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1、PyTorch 复杂模型构建
1、模型截图2、模型部件实现3、模型组装
2、模型定义
2.1、Sequential
1、当模型的前向计算为简单串联各个层的计算时#xff0c; Sequential 类可以通过更加简单的方式定义模型。2、可以接收…https://github.com/ExpressGit/Pytorch_Study_Demo
1、PyTorch 复杂模型构建
1、模型截图2、模型部件实现3、模型组装
2、模型定义
2.1、Sequential
1、当模型的前向计算为简单串联各个层的计算时 Sequential 类可以通过更加简单的方式定义模型。2、可以接收一个子模块的有序字典(OrderedDict) 或者一系列子模块作为参数来逐一添加 Module 的实例模型的前向计算就是将这些实例按添加的顺序逐⼀计算 - 3、使用Sequential定义模型的好处在于简单、易读同时使用Sequential定义的模型不需要再写forward
import torch.nn as nn
net nn.Sequential(nn.Linear(784, 256),nn.ReLU(),nn.Linear(256, 10), )
print(net)
Sequential((0): Linear(in_features784, out_features256, biasTrue)(1): ReLU()(2): Linear(in_features256, out_features10, biasTrue)
)import collections
import torch.nn as nn
net2 nn.Sequential(collections.OrderedDict([(fc1, nn.Linear(784, 256)),(relu1, nn.ReLU()),(fc2, nn.Linear(256, 10))]))
print(net2)Sequential((fc1): Linear(in_features784, out_features256, biasTrue)(relu1): ReLU()(fc2): Linear(in_features256, out_features10, biasTrue)
)2.2、ModuleList
ModuleList 接收一个子模块或层需属于nn.Module类的列表作为输入然后也可以类似List那样进行append和extend操作nn.ModuleList 并没有定义一个网络它只是将不同的模块储存在一起。ModuleList中元素的先后顺序并不代表其在网络中的真实位置顺序
net nn.ModuleList([nn.Linear(784, 256), nn.ReLU()])
net.append(nn.Linear(256, 10)) # # 类似List的append操作
print(net[-1]) # 类似List的索引访问
print(net)Linear(in_features256, out_features10, biasTrue)
ModuleList((0): Linear(in_features784, out_features256, biasTrue)(1): ReLU()(2): Linear(in_features256, out_features10, biasTrue)
)2.3、ModuleDict
ModuleList 接收一个子模块或层需属于nn.Module类的列表作为输入然后也可以类似List那样进行append和extend操作增加子模块或层的同时权重也会自动添加到网络中来
net nn.ModuleDict({linear: nn.Linear(784, 256),act: nn.ReLU(),
})
net[output] nn.Linear(256, 10) # 添加
print(net[linear]) # 访问
print(net.output)
print(net)Linear(in_features784, out_features256, biasTrue)
Linear(in_features256, out_features10, biasTrue)
ModuleDict((linear): Linear(in_features784, out_features256, biasTrue)(act): ReLU()(output): Linear(in_features256, out_features10, biasTrue)
)3、手搓Restnet50
3.1、Restnet50
resnet 在imageNet竞赛中分类任务第一名、目标检测第一名获得COCO数据集中目标检测第一名图像分割第一名。
3.2、手搓思路
resnet50讲解网络的输入照片大小是224x224的经过conv1conv2conv3conv4conv5最后在平均池化全连接层。由于中间有重复利用的模块所以我们需要将它们写成一个类用来重复调用即可
3.3、resetnet核心要点
1、提出residual模块残差2、使用Batch Normalization加速训练均值为0方差为1 虚线代表进行残差的部分 3.4 模型结构解析restnet50
1、conv1,stride2,kernel_size7,out_chnnels642、conv2_x 2.1、 max_pool:kernel_size3, stride22.2、 conv_01:stride1,kernel_size1,out_chnnels642.3、 conv_02:stride2,kernel_size3,out_chnnels642.4、 conv_03:stride1,kernel_size1,out_chnnels2562.5、 layersconv_01conv_02conv_03*3 3、conv3_x 3.1、conv_01:stride1,kernel_size1,out_chnnels1283.2、conv_02:stride2,kernel_size3,out_chnnels1283.3、conv_03:stride1,kernel_size1,out_chnnels5123.4、residual:stride2,kernel_size1,out_chnnels5123.5、layersconv_01conv_02conv_03*4 4、conv4_x 4.1、conv_01:stride1,kernel_size1,out_chnnels2564.2、conv_02:stride2,kernel_size3,out_chnnels2564.3、conv_03:stride1,kernel_size1,out_chnnels10244.4、residual:stride2,kernel_size1,out_chnnels10244.5、layersconv_01conv_02conv_03*6 5、conv5_x 5.1、conv_01:stride1,kernel_size1,out_chnnels5125.2、conv_02:stride2,kernel_size3,out_chnnels5125.3、conv_03:stride1,kernel_size1,out_chnnels20485.4、residual:stride2,kernel_size1,out_chnnels20485.5、layersconv_01conv_02conv_03*3 6、fc 6.1、AdaptiveAvgPool2d:output1,16.2、flatten(x, 1)6.3、fc:linear(512 * 4num_class)
import torch.nn as nn
