This commit is contained in:
Mark Hillebrand 2017-04-19 15:24:08 +02:00
Родитель f99e9a4fce
Коммит a1e10ff9e7
32 изменённых файлов: 53 добавлений и 53 удалений

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@ -26,7 +26,7 @@
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</PropertyGroup>

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@ -99,12 +99,12 @@
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@ -1,4 +1,4 @@
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@ -99,12 +99,12 @@
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@ -1,4 +1,4 @@
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@ -39,8 +39,8 @@
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@ -58,12 +58,12 @@
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@ -1,4 +1,4 @@
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@ -39,8 +39,8 @@
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@ -62,12 +62,12 @@
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@ -1,4 +1,4 @@
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@ -108,12 +108,12 @@
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@ -1,4 +1,4 @@
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@ -106,12 +106,12 @@
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@ -1,4 +1,4 @@
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@ -49,8 +49,8 @@
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@ -88,11 +88,11 @@
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@ -1,4 +1,4 @@
<?xml version="1.0" encoding="utf-8"?>
<packages>
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@ -4,7 +4,7 @@ This example demonstrates how to use sampled softmax for training a token based
The model predicts the next word in a text given the previous ones where the probability of the next word is computed using a softmax.
As the number of different words might be very high this final softmax step can turn out to be costly.
Sampled-softmax is a technique to reduce this cost at training time. For details see also the [sampled softmax tutorial](https://github.com/Microsoft/CNTK/blob/v2.0.rc1/Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb)
Sampled-softmax is a technique to reduce this cost at training time. For details see also the [sampled softmax tutorial](https://github.com/Microsoft/CNTK/blob/v2.0.rc2/Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb)
Note the provided data set has only 10.000 distinct words. This number is still not very high and sampled softmax doesn't show any significant perf improvements here.
The real perf gains will show up with larger vocabularies.

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@ -274,7 +274,7 @@ ORIGINDIR:='$$ORIGIN'
# Components VERSION info
########################################
CNTK_COMPONENT_VERSION := 2.0rc1
CNTK_COMPONENT_VERSION := 2.0rc2
ifeq ("$(BUILDTYPE)","debug")
CNTK_COMPONENT_VERSION := $(CNTK_COMPONENT_VERSION)d
endif

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@ -614,7 +614,7 @@ 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 2.0rc1+ (");
fprintf(stderr, "CNTK 2.0rc2+ (");
#ifdef _GIT_EXIST
fprintf(stderr, "%s %.6s, ", _BUILDBRANCH_, _BUILDSHA1_);
#endif

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@ -25,7 +25,7 @@
<!-- Explicitly defines the version of the dependent assemblies to avoid build errors or stale binaries. -->
<!-- The change of CntkComponentVersion in CNTK.Common.props cannot be populated correctly without restarting VS and cleaning solution. -->
<PropertyGroup>
<CntkCsAssemblyVersion>2.0rc1</CntkCsAssemblyVersion>
<CntkCsAssemblyVersion>2.0rc2</CntkCsAssemblyVersion>
<CntkCsAssemblyVersion Condition="$(DebugBuild)">$(CntkCsAssemblyVersion)d</CntkCsAssemblyVersion>
</PropertyGroup>
<PropertyGroup Condition="$(DebugBuild)">

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@ -25,7 +25,7 @@
<!-- Explicitly defines the version of the dependent assemblies to avoid build errors or stale binaries -->
<!-- The change of CntkComponentVersion in CNTK.Common.props cannot be populated correctly without restarting VS and cleaning solution. -->
<PropertyGroup>
<CntkCsAssemblyVersion>2.0rc1</CntkCsAssemblyVersion>
<CntkCsAssemblyVersion>2.0rc2</CntkCsAssemblyVersion>
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<PropertyGroup Condition="'$(Configuration)|$(Platform)' == 'Debug|x64'">

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@ -30,7 +30,7 @@
<!-- Explicitly defines the version of the dependent assemblies to avoid build errors or stale binaries -->
<!-- The change of CntkComponentVersion in CNTK.Common.props cannot be populated correctly without restarting VS and cleaning solution. -->
<PropertyGroup>
<CntkCsAssemblyVersion>2.0rc1</CntkCsAssemblyVersion>
<CntkCsAssemblyVersion>2.0rc2</CntkCsAssemblyVersion>
<CntkCsAssemblyVersion Condition="$(DebugBuild)">$(CntkCsAssemblyVersion)d</CntkCsAssemblyVersion>
</PropertyGroup>
<PropertyGroup Condition="'$(Configuration)|$(Platform)' == 'Debug|x64'">

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@ -21,7 +21,7 @@
"\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.rc1/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.rc2/Tutorials/HelloWorld-LogisticRegression). \n",
"\n",
"## Introduction\n",
"\n",

