Pytorch实战100例-第8天：C3模块实现
本文为365天深度学习训练营 内部限免文章
参考本文所写记录性文章，请在文章开头注明以下内容，复制粘贴即可
- 本文为365天深度学习训练营 中的学习记录博客
- 参考文章：Pytorch实战 | P8周：C3模块实现
- 原作者：K同学啊|接辅导、项目定制
⏲往期文章：
- 深度学习实战训练 | 第8周：猫狗识别
- 深度学习实战训练 | 第7周：咖啡豆识别
- 深度学习实战训练 | 第6周：好莱坞明星识别
- 深度学习实战训练 | 第5周：运动鞋品牌识别
☕ Pytorch实战
- Pytorch实战 | 第P1周：实现mnist手写数字识别
- Pytorch实战 | 第P2周：彩色图片识别
- Pytorch实战 | 第P3周：天气识别
- 难度：新手入门⭐
- 语言：Python3、Pytorch
要求：
- 本地读取并加载数据。
- 测试集accuracy到达93%
拔高：
- 测试集accuracy到达95%
- 调用模型识别一张本地图片
我的环境：
- 语言环境：Python3.8
- 编译器：jupyter notebook
- 深度学习环境：Pytorch
一、 前期准备
1. 设置GPU
如果设备上支持GPU就使用GPU,否则使用CPU
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasets
import os,PIL,pathlib
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device
device(type='cpu')
2. 导入数据
import os,PIL,random,pathlib
data_dir = '/home/liangjie/test/Modelwhale/deep learning/p8/weather_photos'
data_dir = pathlib.Path(data_dir)
data_paths = list(data_dir.glob('*'))
classeNames = [str(path).split("/")[-1] for path in data_paths]
classeNames
['cloudy', 'rain', 'shine', 'sunrise']
total_datadir = '/deep learning/p8/weather_photos'
# 关于transforms.Compose的更多介绍可以参考：https://blog.csdn.net/qq_38251616/article/details/124878863
train_transforms = transforms.Compose([
transforms.Resize([224, 224]), # 将输入图片resize成统一尺寸
transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换为tensor，并归一化到[0,1]之间
transforms.Normalize( # 标准化处理-->转换为标准正太分布（高斯分布），使模型更容易收敛
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]) # 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
])
total_data = datasets.ImageFolder(total_datadir,transform=train_transforms)
total_data
Dataset ImageFolder
Number of datapoints: 1125
Root location: /home/liangjie/test/Modelwhale/deep learning/p8/weather_photos
StandardTransform
Transform: Compose(
Resize(size=[224, 224], interpolation=bilinear, max_size=None, antialias=None)
ToTensor()
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
)
3. 划分数据集
train_size = int(0.8 * len(total_data))
test_size = len(total_data) - train_size
train_dataset, test_dataset = torch.utils.data.random_split(total_data, [train_size, test_size])
train_dataset, test_dataset
(,
)
train_size,test_size
(900, 225)
batch_size = 32
train_dl = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
test_dl = torch.utils.data.DataLoader(test_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
for X, y in test_dl:
print("Shape of X [N, C, H, W]: ", X.shape)
print("Shape of y: ", y.shape, y.dtype)
break
Shape of X [N, C, H, W]: torch.Size([32, 3, 224, 224])
Shape of y: torch.Size([32]) torch.int64
二、搭建包含C3模块的模型
对于一般的CNN网络来说，都是由特征提取网络和分类网络构成，其中特征提取网络用于提取图片的特征，分类网络用于将图片进行分类。
⭐1. torch.nn.Conv2d()详解
函数原型：
torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode=‘zeros’, device=None, dtype=None)
关键参数说明：
- in_channels ( int ) – 输入图像中的通道数
- out_channels ( int ) – 卷积产生的通道数
- kernel_size ( int or tuple ) – 卷积核的大小
- stride ( int or tuple , optional ) – 卷积的步幅。默认值：1
- padding ( int , tuple或str , optional ) – 添加到输入的所有四个边的填充。默认值：0
- padding_mode (字符串,可选) – ‘zeros’, ‘reflect’, ‘replicate’或’circular’. 默认：‘zeros’
⭐2. torch.nn.Linear()详解
函数原型：
torch.nn.Linear(in_features, out_features, bias=True, device=None, dtype=None)
关键参数说明：
- in_features：每个输入样本的大小
- out_features：每个输出样本的大小
⭐3. torch.nn.MaxPool2d()详解
函数原型：
torch.nn.MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False)
