Python 中 function(#) (X)格式和 (#)在Python3.*中的注意事项_Python

Python 中 function(#) (X)格式和 (#)在Python3.*中的注意事项

2021-04-24 00:18沈子恒 Python

这篇文章主要介绍了Python 中 function(#) (X)格式和 (#)在Python3.*中的注意事项，非常不错，具有一定的参考借鉴价值,需要的朋友可以参考下

python 的语法定义和C++、matlab、java 还是很有区别的。

1. 括号与函数调用

1 2	`def` `devided_3(x):` `return` `x/3.`

print(a) #不带括号调用的结果：<function a at 0x139c756a8>
print(a(3)) #带括号调用的结果：1

不带括号时，调用的是函数在内存在的首地址；带括号时，调用的是函数在内存区的代码块，输入参数后执行函数体。

2. 括号与类调用

									class test():

									  y = 'this is out of __init__()'

									  def __init__(self):

									    self.y = 'this is in the __init__()'

									x = test  # x是类位置的首地址

									print(x.y) # 输出类的内容：this is out of __init__()

									x = test() # 类的实例化

									print(x.y) # 输出类的属性：this is in the __init__() ;

3. function(#) (input)

									def With_func_rtn(a):

									  print("this is func with another func as return")

									  print(a)

									  def func(b):

									    print("this is another function")

									    print(b)

									  return func

									func(2018)(11)

									>>> this is func with another func as return

									  2018

									  this is another function

									  11

其实，这种情况最常用在卷积神经网络中：

									def model(input_shape):

									  # Define the input placeholder as a tensor with shape input_shape.

									  X_input = Input(input_shape)

									  # Zero-Padding: pads the border of X_input with zeroes

									  X = ZeroPadding2D((3, 3))(X_input)

									  # CONV -> BN -> RELU Block applied to X

									  X = Conv2D(32, (7, 7), strides = (1, 1), name = 'conv0')(X)

									  X = BatchNormalization(axis = 3, name = 'bn0')(X)

									  X = Activation('relu')(X)

									  # MAXPOOL

									  X = MaxPooling2D((2, 2), name='max_pool')(X)

									  # FLATTEN X (means convert it to a vector) + FULLYCONNECTED

									  X = Flatten()(X)

									  X = Dense(1, activation='sigmoid', name='fc')(X)

									  # Create model. This creates your Keras model instance, you'll use this instance to train/test the model.

									  model = Model(inputs = X_input, outputs = X, name='HappyModel')

									  return model