new_contents: # Copyright (c) 2018 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.
  
  from __future__ import print_function
  
  import paddle
  import paddle.fluid as fluid
  from functools import partial
  
  CLASS_DIM = 2
  EMB_DIM = 128
  HID_DIM = 512
  STACKED_NUM = 3
  
  
  def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
      assert stacked_num % 2 == 1
  
      emb = fluid.layers.embedding(
          input=data, size=[input_dim, emb_dim], is_sparse=True)
  
      fc1 = fluid.layers.fc(input=emb, size=hid_dim)
      lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)
  
      inputs = [fc1, lstm1]
  
      for i in range(2, stacked_num + 1):
          fc = fluid.layers.fc(input=inputs, size=hid_dim)
          lstm, cell = fluid.layers.dynamic_lstm(
              input=fc, size=hid_dim, is_reverse=(i % 2) == 0)
          inputs = [fc, lstm]
  
      fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
      lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max')
  
      prediction = fluid.layers.fc(input=[fc_last, lstm_last],
                                   size=class_dim,
                                   act='softmax')
      return prediction
  
  
  def inference_network(word_dict):
      data = fluid.layers.data(
          name="words", shape=[1], dtype="int64", lod_level=1)
  
      dict_dim = len(word_dict)
      net = stacked_lstm_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM,
                             STACKED_NUM)
      return net
  
  
  def train_network(word_dict):
      prediction = inference_network(word_dict)
      label = fluid.layers.data(name="label", shape=[1], dtype="int64")
      cost = fluid.layers.cross_entropy(input=prediction, label=label)
      avg_cost = fluid.layers.mean(cost)
      accuracy = fluid.layers.accuracy(input=prediction, label=label)
      return avg_cost, accuracy
  
  
  def train(use_cuda, save_path):
      BATCH_SIZE = 128
      EPOCH_NUM = 5
  
      word_dict = paddle.dataset.imdb.word_dict()
  
      train_data = paddle.batch(
          paddle.reader.shuffle(
              paddle.dataset.imdb.train(word_dict), buf_size=1000),
          batch_size=BATCH_SIZE)
  
      test_data = paddle.batch(
          paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
  
      def event_handler(event):
          if isinstance(event, fluid.EndIteration):
              if (event.batch_id % 10) == 0:
                  avg_cost, accuracy = trainer.test(reader=test_data)
  
                  print('BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'.format(
                      event.batch_id + 1, avg_cost, accuracy))
  
                  if accuracy > 0.01:  # Low threshold for speeding up CI
                      trainer.params.save(save_path)
                      return
  
      place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
      trainer = fluid.Trainer(
          partial(train_network, word_dict),
          optimizer=fluid.optimizer.Adagrad(learning_rate=0.002),
          place=place,
          event_handler=event_handler)
  
      trainer.train(train_data, EPOCH_NUM, event_handler=event_handler)
  
  
  def infer(use_cuda, save_path):
      params = fluid.Params(save_path)
      place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
      word_dict = paddle.dataset.imdb.word_dict()
      inferencer = fluid.Inferencer(
          partial(inference_network, word_dict), params, place=place)
  
      def create_random_lodtensor(lod, place, low, high):
          data = np.random.random_integers(low, high,
                                           [lod[-1], 1]).astype("int64")
          res = fluid.LoDTensor()
          res.set(data, place)
          res.set_lod([lod])
          return res
  
      lod = [0, 4, 10]
      tensor_words = create_random_lodtensor(
          lod, place, low=0, high=len(word_dict) - 1)
      results = inferencer.infer({'words': tensor_words})
      print("infer results: ", results)
  
  
  def main(use_cuda):
      if use_cuda and not fluid.core.is_compiled_with_cuda():
          return
      save_path = "understand_sentiment_stacked_lstm.inference.model"
      train(use_cuda, save_path)
      infer(use_cuda, save_path)
  
  
  if __name__ == '__main__':
      for use_cuda in (False, True):
          main(use_cuda=use_cuda)
  
subject: Write the Understand Sentiment book example with stacked LSTM using new API (#10355)