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

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 funcfunc(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

总结

以上所述是小编给大家介绍的Python 中 function(#) (X)格式 和 (#)在Python3.*中的注意，希望对大家有所帮助，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对网站的支持！