add w2v

models/recall/w2v_evaluate_reader.py

+# Copyright (c) 2019 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 numpy as np
+import io
+import six
+from fleetrec.core.reader import Reader
+from fleetrec.core.utils import envs
+
+
+class EvaluateReader(Reader):
+    def init(self):
+        dict_path = envs.get_global_env("word_id_dict_path", None, "evaluate.reader") 
+        self.word_to_id = dict()
+        self.id_to_word = dict()
+        with io.open(dict_path, 'r', encoding='utf-8') as f:
+            for line in f:
+                self.word_to_id[line.split(' ')[0]] = int(line.split(' ')[1])
+                self.id_to_word[int(line.split(' ')[1])] = line.split(' ')[0]
+        self.dict_size = len(self.word_to_id)
+
+    def native_to_unicode(self, s):
+        if self._is_unicode(s):
+            return s
+        try:
+            return self._to_unicode(s)
+        except UnicodeDecodeError:
+            res = self._to_unicode(s, ignore_errors=True)
+            return res
+
+    def _is_unicode(self, s):
+        if six.PY2:
+            if isinstance(s, unicode):
+                return True
+        else:
+            if isinstance(s, str):
+                return True
+        return False
+    
+    
+    def _to_unicode(self, s, ignore_errors=False):
+        if self._is_unicode(s):
+            return s
+        error_mode = "ignore" if ignore_errors else "strict"
+        return s.decode("utf-8", errors=error_mode)
+    
+    
+    def strip_lines(self, line, vocab):
+        return self._replace_oov(vocab, self.native_to_unicode(line))
+    
+    
+    def _replace_oov(self, original_vocab, line):
+        """Replace out-of-vocab words with "<UNK>".
+      This maintains compatibility with published results.
+      Args:
+        original_vocab: a set of strings (The standard vocabulary for the dataset)
+        line: a unicode string - a space-delimited sequence of words.
+      Returns:
+        a unicode string - a space-delimited sequence of words.
+      """
+        return u" ".join([
+            word if word in original_vocab else u"<UNK>" for word in line.split()
+        ])
+
+    def generate_sample(self, line):
+        def reader():
+            features = self.strip_lines(line.lower(), self.word_to_id)
+            features = features.split()
+            yield [('analogy_a', [self.word_to_id[features[0]]]), ('analogy_b', [self.word_to_id[features[1]]]), ('analogy_c', [self.word_to_id[features[2]]]), ('analogy_d', [self.word_to_id[features[3]]])]
+        return reader

models/recall/w2v_reader.py

+# Copyright (c) 2019 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 numpy as np
+import io
+from fleetrec.core.reader import Reader
+from fleetrec.core.utils import envs
+
+
+class NumpyRandomInt(object):
+    def __init__(self, a, b, buf_size=1000):
+        self.idx = 0
+        self.buffer = np.random.random_integers(a, b, buf_size)
+        self.a = a
+        self.b = b
+
+    def __call__(self):
+        if self.idx == len(self.buffer):
+            self.buffer = np.random.random_integers(self.a, self.b,
+                                                    len(self.buffer))
+            self.idx = 0
+
+        result = self.buffer[self.idx]
+        self.idx += 1
+        return result
+
+
+class TrainReader(Reader):
+    def init(self):
+        dict_path = envs.get_global_env("word_count_dict_path", None, "train.reader") 
+        self.window_size = envs.get_global_env("hyper_parameters.window_size", None, "train.model")
+        self.neg_num = envs.get_global_env("hyper_parameters.neg_num", None, "train.model")
+        self.with_shuffle_batch = envs.get_global_env("hyper_parameters.with_shuffle_batch", None, "train.model")
+        self.random_generator = NumpyRandomInt(1, self.window_size + 1)
+
+        self.cs = None
+        if not self.with_shuffle_batch:
+            id_counts = []
+            word_all_count = 0
+            with io.open(dict_path, 'r', encoding='utf-8') as f:
+                for line in f:
+                    word, count = line.split()[0], int(line.split()[1])
+                    id_counts.append(count)
+                    word_all_count += count
+            id_frequencys = [
+                float(count) / word_all_count for count in id_counts
+            ]
+            np_power = np.power(np.array(id_frequencys), 0.75)
+            id_frequencys_pow = np_power / np_power.sum()
+            self.cs = np.array(id_frequencys_pow).cumsum()
+
+    def get_context_words(self, words, idx):
+        """
+        Get the context word list of target word.
+        words: the words of the current line
+        idx: input word index
+        window_size: window size
+        """
+        target_window = self.random_generator()
+        start_point = idx - target_window  # if (idx - target_window) > 0 else 0
+        if start_point < 0:
+            start_point = 0
+        end_point = idx + target_window
+        targets = words[start_point:idx] + words[idx + 1:end_point + 1]
+        return targets 
+
+    def generate_sample(self, line):
+        def reader():
+            word_ids = [w for w in line.split()]
+            for idx, target_id in enumerate(word_ids):
+                context_word_ids = self.get_context_words(
+                    word_ids, idx)
+                for context_id in context_word_ids:
+                    output = [('input_word', [int(target_id)]), ('true_label', [int(context_id)])]
+                    if not self.with_shuffle_batch:
+                        neg_array = self.cs.searchsorted(np.random.sample(self.neg_num))
+                        output += [('neg_label', [int(str(i)) for i in neg_array ])]
+                    yield output
+        return reader
+

