Merge pull request #1502 from hedaoyuan/sentiment_new_api

Training the understand sentiment model with the new API.

demo/sentiment/train_v2.py

+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],
+                      size=class_dim,
+                      act=activation.Softmax())
+    lbl = layer.data("label", data_type.integer_value(2))
+    cost = layer.classification_cost(input=output, label=lbl)
+    return cost
+
+
+def stacked_lstm_net(input_dim,
+                     class_dim=2,
+                     emb_dim=128,
+                     hid_dim=512,
+                     stacked_num=3,
+                     is_predict=False):
+    """
+    A Wrapper for sentiment classification task.
+    This network uses bi-directional recurrent network,
+    consisting three LSTM layers. This configure is referred to
+    the paper as following url, but use fewer layrs.
+        http://www.aclweb.org/anthology/P15-1109
+
+    input_dim: here is word dictionary dimension.
+    class_dim: number of categories.
+    emb_dim: dimension of word embedding.
+    hid_dim: dimension of hidden layer.
+    stacked_num: number of stacked lstm-hidden layer.
+    is_predict: is predicting or not.
+                Some layers is not needed in network when predicting.
+    """
+    assert stacked_num % 2 == 1
+
+    layer_attr = attrs.ExtraLayerAttribute(drop_rate=0.5)
+    fc_para_attr = attrs.ParameterAttribute(learning_rate=1e-3)
+    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()
+
+    data = layer.data("word", data_type.integer_value_sequence(input_dim))
+    emb = layer.embedding(input=data, size=emb_dim)
+
+    fc1 = layer.fc(input=emb, size=hid_dim, act=linear, bias_attr=bias_attr)
+    lstm1 = 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,
+                      size=hid_dim,
+                      act=linear,
+                      param_attr=para_attr,
+                      bias_attr=bias_attr)
+        lstm = layer.lstmemory(
+            input=fc,
+            reverse=(i % 2) == 0,
+            act=relu,
+            bias_attr=bias_attr,
+            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],
+                      size=class_dim,
+                      act=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)
+    return cost
+
+
+if __name__ == '__main__':
+    # init
+    paddle.init(use_gpu=True, trainer_count=4)
+
+    # network config
+    print 'load dictionary...'
+    word_dict = imdb.word_dict()
+    dict_dim = len(word_dict)
+    class_dim = 2
+
+    # Please choose the way to build the network
+    # by uncommenting the corresponding line.
+    cost = convolution_net(dict_dim, class_dim=class_dim)
+    # cost = stacked_lstm_net(dict_dim, class_dim=class_dim, stacked_num=3)
+
+    # create parameters
+    parameters = paddle.parameters.create(cost)
+
+    # create optimizer
+    adam_optimizer = paddle.optimizer.Adam(
+        learning_rate=2e-3,
+        regularization=paddle.optimizer.L2Regularization(rate=8e-4),
+        model_average=paddle.optimizer.ModelAverage(average_window=0.5))
+
+    # End batch and end pass event handler
+    def event_handler(event):
+        if isinstance(event, paddle.event.EndIteration):
+            if event.batch_id % 100 == 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()
+        if isinstance(event, paddle.event.EndPass):
+            result = trainer.test(
+                reader=paddle.reader.batched(
+                    lambda: imdb.test(word_dict), batch_size=128),
+                reader_dict={'word': 0,
+                             'label': 1})
+            print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
+
+    # create trainer
+    trainer = paddle.trainer.SGD(cost=cost,
+                                 parameters=parameters,
+                                 update_equation=adam_optimizer)
+
+    trainer.train(
+        reader=paddle.reader.batched(
+            paddle.reader.shuffle(
+                lambda: imdb.train(word_dict), buf_size=1000),
+            batch_size=100),
+        event_handler=event_handler,
+        reader_dict={'word': 0,
+                     'label': 1},
+        num_passes=10)

python/paddle/v2/dataset/imdb.py

@@ -116,3 +116,8 @@ def test(word_idx):
     return reader_creator(
         re.compile("aclImdb/test/pos/.*\.txt$"),
         re.compile("aclImdb/test/neg/.*\.txt$"), word_idx, 1000)
+
+
+def word_dict():
+    return build_dict(
+        re.compile("aclImdb/((train)|(test))/((pos)|(neg))/.*\.txt$"), 150)