Python head.regression_head函数代码示例

def _get_default_head(params, weights_name, output_type, name=None):
  """Creates a default head based on a type of a problem."""
  if output_type == ModelBuilderOutputType.MODEL_FN_OPS:
    if params.regression:
      return head_lib.regression_head(
          weight_column_name=weights_name,
          label_dimension=params.num_outputs,
          enable_centered_bias=False,
          head_name=name)
    else:
      return head_lib.multi_class_head(
          params.num_classes,
          weight_column_name=weights_name,
          enable_centered_bias=False,
          head_name=name)
  else:
    if params.regression:
      return core_head_lib.regression_head(
          weight_column=weights_name,
          label_dimension=params.num_outputs,
          name=name,
          loss_reduction=losses.Reduction.SUM_OVER_BATCH_SIZE)
    else:
      if params.num_classes == 2:
        return core_head_lib.binary_classification_head(
            weight_column=weights_name,
            name=name,
            loss_reduction=losses.Reduction.SUM_OVER_BATCH_SIZE)
      else:
        return core_head_lib.multi_class_head(
            n_classes=params.num_classes,
            weight_column=weights_name,
            name=name,
            loss_reduction=losses.Reduction.SUM_OVER_BATCH_SIZE)

def _get_default_head(params, weights_name, output_type, name=None):
  """Creates a default head based on a type of a problem."""
  if output_type == ModelBuilderOutputType.MODEL_FN_OPS:
    if params.regression:
      return head_lib.regression_head(
          weight_column_name=weights_name,
          label_dimension=params.num_outputs,
          enable_centered_bias=False,
          head_name=name)
    else:
      return head_lib.multi_class_head(
          params.num_classes,
          weight_column_name=weights_name,
          enable_centered_bias=False,
          head_name=name)
  else:
    if params.regression:
      return core_head_lib._regression_head(  # pylint:disable=protected-access
          weight_column=weights_name,
          label_dimension=params.num_outputs,
          name=name,
          loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS)
    else:
      return core_head_lib._multi_class_head_with_softmax_cross_entropy_loss(  # pylint:disable=protected-access
          n_classes=params.num_classes,
          weight_column=weights_name,
          name=name,
          loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS)

  def __init__(self,
               learner_config,
               examples_per_layer,
               label_dimension=1,
               num_trees=None,
               feature_columns=None,
               label_name=None,
               weight_column_name=None,
               model_dir=None,
               config=None,
               feature_engineering_fn=None,
               logits_modifier_function=None,
               center_bias=True):
    """Initializes a GradientBoostedDecisionTreeRegressor estimator instance.

    Args:
      learner_config: A config for the learner.
      examples_per_layer: Number of examples to accumulate before growing a
        layer. It can also be a function that computes the number of examples
        based on the depth of the layer that's being built.
      label_dimension: Number of regression labels per example. This is the size
        of the last dimension of the labels `Tensor` (typically, this has shape
        `[batch_size, label_dimension]`).
      num_trees: An int, number of trees to build.
      feature_columns: A list of feature columns.
      label_name: String, name of the key in label dict. Can be null if label
          is a tensor (single headed models).
      weight_column_name: Name of the column for weights, or None if not
        weighted.
      model_dir: Directory for model exports, etc.
      config: `RunConfig` object to configure the runtime settings.
      feature_engineering_fn: Feature engineering function. Takes features and
        labels which are the output of `input_fn` and returns features and
        labels which will be fed into the model.
      logits_modifier_function: A modifier function for the logits.
      center_bias: Whether a separate tree should be created for first fitting
        the bias.
    """
    head = head_lib.regression_head(
        label_name=label_name,
        label_dimension=label_dimension,
        weight_column_name=weight_column_name,
        enable_centered_bias=False)
    super(GradientBoostedDecisionTreeRegressor, self).__init__(
        model_fn=model.model_builder,
        params={
            'head': head,
            'feature_columns': feature_columns,
            'learner_config': learner_config,
            'num_trees': num_trees,
            'weight_column_name': weight_column_name,
            'examples_per_layer': examples_per_layer,
            'logits_modifier_function': logits_modifier_function,
            'center_bias': center_bias,
        },
        model_dir=model_dir,
        config=config,
        feature_engineering_fn=feature_engineering_fn)

  def __init__(self,
               example_id_column,
               feature_columns,
               weight_column_name=None,
               model_dir=None,
               l1_regularization=0.0,
               l2_regularization=1.0,
               num_loss_partitions=None,
               config=None,
               feature_engineering_fn=None,
               partitioner=None):
    """Construct a `SDCALinearRegressor` estimator object.


