https://github.com/modelzoo/priceprediction
Boston Housing Prediction
https://github.com/modelzoo/priceprediction
Last synced: about 1 month ago
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Boston Housing Prediction
- Host: GitHub
- URL: https://github.com/modelzoo/priceprediction
- Owner: ModelZoo
- Created: 2018-10-05T16:23:35.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-11-16T19:04:00.000Z (over 5 years ago)
- Last Synced: 2025-03-28T01:48:30.086Z (about 2 months ago)
- Language: Python
- Size: 11.7 KB
- Stars: 6
- Watchers: 0
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# PricePrediction
Simple Linear Regression 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 BostonHousing dataset for example.
## Usage
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:
```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