71d61f102189f8b7337567ca34ad590f2ddf4808,snntoolbox/simulation/target_simulators/INI_ttfs_target_sim.py,SNN,simulate,#SNN#,34

Before Change

                print("Finished early.")
                break

        for b in range(self.batch_size):
            for l in range(self.num_classes):
                spike = 0
                for t in range(self._num_timesteps):
                    if output_b_l_t[b, l, t]:
                        spike = True
                    output_b_l_t[b, l, t] = spike

        self.avg_rate /= self.batch_size * np.sum(self.num_neurons) * \
            self._num_timesteps

        if self.spiketrains_n_b_l_t is None:

After Change

            none_class_b = -1 * np.ones(self.batch_size)
            clean_guesses_b = np.where(undecided_b, none_class_b, guesses_b)
            current_acc = np.mean(kwargs[str("truth_b")] == clean_guesses_b)
            if self.config.getint("output", "verbose") > 0:
                if sim_step % 1 == 0:
                    echo("{:.2%}_".format(current_acc))
            else:
                sys.stdout.write("\r{:>7.2%}".format(current_acc))
                sys.stdout.flush()

            if self.config.getboolean("conversion", "softmax_to_relu") and \
                    all(np.count_nonzero(output_b_l_t, (1, 2)) >= self.top_k):
                print("Finished early.")
                break

Instances