深度学习笔记——分类模型（二）AlexNet

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

def main():
    width = 227
    height = 227
    batch_size = 16

    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")
    # AlexNet
    model = Sequential()

    model.add(Conv2D(filters=96, kernel_size=(11, 11), strides=(4, 4), padding='valid', input_shape=(width, height, 3),
                     activation='relu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid'))

    model.add(Conv2D(filters=256, kernel_size=(5, 5), strides=(1, 1), padding='same', activation='relu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid'))

    model.add(Conv2D(filters=384, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu'))
    model.add(Conv2D(filters=384, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu'))
    model.add(Conv2D(filters=256, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu'))

    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='relu'))
    model.add(Dropout(0.5))

    # Output Layer
    model.add(Dense(1))
    model.add(Activation('sigmoid'))

    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))