def main(args): new_model = args.new_model rnn = RNN() if not new_model: try: rnn.set_weights(config.rnn_weight) except: print("Either set --new_model or ensure {} exists".format( config.rnn_weight)) raise rnn_input = [] rnn_output = [] for i in range(130): # print('Building {}th...'.format(i)) input = np.load('./rnn_data/rnn_input_' + str(i) + '.npy') output = np.load('./rnn_data/rnn_output_' + str(i) + '.npy') # sequence pre-processing, for training LSTM the rnn_input must be (samples/episodes, time steps, features) input = pad_sequences(input, maxlen=40, dtype='float32', padding='post', truncating='post') output = pad_sequences(output, maxlen=40, dtype='float32', padding='post', truncating='post') rnn_input.append(input) rnn_output.append(output) input = rnn_input[0] output = rnn_output[0] for i in range(len(rnn_input) - 1): input = np.concatenate((input, rnn_input[i + 1]), axis=0) output = np.concatenate((output, rnn_output[i + 1]), axis=0) print(input.shape) print(output.shape) rnn.train(input, output) rnn.plot_loss()