深度学习笔记——分类模型（三）VGG16

# -*- coding: utf-8 -*-
import datetime
from keras.models import Model
from keras.layers import *
from keras.preprocessing.image import ImageDataGenerator

def VGG16(input_shape=(224, 224, 3), classes=1000):

    img_input = Input(shape=input_shape)

    # Block 1
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)

    # Block 2
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)

    # Block 3
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)

    # Block 4
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)

    # Block 5
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)

    x = Flatten(name='flatten')(x)
    x = Dense(4096, activation='relu', name='fc1')(x)
    x = Dense(4096, activation='relu', name='fc2')(x)
    if classes == 2:
        x = Dense(1, activation='sigmoid', name='predictions')(x)
    else:
        x = Dense(classes, activation='softmax', name='predictions')(x)

    model = Model(img_input, x, name='vgg16')
    return model

def main():
    width = 224
    height = 224
    batch_size = 2

    generator = ImageDataGenerator(horizontal_flip=True,
                                   vertical_flip=True,
                                   validation_split=0.2)

    train_generator = generator.flow_from_directory(directory="datasets/train",
                                                    target_size=(width, height),
                                                    batch_size=batch_size,
                                                    class_mode="binary",
                                                    subset="training")

    val_generator = generator.flow_from_directory(directory="datasets/train",
                                                  target_size=(width, height),
                                                  batch_size=batch_size,
                                                  class_mode="binary",
                                                  subset="validation")
    model = VGG16(classes=2)
    model.summary()
    # Compile model
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

    model.fit_generator(train_generator, validation_data=val_generator, epochs=10, verbose=1)

if __name__ == '__main__':
    tic = datetime.datetime.now()
    main()
    toc = datetime.datetime.now()
    print("\nThis model took ", (toc - tic))