Given below is a list of commonly used attributes

1. tensorflow.shape
2. tensorflow.zeros
3. tensorflow.ones
4. tensorflow.dtype

tensorflow.shape

tensorflow.shape used for returning the shape of the tensor

import tensorflow as tf  
  
# Shape of tensor  
m_shape = tf.constant([ [10, 11],  
                        [12, 13],  
                        [14, 15] ]                        
                     )   
m_shape.shape

The output will be TensorShape([Dimension(3), Dimension(2)])

tensorflow.zeros

tensorflow. zeros used for creating a tensor of the given dimension with all elements being zero

import tensorflow as tf  
# Create a vector of 0  
print(tf.zeros(10))

The output will be Tensor(“zeros:0”, shape=(10,), dtype=float32)

tensorflow.ones

tensorflow.ones used for for creating a tensor of the given dimmension with all elements being one

import tensorflow as tf  
# Create a vector of 1  
print(tf.ones([10, 10]))              
# Create a vector of ones with the same number of rows as m_shape  
print(tf.ones(m_shape.shape[0]))  
# Create a vector of ones with the same number of column as m_shape  
print(tf.ones(m_shape.shape[1]))  
  
print(tf.ones(m_shape.shape))

The output will be Tensor(“ones_1:0”, shape=(10, 10), dtype=float32) Tensor(“ones_2:0”, shape=(3,), dtype=float32) Tensor(“ones_3:0”, shape=(2,), dtype=float32) Tensor(“ones_4:0”, shape=(3, 2), dtype=float32)

tensorflow.dtype

tensorflow.dtype used to find the data type of the elements of the tensor

import tensorflow as tf  
m_shape = tf.constant([ [10, 11],  
                        [12, 13],  
                        [14, 15] ]                        
                     )   
print(m_shape.dtype) 

The output will be <dtype: ‘int32’>

import tensorflow as tf  
  
# Change type of data  
type_float = tf.constant(3.123456789, tf.float32)  
type_int = tf.cast(type_float, dtype=tf.int32)  
print(type_float.dtype)  
print(type_int.dtype)

The output of the above code will be <dtype: ‘float32’> <dtype: ‘int32’>