def devided_3(x):
   return x/3.

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__() ;

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

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