Merge pull request #1549 from hedaoyuan/sentiment_new_api

Use the sequence_conv_pool define inside the networks.py

demo/sentiment/train_v2.py

+# Copyright (c) 2016 PaddlePaddle Authors. 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.
+
 import sys
-from os.path import join as join_path
 import paddle.trainer_config_helpers.attrs as attrs
 from paddle.trainer_config_helpers.poolings import MaxPooling
-import paddle.v2.layer as layer
-import paddle.v2.activation as activation
-import paddle.v2.data_type as data_type
-import paddle.v2.dataset.imdb as imdb
 import paddle.v2 as paddle
 
 
-def sequence_conv_pool(input,
-                       input_size,
-                       context_len,
-                       hidden_size,
-                       name=None,
-                       context_start=None,
-                       pool_type=None,
-                       context_proj_layer_name=None,
-                       context_proj_param_attr=False,
-                       fc_layer_name=None,
-                       fc_param_attr=None,
-                       fc_bias_attr=None,
-                       fc_act=None,
-                       pool_bias_attr=None,
-                       fc_attr=None,
-                       context_attr=None,
-                       pool_attr=None):
-    """
-    Text convolution pooling layers helper.
-
-    Text input => Context Projection => FC Layer => Pooling => Output.
-
-    :param name: name of output layer(pooling layer name)
-    :type name: basestring
-    :param input: name of input layer
-    :type input: LayerOutput
-    :param context_len: context projection length. See
-                        context_projection's document.
-    :type context_len: int
-    :param hidden_size: FC Layer size.
-    :type hidden_size: int
-    :param context_start: context projection length. See
-                          context_projection's context_start.
-    :type context_start: int or None
-    :param pool_type: pooling layer type. See pooling_layer's document.
-    :type pool_type: BasePoolingType.
-    :param context_proj_layer_name: context projection layer name.
-                                    None if user don't care.
-    :type context_proj_layer_name: basestring
-    :param context_proj_param_attr: context projection parameter attribute.
-                                    None if user don't care.
-    :type context_proj_param_attr: ParameterAttribute or None.
-    :param fc_layer_name: fc layer name. None if user don't care.
-    :type fc_layer_name: basestring
-    :param fc_param_attr: fc layer parameter attribute. None if user don't care.
-    :type fc_param_attr: ParameterAttribute or None
-    :param fc_bias_attr: fc bias parameter attribute. False if no bias,
-                         None if user don't care.
-    :type fc_bias_attr: ParameterAttribute or None
-    :param fc_act: fc layer activation type. None means tanh
-    :type fc_act: BaseActivation
-    :param pool_bias_attr: pooling layer bias attr. None if don't care.
-                           False if no bias.
-    :type pool_bias_attr: ParameterAttribute or None.
-    :param fc_attr: fc layer extra attribute.
-    :type fc_attr: ExtraLayerAttribute
-    :param context_attr: context projection layer extra attribute.
-    :type context_attr: ExtraLayerAttribute
-    :param pool_attr: pooling layer extra attribute.
-    :type pool_attr: ExtraLayerAttribute
-    :return: output layer name.
-    :rtype: LayerOutput
-    """
-    # Set Default Value to param
-    context_proj_layer_name = "%s_conv_proj" % name \
-        if context_proj_layer_name is None else context_proj_layer_name
-
-    with layer.mixed(
-            name=context_proj_layer_name,
-            size=input_size * context_len,
-            act=activation.Linear(),
-            layer_attr=context_attr) as m:
-        m += layer.context_projection(
-            input=input,
-            context_len=context_len,
-            context_start=context_start,
-            padding_attr=context_proj_param_attr)
-
-    fc_layer_name = "%s_conv_fc" % name \
-        if fc_layer_name is None else fc_layer_name
-    fl = layer.fc(name=fc_layer_name,
-                  input=m,
-                  size=hidden_size,
-                  act=fc_act,
-                  layer_attr=fc_attr,
-                  param_attr=fc_param_attr,
-                  bias_attr=fc_bias_attr)
-
-    return layer.pooling(
-        name=name,
-        input=fl,
-        pooling_type=pool_type,
-        bias_attr=pool_bias_attr,
-        layer_attr=pool_attr)
-
-
 def convolution_net(input_dim,
                     class_dim=2,
                     emb_dim=128,
                     hid_dim=128,
                     is_predict=False):
-    data = layer.data("word", data_type.integer_value_sequence(input_dim))
-    emb = layer.embedding(input=data, size=emb_dim)
-    conv_3 = sequence_conv_pool(
-        input=emb, input_size=emb_dim, context_len=3, hidden_size=hid_dim)
-    conv_4 = sequence_conv_pool(
-        input=emb, input_size=emb_dim, context_len=4, hidden_size=hid_dim)
-    output = layer.fc(input=[conv_3, conv_4],
+    data = paddle.layer.data("word",
+                             paddle.data_type.integer_value_sequence(input_dim))
+    emb = paddle.layer.embedding(input=data, size=emb_dim)
+    conv_3 = paddle.networks.sequence_conv_pool(
+        input=emb, context_len=3, hidden_size=hid_dim)
+    conv_4 = paddle.networks.sequence_conv_pool(
+        input=emb, context_len=4, hidden_size=hid_dim)
+    output = paddle.layer.fc(input=[conv_3, conv_4],
                              size=class_dim,
-                      act=activation.Softmax())
-    lbl = layer.data("label", data_type.integer_value(2))
-    cost = layer.classification_cost(input=output, label=lbl)
+                             act=paddle.activation.Softmax())
+    lbl = paddle.layer.data("label", paddle.data_type.integer_value(2))
+    cost = paddle.layer.classification_cost(input=output, label=lbl)
     return cost
 
