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Convolutional-Neural-Networks/Quizes.md at master · SiHaoShen/Convolutional-Neural-Networks · GitHub
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class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Resources <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="resources-topics-heading">Topics</span> <ul class="list-style-none f5" 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transfer","anchor":"week-4-quiz---special-applications-face-recognition--neural-style-transfer","htmlText":"Week 4 quiz - Special applications: Face recognition \u0026amp; Neural style transfer"}],"lineInfo":{"truncatedLoc":"284","truncatedSloc":"142"},"mode":"file"},"image":false,"isCodeownersFile":null,"isPlain":false,"isValidLegacyIssueTemplate":false,"issueTemplate":null,"discussionTemplate":null,"language":"Markdown","languageID":222,"large":false,"planSupportInfo":{"repoIsFork":null,"repoOwnedByCurrentUser":null,"requestFullPath":"/SiHaoShen/Convolutional-Neural-Networks/blob/master/Quizes.md","showFreeOrgGatedFeatureMessage":null,"showPlanSupportBanner":null,"upgradeDataAttributes":null,"upgradePath":null},"publishBannersInfo":{"dismissActionNoticePath":"/settings/dismiss-notice/publish_action_from_dockerfile","releasePath":"/SiHaoShen/Convolutional-Neural-Networks/releases/new?marketplace=true","showPublishActionBanner":false},"rawBlobUrl":"https://github.com/SiHaoShen/Convolutional-Neural-Networks/raw/refs/heads/master/Quizes.md","renderImageOrRaw":false,"richText":"\u003carticle class=\"markdown-body entry-content container-lg\" itemprop=\"text\"\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWeek 1 quiz - The basics of ConvNets\u003c/h2\u003e\u003ca id=\"user-content-week-1-quiz---the-basics-of-convnets\" class=\"anchor\" aria-label=\"Permalink: Week 1 quiz - The basics of ConvNets\" href=\"#week-1-quiz---the-basics-of-convnets\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eWhat do you think applying this filter to a grayscale image will do?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eDetect horizontal edges\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eDetect vertical edges\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eDetect 45 degree edges\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eDetect image contrast\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"2\" dir=\"auto\"\u003e\n\u003cli\u003eSuppose your input is a 300 by 300 color (RGB) image, and you are not using a convolutional network. If the first hidden layer has 100 neurons, each one fully connected to the input, how many parameters does this hidden layer have (including the bias parameters)?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e9,000,001\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e9,000,100\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e27,000,001\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e27,000,100\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\" dir=\"auto\"\u003e\n\u003cli\u003eSuppose your input is a 300 by 300 color (RGB) image, and you use a convolutional layer with 100 filters that are each 5x5. How many parameters does this hidden layer have (including the bias parameters)?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e2501\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e2600\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e7500\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e7600\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"4\" dir=\"auto\"\u003e\n\u003cli\u003eYou have an input volume that is 63x63x16, and convolve it with 32 filters that are each 7x7, using a stride of 2 and no padding. What is the output volume?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003e16x16x32\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e29x29x16\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e29x29x32\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003e16x16x16\u003c/p\u003e\n\u003col start=\"5\" dir=\"auto\"\u003e\n\u003cli\u003eYou have an input volume that is 15x15x8, and pad it using “pad=2.” What is the dimension of the resulting volume (after padding)?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003e19x19x12\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e17x17x10\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e19x19x8\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003e17x17x8\u003c/p\u003e\n\u003col start=\"6\" dir=\"auto\"\u003e\n\u003cli\u003eYou have an input volume that is 63x63x16, and convolve it with 32 filters that are each 7x7, and stride of 1. You want to use a “same” convolution. What is the padding?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003e1\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e2\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e3\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003e7\u003c/p\u003e\n\u003col start=\"7\" dir=\"auto\"\u003e\n\u003cli\u003eYou have an input volume that is 32x32x16, and apply max pooling with a stride of 2 and a filter size of 2. What is the output volume?