Python – tensorflow.IndexedSlicesSpec()

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_dtype(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]))

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