TensorFlow2:基础组件

悠悠 2023-06-27 06:15 81阅读 0赞

TensorFlow由两部分组成，Tensor(张量) 和 Flow(流，计算图)

# 1.Tensor(张量) #

In TensorFlow, data isn’t stored as integers, floats, or strings. These values are encapsulated in an object called a tensor.

TensorFlow使用Tensor来表示所有的数据，Tensor可以理解为N维数组或列表。

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x1dGVyZXNh_size_16_color_FFFFFF_t_70]

# 2.Session #

TensorFlow’s api is built around the idea of a computational graph, a way of visualizing a mathematical process. Let’s take the TensorFlow code you ran and turn that into a graph:

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x1dGVyZXNh_size_16_color_FFFFFF_t_70 1]

A “TensorFlow Session”, as shown above, is an environment for running a graph. The session is in charge of allocating the operations to GPU(s) and/or CPU(s), including remote machines.

The code creates a session instance, sess, using tf.Session. The sess.run() function then evaluates the tensor and returns the results.

**Tensor是一个张量，用于存放各种数据，多个Tensor按计算图组织数据关系，sess会话调用run（）执行计算，促使Tensor按计算图流动，得到计算结果，是为Tensor Flow。**

More in :  
[https://medium.com/tebs-lab/deep-neural-networks-as-computational-graphs-867fcaa56c9?][https_medium.com_tebs-lab_deep-neural-networks-as-computational-graphs-867fcaa56c9]

# 3.常量 #

Tensor(“Const\_12:0”, shape=(), dtype=string)  
Tensor(“Const\_13:0”, shape=(), dtype=int32)  
<class ‘tensorflow.python.framework.ops.Tensor’>

session = tf.Session()
    print(session.run(hello))

b’hello’

print(str(session.run(hello),'utf-8'))

hello

print(session.run(n))

### 指定常量的数据类型 ###

str_value = tf.constant('hello tensorflow!', dtype=tf.string)
    print(str(session.run(str_value),'utf-8'))

hello tensorflow!

print(session.run(tf.constant(32,tf.int32)))

### 常量运算 ###

x = tf.constant(20)
    y = tf.constant(60)
    
    session = tf.Session()
    add = tf.add(x,y)
    sub = tf.subtract(x,y)
    mul = tf.multiply(x,y)
    div = tf.divide(x,y)

print(session.run(add))
    print(session.run(sub))
    print(session.run(mul))
    print(session.run(div))

80  
\-40  
1200  
0.3333333333333333

# x*x + x*y + 10
    result = x * x + x*y +10
    value = tf.constant(20)
    print(session.run(result))

1610

# 4.占位符,Input #

不指定值，可以重用tensor，必须指定类型；

Tf.placeholder: 传入普通变量，返回一个tensor，tensor用于tf.session.run()。

如果feed\_dict与tensor类型不匹配且不能强制转换， you’ll get the error “ValueError: invalid literal for…”.

input1 = tf.placeholder(tf.int32)
    input2 = tf.placeholder(tf.int32)
    
    output = tf.add(input1, input2)
    session = tf.Session()
    
    # print(session.run(output)) 直接运行，出错
    print(session.run(output, feed_dict={ input1:2, input2:3}))
    print(session.run(output, feed_dict={ input1:10, input2:20}))
    print(session.run(output, feed_dict={ input1:[1,2,3], input2:[2,3,4]}))

5  
30  
\[3 5 7\]

# 5.变量 #

x = tf.Variable(2)
    y = tf.Variable(3)
    
    f = x*x + y +3
    
    session = tf.Session()
    session.run(x.initializer) # 必须调用run, 初始化变量
    session.run(y.initializer)
    session.run(y)

session.run(f)

x.load(10,session)
    y.load(20,session)
    session.run(y)

session.run(f)

123

session.close()

with tf.Session() as session:
        y.initializer.run()
        x.initializer.run()
        r = f.eval() #
    print(r)