import torchclass Block(nn.Module):def __init__(self, in_channels, out_channels, stride1, downsampleFalse):super(Block, self).__init__()out_channel_01, out_channel_02, out_channel_03 out_channelsself.downsample downsampleself.relu nn.ReLU(inplaceTrue)self.conv1 nn.Sequential(nn.Conv2d(in_channels, out_channel_01, kernel_size1, stride1,biasFalse),nn.BatchNorm2d(out_channel_01),nn.ReLU(inplaceTrue))self.conv2 nn.Sequential(nn.Conv2d(out_channel_01, out_channel_02, kernel_size3, stridestride, padding1, biasFalse),nn.BatchNorm2d(out_channel_02),nn.ReLU(inplaceTrue))self.conv3 nn.Sequential(nn.Conv2d(out_channel_02, out_channel_03, kernel_size1, stride1, biasFalse),nn.BatchNorm2d(out_channel_03),)if downsample:self.shortcut nn.Sequential(nn.Conv2d(in_channels, out_channel_03, kernel_size1, stridestride, biasFalse),nn.BatchNorm2d(out_channel_03))def forward(self,x):x_shortcut xx self.conv1(x)x self.conv2(x)x self.conv3(x)if self.downsample:x_shortcut self.shortcut(x_shortcut)x x x_shortcutx self.relu(x)return x
class Resnet50(nn.Module):def __init__(self):super(Resnet50,self).__init__()self.conv1 nn.Sequential(nn.Conv2d(3, 64, kernel_size7, stride2, padding3),nn.BatchNorm2d(64),nn.ReLU(),)Layers [3, 4, 6, 3]self.maxpool nn.MaxPool2d(kernel_size3, stride2, padding1)self.conv2 self._make_layer(64, (64, 64, 256), Layers[0],1)self.conv3 self._make_layer(256, (128, 128, 512), Layers[1], 2)self.conv4 self._make_layer(512, (256, 256, 1024), Layers[2], 2)self.conv5 self._make_layer(1024, (512, 512, 2048), Layers[3], 2)self.avgpool nn.AdaptiveAvgPool2d((1, 1))self.fc nn.Sequential(nn.Linear(2048, 1000))def forward(self, input):x self.conv1(input)x self.maxpool(x)x self.conv2(x)x self.conv3(x)x self.conv4(x)x self.conv5(x)x self.avgpool(x)x torch.flatten(x, 1)x self.fc(x)return xdef _make_layer(self, in_channels, out_channels, blocks, stride1):layers []block_1 Block(in_channels, out_channels, stridestride, downsampleTrue)layers.append(block_1)for i in range(1, blocks):layers.append(Block(out_channels[2], out_channels, stride1, downsampleFalse))return nn.Sequential(*layers)
#打印网络结构
net Resnet50()
x torch.rand((10, 3, 224, 224))
for name,layer in net.named_children():if name ! fc:x layer(x)print(name, output shaoe:, x.shape)else:x x.view(x.size(0), -1)x layer(x)print(name, output shaoe:, x.shape)
conv1 output shaoe: torch.Size([10, 64, 112, 112])
maxpool output shaoe: torch.Size([10, 64, 56, 56])
conv2 output shaoe: torch.Size([10, 256, 56, 56])
conv3 output shaoe: torch.Size([10, 512, 28, 28])
conv4 output shaoe: torch.Size([10, 1024, 14, 14])
conv5 output shaoe: torch.Size([10, 2048, 7, 7])
avgpool output shaoe: torch.Size([10, 2048, 1, 1])
fc output shaoe: torch.Size([10, 1000])#torchinfo 可视化网络结构
from torchinfo import summary
net Resnet50()summary(net,((10, 3, 224, 224))) Layer (type:depth-idx) Output Shape Param #Resnet50 [10, 1000] --
├─Sequential: 1-1 [10, 64, 112, 112] --
│ └─Conv2d: 2-1 [10, 64, 112, 112] 9,472
│ └─BatchNorm2d: 2-2 [10, 64, 112, 112] 128
│ └─ReLU: 2-3 [10, 64, 112, 112] --
├─MaxPool2d: 1-2 [10, 64, 56, 56] --
├─Sequential: 1-3 [10, 256, 56, 56] --
│ └─Block: 2-4 [10, 256, 56, 56] --
│ │ └─Sequential: 3-1 [10, 64, 56, 56] 4,224
│ │ └─Sequential: 3-2 [10, 64, 56, 56] 36,992
│ │ └─Sequential: 3-3 [10, 256, 56, 56] 16,896
│ │ └─Sequential: 3-4 [10, 256, 56, 56] 16,896
│ │ └─ReLU: 3-5 [10, 256, 56, 56] --
│ └─Block: 2-5 [10, 256, 56, 56] --
│ │ └─Sequential: 3-6 [10, 64, 56, 56] 16,512
│ │ └─Sequential: 3-7 [10, 64, 56, 56] 36,992
│ │ └─Sequential: 3-8 [10, 256, 56, 56] 16,896
│ │ └─ReLU: 3-9 [10, 256, 56, 56] --
│ └─Block: 2-6 [10, 256, 56, 56] --
│ │ └─Sequential: 3-10 [10, 64, 56, 56] 16,512
│ │ └─Sequential: 3-11 [10, 64, 56, 56] 36,992
│ │ └─Sequential: 3-12 [10, 256, 56, 56] 16,896
│ │ └─ReLU: 3-13 [10, 256, 56, 56] --
