Update Getting started section of the Python API docs

contrib/Python/doc/gettingstarted.rst

@@ -32,15 +32,16 @@ Installing the Python module
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #. Go to ``<cntkpath>/contrib/Python`` and run ``python setup.py install``
 #. Set up the environment variable ``CNTK_EXECUTABLE_PATH`` to point to the
-   CNTK executable
+   CNTK executable. Make sure the executable is also included
 #. Enjoy Python's ease of use with CNTK's speed::
 
-    >>> import cntk as cn
-    >>> cn.__version__
+    >>> import cntk as C
+    >>> C.__version__
     1.4
-    >>> with cn.Context('demo', clean_up=False) as ctx:
-    ...     a = cn.constant([[1,2], [3,4]])
-    ...     print(ctx.eval(a + [[10,20], [30, 40]]))
+    >>> with C.LocalExecutionContext('demo', clean_up=False) as ctx:
+    ...     a = C.constant([[1,2], [3,4]])
+    ...     i = C.input_numpy([[[10,20], [30, 40]]])
+    ...     print(ctx.eval(a + i))
     [[11.0, 22.0], [33.0, 44.0]]
 
 In this case, we have set ``clean_up=False`` so that you can now peek into the
@@ -71,7 +72,6 @@ explained::
     import cntk as C
     import numpy as np
 
-    def simple_network():
     # 500 samples, 250-dimensional data
     N = 500
     d = 250
@@ -82,16 +82,16 @@ explained::
     Y = np.hstack((Y, 1-Y))
 
     # set up the training data for CNTK
-        x = C.input_numpy(X, has_dynamic_axis=False)
-        y = C.input_numpy(Y, has_dynamic_axis=False)
+    x = C.input_numpy(X)
+    y = C.input_numpy(Y)
 
     # define our network parameters: a weight tensor and a bias
-        W = C.parameter((2, d))
-        b = C.parameter((2, 1))
+    W = C.parameter((d, 2))
+    b = C.parameter((1, 2))
 
     # create a dense 'layer' by multiplying the weight tensor and  
     # the features and adding the bias
-        out = C.times(W, x) + b
+    out = C.times(x, W) + b
 
     # setup the criterion node using cross entropy with softmax
     ce = C.cross_entropy_with_softmax(y, out, name='loss')
@@ -222,7 +222,7 @@ We can define this network as follows in the CNTK Python API::
     cell_dim = 128
 
     t = C.dynamic_axis(name='t')
-        # temporarily using cntk1 SpareInput because cntk2's input() will simply allow sparse as a parameter
+    # temporarily using cntk1 SparseInput because cntk2's input() will simply allow sparse as a parameter
     features = cntk1.SparseInput(vocab, dynamicAxis=t, name='features')
     labels = C.input(num_labels, name='labels')
 
@@ -251,6 +251,7 @@ We can define this network as follows in the CNTK Python API::
     ce = C.cross_entropy_with_softmax(labels, pred)
     ce.tag = "criterion"
 
+
 Let's go through some of the intricacies of the above network definition. First, we define 
 some parameters of the data and the network. We have 5 possible classes for the sequences; 
 we're working with a vocabulary of 2000 words; and our embedding vectors have a dimension of 
@@ -282,7 +283,7 @@ the criterion node that adds a softmax and then implements the cross entropy los
 we add the criterion node, however, we call :func:`cntk.ops.reconcile_dynamic_axis` which will ensure 
 that the minibatch layout for the labels and the data with dynamic axes is compatible.
 
-For the full explanation of how ``lstm_layer()`` is defined, please see the full example in the 
+For the full explanation of how ``lstm_layer()`` is defined, please see the full example (`seqcla.py <https://github.com/Microsoft/CNTK/blob/master/contrib/Python/cntk/examples/LSTM/seqcla.py>`_) in the 
 Examples section.
 
 How to pass Python data as train/test data