Basic math with TensorFlow

The tf.add() function takes two numbers, two tensors, or one of each, and it returns their sum as a tensor:

Addition
x = tf.add(1, 2, name=None) # 3

Here's an example with subtraction and multiplication:

x = tf.subtract(1, 2,name=None) # -1
y = tf.multiply(2, 5,name=None) # 10
 

What if we want to use a non-constant? How to feed an input dataset to TensorFlow? For this, TensorFlow provides an API, tf.placeholder(), and uses feed_dict.

A placeholder is a variable that data is assigned to later in the tf.session.run() function. With the help of this, our operations can be created and we can build our computational graph without needing the data. Afterwards, this data is fed into the graph through these placeholders with the help of the feed_dict parameter in tf.session.run() to set the placeholder tensor. In the following example, the tensor x is set to the string Hello World before the session runs:

x = tf.placeholder(tf.string)

with tf.Session() as sess:
    output = sess.run(x, feed_dict={x: 'Hello World'})

It's also possible to set more than one tensor using feed_dict, as follows:

x = tf.placeholder(tf.string)
y = tf.placeholder(tf.int32, None)
z = tf.placeholder(tf.float32, None)

with tf.Session() as sess:
    output = sess.run(x, feed_dict={x: 'Welcome to CNN', y: 123, z: 123.45}) 

Placeholders can also allow storage of arrays with the help of multiple dimensions. Please see the following example:

import tensorflow as tf

x = tf.placeholder("float", [None, 3])
y = x * 2

with tf.Session() as session:
    input_data = [[1, 2, 3],
                 [4, 5, 6],]
    result = session.run(y, feed_dict={x: input_data})
    print(result)

The tf.truncated_normal() function returns a tensor with random values from a normal distribution. This is mostly used for weight initialization in a network:

n_features = 5
n_labels = 2
weights = tf.truncated_normal((n_features, n_labels))
with tf.Session() as sess:
  print(sess.run(weights))