类型错误：只能将元组(不是"float")连接到元组 cousera deeplearnig.ai

Question

谁能帮忙？ 我正在从 deeplearning.ai 进行深度学习 我在我第 2 周当然 1 我的传播函数如下 前向传播：

你得到 X 你计算 A=σ(wTX+b(=(a(1(，a(2(,...,a(m−1(，a(m((A=σ(wTX+b(=(a(1(，a(2(,...,a(m−1(，a(m(( 计算成本函数：J=−1m∑mi=1y(i(log(a(i((+(1−y(i((log(1−a(i((J=−1m∑i=1my(i(log(a(i((+(1−y(i((log(1−a(i((

# GRADED FUNCTION: propagate
def propagate(w, b, X, Y):
"""
Implement the cost function and its gradient for the propagation explained above
Arguments:
w -- weights, a numpy array of size (num_px * num_px * 3, 1)
b -- bias, a scalar
X -- data of size (num_px * num_px * 3, number of examples)
Y -- true "label" vector (containing 0 if non-cat, 1 if cat) of size (1, number of examples)
Return:
cost -- negative log-likelihood cost for logistic regression
dw -- gradient of the loss with respect to w, thus same shape as w
db -- gradient of the loss with respect to b, thus same shape as b
Tips:
- Write your code step by step for the propagation. np.log(), np.dot()
"""
m = X.shape[1]
# FORWARD PROPAGATION (FROM X TO COST)
### START CODE HERE ### (≈ 2 lines of code)
A = sigmoid(np.dot((w.T,X)+b))                                    # compute activation
cost = -1/m*np.sum(Y*np.log(A)+(1-Y)*np.log(1-A), axis=1,keepdims=True)                                 # compute cost
### END CODE HERE ###
# BACKWARD PROPAGATION (TO FIND GRAD)
### START CODE HERE ### (≈ 2 lines of code)
dw = 1/m*dot((X,(A-Y).T))
db = 1/m*np.sum(A-Y)
### END CODE HERE ###
assert(dw.shape == w.shape)
assert(db.dtype == float)
cost = np.squeeze(cost)
assert(cost.shape == ())
grads = {"dw": dw,
"db": db}
return grads, cost

和

w, b, X, Y = np.array([[1.],[2.]]), 2., np.array([[1.,2.,-1.],[3.,4.,-3.2]]), np.array([[1,0,1]])
grads, cost = propagate(w, b, X, Y)
print ("dw = " + str(grads["dw"]))
print ("db = " + str(grads["db"]))
print ("cost = " + str(cost))

但是我收到以下错误

TypeError                                 Traceback (most recent call last)
----> 3 grads, cost = propagate(w, b, X, Y)
---> 26     A = sigmoid(np.dot((w.T,X)+b))                                    # compute activation
TypeError: can only concatenate tuple (not "float") to tuple

如何解决？ 我的 S 形函数工作正常。.

Answer 1

您的错误在表达式np.dot((w.T,X)+b)中。在此表达式中，将函数np.dot应用于一个参数(w.T,X)+b。反过来，它由元组(w.T, X)和浮点数组成，b您尝试将其相加(这是不可能的(。

问题出在您的括号上。您希望使用两个参数w.T和X调用函数，然后将b添加到结果中：np.dot(w.T,X)+b。