Current Teaclave supports any python machine learning modules? can I load a
pickle-dumped machine learning model file in Teaclave and run it?
`import numpy as np
class Linear_Regression:
def __init__(self):
self._w = None
def fit(self, X, y, lr=0.01, epsilon=0.01, epoch=1000):
#训练数据
#将输入的X,y转换为numpy数组
X, y = np.asarray(X, np.float32), np.asarray(y, np.float32)
#给X增加一列常数项
X=np.hstack((X,np.ones((X.shape[0],1))))
#初始化w
self._w = np.zeros((X.shape[1],1))
for _ in range(epoch):
#随机选择一组样本计算梯度
random_num=np.random.choice(len(X))
x_random=X[random_num].reshape(1,2)
y_random=y[random_num]
gradient=(x_random.T)*(np.dot(x_random,self._w)-y_random)
#如果收敛，那么停止迭代
if (np.abs(self._w-lr*gradient)<epsilon).all():
break
#否则，更新w
else:
self._w =self._w-lr*gradient
return self._w
def print_results(self):
print("参数w:{}".format(self._w))
print("回归拟合线:y={}x+{}".format(self._w[0],self._w[1]))
def predict(self,x):
x=np.asarray(x, np.float32)
x=x.reshape(x.shape[0],1)
x=np.hstack((x,np.ones((x.shape[0],1))))
return np.dot(x,self._w)
训练并保存模型：
import pickle
#创建数据
x=np.linspace(0,100,10).reshape(10,1)
rng=np.random.RandomState(4)
noise=rng.randint(-10,10,size=(10,1))*4
y=4*x+4+noise
model=Linear_Regression()
model.fit(x,y,lr=0.0001,epsilon=0.001,epoch=20)
with open('model.pickle', 'wb') as file:
pickle.dump(model, file)
然后调取模型并进行预测和打印结果：
with open('model.pickle', 'rb') as file:
model=pickle.load(file)
print(model.predict([50]))
model.print_results()`
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