vgg16.py

from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Dropout
from keras.models import Sequential

def vgg16():
	model = Sequential()
	model.add(Conv2D(64, (3, 3), padding='same', activation='relu', input_shape=(224, 224, 3)))
	model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
	model.add(MaxPooling2D((2,2), strides=(2,2)))

	model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))
	model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))
	model.add(MaxPooling2D((2,2), strides=(2,2)))

	model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))
	model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))
	model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))
	model.add(MaxPooling2D((2,2), strides=(2,2)))

	model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
	model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
	model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
	model.add(MaxPooling2D((2,2), strides=(2,2)))

	model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
	model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
	model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
	model.add(MaxPooling2D((2,2), strides=(2,2)))

	model.add(Flatten())

	model.add(Dense(4096, activation='relu'))
	model.add(Dropout(0.5))

	model.add(Dense(4096, activation='relu'))
	model.add(Dropout(0.5))

	model.add(Dense(1000, activation='softmax'))

	return model