Question: Referring to Convolutional Neural Network (CNN) code in below cnn_model = models.Sequential () cnn_model. add(layers. Conv2D (16,(11,11), activation='relu', input_shape =(256,256,3))) cnn_model. add(layers.MaxPooling2D ((2,2))) cnn_model. add(layers.

Referring to Convolutional Neural Network (CNN) code in below cnn_model =

Referring to Convolutional Neural Network (CNN) code in below cnn_model = models.Sequential () cnn_model. add(layers. Conv2D (16,(11,11), activation='relu', input_shape =(256,256,3))) cnn_model. add(layers.MaxPooling2D ((2,2))) cnn_model. add(layers. Conv2D (32,(7,7), activation='relu' ) ) cnn_model. add(layers.MaxPooling2D ((2,2))) cnn_model. add(layers. Conv2D (64,(1,1), activation='relu' ) ) cnn_model. add(layers. Conv2D (128,(5,5), activation='relu')) cnn_model.add(layers.MaxPooling2D ((2,2))) cnn_model. add(layers. Conv2D (256,(3,3), activation='relu' )) cnn_model.add(layers.MaxPooling2D ((2,2))) cnn_model.add(layers. Conv2D (512,(3,3), activation='relu' )) cnn_model.add(layers. Flatten()) cnn_model. add(layers. Dense(512, activation='relu')) cnn_model.add(layers. Dense(1, activation='sigmoid')) a. Illustrate the model structure with details of layer labeling. (3 marks) b. Analyze image output shape and its total trainable parameters for each stack of the CNN layer. (20 marks) c. Illustrate the new model structure if VGG pre-trained model is to be integrated to the existing CNN structure. (2 marks)

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