linear.py 文件源码

def sdca_classifier_model_fn(features, targets, mode, params):
  """Estimator's linear model_fn."""
  feature_columns = params["feature_columns"]
  optimizer = params["optimizer"]
  weight_column_name = params["weight_column_name"]
  loss_type = params["loss_type"]

  if not isinstance(optimizer, sdca_optimizer.SDCAOptimizer):
    raise ValueError("Optimizer must be of type SDCAOptimizer")

  loss_fn = {
      "logistic_loss": _log_loss_with_two_classes,
      "hinge_loss": _hinge_loss,
  }[loss_type]

  logits, columns_to_variables, bias = (
      layers.weighted_sum_from_feature_columns(
          columns_to_tensors=features,
          feature_columns=feature_columns,
          num_outputs=1))

  _add_bias_column(feature_columns, features, bias, targets,
                   columns_to_variables)

  loss = None
  if mode != estimator.ModeKeys.INFER:
    loss = math_ops.reduce_mean(loss_fn(logits, targets), name="loss")
    logging_ops.scalar_summary("loss", loss)

  train_op = None
  if mode == estimator.ModeKeys.TRAIN:
    global_step = contrib_variables.get_global_step()
    train_op = optimizer.get_train_step(
        columns_to_variables, weight_column_name, loss_type, features,
        targets, global_step)

  predictions = {}
  predictions[_LOGISTIC] = math_ops.sigmoid(logits)
  logits = array_ops.concat(1, [array_ops.zeros_like(logits), logits])
  predictions[_PROBABILITIES] = nn.softmax(logits)
  predictions[_CLASSES] = math_ops.argmax(logits, 1)

  return predictions, loss, train_op


# Ensures consistency with LinearComposableModel.