关键参数说明：
- kernel_size：最大的窗口大小
- stride：窗口的步幅，默认值为
kernel_size - padding：填充值，默认为0
- dilation：控制窗口中元素步幅的参数
def autopad(k, p=None): # kernel, padding
# Pad to 'same'
if p is None:
p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
return p
class Conv(nn.Module):
def __init__(self, in_channel, out_channel, k=1, s=1, p=None, g=1, act=True):
super(Conv, self).__init__()
self.conv = nn.Conv2d(in_channel, out_channel, kernel_size=k, stride=s, padding = autopad(k, p), groups=g, bias=False)
self.bn = nn.BatchNorm2d(out_channel)
self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
def forward(self, x):
return self.act(self.bn(self.conv(x)))
class Bottleneck(nn.Module):
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):
super(Bottleneck, self).__init__()
self.c_ = int(c2 * e)
self.conv1 = Conv(c1, self.c_)
self.conv2 = Conv(self.c_, c2)
self.add = shortcut and c1 == c2
def forward(self, x):
out = self.conv1(x)
out = self.conv2(out)
return out + x if self.add else out
class C3(nn.Module):
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
super(C3, self).__init__()
self.c_ = int(c2 * e)
self.conv1 = Conv(c1, self.c_)
self.conv2 = Conv(c1, self.c_)
self.conv3 = Conv(2*self.c_, c2)
self.m = nn.Sequential(*(Bottleneck(self.c_, self.c_, shortcut, g, e=1.0) for _ in range(n)))
def forward(self, x):
out1 = self.conv1(x)
out1 = self.m(out1)
out2 = self.conv2(x)
out = torch.cat((out1, out2), dim=1)
return self.conv3(out)
class C3_net(nn.Module):
def __init__(self):
super(C3_net, self).__init__()
self.Conv1 = Conv(3, 32, 3, 2)
self.C3_1 = C3(32, 64, 3, 2)
self.classifier = nn.Sequential(
nn.Linear(in_features=802816, out_features=100),
nn.ReLU(),
nn.Linear(in_features=100, out_features=4)
)
def forward(self, x):
x = self.Conv1(x)
x = self.C3_1(x)
x = torch.flatten(x, start_dim=1)
x = self.classifier(x)
return x
torch.manual_seed(42)
model = C3_net()
model
C3_net(
(Conv1): Conv(
(conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(C3_1): C3(
(conv1): Conv(
(conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(conv2): Conv(
(conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(conv3): Conv(
(conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(m): Sequential(
(0): Bottleneck(
(conv1): Conv(
(conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(conv2): Conv(
(conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
)
(1): Bottleneck(
(conv1): Conv(
(conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(conv2): Conv(
(conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
)
(2): Bottleneck(
(conv1): Conv(
(conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(conv2): Conv(
(conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
)
)
)
(classifier): Sequential(
(0): Linear(in_features=802816, out_features=100, bias=True)
(1): ReLU()
(2): Linear(in_features=100, out_features=4, bias=True)
)
)
import torchsummary as summary
model.to(device)
summary.summary(model, (3, 224, 224))
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 32, 112, 112] 864
BatchNorm2d-2 [-1, 32, 112, 112] 64
SiLU-3 [-1, 32, 112, 112] 0
Conv-4 [-1, 32, 112, 112] 0
Conv2d-5 [-1, 32, 112, 112] 1,024
BatchNorm2d-6 [-1, 32, 112, 112] 64
SiLU-7 [-1, 32, 112, 112] 0
Conv-8 [-1, 32, 112, 112] 0
Conv2d-9 [-1, 32, 112, 112] 1,024
BatchNorm2d-10 [-1, 32, 112, 112] 64
