Multi-output model evaluation

In this exercise, you will practice model evaluation for multi-output models. Your task is to write a function called evaluate_model() that takes an alphabet-and-character-predicting model as input, runs the evaluation loop, and prints the model's accuracy in the two tasks.

You can assume that the function will have access to dataloader_test. The following imports have already been run for you:

import torch
from torchmetrics import Accuracy

Once you have implemented evaluate_model(), you will use it in the following exercise!

This exercise is part of the course

Intermediate Deep Learning with PyTorch

View Course

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

def evaluate_model(model):
    # Define accuracy metrics
    acc_alpha = ____(____, ____)
    acc_char = ____(____, ____)