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train.py
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train.py
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import os
import six
import argparse
import importlib
import theano
import keras
from keras.optimizers import SGD
from load import load_data
def main():
parser = argparse.ArgumentParser(description='Train a neural network')
parser.add_argument('--model', type=str)
parser.add_argument('--lr', type=float, default=0.001)
parser.add_argument('--decay', type=float, default=1e-4)
parser.add_argument('--momentum', type=float, default=0.9)
parser.add_argument('--batch', type=int, default=128)
parser.add_argument('--epoch', type=int, default=100)
parser.add_argument('--output', type=str, default='weight')
args = parser.parse_args()
model = importlib.import_module(args.model).build()
six.print_('loading data')
(train_x, train_y, val_x, val_y) = load_data()
six.print_('load data complete')
sgd = SGD(lr=args.lr,
decay=args.decay,
momentum=args.momentum,
nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=sgd)
six.print_('build model complete')
six.print_('start training')
model.fit(train_x, train_y, batch_size=args.batch, nb_epoch=args.epoch,
verbose=2,
show_accuracy=True,
shuffle=True,
validation_data=(val_x, val_y))
model.save_weights(args.output + '.hdf5')
if __name__ == '__main__':
main()