SiLU-11 [-1, 32, 112, 112] 0
Conv-12 [-1, 32, 112, 112] 0
Conv2d-13 [-1, 32, 112, 112] 1,024
BatchNorm2d-14 [-1, 32, 112, 112] 64
SiLU-15 [-1, 32, 112, 112] 0
Conv-16 [-1, 32, 112, 112] 0
Bottleneck-17 [-1, 32, 112, 112] 0
Conv2d-18 [-1, 32, 112, 112] 1,024
BatchNorm2d-19 [-1, 32, 112, 112] 64
SiLU-20 [-1, 32, 112, 112] 0
Conv-21 [-1, 32, 112, 112] 0
Conv2d-22 [-1, 32, 112, 112] 1,024
BatchNorm2d-23 [-1, 32, 112, 112] 64
SiLU-24 [-1, 32, 112, 112] 0
Conv-25 [-1, 32, 112, 112] 0
Bottleneck-26 [-1, 32, 112, 112] 0
Conv2d-27 [-1, 32, 112, 112] 1,024
BatchNorm2d-28 [-1, 32, 112, 112] 64
SiLU-29 [-1, 32, 112, 112] 0
Conv-30 [-1, 32, 112, 112] 0
Conv2d-31 [-1, 32, 112, 112] 1,024
BatchNorm2d-32 [-1, 32, 112, 112] 64
SiLU-33 [-1, 32, 112, 112] 0
Conv-34 [-1, 32, 112, 112] 0
Bottleneck-35 [-1, 32, 112, 112] 0
Conv2d-36 [-1, 32, 112, 112] 1,024
BatchNorm2d-37 [-1, 32, 112, 112] 64
SiLU-38 [-1, 32, 112, 112] 0
Conv-39 [-1, 32, 112, 112] 0
Conv2d-40 [-1, 64, 112, 112] 4,096
BatchNorm2d-41 [-1, 64, 112, 112] 128
SiLU-42 [-1, 64, 112, 112] 0
Conv-43 [-1, 64, 112, 112] 0
C3-44 [-1, 64, 112, 112] 0
Linear-45 [-1, 100] 80,281,700
ReLU-46 [-1, 100] 0
Linear-47 [-1, 4] 404
================================================================
Total params: 80,295,960
Trainable params: 80,295,960
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 150.06
Params size (MB): 306.30
Estimated Total Size (MB): 456.94
----------------------------------------------------------------
三、 训练模型
1. 设置超参数
2. 编写训练函数
1. optimizer.zero_grad()
函数会遍历模型的所有参数，通过内置方法截断反向传播的梯度流，再将每个参数的梯度值设为0，即上一次的梯度记录被清空。
2. loss.backward()
PyTorch的反向传播(即tensor.backward())是通过autograd包来实现的，autograd包会根据tensor进行过的数学运算来自动计算其对应的梯度。
具体来说，torch.tensor是autograd包的基础类，如果你设置tensor的requires_grads为True，就会开始跟踪这个tensor上面的所有运算，如果你做完运算后使用tensor.backward()，所有的梯度就会自动运算，tensor的梯度将会累加到它的.grad属性里面去。
更具体地说，损失函数loss是由模型的所有权重w经过一系列运算得到的，若某个w的requires_grads为True，则w的所有上层参数（后面层的权重w）的.grad_fn属性中就保存了对应的运算，然后在使用loss.backward()后，会一层层的反向传播计算每个w的梯度值，并保存到该w的.grad属性中。
如果没有进行tensor.backward()的话，梯度值将会是None，因此loss.backward()要写在optimizer.step()之前。
3. optimizer.step()
step()函数的作用是执行一次优化步骤，通过梯度下降法来更新参数的值。因为梯度下降是基于梯度的，所以在执行optimizer.step()函数前应先执行loss.backward()函数来计算梯度。
注意：optimizer只负责通过梯度下降进行优化，而不负责产生梯度，梯度是tensor.backward()方法产生的。
# 训练循环
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset) # 训练集的大小，一共60000张图片
num_batches = len(dataloader) # 批次数目，1875（60000/32）
train_loss, train_acc = 0, 0 # 初始化训练损失和正确率
for X, y in dataloader: # 获取图片及其标签
X, y = X.to(device), y.to(device)
# 计算预测误差
pred = model(X) # 网络输出
loss = loss_fn(pred, y) # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失
# 反向传播
optimizer.zero_grad() # grad属性归零
loss.backward() # 反向传播
optimizer.step() # 每一步自动更新
# 记录acc与loss
train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()
train_loss += loss.item()
train_acc /= size
train_loss /= num_batches
return train_acc, train_loss
3. 编写测试函数
测试函数和训练函数大致相同，但是由于不进行梯度下降对网络权重进行更新，所以不需要传入优化器
def test (dataloader, model, loss_fn):
size = len(dataloader.dataset) # 测试集的大小，一共10000张图片
num_batches = len(dataloader) # 批次数目，313（10000/32=312.5，向上取整）
test_loss, test_acc = 0, 0
# 当不进行训练时，停止梯度更新，节省计算内存消耗
with torch.no_grad():
for imgs, target in dataloader:
imgs, target = imgs.to(device), target.to(device)
# 计算loss
target_pred = model(imgs)
loss = loss_fn(target_pred, target)
