从头开始计算逻辑回归的对数损失

Question

计算日志损失

def logloss(y_true,y_pred):
'''In this function, we will compute log loss '''
log_loss = (-((y_true * np.log10(y_pred)) + (1-y_true) * np.log10(1-y_pred)).mean())
return log_loss

计算逻辑回归

def train(X_train,y_train,X_test,y_test,epochs,alpha,eta0):
w,b = initialize_weights(X_train[0])
train_loss = []
test_loss = []
for e in range(epochs):
for x,y in zip(X_train,y_train):
dw = gradient_dw(x,w,y,b,alpha,N)
db = gradient_db(x,y,w,b)
w = w + (eta0 * dw)
b = b + (eta0 * db)
train_pred = []
for i in X_train:
y_pred = sigmoid(np.dot(w.T, i) + b)
train_pred.append(y_pred)
train_loss.append(logloss(y_train, train_pred))

test_pred = []
for j in X_test:
y_pred_test = sigmoid(np.dot(w.T, j) + b)
test_pred.append(y_pred_test)
test_loss.append(logloss(y_test, test_pred))
return w,b

alpha=0.0001
eta0=0.0001
epochs=50
N = len(X_train)
w,b = train(X_train,y_train,X_test,y_test,epochs,alpha,eta0)

我得到的错误

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-112-9a34879eb072> in <module>
3 epochs=50
4 N = len(X_train)
----> 5 w,b = train(X_train,y_train,X_test,y_test,epochs,alpha,eta0)
<ipython-input-110-db0e3d88382d> in train(X_train, y_train, X_test, y_test, epochs, alpha, eta0)
30             y_pred = sigmoid(np.dot(w.T, i) + b)
31             train_pred.append(y_pred)
---> 32         train_loss.append(logloss(y_train, train_pred))
33 
34         test_pred = []
<ipython-input-108-f272288a384c> in logloss(y_true, y_pred)
1 def logloss(y_true,y_pred):
2     '''In this function, we will compute log loss '''
----> 3     log_loss = (-((y_true * np.log10(y_pred)) + (1-y_true) * np.log10(1-y_pred)).mean())
4     return log_loss
TypeError: unsupported operand type(s) for -: 'int' and 'list'

我已经提到了完整的代码，只是我遇到错误的代码。我很困惑是对logloss进行更改还是对逻辑回归代码进行更改，即def-train((。如何纠正这个错误？

Answer 1

您的train_pred是python列表。在CCD_ 2函数中使用时，您计算(1-train_pred)，它是整数减去python列表。因此你得到类型错误

TypeError: unsupported operand type(s) for -: 'int' and 'list'