def __init__(self,
activation,
sample_image,
layer_configuration,
should_pool=True):
self.__prev_neuron = Neuron(sample_image, activation, 'Ly1')
self.__prev_neuron.set_should_pooling(should_pool)
self.__prev_neuron.activate()
self.__prev_neuron.plot_maps(0)
self.__network = [self.__prev_neuron]
size_of_config = len(layer_configuration)
for i in range(size_of_config):
neuron = Neuron(
self.__prev_neuron.get_map(), activation, 'Ly{}'.format(i + 2),
general.get_random_filter(
layer_configuration[i],
self.__prev_neuron.get_map().shape[-1]))
neuron.set_should_pooling(should_pool)
neuron.activate()
neuron.plot_maps(i)
self.__network.append(neuron)
self.__prev_neuron = neuron
tacos = self.__prev_neuron.get_map()
neuron = fc_layer(tacos) # Full Connected Layer
neuron.set_should_pooling(
False) # Do not want down-sampling on output layer
neuron.activate()
self.__network.append(neuron)
class Network:
def __init__(self,
activation,
sample_image,
layer_configuration,
should_pool=True):
self.__prev_neuron = Neuron(sample_image, activation, 'Ly1')
self.__prev_neuron.set_should_pooling(should_pool)
self.__prev_neuron.activate()
self.__prev_neuron.plot_maps(0)
self.__network = [self.__prev_neuron]
size_of_config = len(layer_configuration)
for i in range(size_of_config):
neuron = Neuron(
self.__prev_neuron.get_map(), activation, 'Ly{}'.format(i + 2),
general.get_random_filter(
layer_configuration[i],
self.__prev_neuron.get_map().shape[-1]))
neuron.set_should_pooling(should_pool)
neuron.activate()
neuron.plot_maps(i)
self.__network.append(neuron)
self.__prev_neuron = neuron
tacos = self.__prev_neuron.get_map()
neuron = fc_layer(tacos) # Full Connected Layer
neuron.set_should_pooling(
False) # Do not want down-sampling on output layer
neuron.activate()
self.__network.append(neuron)
def train(self, input_data, cur_epoch, batch_size):
error_sum = 0
for i in range(len(input_data[1])):
has_a_car = (i + cur_epoch) % 2
data = input_data[has_a_car]['{}'.format(i)]
for j in range(len(self.__network) - 1):
if j == 0:
# data -= numpy.mean(data, axis=0)
self.__network[j].set_image(data)
else:
self.__network[j].set_image(self.__network[j -
1].get_map())
self.__network[j].activate()
self.__network[j].plot_maps(i)
classifation = self.__network[len(self.__network) - 1].get_map()
correct_answer = create_indacation(has_a_car)
guess = create_indacation(classifation)
print("\n========================================\n"
"the correct answer for this images is: {}"
"\nhowever the network has guessed: {}"
"\nleading to a error of {}"
"\n========================================\n".format(
correct_answer, guess, .5 * math.pow(
(has_a_car - classifation), 1)))
if i == batch_size:
avg_error = error_sum / batch_size
print("\n==============================\n"
"YELLING AT THE TOP OF MY LUNGS"
"\n{}\n".format(avg_error))
else:
error_sum += .5 * math.pow((has_a_car - classifation), 1)
def test(self): # TODO
pass