pf = strengthen_functions.PF81
nn = NeuralNetwork(strength_function=pf,
image_scale=8,
transmission_history_len=10**4)
'''
train_imgs, _ = skl.load_data()
train_imgs = train_imgs[number]
size = len(train_imgs)
'''
# average_img = skl.average_img_by_number(number)
train_imgs = skl.get_imgs_by_number(number)
plotting_strength = True
if plotting_strength: strength_stats = []
start_time = datetime.datetime.now()
for i in range(iterations):
_, img = random.choice(train_imgs)
nn.propagate_once(img, gray_max=16)
if plotting_strength:
if i % 10 == 0: strength_stats.append(nn.stats()['strength'])
end_time = datetime.datetime.now()
print('%s: ' % number, 'start time:', start_time, 'stop time: ', end_time)
if plotting_strength:
plt.plot(strength_stats)
plt.savefig('./nn_growable_%s.png' % number)
utils.write_pickle(nn.connections_matrix, './pkl/nn_growable_%s.pkl' % number)
action="store",
dest="iterations",
default=30000,
help="default: 30000")
args = parser.parse_args()
number = int(args.number)
iterations = int(args.iterations)
pf = strengthen_functions.PF80
nn = NeuralNetwork(strength_function=pf,
image_scale=8,
transmission_history_len=10**4)
average_img = skl.average_img_by_number(number)
plotting_strength = False
if plotting_strength: strength_stats = []
start_time = datetime.datetime.now()
for i in range(iterations):
nn.propagate_once(average_img, gray_max=16)
if plotting_strength:
if i % 10 == 0: strength_stats.append(nn.stats()['strength'])
end_time = datetime.datetime.now()
print('%s: ' % number, 'start time:', start_time, 'stop time: ', end_time)
if plotting_strength:
plt.plot(strength_stats)
plt.savefig('./nn_growable_%s.png' % number)
utils.write_pickle(nn.connections_matrix, './pkl/nn_growable_%s.pkl' % number)