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TensorFlow Machine Learning Cookbook by Nick McClure

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Operations in a Computational Graph

Now that we can put objects into our computational graph, we will introduce operations that act on such objects.

Getting ready

To start a graph, we load TensorFlow and create a session, as follows:

import tensorflow as tf
sess = tf.Session()

How to do it…

In this example, we will combine what we have learned and feed in each number in a list to an operation in a graph and print the output:

  1. First we declare our tensors and placeholders. Here we will create a numpy array to feed into our operation:
    import numpy as np x_vals = np.array([1., 3., 5., 7., 9.]) x_data = tf.placeholder(tf.float32) m_const = tf.constant(3.) my_product = tf.mul(x_data, m_const) for x_val in x_vals: print(sess.run(my_product, feed_dict={x_data: ...

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