YOLO的conv层和dense层之间应该有一个flat layer吗？

数据挖掘 机器学习 深度学习 神经网络 卷积神经网络 约洛
2021-09-28 12:03:24

YOLO的conv层和dense层之间应该有一个flat layer吗？

这是论文中没有具体说明的东西，但我看到 github 上的大多数 YOLO 实现都是这样做的。在我的实现中，我没有展平 7*7*1024 特征图，而是直接在其后添加一个 Dense(4096) 层（我使用的是带有 tensorflow 后端的 keras）。

有问题的代码：

x = Conv2D(filters=1024, kernel_size=(3,3), padding='same', activation=leaky_relu)(x)
# x = Flatten()(x) # not using this right now
x = Dense(4096, activation=leaky_relu)(x)
x = Dense(30)(x)

我的 model.summary 输出如下：

conv2d_294 (Conv2D)          (None, 14, 14, 1024)      9438208   
_________________________________________________________________
conv2d_295 (Conv2D)          (None, 7, 7, 1024)        9438208   
_________________________________________________________________
conv2d_296 (Conv2D)          (None, 7, 7, 1024)        9438208   
_________________________________________________________________
conv2d_297 (Conv2D)          (None, 7, 7, 1024)        9438208   
_________________________________________________________________
dense_14 (Dense)             (None, 7, 7, 4096)        4198400   
_________________________________________________________________
dense_15 (Dense)             (None, 7, 7, 30)          122910

我觉得这种实现比平面实现更正确（除非作者另有说明），因为在平面化输出时，我们丢失了预测边界框和网格中每个元素的置信度分数所需的空间信息。

3个回答

不确定这对您的项目是否仍然重要，但它很重要：密集层不会首先展平条目！它采用入口张量的最后一维并将其连接到密集层的神经元。确保有一件简单的事情要做：计算层的参数数量。在您的情况下，它是：4096（密集神经元的数量）* 1024（Conv 层的最后一个维度）+ 4096（密集神经元的偏差）= 4198400。

不管有没有展平，密集层都将整个前一层作为输入。不再使用空间结构信息。一些神经网络实现可能无法将空间结构直接映射到密集层，这就是为什么您需要在两者之间使用 Flatten。在这种情况下，数学上是完全相同的。

编辑：正如我所料，在Keras的源代码中提到，如果排名像卷积一样更高，他们会先隐式地将其展平。

coursera 上，Andrew Ng 教授第三周的卷积网络作业就是关于这个的。我建议你看看那个作业。它还实现了该YOLO文件。我这里不能加代码，但是网络的架构如下：

