【575】连续卷积层

　　对于连续的卷积层，filter 的维度是跟输入图像的维度一致

model = Sequential([
    Conv2D(8, 3, input_shape=(28, 28, 1), use_bias=False),
    Conv2D(16, 3, use_bias=False)
])

model.summary() 

　　输出

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_3 (Conv2D)            (None, 26, 26, 8)         72        
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 24, 24, 16)        1152      
=================================================================
Total params: 1,224
Trainable params: 1,224
Non-trainable params: 0
_________________________________________________________________

　　其中：

• 第一层的filter为 3x3x1x8=72（原始数据是 28x28x1，得到数据 26x26x8）

• 第二层的filter为 3x3x8x16=1152（上一个数据是 26x26x8，得到数据 24x24x16）

标签：卷积,575,26x26x8,filter,bias,params,Conv2D,_______________________________________
来源： https://www.cnblogs.com/alex-bn-lee/p/14901087.html