Bump version

Scripts/install/linux/install-cntk.sh

@@ -22,7 +22,7 @@ CNTK_EXAMPLES_PATH="$PWD/Examples"
 CNTK_TUTORIALS_PATH="$PWD/Tutorials"
 CNTK_BINARY="$CNTK_BIN_PATH/cntk"
 CNTK_PY34_ENV_FILE="$SCRIPT_DIR/conda-linux-cntk-py34-environment.yml"
-CNTK_WHEEL_PATH="cntk/python/cntk-2.0.beta3.0-cp34-cp34m-linux_x86_64.whl"
+CNTK_WHEEL_PATH="cntk/python/cntk-2.0.beta4.0-cp34-cp34m-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" &&
 test -d "$CNTK_EXAMPLES_PATH" && test -x "$CNTK_BINARY" &&

Source/CNTK/CNTK.cpp

@@ -645,7 +645,7 @@ 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.beta3.0+ (");
+    fprintf(stderr, "CNTK 2.0.beta4.0+ (");
 #ifdef _GIT_EXIST
     fprintf(stderr, "%s %.6s, ", _BUILDBRANCH_, _BUILDSHA1_);
 #endif

Tutorials/CNTK_101_LogisticRegression.ipynb

@@ -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.beta3.0/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.beta4.0/Tutorials/HelloWorld-LogisticRegression). \n",
     "\n",
     "## Introduction\n",
     "\n",

Tutorials/CNTK_102_FeedForward.ipynb

@@ -768,7 +768,7 @@
     "\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.beta3.0/Tutorials/NumpyInterop/FeedForwardNet.py"
+    "[FeedForwardNet.py]: https://github.com/Microsoft/CNTK/blob/v2.0.beta4.0/Tutorials/NumpyInterop/FeedForwardNet.py"
    ]
   },
   {

Tutorials/CNTK_103A_MNIST_DataLoader.ipynb

@@ -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.beta3.0/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.beta4.0/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"

Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb

@@ -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.beta3.0/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.beta4.0/Examples/Image/GettingStarted)\n",
     "\n",
     "## Introduction\n",
     "\n",
@@ -763,7 +763,7 @@
    "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.beta3.0/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.beta4.0/Examples/Image/Classification/MLP/Python) example."
    ]
   },
   {

bindings/python/doc/conf.py

@@ -63,9 +63,9 @@ author = 'Microsoft'
 # built documents.
 #
 # The short X.Y version.
-version = '2.0.beta3.0'
+version = '2.0.beta4.0'
 # The full version, including alpha/beta/rc tags.
-release = '2.0.beta3.0'
+release = '2.0.beta4.0'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

bindings/python/doc/examples.rst

@@ -1,31 +1,30 @@
 Examples
 ========
 
-The best way to learn about the APIs currently is to look at the
-following examples in the [CNTK clone root]/bindings/python/examples
-directory:
+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.beta3.0/bindings/python/examples/MNIST/SimpleMNIST.py>`__:
+-  `MNIST <https://github.com/Microsoft/CNTK/blob/v2.0.beta4.0/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)
 
--  `CifarResNet <https://github.com/Microsoft/CNTK/blob/v2.0.beta3.0/bindings/python/examples/CifarResNet/CifarResNet.py>`__:
+-  `CifarResNet <https://github.com/Microsoft/CNTK/blob/v2.0.beta4.0/Examples/Image/Classification/ResNet/Python/CifarResNet.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.beta3.0/bindings/python/examples/SequenceClassification/SequenceClassification.py>`__:
+-  `SequenceClassification <https://github.com/Microsoft/CNTK/blob/v2.0.beta4.0/Examples/SequenceClassification/SimpleExample/Python/SequenceClassification.py>`__:
    An LSTM sequence classification model for text data.
 
--  `Sequence2Sequence <https://github.com/Microsoft/CNTK/blob/v2.0.beta3.0/bindings/python/examples/Sequence2Sequence/Sequence2Sequence.py>`__:
+-  `Sequence2Sequence <https://github.com/Microsoft/CNTK/blob/v2.0.beta4.0/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.beta3.0/bindings/python/examples/NumpyInterop/FeedForwardNet.py>`__
+-  `NumpyInterop <https://github.com/Microsoft/CNTK/blob/v2.0.beta4.0/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.beta3.0/bindings/python/examples/LanguageUnderstanding/LanguageUnderstanding.py>`__
+-  `LanguageUnderstanding <https://github.com/Microsoft/CNTK/blob/v2.0.beta4.0/Examples/LanguageUnderstanding/ATIS/Python/LanguageUnderstanding.py>`__
    - Language Understanding.

bindings/python/doc/gettingstarted.rst

@@ -60,8 +60,8 @@ can be vastly improved. To explicitly set the device to GPU, set the target devi
     set_default_device(gpu(0))
 
 Now let's setup a network that will learn a classifier based on the example fully connected classifier network 
-(``examples.common.nn.fully_connected_classifier_net``). This is defined, along with several other simple and more complex DNN building blocks in 
-``bindings/python/examples/common/nn.py``. Go to the ``[CNTK root]/bindings/python`` directory and create a ``simplenet.py`` file with the 
+(``nn.fully_connected_classifier_net``). This is defined, along with several other simple and more complex DNN building blocks in 
+``Examples/common/nn.py``. Go to the ``[CNTK root]/Examples/common/`` directory and create a ``simplenet.py`` file with the 
 following contents::
 
 	import numpy as np
@@ -69,7 +69,7 @@ following contents::
 	import cntk.ops as C
 	from cntk.learner import sgd
 	from cntk.utils import get_train_loss
-	from examples.common.nn import fully_connected_classifier_net
+	from nn import fully_connected_classifier_net
 	from cntk.utils import ProgressPrinter
 
 	def generate_random_data(sample_size, feature_dim, num_classes):
@@ -155,7 +155,7 @@ as easy as that!
 
