My first ML project is a neural network that would say Hello to everyone coming to our office and make coffee in the mornings. Though since I haven’t done it before, I wanted to start with something simpler. Like this task on kaggle https://www.kaggle.com/c/poker-rule-induction.
The solution that finally gave 100% accuracy on the test set required a bit of data augmentation and feature engineering. What I did was:
1. Add distances between cards as features. E.g. distance between Jack and King is 2, between King and Three is 3 and so on.
2. Increase training dataset 4 times.
And the neural network implementation in keras looks like this:
model = keras.Sequential()
model.add(keras.layers.Dense(120, activation='relu', input_shape=(30,)))
model.add(keras.layers.Dropout(0.2))
model.add(keras.layers.Dense(240, activation='relu'))
model.add(keras.layers.Dropout(0.2))
model.add(keras.layers.Dense(120, activation='relu'))
model.add(keras.layers.Dropout(0.1))
model.add(keras.layers.Dense(120, activation='relu'))
model.add(keras.layers.Dense(60, activation='relu'))
model.add(keras.layers.Dense(10, activation='softmax'))
model.compile(optimizer=tf.train.AdamOptimizer(0.0005),
loss='categorical_crossentropy',
metrics=['accuracy'])