Performing matrix manipulations using TensorFlow

Matrix operations, such as performing multiplication, addition, and subtraction, are important operations in the propagation of signals in any neural network. Often in the computation, we require random, zero, ones, or identity matrices.

This recipe will show you how to get different types of matrices and how to perform different matrix manipulation operations on them.

How to do it...

We proceed with the recipe as follows:

1. We start an interactive session so that the results can be evaluated easily:

import tensorflow as tf

#Start an Interactive Session
sess = tf.InteractiveSession()

#Define a 5x5 Identity matrix
I_matrix = tf.eye(5)
print(I_matrix.eval()) 
# This will print a 5x5 Identity matrix

#Define a Variable initialized to a 10x10 identity matrix
X = tf.Variable(tf.eye(10))
X.initializer.run()  # Initialize the Variable
print(X.eval()) 
# Evaluate the Variable and print the result

#Create a random 5x10 matrix
A = tf.Variable(tf.random_normal([5,10]))
A.initializer.run()

#Multiply two matrices
product = tf.matmul(A, X)
print(product.eval())

#create a random matrix of 1s and 0s, size 5x10
b = tf.Variable(tf.random_uniform([5,10], 0, 2, dtype= tf.int32))
b.initializer.run()
print(b.eval())
b_new = tf.cast(b, dtype=tf.float32)
#Cast to float32 data type

# Add the two matrices
t_sum = tf.add(product, b_new)
t_sub = product - b_new
print("A*X _b\n", t_sum.eval())
print("A*X - b\n", t_sub.eval()) 
 

2. Some other useful matrix manipulations, like element-wise multiplication, multiplication with a scalar, elementwise division, elementwise remainder of a division, can be performed as follows:

import tensorflow as tf

# Create two random matrices
a = tf.Variable(tf.random_normal([4,5], stddev=2))
b = tf.Variable(tf.random_normal([4,5], stddev=2))

#Element Wise Multiplication
A = a * b

#Multiplication with a scalar 2
B = tf.scalar_mul(2, A)

# Elementwise division, its result is
C = tf.div(a,b)

#Element Wise remainder of division
D = tf.mod(a,b)

init_op = tf.global_variables_initializer()
with tf.Session() as sess:
     sess.run(init_op)
     writer = tf.summary.FileWriter('graphs', sess.graph)
     a,b,A_R, B_R, C_R, D_R = sess.run([a , b, A, B, C, D])
     print("a\n",a,"\nb\n",b, "a*b\n", A_R, "\n2*a*b\n", B_R, "\na/b\n", C_R, "\na%b\n", D_R)

writer.close() 

tf.div returns a tensor of the same type as the first argument.

How it works...

All arithmetic operations of matrices like add, sub, div, multiply (elementwise multiplication), mod, and cross require that the two tensor matrices should be of the same data type. In case this is not so they will produce an error. We can use tf.cast() to convert Tensors from one data type to another.

There's more...

If we are doing division between integer tensors, it is better to use tf.truediv(a,b) as it first casts the integer tensors to floating points and then performs element-wise division.