- 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
- 🍖 原作者：K同学啊 | 接辅导、项目定制
目录
- 环境
- 步骤
- 环境设置
- 数据准备
- 图像信息查看
- 模型设计
- ResidualBlock块
- stack堆叠
- resnet50v2模型
- 模型训练
- 模型效果展示
- 总结与心得体会
环境
- 系统: Linux
- 语言: Python3.8.10
- 深度学习框架: Pytorch2.0.0+cu118
步骤
环境设置
包引用
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, random_split
from torchvision import datasets, transforms
import copy, random, pathlib
import matplotlib.pyplot as plt
from PIL import Image
from torchinfo import summary
import numpy as np
设置一个全局的设备，使后面的模型和数据放置在统一的设备中
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
数据准备
从K同学提供的网盘中下载鸟类数据集，解压到data目录下，数据集的结构如下：
其中bird_photos下不同的文件夹中保存了不同类型的鸟类图像，这个目录结构可以使用torchvision.datasets.ImageFolder直接加载
图像信息查看
- 获取到所有的图像
root_dir = 'data/bird_photos'
root_directory = pathlib.Path(root_dir)
image_list = root_directory.glob("*/*")
- 随机打印5个图像的尺寸
for _ in range(5):
print(np.array(Image.open(str(random.choice(image_list)))).shape)
发现都是224*224大小的三通道图像，所以我们可以在数据集处理时省略Resize这一步，或者加上224的Resize排除异常情况
3. 随机打印20个图像
plt.figure(figsize=(20, 4))
for i in range(20):
plt.subplot(2, 10, i+1)
plt.axis('off')
image = random.choice(image_list)
class_name = image.parts[-2]
plt.title(class_name)
plt.imshow(Image.open(str(image)))
4. 创建数据集
首先定义一个图像的预处理
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
])
然后通过datasets.ImageFolder加载文件夹
dataset = datasets.ImageFolder(root_dir, transform=transform)
从数据中提取图像不同的分类名称
class_names = [x for x in dataset.class_to_idx]
划分训练集和验证集
train_size = int(len(dataset) * 0.8)
test_size = len(dataset) - train_size
train_dataset, test_dataset = random_split(dataset, [train_size, test_size])
最后，将数据集划分批次
batch_size = 8
train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size)
test_loader = DataLoader(test_dataset, batch_size=batch_size)
模型设计
基于上次的ResNet-50，v2主要改进了BatchNormalization和激活函数的顺序。
ResidualBlock块
class ResidualBlock(nn.Module):
def __init__(self, input_size, filters, kernel_size=3, stride=1, conv_shortcut=False):
super().__init__()
self.preact = nn.Sequential(nn.BatchNorm2d(input_size), nn.ReLU())
self.conv_shortcut = conv_shortcut
if conv_shortcut:
self.shortcut = nn.Conv2d(input_size, 4*filters, 1, stride=stride, bias=False)
elif stride > 1:
self.shortcut = nn.MaxPool2d(1, stride=stride)
else:
self.shortcut = nn.Identity()
self.conv1 = nn.Sequential(
nn.Conv2d(input_size, filters, 1, stride=1, bias=False),
nn.BatchNorm2d(filters),
nn.ReLU()
)
self.conv2 = nn.Sequential(
nn.Conv2d(filters, filters, kernel_size, padding=1, stride=stride, bias=False),
nn.BatchNorm2d(filters),
nn.ReLU()
)
self.conv3 = nn.Conv2d(filters, 4*filters, 1, bias=False)
def forward(self, x):
pre = self.preact(x)
if self.conv_shortcut:
shortcut = self.shortcut(pre)
else:
shortcut = self.shortcut(x)
x = self.conv1(pre)
x = self.conv2(x)
x = self.conv3(x)
x = x + shortcut
return x
stack堆叠
class ResidualStack(nn.Module):
def __init__(self, input_size, filters, blocks, stride=2):
super().__init__()
self.first = ResidualBlock(input_size, filters, conv_shortcut=True)
self.module_list = nn.ModuleList([])
for i in range(2, blocks):
self.module_list.append(ResidualBlock(filters*4, filters))
self.last = ResidualBlock(filters*4, filters, stride=stride)
def forward(self, x):
x = self.first(x)
for layer in self.module_list:
x = layer(x)
x = self.last(x)
return x
resnet50v2模型
class ResNet50v2(nn.Module):
def __init__(self,
include_top=True,
preact=True,
use_bias=True,
input_size=None,
pooling=None,
classes=1000,
classifier_activation = 'softmax'):
super().__init__()
self.input_conv = nn.Conv2d(3, 64, 7, padding=3, stride=2, bias=use_bias)
if not preact:
self.pre = nn.Sequential(nn.BatchNorm2d(64), nn.ReLU())
else:
self.pre = nn.Identity()
self.pool = nn.MaxPool2d(3, padding=1, stride=2)