    Args:
      example_id_column: A string defining the feature column name representing
        example ids. Used to initialize the underlying SDCA optimizer.
      feature_columns: An iterable containing all the feature columns used by
        the model. All items in the iterable should derive from `FeatureColumn`.
        Note that the order of the items is ignored at model construction time.
      weight_column_name: A string defining feature column name representing
        weights. It is used to down weight or boost examples during training. It
        will be multiplied by the loss of the example.
      model_dir: Directory to save model parameters, graph etc. This can also be
        used to load checkpoints from the directory into an estimator to
        continue training a previously saved model.
      l1_regularization: L1-regularization parameter. Refers to global L1
        regularization (across all examples).
      l2_regularization: L2-regularization parameter. Refers to global L2
        regularization (across all examples).
      num_loss_partitions: number of partitions of the (global) loss function
        optimized by the underlying optimizer (SDCAOptimizer).
      config: `RunConfig` object to configure the runtime settings.
      feature_engineering_fn: Feature engineering function. Takes features and
        labels which are the output of `input_fn` and returns features and
        labels which will be fed into the model.
      partitioner: Variable partitioner for the primal weights (`div`
        partitioning strategy will be used).

    Returns:
      A `SDCALinearRegressor` estimator.
    """
    super(SDCALinearRegressor, self).__init__(
        example_id_column=example_id_column,
        feature_columns=feature_columns,
        weight_column_name=weight_column_name,
        model_dir=model_dir,
        head=head_lib.regression_head(weight_column_name=weight_column_name),
        l1_regularization=l1_regularization,
        l2_regularization=l2_regularization,
        num_loss_partitions=num_loss_partitions,
        config=config,
        feature_engineering_fn=None,
        partitioner=partitioner)

def get_default_head(params, weights_name, name=None):
  if params.regression:
    return head_lib.regression_head(
        weight_column_name=weights_name,
        label_dimension=params.num_outputs,
        enable_centered_bias=False,
        head_name=name)
  else:
    return head_lib.multi_class_head(
        params.num_classes,
        weight_column_name=weights_name,
        enable_centered_bias=False,
        head_name=name)

  def __init__(self,
               model_dir=None,
               label_dimension=1,
               weight_column_name=None,
               config=None,
               feature_engineering_fn=None):
    """Initializes a DebugRegressor instance.

    Args:
      model_dir: Directory to save model parameters, graph and etc. This can
        also be used to load checkpoints from the directory into a estimator to
        continue training a previously saved model.
      label_dimension: Number of regression targets per example. This is the
        size of the last dimension of the labels and logits `Tensor` objects
        (typically, these have shape `[batch_size, label_dimension]`).
      weight_column_name: A string defining feature column name representing
        weights. It is used to down weight or boost examples during training. It
        will be multiplied by the loss of the example.
      config: `RunConfig` object to configure the runtime settings.
      feature_engineering_fn: Feature engineering function. Takes features and
                        labels which are the output of `input_fn` and returns
                        features and labels which will be fed into the model.
    Returns:
      A `DebugRegressor` estimator.
    """

    params = {
        "head":
            head_lib.regression_head(
                weight_column_name=weight_column_name,
                label_dimension=label_dimension,
                enable_centered_bias=True)
    }

    super(DebugRegressor, self).__init__(
        model_fn=debug_model_fn,
        model_dir=model_dir,
        config=config,
        params=params,
        feature_engineering_fn=feature_engineering_fn)

 def __init__(self,
              example_id_column,
              feature_columns,
              weight_column_name=None,
              model_dir=None,
              l1_regularization=0.0,
              l2_regularization=1.0,
              num_loss_partitions=None,
              config=None,
              feature_engineering_fn=None):
   """Construct a `SDCARegressor` estimator object. See _SDCAEstimator."""
   super(SDCARegressor, self).__init__(
       example_id_column=example_id_column,
       feature_columns=feature_columns,
       weight_column_name=weight_column_name,
       model_dir=model_dir,
       head=head_lib.regression_head(weight_column_name=weight_column_name),
       l1_regularization=l1_regularization,
       l2_regularization=l2_regularization,
       num_loss_partitions=num_loss_partitions,
       config=config,
       feature_engineering_fn=None)

