def test_bias(model_path):
model = load_model(model_path)
dic = {}
dic[5] = []
dic[10] = []
dic[20] = []
dic[30] = []
dic[50] = []
for extent in [5, 10, 20, 30, 50]:
ratio = 0.0
step = 0.1
for i in range(5):
ratio_temp = 0.0
change = False
acc = 0.98
while change | (acc > 0.9):
ratio = ratio + step
if ratio == ratio_temp:
break
_, _, _, _, model_change = model_mutation_single_neuron(
model, cls='bias', random_ratio=ratio, extent=extent)
acc = accuracy_mnist(model_change, mnist)
print(acc)
if acc <= 0.90:
if step != 0.001:
ratio_temp = ratio
step = step / 10.0
ratio = ratio - step * 10.0
change = True
print(step, ratio)
dic[extent].append((ratio, acc))
ratio = 0.0
step = 0.1
return dic
def Gen_mutaion(model_path,size=100):
model=load_model(model_path)
for index in range(size):
#这个地方可以使用for语句进行多次变异
#这个地方自己选择变异的类型权重和比例
extent = (index/10)*0.5+3
random_ratio = ((index+1) % 10)*0.05
_,_,_,_,model_mut=model_change_standard.model_mutation_single_neuron(model,'kernel',random_ratio,extent)
sys.stdout.write('index:{},extent:{},random_ratio:{}\r'.format(index,extent,random_ratio))
sys.stdout.flush()
model_mut.save('./model_bp/mutation/model_mutation_{}.hdf5'.format(index))
def bp_bias(model_path):
model = load_model(model_path)
extent_lst = [0.01, 0.1, 0.5, 1.5, 2, 3, 5, 10]
statistic = {i: [] for i in extent_lst}
ratio_lst = [0.01, 0.03, 0.05, 0.1, 0.2]
for extent in extent_lst:
for ratio in ratio_lst:
lst = []
for i in range(10):
print(i)
_, _, _, _, model_change = model_mutation_single_neuron(
model, cls='bias', random_ratio=ratio, extent=extent)
acc = accuracy_mnist(model_change, mnist)
lst.append(acc)
statistic[extent].append(lst)
return statistic