70 lines
1.7 KiB
Python
70 lines
1.7 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from torch.nn import CrossEntropyLoss
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import math
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import torchmetrics
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# shift_logits = torch.zeros((16, 4096))
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# shift_logits[:, 2] = 10.0
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# shift_labels = (torch.ones(16) * 2).long()
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# loss = CrossEntropyLoss()(shift_logits, shift_labels)
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# print(loss)
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# loss = nn.CrossEntropyLoss()
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# input = torch.tensor([[1.0, 2.0, 3.0]])
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# target = torch.tensor([0]).long()
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# output = loss(input, target)
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# print(output)
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# target = torch.tensor([0, 1, 2])
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# preds = torch.tensor([[0.1, 0.9, 0], [0.3, 10.1, 0.6], [0.2, 0.3, 0.9]])
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# accuracy = torchmetrics.Accuracy(task="multiclass", num_classes=3)
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# accur = accuracy(preds, target)
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metric_accuracy = torchmetrics.Accuracy(
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task="multiclass",
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num_classes=4,
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)
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shift_logits = torch.rand((128, 4))
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shift_labels = torch.randint(0, 4, size=(128,))
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accur = metric_accuracy(shift_logits, shift_labels)
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print(accur.numpy())
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shift_logits = torch.cat((shift_logits, shift_logits), dim=0)
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shift_labels = torch.cat((shift_labels, shift_labels), dim=0)
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accur = metric_accuracy(shift_logits, shift_labels)
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print(accur.numpy())
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print(accur.numpy())
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# torch.manual_seed(32)
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# criterion = nn.CrossEntropyLoss()
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# output = torch.randn(1, 5)
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# label = torch.ones(1, dtype=torch.long)*3
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# loss = criterion(output, label)
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# print("网络输出为5类:")
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# print(output)
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# print("要计算label的类别:")
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# print(label)
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# print("计算loss的结果:")
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# print(loss)
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# first = 0
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# for i in range(1):
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# first = -output[i][label[i]]
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# second = 0
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# for i in range(1):
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# for j in range(5):
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# second += math.exp(output[i][j])
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# res = 0
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# res = (first + math.log(second))
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# print("自己的计算结果：")
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# print(res)
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