+++ /dev/null
-import numpy as np
-from keras.models import Sequential
-from keras.layers import Dense, Dropout # , Activation
-
-model = Sequential()
-
-model.add(Dense(26, input_shape=(13,), activation='relu'))
-#model.add(Dense(100, activation='relu'))
-#model.add(Dropout(0.25))
-model.add(Dense(100, activation='relu'))
-model.add(Dense(26, activation='relu'))
-model.add(Dense(1, activation='sigmoid'))
-
-model.compile(
- loss='binary_crossentropy',
- optimizer='rmsprop',
- metrics=['accuracy'])
-
-model.summary()
-
-dat = np.genfromtxt('train.txt', dtype=float, delimiter='\t', usecols=range(1, 14))
-lab = np.genfromtxt('train.txt', dtype=int, delimiter='\t', usecols=[0])
-
-model.fit(dat, lab, epochs=10, batch_size=32)
-
-with open('model.json', 'w') as f:
- f.write(model.to_json())
-model.save_weights('model.hdf5')