技術標籤：PythonPyTorchpython深度學習pytorch

訓練模型時，控制檯輸出模板：
【資料集載入類】

import torch
from torch.utils.data import Dataset

__all__ = ["TestDataset"]


class TestDataset(Dataset):

    def __init__(self):
        super().__init__()
        self.dataset = torch.rand(20, 10, 1, 200, 200)

    def __getitem__
 
(self, item):
        return self.dataset[item,], self.dataset[item, 10:]

    def __len__(self):
        return self.dataset.shape[0]

【訓練程式碼】

import time
from random import random
from datetime import datetime

from torch.utils.data import DataLoader
from tqdm import tqdm, trange

from TestDataset import
 
 TestDataset

max_epochs = 5

train_set = TestDataset()
test_set = TestDataset()

train_loader = DataLoader(train_set)
test_loader = DataLoader(test_set)


def training(epoch: int, test_frequency: int = 5):
    for epoch in range(epoch):
        with tqdm(
                iterable=train_loader,
                bar_format=
 
'{desc} {n_fmt:>4s}/{total_fmt:<4s} {percentage:3.0f}%|{bar}| {postfix}',
        ) as t:
            start_time = datetime.now()
            loss_list = []
            for batch, data in enumerate(train_loader):
                t.set_description_str(f"\33[36m【Epoch {epoch + 1:04d}】")
                # 訓練程式碼

                time.sleep(1)

                # 計算當前損失
                loss = random()

                loss_list.append(loss)
                cur_time = datetime.now()

                delta_time = cur_time - start_time

                t.set_postfix_str(f"train_loss={sum(loss_list) / len(loss_list):.6f}， 執行時長：{delta_time}\33[0m")

                t.update()

        if (epoch + 1) % test_frequency == 0:
            with tqdm(
                    iterable=test_loader,
                    bar_format='{desc} {postfix}',
            ) as t:
                # 測試一下
                time.sleep(2)
                test_loss = 3.1415926

                t.set_description_str(f"\33[35m【測試集】")
                t.set_postfix_str(f"test_loss={test_loss:.6f}\33[0m")

            t.update()


training(epoch=max_epochs, test_frequency=1)

【效果圖】