modified gettingstarted.rst

contrib/Python/doc/gettingstarted.rst

@@ -284,3 +284,34 @@ that the minibatch layout for the labels and the data with dynamic axes is compa
 For the full explanation of how ``lstm_layer()`` is defined, please see the full example in the 
 Examples section.
 
+How to pass Python data as train/test data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The Python CNTK API allows to pass training / testing data either by specifing external input files or by using Python data directly to CNTK.
+This second alternative - using internal Python data - is usefull especially if you want to do some quick experimentation with small synthetic data sets.
+In what follows you will learn in what structure these data has to be provided.
+
+Let us start with a scenario coming from one of our code examples (`logreg_numpy.py <https://github.com/Microsoft/CNTK/tree/master/contrib/Python/cntk/examples/LogReg/logreg_numpy.py>`_).
+In this example we want to classify a 250 dimensional feature vector into one of two classes. In this case whe have two *inputs*:
+ - The features values for each training item. In the example these are 500 vectors each of dimension 250. 
+ - The expected class. In this example the class is encoded with a two-dimensonal vector where the element for expected class is set to 1 and the other to 0.
+
+For each of these inputs we have to provide one data structure containing all training instances. 
+
+You might notice that this is conceptually different to the case where we provide the data from external files using the CNTKTextReader. 
+In the input file for CNTKTextReader we provide data for different *inputs* of one instance on the same line, so the data from different inputs are much more interwined.
+
+In Python the feature data are reprensented by a NumPy array of dimension ``number_of_instances X dimension_of_feature_space`` so in out example its a NumPy array of dimension ``500 X 250``.
+Likewise the expected output is reprensented by another NumPy array of dimension ``500 X 2``.
+
+Passing sequence data from Python
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+CNTK can handle sequences with arbitrary maximal length. This feature is also called *dynamic-axis*.
+To represent an input with a dynamic-axis in Python you have to provide each sequence as a NumPy-array where the first axis has a dimension equal to the sequence length.
+The complete dataset is then just a normal one-dimensional numpy array of these sequences.
+
+Take as an artifical example a sentence classification problem. Each sentence has a different number of words, i.e. it is a *sequence* of words. The individual words might each be represented by some lantent vector.
+So each sentence is represented by a NumPy array of dimension ``sequence_length X embedding_dimension``. The whole set of instances (sentences) is then represented by putting them into a one-dimensional array with the size equal to the number of instances.
+
+