def random():
if os.path.exists("log") is False:
os.mkdir("log")
if os.path.exists("model") is False:
os.mkdir("model")
loader_test = DataLoader()
loader_test.load_xls("dlt2.xls")
rnn = ModelRNN("log",
"model",
lstm_size=128,
num_layers=2,
learning_rate=0.001)
rnn.build_lstm_model_lstm(1,
loader_test.get_seq_len(),
1,
loader_test.get_classes_count(),
test_mode=True)
rnn.predict(loader_test, 16, None)
def train():
print("run...")
if os.path.exists("log") is False:
os.mkdir("log")
if os.path.exists("model") is False:
os.mkdir("model")
loader = DataLoader()
loader.load_xls("dlt2.xls")
rnn = ModelRNN("log",
"model",
lstm_size=128,
num_layers=2,
learning_rate=0.001)
rnn.build_lstm_model_lstm(32,
loader.get_seq_len(),
1,
loader.get_classes_count(),
test_mode=False)
rnn.train(loader)