python keras语法Conv2D

Question

我对python和深度学习都很陌生。从其中一个kaggle内核中我看到了以下代码，并且我试图理解这种语法背后的逻辑：

c1 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (s)
c1 = Dropout(0.1) (c1)
c1 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c1)
p1 = MaxPooling2D((2, 2)) (c1)

c2 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p1)
c2 = Dropout(0.1) (c2)
c2 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c2)
p2 = MaxPooling2D((2, 2)) (c2)

我不理解陈述末尾的(s), (c1), (c2) ......有人可以解释一下这背后的逻辑吗？

Answer 1

（s），（c1），（c2）是corrrsponding层的输入