TensorFlow——训练自己的数据（三）模型训练

参考：Tensorflow教程-猫狗大战数据集

文件training.py

导入文件

import os
import numpy as np
import tensorflow as tf
import input_data
import model

变量声明

N_CLASSES = 2 #猫和狗
IMG_W = 208  # resize图像，太大的话训练时间久
IMG_H = 208
BATCH_SIZE = 16
CAPACITY = 2000
MAX_STEP = 10000 # 一般大于10K
learning_rate = 0.0001 # 一般小于0.0001

获取批次batch

train_dir = '/home/kevin/tensorflow/cats_vs_dogs/data/train/'
logs_train_dir = '/home/kevin/tensorflow/cats_vs_dogs/logs/train/'

train, train_label = input_data.get_files(train_dir)
train_batch,train_label_batch=input_data.get_batch(train,
                                train_label,
                                IMG_W,
                                IMG_H,
                                BATCH_SIZE, 
                                CAPACITY)      

操作定义

train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES)
train_loss = model.losses(train_logits, train_label_batch)        
train_op = model.trainning(train_loss, learning_rate)
train__acc = model.evaluation(train_logits, train_label_batch)
summary_op = tf.summary.merge_all() #这个是log汇总记录

#产生一个会话
sess = tf.Session()  
#产生一个writer来写log文件
train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph) 
 #产生一个saver来存储训练好的模型
saver = tf.train.Saver()
#所有节点初始化
sess.run(tf.global_variables_initializer()) 

#队列监控
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)

进行batch的训练

try:
    #执行MAX_STEP步的训练，一步一个batch
    for step in np.arange(MAX_STEP):
        if coord.should_stop():
                break
        #启动以下操作节点，有个疑问，为什么train_logits在这里没有开启？
        _, tra_loss, tra_acc = sess.run([train_op, train_loss, train__acc])
        #每隔50步打印一次当前的loss以及acc，同时记录log，写入writer   
        if step % 50 == 0:
            print('Step %d, train loss = %.2f, train accuracy = %.2f%%' %(step, tra_loss, tra_acc*100.0))
            summary_str = sess.run(summary_op)
            train_writer.add_summary(summary_str, step)
        #每隔2000步，保存一次训练好的模型
        if step % 2000 == 0 or (step + 1) == MAX_STEP:
            checkpoint_path = os.path.join(logs_train_dir, 'model.ckpt')
            saver.save(sess, checkpoint_path, global_step=step)

except tf.errors.OutOfRangeError:
    print('Done training -- epoch limit reached')
finally:
    coord.request_stop()