variable

变量Variable

变量用于存储和更新参数,是内存中用于存放张量(Tensor)的缓存区,必须明确的初始化,并能在训练过程种和结束后保存到磁盘.
* 创建
需要传入一个初始和shape
你将一个 张量 作为初始值传入 Variable() 构造函数。TensorFlow提供了一系列操作符来初始化张量，初始值是常量或是随机值

import tensorflow as tf# Create a variable.w = tf.Variable(<initial-value>, name=<optional-name>)weights = tf. Variable(tf. random_normal([784, 200] , stddev=0.35) ,name="weights")biases = tf. Variable(tf. zeros([200] ) , name="biases")

* 调用 tf.Variable() 添加一些操作(Op, operation)到graph：

    # Use the variable in the graph like any Tensor.    y = tf.matmul(w, ...another variable or tensor...)    # The overloaded operators are available too.    z = tf.sigmoid(w + b)

• 给w赋新值

•    # Assign a new value to the variable with `assign()` or a related method.    w.assign(w + 1.0)    w.assign_add(1.0)

• 初始化

•   # Create two variables.创建两个变量  weights = tf. Variable(tf. random_normal([784, 200] ,  stddev=0.35) ,  name="weights")  biases = tf. Variable(tf. zeros([200] ) , name="biases")  # Add an Op to initialize all variables.添加一个节点用来初始化变量  init_op = tf.initialize_all_variables()  # Later, when launching the model 运行模型  with tf. Session() as sess:  # Run the init operation.   sess. run(init_op)  ...  # Use the model

  • 由另一个变量初始化

    # Create a variable with a random value.weights = tf. Variable(tf. random_normal([784, 200] , stddev=0.35) ,name="weights")# Create another variable with the same value as 'weights'.w2 = tf. Variable(weights. initialized_value() , name="w2")# Create another variable with twice the value of 'weights'w_twice = tf. Variable(weights. initialized_value() * 0.2, name="w_twice")

• 保存和加载

  • 保存 tf.train.saver

    # Create some variables.v1 = tf. Variable(... , name="v1")v2 = tf. Variable(... , name="v2")...# Add an op to initialize the variables.init_op = tf. initialize_all_variables()# Add ops to save and restore all the variables.saver = tf. train. Saver()# Later, launch the model, initialize the variables, do some work, save the# variables to disk.with tf. Session() as sess:sess. run(init_op)# Do some work with the model...# Save the variables to disk.保存变量至检查点save_path = saver. save(sess, "/tmp/model.ckpt")print "Model saved in file: ", save_path

  • 恢复变量
    用同一个 Saver 对象来恢复变量。注意，当你从文件中恢复变量时，不需要事先对它们做初始化。
    save.restore

    # Create some variables.v1 = tf. Variable(... , name="v1")v2 = tf. Variable(... , name="v2")...# Add ops to save and restore all the variables.saver = tf. train. Saver()# Later, launch the model, use the saver to restore variables from disk, and# do some work with the model.with tf. Session() as sess:# Restore variables from disk.saver. restore(sess, "/tmp/model.ckpt")print "Model restored."# Do some work with the model...

  • 恢复部分变量

    # Create some variables.v1 = tf. Variable(... , name="v1")v2 = tf. Variable(... , name="v2")...# Add ops to save and restore only 'v2' using the name "my_v2"saver = tf. train. Saver({"my_v2": v2} )# Use the saver object normally after that.