models/recall/word2vec/config.yaml

+# Copyright (c) 2020 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.
+evaluate:
+  workspace: "fleetrec.models.recall.word2vec"
+  reader:
+    batch_size: 50
+    class: "{workspace}/../w2v_evaluate_reader.py"
+    test_data_path: "{workspace}/data/test"
+    word_id_dict_path: "{workspace}/data/dict/word_id_dict.txt"
+
+train:
+  trainer:
+    # for cluster training
+    strategy: "async"
+
+  epochs: 2
+  workspace: "fleetrec.models.recall.word2vec"
+
+  reader:
+    batch_size: 100
+    class: "{workspace}/../w2v_reader.py"
+    train_data_path: "{workspace}/data/train"
+    test_data_path: "{workspace}/data/test"
+    word_count_dict_path: "{workspace}/data/dict/word_count_dict.txt"
+    word_id_dict_path: "{workspace}/data/dict/word_id_dict.txt"
+
+  model:
+    models: "{workspace}/model.py"
+    hyper_parameters:
+      sparse_feature_number: 354051
+      sparse_feature_dim: 300
+      with_shuffle_batch: False
+      neg_num: 5
+      window_size: 5
+      learning_rate: 1.0
+      decay_steps: 100000
+      decay_rate: 0.999
+      optimizer: sgd
+
+  save:
+    increment:
+      dirname: "increment"
+      epoch_interval: 1
+      save_last: True
+    inference:
+      dirname: "inference"
+      epoch_interval: 1
+      save_last: True

models/recall/word2vec/data/dict/word_count_dict.txt

+<UNK> 2541
+the 256
+to 135
+of 122
+a 106
+in 97
+and 94
+that 54
+for 49
+is 47
+on 44
+s 43
+at 37
+said 34
+be 31
+with 27
+will 26
+are 25
+have 24
+was 23
+it 22
+more 20
+who 20
+an 19
+as 19
+by 18
+his 18
+from 18
+they 17
+not 16
+their 16
+has 15
+there 15
+this 15
+but 15
+we 13
+he 13
+been 12
+out 12
+new 11
+would 11
+than 11
+were 11
+year 10
+or 10
+us 10
+had 9
+first 9
+all 9
+two 9
+after 8
+them 8
+t 8
+most 8
+last 8
+some 8
+so 8
+i 8
+even 7
+when 7
+according 7
+its 7
+during 7
+per 7
+because 7
+up 7
+she 7
+home 7
+about 7
+mr 6
+do 6
+if 6
+just 6
+no 6
+time 6
+team 6
+may 6
+years 6
+city 6
+only 6
+world 6
+you 6
+including 6
+day 6
+cent 6

