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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 osimport numpy as npimport tensorflow as tfimport input_dataimport 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10
Klearning_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()

作者:xinyu3307
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