Tensorflow:SavedModelBuilder，如何以最佳验证精度保存模型

Question

我浏览了tensorflow文档，但找不到使用SavedModelBuilder类以最佳验证精度保存模型的方法。 我正在使用tflearn进行模型构建，下面是我尝试过的解决方法，但是这需要很多时间，在这里我分别为每个纪元运行fit方法并保存模型

for i in range(epoch):
    model.fit(trainX, trainY, n_epoch=1, validation_set=(testX, testY), show_metric=True, batch_size=8)
    builder = tf.saved_model.builder.SavedModelBuilder('/tmp/serving/model/' + str(i))
    builder.add_meta_graph_and_variables(model.session,
                                     ['TRAINING'],
                                     signature_def_map={
                                         'predict': prediction_sig
                                     })
    builder.save()

请建议是否有更好的方法。在

Answer 1

明白了。它可以通过tflearn回调来实现。 谢谢。在

class SaveModelCallback(tflearn.callbacks.Callback):
def __init__(self, accuracy_threshold):
    self.accuracy_threshold = accuracy_threshold
    self.accuracy = []
    self.max_accuracy = -1

def on_epoch_end(self, training_state):
    self.accuracy.append(training_state.global_acc)
    if training_state.val_acc > self.accuracy_threshold and training_state.val_acc > self.max_accuracy:
        self.max_accuracy = training_state.val_acc
        epoch = training_state.epoch
        self.save_model(epoch)

def save_model(self, epoch):
    print('saved epoch ' + str(epoch))
    builder = tf.saved_model.builder.SavedModelBuilder('/tmp/serving/model/' + str(epoch))
    builder.add_meta_graph_and_variables(model.session,
                                         [tf.saved_model.tag_constants.SERVING],
                                         signature_def_map={
                                             'predict': prediction_sig,
                                             tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
                                                 classification_signature,
                                         })
    builder.save()

callback = SaveModelCallback(accuracy_threshold=0.8)
model.fit(trainX, trainY, n_epoch=200, validation_set=(testX, testY), show_metric=True, batch_size=8,
          callbacks=callback)