The Kaggles mushrooms dataset classified with fully connected neural networks. The dataset is pretty simple and we can easily achieve 100% accuracy with most of models.
The bonus code in this repository is implementation of feed forward netowrk using Keras and Tensorflow library. That is for people interested in learning those two libraries.
The dataset used in this project can be found on Kaggle here.
- Python version used in this project: 3.5+
- Pandas 0.18.0
- Numpy 1.10.4
- Matplotlib 1.5.1
- Tensorflow 1.2.0
- Keras
The numpy testing file is Numpy NN.ipynb - use this to test Numpy version of feed forward network on this dataset.
The tensorflow version of network can be found in Tensorflow NN.ipynb.
The Keras version of the network can be found in Keras NN.ipynb.
Methods used to preprocess this dataset are inside data_handlers.py.
There is some bonus code inside folder activation_weights_init_losses. Inside this folder you can find implementation of many activation functions used in DL, different techniques for weight initialization and two loss functions (softmax loss and mean_squared_error).
To run this project you will need some software, like Anaconda, which provides support for running .ipynb files (Jupyter Notebook).
After making sure you have that, you can run from a terminal or cmd next lines:
[for example: ]
ipython notebook 'Numpy NN.ipynb'
or
jupyter notebook 'Numpy NN.ipynb'
If a file has an extension .py instead of .ipynb - you can start it as well from your terminal/cmd with this line:
python name_of_a_file.py
MIT License
Copyright (c) 2017 Luka Anicin
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