init

.gitignore

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+# Created by .ignore support plugin (hsz.mobi)
+### Python template
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+.idea/
+model_zoo
+scores/
+# C extensions
+*.so
+checkpoints/
+notebooks/.ipynb_checkpoints
+debug.log
+# Distribution / packaging
+.Python
+build/
+examples/checkpoints/
+examples/events/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+*.pyc
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+events/
+checkpoints/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+

README.md

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+# TextClassification
+
+TextClassification Model implemented by [ModelZoo](https://github.com/ModelZoo/ModelZoo).
+
+
+## Installation
+
+Firstly you need to clone this repository and install dependencies with pip:
+
+```
+pip3 install -r requirements.txt
+```
+
+## Dataset
+
+We use IMDB dataset for example.
+
+## Usage
+
+We can run this model like this:
+
+```
+python3 train.py
+```
+
+Outputs like this:
+
+```
+...
+```
+
+It runs only 42 epochs and stopped early, because there are no more good evaluation results for 20 epochs.
+
+When finished, we can find two folders generated named `checkpoints` and `events`.
+
+Go to `events` and run TensorBoard:
+
+```
+cd events
+tensorboard --logdir=.
+```
+
+TensorBoard like this:
+
+![](https://ws4.sinaimg.cn/large/006tNbRwgy1fvxrcajse2j31kw0hkgnf.jpg)
+
+There are training batch loss, epoch loss, eval loss.
+
+And also we can find checkpoints in `checkpoints` dir.
+
+It saved the best model named `model.ckpt` according to eval score, and it also saved checkpoints every 2 epochs.
+
+Next we can predict using existing checkpoints and `infer.py`.
+
+Now we've restored the specified model `model.ckpt-38` and prepared test data, outputs like this:
+
+```python
+[[ 9.637125 ]
+ [21.368305 ]
+ [20.898445 ]
+ [33.832504 ]
+ [25.756516 ]
+ [21.264557 ]
+ [29.069794 ]
+ [24.968184 ]
+ ...
+ [36.027283 ]
+ [39.06852  ]
+ [25.728745 ]
+ [41.62165  ]
+ [34.340042 ]
+ [24.821484 ]]
+```
+
+OK, we've finished restoring and predicting. Just so quickly.
+
+## License
+
+MIT

model.py

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+from model_zoo.model import BaseModel
+import tensorflow as tf
+
+
+class DenseModel(BaseModel):
+    def __init__(self, config):
+        super(DenseModel, self).__init__(config)
+        self.embedding = tf.keras.layers.Embedding(config['vocab_size'], config['embedding_size'])
+        self.pool = tf.keras.layers.GlobalAveragePooling1D()
+        self.dense1 = tf.keras.layers.Dense(16, activation=tf.nn.relu)
+        self.dense2 = tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)
+
+    def call(self, inputs, training=None, mask=None):
+        o = self.embedding(inputs)
+        o = self.pool(o)
+        o = self.dense1(o)
+        o = self.dense2(o)
+        return o
+
+    def init(self):
+        self.compile(optimizer=tf.train.AdamOptimizer(),
+                     loss='binary_crossentropy',
+                     metrics=['accuracy'])

requirements.txt

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+absl-py==0.5.0
+astor==0.7.1
+cycler==0.10.0
+gast==0.2.0
+grpcio==1.15.0
+h5py==2.8.0
+Keras-Applications==1.0.6
+Keras-Preprocessing==1.0.5
+kiwisolver==1.0.1
+Markdown==3.0.1
+matplotlib==3.0.0
+model-zoo
+numpy>=1.15.2
+pandas
+protobuf==3.6.1
+pyparsing==2.2.2
+python-dateutil==2.7.3
+scikit-learn==0.20.0
+scipy>=1.1.0
+six==1.11.0
+sklearn
+tensorboard>=1.11.0
+tensorflow>=1.11.0
+termcolor==1.1.0
+Werkzeug==0.14.1

train.py

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+import tensorflow as tf
+from model_zoo.trainer import BaseTrainer
+from tensorflow.python.keras.datasets import imdb
+from tensorflow.python.keras.preprocessing.sequence import pad_sequences
+
+tf.flags.DEFINE_integer('epochs', 50, 'Max epochs')
+tf.flags.DEFINE_float('learning_rate', 0.001, 'Learning rate')
+tf.flags.DEFINE_string('model_class', 'DenseModel', help='Model class name')
+tf.flags.DEFINE_integer('vocab_size', 10000, help='Vocab size')
+tf.flags.DEFINE_integer('embedding_size', 200, help='Embedding size')
+
+
+class Trainer(BaseTrainer):
+
+    def build_word_index(self):
+        word_index = imdb.get_word_index()
+        word_index = {k: (v + 3) for k, v in word_index.items()}
+        word_index['<PAD>'] = 0
+        word_index['<START>'] = 1
+        word_index['<UNK>'] = 2
+        word_index['<UNUSED>'] = 3
+        return word_index
+
+    def prepare_data(self):
+        (x_train, y_train), (_, _) = imdb.load_data(num_words=self.flags.vocab_size)
+        word_index = self.build_word_index()
+        x_train = pad_sequences(x_train, maxlen=250, value=word_index['<PAD>'], padding='post')
+        (x_train, x_eval) = x_train[:20000], x_train[20000:]
+        (y_train, y_eval) = y_train[:20000], y_train[20000:]
+        train_data, eval_data = self.build_generator(x_train, y_train), self.build_generator(x_eval, y_eval)
+        return train_data, eval_data, len(x_train), len(x_eval)
+
+
+if __name__ == '__main__':
+    Trainer().run()