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A Scaffold to help you build Deep-learning Model much more easily, implemented with TensorFlow Eager Execution and Keras
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README.md

ModelZoo

A Scaffold to help you build Deep-learning Model much more easily, implemented with TensorFlow Eager Execution and Keras.

Installation

You can install this package easily with pip:

pip3 install model-zoo

Usage

Let's implement a linear-regression model quickly.

Here we use boston_housing dataset as example.

Define a linear model like this, named model.py:

from model_zoo.model import BaseModel
import tensorflow as tf

class BostonHousingModel(BaseModel):
    def __init__(self, config):
        super(BostonHousingModel, self).__init__(config)
        self.dense = tf.keras.layers.Dense(1)

    def call(self, inputs, training=None, mask=None):
        o = self.dense(inputs)
        return o

Then define a trainer like this, named train.py:

import tensorflow as tf
from model_zoo.trainer import BaseTrainer
from model_zoo.preprocess import standardize

tf.flags.DEFINE_integer('epochs', 20, 'Max epochs')
tf.flags.DEFINE_string('model_class', 'BostonHousingModel', 'Model class name')

class Trainer(BaseTrainer):

    def prepare_data(self):
        from tensorflow.python.keras.datasets import boston_housing
        (x_train, y_train), (x_eval, y_eval) = boston_housing.load_data()
        x_train, x_eval = standardize(x_train, x_eval)
        train_data, eval_data = (x_train, y_train), (x_eval, y_eval)
        return train_data, eval_data

if __name__ == '__main__':
    Trainer().run()

Now, we've finished this model!

Next we can run this model using this cmd:

python3 train.py

Outputs like this:

Epoch 1/100
 1/13 [=>............................] - ETA: 0s - loss: 816.1798
13/13 [==============================] - 0s 4ms/step - loss: 457.9925 - val_loss: 343.2489

Epoch 2/100
 1/13 [=>............................] - ETA: 0s - loss: 361.5632
13/13 [==============================] - 0s 3ms/step - loss: 274.7090 - val_loss: 206.7015
Epoch 00002: saving model to checkpoints/model.ckpt

Epoch 3/100
 1/13 [=>............................] - ETA: 0s - loss: 163.5308
13/13 [==============================] - 0s 3ms/step - loss: 172.4033 - val_loss: 128.0830

Epoch 4/100
 1/13 [=>............................] - ETA: 0s - loss: 115.4743
13/13 [==============================] - 0s 3ms/step - loss: 112.6434 - val_loss: 85.0848
Epoch 00004: saving model to checkpoints/model.ckpt

Epoch 5/100
 1/13 [=>............................] - ETA: 0s - loss: 149.8252
13/13 [==============================] - 0s 3ms/step - loss: 77.0281 - val_loss: 57.9716
....

Epoch 42/100
 7/13 [===============>..............] - ETA: 0s - loss: 20.5911
13/13 [==============================] - 0s 8ms/step - loss: 22.4666 - val_loss: 23.7161
Epoch 00042: saving model to checkpoints/model.ckpt

It 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.

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:

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, define infer.py like this:

from model_zoo.inferer import BaseInferer
from model_zoo.preprocess import standardize
import tensorflow as tf

tf.flags.DEFINE_string('checkpoint_name', 'model.ckpt-20', help='Model name')

class Inferer(BaseInferer):

    def prepare_data(self):
        from tensorflow.python.keras.datasets import boston_housing
        (x_train, y_train), (x_test, y_test) = boston_housing.load_data()
        _, x_test = standardize(x_train, x_test)
        return x_test

if __name__ == '__main__':
    result = Inferer().run()
    print(result)

Now we've restored the specified model model.ckpt-38 and prepared test data, outputs like this:

[[ 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.

Implemented Models

Just see models, welcome to contribute your model to us.

License

MIT

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