解决Tensorflow 内存泄露问题

with tf.Session() as session:
  #codes

#一般使用with以后就会释放内存，否则运行如下释放
session.close()
del session

with tf.Seesion() as session:
  # 在session内部加载保存好的graph
  saver = tf.train.import_meta_graph('./CNN_cracks.meta')
  saver.restore(session, "./CNN_cracks")
  # codes

# 用with新建一个graph，这样在运行完以及异常退出时就会释放内存
graph = tf.Gragh()
with graph.as_default():
  saver = tf.train.import_meta_graph('./CNN_cracks.meta')

with tf.Session(graph=graph) as session:
  saver.restore(session, "./CNN_cracks")

##错误代码

#训练
graph = tf.Graph()
with graph.as_default():
  #codes
  predict = tf.nn.softmax(model(data))

#预测，这里训练文件与预测文件是分离的
with tf.Session(graph=graph) as session:
  #codes
  predict = session.run(predict, feed_dict={data: block})
  prediction = tf.argmax(predict, -1) #这里会对图进行修改

##正确代码

#训练
graph = tf.Graph()
with graph.as_default():
  #codes
  predict = tf.argmax(tf.nn.softmax(model(data)), -1)

#预测
with tf.Session(graph=graph) as session:
  #codes
  prediction = session.run(predict, feed_dict={data: block})