并不会一下子初始化，而是在计算图中创建一个节点，这个节点会在绘画执行时初始化所有变量；惰性初始化

一个TensorFlow程序分为两部分：  
1.用于构建计算图的部分；  
2.用于执行计算图部分；  
我们的主要工作是构建计算图。

init = tf.global_variables_initializer()
    with tf.Session() as sess:
        init.run()
        ret = f.eval() #执行计算图
    print(ret)

# 6.TensorFlow运算函数 #

官网API: [https://www.tensorflow.org/api\_docs/python/tf/math][https_www.tensorflow.org_api_docs_python_tf_math]

import tensorflow as tf
    import os
    
    #os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' #默认显示所有信息
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # 只显示警告和错误
    #os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # 只显示错误
    
    x = tf.Variable(10)
    y = tf.Variable(20)
    z = tf.Variable(-5)
    
    value = tf.Variable(2.5,dtype=tf.float32)
    init = tf.global_variables_initializer()
    
    with tf.Session() as session:
        init.run()
        print(session.run(tf.add(x,y)))
        print(session.run(tf.subtract(x,y)))
        print(session.run(tf.multiply(x,y)))
        print(session.run(tf.divide(x,y)))
        print(session.run(tf.mod(x,y)))
        print(session.run(tf.abs(x)))
        print(session.run(tf.negative(x)))
        print(session.run(tf.square(x)))
        print(session.run(tf.sqrt(value))) #不能是整数
        print(session.run(tf.sign(z)))
        print(session.run(tf.sin(value))) #float

30  
\-10  
200  
0.5  
10  
10  
\-10  
100  
1.5811388  
\-1  
0.5984721

### 类型转换 ###

tf.subtract(tf.constant(2.0),tf.constant(1)) \# Fails with ValueError: Tensor conversion requested dtype float32 for Tensor with dtype int32:

保持表达式类型一致，或强制转换

tf.subtract(tf.cast(tf.constant(2.0), tf.int32), tf.constant(1)) \# 1

# 7.管理计算图 #

import tensorflow as tf
    import os
    
    #os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' #默认显示所有信息
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # 只显示警告和错误
    #os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # 只显示错误
    
    sess = tf.Session()
    x1 = tf.Variable(5, name='x1')
    sess.run(x1.initializer)
    
    print(tf.get_default_graph().get_operation_by_name('x1'))
    print(tf.get_default_graph().get_operation_by_name('x1').get_attr('dtype'))
    print(x1.graph is tf.get_default_graph())
    
    #创建一个新计算图
    graph = tf.Graph()
    with graph.as_default():
        x2 = tf.Variable(20)
        print(x2.graph is graph)
    
    print(x2.graph is tf.get_default_graph())

name: “x1”  
op: “VariableV2”  
attr \{  
key: “container”  
value \{  
s: “”  
\}  
\}  
attr \{  
key: “dtype”  
value \{  
type: DT\_INT32  
\}  
\}  
attr \{  
key: “shape”  
value \{  
shape \{  
\}  
\}  
\}  
attr \{  
key: “shared\_name”  
value \{  
s: “”  
\}  
\}

<dtype: ‘int32’>  
True  
True  
False

# 8.计算图节点的依赖与生命周期 #

import tensorflow as tf
    import os
    
    #os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' #默认显示所有信息
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # 只显示警告和错误
    #os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # 只显示错误
    
    k = tf.constant(333)
    x = k + 1
    y = x + 2
    z = x + 5
    
    # 依赖重复计算两次
    with tf.Session() as sess:
        print(y.eval())
        print(z.eval())
    #依赖只计算一次
    with tf.Session() as sess:
        y_val, z_val = sess.run([y,z])
        print(y_val, z_val)
    #在计算图每次执行时，所有节点都会被丢弃，但变量不会，变量是由Session维护，除非关闭Session,否则变量一直存在

335  
339  
335 339

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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x1dGVyZXNh_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/20200311160311163.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x1dGVyZXNh,size_16,color_FFFFFF,t_70
[https_medium.com_tebs-lab_deep-neural-networks-as-computational-graphs-867fcaa56c9]: https://medium.com/tebs-lab/deep-neural-networks-as-computational-graphs-867fcaa56c9?
[https_www.tensorflow.org_api_docs_python_tf_math]: https://www.tensorflow.org/api_docs/python/tf/math