导语

本文翻译自deeplearning.ai的深度学习课程测试作业，近期将逐步翻译完毕，一共五门课。

翻译：黄海广

本集翻译Lesson1 Week 4：

Lesson1 Neural Networks and Deep Learning (第一门课 神经网络和深度学习)

Week 4 Quiz - Key concepts on Deep Neural Networks（第四周测验 – 深层神经网络）

1.What is the “cache” used for in our implementation of forward propagation and backward propagation?

(在实现前向传播和反向传播中使用的“cache”是什么？)

【 】It is used to cache the intermediate values of the cost function during training.(用于在训练期间缓存成本函数的中间值。)

【★】We use it to pass variables computed during forward propagation to the corresponding backward propagation step. It contains useful values for backward propagation to compute derivatives.(我们用它传递前向传播中计算的变量到相应的反向传播步骤，它包含用于计算导数的反向传播的有用值。)

【 】It is used to keep track of the hyperparameters that we are searching over, to speed up computation.(它用于跟踪我们正在搜索的超参数，以加速计算。)

【 】We use it to pass variables computed during backward propagation to the corresponding forward propagation step. It contains useful values for forward propagation to compute activations.(我们使用它将向后传播计算的变量传递给相应的正向传播步骤，它包含用于计算计算激活的正向传播的有用值。)

Note: the “cache” records values from the forward propagation units and sends it to the backward propagation units because it is needed to compute the chain rule derivatives.(请注意：“cache”记录来自正向传播单元的值并将其发送到反向传播单元，因为需要链式计算导数。)

2. Among the following, which ones are “hyperparameters”? (Check all that apply.) I only list correct options.

(以下哪些是“超参数”？只列出了正确选项)

【★】size of the hidden layers (隐藏层的大小)

【★】learning rate α(学习率α)

【★】number of iterations(迭代次数)

【★】number of layers  in the neural network(神经网络中的层数)

Note: You can check this Quora post orthis blog post.(请注意：你可以查看Quora的这篇文章或者这篇博客.)

3. Which of the following statements is true?(下列哪个说法是正确的？)

【★】The deeper layers of a neural network are typically computing more complex features of the input than the earlier layers. (神经网络的更深层通常比前面的层计算更复杂的输入特征。)

【 】 The earlier layers of a neural network are typically computing more complex features of the input than the deeper layers.(神经网络的前面的层通常比更深层计算更复杂的输入特征。)

Note: You can check the lecture videos. I think Andrew used a CNN example to explain this.(注意：您可以查看视频，我想用吴恩达的用美国有线电视新闻网的例子来解释这个。)

4. Vectorization allows you to compute forward propagation in an -layer neural network without an explicit for-loop (or any other explicit iterative loop) over the layers l=1, 2, …,L. True/False?

(向量化允许您在层神经网络中计算前向传播，而不需要在层(l = 1,2，…，L)上显式的使用for-loop（或任何其他显式迭代循环），正确吗？)

【 】 True(正确)

【★】 False(错误)

Note: We cannot avoid the for-loop iteration over the computations among layers.(请注意：在层间计算中，我们不能避免for循环迭代。)

5. Assume we store the values for  in an array called layers, as follows: layer_dims = [, 4,3,2,1]. So layer 1 has four hidden units, layer 2 has 3 hidden units and so on. Which of the following for-loops will allow you to initialize the parameters for the model?

(假设我们将的值存储在名为layers的数组中，如下所示：layer_dims = [, 4,3,2,1]。因此，第1层有四个隐藏单元，第2层有三个隐藏单元，依此类推。您可以使用哪个for循环初始化模型参数？)

for(i in range(1, len(layer_dims))):parameter[‘W’ + str(i)] = np.random.randn(layers[i], layers[i - 1])) * 0.01 `parameter[‘b’ + str(i)] = np.random.randn(layers[i], 1) * 0.01

6. Consider the following neural network.

(下面关于神经网络的说法正确的是：只列出了正确选项)

【★】The number of layers  is 4. The number of hidden layers is 3.(层数为4，隐藏层数为3)

Note: The input layer () does not count.(注意：输入层（）不计数。)

As seen in lecture, the number of layers is counted as the number of hidden layers + 1. The input and output layers are not counted as hidden layers.(正如视频中所看到的那样，层数被计为隐藏层数+1。输入层和输出层不计为隐藏层。)

7. During forward propagation, in the forward function for a layer  you need to know what is the activation function in a layer (Sigmoid, tanh, ReLU, etc.). During backpropagation, the corresponding backward function also needs to know what is the activation function for layer , since the gradient depends on it. True/False?

(在前向传播期间，在层的前向传播函数中，您需要知道层中的激活函数（Sigmoid，tanh，ReLU等）是什么， 在反向传播期间，相应的反向传播函数也需要知道第层的激活函数是什么，因为梯度是根据它来计算的，正确吗？)

【★】 True(正确)

【 】False(错误)

Note: During backpropagation you need to know which activation was used in the forward propagation to be able to compute the correct derivative.(注：在反向传播期间，您需要知道正向传播中使用哪种激活函数才能计算正确的导数。)

8.There are certain functions with the following properties:

(有一些函数具有以下属性：)

(i) To compute the function using a shallow network circuit, you will need a large network (where we measure size by the number of logic gates in the network), but (ii) To compute it using a deep network circuit, you need only an exponentially smaller network. True/False?((i)使用浅网络电路计算函数时，需要一个大网络（我们通过网络中的逻辑门数量来度量大小），但是（ii）使用深网络电路来计算它，只需要一个指数较小的网络。真/假？)

【★】True(正确)

【 】False(错误)

Note: See lectures, exactly same idea was explained.(参见视频，完全相同的题。)

9. Consider the following 2 hidden layer neural network: Which of the following statements are True? (Check all that apply).

((在2层隐层神经网络中，下列哪个说法是正确的？只列出了正确选项))

【★】 will have shape (4, 4)(的维度为 (4, 4))

【★】 will have shape (4, 1)(的维度为 (4, 1))

【★】 will have shape (3, 4)(的维度为 (3, 4))

【★】 will have shape (3, 1)(的维度为 (3, 1))

【★】 will have shape (1, 1)(的维度为 (1, 1))

【★】 will have shape (1, 3)(的维度为 (1, 3))

Note: See [this image] for general formulas.(注：请参阅图片。)

10. Whereas the previous question used a specific network, in the general case what is the dimension of  , the weight matrix associated with layer ?

(前面的问题使用了一个特定的网络，与层ll有关的权重矩阵在一般情况下， 的维数是多少，只列出了正确选项)

【★】 has shape (,)(的维度是 (,)

Note: See this imagefor general formulas.(注：请参阅图片)

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