自己做国外网站,百度网络推广优化,只做女性的网站,新西兰注册公司做网站PyTorch 模型进阶训练技巧
自定义损失函数动态调整学习率
典型案例#xff1a;loss上下震荡 [外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-BndMyRX0-1692613806232)(attachment:image-2.png)]
1、自定义损失函数
1、PyTorch已经提供了很多常用…PyTorch 模型进阶训练技巧
自定义损失函数动态调整学习率
典型案例loss上下震荡 [外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-BndMyRX0-1692613806232)(attachment:image-2.png)]
1、自定义损失函数
1、PyTorch已经提供了很多常用的损失函数但是有些非通用的损失函数并未提供比如DiceLoss、HuberLoss…等2、模型如果出现loss震荡在经过调整数据集或超参后现象依然存在非通用损失函数或自定义损失函数针对特定模型会有更好的效果
比如DiceLoss是医学影像分割常用的损失函数定义如下 [外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-Fsl0SyZ6-1692613806233)(attachment:image-2.png)]
Dice系数, 是一种集合相似度度量函数通常用于计算两个样本的相似度(值范围为 [0, 1])∣X∩Y∣表示X和Y之间的交集∣ X ∣ 和∣ Y ∣ 分别表示X和Y的元素个数其中分子中的系数 2是因为分母存在重复计算 X 和 Y 之间的共同元素的原因.
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.optim.lr_scheduler import LambdaLR
from torch.optim.lr_scheduler import StepLR
import torchvision
from torch.utils.data import Dataset, DataLoader
from torchvision.transforms import transforms
import matplotlib.pyplot as plt
from torch.utils.tensorboard import SummaryWriter
import time
import numpy as np#DiceLoss 实现 Vnet 医学影像分割模型的损失函数
class DiceLoss(nn.Module):def __init__(self, weightNone, size_averageTrue):super(DiceLoss, self).__init__()def forward(self, inputs, targets, smooth1):inputs F.sigmoid(inputs) inputs inputs.view(-1)targets targets.view(-1)intersection (inputs * targets).sum() dice_loss 1 - (2.*intersection smooth)/(inputs.sum() targets.sum() smooth)return dice_loss#自定义实现多分类损失函数 处理多分类
# cross_entropy L2正则化
class MyLoss(torch.nn.Module):def __init__(self, weight_decay0.01):super(MyLoss, self).__init__()self.weight_decay weight_decaydef forward(self, inputs, targets):ce_loss F.cross_entropy(inputs, targets)l2_loss torch.tensor(0., requires_gradTrue).to(inputs.device)for name, param in self.named_parameters():if weight in name:l2_loss torch.norm(param)loss ce_loss self.weight_decay * l2_lossreturn loss
注
在自定义损失函数时涉及到数学运算时我们最好全程使用PyTorch提供的张量计算接口利用Pytorch张量自带的求导机制
#超参数定义
# 批次的大小
batch_size 16 #可选32、64、128
# 优化器的学习率
lr 1e-4
#运行epoch
max_epochs 2
# 方案二使用“device”后续对要使用GPU的变量用.to(device)即可
device torch.device(cuda:1 if torch.cuda.is_available() else cpu) # 指明调用的GPU为1号# 数据读取
#cifar10数据集为例给出构建Dataset类的方式
from torchvision import datasets#“data_transform”可以对图像进行一定的变换如翻转、裁剪、归一化等操作可自己定义
data_transformtransforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])train_cifar_dataset datasets.CIFAR10(cifar10,trainTrue, downloadFalse,transformdata_transform)
test_cifar_dataset datasets.CIFAR10(cifar10,trainFalse, downloadFalse,transformdata_transform)#构建好Dataset后就可以使用DataLoader来按批次读入数据了
train_loader torch.utils.data.DataLoader(train_cifar_dataset, batch_sizebatch_size, num_workers4, shuffleTrue, drop_lastTrue)test_loader torch.utils.data.DataLoader(test_cifar_dataset, batch_sizebatch_size, num_workers4, shuffleFalse)
# restnet50 pretrained
Resnet50 torchvision.models.resnet50(pretrainedTrue)
Resnet50.fc.out_features10
print(Resnet50)D:\Users\xulele\Anaconda3\lib\site-packages\torchvision\models\_utils.py:208: UserWarning: The parameter pretrained is deprecated since 0.13 and may be removed in the future, please use weights instead.warnings.warn(
D:\Users\xulele\Anaconda3\lib\site-packages\torchvision\models\_utils.py:223: UserWarning: Arguments other than a weight enum or None for weights are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing weightsResNet50_Weights.IMAGENET1K_V1. You can also use weightsResNet50_Weights.DEFAULT to get the most up-to-date weights.warnings.warn(msg)ResNet((conv1): Conv2d(3, 64, kernel_size(7, 7), stride(2, 2), padding(3, 3), biasFalse)(bn1): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(relu): ReLU(inplaceTrue)(maxpool): MaxPool2d(kernel_size3, stride2, padding1, dilation1, ceil_modeFalse)(layer1): Sequential((0): Bottleneck((conv1): Conv2d(64, 64, kernel_size(1, 1), stride(1, 1), biasFalse)(bn1): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(64, 64, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse)(bn2): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(64, 256, kernel_size(1, 1), stride(1, 1), biasFalse)(bn3): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(relu): ReLU(inplaceTrue)(downsample): Sequential((0): Conv2d(64, 256, kernel_size(1, 1), stride(1, 1), biasFalse)(1): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)))(1): Bottleneck((conv1): Conv2d(256, 64, kernel_size(1, 1), stride(1, 1), biasFalse)(bn1): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(64, 64, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse)(bn2): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(64, 256, kernel_size(1, 1), stride(1, 1), biasFalse)(bn3): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(relu): ReLU(inplaceTrue))(2): Bottleneck((conv1): Conv2d(256, 64, kernel_size(1, 1), stride(1, 1), biasFalse)(bn1): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(64, 64, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse)(bn2): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(64, 256, kernel_size(1, 1), stride(1, 1), biasFalse)(bn3): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(relu): ReLU(inplaceTrue)))(layer2): Sequential((0): Bottleneck((conv1): Conv2d(256, 128, kernel_size(1, 1), stride(1, 1), biasFalse)(bn1): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(128, 128, kernel_size(3, 3), stride(2, 2), padding(1, 1), biasFalse)(bn2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(128, 512, kernel_size(1, 1), stride(1, 1), biasFalse)(bn3): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(relu): ReLU(inplaceTrue)(downsample): Sequential((0): Conv2d(256, 512, kernel_size(1, 1), stride(2, 2), biasFalse)(1): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)))(1): Bottleneck((conv1): Conv2d(512, 128, kernel_size(1, 1), stride(1, 1), biasFalse)(bn1): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(128, 128, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse)(bn2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(128, 512, kernel_size(1, 1), stride(1, 1), biasFalse)(bn3): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(relu): ReLU(inplaceTrue))(2): Bottleneck((conv1): Conv2d(512, 128, kernel_size(1, 1), stride(1, 1), biasFalse)(bn1): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(128, 128, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse)(bn2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(128, 512, kernel_size(1, 1), stride(1, 1), biasFalse)(bn3): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(relu): ReLU(inplaceTrue))(3): Bottleneck((conv1): Conv2d(512, 128, kernel_size(1, 1), stride(1, 1), biasFalse)(bn1): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(128, 128, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse)(bn2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(128, 512, kernel_size(1, 1), stride(1, 1), biasFalse)(bn3): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(relu): ReLU(inplaceTrue)))(layer3): Sequential((0): Bottleneck((conv1): Conv2d(512, 256, kernel_size(1, 1), stride(1, 1), biasFalse)(bn1): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(256, 256, kernel_size(3, 3), stride(2, 2), padding(1, 1), biasFalse)(bn2): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(256, 1024, kernel_size(1, 1), stride(1, 1), biasFalse)(bn3): BatchNorm2d(1024, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(relu): ReLU(inplaceTrue)(downsample): Sequential((0): Conv2d(512, 1024, kernel_size(1, 1), stride(2, 2), biasFalse)(1): BatchNorm2d(1024, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)))(1): Bottleneck((conv1): Conv2d(1024, 256, kernel_size(1, 1), stride(1, 1), biasFalse)(bn1): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(256, 256, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse)(bn2): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(256, 1024, kernel_size(1, 1), stride(1, 1), biasFalse)(bn3): BatchNorm2d(1024, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(relu): ReLU(inplaceTrue))(2): Bottleneck((conv1): Conv2d(1024, 256, kernel_size(1, 1), stride(1, 1), biasFalse)(bn1): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(256, 256, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse)(bn2): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(256, 1024, kernel_size(1, 1), stride(1, 1), biasFalse)(bn3): BatchNorm2d(1024, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(relu): ReLU(inplaceTrue))(3): Bottleneck((conv1): Conv2d(1024, 256, kernel_size(1, 1), stride(1, 1), biasFalse)(bn1): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(256, 256, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse)(bn2): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(256, 1024, kernel_size(1, 1), stride(1, 