├─Sequential: 1-4 [10, 512, 28, 28] --
│ └─Block: 2-7 [10, 512, 28, 28] --
│ │ └─Sequential: 3-14 [10, 128, 56, 56] 33,024
│ │ └─Sequential: 3-15 [10, 128, 28, 28] 147,712
│ │ └─Sequential: 3-16 [10, 512, 28, 28] 66,560
│ │ └─Sequential: 3-17 [10, 512, 28, 28] 132,096
│ │ └─ReLU: 3-18 [10, 512, 28, 28] --
│ └─Block: 2-8 [10, 512, 28, 28] --
│ │ └─Sequential: 3-19 [10, 128, 28, 28] 65,792
│ │ └─Sequential: 3-20 [10, 128, 28, 28] 147,712
│ │ └─Sequential: 3-21 [10, 512, 28, 28] 66,560
│ │ └─ReLU: 3-22 [10, 512, 28, 28] --
│ └─Block: 2-9 [10, 512, 28, 28] --
│ │ └─Sequential: 3-23 [10, 128, 28, 28] 65,792
│ │ └─Sequential: 3-24 [10, 128, 28, 28] 147,712
│ │ └─Sequential: 3-25 [10, 512, 28, 28] 66,560
│ │ └─ReLU: 3-26 [10, 512, 28, 28] --
│ └─Block: 2-10 [10, 512, 28, 28] --
│ │ └─Sequential: 3-27 [10, 128, 28, 28] 65,792
│ │ └─Sequential: 3-28 [10, 128, 28, 28] 147,712
│ │ └─Sequential: 3-29 [10, 512, 28, 28] 66,560
│ │ └─ReLU: 3-30 [10, 512, 28, 28] --
├─Sequential: 1-5 [10, 1024, 14, 14] --
│ └─Block: 2-11 [10, 1024, 14, 14] --
│ │ └─Sequential: 3-31 [10, 256, 28, 28] 131,584
│ │ └─Sequential: 3-32 [10, 256, 14, 14] 590,336
│ │ └─Sequential: 3-33 [10, 1024, 14, 14] 264,192
│ │ └─Sequential: 3-34 [10, 1024, 14, 14] 526,336
│ │ └─ReLU: 3-35 [10, 1024, 14, 14] --
│ └─Block: 2-12 [10, 1024, 14, 14] --
│ │ └─Sequential: 3-36 [10, 256, 14, 14] 262,656
│ │ └─Sequential: 3-37 [10, 256, 14, 14] 590,336
│ │ └─Sequential: 3-38 [10, 1024, 14, 14] 264,192
│ │ └─ReLU: 3-39 [10, 1024, 14, 14] --
│ └─Block: 2-13 [10, 1024, 14, 14] --
│ │ └─Sequential: 3-40 [10, 256, 14, 14] 262,656
│ │ └─Sequential: 3-41 [10, 256, 14, 14] 590,336
│ │ └─Sequential: 3-42 [10, 1024, 14, 14] 264,192
│ │ └─ReLU: 3-43 [10, 1024, 14, 14] --
│ └─Block: 2-14 [10, 1024, 14, 14] --
│ │ └─Sequential: 3-44 [10, 256, 14, 14] 262,656
│ │ └─Sequential: 3-45 [10, 256, 14, 14] 590,336
│ │ └─Sequential: 3-46 [10, 1024, 14, 14] 264,192
│ │ └─ReLU: 3-47 [10, 1024, 14, 14] --
│ └─Block: 2-15 [10, 1024, 14, 14] --
│ │ └─Sequential: 3-48 [10, 256, 14, 14] 262,656
│ │ └─Sequential: 3-49 [10, 256, 14, 14] 590,336
│ │ └─Sequential: 3-50 [10, 1024, 14, 14] 264,192
│ │ └─ReLU: 3-51 [10, 1024, 14, 14] --
│ └─Block: 2-16 [10, 1024, 14, 14] --
│ │ └─Sequential: 3-52 [10, 256, 14, 14] 262,656
│ │ └─Sequential: 3-53 [10, 256, 14, 14] 590,336
│ │ └─Sequential: 3-54 [10, 1024, 14, 14] 264,192
│ │ └─ReLU: 3-55 [10, 1024, 14, 14] --
├─Sequential: 1-6 [10, 2048, 7, 7] --
│ └─Block: 2-17 [10, 2048, 7, 7] --
│ │ └─Sequential: 3-56 [10, 512, 14, 14] 525,312
│ │ └─Sequential: 3-57 [10, 512, 7, 7] 2,360,320
│ │ └─Sequential: 3-58 [10, 2048, 7, 7] 1,052,672
│ │ └─Sequential: 3-59 [10, 2048, 7, 7] 2,101,248
│ │ └─ReLU: 3-60 [10, 2048, 7, 7] --
│ └─Block: 2-18 [10, 2048, 7, 7] --
│ │ └─Sequential: 3-61 [10, 512, 7, 7] 1,049,600
│ │ └─Sequential: 3-62 [10, 512, 7, 7] 2,360,320
│ │ └─Sequential: 3-63 [10, 2048, 7, 7] 1,052,672
│ │ └─ReLU: 3-64 [10, 2048, 7, 7] --
│ └─Block: 2-19 [10, 2048, 7, 7] --
│ │ └─Sequential: 3-65 [10, 512, 7, 7] 1,049,600
│ │ └─Sequential: 3-66 [10, 512, 7, 7] 2,360,320
│ │ └─Sequential: 3-67 [10, 2048, 7, 7] 1,052,672
│ │ └─ReLU: 3-68 [10, 2048, 7, 7] --
├─AdaptiveAvgPool2d: 1-7 [10, 2048, 1, 1] --
├─Sequential: 1-8 [10, 1000] --
│ └─Linear: 2-20 [10, 1000] 2,049,000Total params: 25,557,096
Trainable params: 25,557,096
Non-trainable params: 0
Total mult-adds (G): 40.90Input size (MB): 6.02
Forward/backward pass size (MB): 1778.32
Params size (MB): 102.23
Estimated Total Size (MB): 1886.57from torch.utils.data import Dataset, DataLoader
