AutoEncoder-and-Classifier-of-MNIST-images
We aim to build different Neural Networks in order to achieve 2 goals:
- AutoEncoder's implementation.
- Classifier's implementation.
Auto-Encoder is made up of one encoder and one decoder. User will be asked to give hyperparameters in order to build different Auto-Encoders and by observing their results he can save model which performed better.
Classifier will be made up of the half part of previous Auto-Encoder, more specifically only 'Encoder' part, and then a fully connected layer. With same mindset user has the freedom to build several models and plot his experiment's results or save whichever model he prefers.
Compilation & Execution
-
For AutoEncoder: python src/autoencoder.py -d data/train/train-images-idx3-ubyte
-
For Classifier: python src/classification.py -d data/train/train-images-idx3-ubyte -dl data/train/train-labels-idx1-ubyte -t data/test/t10k-images-idx3-ubyte -tl data/test/t10k-labels-idx1-ubyte -model dropoutautoencoder.h5
Results
From directory 'results_of_experiments' we are going display plots, loss vs epochs with specific hyperparameters after a series of experiments (tuning), from our best models. Note that inside this directory we saved some pretty good models for AutoEncoder, but also for Classifier.
- Firstly for Auto-Encoder:
- Secondly for Classifier:
- Correct Labels
- InCorrect Labels
