tensorflow graph 中获取variable operation

tensorflow提供了一些列的方法获取和变量计算图中的variable和operation。

获取单个operation/variable

可以通过如下两个方法获取图中的相关variable和operation：
1. tf.Graph.get_tensor_by_name(tensor_name)
2. tf.Graph.get_operation_by_name(op_name)

批量获取

批量获取的方式主要有如下几种：
1. graph.node

import tensorflow as tf# Create some variables.v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer)v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer)v3 = tf.get_variable("v3", shape=[4], initializer = tf.zeros_initializer)inc_v1 = tf.assign(v1,v1+1,name='inc_v1')dec_v2 = tf.assign(v2,v2-1,name='dec_v2')dec_v3 = tf.assign(v3,v3-2,name='dec_v3')# Add an op to initialize the variables.init_op = tf.global_variables_initializer()# Add ops to save and restore all the variables.saver = tf.train.Saver()# Later, launch the model, initialize the variables, do some work, and save the# variables to disk.with tf.Session() as sess:  sess.run(init_op)  # Do some work with the model.  inc_v1.op.run()  dec_v2.op.run()  dec_v3.op.run()  for n in tf.get_default_graph().as_graph_def().node:    print n

输出：

name: "v1/Initializer/zeros"op: "Const"attr {  key: "_class"  value {    list {      s: "loc:@v1"    }  }}attr {  key: "dtype"  value {    type: DT_FLOAT  }}attr {  key: "value"  value {    tensor {      dtype: DT_FLOAT      tensor_shape {        dim {          size: 3        }      }      float_val: 0.0    }  }}

该方法列出了每个graph中每个node的详细信息。
2. graph.get_operations()

  for op in tf.get_default_graph().get_operations():    print op.name    print op.values()

输出：

name:v1/Initializer/zerosvalue:(<tf.Tensor 'v1/Initializer/zeros:0' shape=(3,) dtype=float32>,)name:v1value:(<tf.Tensor 'v1:0' shape=(3,) dtype=float32_ref>,)

op.valuses()将返回该op对应的tensor对象，可以进一步获取tensor的name，shape等信息。
3. tf.all_variables()

import tensorflow as tf# Create some variables.v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer)v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer)v3 = tf.get_variable("v3", shape=[4], initializer = tf.zeros_initializer)inc_v1 = tf.assign(v1,v1+1,name='inc_v1')dec_v2 = tf.assign(v2,v2-1,name='dec_v2')dec_v3 = tf.assign(v3,v3-2,name='dec_v3')# Add an op to initialize the variables.init_op = tf.global_variables_initializer()# Add ops to save and restore all the variables.saver = tf.train.Saver()# Later, launch the model, initialize the variables, do some work, and save the# variables to disk.with tf.Session() as sess:  sess.run(init_op)  # Do some work with the model.  inc_v1.op.run()  dec_v2.op.run()  dec_v3.op.run()  for variable in tf.all_variables():    print variable    print variable.name

输出：

<tf.Variable 'v1:0' shape=(3,) dtype=float32_ref>v1:0<tf.Variable 'v2:0' shape=(5,) dtype=float32_ref>v2:0<tf.Variable 'v3:0' shape=(4,) dtype=float32_ref>v3:0

该方法返回默认计算图中所有的variable（）对象
4. tf.get_collection(collection_key)

    for variable in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES):        print variable

输出：

<tf.Variable 'v1:0' shape=(3,) dtype=float32_ref><tf.Variable 'v2:0' shape=(5,) dtype=float32_ref><tf.Variable 'v3:0' shape=(4,) dtype=float32_ref>

该方法根据key返回相应collection中的对象。

tensorflow中预定义了一些grapykClass GraphKeys

Standard names to use for graph collections.The standard library uses various well-known names to collect and retrieve values associated with a graph. For example, the tf.Optimizer subclasses default to optimizing the variables collected under tf.GraphKeys.TRAINABLE_VARIABLES if none is specified, but it is also possible to pass an explicit list of variables.The following standard keys are defined:  ● GLOBAL_VARIABLES: the default collection of Variable objects, shared across distributed environment (model variables are subset of these). See tf.global_variables for more details. Commonly, all TRAINABLE_VARIABLES variables will be in MODEL_VARIABLES, and all MODEL_VARIABLES variables will be in GLOBAL_VARIABLES.  ● LOCAL_VARIABLES: the subset of Variable objects that are local to each machine. Usually used for temporarily variables, like counters. Note: use tf.contrib.framework.local_variable to add to this collection.  ● MODEL_VARIABLES: the subset of Variable objects that are used in the model for inference (feed forward). Note: use tf.contrib.framework.model_variable to add to this collection.  ● TRAINABLE_VARIABLES: the subset of Variable objects that will be trained by an optimizer. Seetf.trainable_variables for more details.  ● SUMMARIES: the summary Tensor objects that have been created in the graph. See tf.summary.merge_all for more details.  ● QUEUE_RUNNERS: the QueueRunner objects that are used to produce input for a computation. Seetf.train.start_queue_runners for more details.  ● MOVING_AVERAGE_VARIABLES: the subset of Variable objects that will also keep moving averages. Seetf.moving_average_variables for more details.  ● REGULARIZATION_LOSSES: regularization losses collected during graph construction.The following standard keys are defined, but their collections are not automatically populated as many of the others are:  ● WEIGHTS  ● BIASES  ● ACTIVATIONS 

除了预定义的collecion，tensorflow还支持自定义collection方法–tf.add_collection(key,value),tf.get_collection(key)。tf的collecion提供了一种全局的存储机制，不收命名空间影响。代码如下：

 import tensorflow as tf# Create some variables.v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer)v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer)v3 = tf.get_variable("v3", shape=[4], initializer = tf.zeros_initializer)inc_v1 = tf.assign(v1,v1+1,name='inc_v1')dec_v2 = tf.assign(v2,v2-1,name='dec_v2')dec_v3 = tf.assign(v3,v3-2,name='dec_v3')# Add an op to initialize the variables.init_op = tf.global_variables_initializer()# Add ops to save and restore all the variables.saver = tf.train.Saver()# Later, launch the model, initialize the variables, do some work, and save the# variables to disk.with tf.Session() as sess:  sess.run(init_op)  # Do some work with the model.  inc_v1.op.run()  dec_v2.op.run()  dec_v3.op.run()  # Add variable into   tf.add_to_collection('test',v1)  tf.add_to_collection('test',v2)    tf.add_to_collection('test',inc_v1)  for element in tf.get_collection('test'):    print element

输出：

<tf.Variable 'v1:0' shape=(3,) dtype=float32_ref><tf.Variable 'v2:0' shape=(5,) dtype=float32_ref>Tensor("inc_v1:0", shape=(3,), dtype=float32_ref)

tf还提供了获取graph中所有collection的方法：

   for key in tf.get_default_graph().get_all_collection_keys():      print 'key:'+key      for element in tf.get_collection(key):          print element

输出

   key:variables<tf.Variable 'v1:0' shape=(3,) dtype=float32_ref><tf.Variable 'v2:0' shape=(5,) dtype=float32_ref><tf.Variable 'v3:0' shape=(4,) dtype=float32_ref>key:trainable_variables<tf.Variable 'v1:0' shape=(3,) dtype=float32_ref><tf.Variable 'v2:0' shape=(5,) dtype=float32_ref><tf.Variable 'v3:0' shape=(4,) dtype=float32_ref>