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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。)

答案

【★】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。)

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。)

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\2. Among the following, which ones are “hyperparameters”? (Check all that apply.)(以下哪些是“超参数”？)

【 】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??)

答案

全对

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

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\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。)

答案

【★】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。)

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。)

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\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)

答案

【★】 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。)

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\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？)

答案

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

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\6. Consider the following neural network.(下面关于神经网络的说法正确的是：)

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

答案

显然