TypeError: can only concatenate tuple (not "float") to tuple cousera deeplearnig.ai

Question

有人能帮忙吗？我正在从 deeplearning.ai 进行深度学习我在第 2 周当然 1 我的传播功能如下前向传播：

你得到 X 您计算 A=σ(wTX+b)=(a(1),a(2),...,a(m−1),a(m))A=σ(wTX+b)=(a( 1),a(2),...,a(m−1),a(m)) 您计算成本函数：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))

但是我遇到了以下错误

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 sigmoid 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，您将函数 np.dot 应用于 one 参数 (w.T,X)+b。反过来，它由元组 (w.T, X) 和您尝试加在一起的浮点数 b 组成（这是不可能的）。

问题出在你的括号上。您想使用两个参数 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.

TypeError: can only concatenate tuple (not "float") to tuple cousera deeplearnig.ai

TypeError: can only concatenate tuple (not "float") to tuple cousera deeplearnig.ai

python

logistic-regression

deep-learning