 Now that we've seen some of the basics of setting up and training a network using the CNTK Python API, let's look at a more interesting deep 
 learning problem in more detail (for the full example above along with the function to generate random data, please see 
-``bindings/python/examples/NumpyInterop/FeedForwardNet.py``).
+``Tutorials/NumpyInterop/FeedForwardNet.py``).
 
 
 Sequence classification
@@ -227,7 +227,7 @@ sequence classification. We can think of the network as adding a series of layer
 2. LSTM layer (allow each word to depend on previous words)
 3. Softmax layer (an additional set of parameters and output probabilities per class)
 
-This network is defined as part of the example at ``bindings/python/examples/SequenceClassification/SequenceClassification.py``. Let's go through some 
+This network is defined as part of the example at ``Examples/SequenceClassification/SimpleExample/Python/SequenceClassification.py``. Let's go through some 
 key parts of the code::
 
     # model

bindings/python/doc/index.rst

@@ -2,7 +2,7 @@
 .. some aliases
 .. _CNTK: http://cntk.ai/
 
-Python API for CNTK (2.0.beta3.0)
+Python API for CNTK (2.0.beta4.0)
 ===============================
 
 CNTK_, the Microsoft Cognitive Toolkit, is a system for describing, training,
@@ -12,7 +12,7 @@ neural networks (CNNs), recurrent neural networks (RNNs), long short term
 memory (LSTM), logistic regression, and maximum entropy model. CNTK is an 
 implementation of computational networks that supports both CPU and GPU.
  
-This page describes the Python API for CNTK_ version 2.0.beta3.0. This is an ongoing effort
+This page describes the Python API for CNTK_ version 2.0.beta4.0. 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.

bindings/python/doc/tutorials.rst

@@ -17,12 +17,11 @@ Tutorials
 
 #.  CNTK 203: `Reinforcement learning basics`_ with OpenAI Gym data
 	
-.. _`Logistic Regression`: https://github.com/Microsoft/CNTK/tree/v2.0.beta3.0/bindings/python/tutorials/CNTK_101_LogisticRegression.ipynb
-.. _`Feed Forward Network`: https://github.com/Microsoft/CNTK/tree/v2.0.beta3.0/bindings/python/tutorials/CNTK_102_FeedForward.ipynb
-.. _`MNIST data preparation`: https://github.com/Microsoft/CNTK/tree/v2.0.beta3.0/bindings/python/tutorials/CNTK_103A_MNIST_DataLoader.ipynb
-.. _`Feed Forward Classifier`: https://github.com/Microsoft/CNTK/tree/v2.0.beta3.0/bindings/python/tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
-.. _`CIFAR-10 Data preparation`: https://github.com/Microsoft/CNTK/tree/v2.0.beta3.0/bindings/python/tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb
-.. _`VGG and ResNet classifiers`: https://github.com/Microsoft/CNTK/tree/v2.0.beta3.0/bindings/python/tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb
-.. _`Language understanding`: https://github.com/Microsoft/CNTK/blob/v2.0.beta3.0/bindings/python/tutorials/CNTK_202_Language_Understanding.ipynb
-.. _`Reinforcement learning basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta3.0/bindings/python/tutorials/CNTK_203_Reinforcement_Learning_Basics.ipynb
-  
+.. _`Logistic Regression`: https://github.com/Microsoft/CNTK/tree/2.0.beta4.0/Tutorials/CNTK_101_LogisticRegression.ipynb
+.. _`Feed Forward Network`: https://github.com/Microsoft/CNTK/tree/2.0.beta4.0/Tutorials/CNTK_102_FeedForward.ipynb
+.. _`MNIST data preparation`: https://github.com/Microsoft/CNTK/tree/2.0.beta4.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb
+.. _`Feed Forward Classifier`: https://github.com/Microsoft/CNTK/tree/2.0.beta4.0/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
+.. _`CIFAR-10 Data preparation`: https://github.com/Microsoft/CNTK/tree/2.0.beta4.0/Tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb
+.. _`VGG and ResNet classifiers`: https://github.com/Microsoft/CNTK/tree/2.0.beta4.0/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb
+.. _`Language understanding`: https://github.com/Microsoft/CNTK/tree/2.0.beta4.0/Tutorials/CNTK_202_Language_Understanding.ipynb
+.. _`Reinforcement learning basics`: https://github.com/Microsoft/CNTK/tree/2.0.beta4.0/Tutorials/CNTK_203_Reinforcement_Learning_Basics.ipynb

bindings/python/setup.py

@@ -169,7 +169,7 @@ else:
     kwargs = dict(package_data = package_data)
 
 setup(name="cntk",
-      version="2.0.beta3.0",
+      version="2.0.beta4.0",
       url="http://cntk.ai",
       ext_modules=[cntk_module],
       packages=packages,