#Keras OverFeat代码实现（fast和accurate）

OverFeat keras代码实现

OverFeat快速版模型结构参数：

OverFeat精确版模型结构参数：

对模型结构有疑问详见原论文

# -*- coding: utf-8 -*-
"""
Created on 2022/8/16 14:49

@author: Troye Jcan
"""
import os

os.environ['CUDA_VISIBLE_DEVICES'] = "1"  # use GPU 0
import keras
from keras.layers import Conv2D, Dense, MaxPooling2D, Flatten


# define a overfeat model
def overfeat_model(model_type='accurate'):
    if model_type == 'accurate':
        # accurate model
        model = keras.models.Sequential()
        model.add(Conv2D(input_shape=(221, 221, 3), filters=96, kernel_size=7, strides=2, padding='valid',
                         activation='relu', kernel_initializer='uniform'))
        model.add(MaxPooling2D(pool_size=3, strides=3))
        model.add(Conv2D(filters=256, kernel_size=7, strides=1, kernel_initializer='uniform'))
        model.add(MaxPooling2D(pool_size=2, strides=2))
        model.add(Conv2D(filters=512, kernel_size=3, strides=1, padding='same', kernel_initializer='uniform'))
        model.add(Conv2D(filters=512, kernel_size=3, strides=1, padding='same', kernel_initializer='uniform'))
        model.add(Conv2D(filters=1024, kernel_size=3, strides=1, padding='same', kernel_initializer='uniform'))
        model.add(Conv2D(filters=1024, kernel_size=3, strides=1, padding='same', kernel_initializer='uniform'))
        model.add(MaxPooling2D(pool_size=3, strides=3))
        model.add(Conv2D(filters=4096, kernel_size=5, strides=1, kernel_initializer='uniform'))
        model.add(Conv2D(filters=4096, kernel_size=1, strides=1, kernel_initializer='uniform'))
        model.add(Conv2D(filters=1000, kernel_size=1, strides=1, kernel_initializer='uniform'))
        model = keras.models.Model(inputs=model.input, outputs=model.output)
    else:
        # fast model
        model = keras.models.Sequential()
        model.add(Conv2D(input_shape=(231, 231, 3), filters=96, kernel_size=11, strides=4))
        model.add(MaxPooling2D(pool_size=2, strides=2))
        model.add(Conv2D(filters=256, kernel_size=5, strides=1))
        model.add(MaxPooling2D(pool_size=2, strides=2))
        model.add(Conv2D(filters=512, kernel_size=3, strides=1, padding='same'))
        model.add(Conv2D(filters=1024, kernel_size=3, strides=1, padding='same'))
        model.add(Conv2D(filters=1024, kernel_size=3, strides=1, padding='same'))
        model.add(MaxPooling2D(pool_size=2, strides=2))
        model.add(Conv2D(filters=3072, kernel_size=(6, 6), strides=1))
        model.add(Conv2D(filters=4096, kernel_size=1, strides=1))
        model.add(Conv2D(filters=1000, kernel_size=1, strides=1))
        model = keras.models.Model(inputs=model.input, outputs=model.output)
    return model


overfeat = overfeat_model('accurate')
overfeat.summary()

OverFeat推荐博客阅读：
https://www.cnblogs.com/liaohuiqiang/p/9348276.html
https://yufeigan.github.io/2014/11/23/OverFeat-Integrated-Recognition-Localization-and-Detection-using-Convolutional-Networks/