The project should work with python 2 and 3.
This project provides the python code that supports this blog post (if you are a beginner, you should read it).
The goal is to make a neural network from scratch using numpy, then the same one using TensorFlow.
As a toy example, we try to predict the price of car using online data.
download_lbc_cars_data.py downloads data from leboncoin.fr, which is a website of classified ads. The data retrieved are about BMW Serie 1 (only one model of car).
For each BMW Serie 1 we save an input with the number of km, fuel, age and the price. The data are saved into car_features.csv.
These data are then normalized by normalize_lbc_cars_data.py to produce normalized_car_features.csv.
normalized_car_features.csv is used as input by dnn_from_scratch.py which is the neural network using numpy and dnn_from_scratch_tensorflow.py which is the neural network using TensorFlow.
predict.py is used to transform the data back and forth from the normalized to the human readeable version. For instance to predict a price, the user will input the raw car attributes. predict.py will convert the raw data to the normalized version and return them. The neural network output is also given to predict.py so that the user obtains a readable price and not a normalized one.
Overall results are pretty good knowing that the price is impacted by more than three attributes.
The architecture is pretty simple and well described in the blog post. Here is an illustration:
A requirements.txt file exists at the root of the repository. Run pip install -r requirements.txt .
If you see a bad implementation or you come across a bug, open an issue. I'll help you.