correct += pred.eq(target.view_as(pred)).sum().item()
时间: 2024-05-19 20:11:15 浏览: 14
As an AI language model, I cannot determine the context of this code snippet. However, I can explain what this line does:
This line compares the predicted output (pred) to the target output (target), converts the target output to the same shape as the predicted output using the view_as() function, and then calculates the number of elements in the predicted output that are equal to the corresponding elements in the target output. The .sum() function sums up all the occurrences of equal elements and .item() converts the result to a Python integer. The final result is the number of correct predictions.
相关问题
correct += pred.eq(target.view_as(pred)).sum().item()是什么意思
这段代码是在计算分类任务中模型预测的正确分类数量。其中,
- `pred` 是模型对输入数据的预测输出;
- `target` 是输入数据的真实标签;
- `pred.eq(target.view_as(pred))` 是将模型预测的类别与真实标签进行比较,得到一个布尔类型的 Tensor;
- `.sum().item()` 是将布尔类型的 Tensor 进行求和,并将结果转换为 Python 中的标量。
因此,`correct` 变量记录的是模型在当前 batch 中预测正确的样本数量。
correct += pred.eq(target.data.view_as(pred)).cpu().sum()
This line of code is used to compute the number of correct predictions made by a model during training or evaluation.
`pred` is the predicted output of the model, which is compared to the ground truth `target` using `eq()` to get a tensor of boolean values indicating whether the prediction was correct or not.
`target.data.view_as(pred)` is used to reshape `target` to have the same shape as `pred` so that the `eq()` operation can be applied element-wise.
`cpu()` is used to move the tensor to the CPU if it was computed on a GPU.
Finally, `sum()` is used to compute the total number of correct predictions in the batch.