ud185
Tips
回归问题和二元分类问题经常使用均方损失
前向传播
计算损失
反向传播得到梯度
更新权重（使用优化器）
创建一个分类器
from torch import nn, optim
import torch.nn.functional as F
class Classifier(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(784, 256)
self.fc2 = nn.Linear(256, 128)
self.fc3 = nn.Linear(128, 64)
self.fc4 = nn.Linear(64, 10)
def forward(self, x):
# make sure input tensor is flattened
x = x.view(x.shape[0], -1)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = F.log_softmax(self.fc4(x), dim=1)
return x
#model = nn.Sequential(nn.Linear(784, 128),
# nn.ReLU(),
# nn.Linear(128, 64),
# nn.ReLU(),
# nn.Linear(64, 10),
# nn.LogSoftmax(dim=1))验证分类器
model = Classifier()
criterion = nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=0.003)
epochs = 30
steps = 0
train_losses, test_losses = [], []
for e in range(epochs):
running_loss = 0
for images, labels in trainloader:
optimizer.zero_grad()
log_ps = model(images)
loss = criterion(log_ps, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
else:
test_loss = 0
accuracy = 0
# Turn off gradients for validation, saves memory and computations
with torch.no_grad():
for images, labels in testloader:
log_ps = model(images)
test_loss += criterion(log_ps, labels)
ps = torch.exp(log_ps)
top_p, top_class = ps.topk(1, dim=1)
equals = top_class == labels.view(*top_class.shape)
accuracy += torch.mean(equals.type(torch.FloatTensor))
train_losses.append(running_loss/len(trainloader))
test_losses.append(test_loss/len(testloader))
print("Epoch: {}/{}.. ".format(e+1, epochs),
"Training Loss: {:.3f}.. ".format(running_loss/len(trainloader)),
"Test Loss: {:.3f}.. ".format(test_loss/len(testloader)),
"Test Accuracy: {:.3f}".format(accuracy/len(testloader)))过拟合
早停法 (early stopping) 即：使用验证损失最低的模型，需要频繁保存模型。
丢弃（dropout）即随机丢弃单元
self.dropout = nn.Dropout(p=0.2)class Classifier(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(784, 256) self.fc2 = nn.Linear(256, 128) self.fc3 = nn.Linear(128, 64) self.fc4 = nn.Linear(64, 10) # Dropout module with 0.2 drop probability self.dropout = nn.Dropout(p=0.2) def forward(self, x): # make sure input tensor is flattened x = x.view(x.shape[0], -1) # Now with dropout x = self.dropout(F.relu(self.fc1(x))) x = self.dropout(F.relu(self.fc2(x))) x = self.dropout(F.relu(self.fc3(x))) # output so no dropout here x = F.log_softmax(self.fc4(x), dim=1) return x验证的时候关闭
dropout, 先使用model.eval()设定为推理模式，计算测试损失和精度后再开启模型训练model.train()启动dropout# Turn off gradients for validation, saves memory and computations with torch.no_grad(): model.eval() #set up to eval not use dropout for images, labels in testloader: log_ps = model(images) ... model.train() # set up to train with dropout
保存和加载网络模型
torch.save和torch.loadPyTorch网络的参数保存在模型的state_dict中。状态字典包含每个层级的权重和偏差矩阵
torch.save(model.state_dict(), 'checkpoint.pth')
state_dict = torch.load('checkpoint.pth')
model.load_state_dict(state_dict)将状态加载到神经网络中需要执行
model.load_state_dict(state_dict)只有模型结构和检查点的结构完全一样时，状态字典才能加载成功
可以将模型的架构信息和状态字典都保存在检查点里，可以通过创建一个字典来实现
checkpoint = {'input_size': 784, 'output_size': 10, 'hidden_layers': [each.out_features for each in model.hidden_layers], 'state_dict': model.state_dict()} torch.save(checkpoint, 'checkpoint.pth') def load_checkpoint(filepath): checkpoint = torch.load(filepath) model = fc_model.Network(checkpoint['input_size'], checkpoint['output_size'], checkpoint['hidden_layers']) model.load_state_dict(checkpoint['state_dict']) return model model = load_checkpoint('checkpoint.pth')再次加载模型的时候使用
load_checkpoint函数即可正确完成
加载图像数据
data_dir = 'Cat_Dog_data/train' # 数据集所在目录
# 数据转换，缩放、裁剪，然后转换为张量
transform = transforms.Compose([transforms.Resize(255),
transforms.CenterCrop(224),
