import sys import paddle.v2 as paddle import paddle.v2.layers.beam_search as beam_search def seqToseq_net(source_dict_dim, target_dict_dim, is_generating): ### Network Architecture word_vector_dim = 512 # dimension of word vector decoder_size = 512 # dimension of hidden unit in GRU Decoder network encoder_size = 512 # dimension of hidden unit in GRU Encoder network beam_size = 3 max_length = 250 #### Encoder src_word_id = paddle.layer.data( name='source_language_word', type=paddle.data_type.integer_value_sequence(source_dict_dim)) src_embedding = paddle.layer.embedding( input=src_word_id, size=word_vector_dim, param_attr=paddle.attr.ParamAttr(name='_source_language_embedding')) src_forward = paddle.networks.simple_gru( input=src_embedding, size=encoder_size) src_backward = paddle.networks.simple_gru( input=src_embedding, size=encoder_size, reverse=True) encoded_vector = paddle.layer.concat(input=[src_forward, src_backward]) #### Decoder with paddle.layer.mixed(size=decoder_size) as encoded_proj: encoded_proj += paddle.layer.full_matrix_projection( input=encoded_vector) backward_first = paddle.layer.first_seq(input=src_backward) with paddle.layer.mixed( size=decoder_size, act=paddle.activation.Tanh()) as decoder_boot: decoder_boot += paddle.layer.full_matrix_projection( input=backward_first) def gru_decoder_with_attention(enc_vec, enc_proj, current_word): decoder_mem = paddle.layer.memory( name='gru_decoder', size=decoder_size, boot_layer=decoder_boot) context = paddle.networks.simple_attention( encoded_sequence=enc_vec, encoded_proj=enc_proj, decoder_state=decoder_mem) with paddle.layer.mixed(size=decoder_size * 3) as decoder_inputs: decoder_inputs += paddle.layer.full_matrix_projection(input=context) decoder_inputs += paddle.layer.full_matrix_projection( input=current_word) gru_step = paddle.layer.gru_step( name='gru_decoder', input=decoder_inputs, output_mem=decoder_mem, size=decoder_size) with paddle.layer.mixed( size=target_dict_dim, bias_attr=True, act=paddle.activation.Softmax()) as out: out += paddle.layer.full_matrix_projection(input=gru_step) return out decoder_group_name = "decoder_group" group_input1 = paddle.layer.StaticInputV2(input=encoded_vector, is_seq=True) group_input2 = paddle.layer.StaticInputV2(input=encoded_proj, is_seq=True) group_inputs = [group_input1, group_input2] if not is_generating: trg_embedding = paddle.layer.embedding( input=paddle.layer.data( name='target_language_word', type=paddle.data_type.integer_value_sequence(target_dict_dim)), size=word_vector_dim, param_attr=paddle.attr.ParamAttr(name='_target_language_embedding')) group_inputs.append(trg_embedding) # For decoder equipped with attention mechanism, in training, # target embeding (the groudtruth) is the data input, # while encoded source sequence is accessed to as an unbounded memory. # Here, the StaticInput defines a read-only memory # for the recurrent_group. decoder = paddle.layer.recurrent_group( name=decoder_group_name, step=gru_decoder_with_attention, input=group_inputs) lbl = paddle.layer.data( name='target_language_next_word', type=paddle.data_type.integer_value_sequence(target_dict_dim)) cost = paddle.layer.classification_cost(input=decoder, label=lbl) return cost else: # In generation, the decoder predicts a next target word based on # the encoded source sequence and the last generated target word. # The encoded source sequence (encoder's output) must be specified by # StaticInput, which is a read-only memory. # Embedding of the last generated word is automatically gotten by # GeneratedInputs, which is initialized by a start mark, such as , # and must be included in generation. trg_embedding = beam_search.GeneratedInputV2( size=target_dict_dim, embedding_name='_target_language_embedding', embedding_size=word_vector_dim) group_inputs.append(trg_embedding) beam_gen = beam_search.beam_search( name=decoder_group_name, step=gru_decoder_with_attention, input=group_inputs, bos_id=0, eos_id=1, beam_size=beam_size, max_length=max_length) # # seqtext_printer_evaluator( # input=beam_gen, # id_input=data_layer( # name="sent_id", size=1), # dict_file=trg_dict_path, # result_file=gen_trans_file) return beam_gen def main(): paddle.init(use_gpu=False, trainer_count=1) # source and target dict dim. dict_size = 30000 source_dict_dim = target_dict_dim = dict_size # define network topology cost = seqToseq_net(source_dict_dim, target_dict_dim, False) parameters = paddle.parameters.create(cost) # define optimize method and trainer optimizer = paddle.optimizer.Adam( learning_rate=5e-5, regularization=paddle.optimizer.L2Regularization(rate=1e-3)) trainer = paddle.trainer.SGD(cost=cost, parameters=parameters, update_equation=optimizer) # define data reader feeding = { 'source_language_word': 0, 'target_language_word': 1, 'target_language_next_word': 2 } wmt14_reader = paddle.batch( paddle.reader.shuffle( paddle.dataset.wmt14.train(dict_size=dict_size), buf_size=8192), batch_size=5) # define event_handler callback def event_handler(event): if isinstance(event, paddle.event.EndIteration): if event.batch_id % 10 == 0: print "\nPass %d, Batch %d, Cost %f, %s" % ( event.pass_id, event.batch_id, event.cost, event.metrics) else: sys.stdout.write('.') sys.stdout.flush() # start to train trainer.train( reader=wmt14_reader, event_handler=event_handler, num_passes=10000, feeding=feeding) if __name__ == '__main__': main()