Boolean indexing with keras

Hi everyone,

Is it possible to use boolean indexing in Keras (with TF backend) ?

Here is a little script to see what I want to do, with the errors I get:

import numpy as np
import keras
from keras import backend as K

print(keras.__version__,K.tensorflow_backend.tf.__version__)
# 1.2.0 0.11.0rc2

m = np.random.randn(10,10)
b = np.random.uniform(size=(10,10))
M = K.variable(m)
B = K.variable(B)
boolean_mask = K.greater(B,0.5)

# This works fine, in numpy
np_bool = M.eval(session=K.get_session())[boolean_mask.eval(session=K.get_session())] 

# But this does not
keras_bool = M[boolean_mask]

I am looking for something that is similar to tf.boolean_mask.

Thank you in advance !

[DSLituiev]

this is a very desirable feature to have when writing custom loss functions. The solution by @MarkSeygan might works if loss then aggregated by sum, but breaks if the aggregation is performed with mean.

[chrishmorris]

See tf.boolean_mask