Tensorflow: Graph

import tensorflow as tf

1. 概述

Tensorflow中的运算过程, 会被表示成一个图, 由一些列的op(操作对象)作为节点组成. Tensor对象作为数据节点在op起到连接作用. 在程序运行的开始就会自动的有默认的图的生成.

print(tf.get_default_graph())a = tf.constant(1.0)print(a.graph)'''结果:<tensorflow.python.framework.ops.Graph object at 0x0000024C257BB518><tensorflow.python.framework.ops.Graph object at 0x0000024C257BB518>可见在程序运行的开始, 就已经创建了默认图, 并能通过tf.get_default_graph()方法得到默认图的对象.创建的Tensor, Variable, Op等也都会加入到该图中.'''

这种系统自动创建的方法不便于管理, 通常我们使用如下的方法, 在上下文中管理:
通过 tf.Graph() 创建新的Graph, 并用 as_default() 将这个Graph设置为默认的图, 使用with上下文管理器, 在这个上下文中覆盖默认的图, 将节点加入到新的tf.Graph()图中.

print(tf.get_default_graph())g = tf.Graph()print(g)with g.as_default():    print(tf.get_default_graph())print(tf.get_default_graph())'''结果:<tensorflow.python.framework.ops.Graph object at 0x0000020DEFA2B5C0><tensorflow.python.framework.ops.Graph object at 0x0000020DEFBCD7B8><tensorflow.python.framework.ops.Graph object at 0x0000020DEFBCD7B8><tensorflow.python.framework.ops.Graph object at 0x0000020DEFA2B5C0>'''

综上, 一般使用下述方法管理Graph:

with tf.Graph().as_default():    pass

2.图的函数

一个图中, 会有一个字典属性collection, 将图中的变量节点通过名字归类, 便于查找和区分, 即通过设置变量的collections属性, 将节点以字符串为key值归纳.

collection有以下默认字段:
variables 所有在该图下创建的Variable ;
trainable_variables 该图所有的可训练变量, 在创建该变量时trainable参数设置为True(默认)

Graph类关于collection的函数:

• tf.Graph.get_all_collection_keys()

'''作用: 获取图中collection所有key值字符串输出: string list, 元素为graph._collection字典中的key值字符串'''with tf.Graph().as_default():    a = tf.Variable([1,1], name="a")    b = tf.get_variable("b", shape=[2], collections=["test1", "test2"])    graph = tf.get_default_graph()    print(graph.get_all_collection_keys())'''结果:['test1', 'variables', 'test2', 'trainable_variables']'''

• tf.Graph.add_to_collection()

'''作用: 将变量value添加到指定name一个collection中参数:    name: (must)collection的名称, 只能输入一个collection的名称, 格式为字符串;    value: 变量对象'''with tf.Graph().as_default():    a = tf.Variable([1,1], name="a")    b = tf.get_variable("b", shape=[2], collections=["test1", "test2"])    graph = tf.get_default_graph()    graph.add_to_collection("test3", a)    print(graph.get_all_collection_keys())'''结果:['test1', 'trainable_variables', 'test3', 'test2', 'variables']'''

Graph类其他重要函数:

• tf.Graph.get_operations()

