Merge pull request #1555 from jacquesqiao/refine-import

optimize import of seqToseq_net_v2 for book

Author: jacquesqiao

demo/seqToseq/api_train_v2.py

@@ -72,31 +72,35 @@ def main():
     # define network topology
     cost = seqToseq_net_v2(source_dict_dim, target_dict_dim)
     parameters = paddle.parameters.create(cost)
-    optimizer = paddle.optimizer.Adam(learning_rate=1e-4)
-
-    def event_handler(event):
-        if isinstance(event, paddle.event.EndIteration):
-            if event.batch_id % 10 == 0:
-                print "Pass %d, Batch %d, Cost %f, %s" % (
-                    event.pass_id, event.batch_id, event.cost, event.metrics)
 
+    # define optimize method and trainer
+    optimizer = paddle.optimizer.Adam(learning_rate=1e-4)
     trainer = paddle.trainer.SGD(cost=cost,
                                  parameters=parameters,
                                  update_equation=optimizer)
 
+    # define data reader
     reader_dict = {
         'source_language_word': 0,
         'target_language_word': 1,
         'target_language_next_word': 2
     }
 
-    trn_reader = paddle.reader.batched(
+    wmt14_reader = paddle.reader.batched(
         paddle.reader.shuffle(
             train_reader("data/pre-wmt14/train/train"), 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 "Pass %d, Batch %d, Cost %f, %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics)
+
+    # start to train
     trainer.train(
-        reader=trn_reader,
+        reader=wmt14_reader,
         event_handler=event_handler,
         num_passes=10000,
         reader_dict=reader_dict)

demo/seqToseq/seqToseq_net_v2.py

@@ -1,8 +1,4 @@
-import paddle.v2.activation as activation
-import paddle.v2.attr as attr
-import paddle.v2.data_type as data_type
-import paddle.v2.layer as layer
-import paddle.v2.networks as networks
+import paddle.v2 as paddle
 
 
 def seqToseq_net_v2(source_dict_dim, target_dict_dim):
@@ -12,79 +8,85 @@ def seqToseq_net_v2(source_dict_dim, target_dict_dim):
     encoder_size = 512  # dimension of hidden unit in GRU Encoder network
 
     #### Encoder
-    src_word_id = layer.data(
+    src_word_id = paddle.layer.data(
         name='source_language_word',
-        type=data_type.integer_value_sequence(source_dict_dim))
-    src_embedding = layer.embedding(
+        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=attr.ParamAttr(name='_source_language_embedding'))
-    src_forward = networks.simple_gru(input=src_embedding, size=encoder_size)
-    src_backward = networks.simple_gru(
+        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 = layer.concat(input=[src_forward, src_backward])
+    encoded_vector = paddle.layer.concat(input=[src_forward, src_backward])
 
     #### Decoder
-    with layer.mixed(size=decoder_size) as encoded_proj:
-        encoded_proj += layer.full_matrix_projection(input=encoded_vector)
+    with paddle.layer.mixed(size=decoder_size) as encoded_proj:
+        encoded_proj += paddle.layer.full_matrix_projection(
+            input=encoded_vector)
 
-    backward_first = layer.first_seq(input=src_backward)
+    backward_first = paddle.layer.first_seq(input=src_backward)
 
-    with layer.mixed(size=decoder_size, act=activation.Tanh()) as decoder_boot:
-        decoder_boot += layer.full_matrix_projection(input=backward_first)
+    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 = layer.memory(
+        decoder_mem = paddle.layer.memory(
             name='gru_decoder', size=decoder_size, boot_layer=decoder_boot)
 
-        context = networks.simple_attention(
+        context = paddle.networks.simple_attention(
             encoded_sequence=enc_vec,
             encoded_proj=enc_proj,
             decoder_state=decoder_mem)
 
-        with layer.mixed(size=decoder_size * 3) as decoder_inputs:
-            decoder_inputs += layer.full_matrix_projection(input=context)
-            decoder_inputs += layer.full_matrix_projection(input=current_word)
+        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 = layer.gru_step(
+        gru_step = paddle.layer.gru_step(
             name='gru_decoder',
             input=decoder_inputs,
             output_mem=decoder_mem,
             size=decoder_size)
 
-        with layer.mixed(
-                size=target_dict_dim, bias_attr=True,
-                act=activation.Softmax()) as out:
-            out += layer.full_matrix_projection(input=gru_step)
+        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 = layer.StaticInputV2(input=encoded_vector, is_seq=True)
-    group_input2 = layer.StaticInputV2(input=encoded_proj, is_seq=True)
+    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]
 
-    trg_embedding = layer.embedding(
-        input=layer.data(
+    trg_embedding = paddle.layer.embedding(
+        input=paddle.layer.data(
             name='target_language_word',
-            type=data_type.integer_value_sequence(target_dict_dim)),
+            type=paddle.data_type.integer_value_sequence(target_dict_dim)),
         size=word_vector_dim,
-        param_attr=attr.ParamAttr(name='_target_language_embedding'))
+        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 = layer.recurrent_group(
+    decoder = paddle.layer.recurrent_group(
         name=decoder_group_name,
         step=gru_decoder_with_attention,
         input=group_inputs)
 
-    lbl = layer.data(
+    lbl = paddle.layer.data(
         name='target_language_next_word',
-        type=data_type.integer_value_sequence(target_dict_dim))
-    cost = layer.classification_cost(input=decoder, label=lbl)
+        type=paddle.data_type.integer_value_sequence(target_dict_dim))
+    cost = paddle.layer.classification_cost(input=decoder, label=lbl)
 
     return cost