self.stack1 = ResidualStack(64, 64, 3)
self.stack2 = ResidualStack(256, 128, 4)
self.stack3 = ResidualStack(512, 256, 6)
self.stack4 = ResidualStack(1024, 512, 3, stride=1)
if preact:
self.post = nn.Sequential(nn.BatchNorm2d(2048), nn.ReLU())
else:
self.post = nn.Identity()
if include_top:
self.final = nn.Sequential(nn.AdaptiveAvgPool2d(1), nn.Flatten(), nn.Linear(2048, classes), nn.Softmax() if 'softmax' == classifier_activation else nn.Identity())
elif 'avg' == pooling:
self.final = nn.AdaptiveAvgPool2d(1)
elif 'max' == pooling:
self.final = nn.AdaptiveMaxPool2d(1)
def forward(self, x):
x = self.input_conv(x)
x = self.pre(x)
x = self.pool(x)
x = self.stack1(x)
x = self.stack2(x)
x = self.stack3(x)
x = self.stack4(x)
x = self.post(x)
x = self.final(x)
return x
创建模型并打印
model = ResNet50v2(classes=len(class_names)).to(device)
summary(model, input_size=(8, 3, 224, 224))
===============================================================================================
Layer (type:depth-idx) Output Shape Param #
===============================================================================================
ResNet50v2 [8, 4] --
├─Conv2d: 1-1 [8, 64, 112, 112] 9,472
├─Identity: 1-2 [8, 64, 112, 112] --
├─MaxPool2d: 1-3 [8, 64, 56, 56] --
├─ResidualStack: 1-4 [8, 256, 28, 28] --
│ └─ResidualBlock: 2-1 [8, 256, 56, 56] --
│ │ └─Sequential: 3-1 [8, 64, 56, 56] 128
│ │ └─Conv2d: 3-2 [8, 256, 56, 56] 16,384
│ │ └─Sequential: 3-3 [8, 64, 56, 56] 4,224
│ │ └─Sequential: 3-4 [8, 64, 56, 56] 36,992
│ │ └─Conv2d: 3-5 [8, 256, 56, 56] 16,384
│ └─ModuleList: 2-2 -- --
│ │ └─ResidualBlock: 3-6 [8, 256, 56, 56] 70,400
│ └─ResidualBlock: 2-3 [8, 256, 28, 28] --
│ │ └─Sequential: 3-7 [8, 256, 56, 56] 512
│ │ └─MaxPool2d: 3-8 [8, 256, 28, 28] --
│ │ └─Sequential: 3-9 [8, 64, 56, 56] 16,512
│ │ └─Sequential: 3-10 [8, 64, 28, 28] 36,992
│ │ └─Conv2d: 3-11 [8, 256, 28, 28] 16,384
├─ResidualStack: 1-5 [8, 512, 14, 14] --
│ └─ResidualBlock: 2-4 [8, 512, 28, 28] --
│ │ └─Sequential: 3-12 [8, 256, 28, 28] 512
│ │ └─Conv2d: 3-13 [8, 512, 28, 28] 131,072
│ │ └─Sequential: 3-14 [8, 128, 28, 28] 33,024
│ │ └─Sequential: 3-15 [8, 128, 28, 28] 147,712
│ │ └─Conv2d: 3-16 [8, 512, 28, 28] 65,536
│ └─ModuleList: 2-5 -- --
│ │ └─ResidualBlock: 3-17 [8, 512, 28, 28] 280,064
│ │ └─ResidualBlock: 3-18 [8, 512, 28, 28] 280,064
│ └─ResidualBlock: 2-6 [8, 512, 14, 14] --
│ │ └─Sequential: 3-19 [8, 512, 28, 28] 1,024
│ │ └─MaxPool2d: 3-20 [8, 512, 14, 14] --
│ │ └─Sequential: 3-21 [8, 128, 28, 28] 65,792
│ │ └─Sequential: 3-22 [8, 128, 14, 14] 147,712
│ │ └─Conv2d: 3-23 [8, 512, 14, 14] 65,536
├─ResidualStack: 1-6 [8, 1024, 7, 7] --
│ └─ResidualBlock: 2-7 [8, 1024, 14, 14] --
│ │ └─Sequential: 3-24 [8, 512, 14, 14] 1,024
│ │ └─Conv2d: 3-25 [8, 1024, 14, 14] 524,288
│ │ └─Sequential: 3-26 [8, 256, 14, 14] 131,584
│ │ └─Sequential: 3-27 [8, 256, 14, 14] 590,336
│ │ └─Conv2d: 3-28 [8, 1024, 14, 14] 262,144
│ └─ModuleList: 2-8 -- --
│ │ └─ResidualBlock: 3-29 [8, 1024, 14, 14] 1,117,184
│ │ └─ResidualBlock: 3-30 [8, 1024, 14, 14] 1,117,184
│ │ └─ResidualBlock: 3-31 [8, 1024, 14, 14] 1,117,184
│ │ └─ResidualBlock: 3-32 [8, 1024, 14, 14] 1,117,184
│ └─ResidualBlock: 2-9 [8, 1024, 7, 7] --
│ │ └─Sequential: 3-33 [8, 1024, 14, 14] 2,048
│ │ └─MaxPool2d: 3-34 [8, 1024, 7, 7] --
│ │ └─Sequential: 3-35 [8, 256, 14, 14] 262,656
│ │ └─Sequential: 3-36 [8, 256, 7, 7] 590,336
│ │ └─Conv2d: 3-37 [8, 1024, 7, 7] 262,144
├─ResidualStack: 1-7 [8, 2048, 7, 7] --
│ └─ResidualBlock: 2-10 [8, 2048, 7, 7] --
│ │ └─Sequential: 3-38 [8, 1024, 7, 7] 2,048
│ │ └─Conv2d: 3-39 [8, 2048, 7, 7] 2,097,152
│ │ └─Sequential: 3-40 [8, 512, 7, 7] 525,312
│ │ └─Sequential: 3-41 [8, 512, 7, 7] 2,360,320
│ │ └─Conv2d: 3-42 [8, 2048, 7, 7] 1,048,576
│ └─ModuleList: 2-11 -- --
