Calculate Attention(Softmax) of Variable Length Sequence in TensorFlow – TensorFlow Tutorial

By | July 9, 2019

If you are using LSTM or BiLSTM in tensorflow, you have to process variable length sequence, especially you have added an attention mechanism in your model. In this tutorial, we will introduce you how to calculate the attention of variable length sequence in tensorflow.

tensorflow compute real attention value


import tensorflow as tf
import numpy as np

Prepare data

#a is atttention value by softmax on batch_size * time_step * input_size
a = np.array([[[0.2],[0.3],[0.4],[0.1]],[[0.3],[0.1],[0.2],[0.4]]])
#l is the valid sequence length,size is [batch_size], the first is 2, and the second is 3
l = np.array([2,3])

Convert numpy to tensor

aa = tf.convert_to_tensor(a,dtype=tf.float32)
ll = tf.convert_to_tensor(l,dtype=tf.float32)

Define a function to calculate real attention value

def realAttention(attention,length):
    batch_size = tf.shape(attention)[0]
    max_length = tf.shape(attention)[1]
    mask = tf.sequence_mask(length, max_length, tf.float32)
    mask = tf.reshape(mask,[-1, max_length, 1])
    a = attention * mask
    a = a / tf.reduce_sum(a,axis=1,keep_dims=True)
    return a

Calculate real attention value

real_attention = realAttention(aa,ll)

Print real attention value

init = tf.global_variables_initializer() 
init_local = tf.local_variables_initializer()

with tf.Session() as sess:[init, init_local])

The output is:

[[[0.4       ]
  [0.6       ]
  [0.        ]
  [0.        ]]

 [[0.5       ]
  [0.3333333 ]
  [0.        ]]]

From realAttention() function, you will find to calculate real attention value of variable length sequence, the key is to use mask.

2 thoughts on “Calculate Attention(Softmax) of Variable Length Sequence in TensorFlow – TensorFlow Tutorial

  1. guosuan

    Hi, I think the sequence mask should be applied BEFORE the softmax. Correct?

    1. admin Post author

      Yes, we should apply the sequence mask before the softmax, this article is ony an example for introducing how to process the variable length attention, we should modify it in our model.


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