Data model

The data model in TensorFlow is represented by tensors. Without using complex mathematical definitions, we can say that a tensor (in TensorFlow) identifies a multidimensional numerical array.

This data structure is characterized by three parameters--Rank, Shape, and Type.

Rank

Each tensor is described by a unit of dimensionality called rank. It identifies the number of dimensions of the tensor, for this reason, a rank is a known-as order or n-dimensions of a tensor. A rank zero tensor is a scalar, a rank one tensor ID a vector, while a rank two tensor is a matrix.

The following code defines a TensorFlow scalar, a vector, a matrix and a cube_matrix, in the next example we show how the rank works:

import tensorflow as tf 

scalar = tf.constant(100) 
vector = tf.constant([1,2,3,4,5]) 
matrix = tf.constant([[1,2,3],[4,5,6]]) 
cube_matrix = tf.constant([[[1],[2],[3]],[[4],[5],[6]],[[7],[8],[9]]]) 

print(scalar.get_shape()) 
print(vector.get_shape()) 
print(matrix.get_shape()) 
print(cube_matrix...