python 的語(yǔ)法定義和C++、matlab、java 還是很有區(qū)別的。

1. 括號(hào)與函數(shù)調(diào)用

def devided_3(x):   return x/3.

print(a)    #不帶括號(hào)調(diào)用的結(jié)果：<function a at 0x139c756a8>
print(a(3)) #帶括號(hào)調(diào)用的結(jié)果：1

不帶括號(hào)時(shí)，調(diào)用的是函數(shù)在內(nèi)存在的首地址； 帶括號(hào)時(shí)，調(diào)用的是函數(shù)在內(nèi)存區(qū)的代碼塊，輸入?yún)?shù)后執(zhí)行函數(shù)體。

2. 括號(hào)與類調(diào)用

class test():  y = 'this is out of __init__()'  def __init__(self):    self.y = 'this is in the __init__()' x = test  # x是類位置的首地址print(x.y) # 輸出類的內(nèi)容：this is out of __init__()x = test() # 類的實(shí)例化print(x.y) # 輸出類的屬性：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 funcfunc(2018)(11)>>> this is func with another func as return  2018  this is another function  11

其實(shí)，這種情況最常用在卷積神經(jīng)網(wǎng)絡(luò)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

總結(jié)

以上所述是小編給大家介紹的Python 中 function(#) (X)格式 和 (#)在Python3.*中的注意，希望對(duì)大家有所幫助，如果大家有任何疑問(wèn)請(qǐng)給我留言，小編會(huì)及時(shí)回復(fù)大家的。在此也非常感謝大家對(duì)武林站長(zhǎng)站網(wǎng)站的支持！