keras实现多种分类网络的方式


Posted in Python onJune 11, 2020

Keras应该是最简单的一种深度学习框架了,入门非常的简单.

简单记录一下keras实现多种分类网络:如AlexNet、Vgg、ResNet

采用kaggle猫狗大战的数据作为数据集.

由于AlexNet采用的是LRN标准化,Keras没有内置函数实现,这里用batchNormalization代替

收件建立一个model.py的文件,里面存放着alexnet,vgg两种模型,直接导入就可以了

#coding=utf-8
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D, BatchNormalization
from keras.layers import *
from keras.layers.advanced_activations import LeakyReLU,PReLU
from keras.models import Model
 
def keras_batchnormalization_relu(layer):
 BN = BatchNormalization()(layer)
 ac = PReLU()(BN)
 return ac
 
def AlexNet(resize=227, classes=2):
 model = Sequential()
 # 第一段
 model.add(Conv2D(filters=96, kernel_size=(11, 11),
      strides=(4, 4), padding='valid',
      input_shape=(resize, resize, 3),
      activation='relu'))
 model.add(BatchNormalization())
 model.add(MaxPooling2D(pool_size=(3, 3),
       strides=(2, 2),
       padding='valid'))
 # 第二段
 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'))
 # 第三段
 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(MaxPooling2D(pool_size=(3, 3),
       strides=(2, 2), padding='valid'))
 # 第四段
 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(classes,activation='softmax'))
 # model.add(Activation('softmax'))
 
 return model
 
def AlexNet2(inputs, classes=2, prob=0.5):
 '''
 自己写的函数,尝试keras另外一种写法
 :param inputs: 输入
 :param classes: 类别的个数
 :param prob: dropout的概率
 :return: 模型
 '''
 # Conv2D(32, (3, 3), dilation_rate=(2, 2), padding='same')(inputs)
 print "input shape:", inputs.shape
 
 conv1 = Conv2D(filters=96, kernel_size=(11, 11), strides=(4, 4), padding='valid')(inputs)
 conv1 = keras_batchnormalization_relu(conv1)
 print "conv1 shape:", conv1.shape
 pool1 = MaxPool2D(pool_size=(3, 3), strides=(2, 2))(conv1)
 print "pool1 shape:", pool1.shape
 
 conv2 = Conv2D(filters=256, kernel_size=(5, 5), padding='same')(pool1)
 conv2 = keras_batchnormalization_relu(conv2)
 print "conv2 shape:", conv2.shape
 pool2 = MaxPool2D(pool_size=(3, 3), strides=(2, 2))(conv2)
 print "pool2 shape:", pool2.shape
 
 conv3 = Conv2D(filters=384, kernel_size=(3, 3), padding='same')(pool2)
 conv3 = PReLU()(conv3)
 print "conv3 shape:", conv3.shape
 
 conv4 = Conv2D(filters=384, kernel_size=(3, 3), padding='same')(conv3)
 conv4 = PReLU()(conv4)
 print "conv4 shape:", conv4
 
 conv5 = Conv2D(filters=256, kernel_size=(3, 3), padding='same')(conv4)
 conv5 = PReLU()(conv5)
 print "conv5 shape:", conv5
 
 pool3 = MaxPool2D(pool_size=(3, 3), strides=(2, 2))(conv5)
 print "pool3 shape:", pool3.shape
 
 dense1 = Flatten()(pool3)
 dense1 = Dense(4096, activation='relu')(dense1)
 print "dense2 shape:", dense1
 dense1 = Dropout(prob)(dense1)
 # print "dense1 shape:", dense1
 
 dense2 = Dense(4096, activation='relu')(dense1)
 print "dense2 shape:", dense2
 dense2 = Dropout(prob)(dense2)
 # print "dense2 shape:", dense2
 
 predict= Dense(classes, activation='softmax')(dense2)
 