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003e15x15x16\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e16x16x16\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003e32x32x8\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e16x16x8\u003c/p\u003e\n\u003col start=\"8\" dir=\"auto\"\u003e\n\u003cli\u003eBecause pooling layers do not have parameters, they do not affect the backpropagation (derivatives) calculation.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eTrue\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eFalse\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"9\" dir=\"auto\"\u003e\n\u003cli\u003eIn lecture we talked about “parameter sharing” as a benefit of using convolutional networks. Which of the following statements about parameter sharing in ConvNets are true? (Check all that apply.)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eIt allows parameters learned for one task to be shared even for a different task (transfer learning).\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eIt reduces the total number of parameters, thus reducing overfitting.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003eIt allows gradient descent to set many of the parameters to zero, thus making the connections sparse.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eIt allows a feature detector to be used in multiple locations throughout the whole input image/input volume.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"10\" dir=\"auto\"\u003e\n\u003cli\u003eIn lecture we talked about “sparsity of connections” as a benefit of using convolutional layers. What does this mean?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eEach filter is connected to every channel in the previous layer.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eEach activation in the next layer depends on only a small number of activations from the previous layer.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003eEach layer in a convolutional network is connected only to two other layers\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eRegularization causes gradient descent to set many of the parameters to zero.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWeek 2 quiz - Deep convolutional models\u003c/h2\u003e\u003ca id=\"user-content-week-2-quiz---deep-convolutional-models\" class=\"anchor\" aria-label=\"Permalink: Week 2 quiz - Deep convolutional models\" href=\"#week-2-quiz---deep-convolutional-models\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eWhich of the following do you typically see as you move to deeper layers in a ConvNet?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003enH and nW increases, while nC decreases\u003c/p\u003e\n\u003cp dir=\"auto\"\u003enH and nW decreases, while nC also decreases\u003c/p\u003e\n\u003cp dir=\"auto\"\u003enH and nW increases, while nC also increases\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003enH and nW decrease, while nC increases\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"2\" dir=\"auto\"\u003e\n\u003cli\u003eWhich of the following do you typically see in a ConvNet? (Check all that apply.)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eMultiple CONV layers followed by a POOL layer\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003eMultiple POOL layers followed by a CONV layer\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eFC layers in the last few layers\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003eFC layers in the first few layers\u003c/p\u003e\n\u003col start=\"3\" dir=\"auto\"\u003e\n\u003cli\u003eIn 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.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eTrue\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eFalse\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"4\" dir=\"auto\"\u003e\n\u003cli\u003eTraining 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.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eTrue\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eFalse\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"5\" dir=\"auto\"\u003e\n\u003cli\u003eThe following equation captures the computation in a ResNet block. What goes into the two blanks above?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"a[l+2]=g(W[l+2]g(W[l+1]a[l]+b[l+1])+bl+2+_______ )+_______\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003ea[l+2]=g(W[l+2]g(W[l+1]a[l]+b[l+1])+bl+2+_______ )+_______\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"\u0026gt; a[l] and 0, respectively\n\n0 and z[l+1], respectively\n\nz[l] and a[l], respectively\n\n0 and a[l], respectively\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e\u0026gt; a[l] and 0, respectively\n\n0 and z[l+1], respectively\n\nz[l] and a[l], respectively\n\n0 and a[l], respectively\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"6\" dir=\"auto\"\u003e\n\u003cli\u003eWhich ones of the following statements on Residual Networks are true? (Check all that apply.)