Tensors with different ranks shapes are created and printed out to the output. Tensor Ranks and Tensor Shapes are explained in detail at http://gsrikar.blogspot.com/2017/06/what-is-tensor-rank-and-tensor-shape.html

Tensor Rank and Tensor Shape - Tensorflow

'''
Class creates tensors of different ranks and shapes
'''

import tensorflow as tf


def main():
    '''
    Main method
    '''
    # Rank 0 tensor with 0-D shape and type float
    scalar = tf.constant(48.3)
    print('Scalar: ', scalar)  # Scalar:  Tensor("Const:0", shape=(), dtype=float32)

    # Rank 1 tensor with 1-D shape and type int
    vector = tf.constant([1, 9, -6, 7, 0])
    print('Vector: ', vector)  # Vector:  Tensor("Const_1:0", shape=(5,), dtype=int32)

    # Rank 2 tensor with 2-D shape and type float
    matrix = tf.constant([[2.4, 5.1], [3.3, 7.9], [8.5, 6.1]])
    print('Matrix: ', matrix)  # Matrix:  Tensor("Const_2:0", shape=(3, 2), dtype=float32)

    # Rank 3 tensor with 3-D shape and type int
    tensor = tf.constant([[[2, 5, 6], [5, 3, 3], [6, 7, 8]],
                          [[0, 0, 1], [9, 7, 9], [2, 3, 6]],
                          [[4, 8, 2], [1, 0, 8], [4, 4, 0]]])
    print('Tensor: ', tensor)  # Tensor:  Tensor("Const_3:0", shape=(3, 3, 3), dtype=int32)

    # Start the session
    with tf.Session() as sess:
        # Print the values
        print('Scalar value: ', sess.run(scalar))  # Scalar value:  48.3
        print('Vector value: ', sess.run(vector))  # Vector value:  [ 1  9 -6  7  0]
        print('Matrix value: ', sess.run(matrix))  # Matrix value:  [[ 2.4000001   5.0999999 ]
                                                   #                 [ 3.29999995  7.9000001 ]
                                                   #                 [ 8.5         6.0999999 ]]
        print('Tensor value: ', sess.run(tensor))  # Tensor value:  [[[2 5 6]
                                                   #                  [5 3 3]
                                                   #                  [6 7 8]]
                                                   #
                                                   #                 [[0 0 1]
                                                   #                  [9 7 9]
                                                   #                  [2 3 6]]
                                                   #
                                                   #                 [[4 8 2]
                                                   #                  [1 0 8]
                                                   #                  [4 4 0]]]

        # Print the ranks
        print('Scalar Rank: ', sess.run(tf.rank(scalar)))  # Scalar Rank:  0
        print('Vector Rank: ', sess.run(tf.rank(vector)))  # Vector Rank:  1
        print('Matrix Rank: ', sess.run(tf.rank(matrix)))  # Matrix Rank:  2
        print('Tensor Rank: ', sess.run(tf.rank(tensor)))  # Tensor Rank:  3
        # Print the shapes
        print('Scalar Shape: ', sess.run(tf.shape(scalar)))  # Scalar Shape:  []
        print('Vector Shape: ', sess.run(tf.shape(vector)))  # Vector Shape:  [5]
        print('Matrix Shape: ', sess.run(tf.shape(matrix)))  # Matrix Shape:  [3 2]
        print('Tensor Shape: ', sess.run(tf.shape(tensor)))  # Tensor Shape:  [3 3 3]


if __name__ == '__main__':
    '''
    Starting point
    '''
    main()