 model = Model(inputs=inputs, outputs=predict)
 return model
 
def vgg13(resize=224, classes=2, prob=0.5):
 model = Sequential()
 model.add(Conv2D(64, (3, 3), strides=(1, 1), input_shape=(resize, resize, 3), padding='same', activation='relu',
      kernel_initializer='uniform'))
 model.add(Conv2D(64, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Conv2D(128, (3, 2), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Flatten())
 model.add(Dense(4096, activation='relu'))
 model.add(Dropout(prob))
 model.add(Dense(4096, activation='relu'))
 model.add(Dropout(prob))
 model.add(Dense(classes, activation='softmax'))
 return model
 
def vgg16(resize=224, classes=2, prob=0.5):
 model = Sequential()
 model.add(Conv2D(64, (3, 3), strides=(1, 1), input_shape=(resize, resize, 3), padding='same', activation='relu',
      kernel_initializer='uniform'))
 model.add(Conv2D(64, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Conv2D(128, (3, 2), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Flatten())
 model.add(Dense(4096, activation='relu'))
 model.add(Dropout(prob))
 model.add(Dense(4096, activation='relu'))
 model.add(Dropout(prob))
 model.add(Dense(classes, activation='softmax'))
 return model

然后建立一个train.py文件,用于读取数据和训练数据的.

#coding=utf-8
import keras
import cv2
import os
import numpy as np
import model
import modelResNet
import tensorflow as tf
from keras.layers import Input, Dense
from keras.preprocessing.image import ImageDataGenerator
 
resize = 224
batch_size = 128
path = "/home/hjxu/PycharmProjects/01_cats_vs_dogs/data"
 
trainDirectory = '/home/hjxu/PycharmProjects/01_cats_vs_dogs/data/train/'
def load_data():
 imgs = os.listdir(path + "/train/")
 num = len(imgs)
 train_data = np.empty((5000, resize, resize, 3), dtype="int32")
 train_label = np.empty((5000, ), dtype="int32")
 test_data = np.empty((5000, resize, resize, 3), dtype="int32")
 test_label = np.empty((5000, ), dtype="int32")
 for i in range(5000):
  if i % 2:
   train_data[i] = cv2.resize(cv2.imread(path + '/train/' + 'dog.' + str(i) + '.jpg'), (resize, resize))
   train_label[i] = 1
  else:
   train_data[i] = cv2.resize(cv2.imread(path + '/train/' + 'cat.' + str(i) + '.jpg'), (resize, resize))
   train_label[i] = 0
 for i in range(5000, 10000):
  if i % 2:
   test_data[i-5000] = cv2.resize(cv2.imread(path + '/train/' + 'dog.' + str(i) + '.jpg'), (resize, resize))
   test_label[i-5000] = 1
  else:
   test_data[i-5000] = cv2.resize(cv2.imread(path + '/train/' + 'cat.' + str(i) + '.jpg'), (resize, resize))
   test_label[i-5000] = 0
 return train_data, train_label, test_data, test_label
 
def main():
 
 train_data, train_label, test_data, test_label = load_data()
 train_data, test_data = train_data.astype('float32'), test_data.astype('float32')
 train_data, test_data = train_data/255, test_data/255
 
 train_label = keras.utils.to_categorical(train_label, 2)
 '''
  #one_hot转码,如果使用 categorical_crossentropy,就需要用到to_categorical函数完成转码
 '''
 test_label = keras.utils.to_categorical(test_label, 2)
 
 inputs = Input(shape=(224, 224, 3))
 