1), biasFalse)(bn3): BatchNorm2d(1024, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(relu): ReLU(inplaceTrue))(4): Bottleneck((conv1): Conv2d(1024, 256, kernel_size(1, 1), stride(1, 1), biasFalse)(bn1): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(256, 256, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse)(bn2): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(256, 1024, kernel_size(1, 1), stride(1, 1), biasFalse)(bn3): BatchNorm2d(1024, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(relu): ReLU(inplaceTrue))(5): Bottleneck((conv1): Conv2d(1024, 256, kernel_size(1, 1), stride(1, 1), biasFalse)(bn1): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(256, 256, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse)(bn2): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(256, 1024, kernel_size(1, 1), stride(1, 1), biasFalse)(bn3): BatchNorm2d(1024, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(relu): ReLU(inplaceTrue)))(layer4): Sequential((0): Bottleneck((conv1): Conv2d(1024, 512, kernel_size(1, 1), stride(1, 1), biasFalse)(bn1): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(512, 512, kernel_size(3, 3), stride(2, 2), padding(1, 1), biasFalse)(bn2): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(512, 2048, kernel_size(1, 1), stride(1, 1), biasFalse)(bn3): BatchNorm2d(2048, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(relu): ReLU(inplaceTrue)(downsample): Sequential((0): Conv2d(1024, 2048, kernel_size(1, 1), stride(2, 2), biasFalse)(1): BatchNorm2d(2048, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)))(1): Bottleneck((conv1): Conv2d(2048, 512, kernel_size(1, 1), stride(1, 1), biasFalse)(bn1): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(512, 512, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse)(bn2): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(512, 2048, kernel_size(1, 1), stride(1, 1), biasFalse)(bn3): BatchNorm2d(2048, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(relu): ReLU(inplaceTrue))(2): Bottleneck((conv1): Conv2d(2048, 512, kernel_size(1, 1), stride(1, 1), biasFalse)(bn1): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(512, 512, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse)(bn2): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(512, 2048, kernel_size(1, 1), stride(1, 1), biasFalse)(bn3): BatchNorm2d(2048, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(relu): ReLU(inplaceTrue)))(avgpool): AdaptiveAvgPool2d(output_size(1, 1))(fc): Linear(in_features2048, out_features10, biasTrue)
)#训练验证# 定义损失函数和优化器
device torch.device(cuda:0 if torch.cuda.is_available() else cpu)
# 损失函数自定义损失函数
criterion MyLoss()
# 优化器
optimizer torch.optim.Adam(Resnet50.parameters(), lrlr)
epoch max_epochs
Resnet50 Resnet50.to(device)
total_step len(train_loader)
train_all_loss []
test_all_loss []for i in range(epoch):Resnet50.train()train_total_loss 0train_total_num 0train_total_correct 0for iter, (images,labels) in enumerate(train_loader):images images.to(device)labels labels.to(device)outputs Resnet50(images)loss criterion(outputs,labels)train_total_correct (outputs.argmax(1) labels).sum().item()#backwordoptimizer.zero_grad()loss.backward()optimizer.step()train_total_num labels.shape[0]train_total_loss loss.item()print(Epoch [{}/{}], Iter [{}/{}], train_loss:{:4f}.format(i1,epoch,iter1,total_step,loss.item()/labels.shape[0]))Resnet50.eval()test_total_loss 0test_total_correct 0test_total_num 0for iter,(images,labels) in enumerate(test_loader):images images.to(device)labels labels.to(device)outputs Resnet50(images)loss criterion(outputs,labels)test_total_correct (outputs.argmax(1) labels).sum().item()test_total_loss loss.item()test_total_num labels.shape[0]print(Epoch [{}/{}], train_loss:{:.4f}, train_acc:{:.4f}%, test_loss:{:.4f}, test_acc:{:.4f}%.format(i1, epoch, train_total_loss / train_total_num, train_total_correct / train_total_num * 100, test_total_loss / test_total_num, test_total_correct / test_total_num * 100))train_all_loss.append(np.round(train_total_loss / train_total_num,4))test_all_loss.append(np.round(test_total_loss / test_total_num,4))
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Epoch [1/10], Iter [1021/3125], train_loss:0.090297
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Epoch [1/10], Iter [1035/3125], train_loss:0.044121
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Epoch [1/10], Iter [1211/3125], train_loss:0.067255
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Epoch [1/10], Iter [1219/3125], train_loss:0.069693
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Epoch [1/10], Iter [1224/3125], train_loss:0.073832
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Epoch [1/10], Iter [1229/3125], train_loss:0.066249
Epoch [1/10], Iter [1230/3125], train_loss:0.037475
Epoch [1/10], Iter [1231/3125], train_loss:0.037161
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Epoch [1/10], Iter [1234/3125], train_loss:0.036732
Epoch [1/10], Iter [1235/3125], train_loss:0.080269
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Epoch [1/10], Iter [1238/3125], train_loss:0.064553
Epoch [1/10], Iter [1239/3125], train_loss:0.094893
Epoch [1/10], Iter [1240/3125], train_loss:0.061000
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Epoch [1/10], Iter [1251/3125], train_loss:0.051030
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Epoch [1/10], Iter [1253/3125], train_loss:0.068953
Epoch [1/10], Iter [1254/3125], train_loss:0.077853
Epoch [1/10], Iter [1255/3125], train_loss:0.078600
Epoch [1/10], Iter [1256/3125], train_loss:0.029034
Epoch [1/10], Iter [1257/3125], train_loss:0.067805
Epoch [1/10], Iter [1258/3125], train_loss:0.105204
Epoch [1/10], Iter [1259/3125], train_loss:0.044573
Epoch [1/10], Iter [1260/3125], train_loss:0.098438
Epoch [1/10], Iter [1261/3125], train_loss:0.044922
Epoch [1/10], Iter [1262/3125], train_loss:0.077494
Epoch [1/10], Iter [1263/3125], train_loss:0.068515
Epoch [1/10], Iter [1264/3125], train_loss:0.082361
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Epoch [1/10], Iter [1266/3125], train_loss:0.061101
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Epoch [1/10], Iter [1269/3125], train_loss:0.078264
Epoch [1/10], Iter [1270/3125], train_loss:0.053628
Epoch [1/10], Iter [1271/3125], train_loss:0.076903
Epoch [1/10], Iter [1272/3125], train_loss:0.055117
Epoch [1/10], Iter [1273/3125], train_loss:0.122055
Epoch [1/10], Iter [1274/3125], train_loss:0.041958
Epoch [1/10], Iter [1275/3125], train_loss:0.110160
Epoch [1/10], Iter [1276/3125], train_loss:0.080354
Epoch [1/10], Iter [1277/3125], train_loss:0.036007
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Epoch [1/10], Iter [1279/3125], train_loss:0.103632
Epoch [1/10], Iter [1280/3125], train_loss:0.105166
Epoch [1/10], Iter [1281/3125], train_loss:0.068429
Epoch [1/10], Iter [1282/3125], train_loss:0.072354
Epoch [1/10], Iter [1283/3125], train_loss:0.058038
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Epoch [1/10], Iter [1285/3125], train_loss:0.033587
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Epoch [1/10], Iter [1287/3125], train_loss:0.072158
Epoch [1/10], Iter [1288/3125], train_loss:0.037460
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Epoch [1/10], Iter [1290/3125], train_loss:0.051290
Epoch [1/10], Iter [1291/3125], train_loss:0.076521
Epoch [1/10], Iter [1292/3125], train_loss:0.045308
Epoch [1/10], Iter [1293/3125], train_loss:0.077797
Epoch [1/10], Iter [1294/3125], train_loss:0.050401
Epoch [1/10], Iter [1295/3125], train_loss:0.054285
Epoch [1/10], Iter [1296/3125], train_loss:0.071456
Epoch [1/10], Iter [1297/3125], train_loss:0.069530
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Epoch [1/10], Iter [1299/3125], train_loss:0.060730
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Epoch [1/10], Iter [1301/3125], train_loss:0.049532
Epoch [1/10], Iter [1302/3125], train_loss:0.069171
Epoch [1/10], Iter [1303/3125], train_loss:0.061904
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Epoch [1/10], Iter [1305/3125], train_loss:0.045866
Epoch [1/10], Iter [1306/3125], train_loss:0.042385
Epoch [1/10], Iter [1307/3125], train_loss:0.050176
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Epoch [1/10], Iter [1309/3125], train_loss:0.042563
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Epoch [1/10], Iter [1311/3125], train_loss:0.086524
Epoch [1/10], Iter [1312/3125], train_loss:0.056474
Epoch [1/10], Iter [1313/3125], train_loss:0.037732
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Epoch [1/10], Iter [1315/3125], train_loss:0.082700
Epoch [1/10], Iter [1316/3125], train_loss:0.092105
Epoch [1/10], Iter [1317/3125], train_loss:0.059939
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Epoch [1/10], Iter [1320/3125], train_loss:0.086146
Epoch [1/10], Iter [1321/3125], train_loss:0.061879
