Handling Negative axis
Here, we create Two example tensors t1 and t2 using TensorFlow’s tf.constant function. These tensors are 3-dimensional, with each containing two matrices of size 2×2.
Then, we use the tf.concat() function to concatenate t1 and t2 along the last dimension using a negative axis value (-1). Negative axis values count from the end of the tensor’s shape. In this case, -1 refers to the last dimension.
Python3
import tensorflow as tf# Example tensorst1 = tf.constant([[[1, 2], [2, 3]], [[4, 4], [5, 3]]])t2 = tf.constant([[[7, 4], [8, 4]], [[2, 10], [15, 11]]])print('Tensor 1:\n', t1)print('\nTensor 2:\n', t2)# Concatenate along the last dimension using negative axisresult = tf.concat([t1, t2], axis=-1)print("\nConcatenated along last dimension:\n", result) |
Output:
Tensor 1:
tf.Tensor(
[[[1 2]
[2 3]]
[[4 4]
[5 3]]], shape=(2, 2, 2), dtype=int32)
Tensor 2:
tf.Tensor(
[[[ 7 4]
[ 8 4]]
[[ 2 10]
[15 11]]], shape=(2, 2, 2), dtype=int32)
Concatenated along last dimension:
tf.Tensor(
[[[ 1 2 7 4]
[ 2 3 8 4]]
[[ 4 4 2 10]
[ 5 3 15 11]]], shape=(2, 2, 4), dtype=int32)
Tensor Concatenations in Tensorflow With Example
Tensor concatenation is a fundamental operation in TensorFlow, essential for combining tensors along specified dimensions. In this article, we will learn about concatenation in TensorFlow and demonstrate the concatenations in python.
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