Pytorch之MNIST資料集的訓練和測試
阿新 • • 發佈:2021-01-06
訓練和測試的完整程式碼:
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, models, transforms
from torch.utils.data import DataLoader, Dataset
import argparse
import os
# 訓練
def train(args, model, device, train_loader, optimizer) :
model.train()
num_correct = 0
for batch_index, (images, labels) in enumerate(train_loader):
images = images.to(device)
labels = labels.to(device)
# forward
outputs = model(images)
loss = F.cross_entropy(outputs, labels)
# backward
optimizer.zero_grad () # 梯度清空
loss.backward() # 梯度回傳,更新引數
optimizer.step()
_, predicted = torch.max(outputs, dim=1)
# 每一個batch預測對的個數
batch_correct = (predicted == labels).sum().item()
# 每一個batch的準確率
batch_accuracy = batch_correct / args.batch_size
# 每一個epoch預測對的總個數
num_correct + = (predicted == labels).sum().item()
# print sth.
print(f'Epoch:{epoch},Batch ID:{batch_index}/{len(train_loader)}, loss:{loss}, Batch accuracy:{batch_accuracy*100}%')
# 每一個epoch的準確率
epoch_accuracy = num_correct / len(train_loader.dataset)
# print epoch_accuracy
print(f'Epoch Accuracy:{epoch_accuracy}')
# 儲存模型
if epoch % args.checkpoint_interval == 0:
torch.save(model.state_dict(), f"checkpoints/VGG16_MNIST_%d.pth" % epoch)
# 驗證
def test(args, model, device, test_loader):
model.eval()
total_loss = 0
num_correct = 0
if args.pretrained_weights.endswith(".pth"):
model.load_state_dict(torch.load(args.pretrained_weights))
# 不計算梯度,節省計算資源
with torch.no_grad():
for images, labels in test_loader:
images = images.to(device)
labels = labels.to(device)
output = model(images)
# 總的loss
total_loss += F.cross_entropy(output, labels).item() # item()用於取出tensor裡邊的值
# torch.max():返回的是兩個值,第一個值是具體的value,第二個值是value所在的index
_, predicted = torch.max(output, dim=1)
# 預測對的總個數
num_correct += (predicted == labels).sum().item()
# 平均loss
test_loss = total_loss / len(test_loader.dataset)
# 平均準確率
accuracy = num_correct / len(test_loader.dataset)
# print sth.
print(f'Average loss:{test_loss}\nTest Accuracy:{accuracy*100}%')
if __name__ == '__main__':
parser = argparse.ArgumentParser(description = 'Pytorch-MNIST_classification')
parser.add_argument('--epochs', type=int, default=20, help='number of epochs')
parser.add_argument('--batch_size', type=int, default=32, help='size of each image batch' )
parser.add_argument('--num_classes', type=int, default=10, help='number of classes')
parser.add_argument('--lr', type=float, default=0.001, help='learning rate')
parser.add_argument('--momentum', type=float, default=0.9, help='SGD momentum')
parser.add_argument('--pretrained_weights', type=str, default='checkpoints/', help='pretrained weights')
parser.add_argument("--img_size", type=int, default=224, help="size of each image dimension")
parser.add_argument("--checkpoint_interval", type=int, default=1, help="interval between saving model weights")
parser.add_argument("--train", default=False, help="train or test")
args = parser.parse_args()
print(args)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# os.makedirs() 方法用於遞迴建立目錄
os.makedirs("output", exist_ok=True)
os.makedirs("checkpoints", exist_ok=True)
# transform
data_transform = transforms.Compose([transforms.ToTensor(), transforms.RandomResizedCrop(args.img_size)])
# 下載訓練資料
train_data = datasets.MNIST(root = 'data',
train = True,
transform = data_transform,
target_transform = None,
download = True)
# 下載測試資料
test_data = datasets.MNIST(root = 'data',
train = False,
transform = data_transform,
target_transform = None,
download = True)
# 載入訓練資料
train_loader = DataLoader(dataset = train_data,
batch_size = args.batch_size,
shuffle = True)
# 載入測試資料
test_loader = DataLoader(dataset = test_data,
batch_size = args.batch_size)
# 建立模型
model = models.vgg16(pretrained = True)
# 修改vgg16的輸出維度
model.classifier[6] = nn.Linear(in_features=4096, out_features=args.num_classes, bias=True)
# MNIST資料集是灰度圖,channel數為1
model.features[0] = nn.Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
print(model)
model = model.to(device)
# 優化器(也可以選擇其他優化器)
optimizer = torch.optim.SGD(model.parameters(), lr = args.lr, momentum = args.momentum)
# optimizer = torch.optim.Adam()
if args.train == True:
for epoch in range(1, args.epochs+1):
# 是否載入預訓練好的權重
if args.pretrained_weights.endswith(".pth"):
model.load_state_dict(torch.load(args.pretrained_weights))
train(args, model, device, train_loader, optimizer)
else:
# 是否載入預訓練好的權重
if args.pretrained_weights.endswith(".pth"):
model.load_state_dict(torch.load(args.pretrained_weights))
test(args, model, device, test_loader)
測試結果:
我只訓練了不到10輪,效果不是太好,還有提升空間。
說明:
MNIST資料集可以通過trochvision中的datasets.MNIST下載,也可以自己下載(注意存放路徑);我模型使用的是torchvision中的models中預訓練好的vgg16網路,也可以自己搭建網路。