def continue_bp(net, test_len, testing_name=""):
# load training data
training_inputs = helpers.load_training_data()
# writer for testing data
csv_file = None
writer = None
if testing_name != "":
path = "./results/" + testing_name
csv_file = open(path, 'wb+')
writer = csv.writer(csv_file, delimiter=',')
# train the net
for i in range(test_len):
# Print out progress:
if i % 100 == 0:
per = float(i) / float(test_len) * 100
print("Training: " + str(format(per, '.2f')) + "%")
# write testing results if testing
if writer is not None:
data = [i, helpers.get_testing_error(net)]
writer.writerow(data)
# randomly pick an emotion to train
emotion = random.choice(range(len(cfg.emojis)))
# pick a specific test case
test_case = random.choice(training_inputs[emotion])
net.train(test_case, cfg.outputs[emotion])
# write testing results if testing
if writer is not None:
data = [test_len, helpers.get_testing_error(net)]
writer.writerow(data)
# close the file
if csv_file is not None:
csv_file.close()
return 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