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
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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
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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)
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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，這種情況最常用在卷積神經網絡中：
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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
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，希望對大家有所幫助，如果大家有任何疑問請給我留言，小編會及時回復大家的。在此也非常感謝大家對服務器之家網站的支持！
原文鏈接：https://blog.csdn.net/shenziheng1/article/details/84646453
