1. Fully connected layer(dense layer)

all the neurons are connected

2. Convolution layer

就是过滤器在前一层滑动，每一个位置就对应一个值（点乘）。过滤器的深度就等于前一层的深度。

output of convolution layer 叫做 activation map。注意：一个filter产生的输出就叫做一个activation map

一开始的卷积层学一些lower feature，靠后的卷积层学一些higher feature

过滤之后的长度计算公式： ((N - F) / stride + 1)

此处有几个超参数：

1. stride(1 2)
2. size of filter(3 5 7 …)
3. number of filter(power of 2)
4. amount of padding(sizeOfFilter // 2)

3. Pooling layer

makes the representation smaller and more manageable.

Pooling Operates over each activation map independently.

Pooling主要两种

• max
• avg

Pooling层的参数主要就是

1. size of filter
2. stride

在pooling层，size of filter = stride