Comments (4)
ypwhs
commented on July 24, 2024
因为我想构建 [2, 2, 2, 2, 2] 的结构，这样写比较方便。
你也可以写成这样：
x = Conv2D(32, kernel_size=3, padding='same', kernel_initializer='he_uniform')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(32, kernel_size=3, padding='same', kernel_initializer='he_uniform')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D(2)(x)
x = Conv2D(64, kernel_size=3, padding='same', kernel_initializer='he_uniform')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(64, kernel_size=3, padding='same', kernel_initializer='he_uniform')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D(2)(x)
x = Conv2D(128, kernel_size=3, padding='same', kernel_initializer='he_uniform')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(128, kernel_size=3, padding='same', kernel_initializer='he_uniform')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D(2)(x)
x = Conv2D(256, kernel_size=3, padding='same', kernel_initializer='he_uniform')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(256, kernel_size=3, padding='same', kernel_initializer='he_uniform')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D(2)(x)
x = Conv2D(256, kernel_size=3, padding='same', kernel_initializer='he_uniform')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(256, kernel_size=3, padding='same', kernel_initializer='he_uniform')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D(2)(x)这样写比较清晰，但是想调成其他结构比较费劲。
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MgArcher
commented on July 24, 2024
感谢您的回复！我能不能请问一下您是如何确定的模型结构呢？比如说为什么要用相同的卷积核卷积两次呢？是会加速模型收敛吗？
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ypwhs
commented on July 24, 2024
感谢您的回复！我能不能请问一下您是如何确定的模型结构呢？比如说为什么要用相同的卷积核卷积两次呢？是会加速模型收敛吗？
看论文，学习别人的模型结构设计方法，针对这个问题，可以看这篇论文：
An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
https://arxiv.org/abs/1507.05717
Submitted on 21 Jul 2015
如果希望设计通用的模型结构，可以看下面的参考资料：
模型论文
参考链接：https://paperswithcode.com/sota/image-classification-on-imagenet
VGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://arxiv.org/abs/1409.1556
Submitted on 4 Sep 2014
ResNet
Deep Residual Learning for Image Recognition
https://arxiv.org/abs/1512.03385
Submitted on 10 Dec 2015
InceptionV3
Rethinking the Inception Architecture for Computer Vision
https://arxiv.org/abs/1512.00567
Submitted on 2 Dec 2015
InceptionResNetV2
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://arxiv.org/abs/1602.07261
Submitted on 23 Feb 2016
DenseNet
Densely Connected Convolutional Networks
https://arxiv.org/abs/1608.06993
Submitted on 25 Aug 2016
Xception
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/abs/1610.02357
Submitted on 7 Oct 2016
NASNet
Neural Architecture Search with Reinforcement Learning
https://arxiv.org/abs/1611.01578
Submitted on 5 Nov 2016
AmoebaNet
Regularized Evolution for Image Classifier Architecture Search
https://arxiv.org/abs/1802.01548
Submitted on 5 Feb 2018
EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://arxiv.org/abs/1905.11946
Submitted on 28 May 2019
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MgArcher
commented on July 24, 2024
感谢您的回复，对我帮助很大，谢谢！
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