How To Return Incorrectly Predicted Images
I am having trouble figuring out how to create a list containing the first 10 image IDs that were incorrectly predicted.
import os
import torch
import torchvision
from torch.utils.data import random_split
from torchvision.datasets import ImageFolder
from torchvision.transforms import ToTensor
from torch.utils.data.dataloader import 
DataLoader
import torch.nn as nn
import torch.nn.functional as F
batch_size=128


def predict_image(img, model):
    # Convert to a batch of 1
    xb = to_device(img.unsqueeze(0), device)
    # Get predictions from model
    yb = model(xb)
    # Pick index with highest probability
    _, preds  = torch.max(yb, dim=1)
    # Retrieve the class label
    return dataset.classes[preds[0].item()]

def invalid_predictions(n=10):
    invalid_ids = []
    while invalid_ids
    return invalid_ids
    # This method should return a list of first 
    # 10 image ids that the model could not 
    # predict correctly.
    # For example [40, 35, 20, ...]


def invalid_predictions(n=10, images, labels):
    invalid_ids = []
    image_count = 0
    invalid_count = 0
    while invalid_count &lt; n:
        prediction = predict_image(images[image_count], model)
        if prediction != labels[image_count ]:
            invalid_ids.append(image_count )
            invalid_count +=1
        image_count += 1
    return invalid_ids


