vgg16.py

# -*- coding: utf-8 -*-

import keras.backend as K
from keras.layers import Conv2D, ZeroPadding2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential


def vgg16_model(img_rows, img_cols, channel=3):
    model = Sequential()
    # Encoder
    model.add(ZeroPadding2D((1, 1), input_shape=(img_rows, img_cols, channel), name='input'))
    model.add(Conv2D(64, (3, 3), activation='relu', name='conv1_1'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Conv2D(64, (3, 3), activation='relu', name='conv1_2'))
    model.add(MaxPooling2D((2, 2), strides=(2, 2)))

    model.add(ZeroPadding2D((1, 1)))
    model.add(Conv2D(128, (3, 3), activation='relu', name='conv2_1'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Conv2D(128, (3, 3), activation='relu', name='conv2_2'))
    model.add(MaxPooling2D((2, 2), strides=(2, 2)))

    model.add(ZeroPadding2D((1, 1)))
    model.add(Conv2D(256, (3, 3), activation='relu', name='conv3_1'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Conv2D(256, (3, 3), activation='relu', name='conv3_2'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Conv2D(256, (3, 3), activation='relu', name='conv3_3'))
    model.add(MaxPooling2D((2, 2), strides=(2, 2)))

    model.add(ZeroPadding2D((1, 1)))
    model.add(Conv2D(512, (3, 3), activation='relu', name='conv4_1'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Conv2D(512, (3, 3), activation='relu', name='conv4_2'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Conv2D(512, (3, 3), activation='relu', name='conv4_3'))
    model.add(MaxPooling2D((2, 2), strides=(2, 2)))

    model.add(ZeroPadding2D((1, 1)))
    model.add(Conv2D(512, (3, 3), activation='relu', name='conv5_1'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Conv2D(512, (3, 3), activation='relu', name='conv5_2'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(Conv2D(512, (3, 3), activation='relu', name='conv5_3'))
    model.add(MaxPooling2D((2, 2), strides=(2, 2)))

    # Add Fully Connected Layer
    model.add(Flatten(name='flatten'))
    model.add(Dense(4096, activation='relu', name='dense1'))
    model.add(Dropout(0.5))
    model.add(Dense(4096, activation='relu', name='dense2'))
    model.add(Dropout(0.5))
    model.add(Dense(1000, activation='softmax', name='softmax'))

    # Loads ImageNet pre-trained data
    weights_path = 'models/vgg16_weights_tf_dim_ordering_tf_kernels.h5'
    model.load_weights(weights_path)

    return model


if __name__ == '__main__':
    model = vgg16_model(224, 224, 3)
    # input_layer = model.get_layer('input')
    print(model.summary())

    K.clear_session()