[英]Implement SGD Classifier with Logloss and L2 regularization Using SGD without using sklearn
[英]Computing SGD classifier with logloss & L2 regularization without using sklearn and I am not able to compute loss & getting error in loss calculation
def logloss(y_true,y_pred): # compute log-loss
log_loss = (-y_true * math.log(y_pred, 10) - (1 - y_true) * math.log(1 - y_pred,10)).mean()
return log_loss
def grader_logloss(true,pred): # comparing log-loss using assert
loss = logloss(true,pred)
assert(loss == 0.07644900402910389)
return True
true = [1,1,0,1,0]
pred = [0.9,0.8,0.1,0.8,0.2]
grader_logloss(true,pred)
我得到的錯誤
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-110-edd7da954047> in <module>
5 true = [1,1,0,1,0]
6 pred = [0.9,0.8,0.1,0.8,0.2]
----> 7 grader_logloss(true,pred)
<ipython-input-110-edd7da954047> in grader_logloss(true, pred)
1 def grader_logloss(true,pred):
----> 2 loss = logloss(true,pred)
3 assert(loss == 0.07644900402910389)
4 return True
5 true = [1,1,0,1,0]
<ipython-input-109-b96b3bba92ed> in logloss(y_true, y_pred)
2 '''In this function, we will compute log loss '''
3 n = len(y_true)
----> 4 log_loss = (-y_true * math.log(y_pred, 10) - (1 - y_true) * math.log(1 - y_pred,10)).mean()
5 return log_loss
TypeError: bad operand type for unary -: 'list'
我無法獲得操作數類型。 我已經搜索過,但無法清楚地看到它。
預期的結果是
True
計算梯度
def gradient_dw(x,y,w,b,alpha,N):
'''In this function, we will compute the gardient w.r.to w '''
dw = x*(y - sigmoid(np.dot(w.T,x) + b)) - ((alpha*x)/N)
return dw
計算梯度並進行比較
def grader_dw(x,y,w,b,alpha,N):
grad_dw=gradient_dw(x,y,w,b,alpha,N)
assert(grad_dw==2.613689585)
return True
grad_x=np.array([-2.07864835, 3.31604252, -0.79104357, -3.87045546, -1.14783286,
-2.81434437, -0.86771071, -0.04073287, 0.84827878, 1.99451725,
3.67152472, 0.01451875, 2.01062888, 0.07373904, -5.54586092])
grad_y=0
grad_w,grad_b = initialize_weights(grad_x)
alpha=0.0001
N=len(X_train)
grader_dw(grad_x,grad_y,grad_w,grad_b,alpha,N)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-152-b22fd39ec68a> in <module>
10 alpha=0.0001
11 N=len(X_train)
---> 12 grader_dw(grad_x,grad_y,grad_w,grad_b,alpha,N)
<ipython-input-152-b22fd39ec68a> in grader_dw(x, y, w, b, alpha, N)
1 def grader_dw(x,y,w,b,alpha,N):
2 grad_dw=gradient_dw(x,y,w,b,alpha,N)
----> 3 assert(grad_dw==2.613689585)
4 return True
5 grad_x=np.array([-2.07864835, 3.31604252, -0.79104357, -3.87045546, -1.14783286,
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
我不知道我的梯度評估在哪里不正確以及為什么斷言 function 失敗,即使我嘗試了 a.any() 或 a.all()
使用numpy.array
來存儲您的true
和pred
,並稍微調整您的代碼以使用numpy
中定義的操作。 由於 python 中的普通list
不支持-
和*
和+
等元素操作,因此它沒有mean
方法。
import numpy as np
def logloss(y_true,y_pred): # compute log-loss
log_loss = (-y_true * np.log10(y_pred) - (1 - y_true) * np.log10(1 - y_pred)).mean()
return log_loss
def grader_logloss(true,pred): # comparing log-loss using assert
loss = logloss(true,pred)
assert(loss == 0.07644900402910389)
return True
true = np.array([1,1,0,1,0])
pred = np.array([0.9,0.8,0.1,0.8,0.2])
grader_logloss(true,pred)
新問題的更新:我假設你的dw
是一個標量,因為你有grad_dw==2.613689585
。 然后,在您的gradient_dw
function 中,更改此行:
dw = x*(y - sigmoid(np.dot(w.T,x) + b)) - ((alpha*x)/N)
到這條線
dw = (x*(y - sigmoid(np.dot(w.T,x) + b)) - ((alpha*x))).sum() / N
此外,您應該使用assert grad_dw==2.613689585
而不是assert (grad_dw==2.613689585)
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