'''作用: 返回图中所有op, 以列表的形式输出: op list, 元素为op对象'''with tf.Graph().as_default():    W = tf.Variable([[1,2],[3,4]], dtype=tf.float32, name="W")    W2 = tf.get_variable("W2", initializer=tf.random_normal_initializer, shape=[2,2])    x = tf.placeholder(tf.float32, shape=[2,1], name="x")    b = tf.constant([8,6], dtype=tf.float32, shape=[2,1], name="b")    z = tf.matmul(tf.matmul(W, W2, name="matmul_inside"), x, name="matmul_outside") + b    y = tf.nn.relu(z, name="relu")    init_op = tf.global_variables_initializer()    with tf.Session() as sess:        sess.run(init_op)        sess.run([y], feed_dict={x: [[1.0], [1.0]]})        graph = tf.get_default_graph()        print(graph.get_operations())'''结果:[<tf.Operation 'W/initial_value' type=Const>, <tf.Operation 'W' type=VariableV2>, <tf.Operation 'W/Assign' type=Assign>, <tf.Operation 'W/read' type=Identity>, <tf.Operation 'W2/Initializer/random_normal/shape' type=Const>, <tf.Operation 'W2/Initializer/random_normal/mean' type=Const>, <tf.Operation 'W2/Initializer/random_normal/stddev' type=Const>, <tf.Operation 'W2/Initializer/random_normal/RandomStandardNormal' type=RandomStandardNormal>, <tf.Operation 'W2/Initializer/random_normal/mul' type=Mul>, <tf.Operation 'W2/Initializer/random_normal' type=Add>, <tf.Operation 'W2' type=VariableV2>, <tf.Operation 'W2/Assign' type=Assign>, <tf.Operation 'W2/read' type=Identity>, <tf.Operation 'x' type=Placeholder>, <tf.Operation 'b' type=Const>, <tf.Operation 'matmul_inside' type=MatMul>, <tf.Operation 'matmul_outside' type=MatMul>, <tf.Operation 'add' type=Add>, <tf.Operation 'relu' type=Relu>, <tf.Operation 'init' type=NoOp>]可以看出, 有些函数方法会创造很多op, 比如tf.Variable()和tf.get_variable()虽然返回的是一个Variable, 但对应该变量的操作(如初始化, assign赋值, 以及Variable本身都是op)却有很多'''

• tf.Graph.get_operation_by_name()

'''作用: 通过op的名称获取op参数:    name: op的名称输出: op'''with tf.Graph().as_default():    W = tf.Variable([[1,2],[3,4]], dtype=tf.float32, name="W")    W2 = tf.get_variable("W2", initializer=tf.random_normal_initializer, shape=[2,2])    x = tf.placeholder(tf.float32, shape=[2,1], name="x")    b = tf.constant([8,6], dtype=tf.float32, shape=[2,1], name="b")    z = tf.matmul(tf.matmul(W, W2, name="matmul_inside"), x, name="matmul_outside") + b    y = tf.nn.relu(z, name="relu")    init_op = tf.global_variables_initializer()    with tf.Session() as sess:        sess.run(init_op)        sess.run([y], feed_dict={x: [[1.0], [1.0]]})        graph = tf.get_default_graph()        print(graph.get_operations())        op1 = graph.get_operation_by_name("add")        print(op1)'''结果:name: "add"op: "Add"input: "matmul_outside"input: "b"attr {  key: "T"  value {    type: DT_FLOAT  }}这个'add'op是W*W2+b时产生的'''

• tf.Graph.get_tensor_by_name()

'''作用: 通过名称获取tensor参数:    name: Tensor的名称, 格式为<op_name>:<output_index>, Tensor是由各种Op产生的输出: Tensor, 指定获取的Tensor'''with tf.Graph().as_default():    W = tf.Variable([[1,2],[3,4]], dtype=tf.float32, name="W")    W2 = tf.get_variable("W2", initializer=tf.random_normal_initializer, shape=[2,2])    x = tf.placeholder(tf.float32, shape=[2,1], name="x")    b = tf.constant([8,6], dtype=tf.float32, shape=[2,1], name="b")    z = tf.matmul(tf.matmul(W, W2, name="matmul_inside"), x, name="matmul_outside") + b    y = tf.nn.relu(z, name="relu")    init_op = tf.global_variables_initializer()    with tf.Session() as sess:        sess.run(init_op)        sess.run([y], feed_dict={x: [[1.0], [1.0]]})        graph = tf.get_default_graph()        tensor = graph.get_tensor_by_name("b:0")        print(tensor)        print(tensor.eval())'''结果:Tensor("b:0", shape=(2, 1), dtype=float32)[[ 8.] [ 6.]]'''

