from traindata import *
from model import *

# Function to test the model 
def test(): 
    # Load the model that we saved at the end of the training loop 
    model001 = EEGNet()
    data_dir = "C:/DATA/M1/Stages/Fablab/dataclean"

    # path = "NetModel.pth" 
    model001.load_state_dict(torch.load(data_dir)) 
     
    running_accuracy = 0 
    total = 0 
 
    with torch.no_grad(): 
        for data in test_loader: 
            inputs, outputs = data 
            outputs = outputs.to(torch.float32) 
            predicted_outputs = model(inputs) 
            _, predicted = torch.max(predicted_outputs, 1) 
            total += outputs.size(0) 
            running_accuracy += (predicted == outputs).sum().item() 
 
        print('Accuracy of the model based on the test set of', test_split ,'inputs is: %d %%' % (100 * running_accuracy / total))