from torchvision.transforms import transforms
import torchvision
import os
import numpy as np
import torch#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
# 方案一指定GPU的方式
# os.environ[CUDA_VISIBLE_DEVICES] 0,1 # 指明调用的GPU为0,1号# 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:0 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)# from tensorboard import SummaryWriter
from torch.utils.tensorboard import SummaryWriter
writer SummaryWriter(./runs)#训练验证
writer SummaryWriter(./runs)# Set fixed random number seed
torch.manual_seed(42)
# 定义损失函数和优化器
device torch.device(cuda:0 if torch.cuda.is_available() else cpu)
My_model Resnet50()
My_model My_model.to(device)
# 交叉熵
criterion torch.nn.CrossEntropyLoss()
# 优化器
optimizer torch.optim.Adam(My_model.parameters(), lrlr)
epoch max_epochstotal_step len(train_loader)
train_all_loss []
test_all_loss []
for i in range(epoch):My_model.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)# Write the network graph at epoch 0, batch 0if epoch 0 and iter 0:writer.add_graph(My_model, input_to_model(images,labels)[0], verboseTrue)# Write an image at every batch 0if iter 0:writer.add_image(Example input, images[0], global_stepepoch)outputs My_model(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 statisticswriter.add_scalar(Loss/Minibatches, train_total_loss, train_total_num)print(Epoch [{}/{}], Iter [{}/{}], train_loss:{:4f}.format(i1,epoch,iter1,total_step,loss.item()/labels.shape[0]))# Write loss for epochwriter.add_scalar(Loss/Epochs, train_total_loss, epoch)My_model.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 My_model(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))
Epoch [1/2], Iter [1/3125], train_loss:0.430043
Epoch [1/2], Iter [2/3125], train_loss:0.399217
Epoch [1/2], Iter [3/3125], train_loss:0.391730
Epoch [1/2], Iter [4/3125], train_loss:0.381970
Epoch [1/2], Iter [5/3125], train_loss:0.337084
Epoch [1/2], Iter [6/3125], train_loss:0.322986
Epoch [1/2], Iter [7/3125], train_loss:0.328911
Epoch [1/2], Iter [8/3125], train_loss:0.287385
Epoch [1/2], Iter [9/3125], train_loss:0.289794
Epoch [1/2], Iter [10/3125], train_loss:0.247583
Epoch [1/2], Iter [11/3125], train_loss:0.239406
Epoch [1/2], Iter [12/3125], train_loss:0.252444
Epoch [1/2], Iter [13/3125], train_loss:0.204779
Epoch [1/2], Iter [14/3125], train_loss:0.197130
Epoch [1/2], Iter [15/3125], train_loss:0.198398
Epoch [1/2], Iter [16/3125], train_loss:0.234318
Epoch [1/2], Iter [17/3125], train_loss:0.175272
Epoch [1/2], Iter [18/3125], train_loss:0.175399
Epoch [1/2], Iter [19/3125], train_loss:0.166614
Epoch [1/2], Iter [20/3125], train_loss:0.193967
Epoch [1/2], Iter [21/3125], train_loss:0.197993
Epoch [1/2], Iter [22/3125], train_loss:0.159795
Epoch [1/2], Iter [23/3125], train_loss:0.164005
Epoch [1/2], Iter [24/3125], train_loss:0.170589
Epoch [1/2], Iter [25/3125], train_loss:0.138586
Epoch [1/2], Iter [26/3125], train_loss:0.160901
Epoch [1/2], Iter [27/3125], train_loss:0.159692
Epoch [1/2], Iter [28/3125], train_loss:0.174360
Epoch [1/2], Iter [29/3125], train_loss:0.166350
Epoch [1/2], Iter [30/3125], train_loss:0.163763
Epoch [1/2], Iter [31/3125], train_loss:0.174778
Epoch [1/2], Iter [32/3125], train_loss:0.169331
Epoch [1/2], Iter [33/3125], train_loss:0.151900
Epoch [1/2], Iter [34/3125], train_loss:0.167900
Epoch [1/2], Iter [35/3125], train_loss:0.174177
Epoch [1/2], Iter [36/3125], train_loss:0.174313
Epoch [1/2], Iter [37/3125], train_loss:0.165772
Epoch [1/2], Iter [38/3125], train_loss:0.163259
Epoch [1/2], Iter [39/3125], train_loss:0.157740
Epoch [1/2], Iter [40/3125], train_loss:0.176562
