dnn-poker-hands

Using deep learning to predict the quality of poker hands. The data set is the Poker Hand Data Set

This was created mostly so I could give myself a go at learning the high-level TensorFlow APIs, namely Estimators and Datsets.

Installing dependencies

This assumes you have virtualenv with support for Python 3 installed.

./setup.sh

Training and evaluating the model

You can run it using default parameters like so:

python estimator.py 

Results

I don't know much about how to scientifically test and tune neutral networks (yet). But here are some experimental results against the test set.

• 1 hidden layer of 10 neurons, 1000 training steps: 0.500
• 1 hidden layer of 1000 neurons, 50000 training steps: 0.705
• 2 hidden layers of 10 neurons, 1000 training steps: 0.504
• 2 hidden layers of 1000 neurons, 50000 training steps: 0.882
• 5 hidden layers of 10 neurons, 50000 training steps: 0.640
• 5 hidden layers of 100 neurons, 50000 training steps: 0.954
• 10 hidden layers of 100 neurons, 50000 training steps: 0.694

The original paper achieved 0.924.