-
Notifications
You must be signed in to change notification settings - Fork 1
/
6-logger.py
49 lines (41 loc) · 1.74 KB
/
6-logger.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
"""
利用Tensorboad记录训练过程
"""
from deepepochs import Trainer, LogCallback, metrics as dm
import torch
from torch import nn
from torch.nn import functional as F
from torchvision.datasets import MNIST
from torchvision import transforms
from torch.utils.data import DataLoader, random_split
data_dir = './datasets'
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
mnist_full = MNIST(data_dir, train=True, transform=transform, download=True)
train_ds, val_ds, _ = random_split(mnist_full, [5000, 5000, 50000])
test_ds = MNIST(data_dir, train=False, transform=transform, download=True)
train_dl = DataLoader(train_ds, batch_size=32)
val_dl = DataLoader(val_ds, batch_size=32)
test_dl = DataLoader(test_ds, batch_size=32)
channels, width, height = (1, 28, 28)
model = nn.Sequential(
nn.Flatten(),
nn.Linear(channels * width * height, 64),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(64, 64),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(64, 10)
)
def acc(preds, targets):
return dm.accuracy(preds, targets)
logger = LogCallback(
log_dir='./logs', # 日志保存位置,默认为 ./logs
log_graph=True, # 是否保存模型结构图,默认为False
# example_input=None # 保存模型结构图时的输入样例,默认以第一个训练batch_x作为样例输入
# example_input=train_ds[0][0] # 指定样例输入
) # tensorboard日志callback
opt = torch.optim.Adam(model.parameters(), lr=2e-4)
trainer = Trainer(model, F.cross_entropy, opt=opt, epochs=2, callbacks=[logger])
progress = trainer.fit(train_dl, val_dl, metrics=[acc])
logger.run_tensorboard() # 启动tensorboard,在GRAPHS中查看模型结构图