Epoch [1/2], Iter [41/3125], train_loss:0.173564
Epoch [1/2], Iter [42/3125], train_loss:0.167849
Epoch [1/2], Iter [43/3125], train_loss:0.158219
Epoch [1/2], Iter [44/3125], train_loss:0.153129
Epoch [1/2], Iter [45/3125], train_loss:0.165890
Epoch [1/2], Iter [46/3125], train_loss:0.175445
Epoch [1/2], Iter [47/3125], train_loss:0.161246
Epoch [1/2], Iter [48/3125], train_loss:0.152963
Epoch [1/2], Iter [49/3125], train_loss:0.159098
Epoch [1/2], Iter [50/3125], train_loss:0.149376
Epoch [1/2], Iter [51/3125], train_loss:0.169790
Epoch [1/2], Iter [52/3125], train_loss:0.156566
Epoch [1/2], Iter [53/3125], train_loss:0.137577
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Epoch [1/2], Iter [2250/3125], train_loss:0.132058
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Epoch [1/2], Iter [2260/3125], train_loss:0.103699
Epoch [1/2], Iter [2261/3125], train_loss:0.099706
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Epoch [1/2], Iter [2263/3125], train_loss:0.100194
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Epoch [1/2], Iter [2270/3125], train_loss:0.086729
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Epoch [2/2], Iter [809/3125], train_loss:0.078759
Epoch [2/2], Iter [810/3125], train_loss:0.093516
Epoch [2/2], Iter [811/3125], train_loss:0.093906
Epoch [2/2], Iter [812/3125], train_loss:0.095975
Epoch [2/2], Iter [813/3125], train_loss:0.103670
Epoch [2/2], Iter [814/3125], train_loss:0.096167
Epoch [2/2], Iter [815/3125], train_loss:0.085567
Epoch [2/2], Iter [816/3125], train_loss:0.095904
Epoch [2/2], Iter [817/3125], train_loss:0.095014
Epoch [2/2], Iter [818/3125], train_loss:0.095261
Epoch [2/2], Iter [819/3125], train_loss:0.105797
Epoch [2/2], Iter [820/3125], train_loss:0.076541
Epoch [2/2], Iter [821/3125], train_loss:0.076522
Epoch [2/2], Iter [822/3125], train_loss:0.104505
Epoch [2/2], Iter [823/3125], train_loss:0.106988
Epoch [2/2], Iter [824/3125], train_loss:0.103925
Epoch [2/2], Iter [825/3125], train_loss:0.109792
Epoch [2/2], Iter [826/3125], train_loss:0.091824
Epoch [2/2], Iter [827/3125], train_loss:0.101664
Epoch [2/2], Iter [828/3125], train_loss:0.135664
Epoch [2/2], Iter [829/3125], train_loss:0.062098
Epoch [2/2], Iter [830/3125], train_loss:0.096688
Epoch [2/2], Iter [831/3125], train_loss:0.083266
Epoch [2/2], Iter [832/3125], train_loss:0.074664
Epoch [2/2], Iter [833/3125], train_loss:0.136668
Epoch [2/2], Iter [834/3125], train_loss:0.117845
Epoch [2/2], Iter [835/3125], train_loss:0.109683
Epoch [2/2], Iter [836/3125], train_loss:0.080236
Epoch [2/2], Iter [837/3125], train_loss:0.063216
Epoch [2/2], Iter [838/3125], train_loss:0.128305
Epoch [2/2], Iter [839/3125], train_loss:0.062488
Epoch [2/2], Iter [840/3125], train_loss:0.144444
Epoch [2/2], Iter [841/3125], train_loss:0.119419
Epoch [2/2], Iter [842/3125], train_loss:0.077271
Epoch [2/2], Iter [843/3125], train_loss:0.108360
Epoch [2/2], Iter [844/3125], train_loss:0.093583
Epoch [2/2], Iter [845/3125], train_loss:0.103373
Epoch [2/2], Iter [846/3125], train_loss:0.105248
Epoch [2/2], Iter [847/3125], train_loss:0.071489
Epoch [2/2], Iter [848/3125], train_loss:0.091004
Epoch [2/2], Iter [849/3125], train_loss:0.104574
Epoch [2/2], Iter [850/3125], train_loss:0.066352
Epoch [2/2], Iter [851/3125], train_loss:0.075491
Epoch [2/2], Iter [852/3125], train_loss:0.090248
Epoch [2/2], Iter [853/3125], train_loss:0.141754
Epoch [2/2], Iter [854/3125], train_loss:0.111203
Epoch [2/2], Iter [855/3125], train_loss:0.101882
Epoch [2/2], Iter [856/3125], train_loss:0.080121