│ │ └─ResidualBlock: 3-43 [8, 2048, 7, 7] 4,462,592
│ └─ResidualBlock: 2-12 [8, 2048, 7, 7] --
│ │ └─Sequential: 3-44 [8, 2048, 7, 7] 4,096
│ │ └─Identity: 3-45 [8, 2048, 7, 7] --
│ │ └─Sequential: 3-46 [8, 512, 7, 7] 1,049,600
│ │ └─Sequential: 3-47 [8, 512, 7, 7] 2,360,320
│ │ └─Conv2d: 3-48 [8, 2048, 7, 7] 1,048,576
├─Sequential: 1-8 [8, 2048, 7, 7] --
│ └─BatchNorm2d: 2-13 [8, 2048, 7, 7] 4,096
│ └─ReLU: 2-14 [8, 2048, 7, 7] --
├─Sequential: 1-9 [8, 4] --
│ └─AdaptiveAvgPool2d: 2-15 [8, 2048, 1, 1] --
│ └─Flatten: 2-16 [8, 2048] --
│ └─Linear: 2-17 [8, 4] 8,196
│ └─Softmax: 2-18 [8, 4] --
===============================================================================================
Total params: 23,508,612
Trainable params: 23,508,612
Non-trainable params: 0
Total mult-adds (G): 27.85
===============================================================================================
Input size (MB): 4.82
Forward/backward pass size (MB): 1051.69
Params size (MB): 94.03
Estimated Total Size (MB): 1150.54
===============================================================================================
模型训练
训练代码和上一节完全一样，就不再写了， 训练结果如下：
Epoch: 1, Lr:1e-05, TrainAcc: 34.1, TrainLoss: 1.368, TestAcc: 49.6， TestLoss: 1.338
Epoch: 2, Lr:1e-05, TrainAcc: 52.4, TrainLoss: 1.297, TestAcc: 63.7， TestLoss: 1.192
Epoch: 3, Lr:1e-05, TrainAcc: 59.3, TrainLoss: 1.190, TestAcc: 68.1， TestLoss: 1.090
Epoch: 4, Lr:1e-05, TrainAcc: 61.1, TrainLoss: 1.150, TestAcc: 74.3， TestLoss: 1.049
Epoch: 5, Lr:9.5e-06, TrainAcc: 69.0, TrainLoss: 1.078, TestAcc: 74.3， TestLoss: 1.019
Epoch: 6, Lr:9.5e-06, TrainAcc: 75.7, TrainLoss: 1.029, TestAcc: 77.0， TestLoss: 0.984
Epoch: 7, Lr:9.5e-06, TrainAcc: 75.9, TrainLoss: 1.011, TestAcc: 78.8， TestLoss: 0.972
Epoch: 8, Lr:9.5e-06, TrainAcc: 78.5, TrainLoss: 0.986, TestAcc: 76.1， TestLoss: 0.974
Epoch: 9, Lr:9.5e-06, TrainAcc: 79.6, TrainLoss: 0.973, TestAcc: 82.3， TestLoss: 0.934
Epoch: 10, Lr:9.025e-06, TrainAcc: 83.6, TrainLoss: 0.938, TestAcc: 80.5， TestLoss: 0.931
Epoch: 11, Lr:9.025e-06, TrainAcc: 86.3, TrainLoss: 0.914, TestAcc: 80.5， TestLoss: 0.940
Epoch: 12, Lr:9.025e-06, TrainAcc: 86.1, TrainLoss: 0.914, TestAcc: 79.6， TestLoss: 0.934
Epoch: 13, Lr:9.025e-06, TrainAcc: 87.2, TrainLoss: 0.893, TestAcc: 84.1， TestLoss: 0.918
Epoch: 14, Lr:9.025e-06, TrainAcc: 89.6, TrainLoss: 0.879, TestAcc: 84.1， TestLoss: 0.917
Epoch: 15, Lr:8.573749999999999e-06, TrainAcc: 91.6, TrainLoss: 0.853, TestAcc: 80.5， TestLoss: 0.944
Epoch: 16, Lr:8.573749999999999e-06, TrainAcc: 90.3, TrainLoss: 0.863, TestAcc: 83.2， TestLoss: 0.919
Epoch: 17, Lr:8.573749999999999e-06, TrainAcc: 93.6, TrainLoss: 0.837, TestAcc: 77.0， TestLoss: 0.979
Epoch: 18, Lr:8.573749999999999e-06, TrainAcc: 93.1, TrainLoss: 0.838, TestAcc: 81.4， TestLoss: 0.934
Epoch: 19, Lr:8.573749999999999e-06, TrainAcc: 94.9, TrainLoss: 0.819, TestAcc: 81.4， TestLoss: 0.932
Epoch: 20, Lr:8.1450625e-06, TrainAcc: 94.7, TrainLoss: 0.825, TestAcc: 85.8， TestLoss: 0.918
Epoch: 21, Lr:8.1450625e-06, TrainAcc: 95.8, TrainLoss: 0.810, TestAcc: 80.5， TestLoss: 0.928
Epoch: 22, Lr:8.1450625e-06, TrainAcc: 93.8, TrainLoss: 0.823, TestAcc: 84.1， TestLoss: 0.900
Epoch: 23, Lr:8.1450625e-06, TrainAcc: 94.0, TrainLoss: 0.821, TestAcc: 86.7， TestLoss: 0.891
Epoch: 24, Lr:8.1450625e-06, TrainAcc: 94.5, TrainLoss: 0.829, TestAcc: 84.1， TestLoss: 0.909
Epoch: 25, Lr:7.737809374999999e-06, TrainAcc: 92.3, TrainLoss: 0.836, TestAcc: 77.0， TestLoss: 0.938
Epoch: 26, Lr:7.737809374999999e-06, TrainAcc: 93.8, TrainLoss: 0.828, TestAcc: 85.8， TestLoss: 0.897
Epoch: 27, Lr:7.737809374999999e-06, TrainAcc: 95.1, TrainLoss: 0.806, TestAcc: 85.0， TestLoss: 0.912
Epoch: 28, Lr:7.737809374999999e-06, TrainAcc: 96.9, TrainLoss: 0.794, TestAcc: 81.4， TestLoss: 0.919
