ModelZoo

A framework to help you build model much more easily.

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:

from model import BostonHousingModel
from model_zoo.trainer import BaseTrainer
from tensorflow.python.keras.datasets import boston_housing
from sklearn.preprocessing import StandardScaler

class Trainer(BaseTrainer):

    def __init__(self):
        BaseTrainer.__init__(self)
        self.model_class = BostonHousingModel

    def prepare_data(self):
        (x_train, y_train), (x_eval, y_eval) = boston_housing.load_data()
        ss = StandardScaler()
        ss.fit(x_train)
        x_train, x_eval = ss.transform(x_train), ss.transform(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 like this:

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 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 import BostonHousingModel
from model_zoo.inferer import BaseInferer
import tensorflow as tf
from tensorflow.python.keras.datasets import boston_housing
from sklearn.preprocessing import StandardScaler

tf.flags.DEFINE_string('checkpoint_name', 'model.ckpt-38', help='Model name')

class Inferer(BaseInferer):
    def __init__(self):
        BaseInferer.__init__(self)
        self.model_class = BostonHousingModel

    def prepare_data(self):
        (x_train, y_train), (x_test, y_test) = boston_housing.load_data()
        ss = StandardScaler()
        ss.fit(x_train)
        x_test = ss.transform(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.

License

MIT