transforms.ToTensor()])
# 加载数据集
dataset = datasets.ImageFolder(data_dir, transform=transform)
# use the ImageFolder dataset to create the DataLoader
dataloader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True)
# 测试数据加载器
images, labels = next(iter(dataloader))
helper.imshow(images[0], normalize=False)数据增强
训练神经网络的一个常见策略是在输入数据本身里引入随机性。例如，你可以在训练过程中随机地旋转、翻转、缩放和/或裁剪图像。这样一来，你的神经网络在处理位置、大小、方向不同的相同图像时，可以更好地进行泛化。
train_transforms = transforms.Compose([transforms.RandomRotation(30),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5],
[0.5, 0.5, 0.5])])另外，还需要使用 transforms.Normalize 标准化图像。传入均值和标准偏差列表，然后标准化颜色通道
迁移学习
加载 DenseNet 等模型
model = models.densenet121(pretrained=True)
# Freeze parameters so we don't backprop through them 冻结特征层
for param in model.parameters():
param.requires_grad = False测试GPU是否可用
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")PyTorch 和其他深度学习框架一样，也使用 CUDA 在 GPU 上高效地进行前向和反向运算。在 PyTorch 中，你需要使用
model.to('cuda')将模型参数和其他张量转移到 GPU 内存中。你可以使用model.to('cpu')将它们从 GPU 移到 CPU，比如在你需要在 PyTorch 之外对网络输出执行运算时。model.to(device) for epoch in range(epochs): for ii, (inputs, labels) in enumerate(trainloader): # Move input and label tensors to the default device inputs, labels = inputs.to(device), labels.to(device)
Review
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
from torchvision import datasets, transforms, models
from collections import OrderedDict
data_dir = 'Cat_Dog_data'
# Define transforms for the training data and testing data
train_transforms = transforms.Compose([transforms.RandomRotation(30),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
test_transforms = transforms.Compose([transforms.Resize(255),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
# Pass transforms in here, then run the next cell to see how the transforms look
train_data = datasets.ImageFolder(data_dir + '/train', transform=train_transforms)
test_data = datasets.ImageFolder(data_dir + '/test', transform=test_transforms)
trainloader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)
testloader = torch.utils.data.DataLoader(test_data, batch_size=64)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = models.densenet121(pretrained=True)
for param in model.parameters():
param.requires_grad = False
model.classifier = nn.Sequential(OrderedDict([
('fc1', nn.Linear(1024, 500)),
('relu', nn.ReLU()),
('fc2', nn.Linear(500, 2)),
('output', nn.LogSoftmax(dim=1))
]))
criterion = nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=0.003)
model.to(device);
epochs = 30
steps = 0
running_loss = 0
print_every = 5
train_losses, test_losses = [], []
for epoch in range(epochs):
for images, labels in trainloader:
steps += 1
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad() # clearing the gradients
log_ps = model(images)
loss = criterion(log_ps, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if steps %% print_every == 0:
model.eval() # set the network to evaluation mode
test_loss = 0
accuracy = 0
for images, labels in testloader:
images, labels = images.to(device), labels.to(device)
logps = model(images)
loss = criterion(logps, labels)
test_loss += loss.item()
# calculate our accuracy
ps = torch.exp(logps)
top_ps, top_class = ps.topk(1, dim=1)
equality = top_class == labels.view(*top_class,shape)
accuracy += torch.mean(equality.type(torch.FloatTensor)).item()
running_loss = 0
model.train() # back to training mode
print("Epoch: {}/{}.. ".format(e+1, epochs),
"Training Loss: {:.3f}.. ".format(running_loss/len(trainloader)),
"Test Loss: {:.3f}.. ".format(test_loss/len(testloader)),
"Test Accuracy: {:.3f}".format(accuracy/len(testloader)))Last updated