Epoch: 29, Lr:7.737809374999999e-06, TrainAcc: 96.9, TrainLoss: 0.796, TestAcc: 83.2， TestLoss: 0.901
Epoch: 30, Lr:7.350918906249998e-06, TrainAcc: 97.3, TrainLoss: 0.789, TestAcc: 85.8， TestLoss: 0.885
Epoch: 31, Lr:7.350918906249998e-06, TrainAcc: 96.2, TrainLoss: 0.805, TestAcc: 83.2， TestLoss: 0.898
Epoch: 32, Lr:7.350918906249998e-06, TrainAcc: 97.8, TrainLoss: 0.791, TestAcc: 81.4， TestLoss: 0.920
Epoch: 33, Lr:7.350918906249998e-06, TrainAcc: 95.4, TrainLoss: 0.802, TestAcc: 82.3， TestLoss: 0.910
Epoch: 34, Lr:7.350918906249998e-06, TrainAcc: 96.5, TrainLoss: 0.792, TestAcc: 87.6， TestLoss: 0.882
Epoch: 35, Lr:6.983372960937498e-06, TrainAcc: 96.2, TrainLoss: 0.793, TestAcc: 85.8， TestLoss: 0.877
Epoch: 36, Lr:6.983372960937498e-06, TrainAcc: 98.2, TrainLoss: 0.780, TestAcc: 86.7， TestLoss: 0.874
Epoch: 37, Lr:6.983372960937498e-06, TrainAcc: 97.8, TrainLoss: 0.775, TestAcc: 87.6， TestLoss: 0.875
Epoch: 38, Lr:6.983372960937498e-06, TrainAcc: 97.6, TrainLoss: 0.790, TestAcc: 87.6， TestLoss: 0.881
Epoch: 39, Lr:6.983372960937498e-06, TrainAcc: 96.9, TrainLoss: 0.787, TestAcc: 81.4， TestLoss: 0.923
Epoch: 40, Lr:6.634204312890623e-06, TrainAcc: 98.5, TrainLoss: 0.774, TestAcc: 85.0， TestLoss: 0.903
Epoch: 41, Lr:6.634204312890623e-06, TrainAcc: 97.3, TrainLoss: 0.793, TestAcc: 90.3， TestLoss: 0.866
Epoch: 42, Lr:6.634204312890623e-06, TrainAcc: 98.2, TrainLoss: 0.779, TestAcc: 87.6， TestLoss: 0.882
Epoch: 43, Lr:6.634204312890623e-06, TrainAcc: 97.6, TrainLoss: 0.784, TestAcc: 84.1， TestLoss: 0.902
Epoch: 44, Lr:6.634204312890623e-06, TrainAcc: 97.3, TrainLoss: 0.786, TestAcc: 85.8， TestLoss: 0.889
Epoch: 45, Lr:6.302494097246091e-06, TrainAcc: 99.1, TrainLoss: 0.774, TestAcc: 86.7， TestLoss: 0.883
Epoch: 46, Lr:6.302494097246091e-06, TrainAcc: 98.2, TrainLoss: 0.774, TestAcc: 83.2， TestLoss: 0.891
Epoch: 47, Lr:6.302494097246091e-06, TrainAcc: 98.9, TrainLoss: 0.770, TestAcc: 85.0， TestLoss: 0.890
Epoch: 48, Lr:6.302494097246091e-06, TrainAcc: 99.1, TrainLoss: 0.766, TestAcc: 85.0， TestLoss: 0.886
Epoch: 49, Lr:6.302494097246091e-06, TrainAcc: 98.7, TrainLoss: 0.771, TestAcc: 86.7， TestLoss: 0.884
Epoch: 50, Lr:5.987369392383788e-06, TrainAcc: 99.3, TrainLoss: 0.758, TestAcc: 87.6， TestLoss: 0.874
Epoch: 51, Lr:5.987369392383788e-06, TrainAcc: 98.5, TrainLoss: 0.772, TestAcc: 87.6， TestLoss: 0.870
Epoch: 52, Lr:5.987369392383788e-06, TrainAcc: 98.7, TrainLoss: 0.767, TestAcc: 82.3， TestLoss: 0.900
Epoch: 53, Lr:5.987369392383788e-06, TrainAcc: 100.0, TrainLoss: 0.754, TestAcc: 85.8， TestLoss: 0.882
Epoch: 54, Lr:5.987369392383788e-06, TrainAcc: 98.7, TrainLoss: 0.770, TestAcc: 85.0， TestLoss: 0.884
Epoch: 55, Lr:5.688000922764597e-06, TrainAcc: 100.0, TrainLoss: 0.756, TestAcc: 85.8， TestLoss: 0.870
Epoch: 56, Lr:5.688000922764597e-06, TrainAcc: 98.5, TrainLoss: 0.772, TestAcc: 86.7， TestLoss: 0.881
Epoch: 57, Lr:5.688000922764597e-06, TrainAcc: 98.5, TrainLoss: 0.768, TestAcc: 85.0， TestLoss: 0.884
Epoch: 58, Lr:5.688000922764597e-06, TrainAcc: 98.9, TrainLoss: 0.766, TestAcc: 85.8， TestLoss: 0.883
Epoch: 59, Lr:5.688000922764597e-06, TrainAcc: 99.6, TrainLoss: 0.765, TestAcc: 86.7， TestLoss: 0.880
Epoch: 60, Lr:5.403600876626367e-06, TrainAcc: 99.3, TrainLoss: 0.766, TestAcc: 85.8， TestLoss: 0.880
Epoch: 61, Lr:5.403600876626367e-06, TrainAcc: 99.8, TrainLoss: 0.755, TestAcc: 85.8， TestLoss: 0.876
Epoch: 62, Lr:5.403600876626367e-06, TrainAcc: 99.6, TrainLoss: 0.759, TestAcc: 85.0， TestLoss: 0.874
Epoch: 63, Lr:5.403600876626367e-06, TrainAcc: 97.6, TrainLoss: 0.772, TestAcc: 86.7， TestLoss: 0.878
Epoch: 64, Lr:5.403600876626367e-06, TrainAcc: 99.6, TrainLoss: 0.761, TestAcc: 88.5， TestLoss: 0.866
Epoch: 65, Lr:5.133420832795049e-06, TrainAcc: 98.5, TrainLoss: 0.771, TestAcc: 85.0， TestLoss: 0.890
