什么是conv3，conv4，conv5输出VGG16？
什么是conv3，conv4，conv5输出VGG16？
提问于 2020-07-13 11:20:43
一些研究论文提到，他们使用了在Imagenet上训练的conv3、conv4、conv5网络的输出。
如果我像这样显示VGG16层的名称：
base_model = tf.keras.applications.VGG16(input_shape=[h, h, 3], include_top=False)
base_model.summary()我得到了不同名字的层次。
input_1 (InputLayer) [(None, 512, 512, 3)] 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 512, 512, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 512, 512, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 256, 256, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 256, 256, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 256, 256, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 128, 128, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 128, 128, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 128, 128, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 128, 128, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 64, 64, 256) 0
.....那么，它们指的是conv3、conv4、conv5等层吗？它们是指每个池之前的第3层、第4层、第5层(因为vgg16有5个阶段)吗？
回答 1
Stack Overflow用户
回答已采纳
发布于 2020-07-15 14:20:33
VGG16的体系结构可以通过如下代码获得：
import tensorflow as tf
from tensorflow.keras.applications import VGG16
model = VGG16(include_top=False, weights = 'imagenet')
print(model.summary())VGG16的架构如下所示：
Model: "vgg16"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, None, None, 3)] 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, None, None, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, None, None, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, None, None, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, None, None, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, None, None, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, None, None, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, None, None, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, None, None, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, None, None, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, None, None, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, None, None, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, None, None, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, None, None, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, None, None, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, None, None, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, None, None, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, None, None, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, None, None, 512) 0
=================================================================
Total params: 14,714,688
Trainable params: 14,714,688
Non-trainable params: 0从上面的体系结构来看，在通用意义上的，
Conv3表示Layer的输出，block3_pool (MaxPooling2D)Conv4表示Layer的输出，block4_pool (MaxPooling2D)Conv5表示Layer、block5_pool (MaxPooling2D)
的输出
如果你觉得我提供的解释是不正确的，请分享你所指的Research Papers，我可以相应地更新答案。
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/62874773
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