原文翻译：深度学习测试题（L1 W4 测试题）本文翻译自deeplearning.ai的深度学习课程测试作业，近期将逐步

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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原文翻译：深度学习测试题（L1 W4 测试题）

导语

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