)
#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)