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Epoch [1/10], Iter [1325/3125], train_loss:0.061338
Epoch [1/10], Iter [1326/3125], train_loss:0.057086
Epoch [1/10], Iter [1327/3125], train_loss:0.051174
Epoch [1/10], Iter [1328/3125], train_loss:0.054015
Epoch [1/10], Iter [1329/3125], train_loss:0.061765
Epoch [1/10], Iter [1330/3125], train_loss:0.066730
Epoch [1/10], Iter [1331/3125], train_loss:0.054490
Epoch [1/10], Iter [1332/3125], train_loss:0.057822
Epoch [1/10], Iter [1333/3125], train_loss:0.063132
Epoch [1/10], Iter [1334/3125], train_loss:0.069564
Epoch [1/10], Iter [1335/3125], train_loss:0.044150
Epoch [1/10], Iter [1336/3125], train_loss:0.080780
Epoch [1/10], Iter [1337/3125], train_loss:0.058406
Epoch [1/10], Iter [1338/3125], train_loss:0.049550
Epoch [1/10], Iter [1339/3125], train_loss:0.044474
Epoch [1/10], Iter [1340/3125], train_loss:0.055215
Epoch [1/10], Iter [1341/3125], train_loss:0.097746
Epoch [1/10], Iter [1342/3125], train_loss:0.071166
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Epoch [1/10], Iter [1344/3125], train_loss:0.065595
Epoch [1/10], Iter [1345/3125], train_loss:0.069312
Epoch [1/10], Iter [1346/3125], train_loss:0.068984
Epoch [1/10], Iter [1347/3125], train_loss:0.114133
Epoch [1/10], Iter [1348/3125], train_loss:0.053902
Epoch [1/10], Iter [1349/3125], train_loss:0.039486
Epoch [1/10], Iter [1350/3125], train_loss:0.077412
Epoch [1/10], Iter [1351/3125], train_loss:0.105866
Epoch [1/10], Iter [1352/3125], train_loss:0.036934
Epoch [1/10], Iter [1353/3125], train_loss:0.028790
Epoch [1/10], Iter [1354/3125], train_loss:0.044115
Epoch [1/10], Iter [1355/3125], train_loss:0.050180
Epoch [1/10], Iter [1356/3125], train_loss:0.035173
Epoch [1/10], Iter [1357/3125], train_loss:0.066359
Epoch [1/10], Iter [1358/3125], train_loss:0.061649
Epoch [1/10], Iter [1359/3125], train_loss:0.090383
Epoch [1/10], Iter [1360/3125], train_loss:0.094560
Epoch [1/10], Iter [1361/3125], train_loss:0.051187
Epoch [1/10], Iter [1362/3125], train_loss:0.051535
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Epoch [1/10], Iter [1364/3125], train_loss:0.064312
Epoch [1/10], Iter [1365/3125], train_loss:0.035589
Epoch [1/10], Iter [1366/3125], train_loss:0.074556
Epoch [1/10], Iter [1367/3125], train_loss:0.095972
Epoch [1/10], Iter [1368/3125], train_loss:0.079113
Epoch [1/10], Iter [1369/3125], train_loss:0.075476
Epoch [1/10], Iter [1370/3125], train_loss:0.055053
Epoch [1/10], Iter [1371/3125], train_loss:0.036419
Epoch [1/10], Iter [1372/3125], train_loss:0.082008
Epoch [1/10], Iter [1373/3125], train_loss:0.035035
Epoch [1/10], Iter [1374/3125], train_loss:0.061965
Epoch [1/10], Iter [1375/3125], train_loss:0.090616
Epoch [1/10], Iter [1376/3125], train_loss:0.071584
Epoch [1/10], Iter [1377/3125], train_loss:0.062969
Epoch [1/10], Iter [1378/3125], train_loss:0.049597
Epoch [1/10], Iter [1379/3125], train_loss:0.042371
Epoch [1/10], Iter [1380/3125], train_loss:0.058470
Epoch [1/10], Iter [1381/3125], train_loss:0.089132
Epoch [1/10], Iter [1382/3125], train_loss:0.042923
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Epoch [1/10], Iter [1648/3125], train_loss:0.074777
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Epoch [1/10], Iter [1655/3125], train_loss:0.054383
Epoch [1/10], Iter [1656/3125], train_loss:0.033800
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Epoch [1/10], Iter [1660/3125], train_loss:0.060077
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Epoch [1/10], Iter [1664/3125], train_loss:0.048353
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Epoch [1/10], Iter [1670/3125], train_loss:0.042728
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Epoch [1/10], Iter [2137/3125], train_loss:0.061121
Epoch [1/10], Iter [2138/3125], train_loss:0.045141
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Epoch [1/10], Iter [2152/3125], train_loss:0.062930
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Epoch [1/10], Iter [2154/3125], train_loss:0.076227
Epoch [1/10], Iter [2155/3125], train_loss:0.083203
Epoch [1/10], Iter [2156/3125], train_loss:0.063778
Epoch [1/10], Iter [2157/3125], train_loss:0.045961
Epoch [1/10], Iter [2158/3125], train_loss:0.070411
Epoch [1/10], Iter [2159/3125], train_loss:0.064471
Epoch [1/10], Iter [2160/3125], train_loss:0.056950
Epoch [1/10], Iter [2161/3125], train_loss:0.074447
Epoch [1/10], Iter [2162/3125], train_loss:0.052749
Epoch [1/10], Iter [2163/3125], train_loss:0.057865
Epoch [1/10], Iter [2164/3125], train_loss:0.037370
Epoch [1/10], Iter [2165/3125], train_loss:0.103615
Epoch [1/10], Iter [2166/3125], train_loss:0.076190
Epoch [1/10], Iter [2167/3125], train_loss:0.044481
Epoch [1/10], Iter [2168/3125], train_loss:0.050516
Epoch [1/10], Iter [2169/3125], train_loss:0.036114
Epoch [1/10], Iter [2170/3125], train_loss:0.037495
Epoch [1/10], Iter [2171/3125], train_loss:0.058162
Epoch [1/10], Iter [2172/3125], train_loss:0.072126
Epoch [1/10], Iter [2173/3125], train_loss:0.058480
Epoch [1/10], Iter [2174/3125], train_loss:0.057047
Epoch [1/10], Iter [2175/3125], train_loss:0.058543
Epoch [1/10], Iter [2176/3125], train_loss:0.044135
Epoch [1/10], Iter [2177/3125], train_loss:0.021453
Epoch [1/10], Iter [2178/3125], train_loss:0.091287
Epoch [1/10], Iter [2179/3125], train_loss:0.030686
Epoch [1/10], Iter [2180/3125], train_loss:0.043142
Epoch [1/10], Iter [2181/3125], train_loss:0.061297
Epoch [1/10], Iter [2182/3125], train_loss:0.052431
Epoch [1/10], Iter [2183/3125], train_loss:0.064683
Epoch [1/10], Iter [2184/3125], train_loss:0.052090
Epoch [1/10], Iter [2185/3125], train_loss:0.059552
Epoch [1/10], Iter [2186/3125], train_loss:0.043549
Epoch [1/10], Iter [2187/3125], train_loss:0.039106
Epoch [1/10], Iter [2188/3125], train_loss:0.033696
Epoch [1/10], Iter [2189/3125], train_loss:0.059473
Epoch [1/10], Iter [2190/3125], train_loss:0.042966
Epoch [1/10], Iter [2191/3125], train_loss:0.038413
Epoch [1/10], Iter [2192/3125], train_loss:0.048166
Epoch [1/10], Iter [2193/3125], train_loss:0.062529
Epoch [1/10], Iter [2194/3125], train_loss:0.063281
Epoch [1/10], Iter [2195/3125], train_loss:0.068794
Epoch [1/10], Iter [2196/3125], train_loss:0.060039
Epoch [1/10], Iter [2197/3125], train_loss:0.059375
Epoch [1/10], Iter [2198/3125], train_loss:0.052642
Epoch [1/10], Iter [2199/3125], train_loss:0.046952
Epoch [1/10], Iter [2200/3125], train_loss:0.071861
Epoch [1/10], Iter [2201/3125], train_loss:0.044257
Epoch [1/10], Iter [2202/3125], train_loss:0.057232
Epoch [1/10], Iter [2203/3125], train_loss:0.039750
Epoch [1/10], Iter [2204/3125], train_loss:0.074284
Epoch [1/10], Iter [2205/3125], train_loss:0.029797
Epoch [1/10], Iter [2206/3125], train_loss:0.058231
Epoch [1/10], Iter [2207/3125], train_loss:0.066111
Epoch [1/10], Iter [2208/3125], train_loss:0.067477
Epoch [1/10], Iter [2209/3125], train_loss:0.065425
Epoch [1/10], Iter [2210/3125], train_loss:0.039687
Epoch [1/10], Iter [2211/3125], train_loss:0.054980
Epoch [1/10], Iter [2212/3125], train_loss:0.052664
Epoch [1/10], Iter [2213/3125], train_loss:0.065844
Epoch [1/10], Iter [2214/3125], train_loss:0.094000
Epoch [1/10], Iter [2215/3125], train_loss:0.053468
Epoch [1/10], Iter [2216/3125], train_loss:0.061695
Epoch [1/10], Iter [2217/3125], train_loss:0.067787
Epoch [1/10], Iter [2218/3125], train_loss:0.035557
Epoch [1/10], Iter [2219/3125], train_loss:0.054791
Epoch [1/10], Iter [2220/3125], train_loss:0.074102
Epoch [1/10], Iter [2221/3125], train_loss:0.053827
Epoch [1/10], Iter [2222/3125], train_loss:0.064904
Epoch [1/10], Iter [2223/3125], train_loss:0.048594
Epoch [1/10], Iter [2224/3125], train_loss:0.038459
Epoch [1/10], Iter [2225/3125], train_loss:0.033388
Epoch [1/10], Iter [2226/3125], train_loss:0.053181
Epoch [1/10], Iter [2227/3125], train_loss:0.070912
Epoch [1/10], Iter [2228/3125], train_loss:0.087150
Epoch [1/10], Iter [2229/3125], train_loss:0.043372
Epoch [1/10], Iter [2230/3125], train_loss:0.053783
Epoch [1/10], Iter [2231/3125], train_loss:0.040672
Epoch [1/10], Iter [2232/3125], train_loss:0.045534
Epoch [1/10], Iter [2233/3125], train_loss:0.040906
Epoch [1/10], Iter [2234/3125], train_loss:0.046060
Epoch [1/10], Iter [2235/3125], train_loss:0.073936
Epoch [1/10], Iter [2236/3125], train_loss:0.048040
Epoch [1/10], Iter [2237/3125], train_loss:0.044033
Epoch [1/10], Iter [2238/3125], train_loss:0.058578
Epoch [1/10], Iter [2239/3125], train_loss:0.046442
Epoch [1/10], Iter [2240/3125], train_loss:0.070717
Epoch [1/10], Iter [2241/3125], train_loss:0.057559
Epoch [1/10], Iter [2242/3125], train_loss:0.071514
Epoch [1/10], Iter [2243/3125], train_loss:0.072684
Epoch [1/10], Iter [2244/3125], train_loss:0.071098
Epoch [1/10], Iter [2245/3125], train_loss:0.029106
Epoch [1/10], Iter [2246/3125], train_loss:0.047889
