Skip to content
Related Articles

Related Articles

Python – tensorflow.IndexedSlicesSpec()

View Discussion
Improve Article
Save Article
  • Last Updated : 15 Sep, 2021
View Discussion
Improve Article
Save Article

TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning  neural networks.

IndexedSlicesSpec inherits from TypeSpec and provides Type specification for IndexedSlices.

Syntax: tensorflow.IndexedSlicesSpec( shape, dtype, indices_dtype, dense_shape_dtype, indices_shape )

Parameters:

  • shape(optional): It defines the dense shape of IndexedSlices. Default value is None which allows any dense shape.
  • dtype(optional): It defines the dtype of IndexedSlices values. Default value is float32.
  • indices_dtype(optional): It defines the dtype of indices in the IndexedSlices. It can either be int32 or int64 with default value int64.
  • dense_shape_dtye(optional): It defines the dtype of dense shape in the IndexedSlices. It can either be int32, int64 or None with default value None.
  • indices_shape(optional): It defines the shape of the indices component, which indicates how many slices are in the IndexedSlices.

Example 1: This example uses all the default values.

Python3




# Importing the library
import tensorflow as tf
 
# Calculating result
res = tf.IndexedSlicesSpec()
 
# Printing the result
print('IndexedSlicesSpec: ', res)


Output:

IndexedSlicesSpec:  IndexedSlicesSpec(TensorShape(None), tf.float32, tf.int64, None, TensorShape([None]))

Example 2:

Python3




# Importing the library
import tensorflow as tf
 
# Calculating result
res = tf.IndexedSlicesSpec((2, 3))
 
# Printing the result
print('IndexedSlicesSpec: ', res)


Output:

IndexedSlicesSpec:  IndexedSlicesSpec(TensorShape([2, 3]), tf.float32, tf.int64, None, TensorShape([None]))
My Personal Notes arrow_drop_up
Recommended Articles
Page :

Start Your Coding Journey Now!