models/recall/word2vec/data/dict/word_id_dict.txt

+and 6
+all 48
+because 64
+just 72
+per 63
+when 59
+is 9
+year 43
+some 55
+it 20
+an 23
+as 24
+including 82
+at 12
+have 18
+in 5
+home 67
+its 61
+<UNK> 0
+even 58
+city 78
+said 13
+from 27
+for 8
+their 30
+there 32
+had 46
+two 49
+been 37
+than 41
+up 65
+to 2
+only 79
+time 74
+new 39
+you 81
+has 31
+was 19
+day 83
+more 21
+be 14
+we 35
+his 26
+may 76
+do 70
+that 7
+mr 69
+she 66
+team 75
+who 22
+but 34
+if 71
+most 53
+cent 84
+them 51
+they 28
+not 29
+during 62
+years 77
+with 15
+by 25
+after 50
+he 36
+a 4
+on 10
+about 68
+last 54
+would 40
+world 80
+this 33
+of 3
+no 73
+according 60
+us 45
+will 16
+i 57
+s 11
+so 56
+t 52
+were 42
+the 1
+first 47
+out 38
+or 44
+are 17

models/recall/word2vec/data/test/sample.txt

+Athens Greece Baghdad Iraq
+Athens Greece Bangkok Thailand
+Athens Greece Beijing China
+Athens Greece Berlin Germany
+Athens Greece Bern Switzerland
+Athens Greece Cairo Egypt
+Athens Greece Canberra Australia
+Athens Greece Hanoi Vietnam
+Athens Greece Havana Cuba
+Athens Greece Helsinki Finland
+Athens Greece Islamabad Pakistan
+Athens Greece Kabul Afghanistan
+Athens Greece London England
+Athens Greece Madrid Spain
+Athens Greece Moscow Russia
+Athens Greece Oslo Norway
+Athens Greece Ottawa Canada
+Athens Greece Paris France
+Athens Greece Rome Italy
+Athens Greece Stockholm Sweden
+Athens Greece Tehran Iran
+Athens Greece Tokyo Japan
+Baghdad Iraq Bangkok Thailand
+Baghdad Iraq Beijing China
+Baghdad Iraq Berlin Germany
+Baghdad Iraq Bern Switzerland
+Baghdad Iraq Cairo Egypt
+Baghdad Iraq Canberra Australia
+Baghdad Iraq Hanoi Vietnam
+Baghdad Iraq Havana Cuba
+Baghdad Iraq Helsinki Finland
+Baghdad Iraq Islamabad Pakistan
+Baghdad Iraq Kabul Afghanistan
+Baghdad Iraq London England
+Baghdad Iraq Madrid Spain
+Baghdad Iraq Moscow Russia
+Baghdad Iraq Oslo Norway
+Baghdad Iraq Ottawa Canada
+Baghdad Iraq Paris France
+Baghdad Iraq Rome Italy
+Baghdad Iraq Stockholm Sweden
+Baghdad Iraq Tehran Iran
+Baghdad Iraq Tokyo Japan
+Baghdad Iraq Athens Greece
+Bangkok Thailand Beijing China
+Bangkok Thailand Berlin Germany
+Bangkok Thailand Bern Switzerland
+Bangkok Thailand Cairo Egypt
+Bangkok Thailand Canberra Australia
+Bangkok Thailand Hanoi Vietnam
+Bangkok Thailand Havana Cuba
+Bangkok Thailand Helsinki Finland
+Bangkok Thailand Islamabad Pakistan
+Bangkok Thailand Kabul Afghanistan
+Bangkok Thailand London England
+Bangkok Thailand Madrid Spain
+Bangkok Thailand Moscow Russia
+Bangkok Thailand Oslo Norway
+Bangkok Thailand Ottawa Canada
+Bangkok Thailand Paris France
+Bangkok Thailand Rome Italy
+Bangkok Thailand Stockholm Sweden
+Bangkok Thailand Tehran Iran
+Bangkok Thailand Tokyo Japan
+Bangkok Thailand Athens Greece
+Bangkok Thailand Baghdad Iraq
+Beijing China Berlin Germany
+Beijing China Bern Switzerland
+Beijing China Cairo Egypt
+Beijing China Canberra Australia
+Beijing China Hanoi Vietnam
+Beijing China Havana Cuba
+Beijing China Helsinki Finland
+Beijing China Islamabad Pakistan
+Beijing China Kabul Afghanistan
+Beijing China London England
+Beijing China Madrid Spain
+Beijing China Moscow Russia
+Beijing China Oslo Norway
+Beijing China Ottawa Canada
+Beijing China Paris France
+Beijing China Rome Italy
+Beijing China Stockholm Sweden
+Beijing China Tehran Iran
+Beijing China Tokyo Japan
+Beijing China Athens Greece
+Beijing China Baghdad Iraq
+Beijing China Bangkok Thailand
