I am trying to do MNIST classification using SNN.
For the same, I wish to introduce a pause of 100ms between subsequent samples.
I tried to modify the PresentInput process of nengo as follows but this does not work. Any idea where am I going wrong?
class PresentInputWithPause(Process):
"""Present a series of inputs, each for the same fixed length of time.
Parameters
----------
inputs : array_like
Inputs to present, where each row is an input. Rows will be flattened.
presentation_time : float
Show each input for this amount of time (in seconds).
pause_time : float
Pause time after each input (in seconds).
"""
inputs = NdarrayParam("inputs", shape=("...",))
presentation_time = NumberParam("presentation_time", low=0, low_open=True)
pause_time = NumberParam("pause_time", low=0, low_open=True)
def __init__(self, inputs, presentation_time,pause_time, **kwargs):
self.inputs = inputs
self.presentation_time = presentation_time
self.pause_time = pause_time
self.localT = 0
self.index = 0
super().__init__(
default_size_in=0, default_size_out=self.inputs[0].size, **kwargs
)
def make_step(self, shape_in, shape_out, dt, rng, state):
assert shape_in == (0,)
assert shape_out == (self.inputs[0].size,)
n = len(self.inputs)
inputs = self.inputs.reshape(n, -1)
presentation_time = float(self.presentation_time)
pause_time = float(self.pause_time)
self.localT = round((dt if self.localT == 0 else self.localT),2)
def step_presentinput(t):
#t = abs(t - pause_time)
t = round(t,6)
# Pause
if t > ((presentation_time + pause_time ) * self.index + presentation_time) and t < round((presentation_time + pause_time) * (self.index + 1),6) :
return None
else:
# Send input
#if t >= (presentation_time + pause_time) * i:
# t = t - (pause_time * i)
i = int((self.localT - dt) / (presentation_time))
self.localT += dt
if t == round((presentation_time + pause_time) * (self.index + 1),6):
self.index +=1
i = 0
return inputs[i % n]
return step_presentinput