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train.py
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train.py
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import numpy as np
np.random.seed(1337) # for reproducibility
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
from keras.optimizers import Adam
batch_size = 100
nb_epoch = 250
def main():
train_X = np.load('train_X.npy')
train_y = np.load('train_y.npy')
test_X = np.load('test_X.npy')
test_y = np.load('test_y.npy')
model = Sequential()
model.add(Flatten(input_shape=(15,60,2)))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dense(900))
model.add(Activation('sigmoid'))
print model.summary()
adam = Adam(0.001)
#adagrad = Adagrad(lr=0.01)
model.compile(loss='mse', optimizer=adam)
model.fit(train_X, train_y, batch_size=batch_size, nb_epoch=nb_epoch,
verbose=1, validation_data=(test_X, test_y))
model.save_weights('model.h5', overwrite=True)
if __name__ == "__main__":
main()