# Build neural network
# Neural net with multiple layers
model = Sequential()

model.add(Dense(32, input_dim=17, init='uniform', activation='sigmoid'))
model.add(Dense(64, init='uniform', activation='relu'))
model.add(Dense(64, init='uniform', activation='relu'))
model.add(Dense(64, init='uniform', activation='relu'))
model.add(Dense(32, init='uniform', activation='relu'))
model.add(Dense(16, init='uniform', activation='sigmoid'))
model.add(Dense(4, init='uniform', activation='sigmoid'))
model.add(Dense(1, init='uniform', activation='sigmoid'))

# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# Fit model
history = model.fit(X, Y, validation_split=0.46, nb_epoch=150, batch_size=3)