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A jupyter notebook version of the simple 'starter' example. First, define a constant, and define the TensorFlow graph.



In [ ]:

    
import numpy as np

import tensorflow as tf


m1 = np.array([[1., 2.], [3., 4.], [5., 6.], [7., 8.]], dtype=np.float32)

# Input data.
m1_input = tf.placeholder(tf.float32, shape=[4, 2])

m2 = tf.Variable(tf.random_uniform([2, 3], -1.0, 1.0))

m3 = tf.matmul(m1_input, m2)

# This is an identity op with the side effect of printing data when
# evaluating.
m3 = tf.Print(m3, [m3], message="m3 is: ")

# Add variable initializer.
init = tf.initialize_all_variables()

Then, run the graph in a session, specifying a value for the m1_input placeholder.



In [ ]:

    
with tf.Session() as session:
    # We must initialize all variables before we use them.
    init.run()
    print("Initialized")

    print("m2: {}".format(m2))
    print("eval m2: {}".format(m2.eval()))

    feed_dict = {m1_input: m1}

    result = session.run([m3], feed_dict=feed_dict)
    print("\nresult: {}\n".format(result))