Implementation of a feed-forward neural network in C/C++.
Trained it on a Fashion-MNIST dataset using a backpropagation algorithm.
Solution met ALL of the following requirements:
- Your solution must be compilable and runnable on the AISA server.
- Your solution achieves at least 88% accuracy.
- Your solution must finish within 30 minutes. (parse inputs, train, evaluate, export results.)
- Your solution must contain a runnable script called "RUN" (not run, not RUN.sh, not RUN.exe etc) which compiles, executes and exports the results into a files.
- Your solution must output two files:
- "trainPredictions" - network predictions for training input vectors
- "actualTestPredictions" - network predictions for testing input vectors The format of these files is the same as the supplied training/testing labels:
- One prediction per line.
- Prediction for i-th input vector (ordered by the input .csv file) must be on i-th line in the associated output file.
- Each prediction is a single integer 0 - 9.
Fashion MNIST (https://arxiv.org/pdf/1708.07747.pdf) a modern version of a well-known MNIST (http://yann.lecun.com/exdb/mnist/). It is a dataset of Zalando's article images ‒ consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. The dataset is in CSV format. There are four data files included: - fashion_mnist_train_vectors.csv - training input vectors - fashion_mnist_test_vectors.csv - testing input vectors - fashion_mnist_train_labels.csv - training labels - fashion_mnist_test_labels.csv - testing labels
- Matej Horniak
- Lukas Mikula