4461f2d4c1f140667b8fe9305efffb4351e54460,snntoolbox/simulation/target_simulators/INI_target_sim.py,SNN,simulate,#SNN#,112

Before Change

                output_b_l_t[:, :, sim_step_int] = np.argmax(np.reshape(
                    out_spikes.astype("int32"), (out_spikes.shape[0], -1)), 1)
            else:
                output_b_l_t[:, :, sim_step_int] = out_spikes.astype("int32")

            // Record neuron variables.
            i = j = 0
            for layer in self.snn.layers:

After Change

                    spike_sums_b_l = np.sum(output_b_l_t, 2)
                    undecided_b = np.sum(spike_sums_b_l, 1) == 0
                    guesses_b = np.argmax(spike_sums_b_l, 1)
                none_class_b = -1 * np.ones(self.batch_size)
                clean_guesses_b = np.where(undecided_b, none_class_b, guesses_b)
                echo("{:.2%}_".format(np.mean(kwargs[str("truth_b")] ==
                                              clean_guesses_b)))

Instances