Epoch: 66, Lr:5.133420832795049e-06, TrainAcc: 98.9, TrainLoss: 0.763, TestAcc: 85.8， TestLoss: 0.887
Epoch: 67, Lr:5.133420832795049e-06, TrainAcc: 99.1, TrainLoss: 0.766, TestAcc: 85.0， TestLoss: 0.889
Epoch: 68, Lr:5.133420832795049e-06, TrainAcc: 98.7, TrainLoss: 0.763, TestAcc: 88.5， TestLoss: 0.872
Epoch: 69, Lr:5.133420832795049e-06, TrainAcc: 99.1, TrainLoss: 0.762, TestAcc: 87.6， TestLoss: 0.872
Epoch: 70, Lr:4.876749791155296e-06, TrainAcc: 99.6, TrainLoss: 0.757, TestAcc: 85.8， TestLoss: 0.876
Epoch: 71, Lr:4.876749791155296e-06, TrainAcc: 99.3, TrainLoss: 0.762, TestAcc: 85.0， TestLoss: 0.884
Epoch: 72, Lr:4.876749791155296e-06, TrainAcc: 98.9, TrainLoss: 0.767, TestAcc: 85.0， TestLoss: 0.882
Epoch: 73, Lr:4.876749791155296e-06, TrainAcc: 99.3, TrainLoss: 0.758, TestAcc: 85.0， TestLoss: 0.892
Epoch: 74, Lr:4.876749791155296e-06, TrainAcc: 98.7, TrainLoss: 0.768, TestAcc: 84.1， TestLoss: 0.880
Epoch: 75, Lr:4.632912301597531e-06, TrainAcc: 99.1, TrainLoss: 0.763, TestAcc: 87.6， TestLoss: 0.863
Epoch: 76, Lr:4.632912301597531e-06, TrainAcc: 99.6, TrainLoss: 0.757, TestAcc: 87.6， TestLoss: 0.865
Epoch: 77, Lr:4.632912301597531e-06, TrainAcc: 99.1, TrainLoss: 0.759, TestAcc: 81.4， TestLoss: 0.913
Epoch: 78, Lr:4.632912301597531e-06, TrainAcc: 99.3, TrainLoss: 0.758, TestAcc: 86.7， TestLoss: 0.870
Epoch: 79, Lr:4.632912301597531e-06, TrainAcc: 99.3, TrainLoss: 0.760, TestAcc: 81.4， TestLoss: 0.911
Epoch: 80, Lr:4.401266686517654e-06, TrainAcc: 99.3, TrainLoss: 0.761, TestAcc: 86.7， TestLoss: 0.883
Epoch: 81, Lr:4.401266686517654e-06, TrainAcc: 99.6, TrainLoss: 0.761, TestAcc: 92.0， TestLoss: 0.844
Epoch: 82, Lr:4.401266686517654e-06, TrainAcc: 99.6, TrainLoss: 0.758, TestAcc: 86.7， TestLoss: 0.870
Epoch: 83, Lr:4.401266686517654e-06, TrainAcc: 99.8, TrainLoss: 0.757, TestAcc: 88.5， TestLoss: 0.865
Epoch: 84, Lr:4.401266686517654e-06, TrainAcc: 99.6, TrainLoss: 0.756, TestAcc: 88.5， TestLoss: 0.860
Epoch: 85, Lr:4.181203352191771e-06, TrainAcc: 98.2, TrainLoss: 0.769, TestAcc: 87.6， TestLoss: 0.884
Epoch: 86, Lr:4.181203352191771e-06, TrainAcc: 99.3, TrainLoss: 0.756, TestAcc: 90.3， TestLoss: 0.858
Epoch: 87, Lr:4.181203352191771e-06, TrainAcc: 99.1, TrainLoss: 0.758, TestAcc: 85.0， TestLoss: 0.878
Epoch: 88, Lr:4.181203352191771e-06, TrainAcc: 99.6, TrainLoss: 0.756, TestAcc: 91.2， TestLoss: 0.853
Epoch: 89, Lr:4.181203352191771e-06, TrainAcc: 99.8, TrainLoss: 0.752, TestAcc: 86.7， TestLoss: 0.878
Epoch: 90, Lr:3.972143184582182e-06, TrainAcc: 98.5, TrainLoss: 0.762, TestAcc: 87.6， TestLoss: 0.874
Epoch: 91, Lr:3.972143184582182e-06, TrainAcc: 100.0, TrainLoss: 0.751, TestAcc: 88.5， TestLoss: 0.865
Epoch: 92, Lr:3.972143184582182e-06, TrainAcc: 99.3, TrainLoss: 0.756, TestAcc: 85.0， TestLoss: 0.898
Epoch: 93, Lr:3.972143184582182e-06, TrainAcc: 99.3, TrainLoss: 0.758, TestAcc: 85.0， TestLoss: 0.873
Epoch: 94, Lr:3.972143184582182e-06, TrainAcc: 99.3, TrainLoss: 0.755, TestAcc: 85.8， TestLoss: 0.894
Epoch: 95, Lr:3.7735360253530726e-06, TrainAcc: 99.3, TrainLoss: 0.759, TestAcc: 85.8， TestLoss: 0.888
Epoch: 96, Lr:3.7735360253530726e-06, TrainAcc: 99.6, TrainLoss: 0.755, TestAcc: 86.7， TestLoss: 0.868
Epoch: 97, Lr:3.7735360253530726e-06, TrainAcc: 99.3, TrainLoss: 0.756, TestAcc: 78.8， TestLoss: 0.940
Epoch: 98, Lr:3.7735360253530726e-06, TrainAcc: 98.7, TrainLoss: 0.761, TestAcc: 85.0， TestLoss: 0.893
Epoch: 99, Lr:3.7735360253530726e-06, TrainAcc: 99.8, TrainLoss: 0.755, TestAcc: 85.0， TestLoss: 0.888
Epoch: 100, Lr:3.584859224085419e-06, TrainAcc: 98.9, TrainLoss: 0.763, TestAcc: 87.6， TestLoss: 0.866
Epoch: 101, Lr:3.584859224085419e-06, TrainAcc: 99.8, TrainLoss: 0.753, TestAcc: 86.7， TestLoss: 0.879