• tf.Graph.is_feedable()
• tf.Graph.is_fetchable()

'''作用:    is_feedable: 判断Tensor能否被feed;    is_fetchable: 判断Tensor或Op能否被fetch;参数:    is_feedable:        name: Tensor名称或Tensor类    is_fetchable        name: Tensor或Op的名称或类输出: bool'''with tf.Graph().as_default():    W = tf.Variable([[1,2],[3,4]], dtype=tf.float32, name="W")    W2 = tf.get_variable("W2", initializer=tf.random_normal_initializer, shape=[2,2])    x = tf.placeholder(tf.float32, shape=[2,1], name="x")    b = tf.constant([8,6], dtype=tf.float32, shape=[2,1], name="b")    z = tf.matmul(tf.matmul(W, W2, name="matmul_inside"), x, name="matmul_outside") + b    y = tf.nn.relu(z, name="relu")    init_op = tf.global_variables_initializer()    with tf.Session() as sess:        sess.run(init_op)        sess.run([y], feed_dict={x: [[1.0], [1.0]]})        graph = tf.get_default_graph()        tensor1 = graph.get_tensor_by_name("W2:0")        tensor2 = graph.get_tensor_by_name("x:0")        print(tensor1, graph.is_feedable("W2:0"), graph.is_fetchable(tensor1))        print(tensor2, graph.is_feedable(tensor2), graph.is_fetchable("x:0"))        op1 = graph.get_operation_by_name("relu")        op2 = graph.get_operation_by_name("W")        print("op1", graph.is_feedable("relu"), graph.is_fetchable(op1))        print("op2", graph.is_feedable(op2), graph.is_fetchable("W"))'''结果:Tensor("W2:0", shape=(2, 2), dtype=float32_ref) True TrueTensor("x:0", shape=(2, 1), dtype=float32) True Trueop1 True Trueop2 True True'''

• tf.Graph.device()

'''作用: 在with上下文管理器中使用, 在with下创建的Op使用graph.device()指定的设备参数:    device_name_or_function: 设备的名称, 形如"/gpu:0"或"/cpu:0", 不能使用list传递多个'''with tf.Graph().as_default():    graph = tf.get_default_graph()    with graph.device("/cpu:0"):        a = tf.constant(1.0)        print(a.device)'''结果:/device:CPU:0'''# 一般地, 我们直接使用tf.device()来指定op的运行设备with tf.device("/cpu:0"):    a = tf.constant(1.0)    print(a.device)'''结果:/device:CPU:0'''

• tf.Graph.name_scope()

'''作用: 返回一个上下文管理器, 为图中的Op创建层级的名称;参数:    name: 该层scope的名称注意:    1. name_scope只给Op(操作)加前缀(tf.Variable也是操作), 不会给通过tf.get_variable()获取的Variable加前缀(没有Op产生这个变量, 或者产生这个变量的Op不受管理?)    2. tf.Graph中没有variable_scope()方法'''with tf.Graph().as_default() as g:    a = tf.constant(1.0, name="a")    with g.name_scope("level1_a"):        b = tf.constant([1.1, 1.2], name="b")        c = tf.Variable(0 ,name="c")        with g.name_scope("level2_a"):            d = tf.get_variable(shape=[3, 3, 3], name="d")            e = tf.nn.relu(b, name="e")            f = tf.zeros(shape=[3, 3], dtype=tf.float32, name="f")print(a.op.name)print(b.op.name)print(c.op.name)print(d.op.name)print(e.op.name)print(f.op.name)'''结果:alevel1_a/blevel1_a/cdlevel1_a/level2_a/elevel1_a/level2_a/f可以看出, name_scope()方法只给Op加前缀, 而由get_varialbe()方法创建的d, 就没有被加前缀'''

• tf.Graph.finalize()

'''作用: 结束图, 使得图只读(read-only). 调用 g.finalize()之后,新的操作就不能够添加到g里面去了'''with tf.Graph().as_default() as g:    a = tf.constant(1.0, name="a")    g.finalize()    b = tf.constant(1.0, name="b")'''结果: (报错)RuntimeError: Graph is finalized and cannot be modified.'''