Python TensorFlow element-wise addition

Last update on August 28 2025 12:34:55 (UTC/GMT +8 hours)

Python TensorFlow Basic: Exercise-2 with Solution

Write a Python program that implements element-wise addition on two TensorFlow tensors of shape (2, 3) filled with random values.

Sample Solution:

Python Code:

import tensorflow as tf
import numpy as np

# Enable eager execution in TensorFlow 2.x
tf.config.run_functions_eagerly(True)

# Create two random tensors with shape (2, 3)
# Tensors are multi-dimensional arrays with a uniform type (called a dtype ).
ts1 = tf.constant(np.random.rand(2, 3))
ts2 = tf.constant(np.random.rand(2, 3))

# Perform element-wise addition
result_tensor = tf.add(ts1, ts2)

# Print the original tensors and the result
print("Original tensors:")
print("Tensor1:")
print(ts1)
print("Tensor2:")
print(ts2)
print("\nResult of Element-wise Addition:")
print(result_tensor)

Output:

Original tensors:
Tensor1:
tf.Tensor(
[[0.16777132 0.45360842 0.72095194]
 [0.5060912  0.80159217 0.06390026]], shape=(2, 3), dtype=float64)
Tensor2:
tf.Tensor(
[[0.01992499 0.4548276  0.55259358]
 [0.67508427 0.1676432  0.77147187]], shape=(2, 3), dtype=float64)

Result of Element-wise Addition:
tf.Tensor(
[[0.18769631 0.90843602 1.27354552]
 [1.18117547 0.96923538 0.83537213]], shape=(2, 3), dtype=float64)

Explanation:

In the exercise above -

import tensorflow as tf - It imports the TensorFlow library ('tf').
import numpy as np - It imports the NumPy library ('np').
tf.config.run_functions_eagerly(True) - Enable eager execution in TensorFlow 2.x.
ts1 = tf.constant(np.random.rand(2, 3)) - It creates a TensorFlow constant tensors, ts1.
ts2 = tf.constant(np.random.rand(2, 3)) - It creates a TensorFlow constant tensors, ts2.
result_tensor = tf.add(ts1, ts2) - It performs element-wise addition between 'ts1' and 'ts2' using the tf.add() function, stored in 'result_tensor'.
Finally, the print() function prints the original tensors (ts1 and ts2) and the result of element-wise addition (result_tensor).

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Python Code Editor:

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