Epoch [1/10], Iter [2247/3125], train_loss:0.074630
Epoch [1/10], Iter [2248/3125], train_loss:0.039345
Epoch [1/10], Iter [2249/3125], train_loss:0.076240
Epoch [1/10], Iter [2250/3125], train_loss:0.046938
Epoch [1/10], Iter [2251/3125], train_loss:0.051236
Epoch [1/10], Iter [2252/3125], train_loss:0.060951
Epoch [1/10], Iter [2253/3125], train_loss:0.072658
Epoch [1/10], Iter [2254/3125], train_loss:0.072621
Epoch [1/10], Iter [2255/3125], train_loss:0.071780
Epoch [1/10], Iter [2256/3125], train_loss:0.047900
Epoch [1/10], Iter [2257/3125], train_loss:0.083139
Epoch [1/10], Iter [2258/3125], train_loss:0.042750
Epoch [1/10], Iter [2259/3125], train_loss:0.030537
Epoch [1/10], Iter [2260/3125], train_loss:0.071231
Epoch [1/10], Iter [2261/3125], train_loss:0.058627
Epoch [1/10], Iter [2262/3125], train_loss:0.061551
Epoch [1/10], Iter [2263/3125], train_loss:0.057065
Epoch [1/10], Iter [2264/3125], train_loss:0.063427
Epoch [1/10], Iter [2265/3125], train_loss:0.052468
Epoch [1/10], Iter [2266/3125], train_loss:0.052080
Epoch [1/10], Iter [2267/3125], train_loss:0.033376
Epoch [1/10], Iter [2268/3125], train_loss:0.041073
Epoch [1/10], Iter [2269/3125], train_loss:0.065047
Epoch [1/10], Iter [2270/3125], train_loss:0.062026
Epoch [1/10], Iter [2271/3125], train_loss:0.109442
Epoch [1/10], Iter [2272/3125], train_loss:0.056198
Epoch [1/10], Iter [2273/3125], train_loss:0.063348
Epoch [1/10], Iter [2274/3125], train_loss:0.039659
Epoch [1/10], Iter [2275/3125], train_loss:0.062523
Epoch [1/10], Iter [2276/3125], train_loss:0.057241
Epoch [1/10], Iter [2277/3125], train_loss:0.026030
Epoch [1/10], Iter [2278/3125], train_loss:0.060936
Epoch [1/10], Iter [2279/3125], train_loss:0.037769
Epoch [1/10], Iter [2280/3125], train_loss:0.047071
Epoch [1/10], Iter [2281/3125], train_loss:0.067723
Epoch [1/10], Iter [2282/3125], train_loss:0.071875
Epoch [1/10], Iter [2283/3125], train_loss:0.049202
Epoch [1/10], Iter [2284/3125], train_loss:0.060309
Epoch [1/10], Iter [2285/3125], train_loss:0.068315
Epoch [1/10], Iter [2286/3125], train_loss:0.072877
Epoch [1/10], Iter [2287/3125], train_loss:0.063042
Epoch [1/10], Iter [2288/3125], train_loss:0.078719
Epoch [1/10], Iter [2289/3125], train_loss:0.026097
Epoch [1/10], Iter [2290/3125], train_loss:0.060497
Epoch [1/10], Iter [2291/3125], train_loss:0.078648
Epoch [1/10], Iter [2292/3125], train_loss:0.068681
Epoch [1/10], Iter [2293/3125], train_loss:0.044549
Epoch [1/10], Iter [2294/3125], train_loss:0.079612
Epoch [1/10], Iter [2295/3125], train_loss:0.036360
Epoch [1/10], Iter [2296/3125], train_loss:0.029000
Epoch [1/10], Iter [2297/3125], train_loss:0.055833
Epoch [1/10], Iter [2298/3125], train_loss:0.078257
Epoch [1/10], Iter [2299/3125], train_loss:0.064521
Epoch [1/10], Iter [2300/3125], train_loss:0.053077
Epoch [1/10], Iter [2301/3125], train_loss:0.061464
Epoch [1/10], Iter [2302/3125], train_loss:0.054382
Epoch [1/10], Iter [2303/3125], train_loss:0.029077
Epoch [1/10], Iter [2304/3125], train_loss:0.047081
Epoch [1/10], Iter [2305/3125], train_loss:0.034250
Epoch [1/10], Iter [2306/3125], train_loss:0.067229
Epoch [1/10], Iter [2307/3125], train_loss:0.038814
Epoch [1/10], Iter [2308/3125], train_loss:0.059177
Epoch [1/10], Iter [2309/3125], train_loss:0.029574
Epoch [1/10], Iter [2310/3125], train_loss:0.034070
Epoch [1/10], Iter [2311/3125], train_loss:0.077129
Epoch [1/10], Iter [2312/3125], train_loss:0.036397
Epoch [1/10], Iter [2313/3125], train_loss:0.065701
Epoch [1/10], Iter [2314/3125], train_loss:0.044045
Epoch [1/10], Iter [2315/3125], train_loss:0.078438
Epoch [1/10], Iter [2316/3125], train_loss:0.099388
Epoch [1/10], Iter [2317/3125], train_loss:0.053328
Epoch [1/10], Iter [2318/3125], train_loss:0.033426
Epoch [1/10], Iter [2319/3125], train_loss:0.045820
Epoch [1/10], Iter [2320/3125], train_loss:0.071173
Epoch [1/10], Iter [2321/3125], train_loss:0.058071
Epoch [1/10], Iter [2322/3125], train_loss:0.032791
Epoch [1/10], Iter [2323/3125], train_loss:0.049563
Epoch [1/10], Iter [2324/3125], train_loss:0.037852
Epoch [1/10], Iter [2325/3125], train_loss:0.071495
Epoch [1/10], Iter [2326/3125], train_loss:0.051821
Epoch [1/10], Iter [2327/3125], train_loss:0.049604
Epoch [1/10], Iter [2328/3125], train_loss:0.084093
Epoch [1/10], Iter [2329/3125], train_loss:0.050646
Epoch [1/10], Iter [2330/3125], train_loss:0.035999
Epoch [1/10], Iter [2331/3125], train_loss:0.079603
Epoch [1/10], Iter [2332/3125], train_loss:0.036003
Epoch [1/10], Iter [2333/3125], train_loss:0.029306
Epoch [1/10], Iter [2334/3125], train_loss:0.080034
Epoch [1/10], Iter [2335/3125], train_loss:0.056424
Epoch [1/10], Iter [2336/3125], train_loss:0.067404
Epoch [1/10], Iter [2337/3125], train_loss:0.048945
Epoch [1/10], Iter [2338/3125], train_loss:0.034922
Epoch [1/10], Iter [2339/3125], train_loss:0.060189
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Epoch [1/10], Iter [2459/3125], train_loss:0.091389
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Epoch [1/10], Iter [2499/3125], train_loss:0.039835
Epoch [1/10], Iter [2500/3125], train_loss:0.037947
Epoch [1/10], Iter [2501/3125], train_loss:0.087482
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Epoch [1/10], Iter [2508/3125], train_loss:0.060091
Epoch [1/10], Iter [2509/3125], train_loss:0.038879
Epoch [1/10], Iter [2510/3125], train_loss:0.073510
Epoch [1/10], Iter [2511/3125], train_loss:0.078042
Epoch [1/10], Iter [2512/3125], train_loss:0.018318
Epoch [1/10], Iter [2513/3125], train_loss:0.071369
Epoch [1/10], Iter [2514/3125], train_loss:0.055521
Epoch [1/10], Iter [2515/3125], train_loss:0.074205
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Epoch [1/10], Iter [2518/3125], train_loss:0.044943
Epoch [1/10], Iter [2519/3125], train_loss:0.039163
Epoch [1/10], Iter [2520/3125], train_loss:0.033841
Epoch [1/10], Iter [2521/3125], train_loss:0.095452
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Epoch [1/10], Iter [2524/3125], train_loss:0.043344
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Epoch [1/10], Iter [2528/3125], train_loss:0.028267
Epoch [1/10], Iter [2529/3125], train_loss:0.048699
Epoch [1/10], Iter [2530/3125], train_loss:0.089290
Epoch [1/10], Iter [2531/3125], train_loss:0.080898
Epoch [1/10], Iter [2532/3125], train_loss:0.040260
Epoch [1/10], Iter [2533/3125], train_loss:0.079006
Epoch [1/10], Iter [2534/3125], train_loss:0.044073
Epoch [1/10], Iter [2535/3125], train_loss:0.056003
Epoch [1/10], Iter [2536/3125], train_loss:0.049989
Epoch [1/10], Iter [2537/3125], train_loss:0.045744
Epoch [1/10], Iter [2538/3125], train_loss:0.049811
Epoch [1/10], Iter [2539/3125], train_loss:0.059298
Epoch [1/10], Iter [2540/3125], train_loss:0.041965
Epoch [1/10], Iter [2541/3125], train_loss:0.044184
Epoch [1/10], Iter [2542/3125], train_loss:0.070333
Epoch [1/10], Iter [2543/3125], train_loss:0.061322
Epoch [1/10], Iter [2544/3125], train_loss:0.033247
Epoch [1/10], Iter [2545/3125], train_loss:0.037805
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Epoch [1/10], Iter [2548/3125], train_loss:0.053322
Epoch [1/10], Iter [2549/3125], train_loss:0.081269
Epoch [1/10], Iter [2550/3125], train_loss:0.078102
Epoch [1/10], Iter [2551/3125], train_loss:0.022630
Epoch [1/10], Iter [2552/3125], train_loss:0.032897
Epoch [1/10], Iter [2553/3125], train_loss:0.050063
Epoch [1/10], Iter [2554/3125], train_loss:0.053164
Epoch [1/10], Iter [2555/3125], train_loss:0.033120
Epoch [1/10], Iter [2556/3125], train_loss:0.046334
Epoch [1/10], Iter [2557/3125], train_loss:0.068456
Epoch [1/10], Iter [2558/3125], train_loss:0.070154
Epoch [1/10], Iter [2559/3125], train_loss:0.036025
Epoch [1/10], Iter [2560/3125], train_loss:0.070635
Epoch [1/10], Iter [2561/3125], train_loss:0.052198
Epoch [1/10], Iter [2562/3125], train_loss:0.043804
Epoch [1/10], Iter [2563/3125], train_loss:0.067197
Epoch [1/10], Iter [2564/3125], train_loss:0.080402
Epoch [1/10], Iter [2565/3125], train_loss:0.071421
Epoch [1/10], Iter [2566/3125], train_loss:0.044109
Epoch [1/10], Iter [2567/3125], train_loss:0.063801
Epoch [1/10], Iter [2568/3125], train_loss:0.075022
Epoch [1/10], Iter [2569/3125], train_loss:0.030197
Epoch [1/10], Iter [2570/3125], train_loss:0.060289
Epoch [1/10], Iter [2571/3125], train_loss:0.041631
Epoch [1/10], Iter [2572/3125], train_loss:0.047699
Epoch [1/10], Iter [2573/3125], train_loss:0.028659
Epoch [1/10], Iter [2574/3125], train_loss:0.046188
Epoch [1/10], Iter [2575/3125], train_loss:0.031889
Epoch [1/10], Iter [2576/3125], train_loss:0.066076
Epoch [1/10], Iter [2577/3125], train_loss:0.062998
Epoch [1/10], Iter [2578/3125], train_loss:0.034345
Epoch [1/10], Iter [2579/3125], train_loss:0.045776
Epoch [1/10], Iter [2580/3125], train_loss:0.063058
Epoch [1/10], Iter [2581/3125], train_loss:0.049935
Epoch [1/10], Iter [2582/3125], train_loss:0.084482