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eUsing a skip-connection helps the gradient to backpropagate and thus helps you to train deeper networks\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003eA ResNet with L layers would have on the order of L2 skip connections in total.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eThe skip-connections compute a complex non-linear function of the input to pass to a deeper layer in the network.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003eThe skip-connection makes it easy for the network to learn an identity mapping between the input and the output within the ResNet block.\u003c/p\u003e\n\u003col start=\"7\" dir=\"auto\"\u003e\n\u003cli\u003eSuppose you have an input volume of dimension 64x64x16. How many parameters would a single 1x1 convolutional filter have (including the bias)?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003e2\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e4097\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e1\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e17\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"8\" dir=\"auto\"\u003e\n\u003cli\u003eSuppose 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.)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eYou can use a 1x1 convolutional layer to reduce nC but not nH, nW.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003eYou can use a 1x1 convolutional layer to reduce nH, nW, and nC.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eYou can use a pooling layer to reduce nH, nW, but not nC.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003eYou can use a pooling layer to reduce nH, nW, and nC.\u003c/p\u003e\n\u003col start=\"9\" dir=\"auto\"\u003e\n\u003cli\u003eWhich ones of the following statements on Inception Networks are true? (Check all that apply.)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eA single inception block allows the network to use a combination of 1x1, 3x3, 5x5 convolutions and pooling.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003eMaking an inception network deeper (by stacking more inception blocks together) should not hurt training set performance.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eInception blocks usually use 1x1 convolutions to reduce the input data volume’s size before applying 3x3 and 5x5 convolutions.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003eInception 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.\u003c/p\u003e\n\u003col start=\"10\" dir=\"auto\"\u003e\n\u003cli\u003eWhich of the following are common reasons for using open-source implementations of ConvNets (both the model and/or weights)? Check all that apply.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eA model trained for one computer vision task can usually be used to perform data augmentation even for a different computer vision task.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eIt is a convenient way to get working an implementation of a complex ConvNet architecture.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003eThe 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.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eParameters trained for one computer vision task are often useful as pretraining for other computer vision tasks.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWeek 3 quiz - Detection algorithms\u003c/h2\u003e\u003ca id=\"user-content-week-3-quiz---detection-algorithms\" class=\"anchor\" aria-label=\"Permalink: Week 3 quiz - Detection algorithms\" href=\"#week-3-quiz---detection-algorithms\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eYou are building a 3-class object classification and localization algorithm. The classes are: pedestrian (c=1), car (c=2), motorcycle (c=3). What would be the label for the following image? Recall y=[pc,bx,by,bh,bw,c1,c2,c3]\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003ey=[1,0.3,0.7,0.3,0.3,0,1,0]\u003c/code\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"2\" dir=\"auto\"\u003e\n\u003cli\u003eContinuing from the previous problem, what should y be for the image below? Remember that “?” means “don’t care”, which means that the neural network loss function won’t care what the neural network gives for that component of the output. As before, y=[pc,bx,by,bh,bw,c1,c2,c3].\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003ey=[0,?,?,?,?,?,?,?]\u003c/code\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"3\" dir=\"auto\"\u003e\n\u003cli\u003eYou are working on a factory automation task. Your system will see a can of soft-drink coming down a conveyor belt, and you want it to take a picture and decide whether (i) there is a soft-drink can in the image, and if so (ii) its bounding box. Since the soft-drink can is round, the bounding box is always square, and the soft drink can always appears as the same size in the image. There is at most one soft drink can in each image. Here’re some typical images in your training set: What is the most appropriate set of output units for your neural network?