Epoch: 102, Lr:3.584859224085419e-06, TrainAcc: 99.3, TrainLoss: 0.758, TestAcc: 85.0， TestLoss: 0.879
Epoch: 103, Lr:3.584859224085419e-06, TrainAcc: 99.3, TrainLoss: 0.758, TestAcc: 85.0， TestLoss: 0.885
Epoch: 104, Lr:3.584859224085419e-06, TrainAcc: 98.9, TrainLoss: 0.758, TestAcc: 87.6， TestLoss: 0.871
Epoch: 105, Lr:3.4056162628811484e-06, TrainAcc: 99.8, TrainLoss: 0.755, TestAcc: 91.2， TestLoss: 0.853
Epoch: 106, Lr:3.4056162628811484e-06, TrainAcc: 99.3, TrainLoss: 0.753, TestAcc: 87.6， TestLoss: 0.872
Epoch: 107, Lr:3.4056162628811484e-06, TrainAcc: 99.8, TrainLoss: 0.755, TestAcc: 91.2， TestLoss: 0.861
Epoch: 108, Lr:3.4056162628811484e-06, TrainAcc: 99.8, TrainLoss: 0.754, TestAcc: 90.3， TestLoss: 0.859
Epoch: 109, Lr:3.4056162628811484e-06, TrainAcc: 99.8, TrainLoss: 0.753, TestAcc: 89.4， TestLoss: 0.870
Epoch: 110, Lr:3.2353354497370905e-06, TrainAcc: 99.6, TrainLoss: 0.754, TestAcc: 85.0， TestLoss: 0.878
Epoch: 111, Lr:3.2353354497370905e-06, TrainAcc: 99.3, TrainLoss: 0.760, TestAcc: 86.7， TestLoss: 0.869
Epoch: 112, Lr:3.2353354497370905e-06, TrainAcc: 99.8, TrainLoss: 0.750, TestAcc: 87.6， TestLoss: 0.870
Epoch: 113, Lr:3.2353354497370905e-06, TrainAcc: 100.0, TrainLoss: 0.752, TestAcc: 88.5， TestLoss: 0.870
Epoch: 114, Lr:3.2353354497370905e-06, TrainAcc: 99.6, TrainLoss: 0.755, TestAcc: 89.4， TestLoss: 0.865
Epoch: 115, Lr:3.073568677250236e-06, TrainAcc: 100.0, TrainLoss: 0.752, TestAcc: 88.5， TestLoss: 0.865
Epoch: 116, Lr:3.073568677250236e-06, TrainAcc: 99.8, TrainLoss: 0.751, TestAcc: 86.7， TestLoss: 0.877
Epoch: 117, Lr:3.073568677250236e-06, TrainAcc: 99.6, TrainLoss: 0.754, TestAcc: 89.4， TestLoss: 0.864
Epoch: 118, Lr:3.073568677250236e-06, TrainAcc: 99.3, TrainLoss: 0.759, TestAcc: 85.0， TestLoss: 0.894
Epoch: 119, Lr:3.073568677250236e-06, TrainAcc: 99.8, TrainLoss: 0.754, TestAcc: 87.6， TestLoss: 0.867
Epoch: 120, Lr:2.919890243387724e-06, TrainAcc: 99.6, TrainLoss: 0.755, TestAcc: 89.4， TestLoss: 0.860
Epoch: 121, Lr:2.919890243387724e-06, TrainAcc: 99.1, TrainLoss: 0.758, TestAcc: 86.7， TestLoss: 0.884
Epoch: 122, Lr:2.919890243387724e-06, TrainAcc: 100.0, TrainLoss: 0.749, TestAcc: 86.7， TestLoss: 0.879
Epoch: 123, Lr:2.919890243387724e-06, TrainAcc: 100.0, TrainLoss: 0.749, TestAcc: 87.6， TestLoss: 0.876
Epoch: 124, Lr:2.919890243387724e-06, TrainAcc: 99.8, TrainLoss: 0.749, TestAcc: 89.4， TestLoss: 0.866
Epoch: 125, Lr:2.7738957312183377e-06, TrainAcc: 99.3, TrainLoss: 0.764, TestAcc: 83.2， TestLoss: 0.895
Epoch: 126, Lr:2.7738957312183377e-06, TrainAcc: 100.0, TrainLoss: 0.747, TestAcc: 86.7， TestLoss: 0.875
Epoch: 127, Lr:2.7738957312183377e-06, TrainAcc: 100.0, TrainLoss: 0.748, TestAcc: 90.3， TestLoss: 0.859
Epoch: 128, Lr:2.7738957312183377e-06, TrainAcc: 99.8, TrainLoss: 0.752, TestAcc: 88.5， TestLoss: 0.871
Epoch: 129, Lr:2.7738957312183377e-06, TrainAcc: 99.8, TrainLoss: 0.750, TestAcc: 88.5， TestLoss: 0.866
Epoch: 130, Lr:2.6352009446574206e-06, TrainAcc: 100.0, TrainLoss: 0.748, TestAcc: 89.4， TestLoss: 0.873
Epoch: 131, Lr:2.6352009446574206e-06, TrainAcc: 99.8, TrainLoss: 0.749, TestAcc: 90.3， TestLoss: 0.861
Epoch: 132, Lr:2.6352009446574206e-06, TrainAcc: 100.0, TrainLoss: 0.746, TestAcc: 89.4， TestLoss: 0.868
Epoch: 133, Lr:2.6352009446574206e-06, TrainAcc: 99.8, TrainLoss: 0.750, TestAcc: 87.6， TestLoss: 0.867
Epoch: 134, Lr:2.6352009446574206e-06, TrainAcc: 99.6, TrainLoss: 0.754, TestAcc: 89.4， TestLoss: 0.868
Epoch: 135, Lr:2.5034408974245495e-06, TrainAcc: 100.0, TrainLoss: 0.748, TestAcc: 87.6， TestLoss: 0.873
Epoch: 136, Lr:2.5034408974245495e-06, TrainAcc: 99.6, TrainLoss: 0.754, TestAcc: 89.4， TestLoss: 0.863
Epoch: 137, Lr:2.5034408974245495e-06, TrainAcc: 100.0, TrainLoss: 0.749, TestAcc: 88.5， TestLoss: 0.860
Epoch: 138, Lr:2.5034408974245495e-06, TrainAcc: 99.8, TrainLoss: 0.751, TestAcc: 89.4， TestLoss: 0.861
