tensorflow--保存加载模型

s=mnist.train.next_batch(batch_size)

print(xs.shape)

print(ys.shape)

# #从集合中取全部变量

# tf.get_collection()

# #列表内对应元素相加

# tf.add_n([])

# #转换类型

# tf.cast(x,dtype=)

# #返回最大值所在的序列好

# tf.argmax(x,axis)

# #添加路径

# import os

# os.path.join("home","name")

# #字符串操作split()

# "./model/momist_model-1001".split("/")[-1].split("-")[-1]

# #模型保存

# saver=tf.train.Saver()

# with tf.Session() as sess:

# for i in range(steps):

# if i%轮数==0:

# saver.save(sess,os.path.join(MODEL,NAME),global=global_step)

# #加载模型

# with tf.Session() as sess:

# ckpt=tf.train.get_checkpoint_state(存储路径)

# if ckpt andckpt.model_path:

# saver.restore(sess,ckpt.model_path)

# #实例化还原滑动pingjun

# ema=tf.train.ExponentialMovingAverage(滑动平均基础)

# ema_restore=ema.variable_to_restore()

# saver=tf.train.Saver(ema_restore)

# #准确率计算方法

# correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_，1))

# accurcy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

#保存和加载模型

Saver=tf.train.Saver()
#保存模型

Saver.save(sess,save_path="C:/Users/Administrator/Desktop/mnist_tensorflow/model/save_net.ckpt")
#加载模型

saver = tf.train.import_meta_graph(\'C:/Users/Administrator/Desktop/mnist_tensorflow/model/save_net.ckpt.meta\')
saver.restore(sess, "C:/Users/Administrator/Desktop/mnist_tensorflow/model/save_net.ckpt")