Kaggle

Nolsigan's code for kaggle competitions using Tensorflow & keras!

Only deals with problems that can be solved with deep learning.

Cat vs Dog

simple_vgg
- Uses simplified version of VGG
- After epoch 8, train loss still decreases but validation loss doesn't (overfitting)
- Achieved 85.65% accuracy

simple_nn.ipynb
- Uses simple neural network model with two 128-neurons hidden layers
- Used extern data from official MNIST site, gaining 33% more data than Kaggle provides.
- Loss decreases to zero after 100 epochs. ( Maybe this model is enough for this data? )
- Achieved 99.714% accuracy
conv2d.ipynb
- Uses CNN model of tensorflow with two convolutional layers and max pooling, a 512-neurons hidden layer
- Training speed is way slower than simple_nn. Local machine had hard time just training for 10 epochs.
- Loss is 0.05 after 10 epochs, but still achieves pretty good Result.
- Achieved 99.38% accuracy

gender.ipynb ( Kaggle Tutorial )
- Uses simple classification using only gender property.
- Achieved 77.65% accuracy
simple_nn.ipynb
- Uses simple neural network model with two 128-neurons hidden layers
- Result is worse than simple gender model. Not suitable for neural network model.
- Achieved 66.96% accuracy