Added Bidi-LSTM and DB-LSTM to quick_start demo

demo/quick_start/train.sh

@@ -18,6 +18,8 @@ cfg=trainer_config.lr.py
 #cfg=trainer_config.emb.py
 #cfg=trainer_config.cnn.py
 #cfg=trainer_config.lstm.py
+#cfg=trainer_config.bidi-lstm.py
+#cfg=trainer_config.db-lstm.py
 paddle train \
   --config=$cfg \
   --save_dir=./output \

demo/quick_start/trainer_config.bidi-lstm.py

@@ -0,0 +1,62 @@
+# edit-mode: -*- python -*-
+
+# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle.trainer_config_helpers import *
+
+dict_file = "./data/dict.txt"
+word_dict = dict()
+with open(dict_file, 'r') as f:
+    for i, line in enumerate(f):
+        w = line.strip().split()[0]
+        word_dict[w] = i
+
+is_predict = get_config_arg('is_predict', bool, False)
+trn = 'data/train.list' if not is_predict else None
+tst = 'data/test.list' if not is_predict else 'data/pred.list'
+process = 'process' if not is_predict else 'process_predict'
+define_py_data_sources2(train_list=trn,
+                        test_list=tst,
+                        module="dataprovider_emb",
+                        obj=process,
+                        args={"dictionary": word_dict})
+
+batch_size = 128 if not is_predict else 1
+settings(
+    batch_size=batch_size,
+    learning_rate=2e-3,
+    learning_method=AdamOptimizer(),
+    regularization=L2Regularization(8e-4),
+    gradient_clipping_threshold=25
+)
+
+bias_attr = ParamAttr(initial_std=0.,l2_rate=0.)
+data = data_layer(name="word", size=len(word_dict))
+emb = embedding_layer(input=data, size=128)
+
+bi_lstm = bidirectional_lstm(input=emb, size=128)
+dropout = dropout_layer(input=bi_lstm, dropout_rate=0.5)
+
+output = fc_layer(input=dropout, size=2,
+                  bias_attr=bias_attr,
+                  act=SoftmaxActivation())
+
+if is_predict:
+    maxid = maxid_layer(output)
+    outputs([maxid, output])
+else:
+    label = data_layer(name="label", size=2)
+    cls = classification_cost(input=output, label=label)
+    outputs(cls)

demo/quick_start/trainer_config.db-lstm.py

@@ -0,0 +1,73 @@
+# edit-mode: -*- python -*-
+
+# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle.trainer_config_helpers import *
+
+dict_file = "./data/dict.txt"
+word_dict = dict()
+with open(dict_file, 'r') as f:
+    for i, line in enumerate(f):
+        w = line.strip().split()[0]
+        word_dict[w] = i
+
+is_predict = get_config_arg('is_predict', bool, False)
+trn = 'data/train.list' if not is_predict else None
+tst = 'data/test.list' if not is_predict else 'data/pred.list'
+process = 'process' if not is_predict else 'process_predict'
+define_py_data_sources2(train_list=trn,
+                        test_list=tst,
+                        module="dataprovider_emb",
+                        obj=process,
+                        args={"dictionary": word_dict})
+
+batch_size = 128 if not is_predict else 1
+settings(
+    batch_size=batch_size,
+    learning_rate=2e-3,
+    learning_method=AdamOptimizer(),
+    regularization=L2Regularization(8e-4),
+    gradient_clipping_threshold=25
+)
+
+bias_attr = ParamAttr(initial_std=0.,l2_rate=0.)
+
+data = data_layer(name="word", size=len(word_dict))
+emb = embedding_layer(input=data, size=128)
+
+hidden_0 = mixed_layer(size=128, input=[full_matrix_projection(input=emb)])
+lstm_0 = lstmemory(input=hidden_0, layer_attr=ExtraAttr(drop_rate=0.1))
+
+input_layers = [hidden_0, lstm_0]
+
+for i in range(1,8):
+    fc = fc_layer(input=input_layers, size=128)
+    lstm = lstmemory(input=fc, layer_attr=ExtraAttr(drop_rate=0.1),
+                    reverse=(i % 2) == 1,)
+    input_layers = [fc, lstm]
+
+lstm_last = pooling_layer(input=lstm, pooling_type=MaxPooling())
+
+output = fc_layer(input=lstm_last, size=2,
+                  bias_attr=bias_attr,
+                  act=SoftmaxActivation())
+
+if is_predict:
+    maxid = maxid_layer(output)
+    outputs([maxid, output])
+else:
+    label = data_layer(name="label", size=2)
+    cls = classification_cost(input=output, label=label)
+    outputs(cls)