test_loss += loss.item()
test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item()
test_acc /= size
test_loss /= num_batches
return test_acc, test_loss
4. 正式训练
1. model.train()
model.train()的作用是启用 Batch Normalization 和 Dropout。
如果模型中有BN层(Batch Normalization）和Dropout，需要在训练时添加model.train()。model.train()是保证BN层能够用到每一批数据的均值和方差。对于Dropout，model.train()是随机取一部分网络连接来训练更新参数。
2. model.eval()
model.eval()的作用是不启用 Batch Normalization 和 Dropout。
如果模型中有BN层(Batch Normalization）和Dropout，在测试时添加model.eval()。model.eval()是保证BN层能够用全部训练数据的均值和方差，即测试过程中要保证BN层的均值和方差不变。对于Dropout，model.eval()是利用到了所有网络连接，即不进行随机舍弃神经元。
训练完train样本后，生成的模型model要用来测试样本。在model(test)之前，需要加上model.eval()，否则的话，有输入数据，即使不训练，它也会改变权值。这是model中含有BN层和Dropout所带来的的性质。
#SGD调优#
epochs = 20
train_loss = []
train_acc = []
test_loss = []
test_acc = []
torch.manual_seed(42)
model=C3_net()
model.to(device)
loss_fn = nn.CrossEntropyLoss() # 创建损失函数
learn_rate = 1e-4 # 学习率
opt = torch.optim.SGD(model.parameters(),lr=learn_rate)
for epoch in range(epochs):
model.train()
epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt)
model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
train_acc.append(epoch_train_acc)
train_loss.append(epoch_train_loss)
test_acc.append(epoch_test_acc)
test_loss.append(epoch_test_loss)
template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}')
print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss))
print('Done')
#Adam调优#
epochs = 20
train_loss = []
train_acc = []
test_loss = []
test_acc = []
torch.manual_seed(42)
model=C3_net()
model.to(device)
loss_fn = nn.CrossEntropyLoss() # 创建损失函数
learn_rate = 1e-4 # 学习率
opt = torch.optim.Adam(model.parameters(),lr=learn_rate)
for epoch in range(epochs):
model.train()
epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt)
model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
# 保存最佳模型到 best_model
if epoch_test_acc > best_acc:
best_acc = epoch_test_acc
best_model = copy.deepcopy(model)
train_acc.append(epoch_train_acc)
train_loss.append(epoch_train_loss)
test_acc.append(epoch_test_acc)
test_loss.append(epoch_test_loss)
# 获取当前的学习率
lr = optimizer.state_dict()['param_groups'][0]['lr']
template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:.2E}')
print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss,
epoch_test_acc*100, epoch_test_loss, lr))
# 保存最佳模型到文件中
PATH = './best_model.pth' # 保存的参数文件名
torch.save(model.state_dict(), PATH)
print('Done')
Epoch: 1, Train_acc:76.7%, Train_loss:1.097, Test_acc:56.0%，Test_loss:2.127
Epoch: 2, Train_acc:92.3%, Train_loss:0.230, Test_acc:83.6%，Test_loss:0.718
Epoch: 3, Train_acc:97.6%, Train_loss:0.178, Test_acc:89.3%，Test_loss:0.359
Epoch: 4, Train_acc:97.2%, Train_loss:0.114, Test_acc:86.7%，Test_loss:0.538
Epoch: 5, Train_acc:97.4%, Train_loss:0.214, Test_acc:84.9%，Test_loss:0.556
Epoch: 6, Train_acc:97.6%, Train_loss:0.096, Test_acc:88.0%，Test_loss:0.541
Epoch: 7, Train_acc:98.7%, Train_loss:0.047, Test_acc:88.0%，Test_loss:0.469
Epoch: 8, Train_acc:99.4%, Train_loss:0.042, Test_acc:85.3%，Test_loss:0.627
Epoch: 9, Train_acc:99.3%, Train_loss:0.016, Test_acc:89.3%，Test_loss:0.469
Epoch:10, Train_acc:99.7%, Train_loss:0.009, Test_acc:88.4%，Test_loss:0.571
Epoch:11, Train_acc:99.9%, Train_loss:0.008, Test_acc:88.0%，Test_loss:0.501
Epoch:12, Train_acc:99.9%, Train_loss:0.007, Test_acc:88.9%，Test_loss:0.436
