Rewrite bidi encoder & code cleanup

seq2seq/nmt_without_attention_v2.py

@@ -2,7 +2,6 @@
 
 import sys
 import gzip
-import sqlite3
 import paddle.v2 as paddle
 
 ### Parameters
@@ -16,29 +15,21 @@ max_length = 50
 def seq2seq_net(source_dict_dim, target_dict_dim, generating=False):
     decoder_size = encoder_size = latent_chain_dim
 
-    ### Encoder
+    #### 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)
-
-    encoder_forward = paddle.networks.simple_gru(
-        input=src_embedding,
-        act=paddle.activation.Tanh(),
-        gate_act=paddle.activation.Sigmoid(),
-        size=encoder_size,
-        reverse=False)
-    encoder_backward = paddle.networks.simple_gru(
+    # use bidirectional_gru
+    encoded_vector = paddle.networks.bidirectional_gru(
         input=src_embedding,
-        act=paddle.activation.Tanh(),
-        gate_act=paddle.activation.Sigmoid(),
         size=encoder_size,
-        reverse=True)
-    encoded_vector = paddle.layer.concat(
-        input=[encoder_forward, encoder_backward])
-
+        fwd_act=paddle.activation.Tanh(),
+        fwd_gate_act=paddle.activation.Sigmoid(),
+        bwd_act=paddle.activation.Tanh(),
+        bwd_gate_act=paddle.activation.Sigmoid(),
+        return_seq=True)
     #### Decoder
     encoder_last = paddle.layer.last_seq(input=encoded_vector)
     with paddle.layer.mixed(
@@ -146,18 +137,8 @@ def train(source_dict_dim, target_dict_dim):
                     parameters.to_tar(f)
 
             if event.batch_id % 10 == 0:
-                #                wmt14_test_batch = paddle.batch(
-                #                        paddle.reader.shuffle(
-                #                            paddle.dataset.wmt14.test(source_dict_dim), 
-                #                            buf_size=8192), batch_size=1)
-
-                #test_result = trainer.test(wmt14_test_batch)
                 print "\nPass %d, Batch %d, Cost %f, %s" % (
-                    event.pass_id,
-                    event.batch_id,
-                    event.cost,
-                    event.metrics,  # test_result.cost, test_result.metrics
-                )
+                    event.pass_id, event.batch_id, event.cost, event.metrics)
             else:
                 sys.stdout.write('.')
                 sys.stdout.flush()
@@ -167,7 +148,7 @@ def train(source_dict_dim, target_dict_dim):
         reader=wmt14_reader, event_handler=event_handler, num_passes=2)
 
 
-def generate(source_dict_dim, target_dict_dim):
+def generate(source_dict_dim, target_dict_dim, init_models_path):
     # load data  samples for generation
     gen_creator = paddle.dataset.wmt14.gen(source_dict_dim)
     gen_data = []
@@ -175,8 +156,7 @@ def generate(source_dict_dim, target_dict_dim):
         gen_data.append((item[0], ))
 
     beam_gen = seq2seq_net(source_dict_dim, target_dict_dim, True)
-
-    with gzip.open('models/nmt_without_att_params_batch_400.tar.gz') as f:
+    with gzip.open(init_models_path) as f:
         parameters = paddle.parameters.Parameters.from_tar(f)
     # prob is the prediction probabilities, and id is the prediction word. 
     beam_result = paddle.infer(
@@ -208,15 +188,37 @@ def generate(source_dict_dim, target_dict_dim):
             print "prob = %f:" % (prob[i][j]), seq_list[i * beam_size + j]
 
 
+def usage_helper():
+    print "Please specify training/generating phase!"
+    print "Usage: python nmt_without_attention_v2.py --train/generate"
+    exit(1)
+
+
 def main():
+    if not (len(sys.argv) == 2):
+        usage_helper()
+    if sys.argv[1] == '--train':
+        generating = False
+    elif sys.argv[1] == '--generate':
+        generating = True
+    else:
+        usage_helper()
+
     paddle.init(use_gpu=False, trainer_count=4)
     source_language_dict_dim = 30000
     target_language_dict_dim = 30000
-    generating = True
 
     if generating:
-        generate(source_language_dict_dim, target_language_dict_dim)
+        # shoud pass the right generated model's path here
+        init_models_path = 'models/nmt_without_att_params_batch_400.tar.gz'
+        if not os.path.exists(init_models_path):
+            print "Cannot find models for generation"
+            exit(1)
+        generate(source_language_dict_dim, target_language_dict_dim,
+                 init_models_path)
     else:
+        if not os.path.exists('./models'):
+            os.system('mkdir ./models')
         train(source_language_dict_dim, target_language_dict_dim)