def recall_for_i_iters(self, should_display_image, num_of_iterations):
rand_in = Tools.get_random_input(49)
self.setup_input(rand_in)
for i in range(num_of_iterations):
# Attempt to set a random input for every iteration:
self.recall()
if should_display_image:
Tools.show_image_from(out_now=self.output_values.get_value())
# print "Returning from chaotic recall after", num_of_iterations, "iterations."
return [rand_in, self.output_values.get_value()]
def recall_until_stability_criteria(self, should_display_image, max_iterations):
# recall until output unchanged three iterations
out_now = np.copy(self.output_values.get_value())
out_min_1 = np.zeros_like(out_now, dtype=np.float32)
found_stable_output = False
ctr = 0
while not found_stable_output and ctr < max_iterations:
out_min_2 = np.copy(out_min_1)
out_min_1 = np.copy(out_now)
# Attempt to set a random input for every iteration:
self.recall()
out_now = np.copy(self.output_values.get_value())
found_stable_output = True
for out_y in xrange(out_now.shape[1]):
if not (out_min_2[0][out_y] == out_min_1[0][out_y] == out_now[0][out_y]):
found_stable_output = False
break
ctr += 1
if should_display_image and found_stable_output:
Tools.show_image_from(out_now=out_now)
print "Reached stability or max. #iterations during chaotic recall after", ctr, "iterations."
return [ctr, found_stable_output, out_now]