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eLogistic unit, bx, by\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"4\" dir=\"auto\"\u003e\n\u003cli\u003eIf you build a neural network that inputs a picture of a person’s face and outputs N landmarks on the face (assume the input image always contains exactly one face), how many output units will the network have?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e2N\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"5\" dir=\"auto\"\u003e\n\u003cli\u003eWhen training one of the object detection systems described in lecture, you need a training set that contains many pictures of the object(s) you wish to detect. However, bounding boxes do not need to be provided in the training set, since the algorithm can learn to detect the objects by itself.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003efalse\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"6\" dir=\"auto\"\u003e\n\u003cli\u003eSuppose you are applying a sliding windows classifier (non-convolutional implementation). Increasing the stride would tend to increase accuracy, but decrease computational cost.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003efalse\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"7\" dir=\"auto\"\u003e\n\u003cli\u003eIn the YOLO algorithm, at training time, only one cell ---the one containing the center/midpoint of an object--- is responsible for detecting this object.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003etrue\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"8\" dir=\"auto\"\u003e\n\u003cli\u003eWhat is the IoU between these two boxes? The upper-left box is 2x2, and the lower-right box is 2x3. The overlapping region is 1x1.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e1/9\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"9\" dir=\"auto\"\u003e\n\u003cli\u003eSuppose you run non-max suppression on the predicted boxes above. The parameters you use for non-max suppression are that boxes with probability ≤ 0.4 are discarded, and the IoU threshold for deciding if two boxes overlap is 0.5. How many boxes will remain after non-max suppression?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e5\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"10\" dir=\"auto\"\u003e\n\u003cli\u003eSuppose you are using YOLO on a 19x19 grid, on a detection problem with 20 classes, and with 5 anchor boxes. During training, for each image you will need to construct an output volume y as the target value for the neural network; this corresponds to the last layer of the neural network. (y may include some “?”, or “don’t cares”). What is the dimension of this output volume?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e19x19x(5x25)\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWeek 4 quiz - Special applications: Face recognition \u0026amp; Neural style transfer\u003c/h2\u003e\u003ca id=\"user-content-week-4-quiz---special-applications-face-recognition--neural-style-transfer\" class=\"anchor\" aria-label=\"Permalink: Week 4 quiz - Special applications: Face recognition \u0026amp; Neural style transfer\" href=\"#week-4-quiz---special-applications-face-recognition--neural-style-transfer\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eFace verification requires comparing a new picture against one person’s face, whereas face recognition requires comparing a new picture against K person’s faces.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003etrue\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"2\" dir=\"auto\"\u003e\n\u003cli\u003eWhy do we learn a function d(img1,img2) for face verification? (Select all that apply.)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eThis allows us to learn to recognize a new person given just a single image of that person.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eWe need to solve a one-shot learning problem.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"3\" dir=\"auto\"\u003e\n\u003cli\u003eIn order to train the parameters of a face recognition system, it would be reasonable to use a training set comprising 100,000 pictures of 100,000 different persons.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003efalse\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"4\" dir=\"auto\"\u003e\n\u003cli\u003eWhich of the following is a correct definition of the triplet loss? Consider that α\u0026gt;0. (We encourage you to figure out the answer from first principles, rather than just refer to the lecture.)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003emax(||f(A)−f(P)||2−||f(A)−f(N)||2+α,0)\u003c/code\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"5\" dir=\"auto\"\u003e\n\u003cli\u003eConsider the following Siamese network architecture: The upper and lower neural networks have different input images, but have exactly the same parameters.