Epoch [1/10], Iter [2583/3125], train_loss:0.057923
Epoch [1/10], Iter [2584/3125], train_loss:0.045246
Epoch [1/10], Iter [2585/3125], train_loss:0.058265
Epoch [1/10], Iter [2586/3125], train_loss:0.035428
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Epoch [1/10], Iter [2588/3125], train_loss:0.067164
Epoch [1/10], Iter [2589/3125], train_loss:0.045646
Epoch [1/10], Iter [2590/3125], train_loss:0.038400
Epoch [1/10], Iter [2591/3125], train_loss:0.038546
Epoch [1/10], Iter [2592/3125], train_loss:0.072927
Epoch [1/10], Iter [2593/3125], train_loss:0.030221
Epoch [1/10], Iter [2594/3125], train_loss:0.056022
Epoch [1/10], Iter [2595/3125], train_loss:0.056454
Epoch [1/10], Iter [2596/3125], train_loss:0.044413
Epoch [1/10], Iter [2597/3125], train_loss:0.031464
Epoch [1/10], Iter [2598/3125], train_loss:0.051813
Epoch [1/10], Iter [2599/3125], train_loss:0.077083
Epoch [1/10], Iter [2600/3125], train_loss:0.040987
Epoch [1/10], Iter [2601/3125], train_loss:0.037267
Epoch [1/10], Iter [2602/3125], train_loss:0.033299
Epoch [1/10], Iter [2603/3125], train_loss:0.049933
Epoch [1/10], Iter [2604/3125], train_loss:0.050345
Epoch [1/10], Iter [2605/3125], train_loss:0.068158
Epoch [1/10], Iter [2606/3125], train_loss:0.063846
Epoch [1/10], Iter [2607/3125], train_loss:0.057081
Epoch [1/10], Iter [2608/3125], train_loss:0.050321
Epoch [1/10], Iter [2609/3125], train_loss:0.084901
Epoch [1/10], Iter [2610/3125], train_loss:0.061853
Epoch [1/10], Iter [2611/3125], train_loss:0.059709
Epoch [1/10], Iter [2612/3125], train_loss:0.057150
Epoch [1/10], Iter [2613/3125], train_loss:0.034964
Epoch [1/10], Iter [2614/3125], train_loss:0.044947
Epoch [1/10], Iter [2615/3125], train_loss:0.089898
Epoch [1/10], Iter [2616/3125], train_loss:0.052279
Epoch [1/10], Iter [2617/3125], train_loss:0.065590
Epoch [1/10], Iter [2618/3125], train_loss:0.079470
Epoch [1/10], Iter [2619/3125], train_loss:0.064696
Epoch [1/10], Iter [2620/3125], train_loss:0.031827
Epoch [1/10], Iter [2621/3125], train_loss:0.057286
Epoch [1/10], Iter [2622/3125], train_loss:0.059908
Epoch [1/10], Iter [2623/3125], train_loss:0.050808
Epoch [1/10], Iter [2624/3125], train_loss:0.076302
Epoch [1/10], Iter [2625/3125], train_loss:0.054479
Epoch [1/10], Iter [2626/3125], train_loss:0.050685
Epoch [1/10], Iter [2627/3125], train_loss:0.057106
Epoch [1/10], Iter [2628/3125], train_loss:0.050811
Epoch [1/10], Iter [2629/3125], train_loss:0.025450
Epoch [1/10], Iter [2630/3125], train_loss:0.035107
Epoch [1/10], Iter [2631/3125], train_loss:0.037918
Epoch [1/10], Iter [2632/3125], train_loss:0.049256
Epoch [1/10], Iter [2633/3125], train_loss:0.062963
Epoch [1/10], Iter [2634/3125], train_loss:0.043879
Epoch [1/10], Iter [2635/3125], train_loss:0.043937
Epoch [1/10], Iter [2636/3125], train_loss:0.043007
Epoch [1/10], Iter [2637/3125], train_loss:0.033700
Epoch [1/10], Iter [2638/3125], train_loss:0.024870
Epoch [1/10], Iter [2639/3125], train_loss:0.039514
Epoch [1/10], Iter [2640/3125], train_loss:0.067759
Epoch [1/10], Iter [2641/3125], train_loss:0.062978
Epoch [1/10], Iter [2642/3125], train_loss:0.073482
Epoch [1/10], Iter [2643/3125], train_loss:0.051648
Epoch [1/10], Iter [2644/3125], train_loss:0.065120
Epoch [1/10], Iter [2645/3125], train_loss:0.023624
Epoch [1/10], Iter [2646/3125], train_loss:0.019855
Epoch [1/10], Iter [2647/3125], train_loss:0.106905
Epoch [1/10], Iter [2648/3125], train_loss:0.058358
Epoch [1/10], Iter [2649/3125], train_loss:0.072519
Epoch [1/10], Iter [2650/3125], train_loss:0.070563
Epoch [1/10], Iter [2651/3125], train_loss:0.073849
Epoch [1/10], Iter [2652/3125], train_loss:0.051423
Epoch [1/10], Iter [2653/3125], train_loss:0.041773
Epoch [1/10], Iter [2654/3125], train_loss:0.042694
Epoch [1/10], Iter [2655/3125], train_loss:0.041109
Epoch [1/10], Iter [2656/3125], train_loss:0.046723
Epoch [1/10], Iter [2657/3125], train_loss:0.032426
Epoch [1/10], Iter [2658/3125], train_loss:0.031085
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Epoch [1/10], Iter [2660/3125], train_loss:0.034657
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Epoch [1/10], Iter [2755/3125], train_loss:0.076867
Epoch [1/10], Iter [2756/3125], train_loss:0.063004
Epoch [1/10], Iter [2757/3125], train_loss:0.055485
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Epoch [1/10], Iter [2767/3125], train_loss:0.027875
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Epoch [1/10], Iter [2777/3125], train_loss:0.049847
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Epoch [1/10], Iter [2781/3125], train_loss:0.037195
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Epoch [1/10], Iter [2784/3125], train_loss:0.089970
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Epoch [1/10], Iter [2791/3125], train_loss:0.031623
Epoch [1/10], Iter [2792/3125], train_loss:0.064973
Epoch [1/10], Iter [2793/3125], train_loss:0.083880
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Epoch [1/10], Iter [2795/3125], train_loss:0.027198
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Epoch [1/10], Iter [2797/3125], train_loss:0.045814
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Epoch [1/10], Iter [2799/3125], train_loss:0.037425
Epoch [1/10], Iter [2800/3125], train_loss:0.040245
Epoch [1/10], Iter [2801/3125], train_loss:0.069127
Epoch [1/10], Iter [2802/3125], train_loss:0.038190
Epoch [1/10], Iter [2803/3125], train_loss:0.076748
Epoch [1/10], Iter [2804/3125], train_loss:0.063528
Epoch [1/10], Iter [2805/3125], train_loss:0.050070
Epoch [1/10], Iter [2806/3125], train_loss:0.043468
Epoch [1/10], Iter [2807/3125], train_loss:0.037768
Epoch [1/10], Iter [2808/3125], train_loss:0.069925
Epoch [1/10], Iter [2809/3125], train_loss:0.027971
Epoch [1/10], Iter [2810/3125], train_loss:0.045305
Epoch [1/10], Iter [2811/3125], train_loss:0.072035
Epoch [1/10], Iter [2812/3125], train_loss:0.027901
Epoch [1/10], Iter [2813/3125], train_loss:0.055258
Epoch [1/10], Iter [2814/3125], train_loss:0.033380
Epoch [1/10], Iter [2815/3125], train_loss:0.035067
Epoch [1/10], Iter [2816/3125], train_loss:0.062196
Epoch [1/10], Iter [2817/3125], train_loss:0.031055
Epoch [1/10], Iter [2818/3125], train_loss:0.027535
Epoch [1/10], Iter [2819/3125], train_loss:0.074925
Epoch [1/10], Iter [2820/3125], train_loss:0.014863
Epoch [1/10], Iter [2821/3125], train_loss:0.040033
Epoch [1/10], Iter [2822/3125], train_loss:0.073055
Epoch [1/10], Iter [2823/3125], train_loss:0.044778
Epoch [1/10], Iter [2824/3125], train_loss:0.041350
Epoch [1/10], Iter [2825/3125], train_loss:0.045701
Epoch [1/10], Iter [2826/3125], train_loss:0.069052
Epoch [1/10], Iter [2827/3125], train_loss:0.070689
Epoch [1/10], Iter [2828/3125], train_loss:0.073792
Epoch [1/10], Iter [2829/3125], train_loss:0.027273
Epoch [1/10], Iter [2830/3125], train_loss:0.070355
Epoch [1/10], Iter [2831/3125], train_loss:0.050928
Epoch [1/10], Iter [2832/3125], train_loss:0.063157
Epoch [1/10], Iter [2833/3125], train_loss:0.052722
Epoch [1/10], Iter [2834/3125], train_loss:0.066621
Epoch [1/10], Iter [2835/3125], train_loss:0.049870
Epoch [1/10], Iter [2836/3125], train_loss:0.045198
Epoch [1/10], Iter [2837/3125], train_loss:0.047708
Epoch [1/10], Iter [2838/3125], train_loss:0.031084
Epoch [1/10], Iter [2839/3125], train_loss:0.054982
Epoch [1/10], Iter [2840/3125], train_loss:0.062080
Epoch [1/10], Iter [2841/3125], train_loss:0.052313
Epoch [1/10], Iter [2842/3125], train_loss:0.027638
Epoch [1/10], Iter [2843/3125], train_loss:0.069474
Epoch [1/10], Iter [2844/3125], train_loss:0.051465
Epoch [1/10], Iter [2845/3125], train_loss:0.047240
Epoch [1/10], Iter [2846/3125], train_loss:0.043358
Epoch [1/10], Iter [2847/3125], train_loss:0.046753
Epoch [1/10], Iter [2848/3125], train_loss:0.059748
Epoch [1/10], Iter [2849/3125], train_loss:0.032166
Epoch [1/10], Iter [2850/3125], train_loss:0.051633
Epoch [1/10], Iter [2851/3125], train_loss:0.032861
Epoch [1/10], Iter [2852/3125], train_loss:0.046734
Epoch [1/10], Iter [2853/3125], train_loss:0.031587
Epoch [1/10], Iter [2854/3125], train_loss:0.028285
Epoch [1/10], Iter [2855/3125], train_loss:0.063359
Epoch [1/10], Iter [2856/3125], train_loss:0.063512
Epoch [1/10], Iter [2857/3125], train_loss:0.048190
Epoch [1/10], Iter [2858/3125], train_loss:0.070683
Epoch [1/10], Iter [2859/3125], train_loss:0.016137
Epoch [1/10], Iter [2860/3125], train_loss:0.045513
Epoch [1/10], Iter [2861/3125], train_loss:0.033696
Epoch [1/10], Iter [2862/3125], train_loss:0.056089
Epoch [1/10], Iter [2863/3125], train_loss:0.040835
Epoch [1/10], Iter [2864/3125], train_loss:0.059301
Epoch [1/10], Iter [2865/3125], train_loss:0.065590
Epoch [1/10], Iter [2866/3125], train_loss:0.054262
Epoch [1/10], Iter [2867/3125], train_loss:0.032128
Epoch [1/10], Iter [2868/3125], train_loss:0.070486