Epoch: 139, Lr:2.5034408974245495e-06, TrainAcc: 99.8, TrainLoss: 0.754, TestAcc: 88.5， TestLoss: 0.853
Epoch: 140, Lr:2.378268852553322e-06, TrainAcc: 100.0, TrainLoss: 0.751, TestAcc: 91.2， TestLoss: 0.861
Epoch: 141, Lr:2.378268852553322e-06, TrainAcc: 100.0, TrainLoss: 0.755, TestAcc: 89.4， TestLoss: 0.852
Epoch: 142, Lr:2.378268852553322e-06, TrainAcc: 100.0, TrainLoss: 0.751, TestAcc: 88.5， TestLoss: 0.865
Epoch: 143, Lr:2.378268852553322e-06, TrainAcc: 99.8, TrainLoss: 0.752, TestAcc: 85.8， TestLoss: 0.872
Epoch: 144, Lr:2.378268852553322e-06, TrainAcc: 99.8, TrainLoss: 0.751, TestAcc: 85.0， TestLoss: 0.883
Epoch: 145, Lr:2.2593554099256557e-06, TrainAcc: 100.0, TrainLoss: 0.750, TestAcc: 88.5， TestLoss: 0.857
Epoch: 146, Lr:2.2593554099256557e-06, TrainAcc: 100.0, TrainLoss: 0.747, TestAcc: 89.4， TestLoss: 0.866
Epoch: 147, Lr:2.2593554099256557e-06, TrainAcc: 99.8, TrainLoss: 0.751, TestAcc: 91.2， TestLoss: 0.856
Epoch: 148, Lr:2.2593554099256557e-06, TrainAcc: 100.0, TrainLoss: 0.749, TestAcc: 88.5， TestLoss: 0.850
Epoch: 149, Lr:2.2593554099256557e-06, TrainAcc: 99.6, TrainLoss: 0.754, TestAcc: 92.0， TestLoss: 0.856
Epoch: 150, Lr:2.146387639429373e-06, TrainAcc: 99.8, TrainLoss: 0.748, TestAcc: 85.0， TestLoss: 0.883
Epoch: 151, Lr:2.146387639429373e-06, TrainAcc: 100.0, TrainLoss: 0.748, TestAcc: 89.4， TestLoss: 0.856
Epoch: 152, Lr:2.146387639429373e-06, TrainAcc: 100.0, TrainLoss: 0.750, TestAcc: 88.5， TestLoss: 0.854
Epoch: 153, Lr:2.146387639429373e-06, TrainAcc: 99.8, TrainLoss: 0.747, TestAcc: 90.3， TestLoss: 0.863
Epoch: 154, Lr:2.146387639429373e-06, TrainAcc: 100.0, TrainLoss: 0.747, TestAcc: 89.4， TestLoss: 0.849
Epoch: 155, Lr:2.039068257457904e-06, TrainAcc: 99.6, TrainLoss: 0.751, TestAcc: 87.6， TestLoss: 0.866
Epoch: 156, Lr:2.039068257457904e-06, TrainAcc: 99.8, TrainLoss: 0.751, TestAcc: 91.2， TestLoss: 0.855
Epoch: 157, Lr:2.039068257457904e-06, TrainAcc: 99.8, TrainLoss: 0.752, TestAcc: 90.3， TestLoss: 0.858
Epoch: 158, Lr:2.039068257457904e-06, TrainAcc: 99.6, TrainLoss: 0.752, TestAcc: 87.6， TestLoss: 0.867
Epoch: 159, Lr:2.039068257457904e-06, TrainAcc: 99.8, TrainLoss: 0.748, TestAcc: 88.5， TestLoss: 0.859
Epoch: 160, Lr:1.937114844585009e-06, TrainAcc: 100.0, TrainLoss: 0.748, TestAcc: 89.4， TestLoss: 0.849
Epoch: 161, Lr:1.937114844585009e-06, TrainAcc: 100.0, TrainLoss: 0.748, TestAcc: 86.7， TestLoss: 0.874
Epoch: 162, Lr:1.937114844585009e-06, TrainAcc: 99.3, TrainLoss: 0.754, TestAcc: 90.3， TestLoss: 0.859
Epoch: 163, Lr:1.937114844585009e-06, TrainAcc: 99.3, TrainLoss: 0.754, TestAcc: 90.3， TestLoss: 0.853
Epoch: 164, Lr:1.937114844585009e-06, TrainAcc: 100.0, TrainLoss: 0.748, TestAcc: 86.7， TestLoss: 0.868
Epoch: 165, Lr:1.8402591023557584e-06, TrainAcc: 99.3, TrainLoss: 0.754, TestAcc: 87.6， TestLoss: 0.861
Epoch: 166, Lr:1.8402591023557584e-06, TrainAcc: 99.8, TrainLoss: 0.750, TestAcc: 90.3， TestLoss: 0.853
Epoch: 167, Lr:1.8402591023557584e-06, TrainAcc: 99.3, TrainLoss: 0.759, TestAcc: 90.3， TestLoss: 0.854
Epoch: 168, Lr:1.8402591023557584e-06, TrainAcc: 99.6, TrainLoss: 0.753, TestAcc: 90.3， TestLoss: 0.854
Epoch: 169, Lr:1.8402591023557584e-06, TrainAcc: 100.0, TrainLoss: 0.746, TestAcc: 86.7， TestLoss: 0.869
Epoch: 170, Lr:1.7482461472379704e-06, TrainAcc: 100.0, TrainLoss: 0.747, TestAcc: 90.3， TestLoss: 0.858
Epoch: 171, Lr:1.7482461472379704e-06, TrainAcc: 99.8, TrainLoss: 0.751, TestAcc: 85.0， TestLoss: 0.870
Epoch: 172, Lr:1.7482461472379704e-06, TrainAcc: 99.8, TrainLoss: 0.749, TestAcc: 87.6， TestLoss: 0.870
Epoch: 173, Lr:1.7482461472379704e-06, TrainAcc: 100.0, TrainLoss: 0.748, TestAcc: 89.4， TestLoss: 0.853
Epoch: 174, Lr:1.7482461472379704e-06, TrainAcc: 98.2, TrainLoss: 0.764, TestAcc: 90.3， TestLoss: 0.857
