add text classification models

text_classification/text_classification_cnn.py

@@ -0,0 +1,125 @@
+# 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
+import paddle.v2 as paddle
+import gzip
+
+
+def convolution_net(input_dim, class_dim=2, emb_dim=128, hid_dim=128):
+    data = paddle.layer.data("word",
+                             paddle.data_type.integer_value_sequence(input_dim))
+    lbl = paddle.layer.data("label", paddle.data_type.integer_value(2))
+
+    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=paddle.activation.Softmax())
+
+    cost = paddle.layer.classification_cost(input=output, label=lbl)
+
+    return cost, output
+
+
+def train_cnn_model(num_pass):
+    # load word dictionary
+    print 'load dictionary...'
+    word_dict = paddle.dataset.imdb.word_dict()
+
+    dict_dim = len(word_dict)
+    class_dim = 2
+    # define data reader
+    train_reader = paddle.batch(
+        paddle.reader.shuffle(
+            lambda: paddle.dataset.imdb.train(word_dict), buf_size=1000),
+        batch_size=100)
+    test_reader = paddle.batch(
+        lambda: paddle.dataset.imdb.test(word_dict), batch_size=100)
+
+    # network config
+    [cost, _] = convolution_net(dict_dim, class_dim=class_dim)
+    # 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))
+
+    # create trainer
+    trainer = paddle.trainer.SGD(
+        cost=cost, parameters=parameters, update_equation=adam_optimizer)
+
+    # Define 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=test_reader, feeding=feeding)
+            print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
+            with gzip.open("cnn_params.tar.gz", 'w') as f:
+                parameters.to_tar(f)
+
+    # begin training network
+    feeding = {'word': 0, 'label': 1}
+    trainer.train(
+        reader=train_reader,
+        event_handler=event_handler,
+        feeding=feeding,
+        num_passes=num_pass)
+
+    print("Training finished.")
+
+
+def cnn_infer():
+    print("Begin to predict...")
+
+    word_dict = paddle.dataset.imdb.word_dict()
+    dict_dim = len(word_dict)
+    class_dim = 2
+
+    [_, output] = convolution_net(dict_dim, class_dim=class_dim)
+    parameters = paddle.parameters.Parameters.from_tar(
+        gzip.open("cnn_params.tar.gz"))
+
+    infer_data = []
+    infer_label_data = []
+    infer_data_num = 100
+    for item in paddle.dataset.imdb.test(word_dict):
+        infer_data.append([item[0]])
+        infer_label_data.append(item[1])
+        if len(infer_data) == infer_data_num:
+            break
+
+    predictions = paddle.infer(
+        output_layer=output,
+        parameters=parameters,
+        input=infer_data,
+        field=['value'])
+    for i, prob in enumerate(predictions):
+        print prob, infer_label_data[i]
+
+
+if __name__ == "__main__":
+    paddle.init(use_gpu=False, trainer_count=10)
+    train_cnn_model(num_pass=10)
+    cnn_infer()

text_classification/text_classification_dnn.py

@@ -0,0 +1,138 @@
+# 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
+import paddle.v2 as paddle
+import gzip
+
+
+def fc_net(input_dim, class_dim=2, emb_dim=256):
+    data = paddle.layer.data("word",
+                             paddle.data_type.integer_value_sequence(input_dim))
+    lbl = paddle.layer.data("label", paddle.data_type.integer_value(class_dim))
+
+    emb = paddle.layer.embedding(input=data, size=emb_dim)
+    seq_pool = paddle.layer.pooling(
+        input=emb, pooling_type=paddle.pooling.Max())
+
+    hd1 = paddle.layer.fc(
+        input=seq_pool,
+        size=128,
+        act=paddle.activation.Tanh(),
+        param_attr=paddle.attr.Param(initial_std=0.01))
+    hd2 = paddle.layer.fc(
+        input=hd1,
+        size=32,
+        act=paddle.activation.Tanh(),
+        param_attr=paddle.attr.Param(initial_std=0.01))
+
+    output = paddle.layer.fc(
+        input=hd2,
+        size=class_dim,
+        act=paddle.activation.Softmax(),
+        param_attr=paddle.attr.Param(initial_std=0.1))
+
+    cost = paddle.layer.classification_cost(input=output, label=lbl)
+
+    return cost, output
+
+
+def train_dnn_model(num_pass):
+    # load word dictionary
+    print 'load dictionary...'
+    word_dict = paddle.dataset.imdb.word_dict()
+
+    dict_dim = len(word_dict)
+    class_dim = 2
+    # define data reader
+    train_reader = paddle.batch(
+        paddle.reader.shuffle(
+            lambda: paddle.dataset.imdb.train(word_dict), buf_size=1000),
+        batch_size=100)
+    test_reader = paddle.batch(
+        lambda: paddle.dataset.imdb.test(word_dict), batch_size=100)
+
+    # network config
+    [cost, _] = fc_net(dict_dim, class_dim=class_dim)
+    # 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))
+
+    # create trainer
+    trainer = paddle.trainer.SGD(
+        cost=cost, parameters=parameters, update_equation=adam_optimizer)
+
+    # Define 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=test_reader, feeding=feeding)
+            print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
+            with gzip.open("dnn_params.tar.gz", 'w') as f:
+                parameters.to_tar(f)
+
+    # begin training network
+    feeding = {'word': 0, 'label': 1}
+    trainer.train(
+        reader=train_reader,
+        event_handler=event_handler,
+        feeding=feeding,
+        num_passes=num_pass)
+
+    print("Training finished.")
+
+
+def dnn_infer():
+    print("Begin to predict...")
+
+    word_dict = paddle.dataset.imdb.word_dict()
+    dict_dim = len(word_dict)
+    class_dim = 2
+
+    [_, output] = fc_net(dict_dim, class_dim=class_dim)
+    parameters = paddle.parameters.Parameters.from_tar(
+        gzip.open("dnn_params.tar.gz"))
+
+    infer_data = []
+    infer_label_data = []
+    infer_data_num = 100
+    for item in paddle.dataset.imdb.test(word_dict):
+        infer_data.append([item[0]])
+        infer_label_data.append(item[1])
+        if len(infer_data) == infer_data_num:
+            break
+
+    predictions = paddle.infer(
+        output_layer=output,
+        parameters=parameters,
+        input=infer_data,
+        field=['value'])
+    for i, prob in enumerate(predictions):
+        print prob, infer_label_data[i]
+
+
+if __name__ == "__main__":
+    paddle.init(use_gpu=False, trainer_count=4)
+    train_dnn_model(2)
+    dnn_infer()