CODEWITHSUNDEEP
tensorflow
x-jeff
x-jeff

Reputation: 3

The result of "tf.subtract" is not the same as expected

import tensorflow as tf
a=tf.random_normal([3, 2], mean=6, stddev=0.1, seed=1)
b=tf.random_normal([3, 2], mean=1, stddev=1, seed=1)
sess=tf.Session()
ra=sess.run(a)
rb=sess.run(b)
r1=ra-rb
r2=sess.run(tf.subtract(a,b))

Why is r1 and r2 not equal? Shouldn't it be the same in theory？

tensorflow version : 1.15.0

Upvotes: 0

Views: 152

Answers (1)

user11530462
user11530462

Reputation:

In Tensorflow 1.x since in each session the tf.random_normal generates the new set of numbers which is the reason for change in results as rightly mentioned by @xdurch0 and @Addy in the comment section.
Instead, you can set the constant numbers using tf.constant and compare the results.

Tensorflow 1.x:

import tensorflow as tf

a = tf.constant([[5.918868 , 6.14846  ],
       [6.006533 , 5.7557297],
       [6.009925 , 6.0591226]])
b = tf.constant([[0.32409406, 1.2866583 ],
       [1.3215888 , 2.2124639 ],
       [0.19414288, 0.86650544]])
sess=tf.Session()
ra=sess.run(a)
rb=sess.run(b)
r1=ra -rb
r2=sess.run(tf.subtract(a,b))
print(r1)
print(r2)

Result:

[[5.5947742 4.8618016]
 [4.684944  3.5432658]
 [5.815782  5.192617 ]]
[[5.5947742 4.8618016]
 [4.684944  3.5432658]
 [5.815782  5.192617 ]]

Tensorflow 2.x:

In Tensorflow 2.x since eager execution is enabled by default the tf.random.normal will execute immediately and keep the result for rest of the code.

import tensorflow as tf
a=tf.random.normal([3, 2], mean=6, stddev=0.1, seed=1)
b=tf.random.normal([3, 2], mean=1, stddev=1, seed=1)
r1=a-b
r2=tf.subtract(a,b)
print(r1)
print(r2)

Result:

tf.Tensor(
[[5.5947742 4.8618016]
 [4.684944  3.5432658]
 [5.815782  5.192617 ]], shape=(3, 2), dtype=float32)
tf.Tensor(
[[5.5947742 4.8618016]
 [4.684944  3.5432658]
 [5.815782  5.192617 ]], shape=(3, 2), dtype=float32)

Upvotes: 1

Related Questions