How to Develop an Encoder-Decoder Model with Attention in Keras

import tensorflow as tf

from keras import backend as K

from keras import regularizers, constraints, initializers, activations

from keras.layers.recurrent import Recurrent, _time_distributed_dense

from keras.engine import InputSpec

 

tfPrint = lambda d, T: tf.Print(input_=T, data=[T, tf.shape(T)], message=d)

 

class AttentionDecoder(Recurrent):

 

    def __init__(self, units, output_dim,

                 activation=‘tanh’,

                 return_probabilities=False,

                 name=‘AttentionDecoder’,

                 kernel_initializer=‘glorot_uniform’,

                 recurrent_initializer=‘orthogonal’,

                 bias_initializer=‘zeros’,

                 kernel_regularizer=None,

                 bias_regularizer=None,

                 activity_regularizer=None,

                 kernel_constraint=None,

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