CNNニューラルネットワークを用いた画像認識分類(keras)

https://blog.csdn.net/zh_JNU/article/details/54342856
                 。
#coding:utf-8
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"   # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = ""    ###         CUDA  
from keras.models import Input,Model
from keras.layers.convolutional import Conv2D
from keras.layers.pooling import MaxPool2D,GlobalAveragePooling2D
from keras.layers import BatchNormalization,Dense
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
import glob
import cv2
import random
import numpy as np
img_h,img_w,n_channels = 128,128,3 ####   ， ，   
classes = 5 ###  
train_batch_size = 40  #####  batch  
test_batch_size= 20   #####  batch  ，         
train_path = './all_data/train/*'
test_path = './all_data/test/*'
def get_one_hot(num,classes):  ###  one-hot        keras    np_utils.to_categorical()
    one_hot = np.zeros(shape=(1,classes))
    one_hot[:,num] = 1
    return one_hot
class data_generate():  ####     ，          ，            
    def __init__(self,img_path,img_h,img_w,n_channels,batch_size=1,flip=True):
        self.img_h = img_h
        self.img_w = img_w
        self.n_channels = n_channels
        self.flip = flip    ###           
        self.all_path = glob.glob(img_path)
        self.all_num = len(self.all_path)
        self.index = 0
        self.batch_size = batch_size
    def get_batch(self,index,size):   ####        label
        all_data = np.zeros(shape=(size,self.img_h,self.img_w,self.n_channels),dtype='float32')
        all_label = np.zeros(shape=(size,classes))
        for i,img_name in enumerate(self.all_path[index:index+size]):
            img = cv2.imread(img_name,1)
            #print(img_name)
            img = cv2.resize(img,dsize=(img_w,img_h))###         
            img = img -128.0  ####     【-1,1】
            img = img /128.0
            if self.flip:
                p = np.random.random()
                if p>0.5:
                    p_v_h = np.random.random()
                    if p_v_h>0.5:
                        img = img[:,::-1,:] ##    
                    else:
                        img = img[::-1,:, :]  ##    
            all_data[i,:,:,:] = img
            label = int(list(img_name)[-7])-3
            all_label[i,:] = get_one_hot(label,classes)###  label  one-hot  
        return all_data,all_label
    def next_data(self):   ###python   ，     label
        while 1:
            data_label = self.get_batch(self.index,self.batch_size)
            self.index += self.batch_size
            if self.index>self.all_num:
                self.index = 0
                random.shuffle(self.all_path)
            yield data_label
if __name__ == '__main__':
    train_data = data_generate(train_path,img_h,img_w,n_channels,40)####     
    test_data = data_generate(test_path,img_h,img_w,n_channels,20,flip=False)
    input_data = Input(shape=(img_h,img_w,n_channels))
    out = Conv2D(filters=64,kernel_size=(3,3),strides=(1,1),padding='same',activation='relu')(input_data)
    out = Conv2D(filters=64,kernel_size=(3,3),strides=(1,1),padding='same',activation='relu')(out)
    out = BatchNormalization(axis=3)(out)
    out = MaxPool2D(pool_size=(2,2),strides=(2,2))(out)
    out = Conv2D(filters=128, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(out)
    out = Conv2D(filters=128, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(out)
    out = BatchNormalization(axis=3)(out)
    out = MaxPool2D(pool_size=(2, 2), strides=(2, 2))(out)
    out = Conv2D(filters=256, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(out)
    out = Conv2D(filters=256, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(out)
    out = BatchNormalization(axis=3)(out)
    out = GlobalAveragePooling2D()(out)
    ####out = Dense(units=512,activation='relu')(out) ###      ，      
    out = Dense(units=5,activation='softmax')(out)
    model = Model(inputs=input_data,outputs=out)
    model.summary()
    model_check = ModelCheckpoint('./model.h5',
                                  monitor='val_loss',
                                  verbose=1,
                                  save_best_only=True,
                                  save_weights_only=False,
                                  mode='auto',
                                  period=1)  ####      epoch              val_loss
    model.compile(optimizer=Adam(lr=0.001),loss='categorical_crossentropy',metrics=['accuracy'])
    model.fit_generator(train_data.next_data(),train_data.all_num//train_batch_size,100,callbacks=[model_check],
                        validation_data=test_data.next_data(),validation_steps=test_data.all_num//test_batch_size)

    model.save('./final_model.h5')