Epoch [2/2], Iter [857/3125], train_loss:0.109338
Epoch [2/2], Iter [858/3125], train_loss:0.074698
Epoch [2/2], Iter [859/3125], train_loss:0.078181
Epoch [2/2], Iter [860/3125], train_loss:0.080490
Epoch [2/2], Iter [861/3125], train_loss:0.070324
Epoch [2/2], Iter [862/3125], train_loss:0.091529
Epoch [2/2], Iter [863/3125], train_loss:0.099398
Epoch [2/2], Iter [864/3125], train_loss:0.116627
Epoch [2/2], Iter [865/3125], train_loss:0.114276
Epoch [2/2], Iter [866/3125], train_loss:0.093187
Epoch [2/2], Iter [867/3125], train_loss:0.069209
Epoch [2/2], Iter [868/3125], train_loss:0.104793
Epoch [2/2], Iter [869/3125], train_loss:0.073808
Epoch [2/2], Iter [870/3125], train_loss:0.092707
Epoch [2/2], Iter [871/3125], train_loss:0.085808
Epoch [2/2], Iter [872/3125], train_loss:0.099127
Epoch [2/2], Iter [873/3125], train_loss:0.094822
Epoch [2/2], Iter [874/3125], train_loss:0.081842
Epoch [2/2], Iter [875/3125], train_loss:0.083346
Epoch [2/2], Iter [876/3125], train_loss:0.109375
Epoch [2/2], Iter [877/3125], train_loss:0.069028
Epoch [2/2], Iter [878/3125], train_loss:0.088529
Epoch [2/2], Iter [879/3125], train_loss:0.082911
Epoch [2/2], Iter [880/3125], train_loss:0.064488
Epoch [2/2], Iter [881/3125], train_loss:0.109088
Epoch [2/2], Iter [882/3125], train_loss:0.086650
Epoch [2/2], Iter [883/3125], train_loss:0.069423
Epoch [2/2], Iter [884/3125], train_loss:0.082668
Epoch [2/2], Iter [885/3125], train_loss:0.101943
Epoch [2/2], Iter [886/3125], train_loss:0.062625
Epoch [2/2], Iter [887/3125], train_loss:0.067995
Epoch [2/2], Iter [888/3125], train_loss:0.085687
Epoch [2/2], Iter [889/3125], train_loss:0.065357
Epoch [2/2], Iter [890/3125], train_loss:0.071787
Epoch [2/2], Iter [891/3125], train_loss:0.081613
Epoch [2/2], Iter [892/3125], train_loss:0.072062
Epoch [2/2], Iter [893/3125], train_loss:0.104661
Epoch [2/2], Iter [894/3125], train_loss:0.087902
Epoch [2/2], Iter [895/3125], train_loss:0.130290
Epoch [2/2], Iter [896/3125], train_loss:0.075751
Epoch [2/2], Iter [897/3125], train_loss:0.083584
Epoch [2/2], Iter [898/3125], train_loss:0.088319
Epoch [2/2], Iter [899/3125], train_loss:0.107320
Epoch [2/2], Iter [900/3125], train_loss:0.069297
Epoch [2/2], Iter [901/3125], train_loss:0.059855
Epoch [2/2], Iter [902/3125], train_loss:0.090469
Epoch [2/2], Iter [903/3125], train_loss:0.083430
Epoch [2/2], Iter [904/3125], train_loss:0.060752
Epoch [2/2], Iter [905/3125], train_loss:0.088156
Epoch [2/2], Iter [906/3125], train_loss:0.089071
Epoch [2/2], Iter [907/3125], train_loss:0.084885
Epoch [2/2], Iter [908/3125], train_loss:0.048224
Epoch [2/2], Iter [909/3125], train_loss:0.113041
Epoch [2/2], Iter [910/3125], train_loss:0.116053
Epoch [2/2], Iter [911/3125], train_loss:0.074417
Epoch [2/2], Iter [912/3125], train_loss:0.091008
Epoch [2/2], Iter [913/3125], train_loss:0.092575
Epoch [2/2], Iter [914/3125], train_loss:0.113760
Epoch [2/2], Iter [915/3125], train_loss:0.120776
Epoch [2/2], Iter [916/3125], train_loss:0.139293
Epoch [2/2], Iter [917/3125], train_loss:0.069343
Epoch [2/2], Iter [918/3125], train_loss:0.098188
Epoch [2/2], Iter [919/3125], train_loss:0.061732
Epoch [2/2], Iter [920/3125], train_loss:0.138873
Epoch [2/2], Iter [921/3125], train_loss:0.108592
Epoch [2/2], Iter [922/3125], train_loss:0.108380
Epoch [2/2], Iter [923/3125], train_loss:0.089235
Epoch [2/2], Iter [924/3125], train_loss:0.098835
Epoch [2/2], Iter [925/3125], train_loss:0.084797
Epoch [2/2], Iter [926/3125], train_loss:0.086078
Epoch [2/2], Iter [927/3125], train_loss:0.096045
Epoch [2/2], Iter [928/3125], train_loss:0.103381
