@ptrblck  it’s the same
with torch.no_grad():    
        output = model((val_x.float()),(val_x1.float()))
        val_y = val_y.to(device)    
        softmax = torch.exp(output).cpu() 
        softmax= F.softmax(softmax, dim = 1)  
        print("softmax",softmax)      
        prob = list(softmax.numpy())
        print("softmax de test", prob)
        predictions = np.argmax(prob, axis=1)
       print('Validation accuracy test: {:.4f}%'.format(float(accuracy_score(val_y, predictions)) * 100))

the values of softmax is not between [0,1]