def run():
image = read()
input = list(read_image(image))
net = Network.load(LAYER1_FILENAME)
hidden = net.forward(input)
for _ in xrange(5):
print_images([net.backwards(hidden)])
def run():
net = Network.load(LAYER1_FILENAME)
input = [.0] * IMAGE_DIM * IMAGE_DIM
while True:
hidden = net.forward(input)
input = net.backwards(hidden)
print_images([input])
time.sleep(.075)
def train_input_list(self, input_list):
assert len(input_list) > 0
assert all(len(input) == self.insz for input in input_list)
weight_deltas = [.0] * len(self.weights)
#sample_size = len(input_list) / 3
#for input in random.sample(input_list, sample_size):
for input in input_list:
train_deltas = self.train_input(input)
weight_deltas = [a + b for a, b in zip(weight_deltas, train_deltas)]
self.apply_deltas(weight_deltas)
mod_inputs = []
if can_show_something():
for input in random.sample(input_list, 1):
mod_inputs.append(input)
mod_inputs.append(self.backwards(self.forward(input)))
print_images(mod_inputs)
self.save()