____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
input_1 (InputLayer)             (None, 608, 608, 3)   0                                            
____________________________________________________________________________________________________
conv2d_1 (Conv2D)                (None, 608, 608, 32)  864         input_1[0][0]                    
____________________________________________________________________________________________________
batch_normalization_1 (BatchNorm (None, 608, 608, 32)  128         conv2d_1[0][0]                   
____________________________________________________________________________________________________
leaky_re_lu_1 (LeakyReLU)        (None, 608, 608, 32)  0           batch_normalization_1[0][0]      
____________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)   (None, 304, 304, 32)  0           leaky_re_lu_1[0][0]              
____________________________________________________________________________________________________
conv2d_2 (Conv2D)                (None, 304, 304, 64)  18432       max_pooling2d_1[0][0]            
____________________________________________________________________________________________________
batch_normalization_2 (BatchNorm (None, 304, 304, 64)  256         conv2d_2[0][0]                   
____________________________________________________________________________________________________
leaky_re_lu_2 (LeakyReLU)        (None, 304, 304, 64)  0           batch_normalization_2[0][0]      
____________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D)   (None, 152, 152, 64)  0           leaky_re_lu_2[0][0]              
____________________________________________________________________________________________________
conv2d_3 (Conv2D)                (None, 152, 152, 128) 73728       max_pooling2d_2[0][0]            
____________________________________________________________________________________________________
batch_normalization_3 (BatchNorm (None, 152, 152, 128) 512         conv2d_3[0][0]                   
____________________________________________________________________________________________________
leaky_re_lu_3 (LeakyReLU)        (None, 152, 152, 128) 0           batch_normalization_3[0][0]      
____________________________________________________________________________________________________
conv2d_4 (Conv2D)                (None, 152, 152, 64)  8192        leaky_re_lu_3[0][0]              
____________________________________________________________________________________________________
batch_normalization_4 (BatchNorm (None, 152, 152, 64)  256         conv2d_4[0][0]                   
____________________________________________________________________________________________________
leaky_re_lu_4 (LeakyReLU)        (None, 152, 152, 64)  0           batch_normalization_4[0][0]      
____________________________________________________________________________________________________
conv2d_5 (Conv2D)                (None, 152, 152, 128) 73728       leaky_re_lu_4[0][0]              
____________________________________________________________________________________________________
batch_normalization_5 (BatchNorm (None, 152, 152, 128) 512         conv2d_5[0][0]                   
____________________________________________________________________________________________________
leaky_re_lu_5 (LeakyReLU)        (None, 152, 152, 128) 0           batch_normalization_5[0][0]      
____________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D)   (None, 76, 76, 128)   0           leaky_re_lu_5[0][0]              
____________________________________________________________________________________________________
conv2d_6 (Conv2D)                (None, 76, 76, 256)   294912      max_pooling2d_3[0][0]            
____________________________________________________________________________________________________
batch_normalization_6 (BatchNorm (None, 76, 76, 256)   1024        conv2d_6[0][0]                   
____________________________________________________________________________________________________
leaky_re_lu_6 (LeakyReLU)        (None, 76, 76, 256)   0           batch_normalization_6[0][0]      
____________________________________________________________________________________________________
conv2d_7 (Conv2D)                (None, 76, 76, 128)   32768       leaky_re_lu_6[0][0]              
____________________________________________________________________________________________________
batch_normalization_7 (BatchNorm (None, 76, 76, 128)   512         conv2d_7[0][0]                   
____________________________________________________________________________________________________
leaky_re_lu_7 (LeakyReLU)        (None, 76, 76, 128)   0           batch_normalization_7[0][0]      
____________________________________________________________________________________________________
conv2d_8 (Conv2D)                (None, 76, 76, 256)   294912      leaky_re_lu_7[0][0]              
____________________________________________________________________________________________________
batch_normalization_8 (BatchNorm (None, 76, 76, 256)   1024        conv2d_8[0][0]                   
____________________________________________________________________________________________________
leaky_re_lu_8 (LeakyReLU)        (None, 76, 76, 256)   0           batch_normalization_8[0][0]      
____________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D)   (None, 38, 38, 256)   0           leaky_re_lu_8[0][0]              
____________________________________________________________________________________________________
conv2d_9 (Conv2D)                (None, 38, 38, 512)   1179648     max_pooling2d_4[0][0]            
____________________________________________________________________________________________________
batch_normalization_9 (BatchNorm (None, 38, 38, 512)   2048        conv2d_9[0][0]                   
____________________________________________________________________________________________________
leaky_re_lu_9 (LeakyReLU)        (None, 38, 38, 512)   0           batch_normalization_9[0][0]      
____________________________________________________________________________________________________
conv2d_10 (Conv2D)               (None, 38, 38, 256)   131072      leaky_re_lu_9[0][0]              
____________________________________________________________________________________________________
batch_normalization_10 (BatchNor (None, 38, 38, 256)   1024        conv2d_10[0][0]                  
____________________________________________________________________________________________________
leaky_re_lu_10 (LeakyReLU)       (None, 38, 38, 256)   0           batch_normalization_10[0][0]     
____________________________________________________________________________________________________
conv2d_11 (Conv2D)               (None, 38, 38, 512)   1179648     leaky_re_lu_10[0][0]             
____________________________________________________________________________________________________
batch_normalization_11 (BatchNor (None, 38, 38, 512)   2048        conv2d_11[0][0]                  
____________________________________________________________________________________________________
leaky_re_lu_11 (LeakyReLU)       (None, 38, 38, 512)   0           batch_normalization_11[0][0]     
____________________________________________________________________________________________________
conv2d_12 (Conv2D)               (None, 38, 38, 256)   131072      leaky_re_lu_11[0][0]             
