cntkbeta2_ja
|
||||
|
|
|
|||
|
|
@ -161,7 +161,7 @@ else:
|
|||
kwargs = dict(package_data = package_data)
|
||||
|
||||
setup(name="cntk",
|
||||
version="2.0.beta9.0",
|
||||
version="2.0.beta10.0",
|
||||
url="http://cntk.ai",
|
||||
ext_modules=[cntk_module],
|
||||
packages=packages,
|
||||
|
|
|
|||
Загрузка…
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