這篇文章給大家分享的是有關(guān)python中語(yǔ)法定義的示例分析的內(nèi)容。小編覺(jué)得挺實(shí)用的,因此分享給大家做個(gè)參考,一起跟隨小編過(guò)來(lái)看看吧。
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def devided_3(x): return x/3.
print(a) #不帶括號(hào)調(diào)用的結(jié)果:
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(lèi)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(lèi)位置的首地址 print(x.y) # 輸出類(lèi)的內(nèi)容:this is out of __init__() x = test() # 類(lèi)的實(shí)例化 print(x.y) # 輸出類(lèi)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 func func(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
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