Epoch [1/10], Iter [2869/3125], train_loss:0.050579
Epoch [1/10], Iter [2870/3125], train_loss:0.048929
Epoch [1/10], Iter [2871/3125], train_loss:0.059329
Epoch [1/10], Iter [2872/3125], train_loss:0.059987
Epoch [1/10], Iter [2873/3125], train_loss:0.038087
Epoch [1/10], Iter [2874/3125], train_loss:0.042215
Epoch [1/10], Iter [2875/3125], train_loss:0.037359
Epoch [1/10], Iter [2876/3125], train_loss:0.064945
Epoch [1/10], Iter [2877/3125], train_loss:0.032644
Epoch [1/10], Iter [2878/3125], train_loss:0.035471
Epoch [1/10], Iter [2879/3125], train_loss:0.054034
Epoch [1/10], Iter [2880/3125], train_loss:0.055840
Epoch [1/10], Iter [2881/3125], train_loss:0.040988
Epoch [1/10], Iter [2882/3125], train_loss:0.076851
Epoch [1/10], Iter [2883/3125], train_loss:0.084683
Epoch [1/10], Iter [2884/3125], train_loss:0.052963
Epoch [1/10], Iter [2885/3125], train_loss:0.033718
Epoch [1/10], Iter [2886/3125], train_loss:0.047949
Epoch [1/10], Iter [2887/3125], train_loss:0.066821
Epoch [1/10], Iter [2888/3125], train_loss:0.062198
Epoch [1/10], Iter [2889/3125], train_loss:0.064902
Epoch [1/10], Iter [2890/3125], train_loss:0.057373
Epoch [1/10], Iter [2891/3125], train_loss:0.048909
Epoch [1/10], Iter [2892/3125], train_loss:0.047169
Epoch [1/10], Iter [2893/3125], train_loss:0.037598
Epoch [1/10], Iter [2894/3125], train_loss:0.044367
Epoch [1/10], Iter [2895/3125], train_loss:0.059186
Epoch [1/10], Iter [2896/3125], train_loss:0.027673
Epoch [1/10], Iter [2897/3125], train_loss:0.046781
Epoch [1/10], Iter [2898/3125], train_loss:0.044963
Epoch [1/10], Iter [2899/3125], train_loss:0.053782
Epoch [1/10], Iter [2900/3125], train_loss:0.037537
Epoch [1/10], Iter [2901/3125], train_loss:0.043916
Epoch [1/10], Iter [2902/3125], train_loss:0.056527
Epoch [1/10], Iter [2903/3125], train_loss:0.025347
Epoch [1/10], Iter [2904/3125], train_loss:0.038642
Epoch [1/10], Iter [2905/3125], train_loss:0.066414
Epoch [1/10], Iter [2906/3125], train_loss:0.041623
Epoch [1/10], Iter [2907/3125], train_loss:0.050016
Epoch [1/10], Iter [2908/3125], train_loss:0.043550
Epoch [1/10], Iter [2909/3125], train_loss:0.039868
Epoch [1/10], Iter [2910/3125], train_loss:0.026067
Epoch [1/10], Iter [2911/3125], train_loss:0.045635
Epoch [1/10], Iter [2912/3125], train_loss:0.070421
Epoch [1/10], Iter [2913/3125], train_loss:0.063436
Epoch [1/10], Iter [2914/3125], train_loss:0.049509
Epoch [1/10], Iter [2915/3125], train_loss:0.071456
Epoch [1/10], Iter [2916/3125], train_loss:0.029413
Epoch [1/10], Iter [2917/3125], train_loss:0.042938
Epoch [1/10], Iter [2918/3125], train_loss:0.060789
Epoch [1/10], Iter [2919/3125], train_loss:0.035195
Epoch [1/10], Iter [2920/3125], train_loss:0.049221
Epoch [1/10], Iter [2921/3125], train_loss:0.032330
Epoch [1/10], Iter [2922/3125], train_loss:0.037042
Epoch [1/10], Iter [2923/3125], train_loss:0.065629
Epoch [1/10], Iter [2924/3125], train_loss:0.022151
Epoch [1/10], Iter [2925/3125], train_loss:0.056095
Epoch [1/10], Iter [2926/3125], train_loss:0.034682
Epoch [1/10], Iter [2927/3125], train_loss:0.081066
Epoch [1/10], Iter [2928/3125], train_loss:0.038369
Epoch [1/10], Iter [2929/3125], train_loss:0.025391
Epoch [1/10], Iter [2930/3125], train_loss:0.043224
Epoch [1/10], Iter [2931/3125], train_loss:0.073949
Epoch [1/10], Iter [2932/3125], train_loss:0.062411
Epoch [1/10], Iter [2933/3125], train_loss:0.048195
Epoch [1/10], Iter [2934/3125], train_loss:0.041265
Epoch [1/10], Iter [2935/3125], train_loss:0.051641
Epoch [1/10], Iter [2936/3125], train_loss:0.051737
Epoch [1/10], Iter [2937/3125], train_loss:0.085035
Epoch [1/10], Iter [2938/3125], train_loss:0.041058
Epoch [1/10], Iter [2939/3125], train_loss:0.052639
Epoch [1/10], Iter [2940/3125], train_loss:0.067252
Epoch [1/10], Iter [2941/3125], train_loss:0.067398
Epoch [1/10], Iter [2942/3125], train_loss:0.035560
Epoch [1/10], Iter [2943/3125], train_loss:0.026009
Epoch [1/10], Iter [2944/3125], train_loss:0.028872
Epoch [1/10], Iter [2945/3125], train_loss:0.100868
Epoch [1/10], Iter [2946/3125], train_loss:0.073545
Epoch [1/10], Iter [2947/3125], train_loss:0.064018
Epoch [1/10], Iter [2948/3125], train_loss:0.038802
Epoch [1/10], Iter [2949/3125], train_loss:0.035678
Epoch [1/10], Iter [2950/3125], train_loss:0.057404
Epoch [1/10], Iter [2951/3125], train_loss:0.038700
Epoch [1/10], Iter [2952/3125], train_loss:0.066487
Epoch [1/10], Iter [2953/3125], train_loss:0.036224
Epoch [1/10], Iter [2954/3125], train_loss:0.049169
Epoch [1/10], Iter [2955/3125], train_loss:0.060712
Epoch [1/10], Iter [2956/3125], train_loss:0.054164
Epoch [1/10], Iter [2957/3125], train_loss:0.045852
Epoch [1/10], Iter [2958/3125], train_loss:0.046974
Epoch [1/10], Iter [2959/3125], train_loss:0.046566
Epoch [1/10], Iter [2960/3125], train_loss:0.029474
Epoch [1/10], Iter [2961/3125], train_loss:0.048267
Epoch [1/10], Iter [2962/3125], train_loss:0.093090
Epoch [1/10], Iter [2963/3125], train_loss:0.059621
Epoch [1/10], Iter [2964/3125], train_loss:0.053808
Epoch [1/10], Iter [2965/3125], train_loss:0.019410
Epoch [1/10], Iter [2966/3125], train_loss:0.080236
Epoch [1/10], Iter [2967/3125], train_loss:0.048073
Epoch [1/10], Iter [2968/3125], train_loss:0.045536
Epoch [1/10], Iter [2969/3125], train_loss:0.037549
Epoch [1/10], Iter [2970/3125], train_loss:0.077696
Epoch [1/10], Iter [2971/3125], train_loss:0.044552
Epoch [1/10], Iter [2972/3125], train_loss:0.028185
Epoch [1/10], Iter [2973/3125], train_loss:0.027866
Epoch [1/10], Iter [2974/3125], train_loss:0.047479
Epoch [1/10], Iter [2975/3125], train_loss:0.047819
Epoch [1/10], Iter [2976/3125], train_loss:0.040483
Epoch [1/10], Iter [2977/3125], train_loss:0.070177
Epoch [1/10], Iter [2978/3125], train_loss:0.021798
Epoch [1/10], Iter [2979/3125], train_loss:0.041524
Epoch [1/10], Iter [2980/3125], train_loss:0.038104
Epoch [1/10], Iter [2981/3125], train_loss:0.050260
Epoch [1/10], Iter [2982/3125], train_loss:0.047825
Epoch [1/10], Iter [2983/3125], train_loss:0.059096
Epoch [1/10], Iter [2984/3125], train_loss:0.036488
Epoch [1/10], Iter [2985/3125], train_loss:0.048905
Epoch [1/10], Iter [2986/3125], train_loss:0.092370
Epoch [1/10], Iter [2987/3125], train_loss:0.065375
Epoch [1/10], Iter [2988/3125], train_loss:0.050387
Epoch [1/10], Iter [2989/3125], train_loss:0.040478
Epoch [1/10], Iter [2990/3125], train_loss:0.070799
Epoch [1/10], Iter [2991/3125], train_loss:0.074366
Epoch [1/10], Iter [2992/3125], train_loss:0.035977
Epoch [1/10], Iter [2993/3125], train_loss:0.050263
Epoch [1/10], Iter [2994/3125], train_loss:0.038603
Epoch [1/10], Iter [2995/3125], train_loss:0.091508
Epoch [1/10], Iter [2996/3125], train_loss:0.041844
Epoch [1/10], Iter [2997/3125], train_loss:0.037022
Epoch [1/10], Iter [2998/3125], train_loss:0.035034
Epoch [1/10], Iter [2999/3125], train_loss:0.035311
Epoch [1/10], Iter [3000/3125], train_loss:0.027116
Epoch [1/10], Iter [3001/3125], train_loss:0.029279
Epoch [1/10], Iter [3002/3125], train_loss:0.033700
Epoch [1/10], Iter [3003/3125], train_loss:0.058413
Epoch [1/10], Iter [3004/3125], train_loss:0.023097
Epoch [1/10], Iter [3005/3125], train_loss:0.045443
Epoch [1/10], Iter [3006/3125], train_loss:0.029848
Epoch [1/10], Iter [3007/3125], train_loss:0.052713
Epoch [1/10], Iter [3008/3125], train_loss:0.035926
Epoch [1/10], Iter [3009/3125], train_loss:0.058838
Epoch [1/10], Iter [3010/3125], train_loss:0.056548
Epoch [1/10], Iter [3011/3125], train_loss:0.039738
Epoch [1/10], Iter [3012/3125], train_loss:0.053625
Epoch [1/10], Iter [3013/3125], train_loss:0.032034
Epoch [1/10], Iter [3014/3125], train_loss:0.099142
Epoch [1/10], Iter [3015/3125], train_loss:0.041366
Epoch [1/10], Iter [3016/3125], train_loss:0.041256
Epoch [1/10], Iter [3017/3125], train_loss:0.037890
Epoch [1/10], Iter [3018/3125], train_loss:0.051505
Epoch [1/10], Iter [3019/3125], train_loss:0.032262
Epoch [1/10], Iter [3020/3125], train_loss:0.108767
Epoch [1/10], Iter [3021/3125], train_loss:0.039950
Epoch [1/10], Iter [3022/3125], train_loss:0.074630
Epoch [1/10], Iter [3023/3125], train_loss:0.074800
Epoch [1/10], Iter [3024/3125], train_loss:0.068196
Epoch [1/10], Iter [3025/3125], train_loss:0.039287
Epoch [1/10], Iter [3026/3125], train_loss:0.052125
Epoch [1/10], Iter [3027/3125], train_loss:0.025400
Epoch [1/10], Iter [3028/3125], train_loss:0.066438
Epoch [1/10], Iter [3029/3125], train_loss:0.038479
Epoch [1/10], Iter [3030/3125], train_loss:0.057109
Epoch [1/10], Iter [3031/3125], train_loss:0.034795
Epoch [1/10], Iter [3032/3125], train_loss:0.027901
Epoch [1/10], Iter [3033/3125], train_loss:0.050128
Epoch [1/10], Iter [3034/3125], train_loss:0.032854
Epoch [1/10], Iter [3035/3125], train_loss:0.053708
Epoch [1/10], Iter [3036/3125], train_loss:0.088014
Epoch [1/10], Iter [3037/3125], train_loss:0.075370
