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))