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@ -905,7 +905,7 @@
"\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.rc1/Tutorials/NumpyInterop/FeedForwardNet.py"
"[FeedForwardNet.py]: https://github.com/Microsoft/CNTK/blob/v2.0.rc2/Tutorials/NumpyInterop/FeedForwardNet.py"
]
}
],

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@ -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.rc1/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.rc2/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"

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@ -23,7 +23,7 @@
"\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.rc1/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.rc2/Examples/Image/GettingStarted)\n",
"\n",
"## Introduction\n",
"\n",
@ -821,7 +821,7 @@
"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.rc1/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.rc2/Examples/Image/Classification/MLP/Python) example."
]
}
],

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@ -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.rc1/Tutorials/SLUHandsOn/atis.train.ctf) \n",
"and [test](https://github.com/Microsoft/CNTK/blob/v2.0.rc1/Tutorials/SLUHandsOn/atis.test.ctf) \n",
"Download the ATIS [training](https://github.com/Microsoft/CNTK/blob/v2.0.rc2/Tutorials/SLUHandsOn/atis.train.ctf) \n",
"and [test](https://github.com/Microsoft/CNTK/blob/v2.0.rc2/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.rc1/Examples/LanguageUnderstanding/ATIS/BrainScript/query.wl) and\n",
"[slots](https://github.com/Microsoft/CNTK/blob/v2.0.rc1/Examples/LanguageUnderstanding/ATIS/BrainScript/slots.wl)"
"[queries](https://github.com/Microsoft/CNTK/blob/v2.0.rc2/Examples/LanguageUnderstanding/ATIS/BrainScript/query.wl) and\n",
"[slots](https://github.com/Microsoft/CNTK/blob/v2.0.rc2/Examples/LanguageUnderstanding/ATIS/BrainScript/slots.wl)"
]
},
{
@ -99,7 +99,7 @@
" print(\"Reusing locally cached:\", item['file'])\n",
" else:\n",
" print(\"Starting download:\", item['file'])\n",
" url = \"https://github.com/Microsoft/CNTK/blob/v2.0.rc1/%s/%s?raw=true\"%(location, item['file'])\n",
" url = \"https://github.com/Microsoft/CNTK/blob/v2.0.rc2/%s/%s?raw=true\"%(location, item['file'])\n",
" download(url, item['file'])\n",
" print(\"Download completed\")\n"
]
@ -782,7 +782,7 @@
"> 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.rc1/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.rc2/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",

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@ -274,7 +274,7 @@
" if os.path.exists(file):\n",
" print(\"Reusing locally cached: \", file)\n",
" else:\n",
" url = \"https://github.com/Microsoft/CNTK/blob/v2.0.rc1/Examples/SequenceToSequence/CMUDict/Data/%s?raw=true\"%file\n",
" url = \"https://github.com/Microsoft/CNTK/blob/v2.0.rc2/Examples/SequenceToSequence/CMUDict/Data/%s?raw=true\"%file\n",
" print(\"Starting download:\", file)\n",
" download(url, file)\n",
" print(\"Download completed\")\n"

Просмотреть файл

@ -246,7 +246,7 @@
" if os.path.exists(file):\n",
" print(\"Reusing locally cached: \", file)\n",
" else:\n",
" url = \"https://github.com/Microsoft/CNTK/blob/v2.0.rc1/Examples/SequenceToSequence/CMUDict/Data/%s?raw=true\"%file\n",
" url = \"https://github.com/Microsoft/CNTK/blob/v2.0.rc2/Examples/SequenceToSequence/CMUDict/Data/%s?raw=true\"%file\n",
" print(\"Starting download:\", file)\n",
" download(url, file)\n",
" print(\"Download completed\")\n"

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@ -20,7 +20,7 @@ call "%VCDIRECTORY%\..\..\VC\vcvarsall.bat" amd64
set MSSdk=1
set DISTUTILS_USE_SDK=1
set CNTK_COMPONENT_VERSION=2.0rc1
set CNTK_COMPONENT_VERSION=2.0rc2
python .\setup.py build_ext --inplace --force --compiler msvc
if errorlevel 1 exit /b 1

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@ -3,7 +3,7 @@
# for full license information.
# ==============================================================================
__version__ = '2.0rc1+'
__version__ = '2.0rc2+'
import numpy as np

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@ -7,7 +7,7 @@ you can start using CNTK from Python right away (don't forget to ``activate`` yo
>>> import cntk
>>> cntk.__version__
'2.0rc1+'
'2.0rc2+'
>>> cntk.minus([1, 2, 3], [4, 5, 6]).eval()
array([-3., -3., -3.], dtype=float32)

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@ -175,7 +175,7 @@ if IS_PY2:
cntk_install_requires.append('enum34>=1.1.6')
setup(name="cntk",
version="2.0rc1",
version="2.0rc2",
url="http://cntk.ai",
ext_modules=[cntk_module],
packages=packages,