TypeError：只能将元组（而不是“ float”）连接到元组cousera deeplearnig.ai

Question

有人可以帮忙吗？我正在从deeplearning.ai做深度学习我在课程1的第二周我的传播功能如下正向传播：

您得到X您计算A =σ（wTX + b）=（a（1），a（2），...，a（m-1），a（m））A =σ（wTX + b）=（a（ 1），（2），...，第（m-1），（M））您可以计算成本函数：J = −1m∑mi = 1y（i）log（a（i））+（1-y（i））log（1-a（i））J = −1m∑i = 1my （ⅰ）log⁡（A（I））+（1-γ（i））的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))

但是我遇到以下错误

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型函数工作正常。]

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one参数(w.T,X)+b。这又由元组(w.T, X)和您尝试加在一起的浮点数b组成（这是不可能的。）

问题在于您的括号。您想使用two

参数w.T和X调用该函数，然后将b添加到结果中：np.dot(w.T,X)+b。