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003etrue\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"6\" dir=\"auto\"\u003e\n\u003cli\u003eYou train a ConvNet on a dataset with 100 different classes. You wonder if you can find a hidden unit which responds strongly to pictures of cats. (I.e., a neuron so that, of all the input/training images that strongly activate that neuron, the majority are cat pictures.) You are more likely to find this unit in layer 4 of the network than in layer 1.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003etrue\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"7\" dir=\"auto\"\u003e\n\u003cli\u003eNeural style transfer is trained as a supervised learning task in which the goal is to input two images (x), and train a network to output a new, synthesized image (y).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003efalse\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"8\" dir=\"auto\"\u003e\n\u003cli\u003eIn the deeper layers of a ConvNet, each channel corresponds to a different feature detector. The style matrix G[l] measures the degree to which the activations of different feature detectors in layer l vary (or correlate) together with each other.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003etrue\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"9\" dir=\"auto\"\u003e\n\u003cli\u003eIn neural style transfer, what is updated in each iteration of the optimization algorithm?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eThe pixel values of the generated image G\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003col start=\"10\" dir=\"auto\"\u003e\n\u003cli\u003eYou are working with 3D data. You are building a network layer whose input volume has size 32x32x32x16 (this volume has 16 channels), and applies convolutions with 32 filters of dimension 3x3x3 (no padding, stride 1). What is the resulting output volume?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eUndefined: the convolution step is impossible\u003c/code\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/article\u003e","renderedFileInfo":null,"shortPath":null,"symbolsEnabled":true,"tabSize":8,"topBannersInfo":{"overridingGlobalFundingFile":false,"globalPreferredFundingPath":null,"showInvalidCitationWarning":false,"citationHelpUrl":"https://docs.github.com/github/creating-cloning-and-archiving-repositories/creating-a-repository-on-github/about-citation-files","actionsOnboardingTip":null},"truncated":false,"viewable":true,"workflowRedirectUrl":null,"symbols":{"timed_out":false,"not_analyzed":false,"symbols":[{"name":"Week 1 quiz - The basics of ConvNets","kind":"section_2","ident_start":3,"ident_end":39,"extent_start":0,"extent_end":2803,"fully_qualified_name":"Week 1 quiz - The basics of 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height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M8 9a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3ZM1.5 9a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Zm13 0a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Z"></path></svg></button></div></div></div></div><div></div></div><div class="Box-sc-g0xbh4-0 hGyMdv"><section aria-labelledby="file-name-id-wide file-name-id-mobile" class="Box-sc-g0xbh4-0 fGqKFv"><div class="Box-sc-g0xbh4-0 eoaCFS js-snippet-clipboard-copy-unpositioned" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Week 1 quiz - The basics of ConvNets</h2><a id="user-content-week-1-quiz---the-basics-of-convnets" class="anchor" aria-label="Permalink: Week 1 quiz - The basics of ConvNets" href="#week-1-quiz---the-basics-of-convnets"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li>What do you think applying this filter to a grayscale image will do?</li> </ol> <ul dir="auto"> <li> <p dir="auto">Detect horizontal edges</p> </li> <li> <blockquote> <p dir="auto">Detect vertical edges</p> </blockquote> </li> <li> <p dir="auto">Detect 45 degree edges</p> </li> <li> <p dir="auto">Detect image contrast</p> </li> </ul> <ol start="2" dir="auto"> <li>Suppose your input is a 300 by 300 color (RGB) image, and you are not using a convolutional network. If the first hidden layer has 100 neurons, each one fully connected to the input, how many parameters does this hidden layer have (including the bias parameters)?</li> </ol> <ul dir="auto"> <li> <p dir="auto">9,000,001</p> </li> <li> <p dir="auto">9,000,100</p> </li> <li> <p dir="auto">27,000,001</p> </li> <li> <blockquote> <p dir="auto">27,000,100</p> </blockquote> </li> </ul> <ol start="3" dir="auto"> <li>Suppose your input is a 300 by 300 color (RGB) image, and you use a convolutional layer with 100 filters that are each 5x5. How many parameters does this hidden layer have (including the bias parameters)?</li> </ol> <ul dir="auto"> <li> <p dir="auto">2501</p> </li> <li> <p dir="auto">2600</p> </li> <li> <p dir="auto">7500</p> </li> <li> <blockquote> <p dir="auto">7600</p> </blockquote> </li> </ul> <ol start="4" dir="auto"> <li>You have an input volume that is 63x63x16, and convolve it with 32 filters that are each 7x7, using a stride of 2 and no padding. What is the output volume?