Add demo of understand_sentiment on paddlecloud

demo/understand_sentiment/train.py

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+# 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 paddle.v2 as paddle
+import paddle.v2.dataset.common as common
+import os
+import sys
+import glob
+import pickle
+
+# NOTE: must change this to your own username on paddlecloud.
+USERNAME = "demo"
+DC = os.getenv("PADDLE_CLOUD_CURRENT_DATACENTER")
+common.DATA_HOME = "/pfs/%s/home/%s" % (DC, USERNAME)
+TRAIN_FILES_PATH = os.path.join(common.DATA_HOME, "imdb")
+TEST_FILES_PATH = os.path.join(common.DATA_HOME, "imdb")
+
+TRAINER_ID = int(os.getenv("PADDLE_INIT_TRAINER_ID", "-1"))
+TRAINER_COUNT = int(os.getenv("PADDLE_INIT_NUM_GRADIENT_SERVERS", "-1"))
+
+def prepare_dataset():
+    word_dict = paddle.dataset.imdb.word_dict()
+    # convert will also split the dataset by line-count
+    common.convert(TRAIN_FILES_PATH,
+            lambda: paddle.dataset.imdb.train(word_dict),
+                1000, "train")
+    common.convert(TEST_FILES_PATH,
+            lambda: paddle.dataset.imdb.test(word_dict),
+                1000, "test")
+
+def cluster_reader_recordio(trainer_id, trainer_count, flag):
+    '''
+        read from cloud dataset which is stored as recordio format
+        each trainer will read a subset of files of the whole dataset.
+    '''
+    import recordio
+    def reader():
+        PATTERN_STR = "%s-*" % flag
+        FILES_PATTERN = os.path.join(TRAIN_FILES_PATH, PATTERN_STR)
+        file_list = glob.glob(FILES_PATTERN)
+        file_list.sort()
+        my_file_list = []
+        # read files for current trainer_id
+        for idx, f in enumerate(file_list):
+            if idx % trainer_count == trainer_id:
+                my_file_list.append(f)
+        for f in my_file_list:
+            print "processing ", f
+            reader = recordio.reader(f)
+            record_raw = reader.read()
+            while record_raw:
+                yield pickle.loads(record_raw)
+                record_raw = reader.read()
+            reader.close()
+    return reader
+
+
+
+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))
+    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())
+    lbl = paddle.layer.data("label", paddle.data_type.integer_value(2))
+    cost = paddle.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):
+    """
+    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.
+    """
+    assert stacked_num % 2 == 1
+
+    layer_attr = paddle.attr.Extra(drop_rate=0.5)
+    fc_para_attr = paddle.attr.Param(learning_rate=1e-3)
+    lstm_para_attr = paddle.attr.Param(initial_std=0., learning_rate=1.)
+    para_attr = [fc_para_attr, lstm_para_attr]
+    bias_attr = paddle.attr.Param(initial_std=0., l2_rate=0.)
+    relu = paddle.activation.Relu()
+    linear = paddle.activation.Linear()
+
+    data = paddle.layer.data("word",
+                             paddle.data_type.integer_value_sequence(input_dim))
+    emb = paddle.layer.embedding(input=data, size=emb_dim)
+
+    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 = paddle.layer.fc(
+            input=inputs,
+            size=hid_dim,
+            act=linear,
+            param_attr=para_attr,
+            bias_attr=bias_attr)
+        lstm = paddle.layer.lstmemory(
+            input=fc,
+            reverse=(i % 2) == 0,
+            act=relu,
+            bias_attr=bias_attr,
+            layer_attr=layer_attr)
+        inputs = [fc, lstm]
+
+    fc_last = paddle.layer.pooling(
+        input=inputs[0], pooling_type=paddle.pooling.Max())
+    lstm_last = paddle.layer.pooling(
+        input=inputs[1], pooling_type=paddle.pooling.Max())
+    output = paddle.layer.fc(
+        input=[fc_last, lstm_last],
+        size=class_dim,
+        act=paddle.activation.Softmax(),
+        bias_attr=bias_attr,
+        param_attr=para_attr)
+
+    lbl = paddle.layer.data("label", paddle.data_type.integer_value(2))
+    cost = paddle.layer.classification_cost(input=output, label=lbl)
+    return cost
+
+
+def main():
+    # init
+    paddle.init()
+    #data
+    print 'load dictionary...'
+    word_dict = paddle.dataset.imdb.word_dict()
+    dict_dim = len(word_dict)
+    class_dim = 2
+    train_reader = paddle.batch(
+        paddle.reader.shuffle(
+            cluster_reader_recordio(TRAINER_ID, TRAINER_COUNT, "train"), buf_size=1000),
+        batch_size=100)
+    test_reader = paddle.batch(
+        cluster_reader_recordio(TRAINER_ID, TRAINER_COUNT, "test"), batch_size=100)
+
+    feeding = {'word': 0, 'label': 1}
+
+    # network config
+    # 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=test_reader, feeding=feeding)
+            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=train_reader,
+        event_handler=event_handler,
+        feeding=feeding,
+        num_passes=2)
+
+if __name__ == '__main__':
+    usage = "python train.py [prepare|train]"
+    if len(sys.argv) != 2:
+        print usage
+        exit(1)
+
+    if TRAINER_ID == -1 or TRAINER_COUNT == -1:
+        print "no cloud environ found, must run on cloud"
+        exit(1)
+
+    if sys.argv[1] == "prepare":
+        prepare_dataset()
+    elif sys.argv[1] == "train":
+        main()