Question

我将this Colab用于BERT模型。

在最后一个单元格中，为了进行预测，我们需要：

def getPrediction(in_sentences):
  labels = ["Negative", "Positive"]
  input_examples = [run_classifier.InputExample(guid="", text_a = x, text_b = None, label = 0) for x in in_sentences] # here, "" is just a dummy label
  input_features = run_classifier.convert_examples_to_features(input_examples, label_list, MAX_SEQ_LENGTH, tokenizer)
  predict_input_fn = run_classifier.input_fn_builder(features=input_features, seq_length=MAX_SEQ_LENGTH, is_training=False, drop_remainder=False)
  predictions = estimator.predict(predict_input_fn)
  return [(sentence, prediction['probabilities'], labels[prediction['labels']]) for sentence, prediction in zip(in_sentences, predictions)]

pred_sentences = [
  "That movie was absolutely awful",
  "The acting was a bit lacking",
  "The film was creative and surprising",
  "Absolutely fantastic!"
]

predictions = getPrediction(pred_sentences)

我想创建一个与TF服务一起使用的“ SavedModel”。如何为此模型创建SavedModel？

通常我将定义以下内容：

def serving_input_fn():
    """Create serving input function to be able to serve predictions later
    using provided inputs
    :return:
    """
    feature_placeholders = {
        'sentence': tf.placeholder(tf.string, [None]),     
    }
    return tf.estimator.export.ServingInputReceiver(feature_placeholders,
                                                    feature_placeholders)


latest_ckpt = tf.train.latest_checkpoint(OUTPUT_DIR)

last_eval = estimator.evaluate(input_fn=test_input_fn, steps=None, checkpoint_path=latest_ckpt)

# Export the model to GCS for serving.
exporter = tf.estimator.LatestExporter('exporter', serving_input_fn, exports_to_keep=None)
exporter.export(estimator, OUTPUT_DIR, latest_ckpt, last_eval, is_the_final_export=True)

不确定如何定义我的“ tf.estimator.export.ServingInputReceiver”

Answer 1

如果您查看笔记本中存在的 create_model 函数。这需要一些争论。这些是将传递给模型的功能。

您需要更新 serving_input_fn 函数以包括它们。

def serving_input_fn():
  feature_spec = {
      "input_ids" : tf.FixedLenFeature([MAX_SEQ_LENGTH], tf.int64),
      "input_mask" : tf.FixedLenFeature([MAX_SEQ_LENGTH], tf.int64),
      "segment_ids" : tf.FixedLenFeature([MAX_SEQ_LENGTH], tf.int64),
      "label_ids" :  tf.FixedLenFeature([], tf.int64)

  }
  serialized_tf_example = tf.placeholder(dtype=tf.string, 
                                         shape=[None],
                                         name='input_example_tensor')
  receiver_tensors = {'example': serialized_tf_example}
  features = tf.parse_example(serialized_tf_example, feature_spec)
  return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)

为BERT创建SavedModel

1 个答案: