test.py

import torch
import torchvision
import torchvision.transforms as transforms

from models.model import NeuralNet

#Transforming the images to Tensors

transform = transforms.ToTensor()

#Loading the Dataset

test_dataset = torchvision.datasets.MNIST(
    root = './data',
    train = False,
    transform = transform ,
    
)

test_loader = torch.utils.data.DataLoader(
    dataset = test_dataset,
    batch_size = 64,
    shuffle = False,

)

#loading the model

model = NeuralNet()
model.load_state_dict(torch.load('model.pth'))

model.eval()

correct = 0

total = 0

with torch.no_grad():
    for images , labels in test_loader:
        outputs = model(images)

        _, predicted = torch.max(outputs.data , 1)

        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print(f"Accuracy of the model on the 10000 test images : {100 * correct / total} %")