python 的語法定義和C++、matlab、java 還是很有區別的。

1. 括號與函數調用

def devided_3(x):return x/3.print(a) #不帶括號調用的結果：<function a at 0x139c756a8> print(a(3)) #帶括號調用的結果：1

不帶括號時，調用的是函數在內存在的首地址； 帶括號時，調用的是函數在內存區的代碼塊，輸入參數后執行函數體。

2. 括號與類調用

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) # 輸出類的內容：this is out of __init__()x = test() # 類的實例化 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 return2018this is another function11

其實，這種情況最常用在卷積神經網絡中：

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 zeroesX = ZeroPadding2D((3, 3))(X_input)# CONV -> BN -> RELU Block applied to XX = Conv2D(32, (7, 7), strides = (1, 1), name = 'conv0')(X)X = BatchNormalization(axis = 3, name = 'bn0')(X)X = Activation('relu')(X)# MAXPOOLX = MaxPooling2D((2, 2), name='max_pool')(X)# FLATTEN X (means convert it to a vector) + FULLYCONNECTEDX = 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

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