| 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() |