不朽情缘什么时间玩容易爆分

書名： TensorFlow Machine Learning Cookbook
作者名： Nick McClure
本章字?jǐn)?shù)： 194字
更新時間： 2021-04-02 20:36:30

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:

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: x_val}))
3.0
9.0
15.0
21.0
27.0

How it works…

Steps 1 and 2 create the data and operations on the computational graph. Then, in step 3, we feed the data through the graph and print the output. Here is what the computational graph looks like:

Figure 1: Here we can see in the graph that the placeholder, x_data, along with our multiplicative constant, feeds into the multiplication operation.

官术网_书友最值得收藏!

TensorFlow Machine Learning Cookbook

Operations in a Computational Graph

Getting ready

How to do it…

How it works…