 modelAlex = model.AlexNet2(inputs, classes=2)
 '''
 导入模型
 '''
 modelAlex.compile(loss='categorical_crossentropy',
     optimizer='sgd',
     metrics=['accuracy'])
 '''
 def compile(self, optimizer, loss, metrics=None, loss_weights=None,
     sample_weight_mode=None, **kwargs):
  optimizer:优化器,为预定义优化器名或优化器对象,参考优化器
  loss: 损失函数,为预定义损失函数名或者一个目标函数
  metrics:列表,包含评估模型在训练和测试时的性能指标,典型用法是 metrics=['accuracy']
  sample_weight_mode:如果需要按时间步为样本赋值,需要将改制设置为"temoral"
  如果想用自定义的性能评估函数:如下
   def mean_pred(y_true, y_pred):
   return k.mean(y_pred)
  model.compile(loss = 'binary_crossentropy', metrics=['accuracy', mean_pred],...)
  损失函数同理,再看 keras内置支持的损失函数有
   mean_squared_error
  mean_absolute_error
  mean_absolute_percentage_error
  mean_squared_logarithmic_error
  squared_hinge
  hinge
  categorical_hinge
  logcosh
  categorical_crossentropy
  sparse_categorical_crossentropy
  binary_crossentropy
  kullback_leibler_divergence
  poisson
  cosine_proximity
 '''
 modelAlex.summary()
 '''
 # 打印模型信息
 '''
 modelAlex.fit(train_data, train_label,
    batch_size=batch_size,
    epochs=50,
    validation_split=0.2,
    shuffle=True)
 '''
 def fit(self, x=None,   # x:输入数据
   y=None,     # y:标签 Numpy array
   batch_size=32,   # batch_size:训练时,一个batch的样本会被计算一次梯度下降
   epochs=1,    # epochs: 训练的轮数,每个epoch会把训练集循环一遍
   verbose=1,    # 日志显示:0表示不在标准输入输出流输出,1表示输出进度条,2表示每个epoch输出
   callbacks=None,   # 回调函数
   validation_split=0.,  # 0-1的浮点数,用来指定训练集一定比例作为验证集,验证集不参与训练
   validation_data=None, # (x,y)的tuple,是指定的验证集
   shuffle=True,   # 如果是"batch",则是用来处理HDF5数据的特殊情况,将在batch内部将数据打乱
   class_weight=None,  # 字典,将不同的类别映射为不同的权值,用来在训练过程中调整损失函数的
   sample_weight=None,  # 权值的numpy array,用于训练的时候调整损失函数
   initial_epoch=0,   # 该参数用于从指定的epoch开始训练,继续之前的训练
   **kwargs):
 返回:返回一个History的对象,其中History.history损失函数和其他指标的数值随epoch变化的情况
 '''
 scores = modelAlex.evaluate(train_data, train_label, verbose=1)
 print(scores)
 
 scores = modelAlex.evaluate(test_data, test_label, verbose=1)
 print(scores)
 modelAlex.save('my_model_weights2.h5')
 
def main2():
 train_datagen = ImageDataGenerator(rescale=1. / 255,
          shear_range=0.2,
          zoom_range=0.2,
          horizontal_flip=True)
 test_datagen = ImageDataGenerator(rescale=1. / 255)
 train_generator = train_datagen.flow_from_directory(trainDirectory,
              target_size=(224, 224),
              batch_size=32,
              class_mode='binary')
 
 validation_generator = test_datagen.flow_from_directory(trainDirectory,
               target_size=(224, 224),
               batch_size=32,
               class_mode='binary')
 
 inputs = Input(shape=(224, 224, 3))
 # modelAlex = model.AlexNet2(inputs, classes=2)
 modelAlex = model.vgg13(resize=224, classes=2, prob=0.5)
 # modelAlex = modelResNet.ResNet50(shape=224, classes=2)
 modelAlex.compile(loss='sparse_categorical_crossentropy',
      optimizer='sgd',
      metrics=['accuracy'])
 modelAlex.summary()
 
 modelAlex.fit_generator(train_generator,
      steps_per_epoch=1000,
      epochs=60,
      validation_data=validation_generator,
      validation_steps=200)
 
 modelAlex.save('model32.hdf5')
 #
if __name__ == "__main__":
 '''
 如果数据是按照猫狗大战的数据,都在同一个文件夹下,使用main()函数
 如果数据按照猫和狗分成两类,则使用main2()函数
 '''
 main2()

得到模型后该怎么测试一张图像呢?

