Simple Linear Regression Model implemented by ModelZoo.
Firstly you need to clone this repository and install dependencies with pip:
pip3 install -r requirements.txt
We use BostonHousing dataset for example.
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 and infer.py.
Now we've restored the specified model model-best.ckpt 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.
MIT