base_tf_model.py 文件源码

def _evaluate_on_validation(self, get_val_instance_generator,
                                batch_size,
                                num_val_steps,
                                session):
        val_batch_gen = DataManager.get_batch_generator(
            get_val_instance_generator, batch_size)
        # Calculate the mean of the validation metrics
        # over the validation set.
        val_accuracies = []
        val_losses = []
        for val_batch in tqdm(val_batch_gen,
                              total=num_val_steps,
                              desc="Validation Batches Completed",
                              leave=False):
            feed_dict = self._get_validation_feed_dict(val_batch)
            val_batch_acc, val_batch_loss = session.run(
                [self.accuracy, self.loss],
                feed_dict=feed_dict)

            val_accuracies.append(val_batch_acc)
            val_losses.append(val_batch_loss)

        # Take the mean of the accuracies and losses.
        # TODO/FIXME this assumes each batch is same shape, which
        # is not necessarily true.
        mean_val_accuracy = np.mean(val_accuracies)
        mean_val_loss = np.mean(val_losses)

        # Create a new Summary object with mean_val accuracy
        # and mean_val_loss and add it to Tensorboard.
        val_summary = tf.Summary(value=[
            tf.Summary.Value(tag="val_summaries/loss",
                             simple_value=mean_val_loss),
            tf.Summary.Value(tag="val_summaries/accuracy",
                             simple_value=mean_val_accuracy)])
        return mean_val_accuracy, mean_val_loss, val_summary