+Berlin Germany Bern Switzerland
+Berlin Germany Cairo Egypt
+Berlin Germany Canberra Australia
+Berlin Germany Hanoi Vietnam
+Berlin Germany Havana Cuba
+Berlin Germany Helsinki Finland
+Berlin Germany Islamabad Pakistan
+Berlin Germany Kabul Afghanistan
+Berlin Germany London England
+Berlin Germany Madrid Spain
+Berlin Germany Moscow Russia
+Berlin Germany Oslo Norway
+Berlin Germany Ottawa Canada
+Berlin Germany Paris France
+Berlin Germany Rome Italy
+Berlin Germany Stockholm Sweden
+Berlin Germany Tehran Iran
+Berlin Germany Tokyo Japan
+Berlin Germany Athens Greece
+Berlin Germany Baghdad Iraq
+Berlin Germany Bangkok Thailand
+Berlin Germany Beijing China
+Bern Switzerland Cairo Egypt
+Bern Switzerland Canberra Australia
+Bern Switzerland Hanoi Vietnam
+Bern Switzerland Havana Cuba
+Bern Switzerland Helsinki Finland
+Bern Switzerland Islamabad Pakistan
+Bern Switzerland Kabul Afghanistan
+Bern Switzerland London England
+Bern Switzerland Madrid Spain
+Bern Switzerland Moscow Russia
+Bern Switzerland Oslo Norway
+Bern Switzerland Ottawa Canada
+Bern Switzerland Paris France
+Bern Switzerland Rome Italy
+Bern Switzerland Stockholm Sweden
+Bern Switzerland Tehran Iran
+Bern Switzerland Tokyo Japan
+Bern Switzerland Athens Greece
+Bern Switzerland Baghdad Iraq
+Bern Switzerland Bangkok Thailand
+Bern Switzerland Beijing China
+Bern Switzerland Berlin Germany
+Cairo Egypt Canberra Australia
+Cairo Egypt Hanoi Vietnam
+Cairo Egypt Havana Cuba
+Cairo Egypt Helsinki Finland
+Cairo Egypt Islamabad Pakistan
+Cairo Egypt Kabul Afghanistan
+Cairo Egypt London England
+Cairo Egypt Madrid Spain
+Cairo Egypt Moscow Russia
+Cairo Egypt Oslo Norway
+Cairo Egypt Ottawa Canada
+Cairo Egypt Paris France
+Cairo Egypt Rome Italy
+Cairo Egypt Stockholm Sweden
+Cairo Egypt Tehran Iran
+Cairo Egypt Tokyo Japan
+Cairo Egypt Athens Greece
+Cairo Egypt Baghdad Iraq
+Cairo Egypt Bangkok Thailand
+Cairo Egypt Beijing China
+Cairo Egypt Berlin Germany
+Cairo Egypt Bern Switzerland
+Canberra Australia Hanoi Vietnam
+Canberra Australia Havana Cuba
+Canberra Australia Helsinki Finland
+Canberra Australia Islamabad Pakistan
+Canberra Australia Kabul Afghanistan
+Canberra Australia London England
+Canberra Australia Madrid Spain
+Canberra Australia Moscow Russia
+Canberra Australia Oslo Norway
+Canberra Australia Ottawa Canada
+Canberra Australia Paris France
+Canberra Australia Rome Italy
+Canberra Australia Stockholm Sweden
+Canberra Australia Tehran Iran
+Canberra Australia Tokyo Japan
+Canberra Australia Athens Greece
+Canberra Australia Baghdad Iraq
+Canberra Australia Bangkok Thailand
+Canberra Australia Beijing China
+Canberra Australia Berlin Germany
+Canberra Australia Bern Switzerland
+Canberra Australia Cairo Egypt
+Hanoi Vietnam Havana Cuba
+Hanoi Vietnam Helsinki Finland
+Hanoi Vietnam Islamabad Pakistan
+Hanoi Vietnam Kabul Afghanistan
+Hanoi Vietnam London England
+Hanoi Vietnam Madrid Spain
+Hanoi Vietnam Moscow Russia
+Hanoi Vietnam Oslo Norway
+Hanoi Vietnam Ottawa Canada
+Hanoi Vietnam Paris France
+Hanoi Vietnam Rome Italy
+Hanoi Vietnam Stockholm Sweden
+Hanoi Vietnam Tehran Iran
+Hanoi Vietnam Tokyo Japan
+Hanoi Vietnam Athens Greece
+Hanoi Vietnam Baghdad Iraq
+Hanoi Vietnam Bangkok Thailand
+Hanoi Vietnam Beijing China
+Hanoi Vietnam Berlin Germany
+Hanoi Vietnam Bern Switzerland
+Hanoi Vietnam Cairo Egypt
+Hanoi Vietnam Canberra Australia
+Havana Cuba Helsinki Finland
+Havana Cuba Islamabad Pakistan

