Siamese network return same loss value while training
Finally, I found a solution to this problem. I completely modified the architecture, Then I reduced the learning rate to 0.00005. Then the loss value of the model falls continuously.

Latest Architecture:

a = 'tanh'
model = Sequential()
model.add(Dense(self.INPUT_DIM, input_shape=(self.INPUT_DIM, ), 
activation=a))
model.add(Dense(600, activation=a))
model.add(Dense(self.INPUT_DIM, activation=a))