## Week 2 quiz - Deep convolutional models 1. Which of the following do you typically see as you move to deeper layers in a ConvNet? nH and nW increases, while nC decreases nH and nW decreases, while nC also decreases nH and nW increases, while nC also increases > nH and nW decrease, while nC increases 2. Which of the following do you typically see in a ConvNet? (Check all that apply.) > Multiple CONV layers followed by a POOL layer Multiple POOL layers followed by a CONV layer > FC layers in the last few layers FC layers in the first few layers 3. In order to be able to build very deep networks, we usually only use pooling layers to downsize the height/width of the activation volumes while convolutions are used with “valid” padding. Otherwise, we would downsize the input of the model too quickly. True > False 4. Training a deeper network (for example, adding additional layers to the network) allows the network to fit more complex functions and thus almost always results in lower training error. For this question, assume we’re referring to “plain” networks. True > False 5. The following equation captures the computation in a ResNet block. What goes into the two blanks above? ``` a[l+2]=g(W[l+2]g(W[l+1]a[l]+b[l+1])+bl+2+_______ )+_______ ``` > a[l] and 0, respectively 0 and z[l+1], respectively z[l] and a[l], respectively 0 and a[l], respectively 6. Which ones of the following statements on Residual Networks are true? (Check all that apply.) > Using a skip-connection helps the gradient to backpropagate and thus helps you to train deeper networks A ResNet with L layers would have on the order of L2 skip connections in total. The skip-connections compute a complex non-linear function of the input to pass to a deeper layer in the network. > The skip-connection makes it easy for the network to learn an identity mapping between the input and the output within the ResNet block. 7. Suppose you have an input volume of dimension 64x64x16. How many parameters would a single 1x1 convolutional filter have (including the bias)? 2 4097 1 > 17 8. Suppose you have an input volume of dimension nH x nW x nC. Which of the following statements you agree with? (Assume that “1x1 convolutional layer” below always uses a stride of 1 and no padding.) > You can use a 1x1 convolutional layer to reduce nC but not nH, nW. You can use a 1x1 convolutional layer to reduce nH, nW, and nC. > You can use a pooling layer to reduce nH, nW, but not nC. You can use a pooling layer to reduce nH, nW, and nC. 9. Which ones of the following statements on Inception Networks are true? (Check all that apply.) > A single inception block allows the network to use a combination of 1x1, 3x3, 5x5 convolutions and pooling. Making an inception network deeper (by stacking more inception blocks together) should not hurt training set performance. > Inception blocks usually use 1x1 convolutions to reduce the input data volume’s size before applying 3x3 and 5x5 convolutions. Inception networks incorporates a variety of network architectures (similar to dropout, which randomly chooses a network architecture on each step) and thus has a similar regularizing effect as dropout. 10. Which of the following are common reasons for using open-source implementations of ConvNets (both the model and/or weights)? Check all that apply. A model trained for one computer vision task can usually be used to perform data augmentation even for a different computer vision task. > It is a convenient way to get working an implementation of a complex ConvNet architecture. The same techniques for winning computer vision competitions, such as using multiple crops at test time, are widely used in practical deployments (or production system deployments) of ConvNets. > Parameters trained for one computer vision task are often useful as pretraining for other computer vision tasks. > 11. Mobile V2 Bottleneck question > W =5, Y = 30, Z= 20 12. Which of the following true about Depth-wise separable convolutions ? > 1. They have lower computation cost than normal convolutions. 2. They combile depthwise convolutions with pointwise convolutions 15. Which of the following about inception networks are tru ? > 1. A single inception allows the network to use a combination of 1 x 1, 3 x 3, 5 x 5 convolutions and pooling. 2. Inception blocks use 1 X 1 convolutions to reduce the input data 3. Making an inception network deeper will lead to overfitting and computational costs. 16. Suppose that in a MobileNet v2 Bottleneck block the input volume has shape 64 × 64 × 16 64 × 64 × 16. If we use 32 filters for the expansion and 16 filters for the projection. What is the size of the input and output volume of the depthwise convolution, assuming a pad='same'? > 64 x 64 x 32 64 x 64 x 32 17. In Depthwise separable convolution: > 1. perform two steps of convolution 2.The final output is of the dimension n_out x n_out x n_c(hat) 3.For Depthwise computation, each filter convolves with one color channel of input image 4. You convolve the input image with n_c number of n_f x n_f filters. 18.When having a small training set to construct a classification model, which of the following is a strategy of transfer learning that you would use to build the model? > Use an open-source network trained in larger dataset, freezing the layers, re-train the softmax layer 20. 1 x 1 convolutions are the same as multiplying by a single number. True/False? > False 22. Adding a ResNet block to the end of a network makes it deeper. Which of the following is true? > The performance of the networks doesn't get hurt since the Resnet block can easily approximate identity function 24. The computation of a ResNet block is expressed in the equation. Which part corresponds to the skip connection? > The term in the orange box marked as B. 26. The motivation of Residual Networks is that very deep networks are so good at fitting complex functions that when training them we almost always overfit the training data. True/False? > False 28.