Now we will try to define such arrays using TensorFlow:

salar_var = tf.constant([4])
vector_var = tf.constant([5,4,2])
matrix_var = tf.constant([[1,2,3],[2,2,4],[3,5,5]])
tensor = tf.constant( [ [[1,2,3],[2,3,4],[3,4,5]] , [[4,5,6],[5,6,7],[6,7,8]] , [[7,8,9],[8,9,10],[9,10,11]] ] )
with tf.Session() as session:
    result = session.run(salar_var)
    print "Scalar (1 entry):
 %s 
" % result
    result = session.run(vector_var)
    print "Vector (3 entries) :
 %s 
" % result
    result = session.run(matrix_var)
    print "Matrix (3x3 entries):
 %s 
" % result
    result = session.run(tensor)
    print "Tensor (3x3x3 entries) :
 %s 
" % result

Output:
Scalar (1 entry):
 [2] 

Vector (3 entries) :
 [5 6 2] 

Matrix (3x3 entries):
 [[1 2 3]
 [2 3 4]
 [3 4 5]] 

Tensor (3x3x3 entries) :
 [[[ 1  2  3]
  [ 2  3  4]
  [ 3  4  5]]

 [[ 4  5  6]
  [ 5  6  7]
  [ 6  7  8]]

 [[ 7  8  9]
  [ 8  9 10]
  [ 9 10 11]]]

Now that you understand these data structures, I encourage you to play with them using some previous functions to see how they will behave, according to their structure types:

Matrix_one = tf.constant([[1,2,3],[2,3,4],[3,4,5]])
Matrix_two = tf.constant([[2,2,2],[2,2,2],[2,2,2]])
first_operation = tf.add(Matrix_one, Matrix_two)
second_operation = Matrix_one + Matrix_two
with tf.Session() as session:
    result = session.run(first_operation)
    print "Defined using tensorflow function :"
    print(result)
    result = session.run(second_operation)
    print "Defined using normal expressions :"
    print(result)

Output:
Defined using tensorflow function :
[[3 4 5]
 [4 5 6]
 [5 6 7]]
Defined using normal expressions :
[[3 4 5]
 [4 5 6]
 [5 6 7]]

With the regular symbol definition and also the tensorflow function, we were able to get an element-wise multiplication, also known as Hadamard product. But what if we want the regular matrix product? We need to use another TensorFlow function called tf.matmul():

Matrix_one = tf.constant([[2,3],[3,4]])
Matrix_two = tf.constant([[2,3],[3,4]])
first_operation = tf.matmul(Matrix_one, Matrix_two)
with tf.Session() as session:
    result = session.run(first_operation)
    print "Defined using tensorflow function :"
    print(result)

Output:
Defined using tensorflow function :
[[13 18]
 [18 25]]

We can also define this multiplication ourselves, but there is a function that already does that, so no need to reinvent the wheel!