def calc_accuracy(model, data):
    model.eval()
    if use_gpu:
      model.cuda()    
    running_corrects = 0
    test_acc = 0
    
    #with torch.no_grad():
    for idx, (inputs, labels) in enumerate(dataloaders[data]):
        if use_gpu:
            inputs, labels = inputs.cuda(), labels.cuda()
        # obtain the outputs from the model
        outputs = model.forward(Variable(inputs))
        # max provides the (maximum probability, max value)
        #_, predicted = outputs.max(dim=1)
        _, predicted = torch.max(outputs.data, 1)
        running_corrects += torch.sum(predicted == labels)
    test_acc = running_corrects / dataset_sizes['test']
    print('Test Accuracy: {:.4f}'.format(test_acc))
    '''# check the 
    if idx == 0:
        print(predicted) #the predicted class
        print(torch.exp(_)) # the predicted probability
    equals = predicted == labels.data
    if idx == 0:
        print(equals)
    print(equals.float().mean())'''
calc_accuracy(model_ft, 'test')

def predict(image_path, model, top_num=5):
    # Process image
    img = process_image(image_path)
    
    # Numpy -> Tensor
    image_tensor = torch.from_numpy(img).type(torch.FloatTensor)
    # Add batch of size 1 to image
    model_input = image_tensor.unsqueeze(0)
    
    # Probs
    probs = torch.exp(model.forward(Variable(model_input.cuda())))
    
    # Top probs
    top_probs, top_labs = probs.topk(top_num)
    top_probs, top_labs =top_probs.data, top_labs.data
    top_probs = top_probs.cpu().numpy().tolist()[0] 
    top_labs = top_labs.cpu().numpy().tolist()[0]
    #print(top_labs)
    # Convert indices to classes
    '''idx_to_class = {val: key for key, val in    
                                      model.class_to_idx.items()}
    top_labels = [idx_to_class[lab] for lab in top_labs]
    top_flowers = [cat_to_name[idx_to_class[lab]] for lab in top_labs]'''
    top_flowers = [class_names[lab] for lab in top_labs]
    return top_probs, top_flowers