Epoch: 175, Lr:1.6608338398760719e-06, TrainAcc: 99.8, TrainLoss: 0.751, TestAcc: 90.3， TestLoss: 0.849
Epoch: 176, Lr:1.6608338398760719e-06, TrainAcc: 99.3, TrainLoss: 0.753, TestAcc: 84.1， TestLoss: 0.892
Epoch: 177, Lr:1.6608338398760719e-06, TrainAcc: 100.0, TrainLoss: 0.746, TestAcc: 88.5， TestLoss: 0.866
Epoch: 178, Lr:1.6608338398760719e-06, TrainAcc: 99.1, TrainLoss: 0.758, TestAcc: 88.5， TestLoss: 0.864
Epoch: 179, Lr:1.6608338398760719e-06, TrainAcc: 100.0, TrainLoss: 0.750, TestAcc: 90.3， TestLoss: 0.861
Epoch: 180, Lr:1.577792147882268e-06, TrainAcc: 100.0, TrainLoss: 0.747, TestAcc: 89.4， TestLoss: 0.863
Epoch: 181, Lr:1.577792147882268e-06, TrainAcc: 100.0, TrainLoss: 0.747, TestAcc: 91.2， TestLoss: 0.851
Epoch: 182, Lr:1.577792147882268e-06, TrainAcc: 100.0, TrainLoss: 0.748, TestAcc: 90.3， TestLoss: 0.863
Epoch: 183, Lr:1.577792147882268e-06, TrainAcc: 100.0, TrainLoss: 0.746, TestAcc: 89.4， TestLoss: 0.855
Epoch: 184, Lr:1.577792147882268e-06, TrainAcc: 100.0, TrainLoss: 0.748, TestAcc: 90.3， TestLoss: 0.846
Epoch: 185, Lr:1.4989025404881547e-06, TrainAcc: 100.0, TrainLoss: 0.747, TestAcc: 89.4， TestLoss: 0.853
Epoch: 186, Lr:1.4989025404881547e-06, TrainAcc: 100.0, TrainLoss: 0.747, TestAcc: 88.5， TestLoss: 0.866
Epoch: 187, Lr:1.4989025404881547e-06, TrainAcc: 100.0, TrainLoss: 0.747, TestAcc: 87.6， TestLoss: 0.862
Epoch: 188, Lr:1.4989025404881547e-06, TrainAcc: 100.0, TrainLoss: 0.747, TestAcc: 86.7， TestLoss: 0.870
Epoch: 189, Lr:1.4989025404881547e-06, TrainAcc: 99.8, TrainLoss: 0.748, TestAcc: 87.6， TestLoss: 0.858
Epoch: 190, Lr:1.4239574134637468e-06, TrainAcc: 100.0, TrainLoss: 0.748, TestAcc: 90.3， TestLoss: 0.841
Epoch: 191, Lr:1.4239574134637468e-06, TrainAcc: 99.6, TrainLoss: 0.752, TestAcc: 87.6， TestLoss: 0.869
Epoch: 192, Lr:1.4239574134637468e-06, TrainAcc: 99.3, TrainLoss: 0.757, TestAcc: 88.5， TestLoss: 0.859
Epoch: 193, Lr:1.4239574134637468e-06, TrainAcc: 100.0, TrainLoss: 0.751, TestAcc: 85.0， TestLoss: 0.886
Epoch: 194, Lr:1.4239574134637468e-06, TrainAcc: 100.0, TrainLoss: 0.746, TestAcc: 92.0， TestLoss: 0.854
Epoch: 195, Lr:1.3527595427905593e-06, TrainAcc: 99.6, TrainLoss: 0.752, TestAcc: 86.7， TestLoss: 0.867
Epoch: 196, Lr:1.3527595427905593e-06, TrainAcc: 100.0, TrainLoss: 0.746, TestAcc: 90.3， TestLoss: 0.858
Epoch: 197, Lr:1.3527595427905593e-06, TrainAcc: 99.8, TrainLoss: 0.750, TestAcc: 84.1， TestLoss: 0.880
Epoch: 198, Lr:1.3527595427905593e-06, TrainAcc: 99.8, TrainLoss: 0.752, TestAcc: 87.6， TestLoss: 0.863
Epoch: 199, Lr:1.3527595427905593e-06, TrainAcc: 100.0, TrainLoss: 0.746, TestAcc: 89.4， TestLoss: 0.860
Epoch: 200, Lr:1.2851215656510314e-06, TrainAcc: 100.0, TrainLoss: 0.747, TestAcc: 87.6， TestLoss: 0.857
Epoch: 201, Lr:1.2851215656510314e-06, TrainAcc: 100.0, TrainLoss: 0.748, TestAcc: 88.5， TestLoss: 0.858
Epoch: 202, Lr:1.2851215656510314e-06, TrainAcc: 99.8, TrainLoss: 0.753, TestAcc: 87.6， TestLoss: 0.876
Epoch: 203, Lr:1.2851215656510314e-06, TrainAcc: 100.0, TrainLoss: 0.746, TestAcc: 91.2， TestLoss: 0.858
Epoch: 204, Lr:1.2851215656510314e-06, TrainAcc: 99.8, TrainLoss: 0.748, TestAcc: 90.3， TestLoss: 0.848
Epoch: 205, Lr:1.2208654873684798e-06, TrainAcc: 100.0, TrainLoss: 0.751, TestAcc: 90.3， TestLoss: 0.855
Epoch: 206, Lr:1.2208654873684798e-06, TrainAcc: 100.0, TrainLoss: 0.748, TestAcc: 90.3， TestLoss: 0.865
Epoch: 207, Lr:1.2208654873684798e-06, TrainAcc: 100.0, TrainLoss: 0.746, TestAcc: 88.5， TestLoss: 0.858
Epoch: 208, Lr:1.2208654873684798e-06, TrainAcc: 98.9, TrainLoss: 0.766, TestAcc: 88.5， TestLoss: 0.861
模型效果展示
损失
准确度
总结与心得体会
可以发现逆置了BN和ReLU之后，模型的收敛速度更快，然后在测试集上的效果也更好。和作者的相比，我的效果提升给加明显。分析原因是作者的模型训练层数较深，提升的是模型的上限，而我的应该还没有收敛。由此可以得出结论逆置的BN和ReLU具有更强的特征提取能力，也有一定的表达能力的提升