Epoch [2/2], Iter [929/3125], train_loss:0.064686
Epoch [2/2], Iter [930/3125], train_loss:0.101205
Epoch [2/2], Iter [931/3125], train_loss:0.083386
Epoch [2/2], Iter [932/3125], train_loss:0.124332
Epoch [2/2], Iter [933/3125], train_loss:0.071771
Epoch [2/2], Iter [934/3125], train_loss:0.068327
Epoch [2/2], Iter [935/3125], train_loss:0.069932
Epoch [2/2], Iter [936/3125], train_loss:0.088089
Epoch [2/2], Iter [937/3125], train_loss:0.088597
Epoch [2/2], Iter [938/3125], train_loss:0.104114
Epoch [2/2], Iter [939/3125], train_loss:0.083072
Epoch [2/2], Iter [940/3125], train_loss:0.101029
Epoch [2/2], Iter [941/3125], train_loss:0.108483
Epoch [2/2], Iter [942/3125], train_loss:0.100051
Epoch [2/2], Iter [943/3125], train_loss:0.106296
Epoch [2/2], Iter [944/3125], train_loss:0.072279
Epoch [2/2], Iter [945/3125], train_loss:0.143448
Epoch [2/2], Iter [946/3125], train_loss:0.084587
Epoch [2/2], Iter [947/3125], train_loss:0.073256
Epoch [2/2], Iter [948/3125], train_loss:0.083115
Epoch [2/2], Iter [949/3125], train_loss:0.076965
Epoch [2/2], Iter [950/3125], train_loss:0.083379
Epoch [2/2], Iter [951/3125], train_loss:0.078656
Epoch [2/2], Iter [952/3125], train_loss:0.080206
Epoch [2/2], Iter [953/3125], train_loss:0.088033
Epoch [2/2], Iter [954/3125], train_loss:0.094281
Epoch [2/2], Iter [955/3125], train_loss:0.109771
Epoch [2/2], Iter [956/3125], train_loss:0.098340
Epoch [2/2], Iter [957/3125], train_loss:0.103174
Epoch [2/2], Iter [958/3125], train_loss:0.070675
Epoch [2/2], Iter [959/3125], train_loss:0.092117
Epoch [2/2], Iter [960/3125], train_loss:0.093642
Epoch [2/2], Iter [961/3125], train_loss:0.128867
Epoch [2/2], Iter [962/3125], train_loss:0.072056
Epoch [2/2], Iter [963/3125], train_loss:0.094215
Epoch [2/2], Iter [964/3125], train_loss:0.091706
Epoch [2/2], Iter [965/3125], train_loss:0.076420
Epoch [2/2], Iter [966/3125], train_loss:0.110798
Epoch [2/2], Iter [967/3125], train_loss:0.066716
Epoch [2/2], Iter [968/3125], train_loss:0.104807
Epoch [2/2], Iter [969/3125], train_loss:0.086580
Epoch [2/2], Iter [970/3125], train_loss:0.105679
Epoch [2/2], Iter [971/3125], train_loss:0.084984
Epoch [2/2], Iter [972/3125], train_loss:0.093323
Epoch [2/2], Iter [973/3125], train_loss:0.088777
Epoch [2/2], Iter [974/3125], train_loss:0.090154
Epoch [2/2], Iter [975/3125], train_loss:0.096426
Epoch [2/2], Iter [976/3125], train_loss:0.107699
Epoch [2/2], Iter [977/3125], train_loss:0.110699
Epoch [2/2], Iter [978/3125], train_loss:0.072643
Epoch [2/2], Iter [979/3125], train_loss:0.078052
Epoch [2/2], Iter [980/3125], train_loss:0.090422
Epoch [2/2], Iter [981/3125], train_loss:0.071456
Epoch [2/2], Iter [982/3125], train_loss:0.095594
Epoch [2/2], Iter [983/3125], train_loss:0.092027
Epoch [2/2], Iter [984/3125], train_loss:0.116863
Epoch [2/2], Iter [985/3125], train_loss:0.114535
Epoch [2/2], Iter [986/3125], train_loss:0.079183
Epoch [2/2], Iter [987/3125], train_loss:0.090277
Epoch [2/2], Iter [988/3125], train_loss:0.124222
Epoch [2/2], Iter [989/3125], train_loss:0.115095
Epoch [2/2], Iter [990/3125], train_loss:0.114542
Epoch [2/2], Iter [991/3125], train_loss:0.106006
Epoch [2/2], Iter [992/3125], train_loss:0.095041
Epoch [2/2], Iter [993/3125], train_loss:0.076730
Epoch [2/2], Iter [994/3125], train_loss:0.109610
Epoch [2/2], Iter [995/3125], train_loss:0.107274
Epoch [2/2], Iter [996/3125], train_loss:0.066058
Epoch [2/2], Iter [997/3125], train_loss:0.065898
Epoch [2/2], Iter [998/3125], train_loss:0.117909
Epoch [2/2], Iter [999/3125], train_loss:0.069444
Epoch [2/2], Iter [1000/3125], train_loss:0.107684
