def visualize_model(model, num_images=6):
    was_training = model.training
    model.eval()
    images_so_far = 0
    fig = plt.figure()

    with [torch.no_grad](https://docs.pytorch.org/docs/stable/generated/torch.no_grad.html#torch.no_grad "torch.no_grad")():
        for i, ([inputs](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor"), labels) in enumerate(dataloaders['val']):
            [inputs](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor") = [inputs](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor").to(device)
            labels = labels.to(device)

            outputs = model([inputs](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor"))
            _, preds = [torch.max](https://docs.pytorch.org/docs/stable/generated/torch.max.html#torch.max "torch.max")(outputs, 1)

            for j in range([inputs](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor").size()[0]):
                images_so_far += 1
                ax = plt.subplot(num_images//2, 2, images_so_far)
                ax.axis('off')
                ax.set_title(f'predicted: {class_names[preds[j]]}')
                imshow([inputs](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor").cpu().data[j])

                if images_so_far == num_images:
                    model.train(mode=was_training)
                    return
        model.train(mode=was_training)