I'm trying code a logistic regression but I'm in trouble getting a convergent COST, can anyone help me? Below are my codes. Thank you!
#input：
m = 3， n = 4
# we have 3 training examples and each of them has 4 features (Sorry, I know it looks weired here). Y is a label matrix.
X = np.array([[1,2,1],[1,1,0],[1,2,1],[1,0,2]])
Y = np.array([[0,1,0]])
h = 100000 #iterations
alpha = 0.05 #learning rate
b = 0 #scalar bias
W = np.zeros(n).reshape(1,n) #weights
J = np.zeros(h).reshape(1,h) #a vector for holing cost value
Yhat = np.zeros(m).reshape(1,m) #predicted value
def activation(yhat):
return 1/(1+np.exp(-yhat))
W=W.T
for g in range(h):
m = X.T.shape[0]
Y_hat = activation(X.dot(W)+b)
cost = -1/m * np.sum(Y*np.log(Y_hat)+(1-Y)*np.log(1-Y_hat))
current_error = Y.T - Y_hat
dW = 1/m * np.dot(X.T, current_error)
db = 1/m * np.sum(current_error)
W = W + alpha * dW
b = b + alpha * db
J[0][g] = cost