models/recall/word2vec/data/train/convert_sample.txt

+45 8 71 53 83 58 71 28 46 3 
+59 68 5 82 0 81 
+61 
+52 
+80 2 4 
+18 
+0 45 10 10 0 8 45 5 0 10 
+16 16 14 10 
+71 73 23 32 16 0 49 53 
+67 6 26 5 
+18 37 30 65 
+16 75 30 
+1 42 25 
+54 43 0 6 0 10 0 66 
+20 13 7 49 5 46 37 0 
+32 1 40 55 74 
+16 14 3 
+76 29 14 3 44 13 42 44 34 3 
+4 80 
+32 37 0 3 0 22 6 8 3 62 
+13 75 9 6 65 79 8 24 0 24 6 73 
+81 0 
+79 7 40 14 5 6 
+58 56 38 
+23 14 6 2 51 
+12 24 6 
+18 37 55 
+0 14 43 50 
+52 53 
+22 19 11 6 6 41 
+20 68 7 66 59 66 31 48 
+31 2 70 15 24 24 44 72 68 14 27 6 
+2 
+28 10 
+35 51 6 0 64 17 4 21 13 0 11 
+9 33 43 
+26 4 
+4 
+69 29 4 8 
+0 76 46 0 51 30 34 20 79 22 
+1 49 9 25 0 25 78 
+10 
+81 57 81 72 
+8 34 31 29 37 
+38 13 
+9 5 6 39 54 43 
+81 70 18 2 53 
+55 7 44 21 30 0 60 
+19 23 3 0 39 82 
+28 56 27 4 38 55 2 
+41 17 0 43 6 21 41 27 
+70 29 59 
+5 36 36 31 26 17 8 39 78 
+28 64 11 8 21 41 11 16 7 16 20 
+8 
+13 40 61 68 
+9 
+57 40 72 7 71 29 2 22 29 38 1 30 
+0 3 
+39 0 4 5 39 21 41 5 54 45 
+22 7 1 1 0 0 
+46 0 0 20 40 29 3 
+11 0 78 4 15 82 51 
+0 2 33 
+0 21 41 19 29 2 59 36 
+27 3 14 0 
+32 63 84 63 84 3 63 84 0 63 84 
+36 13 
+13 15 
+36 57 35 34 54 0 
+13 22 31 5 
+3 78 2 2 
+27 11 57 20 20 11 
+67 
+28 70 44 58 0 28 17 7 17 29 
+53 11 62 17 6 17 12 30 
+32 81 
+80 0 35 22 19 6 35 51 
+55 33 76 0 9 0 
+0 
+56 
+52 
+42 62 0 
+50 1 34 38 0 58 21 
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+44 
+62 26 69 8 
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+34 47 33 68 
+63 16 38 11 67 
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+27 48 39 16 14 76 
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+33 73 7 18 25 35 29 14 58 0 0 35 70 
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+35 27 77 8 4 77 0 
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+37 73 65 
+9 3 
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+23 9 18 62 23 
+47 
+39 50 0 50 26 
+69 26 66 38 14 72 15 1 
+6 21 33 65 24 9 2 
+60 
+16 25 22 16 15 0 
+18 37 0 28 50 
+40 75 5 
+36 66 11 38 0 3 36 
+5 26 59 66 0 
+45 10 6 7 31 21 41 27 4 
+72 30 10 4 0 83 2 30 47 67 33 
+17 6 64 29 0 
+0 30 38 12 5 18 4 0 
+60 83 3 55 3 
+0 4 0 33 43 80 8 75 
+5 77 0 22 30 21 41 27 
+36 19 3 0 82 49 
+6 32 17 
+0 10 0 62 8 82 
+54 11 38 4 2 19 7 35 18 39 0 16 14 
+37 0 47 75 
+61 0 58 1 48 33 32 10 
+47 10 73 47 
+17 34 2 
+7 56 28 0 2 
+39 23 15 15 6 13 
+9 15 0 13 45 2 14 15 
+0 11 0 0 72 11 13 5 26 3 0 
+0 19 38 12 1 3 
+67 12 
+36 26 0 5 
+56 60 18 37 1 44 
+11 13 11 40 12 
+19 56 
+57 0 22 40 35 0 51 6 28 28 13 
+73 
+34 22 65 64 28 52 44 
+13 1 25 63 84 6 7 12 41 63 84 
+69 46 4 0 
+17 3 
+0 3 13 55 3 26 46 
+2 2 21 7 67 
+45 34 0 14 21 60 2 
+80 11 18 34 29 60 4 14 
+48 
+27 
+21 41 0 66 34 
+54 43 0 0 
+79 68 13 23 5 51 8 
+0 49 31 23 4 
+59 20 48 35 16 5 8 
+22 0 8 26 49 39 10 
+37 4 24 0 5 6 65 68 11 0 
+11 0 2 25 7 
+3 82 18 0 

