利用tensorflow训练自己的图片数据（4）——神经网络训练

一  . 说明

在上一篇博客——利用tensorflow训练自己的图片数据（3）中，我们建立好了本次训练的模型，接下来就是开始网络训练，并保存训练后的网络参数，以便测试时使用。

二 . 编程实现

#======================================================================
#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 = 4 #husky,jiwawa,poodle,qiutian
IMG_W = 64 # resize图像，太大的话训练时间久
IMG_H = 64
BATCH_SIZE =20
CAPACITY = 200
MAX_STEP = 200 # 一般大于10K
learning_rate = 0.0001 # 一般小于0.0001

#获取批次batch
train_dir = 'E:/Re_train/image_data/inputdata' #训练样本的读入路径
logs_train_dir = 'E:/Re_train/image_data/inputdata' #logs存储路径
#logs_test_dir = 'E:/Re_train/image_data/test' #logs存储路径

#train, train_label = input_data.get_files(train_dir)
train, train_label, val, val_label = input_data.get_files(train_dir, 0.3)
#训练数据及标签
train_batch,train_label_batch = input_data.get_batch(train, train_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
#测试数据及标签
val_batch, val_label_batch = input_data.get_batch(val, val_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)

#训练操作定义
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)

#测试操作定义
test_logits = model.inference(val_batch, BATCH_SIZE, N_CLASSES)
test_loss = model.losses(test_logits, val_label_batch)
test_acc = model.evaluation(test_logits, val_label_batch)

#这个是log汇总记录
summary_op = tf.summary.merge_all()

#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)
#val_writer = tf.summary.FileWriter(logs_test_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 % 10 == 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100步，保存一次训练好的模型
if (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()

#========================================================================
本次训练300次，ratio设为0.3，学习率设为0.001，批处理量为20；每10次大厅一下训练结果，并将最后训练完成时，将训练数据保存到logs_train_dir的命名为model.ckpt的文件中。