TensorFlow | How to use tf.data.Dataset.from_tensor_slices()

TensorFlow from_tensor_slices()

This post explains how to use tf.data.Dataset.from_tensor_slices in TensorFlow.

This code snippet is using TensorFlow2.0, if you are using earlier versions of TensorFlow than enable eager execution to run the code.

from_tensor_slices(tensor) creates a Dataset whose elements are slices of the given tensors.

Import `tensorflow`


import tensorflow as tf

print(tf.__version__)

Create Tensor



tensor1 = tf.range(12)

Create dataset, this will return TensorSliceDataset object



dataset = tf.data.Dataset.from_tensor_slices(tensor1)
print(dataset)

Iterate through dataset



  
for i in dataset:
	print(type(i))
	print(i)

Output



2.0.0

< TensorSliceDataset shapes: (), types: tf.int32 >

<class 'tensorflow.python.framework.ops.EagerTensor'>
tf.Tensor(0, shape=(), dtype=int32)
<class 'tensorflow.python.framework.ops.EagerTensor'>
tf.Tensor(1, shape=(), dtype=int32)
<class 'tensorflow.python.framework.ops.EagerTensor'>
tf.Tensor(2, shape=(), dtype=int32)
<class 'tensorflow.python.framework.ops.EagerTensor'>
tf.Tensor(3, shape=(), dtype=int32)
<class 'tensorflow.python.framework.ops.EagerTensor'>
tf.Tensor(4, shape=(), dtype=int32)
<class 'tensorflow.python.framework.ops.EagerTensor'>
tf.Tensor(5, shape=(), dtype=int32)
<class 'tensorflow.python.framework.ops.EagerTensor'>
tf.Tensor(6, shape=(), dtype=int32)
<class 'tensorflow.python.framework.ops.EagerTensor'>
tf.Tensor(7, shape=(), dtype=int32)
<class 'tensorflow.python.framework.ops.EagerTensor'>
tf.Tensor(8, shape=(), dtype=int32)
<class 'tensorflow.python.framework.ops.EagerTensor'>
tf.Tensor(9, shape=(), dtype=int32)
<class 'tensorflow.python.framework.ops.EagerTensor'>
tf.Tensor(10, shape=(), dtype=int32)
<class 'tensorflow.python.framework.ops.EagerTensor'>
tf.Tensor(11, shape=(), dtype=int32)