运营企业网站怎么赚钱,网页设计就业工资,企业宣传海报设计制作,wordpress 虎嗅Inception Moudel
1、卷积核超参数选择困难#xff0c;自动找到卷积的最佳组合。
2、1x1卷积核#xff0c;不同通道的信息融合。使用1x1卷积核虽然参数量增加了#xff0c;但是能够显著的降低计算量(operations)
3、Inception Moudel由4个分支组成#xff0c;要分清哪些…Inception Moudel
1、卷积核超参数选择困难自动找到卷积的最佳组合。
2、1x1卷积核不同通道的信息融合。使用1x1卷积核虽然参数量增加了但是能够显著的降低计算量(operations)
3、Inception Moudel由4个分支组成要分清哪些是在Init里定义哪些是在forward里调用。4个分支在dim1(channels)上进行concatenate。24162424 88
4、GoogleNet的Inception(Pytorch实现) 代码说明1、先使用类对Inception Moudel进行封装 2、先是1个卷积层(conv,maxpooling,relu)然后inceptionA模块(输出的channels是2416242488)接下来又是一个卷积层(conv,mp,relu),然后inceptionA模块最后一个全连接层(fc)。 3、1408这个数据可以通过x x.view(in_size, -1)后调用x.shape得到。
import torch
import torch.nn as nn
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim# prepare datasetbatch_size 64
transform transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) # 归一化,均值和方差train_dataset datasets.MNIST(root../dataset/mnist/, trainTrue, downloadTrue, transformtransform)
train_loader DataLoader(train_dataset, shuffleTrue, batch_sizebatch_size)
test_dataset datasets.MNIST(root../dataset/mnist/, trainFalse, downloadTrue, transformtransform)
test_loader DataLoader(test_dataset, shuffleFalse, batch_sizebatch_size)# design model using class
class InceptionA(nn.Module):def __init__(self, in_channels):super(InceptionA, self).__init__()self.branch1x1 nn.Conv2d(in_channels, 16, kernel_size1)self.branch5x5_1 nn.Conv2d(in_channels, 16, kernel_size1)self.branch5x5_2 nn.Conv2d(16, 24, kernel_size5, padding2)self.branch3x3_1 nn.Conv2d(in_channels, 16, kernel_size1)self.branch3x3_2 nn.Conv2d(16, 24, kernel_size3, padding1)self.branch3x3_3 nn.Conv2d(24, 24, kernel_size3, padding1)self.branch_pool nn.Conv2d(in_channels, 24, kernel_size1)def forward(self, x):branch1x1 self.branch1x1(x)branch5x5 self.branch5x5_1(x)branch5x5 self.branch5x5_2(branch5x5)branch3x3 self.branch3x3_1(x)branch3x3 self.branch3x3_2(branch3x3)branch3x3 self.branch3x3_3(branch3x3)branch_pool F.avg_pool2d(x, kernel_size3, stride1, padding1)branch_pool self.branch_pool(branch_pool)outputs [branch1x1, branch5x5, branch3x3, branch_pool]return torch.cat(outputs, dim1) # b,c,w,h c对应的是dim1class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.conv1 nn.Conv2d(1, 10, kernel_size5)self.conv2 nn.Conv2d(88, 20, kernel_size5) # 88 24x3 16self.incep1 InceptionA(in_channels10) # 与conv1 中的10对应self.incep2 InceptionA(in_channels20) # 与conv2 中的20对应self.mp nn.MaxPool2d(2)self.fc nn.Linear(1408, 10) def forward(self, x):in_size x.size(0)x F.relu(self.mp(self.conv1(x)))x self.incep1(x)x F.relu(self.mp(self.conv2(x)))x self.incep2(x)x x.view(in_size, -1)x self.fc(x)return xmodel Net()# construct loss and optimizer
criterion torch.nn.CrossEntropyLoss()
optimizer optim.SGD(model.parameters(), lr0.01, momentum0.5)# training cycle forward, backward, updatedef train(epoch):running_loss 0.0for batch_idx, data in enumerate(train_loader, 0):inputs, target dataoptimizer.zero_grad()outputs model(inputs)loss criterion(outputs, target)loss.backward()optimizer.step()running_loss loss.item()if batch_idx % 300 299:print([%d, %5d] loss: %.3f % (epoch1, batch_idx1, running_loss/300))running_loss 0.0def test():correct 0total 0with torch.no_grad():for data in test_loader:images, labels dataoutputs model(images)_, predicted torch.max(outputs.data, dim1)total labels.size(0)correct (predicted labels).sum().item()print(accuracy on test set: %d %% % (100*correct/total))if __name__ __main__:for epoch in range(10):train(epoch)test()
视频中截图
说明1、要解决的问题梯度消失 2、跳连接H(x) F(x) x,张量维度必须一样加完后再激活。不要做pooling张量的维度会发生变化。 代码说明
1、先是1个卷积层(conv,maxpooling,relu)然后ResidualBlock模块接下来又是一个卷积层(conv,mp,relu),然后esidualBlock模块模块最后一个全连接层(fc)。
import torch
import torch.nn as nn
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim# prepare datasetbatch_size 64
transform transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) # 归一化,均值和方差train_dataset datasets.MNIST(root../dataset/mnist/, trainTrue, downloadTrue, transformtransform)
train_loader DataLoader(train_dataset, shuffleTrue, batch_sizebatch_size)
test_dataset datasets.MNIST(root../dataset/mnist/, trainFalse, downloadTrue, transformtransform)
test_loader DataLoader(test_dataset, shuffleFalse, batch_sizebatch_size)# design model using class
class ResidualBlock(nn.Module):def __init__(self, channels):super(ResidualBlock, self).__init__()self.channels channelsself.conv1 nn.Conv2d(channels, channels, kernel_size3, padding1)self.conv2 nn.Conv2d(channels, channels, kernel_size3, padding1)def forward(self, x):y F.relu(self.conv1(x))y self.conv2(y)return F.relu(x y)class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.conv1 nn.Conv2d(1, 16, kernel_size5)self.conv2 nn.Conv2d(16, 32, kernel_size5) # 88 24x3 16self.rblock1 ResidualBlock(16)self.rblock2 ResidualBlock(32)self.mp nn.MaxPool2d(2)self.fc nn.Linear(512, 10) # 暂时不知道1408咋能自动出来的def forward(self, x):in_size x.size(0)x self.mp(F.relu(self.conv1(x)))x self.rblock1(x)x self.mp(F.relu(self.conv2(x)))x self.rblock2(x)x x.view(in_size, -1)x self.fc(x)return xmodel Net()# construct loss and optimizer
criterion torch.nn.CrossEntropyLoss()
optimizer optim.SGD(model.parameters(), lr0.01, momentum0.5)# training cycle forward, backward, updatedef train(epoch):running_loss 0.0for batch_idx, data in enumerate(train_loader, 0):inputs, target dataoptimizer.zero_grad()outputs model(inputs)loss criterion(outputs, target)loss.backward()optimizer.step()running_loss loss.item()if batch_idx % 300 299:print([%d, %5d] loss: %.3f % (epoch1, batch_idx1, running_loss/300))running_loss 0.0def test():correct 0total 0with torch.no_grad():for data in test_loader:images, labels dataoutputs model(images)_, predicted torch.max(outputs.data, dim1)total labels.size(0)correct (predicted labels).sum().item()print(accuracy on test set: %d %% % (100*correct/total))if __name__ __main__:for epoch in range(10):train(epoch)test()
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