Which layer is responsible for converting the 2-dimensional feature maps into a 1-dimensional vector in a CNN?o   A) Convolutional layero   B) Activation function layero   C) Pooling layero   D) Flatten layer

Which layer in a CNN is responsible for reducing the spatial dimensions of the input data?a.Fully connected layerb.Pooling layerc.Convolutional layerd.Activation layer

Which layer type is typically used to extract local features in a CNN?Question 3Answera.Convolutional layerb. Fully connected layerc. Activation layerd.Pooling layer

Which of the following are types of CNN architecture? Check all that apply:1 pointJoNetAlexNetVGGNetRavNe

Which of the following Layers can be part of Convolution Neural Networks (CNNs)1 pointReluSoftmaxMaxpoolingDropoutAll of the above

11. Which of the following is a correct statement about a CNN (Convolution Neural Network) structure?以下哪個是關於卷積神經網路架構的正確說法？Number classification and identification is the main function of convolution layers and pooling layers, while fully connected layers are mainly used for image feature extraction. 卷積層與池化層的功能主要是數字分類辨識，而全連結層則主要用於擷取影像的特徵。There have to be more convolution layers than pooling layers. 卷積層的數量需要比池化層多。There have to be more fully connected layers than convolution and pooling layers. 全連接層的數量需要比卷積層與池化層多。Convolution layers are used to extract features from images, while fully connected layers are mainly used to make a classification decision. 卷積層主要是用來擷取影像的特徵，而全連結層則主要用於決定如何分類。