20210614

https://blog.csdn.net/kyle1314608/article/details/117910862
dropout，batch norm 區別 順序

https://blog.csdn.net/hexuanji/article/details/103635335
Batch Normalization和Dropout如何搭配使用？

GAN 生成對抗 生成器

Generator((label_emb): Embedding(10, 100)(l1): Sequential((0): Linear(in_features=100, out_features=8192, bias=True))(conv_blocks): Sequential((0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(1): Upsample(scale_factor=2.0, mode=nearest)(2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(3): BatchNorm2d(128, eps=0.8, momentum=0.1, affine=True, track_running_stats=True)(4): LeakyReLU(negative_slope=0.2, inplace=True)(5): Upsample(scale_factor=2.0, mode=nearest)(6): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(7): BatchNorm2d(64, eps=0.8, momentum=0.1, affine=True, track_running_stats=True)(8): LeakyReLU(negative_slope=0.2, inplace=True)(9): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(10): Tanh())
)
Discriminator((conv_blocks): Sequential((0): Conv2d(1, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))(1): LeakyReLU(negative_slope=0.2, inplace=True)(2): Dropout2d(p=0.25, inplace=False)(3): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))(4): LeakyReLU(negative_slope=0.2, inplace=True)(5): Dropout2d(p=0.25, inplace=False)(6): BatchNorm2d(32, eps=0.8, momentum=0.1, affine=True, track_running_stats=True)(7): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))(8): LeakyReLU(negative_slope=0.2, inplace=True)(9): Dropout2d(p=0.25, inplace=False)(10): BatchNorm2d(64, eps=0.8, momentum=0.1, affine=True, track_running_stats=True)(11): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))(12): LeakyReLU(negative_slope=0.2, inplace=True)(13): Dropout2d(p=0.25, inplace=False)(14): BatchNorm2d(128, eps=0.8, momentum=0.1, affine=True, track_running_stats=True))(adv_layer): Sequential((0): Linear(in_features=512, out_features=1, bias=True)(1): Sigmoid())(aux_layer): Sequential((0): Linear(in_features=512, out_features=10, bias=True)(1): Softmax(dim=None))
)生成器和判別器

為什么有些用dropout
有些用batch norm 是因為圖像越來越復雜?
上面的網絡結構時有問題的? 有BN的的時候就不要用dropout了,或者只能在softmax之前用一下

總結

以上是生活随笔為你收集整理的网络结构深度学习的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。