  def __init__(self,  # _joint_linear_weights pylint: disable=invalid-name
               model_dir=None,
               weight_column_name=None,
               linear_feature_columns=None,
               linear_optimizer=None,
               _joint_linear_weights=False,
               dnn_feature_columns=None,
               dnn_optimizer=None,
               dnn_hidden_units=None,
               dnn_activation_fn=nn.relu,
               dnn_dropout=None,
               gradient_clip_norm=None,
               enable_centered_bias=False,
               label_dimension=1,
               config=None,
               feature_engineering_fn=None,
               embedding_lr_multipliers=None,
               input_layer_min_slice_size=None,
               fix_global_step_increment_bug=False):
    """Initializes a DNNLinearCombinedRegressor instance.

    Note: New users must set `fix_global_step_increment_bug=True` when creating
    an estimator.

    Args:
      model_dir: Directory to save model parameters, graph and etc. This can
        also be used to load checkpoints from the directory into a estimator
        to continue training a previously saved model.
      weight_column_name: A string defining feature column name representing
        weights. It is used to down weight or boost examples during training. It
        will be multiplied by the loss of the example.
      linear_feature_columns: An iterable containing all the feature columns
        used by linear part of the model. All items in the set must be
        instances of classes derived from `FeatureColumn`.
      linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to
        the linear part of the model. If `None`, will use a FTRL optimizer.
      _joint_linear_weights: If True a single (possibly partitioned) variable
        will be used to store the linear model weights. It's faster, but
        requires that all columns are sparse and have the 'sum' combiner.
      dnn_feature_columns: An iterable containing all the feature columns used
        by deep part of the model. All items in the set must be instances of
        classes derived from `FeatureColumn`.
      dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to
        the deep part of the model. If `None`, will use an Adagrad optimizer.
      dnn_hidden_units: List of hidden units per layer. All layers are fully
        connected.
      dnn_activation_fn: Activation function applied to each layer. If None,
        will use `tf.nn.relu`.
      dnn_dropout: When not None, the probability we will drop out
        a given coordinate.
      gradient_clip_norm: A float > 0. If provided, gradients are clipped
        to their global norm with this clipping ratio. See
        tf.clip_by_global_norm for more details.
      enable_centered_bias: A bool. If True, estimator will learn a centered
        bias variable for each class. Rest of the model structure learns the
        residual after centered bias.
      label_dimension: Number of regression targets per example. This is the
        size of the last dimension of the labels and logits `Tensor` objects
        (typically, these have shape `[batch_size, label_dimension]`).
      config: RunConfig object to configure the runtime settings.
      feature_engineering_fn: Feature engineering function. Takes features and
        labels which are the output of `input_fn` and returns features and
        labels which will be fed into the model.
      embedding_lr_multipliers: Optional. A dictionary from `EmbeddingColumn` to
        a `float` multiplier. Multiplier will be used to multiply with
        learning rate for the embedding variables.
      input_layer_min_slice_size: Optional. The min slice size of input layer
        partitions. If not provided, will use the default of 64M.
      fix_global_step_increment_bug: If `False`, the estimator needs two fit
        steps to optimize both linear and dnn parts. If `True`, this bug is
        fixed. New users must set this to `True`, but it the default value is
        `False` for backwards compatibility.

    Raises:
      ValueError: If both linear_feature_columns and dnn_features_columns are
        empty at the same time.
    """
    linear_feature_columns = linear_feature_columns or []
    dnn_feature_columns = dnn_feature_columns or []
    self._feature_columns = linear_feature_columns + dnn_feature_columns
    if not self._feature_columns:
      raise ValueError("Either linear_feature_columns or dnn_feature_columns "
                       "must be defined.")