建立一个testOneImg.py脚本,代码如下

#coding=utf-8
from keras.preprocessing.image import load_img#load_image作用是载入图片
from keras.preprocessing.image import img_to_array
from keras.applications.vgg16 import preprocess_input
from keras.applications.vgg16 import decode_predictions
import numpy as np
import cv2
import model
from keras.models import Sequential
 
pats = '/home/hjxu/tf_study/catVsDogsWithKeras/my_model_weights.h5'
modelAlex = model.AlexNet(resize=224, classes=2)
# AlexModel = model.AlexNet(weightPath='/home/hjxu/tf_study/catVsDogsWithKeras/my_model_weights.h5')
 
modelAlex.load_weights(pats)
#
img = cv2.imread('/home/hjxu/tf_study/catVsDogsWithKeras/111.jpg')
img = cv2.resize(img, (224, 224))
x = img_to_array(img/255) # 三维(224,224,3)
 
x = np.expand_dims(x, axis=0) # 四维(1,224,224,3)#因为keras要求的维度是这样的,所以要增加一个维度
# x = preprocess_input(x) # 预处理
print(x.shape)
y_pred = modelAlex.predict(x) # 预测概率 t1 = time.time() print("测试图:", decode_predictions(y_pred)) # 输出五个最高概率(类名, 语义概念, 预测概率)
print y_pred

不得不说,Keras真心简单方便。

补充知识:keras中的函数式API——残差连接+权重共享的理解

1、残差连接

# coding: utf-8
"""残差连接 residual connection:
  是一种常见的类图网络结构,解决了所有大规模深度学习的两个共性问题:
   1、梯度消失
   2、表示瓶颈
  (甚至,向任何>10层的神经网络添加残差连接,都可能会有帮助)

  残差连接:让前面某层的输出作为后面某层的输入,从而在序列网络中有效地创造一条捷径。
       """
from keras import layers

x = ...
y = layers.Conv2D(128, 3, activation='relu', padding='same')(x)
y = layers.Conv2D(128, 3, activation='relu', padding='same')(y)
y = layers.Conv2D(128, 3, activation='relu', padding='same')(y)

y = layers.add([y, x]) # 将原始x与输出特征相加

# ---------------------如果特征图尺寸不同,采用线性残差连接-------------------
x = ...
y = layers.Conv2D(128, 3, activation='relu', padding='same')(x)
y = layers.Conv2D(128, 3, activation='relu', padding='same')(y)
y = layers.MaxPooling2D(2, strides=2)(y)

residual = layers.Conv2D(128, 1, strides=2, padding='same')(x) # 使用1*1的卷积,将原始张量线性下采样为y具有相同的形状

y = layers.add([y, residual]) # 将原始x与输出特征相加

2、权重共享

即多次调用同一个实例

# coding: utf-8
"""函数式子API:权重共享
  能够重复的使用同一个实例,这样相当于重复使用一个层的权重,不需要重新编写"""
from keras import layers
from keras import Input
from keras.models import Model


lstm = layers.LSTM(32) # 实例化一个LSTM层,后面被调用很多次

# ------------------------左边分支--------------------------------
left_input = Input(shape=(None, 128))
left_output = lstm(left_input) # 调用lstm实例

# ------------------------右分支---------------------------------
right_input = Input(shape=(None, 128))
right_output = lstm(right_input) # 调用lstm实例

# ------------------------将层进行连接合并------------------------
merged = layers.concatenate([left_output, right_output], axis=-1)

# -----------------------在上面构建一个分类器---------------------
predictions = layers.Dense(1, activation='sigmoid')(merged)

# -------------------------构建模型,并拟合训练-----------------------------------
model = Model([left_input, right_input], predictions)
model.fit([left_data, right_data], targets)

以上这篇keras实现多种分类网络的方式就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持三水点靠木。

