predic = torch.max(outputs.data, 1)[1].cpu() train_acc = metrics.accuracy_score(true, predic)
时间: 2024-06-05 22:06:58 浏览: 16
These lines of code are used to calculate the accuracy of a trained model on a dataset.
First, the `torch.max()` function is used to get the predicted class for each input in the batch of outputs. The `1` argument specifies that the maximum should be taken along the second dimension of the tensor, which corresponds to the class probabilities for each input. The `.data` attribute is used to extract the underlying tensor from the `outputs` variable, and `.cpu()` is used to move the tensor to the CPU for further processing.
Next, the `metrics.accuracy_score()` function from the scikit-learn library is used to calculate the accuracy of the predictions. This function takes two arguments: the true labels for the inputs, and the predicted labels. These are both passed to the function as arguments to calculate the accuracy.
The resulting accuracy value is then stored in the `train_acc` variable for further analysis or reporting.
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