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Consider the following DNN for image classification of 1 0 classes. The dataset consists of colour images of size 1 6 x 1 6 .
Consider the following DNN for image classification of classes. The dataset consists of colour images of size x marks Input Layer inputs keras. Input shape# #A## Layer x layers. Dense activation'relu', usebiasFalseinputs #Layer x layers. Dense activation'relu'x model keras. Model inputsinputs, outputsoutputs, name"model" model.compile optimizer 'adam', loss #D## metricsaccuracy A Write the value or code of ##A## B## ##C## and ##D# to complete the network. B If we add a dropout layer of value after layer I. What will be the total number of active neurons in layer during a model training b model testing? II What will be the total number of parameters in the network? What is the change in the number of parameters compared to the original network? C Assume that the image dataset consists of train images and test images. We run for epochs with batch size learning rate LR and SGD optimizer. During model training, I. What will be the size of the batch in the last iteration? II What is the number of times we perform forward propagation? III. What is the number of times we perform backpropagation? And why?
Consider the following DNN for image classification of classes. The dataset consists of colour images of size x marks Input Layer inputs keras. Input shape# #A## Layer x layers. Dense activation'relu', usebiasFalseinputs #Layer x layers. Dense activation'relu'x model keras. Model inputsinputs, outputsoutputs, name"model" model.compile optimizer 'adam', loss #D## metricsaccuracy A Write the value or code of ##A## B## ##C## and ##D# to complete the network. B If we add a dropout layer of value after layer I. What will be the total number of active neurons in layer during a model training b model testing? II What will be the total number of parameters in the network? What is the change in the number of parameters compared to the original network? C Assume that the image dataset consists of train images and test images. We run for epochs with batch size learning rate LR and SGD optimizer. During model training, I. What will be the size of the batch in the last iteration? II What is the number of times we perform forward propagation? III. What is the number of times we perform backpropagation? And why?
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