def main():
print 'Loading Data'
x = cPickle.load(open('english_matrices.pkl', 'rb'))
y = cPickle.load(open('chinese_matrices.pkl', 'rb'))
print 'Done'
# x = np.random.random((10, 10, 50, 1))
# y = np.random.random((10, 10, 50, 1))
encoder_lstm = LSTM(50, 100, 50)
encoder_lstm.load_weights('encoder.pkl')
outputs = []
for i in range(10000):
outputs.append(encoder_lstm.predict(x[i]))
# for _ in range(10):
# for i in range(20):
# idx_start = i*500
# idx_end = min((i+1)*500, len(x))
# sys.stdout.write('\n\nTraining Data %d - %d' % (idx_start, idx_end))
# train(encoder_lstm, x[idx_start:idx_end], y[idx_start:idx_end][0], 50, 'encoder')
# encoder_lstm.save_weights('encoder.pkl')
# outputs = encoder_lstm.predict(x[:10000])
# encoder_lstm.save_weights('encoder.pkl')
embed()
decoder_lstm = LSTM(50, 100, 50)
for _ in range(4):
for i in range(20):
idx_start = i * 500
idx_end = min((i + 1) * 500, len(x))
sys.stdout.write('\n\nTraining Data %d - %d' % (idx_start, idx_end))
train(decoder_lstm, outputs[idx_start:idx_end], y[idx_start:idx_end], 50, 'decoder')
decoder_lstm.save_weights('decoder.pkl')
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Test.')
parser.add_argument(nargs='*',
action='store',
dest='input',
type=str,
help='The text to parse.')
args = parser.parse_args()
sentence = args.input
english_dict = cPickle.load(open('english_dictionary.pkl'))
chinese_dict = cPickle.load(open('chinese_dictionary.pkl'))
encoder = LSTM(50, 100, 50)
encoder.load_weights('encoder.pkl')
decoder = LSTM(50, 100, 50)
mat = []
for word in sentence:
mat.append(english_dict[word])
mat = np.array(mat)
mat = mat.reshape((mat.shape[0], mat.shape[1], 1))
output = encoder.predict(mat)
final = decoder.predict([output[-1].v], output[-1].h)
translated_sentence = ''
for word in final:
translated_sentence += findWord(word.v, chinese_dict)