Epoch:13, Train_acc:99.9%, Train_loss:0.005, Test_acc:90.2%，Test_loss:0.513
Epoch:14, Train_acc:99.6%, Train_loss:0.026, Test_acc:88.4%，Test_loss:1.820
Epoch:15, Train_acc:97.8%, Train_loss:0.076, Test_acc:87.6%，Test_loss:0.887
Epoch:16, Train_acc:99.8%, Train_loss:0.007, Test_acc:89.3%，Test_loss:0.686
Epoch:17, Train_acc:99.4%, Train_loss:0.008, Test_acc:90.2%，Test_loss:0.597
Epoch:18, Train_acc:99.0%, Train_loss:0.071, Test_acc:92.4%，Test_loss:0.424
Epoch:19, Train_acc:99.1%, Train_loss:0.056, Test_acc:88.9%，Test_loss:0.662
Epoch:20, Train_acc:99.2%, Train_loss:0.044, Test_acc:88.4%，Test_loss:0.712
Done
修改优化
epochs = 20
train_loss = []
train_acc = []
test_loss = []
test_acc = []
torch.manual_seed(42)
model=C3_net()
model.to(device)
loss_fn = nn.CrossEntropyLoss() # 创建损失函数
learn_rate = 5e-4 # 学习率
opt = torch.optim.SGD(model.parameters(),lr=learn_rate,momentum=3e-4, weight_decay=5e-4)
for epoch in range(epochs):
model.train()
epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt)
model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
train_acc.append(epoch_train_acc)
train_loss.append(epoch_train_loss)
test_acc.append(epoch_test_acc)
test_loss.append(epoch_test_loss)
template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}')
print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss))
print('Done')
Epoch: 1, Train_acc:78.3%, Train_loss:0.633, Test_acc:67.1%，Test_loss:0.764
Epoch: 2, Train_acc:86.2%, Train_loss:0.369, Test_acc:86.2%，Test_loss:0.350
Epoch: 3, Train_acc:91.8%, Train_loss:0.285, Test_acc:86.2%，Test_loss:0.361
Epoch: 4, Train_acc:93.1%, Train_loss:0.249, Test_acc:82.2%，Test_loss:0.402
Epoch: 5, Train_acc:95.0%, Train_loss:0.188, Test_acc:86.2%，Test_loss:0.316
Epoch: 6, Train_acc:96.1%, Train_loss:0.156, Test_acc:86.2%，Test_loss:0.364
Epoch: 7, Train_acc:97.7%, Train_loss:0.127, Test_acc:88.4%，Test_loss:0.317
Epoch: 8, Train_acc:97.9%, Train_loss:0.133, Test_acc:85.3%，Test_loss:0.442
Epoch: 9, Train_acc:97.6%, Train_loss:0.121, Test_acc:85.3%，Test_loss:0.341
Epoch:10, Train_acc:98.4%, Train_loss:0.122, Test_acc:86.2%，Test_loss:0.374
Epoch:11, Train_acc:98.9%, Train_loss:0.086, Test_acc:88.9%，Test_loss:0.276
Epoch:12, Train_acc:98.7%, Train_loss:0.083, Test_acc:89.3%，Test_loss:0.271
Epoch:13, Train_acc:99.2%, Train_loss:0.081, Test_acc:90.2%，Test_loss:0.280
Epoch:14, Train_acc:98.6%, Train_loss:0.087, Test_acc:88.4%，Test_loss:0.390
Epoch:15, Train_acc:98.7%, Train_loss:0.077, Test_acc:92.0%，Test_loss:0.272
Epoch:16, Train_acc:99.3%, Train_loss:0.055, Test_acc:90.7%，Test_loss:0.389
Epoch:17, Train_acc:99.2%, Train_loss:0.052, Test_acc:90.2%，Test_loss:0.264
Epoch:18, Train_acc:99.6%, Train_loss:0.049, Test_acc:89.3%，Test_loss:0.271
Epoch:19, Train_acc:99.1%, Train_loss:0.054, Test_acc:89.3%，Test_loss:0.261
Epoch:20, Train_acc:99.3%, Train_loss:0.063, Test_acc:88.4%，Test_loss:0.324
Done
SGD会下降的比较慢且容易遇到局部最小值导致训练无法继续向下，通过增加momentum，稍微调大学习率可以使训练更好的进行，增加weight_decay是为了减少过拟合
epochs = 20
train_loss = []
train_acc = []
test_loss = []
test_acc = []
torch.manual_seed(42)
model=C3_net()
model.to(device)
loss_fn = nn.CrossEntropyLoss() # 创建损失函数
learn_rate = 1e-4 # 学习率
#opt = torch.optim.Adam(model.parameters(),lr=learn_rate)
opt =torch.optim.Adam(model.parameters(),
lr=0.0001,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=0,
amsgrad=False)
