def train(X_train,y_train,X_test,y_test,epochs,alpha,eta0):
w,b = initialize_weights(X_train[0])
loss_test=[]
N=len(X_train)
for i in range(0,epochs):
print(i)
for j in range(N-1):
grad_dw=gradient_dw(X_train[j],y_train[j],w,b,alpha,N)
grad_db=gradient_db(X_train[j],y_train[j],w,b)
w=np.array(w)+(alpha*(np.array(grad_dw)))
b=b+(alpha*(grad_db))
predict2 = []
for m in range(len(y_test)):
z=np.dot(w[0],X_test[m])+b
if sigmoid(z) == 0: # sigmoid(w,x,b) returns 1/(1+exp(-(dot(x,w)+b)))
predict2.append(0.000001)
elif sigmoid(z) == 1:
predict2.append(0.99999)
else:
predict2.append(sigmoid(z))
loss_test.append(logloss(y_test,predict2))
return w,b,loss_test
我的渐变 dw 函数
def gradient_dw(x,y,w,b,alpha,N):
dw=[]
for i in range(len(x)):
dw.append((x[i]*(y-1/(1+np.exp(abs(w.T[0][i]*x[i]+b)))))+(alpha/N)*(w.T[0][i]))
return dw
我的渐变数据库功能：
def gradient_db(x,y,w,b):
db=0
for i in range(len(x)):
db=(y-1/(1+np.exp(abs(w.T[0][i]*x[i]+b))))
return db
我的损失函数：
def logloss(y_true,y_pred):
loss=0
for i in range(len(y_true)):
loss+=((y_true[i]*math.log10(y_pred[i]))+((1-y_true[i])*math.log10(1-y_pred[i])))
loss=-1*(1/len(y_true))*loss
return loss
我的问题是每个时代之后我的损失都在增加。为什么？
任何帮助将不胜感激
谢谢