 
@@ -152,24 +66,28 @@ def stacked_lstm_net(input_dim,
     lstm_para_attr = attrs.ParameterAttribute(initial_std=0., learning_rate=1.)
     para_attr = [fc_para_attr, lstm_para_attr]
     bias_attr = attrs.ParameterAttribute(initial_std=0., l2_rate=0.)
-    relu = activation.Relu()
-    linear = activation.Linear()
+    relu = paddle.activation.Relu()
+    linear = paddle.activation.Linear()
 
-    data = layer.data("word", data_type.integer_value_sequence(input_dim))
-    emb = layer.embedding(input=data, size=emb_dim)
+    data = paddle.layer.data("word",
+                             paddle.data_type.integer_value_sequence(input_dim))
+    emb = paddle.layer.embedding(input=data, size=emb_dim)
 
-    fc1 = layer.fc(input=emb, size=hid_dim, act=linear, bias_attr=bias_attr)
-    lstm1 = layer.lstmemory(
+    fc1 = paddle.layer.fc(input=emb,
+                          size=hid_dim,
+                          act=linear,
+                          bias_attr=bias_attr)
+    lstm1 = paddle.layer.lstmemory(
         input=fc1, act=relu, bias_attr=bias_attr, layer_attr=layer_attr)
 
     inputs = [fc1, lstm1]
     for i in range(2, stacked_num + 1):
-        fc = layer.fc(input=inputs,
+        fc = paddle.layer.fc(input=inputs,
                              size=hid_dim,
                              act=linear,
                              param_attr=para_attr,
                              bias_attr=bias_attr)
-        lstm = layer.lstmemory(
+        lstm = paddle.layer.lstmemory(
             input=fc,
             reverse=(i % 2) == 0,
             act=relu,
@@ -177,16 +95,16 @@ def stacked_lstm_net(input_dim,
             layer_attr=layer_attr)
         inputs = [fc, lstm]
 
-    fc_last = layer.pooling(input=inputs[0], pooling_type=MaxPooling())
-    lstm_last = layer.pooling(input=inputs[1], pooling_type=MaxPooling())
-    output = layer.fc(input=[fc_last, lstm_last],
+    fc_last = paddle.layer.pooling(input=inputs[0], pooling_type=MaxPooling())
+    lstm_last = paddle.layer.pooling(input=inputs[1], pooling_type=MaxPooling())
+    output = paddle.layer.fc(input=[fc_last, lstm_last],
                              size=class_dim,
-                      act=activation.Softmax(),
+                             act=paddle.activation.Softmax(),
                              bias_attr=bias_attr,
                              param_attr=para_attr)
 
-    lbl = layer.data("label", data_type.integer_value(2))
-    cost = layer.classification_cost(input=output, label=lbl)
+    lbl = paddle.layer.data("label", paddle.data_type.integer_value(2))
+    cost = paddle.layer.classification_cost(input=output, label=lbl)
     return cost
 
 
@@ -196,7 +114,7 @@ if __name__ == '__main__':
 
     # network config
     print 'load dictionary...'
-    word_dict = imdb.word_dict()
+    word_dict = paddle.dataset.imdb.word_dict()
     dict_dim = len(word_dict)
     class_dim = 2
 
@@ -226,7 +144,8 @@ if __name__ == '__main__':
         if isinstance(event, paddle.event.EndPass):
             result = trainer.test(
                 reader=paddle.reader.batched(
-                    lambda: imdb.test(word_dict), batch_size=128),
+                    lambda: paddle.dataset.imdb.test(word_dict),
+                    batch_size=128),
                 reader_dict={'word': 0,
                              'label': 1})
             print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
@@ -239,7 +158,7 @@ if __name__ == '__main__':
     trainer.train(
         reader=paddle.reader.batched(
             paddle.reader.shuffle(
-                lambda: imdb.train(word_dict), buf_size=1000),
+                lambda: paddle.dataset.imdb.train(word_dict), buf_size=1000),
             batch_size=100),
         event_handler=event_handler,
         reader_dict={'word': 0,