Masking layer does not work after training

[Jbiloki]

Before training the model my masking layer correctly 0s out the unwanted inputs. After model compile and training it outputs non-zero values:

import numpy as np
from tensorflow.keras.layers import Masking,Input,TimeDistributed,Dense
a = np.array([[[3,1,2],[-1,-1,-1],[2,1,1],[-1,-1,-1],[-1,-1,-1]], [[3,1,2],[-1,-1,-1],[2,1,1],[-1,-1,-1],[-1,-1,-1]]]).astype(np.float32)

rel_inputs = Input(shape=(a.shape[-2], a.shape[-1],))
masked = Masking(mask_value=-1.0)(rel_inputs)
x = TimeDistributed(Dense(16, activation='relu',
                          kernel_regularizer=regularizers.l1_l2(l1=1e-5, l2=1e-4),
                          bias_regularizer=regularizers.l2(1e-4),
                          activity_regularizer=regularizers.l2(1e-5)))
x2 = TimeDistributed(Dense(32, activation='relu',
                          kernel_regularizer=regularizers.l1_l2(l1=1e-5, l2=1e-4),
                          bias_regularizer=regularizers.l2(1e-4),
                          activity_regularizer=regularizers.l2(1e-5)))
rel_map = x(masked)
rel_map = Dropout(0.3)(rel_map)
rel_map = x2(rel_map)
rel_map = Dropout(0.3)(rel_map)
s = TimeDistributed(Dense(1))
output = s(rel_map)
output = Reshape((5,))(output)
#output = Lambda(lambda x: K.squeeze(x, -1))(output)
#irr_score = s(irr_map)
#diff = Subtract()([rel_score, irr_score])
model = Model(inputs=rel_inputs, outputs=output, name='rank_net_basic')
q = model.predict(a)
print (q)

[sushreebarsa]

@Jbiloki
In order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here. Please have a look at the issue for reference.Thanks!

[google-ml-butler]

This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.

[google-ml-butler]

Closing as stale. Please reopen if you'd like to work on this further.