【吴恩达课后测验】Course 1 - 神经网络和深度学习 - 第二周测验【中英】

【中英】【吴恩达课后测验】Course 1 - 神经网络和深度学习 - 第二周测验


第2周测验 - 神经网络基础

  1. 神经元节点计算什么?

    • 【 】神经元节点先计算激活函数,再计算线性函数(z = Wx + b)

    • 】神经元节点先计算线性函数(z = Wx + b),再计算激活。

    • 【 】神经元节点计算函数g,函数g计算(Wx + b)。

    • 【 】在 将输出应用于激活函数之前,神经元节点计算所有特征的平均值

      请注意:神经元的输出是a = g(Wx + b),其中g是激活函数(sigmoid,tanh,ReLU,…)。

  2. 下面哪一个是Logistic损失?

    请注意:我们使用交叉熵损失函数。

  3. 假设img是一个(32,32,3)数组,具有3个颜色通道:红色、绿色和蓝色的32x32像素的图像。 如何将其重新转换为列向量?

    x = img.reshape((32 * 32 * 3, 1))
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    请问数组c的维度是多少?

    答: B(列向量)复制3次,以便它可以和A的每一列相加,所以:c.shape = (2, 3)

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    请问数组“c”的维度是多少?

    答:运算符 “*” 说明了按元素乘法来相乘,但是元素乘法需要两个矩阵之间的维数相同,所以这将报错,无法计算。

  4. 假设你的每一个实例有n_x个输入特征,想一下在X=[x^(1), x^(2)…x^(m)]中,X的维度是多少?

    答: (n_x, m)

    请注意:一个比较笨的方法是当l=1的时候,那么计算一下Z(l)=W(l)A(l)Z(l)=W(l)A(l) ,所以我们就有:

    • A(1)A(1) = X
    • X.shape = (n_x, m)
    • Z(1)Z(1) .shape = (n(1)n(1) , m)
    • W(1)W(1) .shape = (n(1)n(1) , n_x)
  5. 回想一下,np.dot(a,b)在a和b上执行矩阵乘法,而`a * b’执行元素方式的乘法。

    看一下下面的这两个随机数组“a”和“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)
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    请问c的维度是多少?

    答: c.shape = (12288, 45), 这是一个简单的矩阵乘法例子。

  6. 看一下下面的这个代码片段:


    # 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]
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    请问要怎么把它们向量化?

    答:c = a + b.T

  7. 看一下下面的代码:

    a = np.random.randn(3, 3)
    b = np.random.randn(3, 1)
    c = a * b
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    请问c的维度会是多少?
    答:这将会使用广播机制,b会被复制三次,就会变成(3,3),再使用元素乘法。所以: c.shape = (3, 3).

  8. 看一下下面的计算图:

    J = u + v - w
    = a * b + a * c - (b + c)
    = a * (b + c) - (b + c)
    = (a - 1) * (b + c)
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    答: (a - 1) * (b + c)
    博主注:由于弄不到图,所以很抱歉。


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)

    • [x] 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

    Note: The output of a neuron is a = g(Wx + b) where g is the activation function (sigmoid, tanh, ReLU, …).

  2. Which of these is the “Logistic Loss”?

    Note: We are using a cross-entropy loss function.

  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?

    • x = img.reshape((32 * 32 * 3, 1))
  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
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    What will be the shape of “c”?

    b (column vector) is copied 3 times so that it can be summed to each column of a. Therefore, c.shape = (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
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    What will be the shape of “c”?

    “*” operator indicates element-wise multiplication. Element-wise multiplication requires same dimension between two matrices. It’s going to be an error.

  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?

    (n_x, m)

    Note: A stupid way to validate this is use the formula Z^(l) = W^(l)A^(l) when l = 1, then we have

    • A^(1) = X
    • X.shape = (n_x, m)
    • Z^(1).shape = (n^(1), m)
    • W^(1).shape = (n^(1), n_x)
  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)
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    What is the shape of c?

    c.shape = (12288, 45), this is a simple matrix multiplication example.

  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]
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    How do you vectorize this?

    c = a + b.T

  9. Consider the following code:

    a = np.random.randn(3, 3)
    b = np.random.randn(3, 1)
    c = a * b
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    What will be c?

    This will invoke broadcasting, so b is copied three times to become (3,3), and 鈭� is an element-wise product so c.shape = (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)
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    Answer: (a - 1) * (b + c)

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