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  • 测试

    这是一条测试

    我想我会一直孤单,直到老去

    而手动挡收到收到

    • 我们的最终目的是
    • 实现共产主义
    • 为了明天
    • 今天躺下

    哈哈哈

    哈哈哈
    哈哈哈

    哈哈

    哈哈

    哈哈哈

    博客园主题

    点击就送


    点击查看代码
    def hello_world():
        print(hello world)
    

    nnnn 妈的 torch.nn as nn 的 2021-11-18 23:15:50星期四

    \(y = x^2\)

    \[y = 1+2+3+4+5_2 + 6^2 \]

    \[h_{i}^{\prime}=\sigma\left(\frac{1}{K} \sum_{k=1}^{K} \sum_{j \in N_{i}} \alpha_{i j}^{k} W^{k} h_{j}\right) \]

    [========]

    import torch
    import torch.nn as nn
    from torch.utils.tensorboard.summary import text
    from tqdm import tqdm
    from collections import defaultdict
    import config
    from rmseloss import RMSELoss
    import ipdb
    import pandas as pd
    import numpy as np
    
    rmseloss = nn.MSELoss()
    
    def validate(model, validate_loader):
        val_loss = 0
        test_pred = defaultdict(list)
        model.eval()
        for step, batch in tqdm(enumerate(validate_loader)):
            input_ids = batch['input_ids'].to(config.device)
            attention_mask = batch["attention_mask"].to(config.device)
            text = batch['text']
            character = batch['character']
            # target = batch
            with torch.no_grad():
                logists = model(input_ids=input_ids, attention_mask=attention_mask, text=text, character=character)
                val_loss += rmseloss(logists, batch['labels'].to(config.device))
    
        return val_loss / len(validate_loader)
    
    
    def predict(model, test_loader):
        model.eval()
        label_preds = None
        for step, batch in tqdm(enumerate(test_loader)):
            input_ids = batch['input_ids'].to(config.device)
            attention_mask = batch["attention_mask"].to(config.device)
            text = batch['text']
            character = batch['character']
            with torch.no_grad():
                logists = model(input_ids=input_ids, attention_mask=attention_mask, text=text, character=character)
                if label_preds is None:
                    label_preds = logists
                else:
                    label_preds = torch.cat((label_preds, logists), dim=0)
    
        # ipdb.set_trace()
        sub = pd.read_csv('data/submit_example.tsv', sep='\t')
    
        print(len(sub['emotion']))
        sub['emotion'] = label_preds.tolist()
        sub['emotion'] = sub['emotion'].apply(lambda x: ','.join([str(i) for i in x]))
        sub.to_csv(config.res_tsv, sep='\t', index=False)
        print(sub.head(5))
    
    
    个性签名:时间会解决一切
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  • 原文地址:https://www.cnblogs.com/lfri/p/test.html
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