    # TODO(b/35922130): Replace with `input_layer_partitioner` arg.
    input_layer_partitioner = None
    if input_layer_min_slice_size is not None:
      input_layer_partitioner = (
          partitioned_variables.min_max_variable_partitioner(
              max_partitions=config.num_ps_replicas if config else 0,
              min_slice_size=input_layer_min_slice_size))

    head = head_lib.regression_head(
        weight_column_name=weight_column_name,
        label_dimension=label_dimension,
        enable_centered_bias=enable_centered_bias)
    super(DNNLinearCombinedRegressor, self).__init__(
        model_fn=_dnn_linear_combined_model_fn,
        model_dir=model_dir,
        config=config,
#.........这里部分代码省略.........

  def __init__(self,
               learner_config,
               examples_per_layer,
               label_dimension=1,
               num_trees=None,
               feature_columns=None,
               label_name=None,
               weight_column_name=None,
               model_dir=None,
               config=None,
               feature_engineering_fn=None,
               logits_modifier_function=None,
               center_bias=True,
               use_core_libs=False,
               output_leaf_index=False,
               override_global_step_value=None):
    """Initializes a GradientBoostedDecisionTreeRegressor estimator instance.

    Args:
      learner_config: A config for the learner.
      examples_per_layer: Number of examples to accumulate before growing a
        layer. It can also be a function that computes the number of examples
        based on the depth of the layer that's being built.
      label_dimension: Number of regression labels per example. This is the size
        of the last dimension of the labels `Tensor` (typically, this has shape
        `[batch_size, label_dimension]`).
      num_trees: An int, number of trees to build.
      feature_columns: A list of feature columns.
      label_name: String, name of the key in label dict. Can be null if label
          is a tensor (single headed models).
      weight_column_name: Name of the column for weights, or None if not
        weighted.
      model_dir: Directory for model exports, etc.
      config: `RunConfig` object to configure the runtime settings.
      feature_engineering_fn: Feature engineering function. Takes features and
        labels which are the output of `input_fn` and returns features and
        labels which will be fed into the model.
      logits_modifier_function: A modifier function for the logits.
      center_bias: Whether a separate tree should be created for first fitting
        the bias.
      use_core_libs: Whether feature columns and loss are from the core (as
        opposed to contrib) version of tensorflow.
      output_leaf_index: whether to output leaf indices along with predictions
        during inference. The leaf node indexes are available in predictions
        dict by the key 'leaf_index'. For example,
        result_dict = classifier.predict(...)
        for example_prediction_result in result_dict:
          # access leaf index list by example_prediction_result["leaf_index"]
          # which contains one leaf index per tree
      override_global_step_value: If after the training is done, global step
        value must be reset to this value. This should be used to reset global
        step to a number > number of steps used to train the current ensemble.
        For example, the usual way is to train a number of trees and set a very
        large number of training steps. When the training is done (number of
        trees were trained), this parameter can be used to set the global step
        to a large value, making it look like that number of training steps ran.
        If None, no override of global step will happen.
    """
    head = head_lib.regression_head(
        label_name=label_name,
        label_dimension=label_dimension,
        weight_column_name=weight_column_name,
        enable_centered_bias=False)
    if label_dimension == 1:
      learner_config.num_classes = 2
    else:
      learner_config.num_classes = label_dimension
    super(GradientBoostedDecisionTreeRegressor, self).__init__(
        model_fn=model.model_builder,
        params={
            'head': head,
            'feature_columns': feature_columns,
            'learner_config': learner_config,
            'num_trees': num_trees,
            'weight_column_name': weight_column_name,
            'examples_per_layer': examples_per_layer,
            'logits_modifier_function': logits_modifier_function,
            'center_bias': center_bias,
            'use_core_libs': use_core_libs,
            'output_leaf_index': False,
            'override_global_step_value': override_global_step_value
        },
        model_dir=model_dir,
        config=config,
        feature_engineering_fn=feature_engineering_fn)

  def __init__(self,
               hidden_units,
               feature_columns,
               model_dir=None,
               weight_column_name=None,
               optimizer=None,
               activation_fn=nn.relu,
               dropout=None,
               gradient_clip_norm=None,
               enable_centered_bias=False,
               config=None,
               feature_engineering_fn=None,
               label_dimension=1,
               embedding_lr_multipliers=None,
               input_layer_min_slice_size=None):
    """Initializes a `DNNRegressor` instance.

