13-weight-grad-visualize.py

"""
可视化训练过程中模型各层输出（forward）和梯度（backward）的均值、标准差、分布
"""
from deepepochs import Trainer, AnalyzeCallback
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)
)


analyer = AnalyzeCallback(
    mode='backward',                # 'forward'可视化各层前向输出，'backward'可视化各层梯度
    log_dir='./logs'                # 日志保存位置，默认为 ./logs
    )

opt = torch.optim.Adam(model.parameters(), lr=2e-4)
trainer = Trainer(model, F.cross_entropy, opt=opt, epochs=2, callbacks=[analyer])
progress = trainer.fit(train_dl, val_dl)

analyer.run_tensorboard()            # 启动tensorboard，在GRAPHS中查看模型结构图