models/recall/word2vec/model.py

+# Copyright (c) 2020 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 math
+import numpy as np
+import paddle.fluid as fluid
+
+from fleetrec.core.utils import envs
+from fleetrec.core.model import Model as ModelBase
+
+
+class Model(ModelBase):
+    def __init__(self, config):
+        ModelBase.__init__(self, config)
+
+    def input(self):
+        neg_num = int(envs.get_global_env("hyper_parameters.neg_num", None, self._namespace))
+        self.input_word = fluid.data(name="input_word", shape=[None, 1], dtype='int64')
+        self.true_word = fluid.data(name='true_label', shape=[None, 1], dtype='int64')
+        self._data_var.append(self.input_word)
+        self._data_var.append(self.true_word)
+        with_shuffle_batch = bool(int(envs.get_global_env("hyper_parameters.with_shuffle_batch", None, self._namespace)))
+        if not with_shuffle_batch:
+            self.neg_word = fluid.data(name="neg_label", shape=[None, neg_num], dtype='int64')
+            self._data_var.append(self.neg_word)
+
+        if self._platform != "LINUX":
+            self._data_loader = fluid.io.DataLoader.from_generator(
+                feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)
+
+    def net(self):
+        is_distributed = True if envs.get_trainer() == "CtrTrainer" else False
+        neg_num = int(envs.get_global_env("hyper_parameters.neg_num", None, self._namespace))
+        sparse_feature_number = envs.get_global_env("hyper_parameters.sparse_feature_number", None, self._namespace)
+        sparse_feature_dim = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self._namespace)
+        with_shuffle_batch = bool(int(envs.get_global_env("hyper_parameters.with_shuffle_batch", None, self._namespace)))
+
+        def embedding_layer(input, table_name, emb_dim, initializer_instance=None, squeeze=False):
+            emb = fluid.embedding(
+                input=input,
+                is_sparse=True,
+                is_distributed=is_distributed,
+                size=[sparse_feature_number, emb_dim],
+                param_attr=fluid.ParamAttr(
+                    name=table_name,
+                    initializer=initializer_instance),
+            )
+            if squeeze:
+                return fluid.layers.squeeze(input=emb, axes=[1])
+            else:
+                return emb
+
+        init_width = 0.5 / sparse_feature_dim
+        emb_initializer = fluid.initializer.Uniform(-init_width, init_width)
+        emb_w_initializer = fluid.initializer.Constant(value=0.0)
+
+        input_emb = embedding_layer(self.input_word, "emb", sparse_feature_dim, emb_initializer, True)
+        true_emb_w = embedding_layer(self.true_word, "emb_w", sparse_feature_dim, emb_w_initializer, True)
+        true_emb_b = embedding_layer(self.true_word, "emb_b", 1, emb_w_initializer, True)
+    
+        if with_shuffle_batch:
+            neg_emb_w_list = []
+            for i in range(neg_num):
+                neg_emb_w_list.append(fluid.contrib.layers.shuffle_batch(true_emb_w))  # shuffle true_word
+            neg_emb_w_concat = fluid.layers.concat(neg_emb_w_list, axis=0)      
+            neg_emb_w = fluid.layers.reshape(neg_emb_w_concat, shape=[-1, neg_num, sparse_feature_dim])
