def model_fn(features, labels, mode, params): if mode == tf.estimator.ModeKeys.PREDICT: # predict mode _, _, logits = function_builder.get_race_loss( FLAGS, features, False) predictions_ = tf.argmax(logits, axis=-1, output_type=tf.int32) # 返回最大值的索引 predictions = { 'predictions': predictions_, } predict_spec = tf.estimator.EstimatorSpec(mode=mode, predictions=predictions) return predict_spec
def model_fn(features, labels, mode, params): #### Training or Evaluation is_training = (mode == tf.estimator.ModeKeys.TRAIN) total_loss, per_example_loss, logits = function_builder.get_race_loss( FLAGS, features, is_training) #### Check model parameters num_params = sum([np.prod(v.shape) for v in tf.trainable_variables()]) logger.info('#params: {}'.format(num_params)) #### load pretrained models scaffold_fn = model_utils.init_from_checkpoint(FLAGS) #### Evaluation mode if mode == tf.estimator.ModeKeys.EVAL: assert FLAGS.num_hosts == 1 def metric_fn(per_example_loss, label_ids, logits, is_real_example): predictions = tf.argmax(logits, axis=-1, output_type=tf.int32) eval_input_dict = { 'labels': label_ids, 'predictions': predictions, 'weights': is_real_example } accuracy = tf.metrics.accuracy(**eval_input_dict) loss = tf.metrics.mean(values=per_example_loss, weights=is_real_example) return { 'eval_accuracy': accuracy, 'eval_loss': loss} is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32) #### Constucting evaluation TPUEstimatorSpec with new cache. label_ids = tf.reshape(features['label_ids'], [-1]) metric_args = [per_example_loss, label_ids, logits, is_real_example] if FLAGS.use_tpu: eval_spec = tf.contrib.tpu.TPUEstimatorSpec( mode=mode, loss=total_loss, eval_metrics=(metric_fn, metric_args), scaffold_fn=scaffold_fn) else: eval_spec = tf.estimator.EstimatorSpec( mode=mode, loss=total_loss, eval_metric_ops=metric_fn(*metric_args)) return eval_spec #### Configuring the optimizer train_op, learning_rate, _ = model_utils.get_train_op(FLAGS, total_loss) monitor_dict = {} monitor_dict["lr"] = learning_rate #### Constucting training TPUEstimatorSpec with new cache. if FLAGS.use_tpu: #### Creating host calls host_call = None train_spec = tf.contrib.tpu.TPUEstimatorSpec( mode=mode, loss=total_loss, train_op=train_op, host_call=host_call, scaffold_fn=scaffold_fn) else: train_spec = tf.estimator.EstimatorSpec( mode=mode, loss=total_loss, train_op=train_op) return train_spec