Epoch [1/10], Iter [3038/3125], train_loss:0.075677
Epoch [1/10], Iter [3039/3125], train_loss:0.063172
Epoch [1/10], Iter [3040/3125], train_loss:0.076501
Epoch [1/10], Iter [3041/3125], train_loss:0.058156
Epoch [1/10], Iter [3042/3125], train_loss:0.061623
Epoch [1/10], Iter [3043/3125], train_loss:0.066724
Epoch [1/10], Iter [3044/3125], train_loss:0.053383
Epoch [1/10], Iter [3045/3125], train_loss:0.050633
Epoch [1/10], Iter [3046/3125], train_loss:0.058951
Epoch [1/10], Iter [3047/3125], train_loss:0.042557
Epoch [1/10], Iter [3048/3125], train_loss:0.030441
Epoch [1/10], Iter [3049/3125], train_loss:0.024813
Epoch [1/10], Iter [3050/3125], train_loss:0.033426
Epoch [1/10], Iter [3051/3125], train_loss:0.055847
Epoch [1/10], Iter [3052/3125], train_loss:0.044011
Epoch [1/10], Iter [3053/3125], train_loss:0.027693
Epoch [1/10], Iter [3054/3125], train_loss:0.051109
Epoch [1/10], Iter [3055/3125], train_loss:0.040254
Epoch [1/10], Iter [3056/3125], train_loss:0.022783
Epoch [1/10], Iter [3057/3125], train_loss:0.052132
Epoch [1/10], Iter [3058/3125], train_loss:0.056355
Epoch [1/10], Iter [3059/3125], train_loss:0.058088
Epoch [1/10], Iter [3060/3125], train_loss:0.031884
Epoch [1/10], Iter [3061/3125], train_loss:0.049938
Epoch [1/10], Iter [3062/3125], train_loss:0.039419
Epoch [1/10], Iter [3063/3125], train_loss:0.083298
Epoch [1/10], Iter [3064/3125], train_loss:0.052872
Epoch [1/10], Iter [3065/3125], train_loss:0.035879
Epoch [1/10], Iter [3066/3125], train_loss:0.040194
Epoch [1/10], Iter [3067/3125], train_loss:0.053528
Epoch [1/10], Iter [3068/3125], train_loss:0.036000
Epoch [1/10], Iter [3069/3125], train_loss:0.039297
Epoch [1/10], Iter [3070/3125], train_loss:0.058124
Epoch [1/10], Iter [3071/3125], train_loss:0.032619
Epoch [1/10], Iter [3072/3125], train_loss:0.056250
Epoch [1/10], Iter [3073/3125], train_loss:0.053652
Epoch [1/10], Iter [3074/3125], train_loss:0.033999
Epoch [1/10], Iter [3075/3125], train_loss:0.041154
Epoch [1/10], Iter [3076/3125], train_loss:0.064491
Epoch [1/10], Iter [3077/3125], train_loss:0.051499
Epoch [1/10], Iter [3078/3125], train_loss:0.072850
Epoch [1/10], Iter [3079/3125], train_loss:0.074374
Epoch [1/10], Iter [3080/3125], train_loss:0.037571
Epoch [1/10], Iter [3081/3125], train_loss:0.043772
Epoch [1/10], Iter [3082/3125], train_loss:0.042835
Epoch [1/10], Iter [3083/3125], train_loss:0.049374
Epoch [1/10], Iter [3084/3125], train_loss:0.069075
Epoch [1/10], Iter [3085/3125], train_loss:0.028113
Epoch [1/10], Iter [3086/3125], train_loss:0.037884
Epoch [1/10], Iter [3087/3125], train_loss:0.050082
Epoch [1/10], Iter [3088/3125], train_loss:0.063452
Epoch [1/10], Iter [3089/3125], train_loss:0.053441
Epoch [1/10], Iter [3090/3125], train_loss:0.041038
Epoch [1/10], Iter [3091/3125], train_loss:0.059465
Epoch [1/10], Iter [3092/3125], train_loss:0.027648
Epoch [1/10], Iter [3093/3125], train_loss:0.034605
Epoch [1/10], Iter [3094/3125], train_loss:0.019859
Epoch [1/10], Iter [3095/3125], train_loss:0.031989
Epoch [1/10], Iter [3096/3125], train_loss:0.051489
Epoch [1/10], Iter [3097/3125], train_loss:0.056322
Epoch [1/10], Iter [3098/3125], train_loss:0.046863
Epoch [1/10], Iter [3099/3125], train_loss:0.047653
Epoch [1/10], Iter [3100/3125], train_loss:0.050260
Epoch [1/10], Iter [3101/3125], train_loss:0.080984
Epoch [1/10], Iter [3102/3125], train_loss:0.039387
Epoch [1/10], Iter [3103/3125], train_loss:0.029410
Epoch [1/10], Iter [3104/3125], train_loss:0.038941
Epoch [1/10], Iter [3105/3125], train_loss:0.043713
Epoch [1/10], Iter [3106/3125], train_loss:0.037539
Epoch [1/10], Iter [3107/3125], train_loss:0.025358
Epoch [1/10], Iter [3108/3125], train_loss:0.071836
Epoch [1/10], Iter [3109/3125], train_loss:0.056706
Epoch [1/10], Iter [3110/3125], train_loss:0.033099
Epoch [1/10], Iter [3111/3125], train_loss:0.037032
Epoch [1/10], Iter [3112/3125], train_loss:0.038965
Epoch [1/10], Iter [3113/3125], train_loss:0.041378
Epoch [1/10], Iter [3114/3125], train_loss:0.049832
Epoch [1/10], Iter [3115/3125], train_loss:0.044040
Epoch [1/10], Iter [3116/3125], train_loss:0.029385
Epoch [1/10], Iter [3117/3125], train_loss:0.059979
Epoch [1/10], Iter [3118/3125], train_loss:0.067147
Epoch [1/10], Iter [3119/3125], train_loss:0.057981
Epoch [1/10], Iter [3120/3125], train_loss:0.028045
Epoch [1/10], Iter [3121/3125], train_loss:0.042211
Epoch [1/10], Iter [3122/3125], train_loss:0.056431
Epoch [1/10], Iter [3123/3125], train_loss:0.044317
Epoch [1/10], Iter [3124/3125], train_loss:0.054007
Epoch [1/10], Iter [3125/3125], train_loss:0.042914---------------------------------------------------------------------------NameError Traceback (most recent call last)~\AppData\Local\Temp/ipykernel_14844/2960384600.py in module40 test_total_correct 041 test_total_num 0
--- 42 for iter,(images,labels) in enumerate(test_loader):43 images images.to(device)44 labels labels.to(device)NameError: name test_loader is not defined2、动态调整学习率
2.1 torch.optim.lr_scheduler
学习率选择的问题
1、学习率设置过小会极大降低收敛速度增加训练时间2、学习率设置太大可能导致参数在最优解两侧来回振荡
以上问题都是学习率设置不满足模型训练的需求解决方案
PyTorch中提供了scheduler
官方API提供的torch.optim.lr_scheduler动态学习率 lr_scheduler.LambdaLR lr_scheduler.MultiplicativeLR lr_scheduler.StepLR lr_scheduler.MultiStepLR lr_scheduler.ExponentialLR lr_scheduler.CosineAnnealingLR lr_scheduler.ReduceLROnPlateau lr_scheduler.CyclicLR lr_scheduler.OneCycleLR lr_scheduler.CosineAnnealingWarmRestarts
2.2、torch.optim.lr_scheduler.LambdaLR
torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda, last_epoch- 1, verboseFalse)
# LambdaLR 实现
lr_lambda f(epoch)
new_lr lr_lambda * init_lr思想:初始学习率乘以系数由于每一次乘系数都是乘初始学习率因此系数往往是epoch的函数。
#伪代码Assuming optimizer has two groups.lambda1 lambda epoch: 1 / (epoch1)scheduler LambdaLR(optimizer, lr_lambdalambda1)for epoch in range(100):train(...)validate(...)scheduler.step()[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-f4P8ROuA-1692613806234)(attachment:image-2.png)]
MultiplicativeLR
torch.optim.lr_scheduler.MultiplicativeLR(optimizer, lr_lambda, last_epoch- 1, verboseFalse)
与LambdaLR不同该方法用前一次的学习率乘以lr_lambda,因此通常lr_lambda函数不需要与epoch有关。
new_lr lr_lambda * old_lr
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-g2URgkPf-1692613806234)(attachment:image.png)]
2.2、自定义scheduler
官方给的动态学习率调整的API如果均不能满足我们的诉求应该怎么办
我们可以通过自定义函数adjust_learning_rate来改变param_group中lr的值
1、官方的API均不能满足诉求2、我们根据adjust_learning_rate实现学习率调整方法
# 训练中调用学习率方法
optimizer torch.optim.SGD(model.parameters(),lr args.lr,momentum 0.9)
for epoch in range(10):train(...)validate(...)adjust_learning_rate(optimizer,epoch)#函数分段每隔几(10)段个epoch,第一个epoch为序号0不计使学习率变乘以0.1的epoch次方数
def adjust_learning_rate(optim, epoch, size10, gamma0.1):if (epoch 1) % size 0:pow (epoch 1) // sizelr learning_rate * np.power(gamma, pow)for param_group in optim.param_groups:param_group[lr] lr代码实例
lr_scheduler.LambdaLRadjust_learning_rate #训练验证
writer SummaryWriter(../train_skills)
# 定义损失函数和优化器
device torch.device(cuda:0 if torch.cuda.is_available() else cpu)
# 损失函数
criterion nn.CrossEntropyLoss()
# 优化器
optimizer torch.optim.Adam(Resnet50.parameters(), lrlr)# 自定义 scheduler
scheduler_my LambdaLR(optimizer, lr_lambdalambda epoch: 1/(epoch1),verbose True)
print(初始化的学习率, optimizer.defaults[lr])epoch max_epochs
Resnet50 Resnet50.to(device)
total_step len(train_loader)
train_all_loss []
test_all_loss []for i in range(epoch):Resnet50.train()train_total_loss 0train_total_num 0train_total_correct 0for iter, (images,labels) in enumerate(train_loader):images images.to(device)labels labels.to(device)outputs Resnet50(images)loss criterion(outputs,labels)train_total_correct (outputs.argmax(1) labels).sum().item()#backwordoptimizer.zero_grad()loss.backward()optimizer.step()train_total_num labels.shape[0]train_total_loss loss.item()print(Epoch [{}/{}], Iter [{}/{}], train_loss:{:4f}.format(i1,epoch,iter1,total_step,loss.item()/labels.shape[0]))writer.add_scalar(lr, optim.param_groups[0][lr], i)print(第%d个epoch的学习率%f % (epoch, optimizer.param_groups[0][lr]))scheduler_my.step() #scheduler#自定义调整lr
# adjust_learning_rate(optimizer, i)Resnet50.eval()test_total_loss 0test_total_correct 0test_total_num 0for iter,(images,labels) in enumerate(test_loader):images images.to(device)labels labels.to(device)outputs Resnet50(images)loss criterion(outputs,labels)test_total_correct (outputs.argmax(1) labels).sum().item()test_total_loss loss.item()test_total_num labels.shape[0]print(Epoch [{}/{}], train_loss:{:.4f}, train_acc:{:.4f}%, test_loss:{:.4f}, test_acc:{:.4f}%.format(i1, epoch, train_total_loss / train_total_num, train_total_correct / train_total_num * 100, test_total_loss / test_total_num, test_total_correct / test_total_num * 100))train_all_loss.append(np.round(train_total_loss / train_total_num,4))test_all_loss.append(np.round(test_total_loss / test_total_num,4))
writer.close()Adjusting learning rate of group 0 to 1.0000e-04.
