rm file

Code/cross_entropy.py

-import torch
-import tqdm
-import numpy as np
-import evaluation
-import evaluate
-import json
-import random
-import math
-from tqdm.auto import tqdm
-from transformers import BertTokenizer, RobertaTokenizer, BertModel, RobertaModel, RobertaPreTrainedModel, RobertaConfig,  BertConfig, BertPreTrainedModel, PreTrainedModel, AutoModel, AutoTokenizer
-from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
-from transformers import AdamW, get_scheduler
-from torch import nn
-from torch.nn import CrossEntropyLoss
-import matplotlib.pyplot as plt
-import os
-import pandas as pd
-import sklearn
-
-logits = tensor([[ 0.4902, -0.4691]])
-
-target = tensor([0.])
-
-
-print(torch.nn.Softmax(logits, dim=1))
-
-# def cross_entropy(logits, target):
-#     y_pred = torch.nn.Softmax(logits, dim=1) #functional. für was functional?
-# 	#find higher pred? For more proable class?
-#     print(y_pred)
-#     loss = -torch.sum(target * y_pred, dim=1)
-#     return loss
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