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()) 
 

1. Some other useful matrix manipulations, like element-wise multiplication, multiplication with a scalar, elementwise pision, elementwise remainder of a pision, 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 pision, its result is
C = tf.p(a,b)

#Element Wise remainder of pision
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.p returns a tensor of the same type as the first argument.