decoder.py

	def decoder(self,encoder_outputs,dropout,mode):
	
		cell, decoder_initial_state = self.decoder_cell(encoder_outputs,dropout,mode)
		if mode != "INFER":
		tgt_text = self.tgt_text
		if self.time_major:
				tgt_text = tf.transpose(self.tgt_text)
			inputs = tf.nn.embedding_lookup(self.tgt_embedding,tgt_text)	  
		helper = tf.contrib.seq2seq.TrainingHelper(inputs, self.tgt_sequence_length, time_major=True)	
		basic_decoder = tf.contrib.seq2seq.BasicDecoder(cell, helper, decoder_initial_state)
		outputs, final_context_state, _ = tf.contrib.seq2seq.dynamic_decode(basic_decoder, output_time_major=self.time_major)
		sample_id = outputs.sample_id
		logits = self.output_layer(outputs.rnn_output) 	
		
		## To add attention
		
	else:
		start_tokens = tf.fill([self.batch_size], self.start_token)
		end_token = self.end_token
		maximum_iterations = tf.round(tf.reduce_max(self.src_sequence_length) * 2)
	 
		## Currently only beam search decoder
		beam_decoder = tf.contrib.seq2seq.BeamSearchDecoder(cell=cell, embedding=self.tgt_embedding, start_tokens=start_tokens, end_token=end_token,initial_state=decoder_initial_state, beam_width=self.beam_width,output_layer=self.output_layer)

		outputs, final_context_state, _ = tf.contrib.seq2seq.dynamic_decode( beam_decoder, maximum_iterations=maximum_iterations,output_time_major=self.time_major)
		logits = tf.no_op()
		sample_id = outputs.predicted_ids

	return logits, sample_id, final_context_state
	
 def decoder_cell(self,encoder_state,dropout,mode):

	cell = self.create_rnn_cell(dropout)
	if mode == "INFER" and self.beam_width!=0:
		decoder_initial_state = tf.contrib.seq2seq.tile_batch(encoder_state, multiplier= self.beam_width)
	else:
		decoder_initial_state = encoder_state
	return cell, decoder_initial_state