#!/usr/bin/env python3
 from keras.models import Sequential
 from keras.layers import Dense
 from keras.utils import np_utils
 import numpy

 data = numpy.loadtxt("shows.csv",
         delimiter=",", skiprows=1)
 X = data[:,0:3]
 Y = data[:,3]

 categories=np_utils.to_categorical(Y)

 model = Sequential()
 model.add(Dense(10, input_dim=3,
     activation='relu'))
 model.add(Dense(3, activation='relu'))
 model.add(Dense(3, activation='sigmoid'))

 model.compile(loss='binary_crossentropy',
         optimizer='adam')
 model.fit(X, categories, epochs=100,
         batch_size=100, verbose=0)

 test_data = numpy.array(
     [[0,1,2], [0,2,1], [1,0,2],
      [1,2,0], [2,0,1], [2,1,0]
     ])

 pred = model.predict(test_data)

 for (idx,row) in enumerate(test_data):
     print(row, pred[idx].argmax())