) #Evaluate our model score = model.evaluate( x=test_x, y=test_y, batch_size=batch_size ) samples_seen += len(x) result_point = { "samples_seens" : samples_seen, "categorial_accuracy" : score[categorial_accuracy_index], "top_2_accuracy" : score[top_2_accuracy_index], "loss" : score[loss_index], "epoch" : syllabus.current_epoch, "task" : syllabus.current_task_index, } results.append(result_point) syllabus.task_finished() #We have finished training this model, save the results model_results = { "name" : name, "id" : model_id, "task_count" : task_count, "results" : results } results_util.save('./results/data', name, model_results)