迁移学习是深度学习中一种常用的方法,核心思想为利用一个已经在其他训练集训练好的模型的材料(权重值或者特征层)来对目标训练集进行训练。
利用另一个训练集训练好的模型,我们可以:
演示平台: python3.6、pytorch0.2
from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
plt.ion() # 开启plt的交互模式
接下来对数据进行部分展示,注意torch.utils.data.Dataloaders读取之后的数据为Tensor型,数据格式为C×W×H(C为颜色通道,W、H为图像宽和高),但是如果要用plt.imshow工具箱进行显示则必须转化为W×H×C的格式,另外也要进行反规范化。
def imshow(inp, title=None):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp)
if title is not None:
plt.title(title)
plt.pause(0.001) # 进行少量延时来确保图像正确显示
# 获取训练数据中的一个 batch
inputs, classes = next(iter(dataloaders['train']))
# 创建网格,注意之前的batch_size = 4
out = torchvision.utils.make_grid(inputs)
imshow(out, title=[class_names[x] for x in classes])
1、可以对学习率进行调控; 2、寻找并保存最佳的模型。
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
since = time.time() # 计时开始
best_model_wts = model.state_dict() # 读取训练好的模型权重
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# 每个epoch中游训练和验证部分
for phase in ['train', 'val']:
if phase == 'train':
scheduler.step()
model.train(True)
else:
model.train(False)
running_loss = 0.0
running_corrects = 0
for data in dataloaders[phase]:
inputs, labels = data
# 如果使用GPU,则使用Variable
if use_gpu:
inputs = Variable(inputs.cuda())
labels = Variable(labels.cuda())
else:
inputs, labels = Variable(inputs), Variable(labels)
# 初始化梯度值
optimizer.zero_grad()
# 前向
outputs = model(inputs)
_, preds = torch.max(outputs.data, 1)
loss = criterion(outputs, labels)
# 后向,如果为训练集则进行梯度优化
if phase == 'train':
loss.backward()
optimizer.step()
# 统计损失
running_loss += loss.data[0]
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects / dataset_sizes[phase]
print('{} Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
# 深度复制该模型
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = model.state_dict()
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# 载入最佳的模型
model.load_state_dict(best_model_wts)
return model
定义一个可视化函数
def visualize_model(model, num_images=6):
images_so_far = 0
fig = plt.figure()
for i, data in enumerate(dataloaders['val']):
inputs, labels = data
if use_gpu:
inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())
else:
inputs, labels = Variable(inputs), Variable(labels)
outputs = model(inputs)
_, preds = torch.max(outputs.data, 1)
for j in range(inputs.size()[0]):
images_so_far += 1
ax = plt.subplot(num_images//2, 2, images_so_far)
ax.axis('off')
ax.set_title('predicted: {}'.format(class_names[preds[j]]))
imshow(inputs.cpu().data[j])
if images_so_far == num_images:
return
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
num_epochs=25)
以下是训练结果,程序执行时间带CPU中为10-25分钟,GPU为1-2分钟。 (GTX1060为2分钟)
Epoch 0/24
----------
train Loss: 0.1660 Acc: 0.6762
val Loss: 0.0445 Acc: 0.9542
Epoch 1/24
----------
train Loss: 0.1141 Acc: 0.8033
val Loss: 0.0877 Acc: 0.8693
Epoch 2/24
----------
train Loss: 0.1440 Acc: 0.7623
val Loss: 0.0484 Acc: 0.9346
Epoch 3/24
----------
train Loss: 0.1082 Acc: 0.8074
val Loss: 0.0787 Acc: 0.8824
Epoch 4/24
----------
train Loss: 0.1751 Acc: 0.7500
val Loss: 0.2313 Acc: 0.7647
Epoch 5/24
----------
train Loss: 0.1367 Acc: 0.8074
val Loss: 0.1766 Acc: 0.7908
Epoch 6/24
----------
train Loss: 0.1456 Acc: 0.8156
val Loss: 0.1116 Acc: 0.7908
Epoch 7/24
----------
train Loss: 0.1259 Acc: 0.8033
val Loss: 0.0793 Acc: 0.8627
Epoch 8/24
----------
train Loss: 0.0807 Acc: 0.8607
val Loss: 0.0781 Acc: 0.8758
Epoch 9/24
----------
train Loss: 0.0618 Acc: 0.8730
val Loss: 0.0778 Acc: 0.8824
Epoch 10/24
----------
train Loss: 0.0804 Acc: 0.8566
val Loss: 0.0876 Acc: 0.8758
Epoch 11/24
----------
train Loss: 0.0751 Acc: 0.8607
val Loss: 0.0945 Acc: 0.8693
Epoch 12/24
----------
train Loss: 0.0695 Acc: 0.8770
val Loss: 0.0950 Acc: 0.8824
Epoch 13/24
----------
train Loss: 0.0596 Acc: 0.8852
val Loss: 0.0907 Acc: 0.8889
Epoch 14/24
----------
train Loss: 0.0624 Acc: 0.9016
val Loss: 0.0785 Acc: 0.8824
Epoch 15/24
----------
train Loss: 0.0546 Acc: 0.9139
val Loss: 0.0810 Acc: 0.8824
Epoch 16/24
----------
train Loss: 0.0982 Acc: 0.8484
val Loss: 0.1054 Acc: 0.8824
