Python - tensorflow.math.reduce_sum()

Introduction

In TensorFlow, tensorflow.math.reduce_sum() is a function that computes the sum of elements along specified axes of a given tensor. This function is often used in various machine learning and deep learning tasks, such as calculating loss functions and evaluating model performance.

Syntax

The syntax of tensorflow.math.reduce_sum() function is as follows:

tensorflow.math.reduce_sum(input_tensor, axis=None, keepdims=False, name=None)

Parameters

The reduce_sum() function takes the following parameters:

• input_tensor: The input tensor on which the sum operation will be performed.
• axis (optional): The dimensions along which the sum will be computed. If not provided, the sum is calculated over all dimensions.
• keepdims (optional): A boolean value indicating whether to keep the dimensions of the input tensor after summing. If set to True, the output tensor will have the same number of dimensions as the input tensor.
• name (optional): A name for the operation.

Return Value

The function returns a tensor that contains the element-wise sum along the specified axes of the input tensor.

Example

import tensorflow as tf

# Create a tensor
tensor = tf.constant([[1, 2, 3], [4, 5, 6]])

# Calculate the sum along axis 1
result = tf.math.reduce_sum(tensor, axis=1)

print(result)

Output:

tf.Tensor([ 6 15], shape=(2,), dtype=int32)

In this example, we create a 2-dimensional tensor and compute the sum along axis 1 using reduce_sum(). The result is a tensor with shape (2,) containing the sum of each row.

Conclusion

The tensorflow.math.reduce_sum() function is a useful tool for calculating the sum of elements along specified axes in TensorFlow. Understanding and utilizing this function can greatly help in various machine learning tasks, where summing values is required.