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TensorFlow | How to use tf.reduce_sum in TensorFlow

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tf.reduce_sum in TensorFlow reduces input_tensor along the dimensions given in axis. If axis is None, all dimensions are reduced, and a tensor with a single element is returned. Below are the example for tf.reduce_sum in TensorFlow.

  • Reducing Tensor to scalar value using tf.reduce_sum

    •  
import tensorflow as tf
tensor1 = tf.constant(value=[1,2,3])
tensor2 = tf.reduce_sum(tensor1)
print(tensor2)
====Output====
tf.Tensor(6, shape=(), dtype=int32)


     
tensor1 = tf.constant(value=[[1,2,3],[2,3,5]])
tensor2 = tf.reduce_sum(tensor1)                
print(tensor2)

=====Output=====
tf.Tensor(16, shape=(), dtype=int32)

  • Use of axis paramter in tf.reduce_sum 

    •  
# tf.reduce_sum with axis = 0
tensor1 = tf.constant(value=[[1,2,3],[2,3,5]])
tensor2 = tf.reduce_sum(tensor1,axis=0)

print(tensor2)
====Output====
tf.Tensor([3 5 8], shape=(3,), dtype=int32)


     
# tf.reduce_sum with axis = 1
tensor1 = tf.constant(value=[[1,2,3],[2,3,5]])
tensor2 = tf.reduce_sum(tensor1,axis=1)

print(tensor2)

====Output====
tf.Tensor([ 6 10], shape=(2,), dtype=int32)

    Category: TensorFlow