</li> </ol> <p dir="auto">16x16x32</p> <p dir="auto">29x29x16</p> <blockquote> <p dir="auto">29x29x32</p> </blockquote> <p dir="auto">16x16x16</p> <ol start="5" dir="auto"> <li>You have an input volume that is 15x15x8, and pad it using “pad=2.” What is the dimension of the resulting volume (after padding)?</li> </ol> <p dir="auto">19x19x12</p> <p dir="auto">17x17x10</p> <blockquote> <p dir="auto">19x19x8</p> </blockquote> <p dir="auto">17x17x8</p> <ol start="6" dir="auto"> <li>You have an input volume that is 63x63x16, and convolve it with 32 filters that are each 7x7, and stride of 1. You want to use a “same” convolution. What is the padding?</li> </ol> <p dir="auto">1</p> <p dir="auto">2</p> <blockquote> <p dir="auto">3</p> </blockquote> <p dir="auto">7</p> <ol start="7" dir="auto"> <li>You have an input volume that is 32x32x16, and apply max pooling with a stride of 2 and a filter size of 2. What is the output volume?</li> </ol> <p dir="auto">15x15x16</p> <blockquote> <p dir="auto">16x16x16</p> </blockquote> <p dir="auto">32x32x8</p> <p dir="auto">16x16x8</p> <ol start="8" dir="auto"> <li>Because pooling layers do not have parameters, they do not affect the backpropagation (derivatives) calculation.</li> </ol> <p dir="auto">True</p> <blockquote> <p dir="auto">False</p> </blockquote> <ol start="9" dir="auto"> <li>In lecture we talked about “parameter sharing” as a benefit of using convolutional networks. Which of the following statements about parameter sharing in ConvNets are true? (Check all that apply.)</li> </ol> <p dir="auto">It allows parameters learned for one task to be shared even for a different task (transfer learning).</p> <blockquote> <p dir="auto">It reduces the total number of parameters, thus reducing overfitting.</p> </blockquote> <p dir="auto">It allows gradient descent to set many of the parameters to zero, thus making the connections sparse.</p> <blockquote> <p dir="auto">It allows a feature detector to be used in multiple locations throughout the whole input image/input volume.</p> </blockquote> <ol start="10" dir="auto"> <li>In lecture we talked about “sparsity of connections” as a benefit of using convolutional layers. What does this mean?</li> </ol> <p dir="auto">Each filter is connected to every channel in the previous layer.</p> <blockquote> <p dir="auto">Each activation in the next layer depends on only a small number of activations from the previous layer.</p> </blockquote> <p dir="auto">Each layer in a convolutional network is connected only to two other layers</p> <p dir="auto">Regularization causes gradient descent to set many of the parameters to zero.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Week 2 quiz - Deep convolutional models</h2><a id="user-content-week-2-quiz---deep-convolutional-models" class="anchor" aria-label="Permalink: Week 2 quiz - Deep convolutional models" href="#week-2-quiz---deep-convolutional-models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li>Which of the following do you typically see as you move to deeper layers in a ConvNet?</li> </ol> <p dir="auto">nH and nW increases, while nC decreases</p> <p dir="auto">nH and nW decreases, while nC also decreases</p> <p dir="auto">nH and nW increases, while nC also increases</p> <blockquote> <p dir="auto">nH and nW decrease, while nC increases</p> </blockquote> <ol start="2" dir="auto"> <li>Which of the following do you typically see in a ConvNet? (Check all that apply.)</li> </ol> <blockquote> <p dir="auto">Multiple CONV layers followed by a POOL layer</p> </blockquote> <p dir="auto">Multiple POOL layers followed by a CONV layer</p> <blockquote> <p dir="auto">FC layers in the last few layers</p> </blockquote> <p dir="auto">FC layers in the first few layers</p> <ol start="3" dir="auto"> <li>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.</li> </ol> <p dir="auto">True</p> <blockquote> <p dir="auto">False</p> </blockquote> <ol start="4" dir="auto"> <li>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.</li> </ol> <p dir="auto">True</p> <blockquote> <p dir="auto">False</p> </blockquote> <ol start="5" dir="auto"> <li>The following equation captures the computation in a ResNet block. What goes into the two blanks above?</li> </ol> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="a[l+2]=g(W[l+2]g(W[l+1]a[l]+b[l+1])+bl+2+_______ )+_______"><pre class="notranslate"><code>a[l+2]=g(W[l+2]g(W[l+1]a[l]+b[l+1])+bl+2+_______ )+_______ </code></pre></div> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="> a[l] and 0, respectively 0 and z[l+1], respectively z[l] and a[l], respectively 0 and a[l], respectively"><pre class="notranslate"><code>> a[l] and 0, respectively 0 and z[l+1], respectively z[l] and a[l], respectively 0 and a[l], respectively </code></pre></div> <ol start="6" dir="auto"> <li>Which ones of the following statements on Residual Networks are true? (Check all that apply.)