Chapter 2.2 - Data representations for neural networks



In [1]:

    
# importing numpy
import numpy as np

Scalar (0-D tensor)



In [2]:

    
x = np.array(10)



In [3]:

    
x.ndim









    Out[3]:





0



In [4]:

    
x.shape









    Out[4]:





()

Vector (1-D tensor)



In [5]:

    
x = np.array([12, 13, 14])



In [6]:

    
x









    Out[6]:





array([12, 13, 14])



In [7]:

    
x.ndim









    Out[7]:





1



In [8]:

    
x.shape









    Out[8]:





(3,)

Matrices (2-D tensor)



In [9]:

    
x = np.array([[1,2,3],
             [4,5,6]])



In [10]:

    
x.ndim









    Out[10]:





2



In [11]:

    
x.shape









    Out[11]:





(2, 3)

3-D tensors and higher-dimensional tensors



In [12]:

    
x = np.array([[[5, 78, 2, 34, 0],
               [6, 79, 3, 35, 1],
               [7, 80, 4, 36, 2]],
              [[5, 78, 2, 34, 0],
               [6, 79, 3, 35, 1],
               [7, 80, 4, 36, 2]],
              [[5, 78, 2, 34, 0],
               [6, 79, 3, 35, 1],
               [7, 80, 4, 36, 2]]])



In [13]:

    
x.ndim









    Out[13]:





3



In [14]:

    
x.shape









    Out[14]:





(3, 3, 5)