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為TFX設計TensorFlow建模代碼

在為TFX設計TensorFlow建模代碼時,需要注意一些事項,包括選擇建模API。

模型的輸入層應使用由Transform組件創建的SavedModel,並且應將Transform模型的層包含在模型中,以便在導出SavedModel和EvalSavedModel時,它們將包含由Transform創建的轉換零件。

TFX的典型TensorFlow模型設計如下所示:

def _build_estimator(tf_transform_dir,
                     config,
                     hidden_units=None,
                     warm_start_from=None):
  """Build an estimator for predicting the tipping behavior of taxi riders.

  Args:
    tf_transform_dir: directory in which the tf-transform model was written
      during the preprocessing step.
    config: tf.contrib.learn.RunConfig defining the runtime environment for the
      estimator (including model_dir).
    hidden_units: [int], the layer sizes of the DNN (input layer first)
    warm_start_from: Optional directory to warm start from.

  Returns:
    Resulting DNNLinearCombinedClassifier.
  """
  metadata_dir = os.path.join(tf_transform_dir,
                              transform_fn_io.TRANSFORMED_METADATA_DIR)
  transformed_metadata = metadata_io.read_metadata(metadata_dir)
  transformed_feature_spec = transformed_metadata.schema.as_feature_spec()

  transformed_feature_spec.pop(_transformed_name(_LABEL_KEY))

  real_valued_columns = [
      tf.feature_column.numeric_column(key, shape=())
      for key in _transformed_names(_DENSE_FLOAT_FEATURE_KEYS)
  ]
  categorical_columns = [
      tf.feature_column.categorical_column_with_identity(
          key, num_buckets=_VOCAB_SIZE + _OOV_SIZE, default_value=0)
      for key in _transformed_names(_VOCAB_FEATURE_KEYS)
  ]
  categorical_columns += [
      tf.feature_column.categorical_column_with_identity(
          key, num_buckets=_FEATURE_BUCKET_COUNT, default_value=0)
      for key in _transformed_names(_BUCKET_FEATURE_KEYS)
  ]
  categorical_columns += [
      tf.feature_column.categorical_column_with_identity(
          key, num_buckets=num_buckets, default_value=0)
      for key, num_buckets in zip(
          _transformed_names(_CATEGORICAL_FEATURE_KEYS),  #
          _MAX_CATEGORICAL_FEATURE_VALUES)
  ]
  return tf.estimator.DNNLinearCombinedClassifier(
      config=config,
      linear_feature_columns=categorical_columns,
      dnn_feature_columns=real_valued_columns,
      dnn_hidden_units=hidden_units or [100, 70, 50, 25],
      warm_start_from=warm_start_from)