</li> </ol> <blockquote> <p dir="auto">Using a skip-connection helps the gradient to backpropagate and thus helps you to train deeper networks</p> </blockquote> <p dir="auto">A ResNet with L layers would have on the order of L2 skip connections in total.</p> <blockquote> <p dir="auto">The skip-connections compute a complex non-linear function of the input to pass to a deeper layer in the network.</p> </blockquote> <p dir="auto">The skip-connection makes it easy for the network to learn an identity mapping between the input and the output within the ResNet block.</p> <ol start="7" dir="auto"> <li>Suppose you have an input volume of dimension 64x64x16. How many parameters would a single 1x1 convolutional filter have (including the bias)?</li> </ol> <p dir="auto">2</p> <p dir="auto">4097</p> <p dir="auto">1</p> <blockquote> <p dir="auto">17</p> </blockquote> <ol start="8" dir="auto"> <li>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.)</li> </ol> <blockquote> <p dir="auto">You can use a 1x1 convolutional layer to reduce nC but not nH, nW.</p> </blockquote> <p dir="auto">You can use a 1x1 convolutional layer to reduce nH, nW, and nC.</p> <blockquote> <p dir="auto">You can use a pooling layer to reduce nH, nW, but not nC.</p> </blockquote> <p dir="auto">You can use a pooling layer to reduce nH, nW, and nC.</p> <ol start="9" dir="auto"> <li>Which ones of the following statements on Inception Networks are true? (Check all that apply.)</li> </ol> <blockquote> <p dir="auto">A single inception block allows the network to use a combination of 1x1, 3x3, 5x5 convolutions and pooling.</p> </blockquote> <p dir="auto">Making an inception network deeper (by stacking more inception blocks together) should not hurt training set performance.</p> <blockquote> <p dir="auto">Inception blocks usually use 1x1 convolutions to reduce the input data volume’s size before applying 3x3 and 5x5 convolutions.</p> </blockquote> <p dir="auto">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.</p> <ol start="10" dir="auto"> <li>Which of the following are common reasons for using open-source implementations of ConvNets (both the model and/or weights)? Check all that apply.</li> </ol> <p dir="auto">A model trained for one computer vision task can usually be used to perform data augmentation even for a different computer vision task.</p> <blockquote> <p dir="auto">It is a convenient way to get working an implementation of a complex ConvNet architecture.</p> </blockquote> <p dir="auto">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.</p> <blockquote> <p dir="auto">Parameters trained for one computer vision task are often useful as pretraining for other computer vision tasks.</p> </blockquote> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Week 3 quiz - Detection algorithms</h2><a id="user-content-week-3-quiz---detection-algorithms" class="anchor" aria-label="Permalink: Week 3 quiz - Detection algorithms" href="#week-3-quiz---detection-algorithms"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li>You are building a 3-class object classification and localization algorithm. The classes are: pedestrian (c=1), car (c=2), motorcycle (c=3). What would be the label for the following image? Recall y=[pc,bx,by,bh,bw,c1,c2,c3]</li> </ol> <blockquote> <p dir="auto"><code>y=[1,0.3,0.7,0.3,0.3,0,1,0]</code></p> </blockquote> <ol start="2" dir="auto"> <li>Continuing from the previous problem, what should y be for the image below? Remember that “?” means “don’t care”, which means that the neural network loss function won’t care what the neural network gives for that component of the output. As before, y=[pc,bx,by,bh,bw,c1,c2,c3].</li> </ol> <blockquote> <p dir="auto"><code>y=[0,?,?,?,?,?,?,?]</code></p> </blockquote> <ol start="3" dir="auto"> <li>You are working on a factory automation task. Your system will see a can of soft-drink coming down a conveyor belt, and you want it to take a picture and decide whether (i) there is a soft-drink can in the image, and if so (ii) its bounding box. Since the soft-drink can is round, the bounding box is always square, and the soft drink can always appears as the same size in the image. There is at most one soft drink can in each image. Here’re some typical images in your training set: What is the most appropriate set of output units for your neural network?</li> </ol> <blockquote> <p dir="auto">Logistic unit, bx, by</p> </blockquote> <ol start="4" dir="auto"> <li>If you build a neural network that inputs a picture of a person’s face and outputs N landmarks on the face (assume the input image always contains exactly one face), how many output units will the network have?</li> </ol> <blockquote> <p dir="auto">2N</p> </blockquote> <ol start="5" dir="auto"> <li>When training one of the object detection systems described in lecture, you need a training set that contains many pictures of the object(s) you wish to detect. However, bounding boxes do not need to be provided in the training set, since the algorithm can learn to detect the objects by itself.