Special thanks to Adrian Rosebrock for his great post that was used as baseline for this tutourial.
This simple code creates and train a neural network to predict house prices based on 4 images for each house , you can try it by running the command 'python cnn_regression.py'.
The model used is as the following:
The dataset is from https://github.com/emanhamed/Houses-dataset, this house dataset includes four numerical and categorical attributes as input and the one continous variable as output:
- Number of bedrooms (continous)
- Number of bathrooms(continous)
- Area (continous)
- Zip code (Cateogiral)
- Price (continous)
Moreover the dataset includes 4 images for each house and this what will be used for training, The 4 images of each house (Bathroom/Kitchen/Frontal/bedroom) will be tiled together into one image 64*64 px which will be the input to our CNN and the output is the price. .
When training finishes the a curves will show the traning and validation loss. Another curve will also be shown for actual vs predicted prices. Both curves are saved to local drive. Also the trained model is saved as housePrice.keras2