+        
+            neg_emb_b_list = []
+            for i in range(neg_num):
+                neg_emb_b_list.append(fluid.contrib.layers.shuffle_batch(true_emb_b))  # shuffle true_word
+            neg_emb_b = fluid.layers.concat(neg_emb_b_list, axis=0)
+            neg_emb_b_vec = fluid.layers.reshape(neg_emb_b, shape=[-1, neg_num])
+ 
+        else:
+            neg_emb_w = embedding_layer(self.neg_word, "emb_w", sparse_feature_dim, emb_w_initializer)
+            neg_emb_b = embedding_layer(self.neg_word, "emb_b", 1, emb_w_initializer)
+            neg_emb_b_vec = fluid.layers.reshape(neg_emb_b, shape=[-1, neg_num])
+        
+        true_logits = fluid.layers.elementwise_add(
+            fluid.layers.reduce_sum(
+                fluid.layers.elementwise_mul(input_emb, true_emb_w),
+                dim=1,
+                keep_dim=True),
+            true_emb_b)
+
+        input_emb_re = fluid.layers.reshape(
+	    input_emb, shape=[-1, 1, sparse_feature_dim])
+        neg_matmul = fluid.layers.matmul(input_emb_re, neg_emb_w, transpose_y=True)
+        neg_logits = fluid.layers.elementwise_add(
+            fluid.layers.reshape(neg_matmul, shape=[-1, neg_num]),
+            neg_emb_b_vec)
+        
+	label_ones = fluid.layers.fill_constant_batch_size_like(
+            true_logits, shape=[-1, 1], value=1.0, dtype='float32')
+        label_zeros = fluid.layers.fill_constant_batch_size_like(
+            true_logits, shape=[-1, neg_num], value=0.0, dtype='float32')
+    
+        true_xent = fluid.layers.sigmoid_cross_entropy_with_logits(true_logits,
+                                                                   label_ones)
+        neg_xent = fluid.layers.sigmoid_cross_entropy_with_logits(neg_logits,
+                                                                  label_zeros)
+        cost = fluid.layers.elementwise_add(
+            fluid.layers.reduce_sum(
+                true_xent, dim=1),
+            fluid.layers.reduce_sum(
+                neg_xent, dim=1))
+        self.avg_cost = fluid.layers.reduce_mean(cost)
+        global_right_cnt = fluid.layers.create_global_var(name="global_right_cnt", persistable=True, dtype='float32', shape=[1], value=0)
+        global_total_cnt = fluid.layers.create_global_var(name="global_total_cnt", persistable=True, dtype='float32', shape=[1], value=0)
+        global_right_cnt.stop_gradient = True
+        global_total_cnt.stop_gradient = True 
+
+    def avg_loss(self):
+        self._cost = self.avg_cost
+
+    def metrics(self):
+        self._metrics["LOSS"] = self.avg_cost
+
+    def train_net(self):
+        self.input()
+        self.net()
+        self.avg_loss()
+        self.metrics()
+
+    def optimizer(self):
+        learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace)
+        decay_steps = envs.get_global_env("hyper_parameters.decay_steps", None, self._namespace)
+        decay_rate = envs.get_global_env("hyper_parameters.decay_rate", None, self._namespace)
+        optimizer = fluid.optimizer.SGD(
+            learning_rate=fluid.layers.exponential_decay(
+                learning_rate=learning_rate,
+                decay_steps=decay_steps,
+                decay_rate=decay_rate,
+                staircase=True))
+        return optimizer
