--fixed-lr
时间: 2024-07-18 15:00:44 浏览: 51
在深度学习中,"fixed-lr"通常指的是固定学习率(Fixed Learning Rate)。学习率是优化算法中的一个重要参数,它控制着模型参数在每次迭代中的更新幅度。固定学习率意味着在整个训练过程中,不论训练了多少轮,模型的学习速率始终保持一个恒定值。
使用固定学习率的优点包括简单易用,不需要对学习过程进行复杂的调整。然而,缺点也很明显,尤其是在训练初期和后期,如果初始学习率设置得太高,可能会导致模型无法收敛;而如果设置得太低,模型可能需要更多的迭代次数才能达到较好的性能,从而增加训练时间。
在某些情况下,如果发现模型在训练过程中性能停滞不前或下降,可能需要尝试降低学习率来改进训练效果。固定学习率策略并不适用于所有情况,因为它没有自我适应能力。相比之下,动态调整学习率的方法,如学习率衰减(learning rate schedule)或自适应学习率算法(如Adam、Adagrad等),更为常见,它们可以在训练的不同阶段自动调整学习率,以找到最优的学习路径。
相关问题
基于pytorch的DCGAN生成11*11的1-bit color图像代码
以下是基于 PyTorch 的 DCGAN 生成 11x11 的 1-bit color 图像的代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.utils as vutils
import numpy as np
# 设置随机数种子
manualSeed = 999
np.random.seed(manualSeed)
torch.manual_seed(manualSeed)
# 超参数设置
batch_size = 64
image_size = 11
nz = 100
nc = 1
ngf = 64
ndf = 64
num_epochs = 100
lr = 0.0002
beta1 = 0.5
# 加载数据集,这里使用的是随机生成的数据
dataset = torch.zeros((batch_size, nc, image_size, image_size))
for i in range(batch_size):
dataset[i] = torch.from_numpy(np.random.randint(2, size=(nc, image_size, image_size))).float()
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)
# 定义生成器
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.main = nn.Sequential(
nn.ConvTranspose2d(nz, ngf * 4, 4, 1, 0, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True),
nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True),
nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
nn.Tanh()
)
def forward(self, input):
return self.main(input)
# 定义判别器
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.main = nn.Sequential(
nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 2),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 4),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(ndf * 4, 1, 4, 1, 0, bias=False),
nn.Sigmoid()
)
def forward(self, input):
return self.main(input).view(-1, 1).squeeze(1)
# 初始化生成器和判别器
netG = Generator()
netD = Discriminator()
print(netG)
print(netD)
# 定义损失函数和优化器
criterion = nn.BCELoss()
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))
# 定义固定的噪声,用于生成图像
fixed_noise = torch.randn(64, nz, 1, 1)
# 开始训练
for epoch in range(num_epochs):
for i, data in enumerate(dataloader, 0):
# 更新判别器
netD.zero_grad()
real_cpu = data
batch_size = real_cpu.size(0)
label = torch.full((batch_size,), 1)
output = netD(real_cpu)
errD_real = criterion(output, label)
errD_real.backward()
D_x = output.mean().item()
noise = torch.randn(batch_size, nz, 1, 1)
fake = netG(noise)
label.fill_(0)
output = netD(fake.detach())
errD_fake = criterion(output, label)
errD_fake.backward()
D_G_z1 = output.mean().item()
errD = errD_real + errD_fake
optimizerD.step()
# 更新生成器
netG.zero_grad()
label.fill_(1)
output = netD(fake)
errG = criterion(output, label)
errG.backward()
D_G_z2 = output.mean().item()
optimizerG.step()
# 打印损失和生成的图像
if i % 100 == 0:
print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f'
% (epoch, num_epochs, i, len(dataloader),
errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))
if (epoch == 0 and i == 0) or ((epoch + 1) % 10 == 0 and i == len(dataloader) - 1):
with torch.no_grad():
fake = netG(fixed_noise).detach().cpu()
vutils.save_image(fake, 'result/epoch_%03d_iter_%d.png' % (epoch, i), normalize=True)
# 保存模型
torch.save(netG.state_dict(), 'netG.pth')
torch.save(netD.state_dict(), 'netD.pth')
```
DLR. A total number of 512 deep features are extracted (256 from PET images and 256 from CT images) as a byproduct of a multimodal neural network. The network is trained on CT and PET simultaneously [39], with two identical and parallel convolutional branches merged in a fully connected layer (see Fig. 4 for the details on the architecture). An internal transfer learning procedure is applied by first training the whole network on the T-stage dataset, then predicting LR by fine-tuning, i.e. retraining only the linear blocks (final blue box in Fig. 4). Fixed hyper-parameters are used to regulate the training process with Adam [43] optimizer (batch size: 32, epochs: 500, learning rate 10−3). Data augmentation procedures were used to improve the performance and reduce overfitting: i.e., minimal rotations, translations and Gaussian noise. The transformed images were resized to cubes of 64 × 64 × 64 to better fit the GPU memory size. 解释
这段文字描述了一个多模态神经网络,该网络从 PET(正电子发射断层扫描)和 CT(计算机断层扫描)图像中提取512个深度特征(256个来自PET图像,256个来自CT图像)。网络采用两个相同的卷积分支,然后将它们合并成一个全连接层。该网络首先在T-stage数据集上进行训练,然后通过微调(只重新训练线性块)来预测LR。训练过程中使用了固定的超参数来调节,使用Adam优化器,批大小为32,训练时代数为500,学习率为10^-3。数据增强过程被用来提高性能和减少过拟合:最小旋转、平移和高斯噪声等。转换后的图像被调整为64×64×64的立方体以更好地适应GPU内存大小。
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