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(2000, input_shape=(13,), activation='relu'))
   8 model.add(Dense(1, activation='sigmoid'))
   9
  10 model.compile(
  11     loss='binary_crossentropy',
  12     optimizer='rmsprop',
  13     metrics=['accuracy'])
  14
  15 model.summary()
  16
  17 dat = np.genfromtxt('train.txt', dtype=float, delimiter='\t', usecols=range(1, 14))
  18 lab = np.genfromtxt('train.txt', dtype=int, delimiter='\t', usecols=[0])
  19
  20 model.fit(dat, lab, epochs=10, batch_size=32)
  21
  22 with open('model.json', 'w') as f:
  23     f.write(model.to_json())
  24 model.save_weights('model.hdf5')