for epoch in range(epochs):
model.train()
epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt)
model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
train_acc.append(epoch_train_acc)
train_loss.append(epoch_train_loss)
test_acc.append(epoch_test_acc)
test_loss.append(epoch_test_loss)
template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}')
print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss))
print('Done')
Epoch: 1, Train_acc:77.6%, Train_loss:1.456, Test_acc:79.1%，Test_loss:0.683
Epoch: 2, Train_acc:91.2%, Train_loss:0.264, Test_acc:85.8%，Test_loss:0.389
Epoch: 3, Train_acc:97.6%, Train_loss:0.093, Test_acc:90.7%，Test_loss:0.335
Epoch: 4, Train_acc:98.4%, Train_loss:0.176, Test_acc:90.2%，Test_loss:0.359
Epoch: 5, Train_acc:98.0%, Train_loss:0.060, Test_acc:85.3%，Test_loss:0.539
Epoch: 6, Train_acc:98.4%, Train_loss:0.041, Test_acc:85.8%，Test_loss:0.484
Epoch: 7, Train_acc:99.7%, Train_loss:0.021, Test_acc:86.7%，Test_loss:0.479
Epoch: 8, Train_acc:99.8%, Train_loss:0.047, Test_acc:88.4%，Test_loss:0.515
Epoch: 9, Train_acc:98.2%, Train_loss:0.081, Test_acc:86.7%，Test_loss:0.550
Epoch:10, Train_acc:99.4%, Train_loss:0.017, Test_acc:88.0%，Test_loss:0.461
Epoch:11, Train_acc:99.9%, Train_loss:0.003, Test_acc:88.0%，Test_loss:0.403
Epoch:12, Train_acc:99.8%, Train_loss:0.007, Test_acc:87.6%，Test_loss:0.468
Epoch:13, Train_acc:99.3%, Train_loss:0.027, Test_acc:83.1%，Test_loss:0.805
Epoch:14, Train_acc:99.7%, Train_loss:0.013, Test_acc:85.8%，Test_loss:0.893
Epoch:15, Train_acc:98.0%, Train_loss:0.165, Test_acc:83.1%，Test_loss:0.753
Epoch:16, Train_acc:97.6%, Train_loss:0.139, Test_acc:84.9%，Test_loss:1.345
Epoch:17, Train_acc:99.1%, Train_loss:0.050, Test_acc:85.3%，Test_loss:0.765
Epoch:18, Train_acc:99.6%, Train_loss:0.011, Test_acc:86.7%，Test_loss:0.695
Epoch:19, Train_acc:99.7%, Train_loss:0.014, Test_acc:85.8%，Test_loss:0.668
Epoch:20, Train_acc:99.7%, Train_loss:0.172, Test_acc:88.4%，Test_loss:0.691
Done
四、 结果可视化
import matplotlib.pyplot as plt
#隐藏警告
import warnings
warnings.filterwarnings("ignore") #忽略警告信息
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
plt.rcParams['figure.dpi'] = 100 #分辨率
epochs_range = range(epochs)
plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, train_acc, label='Training Accuracy')
plt.plot(epochs_range, test_acc, label='Test Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.
findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei
findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.
findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei
模型评估
model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
epoch_test_acc, epoch_test_loss
(0.8844444444444445, 0.7120169894769788)
五 识别本地图片
local_test_image = PIL.Image.open ("/deep learning/p3/p3_testdata/r1.jpg").convert('RGB')
#local_test_data = torchvision.transforms.functional.resize(local_test_data,[224,224])
local_transforms = transforms.Compose([
transforms.Resize([224, 224]), # 将输入图片resize成统一尺寸
transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换为tensor，并归一化到[0,1]之间
])
local_test_data = local_transforms(local_test_image)
PIL.Image.open ("/deep learning/p3/p3_testdata/r1.jpg").convert('RGB')##显示照片
_,result=torch.max(model(local_test_data.to(device).unsqueeze(0)),1)
classeNames[result]
'rain'
优化之后的模型预测效果并不好，将雨预测为云，用Adam效果最好