Epoch 17/24
----------
train Loss: 0.0659 Acc: 0.8893
val Loss: 0.0839 Acc: 0.8889
Epoch 18/24
----------
train Loss: 0.0645 Acc: 0.8893
val Loss: 0.0760 Acc: 0.8824
Epoch 19/24
----------
train Loss: 0.0723 Acc: 0.8934
val Loss: 0.0699 Acc: 0.8758
Epoch 20/24
----------
train Loss: 0.0689 Acc: 0.8852
val Loss: 0.0733 Acc: 0.8627
Epoch 21/24
----------
train Loss: 0.0656 Acc: 0.8893
val Loss: 0.0915 Acc: 0.8954
Epoch 22/24
----------
train Loss: 0.0756 Acc: 0.8770
val Loss: 0.0772 Acc: 0.8889
Epoch 23/24
----------
train Loss: 0.0695 Acc: 0.8934
val Loss: 0.0724 Acc: 0.8627
Epoch 24/24
----------
train Loss: 0.0556 Acc: 0.9139
val Loss: 0.0821 Acc: 0.8889
Training complete in 1m 26s
Best val Acc: 0.954248
由此可知,第一次epoch中的参数为最好参数,将其记录下来。 观察结果:
visualize_model(model_ft)
可以看到,95%的正确率还是很高的。
与之前的步骤类似,不同的是除了自己添加的全连接层需要更新外,其他的卷积层保持之前训练好的参数,不进行更新。
model_conv = torchvision.models.resnet18(pretrained=True)
for param in model_conv.parameters():
param.requires_grad = False # 设置为false则梯度不会进行更新
# 新创建的parameters,默认param.requires_grad = True
num_ftrs = model_conv.fc.in_features
model_conv.fc = nn.Linear(num_ftrs, 2)
if use_gpu:
model_conv = model_conv.cuda()
criterion = nn.CrossEntropyLoss()
# 注意这里仅仅对fc进行参数更新(fc为整个网络中的最后一层)
optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)
# 学习率每7个epoch下降0.1
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)
开始进行训练
model_conv = train_model(model_conv, criterion, optimizer_conv,
exp_lr_scheduler, num_epochs=25)
Epoch 0/24
----------
train Loss: 0.1913 Acc: 0.5738
val Loss: 0.1566 Acc: 0.6732
Epoch 1/24
----------
train Loss: 0.1469 Acc: 0.7295
val Loss: 0.0659 Acc: 0.9085
Epoch 2/24
----------
train Loss: 0.1189 Acc: 0.7623
val Loss: 0.0458 Acc: 0.9477
Epoch 3/24
----------
train Loss: 0.1191 Acc: 0.8033
val Loss: 0.0463 Acc: 0.9281
Epoch 4/24
----------
train Loss: 0.1470 Acc: 0.7582
val Loss: 0.1730 Acc: 0.7255
Epoch 5/24
----------
train Loss: 0.1590 Acc: 0.7746
val Loss: 0.0451 Acc: 0.9346
Epoch 6/24
----------
train Loss: 0.0950 Acc: 0.8361
val Loss: 0.0486 Acc: 0.9412
Epoch 7/24
----------
train Loss: 0.0734 Acc: 0.8975
val Loss: 0.0502 Acc: 0.9412
Epoch 8/24
----------
train Loss: 0.0821 Acc: 0.8689
val Loss: 0.0417 Acc: 0.9477
Epoch 9/24
----------
train Loss: 0.1085 Acc: 0.7910
val Loss: 0.0513 Acc: 0.9346
Epoch 10/24
----------
train Loss: 0.0908 Acc: 0.8443
val Loss: 0.0468 Acc: 0.9477
Epoch 11/24
----------
train Loss: 0.0803 Acc: 0.8484
val Loss: 0.0416 Acc: 0.9477
Epoch 12/24
----------
train Loss: 0.0907 Acc: 0.8525
val Loss: 0.0425 Acc: 0.9477
Epoch 13/24
----------
train Loss: 0.0811 Acc: 0.8443
val Loss: 0.0433 Acc: 0.9542
Epoch 14/24
----------
train Loss: 0.1185 Acc: 0.8115
val Loss: 0.0460 Acc: 0.9542
Epoch 15/24
----------
train Loss: 0.0851 Acc: 0.8361
val Loss: 0.0434 Acc: 0.9542
Epoch 16/24
----------
train Loss: 0.0975 Acc: 0.8361
val Loss: 0.0431 Acc: 0.9542
Epoch 17/24
----------
train Loss: 0.0756 Acc: 0.8730
val Loss: 0.0518 Acc: 0.9346
Epoch 18/24
----------
train Loss: 0.0938 Acc: 0.8361
val Loss: 0.0448 Acc: 0.9477
Epoch 19/24
----------
train Loss: 0.0837 Acc: 0.8402
val Loss: 0.0462 Acc: 0.9412
Epoch 20/24
----------
train Loss: 0.0849 Acc: 0.8443
val Loss: 0.0448 Acc: 0.9477
Epoch 21/24
----------
train Loss: 0.0701 Acc: 0.8934
val Loss: 0.0470 Acc: 0.9477
Epoch 22/24
----------
train Loss: 0.0822 Acc: 0.8525
val Loss: 0.0403 Acc: 0.9477
Epoch 23/24
----------
train Loss: 0.0934 Acc: 0.8525
val Loss: 0.0433 Acc: 0.9412
Epoch 24/24
----------
train Loss: 0.0872 Acc: 0.8361
val Loss: 0.0393 Acc: 0.9477
Training complete in 0m 51s
Best val Acc: 0.954248
Transfer Learning关心的问题是:什么是“知识”以及如何更好地运用之前得到的“知识”。这可以有很多方法和手段。fine-tune是其中的一种手段。在实际操作中有很多的方法可以使用,也可以对不同的特征层进行不同策略的参数调整。
迁移学习是一种思想,在众多方法的修饰下,可以很好的完成任务。
参考资料: 1、http://cs231n.github.io/transfer-learning/ 2、http://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html#convnet-as-fixed-feature-extractor 3、https://www.zhihu.com/question/49534423
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