seq, targets, tokens = c.encode(n_seq, n_steps, tokens, fs) _, __, n_in = seq.shape t0 = time.time() #Creates the model to run the RNN. params = { 'n_in': n_in, 'n_hid': n_hid, 'n_out': n_out, 'n_epochs': 250 } model = Model(logger, params) #Trains the RNN and runs the softmax signal. while seq is not None and targets is not None: model.fit(seq, targets, validation_freq=1000) seqs = xrange(n_seq) for seq_num in seqs: tsm = time.time() guess = model.predict_probability(seq[seq_num]) tsm = time.time() - tsm softmax_time += tsm logger.info("Softmax elapsed time: %f" % (tsm)) seq, targets, tokens = c.encode(n_seq, n_steps, tokens, fs) logger.info("Total elapsed time: {} and softmax time: {} ".format(time.time() - t0, softmax_time))