____________________________________________________________________________________________________
batch_normalization_12 (BatchNor (None, 38, 38, 256)   1024        conv2d_12[0][0]                  
____________________________________________________________________________________________________
leaky_re_lu_12 (LeakyReLU)       (None, 38, 38, 256)   0           batch_normalization_12[0][0]     
____________________________________________________________________________________________________
conv2d_13 (Conv2D)               (None, 38, 38, 512)   1179648     leaky_re_lu_12[0][0]             
____________________________________________________________________________________________________
batch_normalization_13 (BatchNor (None, 38, 38, 512)   2048        conv2d_13[0][0]                  
____________________________________________________________________________________________________
leaky_re_lu_13 (LeakyReLU)       (None, 38, 38, 512)   0           batch_normalization_13[0][0]     
____________________________________________________________________________________________________
max_pooling2d_5 (MaxPooling2D)   (None, 19, 19, 512)   0           leaky_re_lu_13[0][0]             
____________________________________________________________________________________________________
conv2d_14 (Conv2D)               (None, 19, 19, 1024)  4718592     max_pooling2d_5[0][0]            
____________________________________________________________________________________________________
batch_normalization_14 (BatchNor (None, 19, 19, 1024)  4096        conv2d_14[0][0]                  
____________________________________________________________________________________________________
leaky_re_lu_14 (LeakyReLU)       (None, 19, 19, 1024)  0           batch_normalization_14[0][0]     
____________________________________________________________________________________________________
conv2d_15 (Conv2D)               (None, 19, 19, 512)   524288      leaky_re_lu_14[0][0]             
____________________________________________________________________________________________________
batch_normalization_15 (BatchNor (None, 19, 19, 512)   2048        conv2d_15[0][0]                  
____________________________________________________________________________________________________
leaky_re_lu_15 (LeakyReLU)       (None, 19, 19, 512)   0           batch_normalization_15[0][0]     
____________________________________________________________________________________________________
conv2d_16 (Conv2D)               (None, 19, 19, 1024)  4718592     leaky_re_lu_15[0][0]             
____________________________________________________________________________________________________
batch_normalization_16 (BatchNor (None, 19, 19, 1024)  4096        conv2d_16[0][0]                  
____________________________________________________________________________________________________
leaky_re_lu_16 (LeakyReLU)       (None, 19, 19, 1024)  0           batch_normalization_16[0][0]     
____________________________________________________________________________________________________
conv2d_17 (Conv2D)               (None, 19, 19, 512)   524288      leaky_re_lu_16[0][0]             
____________________________________________________________________________________________________
batch_normalization_17 (BatchNor (None, 19, 19, 512)   2048        conv2d_17[0][0]                  
____________________________________________________________________________________________________
leaky_re_lu_17 (LeakyReLU)       (None, 19, 19, 512)   0           batch_normalization_17[0][0]     
____________________________________________________________________________________________________
conv2d_18 (Conv2D)               (None, 19, 19, 1024)  4718592     leaky_re_lu_17[0][0]             
____________________________________________________________________________________________________
batch_normalization_18 (BatchNor (None, 19, 19, 1024)  4096        conv2d_18[0][0]                  
____________________________________________________________________________________________________
leaky_re_lu_18 (LeakyReLU)       (None, 19, 19, 1024)  0           batch_normalization_18[0][0]     
____________________________________________________________________________________________________
conv2d_19 (Conv2D)               (None, 19, 19, 1024)  9437184     leaky_re_lu_18[0][0]             
____________________________________________________________________________________________________
batch_normalization_19 (BatchNor (None, 19, 19, 1024)  4096        conv2d_19[0][0]                  
____________________________________________________________________________________________________
conv2d_21 (Conv2D)               (None, 38, 38, 64)    32768       leaky_re_lu_13[0][0]             
____________________________________________________________________________________________________
leaky_re_lu_19 (LeakyReLU)       (None, 19, 19, 1024)  0           batch_normalization_19[0][0]     
____________________________________________________________________________________________________
batch_normalization_21 (BatchNor (None, 38, 38, 64)    256         conv2d_21[0][0]                  
____________________________________________________________________________________________________
conv2d_20 (Conv2D)               (None, 19, 19, 1024)  9437184     leaky_re_lu_19[0][0]             
____________________________________________________________________________________________________
leaky_re_lu_21 (LeakyReLU)       (None, 38, 38, 64)    0           batch_normalization_21[0][0]     
____________________________________________________________________________________________________
batch_normalization_20 (BatchNor (None, 19, 19, 1024)  4096        conv2d_20[0][0]                  
____________________________________________________________________________________________________
space_to_depth_x2 (Lambda)       (None, 19, 19, 256)   0           leaky_re_lu_21[0][0]             
____________________________________________________________________________________________________
leaky_re_lu_20 (LeakyReLU)       (None, 19, 19, 1024)  0           batch_normalization_20[0][0]     
____________________________________________________________________________________________________
concatenate_1 (Concatenate)      (None, 19, 19, 1280)  0           space_to_depth_x2[0][0]          
                                                                   leaky_re_lu_20[0][0]             
____________________________________________________________________________________________________
conv2d_22 (Conv2D)               (None, 19, 19, 1024)  11796480    concatenate_1[0][0]              
____________________________________________________________________________________________________
batch_normalization_22 (BatchNor (None, 19, 19, 1024)  4096        conv2d_22[0][0]                  
____________________________________________________________________________________________________
leaky_re_lu_22 (LeakyReLU)       (None, 19, 19, 1024)  0           batch_normalization_22[0][0]     
____________________________________________________________________________________________________
conv2d_23 (Conv2D)               (None, 19, 19, 425)   435625      leaky_re_lu_22[0][0]             
====================================================================================================
Total params: 50,983,561
Trainable params: 50,962,889
Non-trainable params: 20,672

像你一样，我看不到任何展平层。

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