def load_net(name):
print("Loading net...")
path = "./nets/" + name
input = open(path, "r")
net = nn.init_net()
weights = []
with open(path, 'rb') as csv_file:
weight_reader = csv.reader(csv_file, delimiter=',')
for row in weight_reader:
for el in row:
weights.append(float(el))
net.put_weights1d(weights)
print("Net loaded!")
return net
def continue_gen(net, train_len, pop_size):
w1 = net.get_weights()
mut_cnt = int(cfg.MUT_RATE * len(w1))
print("Training the net!")
# Initializes the first population
print("Generation: 0")
pop = []
for x in xrange(pop_size):
new_net = nn.init_net()
weights = gen.mutate(w1, mut_cnt)
new_net.put_weights1d(weights)
pop.append(new_net)
training_data = helpers.load_training_data()
# Calculate errors
errors = gen_err(pop, training_data)
# Sorts the errors array but only stores indices
idx_err = sorted(range(len(errors)), key=lambda k: errors[k])
print(" Smallest error: " + str(errors[idx_err[0]]))
# Goes through the generations
counter = 1
while counter < train_len:
print("Generation: " + str(counter))
# Decides best 2 parents based on confidence matrix
parent1 = pop[idx_err[0]]
parent2 = pop[idx_err[1]]
w1 = parent1.get_weights()
w2 = parent2.get_weights()
for x in idx_err[2:]:
new_w = gen.recombine(w1, w2)
new_w = gen.mutate(new_w, mut_cnt)
pop[x].put_weights1d(new_w)
errors = gen_err(pop, training_data)
idx_err = sorted(range(len(errors)), key=lambda k: errors[k])
print(" Smallest error: " + str(errors[idx_err[0]]))
counter += 1
best_net = pop[idx_err[0]]
return best_net
def genetic_train(pop_size, test_len, testing_name):
writer = None
csv_file = None
if testing_name != "":
path = "./results/" + testing_name
csv_file = open(path, 'wb+')
writer = csv.writer(csv_file, delimiter=',')
print("Training the net!")
# Initializes the first population
print("Generation: 0")
pop = []
for x in xrange(pop_size):
net = nn.init_net()
pop.append(net)
training_data = helpers.load_training_data()
# Calculate errors
errors = gen_err(pop, training_data)
# Sorts the errors array but only stores indices
idx_err = sorted(range(len(errors)), key=lambda k: errors[k])
print(" Smallest error: " + str(errors[idx_err[0]]))
if writer is not None:
data = [0, helpers.get_testing_error(pop[idx_err[0]])]
writer.writerow(data)
# Goes through the generations
counter = 1
while errors[idx_err[0]] > 0.1 and counter < test_len:
print("Generation: " + str(counter))
# Decides best 2 parents based on confidence matrix
parent1 = pop[idx_err[0]]
parent2 = pop[idx_err[1]]
w1 = parent1.get_weights()
w2 = parent2.get_weights()
mut_cnt = int(cfg.MUT_RATE * len(w1))
for x in idx_err[2:]:
new_w = gen.recombine(w1, w2)
new_w = gen.mutate(new_w, mut_cnt)
pop[x].put_weights1d(new_w)
errors = gen_err(pop, training_data)
idx_err = sorted(range(len(errors)), key=lambda k: errors[k])
print(" Smallest error: " + str(errors[idx_err[0]]))
# write the current test_error
if writer is not None and counter % 5 == 0:
data = [counter, helpers.get_testing_error(pop[idx_err[0]])]
writer.writerow(data)
counter += 1
# write the last test error:
if writer is not None:
data = [counter, helpers.get_testing_error(pop[idx_err[0]])]
writer.writerow(data)
# close the file
if csv_file is not None:
csv_file.close()
best_net = pop[idx_err[0]]
return best_net