本小节使用torch搭建线性回归模型,训练和测试:
(1)定义模型超参数:输入大小、隐含层、输出、迭代次数、批量大小、学习率。
(2)定义训练数据,加餐部分是使用自己的数据集:(可参考:https://cloud.tencent.com/developer/article/1997099)
(3)定义模型(定义全连接神经网络)。
(4)定义损失函数,选用适合的损失函数。
(5)定义优化算法(SGD、Adam等)。
(6)保存模型。
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代码部分:
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
# 判定GPU是否存在
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 定义超参数
input_size = 784
hidden_size = 500
num_classes = 10
num_epochs = 5
batch_size = 100
learning_rate = 0.001
# 手写体数据
train_dataset = torchvision.datasets.MNIST(root='./data',
train=True,
transform=transforms.ToTensor(),
download=True)
test_dataset = torchvision.datasets.MNIST(root='./data',
train=False,
transform=transforms.ToTensor())
# 构建数据管道, 使用自己的数据集请参考:https://blog.csdn.net/u014365862/article/details/80506147
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False)
# 定义含有一个隐含层的全连接神经网络。
class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNet, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size, num_classes)
def forward(self, x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out
# 定义模型
model = NeuralNet(input_size, hidden_size, num_classes).to(device)
# 损失函数和优化算法
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# 训练模型
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
# Move tensors to the configured device
images = images.reshape(-1, 28*28).to(device)
labels = labels.to(device)
# 前向传播和计算loss
outputs = model(images)
loss = criterion(outputs, labels)
# 后向传播和调整参数
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 每100个batch打印一次数据
if (i+1) % 100 == 0:
print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
# 模型测试部分
# 测试阶段不需要计算梯度,注意
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.reshape(-1, 28*28).to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))
# 保存模型参数
torch.save(model.state_dict(), 'model.ckpt')
加餐:在自己数据集上使用:
其中,train.txt中的数据格式:
gender/0male/0(2).jpg 1
gender/0male/0(3).jpeg 1
gender/0male/0(1).jpg 0
test.txt中的数据格式如下:
gender/0male/0(3).jpeg 1
gender/0male/0(1).jpg 0
gender/1female/1(6).jpg 1
代码部分:
# coding=utf-8
import torch
import torch.nn as nn
import torchvision
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from PIL import Image
# 判定GPU是否存在
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 定义超参数
input_size = 784*3
hidden_size = 500
num_classes = 10
num_epochs = 5
batch_size = 16
learning_rate = 0.001
def default_loader(path):
# 注意要保证每个batch的tensor大小时候一样的。
return Image.open(path).convert('RGB')
class MyDataset(Dataset):
def __init__(self, txt, transform=None, target_transform=None, loader=default_loader):
fh = open(txt, 'r')
imgs = []
for line in fh:
line = line.strip('\n')
# line = line.rstrip()
words = line.split(' ')
imgs.append((words[0],int(words[1])))
self.imgs = imgs
self.transform = transform
self.target_transform = target_transform
self.loader = loader
def __getitem__(self, index):
fn, label = self.imgs[index]
img = self.loader(fn)
if self.transform is not None:
img = self.transform(img)
return img,label
def __len__(self):
return len(self.imgs)
def get_loader(dataset='train.txt', crop_size=128, image_size=28, batch_size=2, mode='train', num_workers=1):
"""Build and return a data loader."""
transform = []
if mode == 'train':
transform.append(transforms.RandomHorizontalFlip())
transform.append(transforms.CenterCrop(crop_size))
transform.append(transforms.Resize(image_size))
transform.append(transforms.ToTensor())
transform.append(transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)))
transform = transforms.Compose(transform)
train_data=MyDataset(txt=dataset, transform=transform)
data_loader = DataLoader(dataset=train_data,
batch_size=batch_size,
shuffle=(mode=='train'),
num_workers=num_workers)
return data_loader
# 注意要保证每个batch的tensor大小时候一样的。
# data_loader = DataLoader(train_data, batch_size=2,shuffle=True)
train_loader = get_loader('train.txt', batch_size=batch_size)
print(len(train_loader))
test_loader = get_loader('test.txt', batch_size=batch_size)
print(len(test_loader))
# 定义含有一个隐含层的全连接神经网络。
class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNet, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size, num_classes)
def forward(self, x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out
# 定义模型
model = NeuralNet(input_size, hidden_size, num_classes).to(device)
# 损失函数和优化算法
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# 训练模型
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
# Move tensors to the configured device
images = images.reshape(-1, 28*28*3).to(device)
labels = labels.to(device)
# print (images, labels)
# 前向传播和计算loss
outputs = model(images)
loss = criterion(outputs, labels)
# 后向传播和调整参数
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 每100个batch打印一次数据
if (i+1) % 100 == 0:
print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
# 模型测试部分
# 测试阶段不需要计算梯度,注意
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.reshape(-1, 28*28*3).to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))
# 保存模型参数
torch.save(model.state_dict(), 'model.ckpt')
总结:
加餐部分加入:在自己数据集上使用torch,是不是犹如画龙点睛的一笔,可以训练自己的很多分类模型,剩下的部分主要在搭建模型了,后面我们慢慢搞起来。
上面加餐部分需要生成自己的txt文件(数据+标签),可以参考这个,自己以前调试用的:https://github.com/MachineLP/py_workSpace/blob/master/g_img_path.py
torch系列: