number of layers in vgg16

1model = Sequential()
2model.add(Conv2D(input_shape=(224,224,3),filters=64,kernel_size=(3,3),padding="same", activation="relu"))
3model.add(Conv2D(filters=64,kernel_size=(3,3),padding="same", activation="relu"))
4model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
5model.add(Conv2D(filters=128, kernel_size=(3,3), padding="same", activation="relu"))
6model.add(Conv2D(filters=128, kernel_size=(3,3), padding="same", activation="relu"))
7model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
8model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"))
9model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"))
10model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"))
11model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
12model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
13model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
14model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
15model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
16model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
17model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
18model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
19model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
20model.add(Flatten())
21model.add(Dense(units=4096,activation="relu"))
22model.add(Dense(units=4096,activation="relu"))
23model.add(Dense(units=2, activation="softmax"))