TensorFlow - How to broadcasts parameters for evaluation on an N-D grid

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

While working with TensorFlow some operations automatically broadcast parameters and sometimes we have to explicitly broadcast parameters. To explicitly broadcast parameters meshgrid method is used.

Method Used:

• meshgrid: This method is used to broadcasts parameters for evaluation on an N-D grid. It accepts rank-1 tensors and broadcast all of them to same shape and returns a list of N Tensors with rank N. Default indexing for this method is 'xy'.

Example 1: In this method default indexing is used.

# importing the library
import tensorflow as tf

# Initializing Input
x = [1, 2, 3]
y = [4, 5, 6, 7]

# Printing the Input
print("x: ", x)
print("y: ", y)

# Broadcasting the Tensors
X, Y = tf.meshgrid(x, y)

# Printing the resulting Tensors
print("X: ", X)
print("Y: ", Y)

Output:

x:  [1, 2, 3]
y:  [4, 5, 6, 7]
X:  tf.Tensor(
[[1 2 3]
 [1 2 3]
 [1 2 3]
 [1 2 3]], shape=(4, 3), dtype=int32)
Y:  tf.Tensor(
[[4 4 4]
 [5 5 5]
 [6 6 6]
 [7 7 7]], shape=(4, 3), dtype=int32)

Example 2: In this example indexing is changed to 'ij'.

# importing the library
import tensorflow as tf

# Initializing Input
x = [1, 2, 3]
y = [4, 5, 6, 7]

# Printing the Input
print("x: ", x)
print("y: ", y)

# Broadcasting the Tensors
X, Y = tf.meshgrid(x, y, indexing = 'ij')

# Printing the resulting Tensors
print("X: ", X)
print("Y: ", Y)

Output:

x:  [1, 2, 3]
y:  [4, 5, 6, 7]
X:  tf.Tensor(
[[1 1 1 1]
 [2 2 2 2]
 [3 3 3 3]], shape=(3, 4), dtype=int32)
Y:  tf.Tensor(
[[4 5 6 7]
 [4 5 6 7]
 [4 5 6 7]], shape=(3, 4), dtype=int32)

aman neekhara

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Article Tags :

• Machine Learning
• AI-ML-DS
• Tensorflow

Practice Tags :

• Machine Learning