batch_dot has different behaviour in keras.backend and tf.keras.backend

[MarshallCN]

I found that the output of keras.backend.batch_dot function is different from the tensorflow.keras.backend.batch_dot. The tensorflow version is 1.14.0, and the Keras version is 2.2.5

#using Google Colab
import tensorflow as tf   #1.14.0  #v1.14.0-0-g87989f6959 
from keras import backend as K #2.2.5

c = tf.ones(shape=(1,2,1))
h = tf.ones(shape=(1,2,1,16))

p1 = K.batch_dot(c, h, [2, 2])
print(K.int_shape(p1))  
####output: (1,2,2,16)

p2 = tf.keras.backend.batch_dot(c, h, [2, 2])
print(p2.shape)  
####output: (1,2,16)

However, in Keras version 2.2.4, these two functions produce identical results. So I think it might have some problem. Also, I wonder which result is actually correct?

#using localhost Jupyter
import tensorflow as tf
from keras import backend as K  #2.2.4

c = tf.ones(shape=(1,2,1))
h = tf.ones(shape=(1,2,1,16))

p3 = K.batch_dot(c, h, [2, 2])
print(K.int_shape(p3))  
####ourput: (1,2,16)

p4 = tf.keras.backend.batch_dot(c, h, [2, 2])
print(p4.shape)  
####output: (1,2,16)

[fchollet]

I guess some change was made in multi-backend Keras that wasn't ported to tf.keras. @farizrahman4u do you remember what it was?

[johnny12150]

I thought below code should output shape (9, 8, 7, 4, 5) in Google Colab

from keras import backend as K
a = K.ones((9, 8, 7, 4, 2))
b = K.ones((9, 8, 7, 2, 5))
c = K.batch_dot(a, b)
print(c.shape)

But why am i getting shape (9, 8, 7, 4, 8, 7, 5)?
Is there any way to get shape (9, 8, 7, 4, 5) again?

[fchollet]

The correct behavior is now the Keras behavior in 2.2.5 and higher. We will update tf.keras to follow the same behavior.