+
+    def analogy_input(self):
+        sparse_feature_number = envs.get_global_env("hyper_parameters.sparse_feature_number", None, self._namespace)
+	self.analogy_a = fluid.data(name="analogy_a", shape=[None], dtype='int64')
+        self.analogy_b = fluid.data(name="analogy_b", shape=[None], dtype='int64')
+        self.analogy_c = fluid.data(name="analogy_c", shape=[None], dtype='int64')
+        self.analogy_d = fluid.data(name="analogy_d", shape=[None], dtype='int64')
+        self._infer_data_var = [self.analogy_a, self.analogy_b, self.analogy_c, self.analogy_d]
+
+        self._infer_data_loader = fluid.io.DataLoader.from_generator(
+            feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
+ 
+    def infer_net(self):
+        sparse_feature_dim = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self._namespace)
+        sparse_feature_number = envs.get_global_env("hyper_parameters.sparse_feature_number", None, self._namespace)
+
+        def embedding_layer(input, table_name, initializer_instance=None):
+            emb = fluid.embedding(
+                input=input,
+                size=[sparse_feature_number, sparse_feature_dim],
+                param_attr=table_name)
+            return emb
+        
+        self.analogy_input()
+        all_label = np.arange(sparse_feature_number).reshape(sparse_feature_number).astype('int32')
+        self.all_label = fluid.layers.cast(x=fluid.layers.assign(all_label), dtype='int64')
+        emb_all_label = embedding_layer(self.all_label, "emb")
+        emb_a = embedding_layer(self.analogy_a, "emb")
+        emb_b = embedding_layer(self.analogy_b, "emb")
+        emb_c = embedding_layer(self.analogy_c, "emb")
+        
+        target = fluid.layers.elementwise_add(
+            fluid.layers.elementwise_sub(emb_b, emb_a), emb_c)
+
+        emb_all_label_l2 = fluid.layers.l2_normalize(x=emb_all_label, axis=1)
+        dist = fluid.layers.matmul(x=target, y=emb_all_label_l2, transpose_y=True)
+        values, pred_idx = fluid.layers.topk(input=dist, k=4)
+        label = fluid.layers.expand(fluid.layers.unsqueeze(self.analogy_d, axes=[1]), expand_times=[1, 4])
+        label_ones = fluid.layers.fill_constant_batch_size_like(
+            label, shape=[-1, 1], value=1.0, dtype='float32')
+        right_cnt = fluid.layers.reduce_sum(
+            input=fluid.layers.cast(fluid.layers.equal(pred_idx, label), dtype='float32'))
+        total_cnt = fluid.layers.reduce_sum(label_ones)
+
+        global_right_cnt = fluid.layers.create_global_var(name="global_right_cnt", persistable=True, dtype='float32', shape=[1], value=0)
+        global_total_cnt = fluid.layers.create_global_var(name="global_total_cnt", persistable=True, dtype='float32', shape=[1], value=0)
+        global_right_cnt.stop_gradient = True
+        global_total_cnt.stop_gradient = True
+
+        tmp1 = fluid.layers.elementwise_add(right_cnt, global_right_cnt)
+        fluid.layers.assign(tmp1, global_right_cnt)
+        tmp2 = fluid.layers.elementwise_add(total_cnt, global_total_cnt)
+        fluid.layers.assign(tmp2, global_total_cnt)
+        
+        acc = fluid.layers.elementwise_div(global_right_cnt, global_total_cnt, name="total_acc")
+        self._infer_results['acc'] = acc