【TensorFlow学习小组】Week 1动手任务list

作业

#21

看到有很多同学做了这道题,都很认真。这里,我注意到一个with as语句,有的学生没有使用。我在这里贴一个with as语句的优点及用法。

Python的with语句是提供一个有效的机制,让代码更简练,同时在异常产生时,清理工作更简单。

  • With语句是什么?

有一些任务,可能事先需要设置,事后做清理工作。对于这种场景,Python的with语句提供了一种非常方便的处理方式。一个很好的例子是文件处理,你需要获取一个文件句柄,从文件中读取数据,然后关闭文件句柄。 如果不用with语句,代码如下:

file = open("/tmp/datageekers.txt")
data = file.read()
file.close()

这里有两个问题。一是可能忘记关闭文件句柄;二是文件读取数据发生异常,没有进行任何处理。下面是处理异常的加强版本:

file = open("/tmp/datageekers.txt")
try:
    data = file.read()
finally:
    file.close()

虽然这段代码运行良好,但是太冗长了。这时候就是with一展身手的时候了。除了有更优雅的语法,with还可以很好的处理上下文环境产生的异常。下面是with版本的代码:

with open("/tmp/datageekers.txt") as file:
    data = file.read()
  • with如何工作?

这看起来充满魔法,但不仅仅是魔法,Python对with的处理还很聪明。基本思想是with所求值的对象必须有一个__enter__()方法,一个__exit__()方法。

紧跟with后面的语句被求值后,返回对象的__enter__()方法被调用,这个方法的返回值将被赋值给as后面的变量。当with后面的代码块全部被执行完之后,将调用前面返回对象的__exit__()方法。

下面例子可以具体说明with如何工作:

#!/usr/bin/env python
# with_example01.py
 
class Sample:
    def __enter__(self):
        print "In __enter__()"
        return "datageekers"
 
    def __exit__(self, type, value, trace):
        print "In __exit__()"
 
def get_sample():
    return Sample()
 
with get_sample() as sample:
    print "sample:", sample

输出结果如下:

In __enter__()
sample: datageekers
In __exit__()

正如你看到的,

  1. enter()方法被执行
  2. enter()方法返回的值 - 这个例子中是"datageekers",赋值给变量’sample’
  3. 执行代码块,打印变量"sample"的值为 “datageekers”
  4. exit()方法被调用 with真正强大之处是它可以处理异常。可能你已经注意到Sample类的__exit__方法有三个参数- val, type 和 trace。 这些参数在异常处理中相当有用。我们来改一下代码,看看具体如何工作的。
#!/usr/bin/env python
# with_example02.py
 
class Sample:
    def __enter__(self):
        return self
 
    def __exit__(self, type, value, trace):
        print "type:", type
        print "value:", value
        print "trace:", trace
 
    def do_something(self):
        bar = 1/0
        return bar + 10
 
with Sample() as sample:
    sample.do_something()

这个例子中,with后面的get_sample()变成了Sample()。这没有任何关系,只要紧跟with后面的语句所返回的对象有__enter__()和__exit__()方法即可。此例中,Sample()的__enter__()方法返回新创建的Sample对象,并赋值给变量sample。 执行结果:

bash-3.2$ ./with_example02.py
type: <type 'exceptions.ZeroDivisionError'>
value: integer division or modulo by zero
trace: <traceback object at 0x1004a8128>
Traceback (most recent call last):
  File "./with_example02.py", line 19, in <module>
    sample.do_something()
  File "./with_example02.py", line 15, in do_something
    bar = 1/0
ZeroDivisionError: integer division or modulo by zero

实际上,在with后面的代码块抛出任何异常时,exit()方法被执行。正如例子所示,异常抛出时,与之关联的type,value和stack trace传给__exit__()方法,因此抛出的ZeroDivisionError异常被打印出来了。开发库时,清理资源,关闭文件等等操作,都可以放在__exit__方法当中。

##官方文档

###8.5. The with statement

The with statement is used to wrap the execution of a block with methods defined by a context manager (see section With Statement Context Managers). This allows common try…except…finally usage patterns to be encapsulated for convenient reuse.

with_stmt ::=  "with" with_item ("," with_item)* ":" suite
with_item ::=  expression ["as" target]

The execution of the with statement with one “item” proceeds as follows:

  • The context expression (the expression given in the with_item) is evaluated to obtain a context manager.

