How to continuously update the value of a tensor in a loop

So I am trying to continuously update a tensor in my code in a loop by assigning it with a new value in each iteration. genRandMat function assigns the variable a1 with a random MxN matrix comprising 0 and 1 with frequency of 1 being decided with probability pt.

Here is the code I ran -

np.random.seed(0)
tf.set_random_seed(0)
def genRandMat(M,N,pt):
    return tf.convert_to_tensor(np.random.choice([0, 1], size=(M,N), p=[1-pt, pt]), dtype=tf.float32)

a1=tf.Variable(genRandMat(1,10,0.5))
a2=a1.assign(genRandMat(1,10,0.5))
init = tf.global_variables_initializer()


with tf.Session() as sess:
    sess.run(init)
    for i in range(5):
        print(a1.eval())
        sess.run(a2)
        print(a1.eval())
        print("*************")

The result I expected was a new random tensor after every second print statement (due to update statement a2), ie 2nd, 4th, 6th... matrices should be updated, new random matrices.

Here is what I got instead

[[0. 0. 1. 1. 0. 0. 1. 1. 1. 0.]]
[[1. 1. 1. 1. 0. 1. 0. 0. 1. 1.]]
*************
[[1. 1. 1. 1. 0. 1. 0. 0. 1. 1.]]
[[1. 1. 1. 1. 0. 1. 0. 0. 1. 1.]]
*************
[[1. 1. 1. 1. 0. 1. 0. 0. 1. 1.]]
[[1. 1. 1. 1. 0. 1. 0. 0. 1. 1.]]
*************
[[1. 1. 1. 1. 0. 1. 0. 0. 1. 1.]]
[[1. 1. 1. 1. 0. 1. 0. 0. 1. 1.]]
*************
[[1. 1. 1. 1. 0. 1. 0. 0. 1. 1.]]
[[1. 1. 1. 1. 0. 1. 0. 0. 1. 1.]]
*************

As you can see, the value of a1 changes once at the start with the 2nd print statement and then not anymore. I tried commenting out both random seeds but the result doesn't change. I want a new matrix after every update statement. How can I achieve this?