</li> </ol> <blockquote> <p dir="auto">false</p> </blockquote> <ol start="6" dir="auto"> <li>Suppose you are applying a sliding windows classifier (non-convolutional implementation). Increasing the stride would tend to increase accuracy, but decrease computational cost.</li> </ol> <blockquote> <p dir="auto">false</p> </blockquote> <ol start="7" dir="auto"> <li>In the YOLO algorithm, at training time, only one cell ---the one containing the center/midpoint of an object--- is responsible for detecting this object.</li> </ol> <blockquote> <p dir="auto">true</p> </blockquote> <ol start="8" dir="auto"> <li>What is the IoU between these two boxes? The upper-left box is 2x2, and the lower-right box is 2x3. The overlapping region is 1x1.</li> </ol> <blockquote> <p dir="auto">1/9</p> </blockquote> <ol start="9" dir="auto"> <li>Suppose you run non-max suppression on the predicted boxes above. The parameters you use for non-max suppression are that boxes with probability ≤ 0.4 are discarded, and the IoU threshold for deciding if two boxes overlap is 0.5. How many boxes will remain after non-max suppression?</li> </ol> <blockquote> <p dir="auto">5</p> </blockquote> <ol start="10" dir="auto"> <li>Suppose you are using YOLO on a 19x19 grid, on a detection problem with 20 classes, and with 5 anchor boxes. During training, for each image you will need to construct an output volume y as the target value for the neural network; this corresponds to the last layer of the neural network. (y may include some “?”, or “don’t cares”). What is the dimension of this output volume?</li> </ol> <blockquote> <p dir="auto">19x19x(5x25)</p> </blockquote> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Week 4 quiz - Special applications: Face recognition & Neural style transfer</h2><a id="user-content-week-4-quiz---special-applications-face-recognition--neural-style-transfer" class="anchor" aria-label="Permalink: Week 4 quiz - Special applications: Face recognition & Neural style transfer" href="#week-4-quiz---special-applications-face-recognition--neural-style-transfer"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li>Face verification requires comparing a new picture against one person’s face, whereas face recognition requires comparing a new picture against K person’s faces.</li> </ol> <blockquote> <p dir="auto">true</p> </blockquote> <ol start="2" dir="auto"> <li>Why do we learn a function d(img1,img2) for face verification? (Select all that apply.)</li> </ol> <blockquote> <p dir="auto">This allows us to learn to recognize a new person given just a single image of that person.</p> </blockquote> <blockquote> <p dir="auto">We need to solve a one-shot learning problem.</p> </blockquote> <ol start="3" dir="auto"> <li>In order to train the parameters of a face recognition system, it would be reasonable to use a training set comprising 100,000 pictures of 100,000 different persons.</li> </ol> <blockquote> <p dir="auto">false</p> </blockquote> <ol start="4" dir="auto"> <li>Which of the following is a correct definition of the triplet loss? Consider that α>0. (We encourage you to figure out the answer from first principles, rather than just refer to the lecture.)</li> </ol> <blockquote> <p dir="auto"><code>max(||f(A)−f(P)||2−||f(A)−f(N)||2+α,0)</code></p> </blockquote> <ol start="5" dir="auto"> <li>Consider the following Siamese network architecture: The upper and lower neural networks have different input images, but have exactly the same parameters.</li> </ol> <blockquote> <p dir="auto">true</p> </blockquote> <ol start="6" dir="auto"> <li>You train a ConvNet on a dataset with 100 different classes. You wonder if you can find a hidden unit which responds strongly to pictures of cats. (I.e., a neuron so that, of all the input/training images that strongly activate that neuron, the majority are cat pictures.) You are more likely to find this unit in layer 4 of the network than in layer 1.</li> </ol> <blockquote> <p dir="auto">true</p> </blockquote> <ol start="7" dir="auto"> <li>Neural style transfer is trained as a supervised learning task in which the goal is to input two images (x), and train a network to output a new, synthesized image (y).</li> </ol> <blockquote> <p dir="auto">false</p> </blockquote> <ol start="8" dir="auto"> <li>In the deeper layers of a ConvNet, each channel corresponds to a different feature detector. The style matrix G[l] measures the degree to which the activations of different feature detectors in layer l vary (or correlate) together with each other.</li> </ol> <blockquote> <p dir="auto">true</p> </blockquote> <ol start="9" dir="auto"> <li>In neural style transfer, what is updated in each iteration of the optimization algorithm?</li> </ol> <blockquote> <p dir="auto">The pixel values of the generated image G</p> </blockquote> <ol start="10" dir="auto"> <li>You are working with 3D data. You are building a network layer whose input volume has size 32x32x32x16 (this volume has 16 channels), and applies convolutions with 32 filters of dimension 3x3x3 (no padding, stride 1). 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