原文链接：https://blog.csdn.net/u013733326/article/details/79862336
Week 2 Quiz - Neural Network Basics
1.What does a neuron compute?
- A neuron computes an activation function followed by a linear function (z = Wx + b)
- A neuron computes a linear function (z = Wx + b) followed by an activation function
- A neuron computes a function g that scales the input x linearly (Wx + b)
- A neuron computes the mean of all features before applying the output to an activation function
解：2
神经元绩点先计算线性函数的结果z，然后在计算激活函数的值a。其中a=g(z)，g是激活函数（sigmoid，tanh，ReLU……）。
2.Which of these is the “Logistic Loss”?
解：我们使用交叉熵损失函数。
3.Suppose img is a (32,32,3) array, representing a 32x32 image with 3 color channels red, green and blue. How do you reshape this into a column vector?
解：
x = img.reshape((32 * 32 * 3, 1))
列向量是(n, 1)的向量，使用reshape函数来更改元素位置。
4.Consider the two following random arrays “a” and “b”:
a = np.random.randn(2, 3) # a.shape = (2, 3)
b = np.random.randn(2, 1) # b.shape = (2, 1)
c = a + b
What will be the shape of “c”?
解：a和b的形状不同，会触发广播机制，b将元素进行复制到(2, 3)，c的形状为(2, 3)
5.Consider the two following random arrays “a” and “b”:
a = np.random.randn(4, 3) # a.shape = (4, 3)
b = np.random.randn(3, 2) # b.shape = (3, 2)
c = a * b
What will be the shape of “c”?
解：“*”运算符用于按元素乘法来相乘，但是元素乘法需要两个矩阵之间的，所以这将报错，无法计算。
6.Suppose you have n_x input features per example. Recall that X=[x^(1), x(2)…x(m)]. What is the dimension of X?
解：(n_x, m)
我们将样本转为列向量进行横向堆叠，那么行数就是特征的数量，列数就是样本数。
7.Recall that np.dot(a,b) performs a matrix multiplication on a and b, whereas a*b performs an element-wise multiplication.
Consider the two following random arrays “a” and “b”:
a = np.random.randn(12288, 150) # a.shape = (12288, 150)
b = np.random.randn(150, 45) # b.shape = (150, 45)
c = np.dot(a, b)
What is the shape of c?
解：(12288, 45), (m, n)的矩阵与(n, l)的矩阵相乘，结果为(m, l)的矩阵
8.Consider the following code snippet:
a.shape = (3,4)b.shape = (4,1)for i in range(3):for j in range(4):c[i][j] = a[i][j] + b[j]
How do you vectorize this?
解：c = a + b.T
可以看出，c每行的元素等于a每行的元素加上一个相同的元素b对应的元素。所以应该是广播的用法。
9.Consider the following code:
a = np.random.randn(3, 3)
b = np.random.randn(3, 1)
c = a * b
What will be c?
解：仍然是广播机制，所以c的形状是(3, 3)。
10.Consider the following computation graph.
J = u + v - w= a * b + a * c - (b + c)= a * (b + c) - (b + c)= (a - 1) * (b + c)
解：(a - 1) * (b + c)