Create main.py

main.py

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+import tensorflow as tf
+
+# 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)
+
+# Launch the default graph.
+sess = tf.Session()
+
+# 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.
+result = sess.run(product)
+print(result)
+# ==> [[ 12.]]
+
+# Close the Session when we're done.
+sess.close()