Tensorflow_入门学习_1

女爷i 2021-12-24 14:41 334阅读 0赞

# 1.0 TensorFlow graphs #

Tensorflow是基于graph based computation：

如：

a=(b\+c)∗(c\+2)

可分解为

d=b\+c

e=c\+2

a=d∗e

![704960-20190517140916407-1627017519.png][]

这样做的目的在于，d=b\+c，e=c\+2，这两个式子就可以并行地计算。

# 2.0 A Simple TensorFlow example #

import tensorflow as tf
    
    # first, create a TensorFlow constant
    const = tf.constant(2.0, name="const")
        
    # create TensorFlow variables
    b = tf.Variable(2.0, name='b')
    c = tf.Variable(1.0, name='c')
    
    # now create some operations
    d = tf.add(b, c, name='d')
    e = tf.add(c, const, name='e')
    a = tf.multiply(d, e, name='a')

声明地常量和变量可以可选地使用一个string来标记，Tensorflow会根据初始值推断变量或常量地类型，也可以使用dtype参数来显式指定类型，如*tf.float32*, *tf.int32*等，[更多类型][Link 1]

注意：

It’s important to note that, as the Python code runs through these commands, the variables haven’t actually been declared as they would have been if you just had a standard Python declaration (i.e. b = 2.0). Instead, all the constants, variables, operations and the computational graph are only created when the initialisation commands are run.

在执行上述python代码地时候，这些变量和常量并不像标准python那样立刻被声明，而是所有地常量、变量、运算和计算图(computational graph)必须在初始化命令执行时才会被创建。

# setup the variable initialisation
    init_op = tf.global_variables_initializer()

To run the operations between the variables, we need to start a TensorFlow session – *tf.Session.* The TensorFlow session is an object where all operations are run. TensorFlow was initially created in a *static* graph paradigm – in other words, first all the operations and variables are defined (the graph structure) and then these are compiled within the *tf.Session*object.

为了执行变量间的运算，需要启动一个tf.Session，所有运算都是在session对象中运行的。TensorFlow首先创建静态图。

# start the session
    with tf.Session() as sess:
        # initialise the variables
        sess.run(init_op)
        # compute the output of the graph
        a_out = sess.run(a)
        print("Variable a is {}".format(a_out))

此处a是运算(operation)而不是变量，所以可以run。

Note something cool - *在a之前还声明了d和e，在计算a之前需要先执行d和e，我们不需要显式执行d和e，Tensorflow通过数据流图(data flow graph)知道a所有的依赖，自动执行依赖的运算。使用TensorBoard的功能可以看到TensorFlow创建的graph：*

![704960-20190517144228667-1234270746.png][]

## 2.1 The TensorFlow placeholder ##

Let’s also say that we didn’t know what the value of the array *b* would be during the declaration phase of the TensorFlow problem (i.e. before the *with tf.Session() as sess*) stage. In this case, TensorFlow requires us to declare the basic structure of the data by using the *tf.placeholder* variable declaration. Let’s use it for *b*:

在Tensorflow训练问题声明阶段，我们可能不知道数组b的具体值。这种情况下，需要我们使用变量声明 *tf.placeholder* 来声明数据的基本结构。

# create TensorFlow variables
    b = tf.placeholder(tf.float32, [None, 1], name='b')

第二个参数是data的shape，placeholder可以接收一个None的size参数。

a_out = sess.run(a, feed_dict={b: np.arange(0, 10)[:, np.newaxis]})

调用session.run时需要将b的具体数据feed进去。

转载于:https://www.cnblogs.com/jasonlixuetao/p/10881002.html

[704960-20190517140916407-1627017519.png]: /images/20211223/75137b24efbd4b51a972bebce6d8d597.png
[Link 1]: https://www.tensorflow.org/api_docs/python/tf/dtypes/DType
[704960-20190517144228667-1234270746.png]: /images/20211223/76d15c6c57454105ad1686a541b71e53.png