Epoch [2/2], Iter [1001/3125], train_loss:0.094535
Epoch [2/2], Iter [1002/3125], train_loss:0.098872
Epoch [2/2], Iter [1003/3125], train_loss:0.097507
Epoch [2/2], Iter [1004/3125], train_loss:0.091864
Epoch [2/2], Iter [1005/3125], train_loss:0.078213
Epoch [2/2], Iter [1006/3125], train_loss:0.099576
Epoch [2/2], Iter [1007/3125], train_loss:0.100277
Epoch [2/2], Iter [1008/3125], train_loss:0.124750
Epoch [2/2], Iter [1009/3125], train_loss:0.104891
Epoch [2/2], Iter [1010/3125], train_loss:0.079731
Epoch [2/2], Iter [1011/3125], train_loss:0.085950
Epoch [2/2], Iter [1012/3125], train_loss:0.084804
Epoch [2/2], Iter [1013/3125], train_loss:0.075454
Epoch [2/2], Iter [1014/3125], train_loss:0.130603
Epoch [2/2], Iter [1015/3125], train_loss:0.096016
Epoch [2/2], Iter [1016/3125], train_loss:0.090073
Epoch [2/2], Iter [1017/3125], train_loss:0.074195
Epoch [2/2], Iter [1018/3125], train_loss:0.122536
Epoch [2/2], Iter [1019/3125], train_loss:0.112131
Epoch [2/2], Iter [1020/3125], train_loss:0.109132
Epoch [2/2], Iter [1021/3125], train_loss:0.115335
Epoch [2/2], Iter [1022/3125], train_loss:0.140687
Epoch [2/2], Iter [1023/3125], train_loss:0.083916
Epoch [2/2], Iter [1024/3125], train_loss:0.095654
Epoch [2/2], Iter [1025/3125], train_loss:0.084160
Epoch [2/2], Iter [1026/3125], train_loss:0.114870
Epoch [2/2], Iter [1027/3125], train_loss:0.101187
Epoch [2/2], Iter [1028/3125], train_loss:0.082069
Epoch [2/2], Iter [1029/3125], train_loss:0.072046
Epoch [2/2], Iter [1030/3125], train_loss:0.086769
Epoch [2/2], Iter [1031/3125], train_loss:0.089113
Epoch [2/2], Iter [1032/3125], train_loss:0.061093
Epoch [2/2], Iter [1033/3125], train_loss:0.090316
Epoch [2/2], Iter [1034/3125], train_loss:0.085117
Epoch [2/2], Iter [1035/3125], train_loss:0.104584
Epoch [2/2], Iter [1036/3125], train_loss:0.081303
Epoch [2/2], Iter [1037/3125], train_loss:0.091452
Epoch [2/2], Iter [1038/3125], train_loss:0.112761
Epoch [2/2], Iter [1039/3125], train_loss:0.088501
Epoch [2/2], Iter [1040/3125], train_loss:0.084058
Epoch [2/2], Iter [1041/3125], train_loss:0.078801
Epoch [2/2], Iter [1042/3125], train_loss:0.087638
Epoch [2/2], Iter [1043/3125], train_loss:0.106893
Epoch [2/2], Iter [1044/3125], train_loss:0.087472
Epoch [2/2], Iter [1045/3125], train_loss:0.130255
Epoch [2/2], Iter [1046/3125], train_loss:0.097685
Epoch [2/2], Iter [1047/3125], train_loss:0.095756
Epoch [2/2], Iter [1048/3125], train_loss:0.115433
Epoch [2/2], Iter [1049/3125], train_loss:0.079820
Epoch [2/2], Iter [1050/3125], train_loss:0.116015
Epoch [2/2], Iter [1051/3125], train_loss:0.146984
Epoch [2/2], Iter [1052/3125], train_loss:0.129607
Epoch [2/2], Iter [1053/3125], train_loss:0.098001
Epoch [2/2], Iter [1054/3125], train_loss:0.076012
Epoch [2/2], Iter [1055/3125], train_loss:0.098679
Epoch [2/2], Iter [1056/3125], train_loss:0.079336
Epoch [2/2], Iter [1057/3125], train_loss:0.127889
Epoch [2/2], Iter [1058/3125], train_loss:0.093738
Epoch [2/2], Iter [1059/3125], train_loss:0.096781
Epoch [2/2], Iter [1060/3125], train_loss:0.079172
Epoch [2/2], Iter [1061/3125], train_loss:0.074400
Epoch [2/2], Iter [1062/3125], train_loss:0.094194
Epoch [2/2], Iter [1063/3125], train_loss:0.085245
Epoch [2/2], Iter [1064/3125], train_loss:0.094455
Epoch [2/2], Iter [1065/3125], train_loss:0.081712
Epoch [2/2], Iter [1066/3125], train_loss:0.096517
Epoch [2/2], Iter [1067/3125], train_loss:0.140057
Epoch [2/2], Iter [1068/3125], train_loss:0.087830
Epoch [2/2], Iter [1069/3125], train_loss:0.083283
Epoch [2/2], Iter [1070/3125], train_loss:0.081132
E
