update basic op + update comments

basic_operations.py

@@ -1,22 +1,73 @@
+'''
+Basic Operations example using TensorFlow library.
+
+Author: Aymeric Damien
+Project: https://github.com/aymericdamien/TensorFlow-Examples/
+'''
+
 import tensorflow as tf
 
-#With constants
+# Basic constant operations
+# The value returned by the constructor represents the output
+# of the Constant op.
 a = tf.constant(2)
 b = tf.constant(3)
 
+# Launch the default graph.
 with tf.Session() as sess:
     print "a=2, b=3"
     print "Addition with constants: %i" % sess.run(a+b)
     print "Multiplication with constants: %i" % sess.run(a*b)
 
-
-#With variables
+# Basic Operations with variable as graph input
+# The value returned by the constructor represents the output
+# of the Variable op. (define as input when running session)
+# tf Graph input
 a = tf.placeholder(tf.types.int16)
 b = tf.placeholder(tf.types.int16)
 
+# Define some operations
 add = tf.add(a, b)
 mul = tf.mul(a, b)
 
+# Launch the default graph.
 with tf.Session() as sess:
+    # Run every operation with variable input
     print "Addition with variables: %i" % sess.run(add, feed_dict={a: 2, b: 3})
     print "Multiplication with variables: %i" % sess.run(mul, feed_dict={a: 2, b: 3})
+
+
+# ----------------
+# More in details:
+# Matrix Multiplication from TensorFlow official tutorial
+
+# Create a Constant op that produces a 1x2 matrix.  The op is
+# added as a node to the default graph.
+#
+# The value returned by the constructor represents the output
+# of the Constant op.
+matrix1 = tf.constant([[3., 3.]])
+
+# Create another Constant that produces a 2x1 matrix.
+matrix2 = tf.constant([[2.],[2.]])
+
+# Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.
+# The returned value, 'product', represents the result of the matrix
+# multiplication.
+product = tf.matmul(matrix1, matrix2)
+
+# To run the matmul op we call the session 'run()' method, passing 'product'
+# which represents the output of the matmul op.  This indicates to the call
+# that we want to get the output of the matmul op back.
+#
+# All inputs needed by the op are run automatically by the session.  They
+# typically are run in parallel.
+#
+# The call 'run(product)' thus causes the execution of threes ops in the
+# graph: the two constants and matmul.
+#
+# The output of the op is returned in 'result' as a numpy `ndarray` object.
+with tf.Session() as sess:
+    result = sess.run(product)
+    print result
+    # ==> [[ 12.]]

helloworld.py

@@ -1,9 +1,22 @@
+'''
+HelloWorld example using TensorFlow library.
+
+Author: Aymeric Damien
+Project: https://github.com/aymericdamien/TensorFlow-Examples/
+'''
+
 import tensorflow as tf
 
 #Simple hello world using TensorFlow
 
+# Create a Constant op
+# The op is added as a node to the default graph.
+#
+# The value returned by the constructor represents the output
+# of the Constant op.
 hello = tf.constant('Hello, TensorFlow!')
 
+# Start tf session
 sess = tf.Session()
 
 print sess.run(hello)