Evaluate model accuracy using Torchmetrics
Evaluating how well your model performs is essential - especially when preparing it for deployment! Let's smoothly integrate accuracy calculation using Torchmetrics right into the validation_step(). Don't forget to log the results, so you can easily monitor your model's progress.
本练习是课程的一部分
Scalable AI Models with PyTorch Lightning
练习说明
- Import
Accuracyfromtorchmetrics. - Instantiate the accuracy metric inside
__init__(). - Calculate accuracy within
validation_step()and log it as'val_acc'.
交互式实操练习
通过完成这段示例代码来试试这个练习。
# Import relevant metric
from torchmetrics import ____
import lightning.pytorch as pl
class ClassifierModel(pl.LightningModule):
def __init__(self):
super().__init__()
# Instantiate accuracy metric
self.accuracy = ____()
def validation_step(self, batch, batch_idx):
x, y = batch
preds = self(x)
# Calculate accuracy and log it as val_acc
acc = self.____(preds, y)
self.log(____, acc)