Python 相关文章推荐
python实现人人网登录示例分享
Jan 19 Python
在Django中编写模版节点及注册标签的方法
Jul 20 Python
玩转python爬虫之URLError异常处理
Feb 17 Python
简述Python中的进程、线程、协程
Mar 18 Python
linux安装python修改默认python版本方法
Mar 31 Python
opencv python 图像轮廓/检测轮廓/绘制轮廓的方法
Jul 03 Python
python selenium 查找隐藏元素 自动播放视频功能
Jul 24 Python
python实现通过flask和前端进行数据收发
Aug 22 Python
python基于socket实现的UDP及TCP通讯功能示例
Nov 01 Python
python 实现从高分辨图像上抠取图像块
Jan 02 Python
python如何实现图片压缩
Sep 11 Python
Python就将所有的英文单词首字母变成大写
Feb 12 Python
python的help函数如何使用
Jun 11 #Python
新手学python应该下哪个版本
Jun 11 #Python
python开发前景如何
Jun 11 #Python
python编写softmax函数、交叉熵函数实例
Jun 11 #Python
python能开发游戏吗
Jun 11 #Python
使用sklearn对多分类的每个类别进行指标评价操作
Jun 11 #Python
python属于解释语言吗
Jun 11 #Python
You might like
PHP嵌套输出缓冲代码实例
2015/05/12 PHP
PHP获取文件夹大小函数用法实例
2015/07/01 PHP
php使用FFmpeg接口获取视频的播放时长、码率、缩略图以及创建时间
2016/11/07 PHP
[原创]PHP实现生成vcf vcard文件功能类定义与使用方法详解【附demo源码下载】
2017/09/02 PHP
为jquery.ui.dialog 增加“自动记住关闭时的位置”的功能
2009/11/24 Javascript
jQuery 处理表单元素的代码
2010/02/15 Javascript
js模拟点击事件实现代码
2012/11/06 Javascript
深入理解JavaScript 闭包究竟是什么
2013/04/12 Javascript
jQuery实现友好的轮播图片特效
2015/01/12 Javascript
Node.js开发之访问Redis数据库教程
2015/01/14 Javascript
JavaScript基本数据类型及值类型和引用类型
2015/08/25 Javascript
JS实现的仿QQ空间图片弹出效果代码
2016/02/23 Javascript
js canvas实现擦除动画
2016/07/16 Javascript
微信小程序Server端环境配置详解(SSL, Nginx HTTPS,TLS 1.2 升级)
2017/01/12 Javascript
JavaScript 事件对内存和性能的影响
2017/01/22 Javascript
基于jQuery实现的单行公告活动轮播效果
2017/08/23 jQuery
vue-cli下的vuex的简单Demo图解(实现加1减1操作)
2018/02/26 Javascript
JS中类的静态方法,静态变量,实例方法,实例变量区别与用法实例分析
2020/03/14 Javascript
Vue管理系统前端之组件拆分封装详解
2020/08/23 Javascript
Python查找相似单词的方法
2015/03/05 Python
python使用正则来处理各种匹配问题
2019/12/22 Python
什么是Python变量作用域
2020/06/03 Python
一篇文章带你搞定Ubuntu中打开Pycharm总是卡顿崩溃
2020/11/02 Python
从一次项目重构说起CSS3自定义变量在项目的使用方法
2021/03/01 HTML / CSS
html5是什么_动力节点Java学院整理
2017/07/07 HTML / CSS
工作失职检讨书范文
2014/01/16 职场文书
优质服务活动实施方案
2014/05/02 职场文书
专家推荐信模板
2014/05/09 职场文书
2014年大学生职业规划书:未来不是梦,只要勇敢冲!
2014/09/22 职场文书
2014年物流工作总结
2014/11/25 职场文书
营业用房租赁协议书
2014/11/26 职场文书
2014小学一年级班主任工作总结
2014/12/05 职场文书
公司仓管员岗位职责
2015/04/01 职场文书
2015年底工作总结范文
2015/05/15 职场文书
深入解读Java三大集合之map list set的用法
2021/11/11 Java/Android
《游戏王:大师决斗》新活动上线 若无符合卡组可免费租用
2022/04/13 其他游戏