train.py

   1 import numpy as np
   2 from keras.models import Sequential
   3 from keras.layers import Dense, Dropout  # , Activation
   4
   5 model = Sequential()
   6
   7 model.add(Dense(50, input_shape=(13,), activation='relu'))
   8 model.add(Dense(50, activation='relu'))
   9 model.add(Dense(50, activation='relu'))
  10 model.add(Dense(1, activation='sigmoid'))
  11
  12 model.compile(
  13     loss='binary_crossentropy',
  14     optimizer='rmsprop',
  15     metrics=['accuracy'])
  16
  17 model.summary()
  18
  19 dat = np.genfromtxt('train.txt', dtype=float, delimiter='\t', usecols=range(1, 14))
  20 lab = np.genfromtxt('train.txt', dtype=int, delimiter='\t', usecols=[0])
  21
  22 model.fit(dat, lab, epochs=10, batch_size=32)
  23
  24 with open('model.json', 'w') as f:
  25     f.write(model.to_json())
  26 model.save_weights('model.hdf5')