Handling mismatched dimensions

Here, we create Two example tensors t1 and t2 using TensorFlow’s tf.constant() function. However, t1 has a shape of (2, 3) while t2 has a shape of (2, 2), resulting in mismatched dimensions along axis 1.

Then, we used TensorFlow’s tf.concat() function to concatenate t1 and t2 along axis 0. However, since they have mismatched dimensions along this axis (3 for t1 and 2 for t2), TensorFlow will raise an error when attempting this concatenation.

Output:

Tensor 1:
 tf.Tensor(
[[1 2 3]
 [4 5 6]], shape=(2, 3), dtype=int32)
Tensor 2:
 tf.Tensor(
[[ 7  8]
 [ 9 10]], shape=(2, 2), dtype=int32)
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-5-1c59130f5f55> in <cell line: 7>()
      5 # Concatenation with mismatched dimensions (will raise an error)
      6 # Uncomment the following line to see the error
----> 7 result = tf.concat([t1, t2], axis=0)

Concatenating tensors with varying shapes along the same axis

Here, we create two example tensors t1 and t2 using TensorFlow’s tf.constant() function. t1 has a shape of (2, 3) and t2 has a shape of (2, 2), resulting in differing shapes along axis 1.

Then, we use The tf.pad function to pad t2 with one extra element along axis 1 to match the shape of t1. The padding configuration [[0, 0], [0, 1]] specifies no padding along axis 0 and one element of padding at the end along axis 1.

Finally, we used tf.concat() to concatenate t1 and t2_reshaped along axis 1

Output:

Concatenated tensors with varying shapes: tf.Tensor(
[[ 1  2  3  7  8  0]
 [ 4  5  6  9 10  0]], shape=(2, 6), dtype=int32)

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.