correct += pred.eq(target.data.view_as(pred)).cpu().sum()
时间: 2024-05-31 11:09:21 浏览: 14
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.
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pred = output.data.max(1, keepdim=True)[1] correct += pred.eq(target.data.view_as(pred)).cpu().sum()
这段代码看起来像是在进行模型的评估,其中`output`是模型的预测输出,`target`是模型的真实标签。`pred`是取`output`中概率最大的类别作为预测结果,并且保持与`target`相同的维度。`pred.eq(target.data.view_as(pred))`是将`pred`与`target`进行比较,返回一个布尔类型的tensor,表示哪些位置预测结果与真实标签相同。`.cpu().sum()`是将所有相同的位置求和,最终得到模型预测正确的样本数。
解释代码:y_pred = log_probs.data.max(1, keepdim=True)[1] correct += y_pred.eq(target.data.view_as(y_pred)).long().cpu().sum()
这段代码是用来计算模型预测结果和真实标签的准确率,具体解释如下:
1. log_probs是模型预测的各类别得分的对数概率;
2. log_probs.data.max(1, keepdim=True)是取得分最高的类别编号的位置;
3. [1]表示取编号而非得分;
4. y_pred是模型预测的类别编号;
5. target是真实标签;
6. .eq(target.data.view_as(y_pred))判断y_pred和target是否相等,返回一个布尔值(True/False)的Tensor;
7. .long()把布尔值的Tensor转换成整型Tensor;
8. .cpu()是把Tensor转移到CPU上,计算机上的CPU处理速度比GPU要慢,但是可以保证对于Tensor的操作不会误差积累;
9. .sum()是把每个样本预测结果的整型Tensor相加,得到正确预测的样本数。