Q2 By referring to Convolutional Neural Network (CNN) code in Figure Q2: (a) Illustrate the model structure with details of layer labelling (3 marks) (b) Analyze image output shape and its total trainable parameters for each stack of the CNN layer. (17 marks) cnn_model = models. Sequential() cnn_model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(150,150,3))) cnn_model.add(layers.MaxPooling2D((2, 2))) cnn_model.add(layers.Conv2D(64, (3,3), activation='relu')) cnn_model.add(layers.MaxPooling2D((2,2))) cnn_model.add(layers.Conv2D(128, (3,3), activation='relu')) cnn_model.add(layers.MaxPooling2D((2,2))) cnn_model.add(layers.Conv2D(128, (3,3), activation='relu')) cnn_model.add(layers. MaxPooling2D((2,2))) cnn_model.add(layers. Flatten()) cnn_model.add(layers. Dense(512, activation='relu')) cnn_model.add(layers. Dense (1, activation='sigmoid')) Figure Q2 Q2 By referring to Convolutional Neural Network (CNN) code in Figure Q2: (a) Illustrate the model structure with details of layer labelling (3 marks) (b) Analyze image output shape and its total trainable parameters for each stack of the CNN layer. (17 marks) cnn_model = models. Sequential() cnn_model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(150,150,3))) cnn_model.add(layers.MaxPooling2D((2, 2))) cnn_model.add(layers.Conv2D(64, (3,3), activation='relu')) cnn_model.add(layers.MaxPooling2D((2,2))) cnn_model.add(layers.Conv2D(128, (3,3), activation='relu')) cnn_model.add(layers.MaxPooling2D((2,2))) cnn_model.add(layers.Conv2D(128, (3,3), activation='relu')) cnn_model.add(layers. MaxPooling2D((2,2))) cnn_model.add(layers. Flatten()) cnn_model.add(layers. Dense(512, activation='relu')) cnn_model.add(layers. Dense (1, activation='sigmoid')) Figure Q2