  • The context manager’s exit() is loaded for later use.

  • The context manager’s enter() method is invoked.

  • If a target was included in the with statement, the return value from enter() is assigned to it.

  • Note The with statement guarantees that if the enter() method returns without an error, then exit() will always be called. Thus, if an error occurs during the assignment to the target list, it will be treated the same as an error occurring within the suite would be. See step 6 below.

  • The suite is executed.

  • The context manager’s exit() method is invoked. If an exception caused the suite to be exited, its type, value, and traceback are passed as arguments to exit(). Otherwise, three None arguments are supplied. If the suite was exited due to an exception, and the return value from the exit() method was false, the exception is reraised. If the return value was true, the exception is suppressed, and execution continues with the statement following the with statement. If the suite was exited for any reason other than an exception, the return value from exit() is ignored, and execution proceeds at the normal location for the kind of exit that was taken.

With more than one item, the context managers are processed as if multiple with statements were nested:

with A() as a, B() as b:
    suite
is equivalent to

with A() as a:
    with B() as b:
        suite

Changed in version 3.1: Support for multiple context expressions.

参考文献: [1] https://stackoverflow.com/questions/1369526/what-is-the-python-keyword-with-used-for [2] https://docs.python.org/3/reference/compound_stmts.html#the-async-with-statement [3] http://preshing.com/20110920/the-python-with-statement-by-example/ [4] https://www.cnblogs.com/DswCnblog/p/6126588.html


#22

交作业啦啦啦啦

使用tf计算下面算式的值

x=2, y=3, z=7 求解: res = x*y +z;

Vim 环境下编写代码 , 新建文件 vim T02_week1_task1.py

# 使用tf计算下面算式的值
# x=2, y=3, z=7 求解: res = x*y +z;


import tensorflow as tf

x = tf.constant(2)
y = tf.constant(3)
z = tf.constant(7)

sess = tf.Session()
res = sess.run((x * y) + z)

# print
print(res)

输出结果:

13

矩阵乘法

A: [[3., 3.]]
B: [[2.], [2.]] A矩阵和B矩阵的乘法运算。

Vim 环境下编写代码, vim T03_week1_task2.py

import tensorflow as tf

# define A 、 B  Matrix
A = tf.constant([[3., 3.]])
B = tf.constant([[2.], [2.]])

# create sesstion
sess = tf.Session()
res = tf.matmul(A,B)

# print result
print(sess.run(res))

运行结果:

[[ 12.]]

#23

1、使用tf计算下面算式的值: x=2 ,y=3,z=7 求解:res=x*y+z的结果

import tensorflow as tf

x = tf.constant(2) y = tf.constant(3) z = tf.constant(7)

with tf.Session() as sess: result = sess.run(x*y+z) print(result) 运行结果: 13

2、使用tf计算求解,矩阵乘法结果: 矩阵乘法: A :[[3., 3.]] B: [[2.],[2.]] A矩阵和B矩阵的乘法运算

import tensorflow as tf

A = tf.constant([[3.,3.]]) B = tf.constant([[2.],[2.]])

with tf.Session() as sess: result = sess.run(tf.matmul(A, B)) print(result)

运行结果: [[ 12.]]


#24

import tensorflow as tf

x = tf.constant(2) y = tf.constant(3) z = tf.constant(7)

res = tf.add(tf.multiply(x,y,name = None),z,name = None)

with tf.Session() as sess: print(sess.run(res))

with tf.Session() as sess: print(sess.run(tf.matmul([[3,3]],[[2],[2]])))


#25

[happywsw]

1.使用tf计算下面算式的值:x = 2, y = 3, z = 7, 求解:res = x * y + z的结果

import tensorflow as tf

x = tf.constant(2)
y = tf.constant(3)	
z = tf.constant(7)

res = x * y + z

with tf.Session() as sess: 
	print(sess.run(res))      

运行结果为:

13

2.使用tf计算求解,矩阵乘法结果: 矩阵乘法: A:[[3., 3.]] B:[[2.],[2.]] A矩阵和B矩阵的乘法运算

import tensorflow as tf

A = tf.constant([[3., 3.]])
B = tf.constant([[2.], [2.]])

result = tf.matmul(A, B)

with tf.Session() as sess:
	print(sess.run(result))   

运行结果为:

[[ 12.]]