{
    "info": {
        "author": "Germey",
        "author_email": "cqc@cuiqingcai.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy"
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        "description": "\n# ModelZoo\n\nA Scaffold to help you build Deep-learning Model much more easily, implemented with TensorFlow Eager Execution and Keras.\n\n## Installation\n\nYou can install this package easily with pip:\n\n```\npip3 install model-zoo\n```\n\n## Usage\n\nLet's implement a linear-regression model quickly.\n\nHere we use boston_housing dataset as example.\n\nDefine a linear model like this, named `model.py`:\n\n```python\nfrom model_zoo.model import BaseModel\nimport tensorflow as tf\n\nclass BostonHousingModel(BaseModel):\n    def __init__(self, config):\n        super(BostonHousingModel, self).__init__(config)\n        self.dense = tf.keras.layers.Dense(1)\n\n    def call(self, inputs, training=None, mask=None):\n        o = self.dense(inputs)\n        return o\n\n```\n\nThen define a trainer like this, named `train.py`:\n\n```python\nimport tensorflow as tf\nfrom model_zoo.trainer import BaseTrainer\nfrom model_zoo.preprocess import standardize\n\ntf.flags.DEFINE_integer('epochs', 20, 'Max epochs')\ntf.flags.DEFINE_string('model_class', 'BostonHousingModel', 'Model class name')\n\nclass Trainer(BaseTrainer):\n\n    def prepare_data(self):\n        from tensorflow.python.keras.datasets import boston_housing\n        (x_train, y_train), (x_eval, y_eval) = boston_housing.load_data()\n        x_train, x_eval = standardize(x_train, x_eval)\n        train_data, eval_data = (x_train, y_train), (x_eval, y_eval)\n        return train_data, eval_data\n\nif __name__ == '__main__':\n    Trainer().run()\n```\n\nNow, we've finished this model!\n\nNext we can run this model using this cmd:\n\n```\npython3 train.py\n```\n\nOutputs like this:\n\n```\nEpoch 1/100\n 1/13 [=>............................] - ETA: 0s - loss: 816.1798\n13/13 [==============================] - 0s 4ms/step - loss: 457.9925 - val_loss: 343.2489\n\nEpoch 2/100\n 1/13 [=>............................] - ETA: 0s - loss: 361.5632\n13/13 [==============================] - 0s 3ms/step - loss: 274.7090 - val_loss: 206.7015\nEpoch 00002: saving model to checkpoints/model.ckpt\n\nEpoch 3/100\n 1/13 [=>............................] - ETA: 0s - loss: 163.5308\n13/13 [==============================] - 0s 3ms/step - loss: 172.4033 - val_loss: 128.0830\n\nEpoch 4/100\n 1/13 [=>............................] - ETA: 0s - loss: 115.4743\n13/13 [==============================] - 0s 3ms/step - loss: 112.6434 - val_loss: 85.0848\nEpoch 00004: saving model to checkpoints/model.ckpt\n\nEpoch 5/100\n 1/13 [=>............................] - ETA: 0s - loss: 149.8252\n13/13 [==============================] - 0s 3ms/step - loss: 77.0281 - val_loss: 57.9716\n....\n\nEpoch 42/100\n 7/13 [===============>..............] - ETA: 0s - loss: 20.5911\n13/13 [==============================] - 0s 8ms/step - loss: 22.4666 - val_loss: 23.7161\nEpoch 00042: saving model to checkpoints/model.ckpt\n```\n\nIt runs only 42 epochs and stopped early, because the framework auto enabled early stop mechanism and there are no more good evaluation results for 20 epochs.\n\nWhen finished, we can find two folders generated named `checkpoints` and `events`.\n\nGo to `events` and run TensorBoard:\n\n```\ncd events\ntensorboard --logdir=.\n```\n\nTensorBoard like this:\n\n![](https://ws4.sinaimg.cn/large/006tNbRwgy1fvxrcajse2j31kw0hkgnf.jpg)\n\nThere are training batch loss, epoch loss, eval loss.\n\nAnd also we can find checkpoints in `checkpoints` dir.\n\nIt saved the best model named `model.ckpt` according to eval score, and it also saved checkpoints every 2 epochs.\n\nNext we can predict using existing checkpoints, define `infer.py` like this:\n\n```python\nfrom model_zoo.inferer import BaseInferer\nfrom model_zoo.preprocess import standardize\nimport tensorflow as tf\n\ntf.flags.DEFINE_string('checkpoint_name', 'model.ckpt-20', help='Model name')\n\nclass Inferer(BaseInferer):\n\n    def prepare_data(self):\n        from tensorflow.python.keras.datasets import boston_housing\n        (x_train, y_train), (x_test, y_test) = boston_housing.load_data()\n        _, x_test = standardize(x_train, x_test)\n        return x_test\n\nif __name__ == '__main__':\n    result = Inferer().run()\n    print(result)\n```\n\nNow we've restored the specified model `model.ckpt-38` and prepared test data, outputs like this:\n\n```python\n[[ 9.637125 ]\n [21.368305 ]\n [20.898445 ]\n [33.832504 ]\n [25.756516 ]\n [21.264557 ]\n [29.069794 ]\n [24.968184 ]\n ...\n [36.027283 ]\n [39.06852  ]\n [25.728745 ]\n [41.62165  ]\n [34.340042 ]\n [24.821484 ]]\n```\n\nOK, we've finished restoring and predicting. Just so quickly.\n\n## Implemented Models\n\nJust see [models](./models), welcome to contribute your model to us.\n\n## License\n\nMIT\n\n\n\n",
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