初始化的学习率 0.0001
Epoch [1/2], Iter [1/3125], train_loss:0.777986
Epoch [1/2], Iter [2/3125], train_loss:0.662992
Epoch [1/2], Iter [3/3125], train_loss:0.767887
Epoch [1/2], Iter [4/3125], train_loss:0.748286
Epoch [1/2], Iter [5/3125], train_loss:0.686887
Epoch [1/2], Iter [6/3125], train_loss:0.675070
Epoch [1/2], Iter [7/3125], train_loss:0.655532
Epoch [1/2], Iter [8/3125], train_loss:0.713970
Epoch [1/2], Iter [9/3125], train_loss:0.675706
Epoch [1/2], Iter [10/3125], train_loss:0.665308
Epoch [1/2], Iter [11/3125], train_loss:0.670263
Epoch [1/2], Iter [12/3125], train_loss:0.597091
Epoch [1/2], Iter [13/3125], train_loss:0.541138
Epoch [1/2], Iter [14/3125], train_loss:0.471112
Epoch [1/2], Iter [15/3125], train_loss:0.570017
Epoch [1/2], Iter [16/3125], train_loss:0.569556
Epoch [1/2], Iter [17/3125], train_loss:0.552114
Epoch [1/2], Iter [18/3125], train_loss:0.569929
Epoch [1/2], Iter [19/3125], train_loss:0.524716
Epoch [1/2], Iter [20/3125], train_loss:0.522762
Epoch [1/2], Iter [21/3125], train_loss:0.499370
Epoch [1/2], Iter [22/3125], train_loss:0.459812
Epoch [1/2], Iter [23/3125], train_loss:0.407852
Epoch [1/2], Iter [24/3125], train_loss:0.472173
Epoch [1/2], Iter [25/3125], train_loss:0.370801
Epoch [1/2], Iter [26/3125], train_loss:0.459706
Epoch [1/2], Iter [27/3125], train_loss:0.403983
Epoch [1/2], Iter [28/3125], train_loss:0.372209
Epoch [1/2], Iter [29/3125], train_loss:0.357835
Epoch [1/2], Iter [30/3125], train_loss:0.501332
Epoch [1/2], Iter [31/3125], train_loss:0.354409
Epoch [1/2], Iter [32/3125], train_loss:0.352994
Epoch [1/2], Iter [33/3125], train_loss:0.359231
Epoch [1/2], Iter [34/3125], train_loss:0.378708
Epoch [1/2], Iter [35/3125], train_loss:0.445062
Epoch [1/2], Iter [36/3125], train_loss:0.345325
Epoch [1/2], Iter [37/3125], train_loss:0.290598
Epoch [1/2], Iter [38/3125], train_loss:0.355161
Epoch [1/2], Iter [39/3125], train_loss:0.295590
Epoch [1/2], Iter [40/3125], train_loss:0.269099
Epoch [1/2], Iter [41/3125], train_loss:0.339802
Epoch [1/2], Iter [42/3125], train_loss:0.251694
Epoch [1/2], Iter [43/3125], train_loss:0.328401
Epoch [1/2], Iter [44/3125], train_loss:0.257955
Epoch [1/2], Iter [45/3125], train_loss:0.325558
Epoch [1/2], Iter [46/3125], train_loss:0.342137
Epoch [1/2], Iter [47/3125], train_loss:0.259149
Epoch [1/2], Iter [48/3125], train_loss:0.249372
Epoch [1/2], Iter [49/3125], train_loss:0.257600
Epoch [1/2], Iter [50/3125], train_loss:0.289483
Epoch [1/2], Iter [51/3125], train_loss:0.301230
Epoch [1/2], Iter [52/3125], train_loss:0.217237
Epoch [1/2], Iter [53/3125], train_loss:0.279841
Epoch [1/2], Iter [54/3125], train_loss:0.261875
Epoch [1/2], Iter [55/3125], train_loss:0.216530
Epoch [1/2], Iter [56/3125], train_loss:0.279174
Epoch [1/2], Iter [57/3125], train_loss:0.188948
Epoch [1/2], Iter [58/3125], train_loss:0.207412
Epoch [1/2], Iter [59/3125], train_loss:0.239609
Epoch [1/2], Iter [60/3125], train_loss:0.195655
Epoch [1/2], Iter [61/3125], train_loss:0.196358
Epoch [1/2], Iter [62/3125], train_loss:0.264320
Epoch [1/2], Iter [63/3125], train_loss:0.193350
Epoch [1/2], Iter [64/3125], train_loss:0.165940
Epoch [1/2], Iter [65/3125], train_loss:0.267849
Epoch [1/2], Iter [66/3125], train_loss:0.221301
Epoch [1/2], Iter [67/3125], train_loss:0.269790
Epoch [1/2], Iter [68/3125], train_loss:0.227033
Epoch [1/2], Iter [69/3125], train_loss:0.156358
Epoch [1/2], Iter [70/3125], train_loss:0.210391
Epoch [1/2], Iter [71/3125], train_loss:0.251990
Epoch [1/2], Iter [72/3125], train_loss:0.177134
Epoch [1/2], Iter [73/3125], train_loss:0.155195
Epoch [1/2], Iter [74/3125], train_loss:0.251515
Epoch [1/2], Iter [75/3125], train_loss:0.159152
Epoch [1/2], Iter [76/3125], train_loss:0.166255
Epoch [1/2], Iter [77/3125], train_loss:0.115882
Epoch [1/2], Iter [78/3125], train_loss:0.175745
Epoch [1/2], Iter [79/3125], train_loss:0.138844
Epoch [1/2], Iter [80/3125], train_loss:0.176611
Epoch [1/2], Iter [81/3125], train_loss:0.161312
Epoch [1/2], Iter [82/3125], train_loss:0.148712
Epoch [1/2], Iter [83/3125], train_loss:0.207151
Epoch [1/2], Iter [84/3125], train_loss:0.111603
Epoch [1/2], Iter [85/3125], train_loss:0.107699
Epoch [1/2], Iter [86/3125], train_loss:0.162084
Epoch [1/2], Iter [87/3125], train_loss:0.199193
Epoch [1/2], Iter [88/3125], train_loss:0.138881
Epoch [1/2], Iter [89/3125], train_loss:0.161221
Epoch [1/2], Iter [90/3125], train_loss:0.149200
Epoch [1/2], Iter [91/3125], train_loss:0.151864
Epoch [1/2], Iter [92/3125], train_loss:0.201360
Epoch [1/2], Iter [93/3125], train_loss:0.169258
Epoch [1/2], Iter [94/3125], train_loss:0.149062
Epoch [1/2], Iter [95/3125], train_loss:0.149584
Epoch [1/2], Iter [96/3125], train_loss:0.145563
Epoch [1/2], Iter [97/3125], train_loss:0.126489
Epoch [1/2], Iter [98/3125], train_loss:0.139146
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Epoch [1/2], Iter [904/3125], train_loss:0.044780
Epoch [1/2], Iter [905/3125], train_loss:0.089853
Epoch [1/2], Iter [906/3125], train_loss:0.101245
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Epoch [1/2], Iter [920/3125], train_loss:0.080133
Epoch [1/2], Iter [921/3125], train_loss:0.088354
Epoch [1/2], Iter [922/3125], train_loss:0.079794
Epoch [1/2], Iter [923/3125], train_loss:0.082035
Epoch [1/2], Iter [924/3125], train_loss:0.043397
Epoch [1/2], Iter [925/3125], train_loss:0.101342
Epoch [1/2], Iter [926/3125], train_loss:0.070656
Epoch [1/2], Iter [927/3125], train_loss:0.068928
Epoch [1/2], Iter [928/3125], train_loss:0.086801
Epoch [1/2], Iter [929/3125], train_loss:0.059911
Epoch [1/2], Iter [930/3125], train_loss:0.079392
Epoch [1/2], Iter [931/3125], train_loss:0.083579
Epoch [1/2], Iter [932/3125], train_loss:0.051975
Epoch [1/2], Iter [933/3125], train_loss:0.083430
Epoch [1/2], Iter [934/3125], train_loss:0.066587
Epoch [1/2], Iter [935/3125], train_loss:0.087434
Epoch [1/2], Iter [936/3125], train_loss:0.087518
Epoch [1/2], Iter [937/3125], train_loss:0.075971
Epoch [1/2], Iter [938/3125], train_loss:0.060921
Epoch [1/2], Iter [939/3125], train_loss:0.059609
Epoch [1/2], Iter [940/3125], train_loss:0.053374
Epoch [1/2], Iter [941/3125], train_loss:0.059154
Epoch [1/2], Iter [942/3125], train_loss:0.037160
Epoch [1/2], Iter [943/3125], train_loss:0.094307
Epoch [1/2], Iter [944/3125], train_loss:0.069412
Epoch [1/2], Iter [945/3125], train_loss:0.093543
Epoch [1/2], Iter [946/3125], train_loss:0.057713
Epoch [1/2], Iter [947/3125], train_loss:0.050613
Epoch [1/2], Iter [948/3125], train_loss:0.101521
Epoch [1/2], Iter [949/3125], train_loss:0.099398
Epoch [1/2], Iter [950/3125], train_loss:0.098440
Epoch [1/2], Iter [951/3125], train_loss:0.036929
Epoch [1/2], Iter [952/3125], train_loss:0.062752
Epoch [1/2], Iter [953/3125], train_loss:0.048165
Epoch [1/2], Iter [954/3125], train_loss:0.075584
Epoch [1/2], Iter [955/3125], train_loss:0.080492
Epoch [1/2], Iter [956/3125], train_loss:0.087700
Epoch [1/2], Iter [957/3125], train_loss:0.043403
Epoch [1/2], Iter [958/3125], train_loss:0.069215
Epoch [1/2], Iter [959/3125], train_loss:0.044430
Epoch [1/2], Iter [960/3125], train_loss:0.066561
Epoch [1/2], Iter [961/3125], train_loss:0.106058
Epoch [1/2], Iter [962/3125], train_loss:0.066117
Epoch [1/2], Iter [963/3125], train_loss:0.075821
Epoch [1/2], Iter [964/3125], train_loss:0.076452
Epoch [1/2], Iter [965/3125], train_loss:0.068917
Epoch [1/2], Iter [966/3125], train_loss:0.073009
Epoch [1/2], Iter [967/3125], train_loss:0.066570
Epoch [1/2], Iter [968/3125], train_loss:0.078626
Epoch [1/2], Iter [969/3125], train_loss:0.071714
Epoch [1/2], Iter [970/3125], train_loss:0.073739
Epoch [1/2], Iter [971/3125], train_loss:0.036135
Epoch [1/2], Iter [972/3125], train_loss:0.077290
Epoch [1/2], Iter [973/3125], train_loss:0.108345
Epoch [1/2], Iter [974/3125], train_loss:0.085700
Epoch [1/2], Iter [975/3125], train_loss:0.081209
Epoch [1/2], Iter [976/3125], train_loss:0.034647
Epoch [1/2], Iter [977/3125], train_loss:0.056354
Epoch [1/2], Ite