    Args:
      hidden_units: List of hidden units per layer. All layers are fully
        connected. Ex. `[64, 32]` means first layer has 64 nodes and second one
        has 32.
      feature_columns: An iterable containing all the feature columns used by
        the model. All items in the set should be instances of classes derived
        from `FeatureColumn`.
      model_dir: Directory to save model parameters, graph and etc. This can
        also be used to load checkpoints from the directory into a estimator to
        continue training a previously saved model.
      weight_column_name: A string defining feature column name representing
        weights. It is used to down weight or boost examples during training. It
        will be multiplied by the loss of the example.
      optimizer: An instance of `tf.Optimizer` used to train the model. If
        `None`, will use an Adagrad optimizer.
      activation_fn: Activation function applied to each layer. If `None`, will
        use `tf.nn.relu`.
      dropout: When not `None`, the probability we will drop out a given
        coordinate.
      gradient_clip_norm: A `float` > 0. If provided, gradients are clipped
        to their global norm with this clipping ratio. See
        `tf.clip_by_global_norm` for more details.
      enable_centered_bias: A bool. If True, estimator will learn a centered
        bias variable for each class. Rest of the model structure learns the
        residual after centered bias.
      config: `RunConfig` object to configure the runtime settings.
      feature_engineering_fn: Feature engineering function. Takes features and
                        labels which are the output of `input_fn` and
                        returns features and labels which will be fed
                        into the model.
      label_dimension: Number of regression targets per example. This is the
        size of the last dimension of the labels and logits `Tensor` objects
        (typically, these have shape `[batch_size, label_dimension]`).
      embedding_lr_multipliers: Optional. A dictionary from `EbeddingColumn` to
          a `float` multiplier. Multiplier will be used to multiply with
          learning rate for the embedding variables.
      input_layer_min_slice_size: Optional. The min slice size of input layer
          partitions. If not provided, will use the default of 64M.

    Returns:
      A `DNNRegressor` estimator.
    """
    self._feature_columns = tuple(feature_columns or [])
    super(DNNRegressor, self).__init__(
        model_fn=_dnn_model_fn,
        model_dir=model_dir,
        config=config,
        params={
            "head":
                head_lib.regression_head(
                    label_dimension=label_dimension,
                    weight_column_name=weight_column_name,
                    enable_centered_bias=enable_centered_bias),
            "hidden_units": hidden_units,
            "feature_columns": self._feature_columns,
            "optimizer": optimizer,
            "activation_fn": activation_fn,
            "dropout": dropout,
            "gradient_clip_norm": gradient_clip_norm,
            "embedding_lr_multipliers": embedding_lr_multipliers,
            "input_layer_min_slice_size": input_layer_min_slice_size,
        },
        feature_engineering_fn=feature_engineering_fn)

  def __init__(self,
               dnn_hidden_units,
               dnn_feature_columns,
               tree_learner_config,
               num_trees,
               tree_examples_per_layer,
               weight_column_name=None,
               model_dir=None,
               config=None,
               label_name=None,
               label_dimension=1,
               feature_engineering_fn=None,
               dnn_optimizer="Adagrad",
               dnn_activation_fn=nn.relu,
               dnn_dropout=None,
               dnn_input_layer_partitioner=None,
               dnn_input_layer_to_tree=True,
               dnn_steps_to_train=10000,
               predict_with_tree_only=False,
               tree_feature_columns=None,
               tree_center_bias=False,
               dnn_to_tree_distillation_param=None,
               use_core_versions=False):
    """Initializes a DNNBoostedTreeCombinedRegressor instance.

    Args:
      dnn_hidden_units: List of hidden units per layer for DNN.
      dnn_feature_columns: An iterable containing all the feature columns
        used by the model's DNN.
      tree_learner_config: A config for the tree learner.
      num_trees: Number of trees to grow model to after training DNN.
      tree_examples_per_layer: Number of examples to accumulate before
        growing the tree a layer. This value has a big impact on model
        quality and should be set equal to the number of examples in
        training dataset if possible. It can also be a function that computes
        the number of examples based on the depth of the layer that's
        being built.
      weight_column_name: The name of weight column.
      model_dir: Directory for model exports.
      config: `RunConfig` of the estimator.
      label_name: String, name of the key in label dict. Can be null if label
        is a tensor (single headed models).
      label_dimension: Number of regression labels per example. This is the size
        of the last dimension of the labels `Tensor` (typically, this has shape
        `[batch_size, label_dimension]`).
      feature_engineering_fn: Feature engineering function. Takes features and
        labels which are the output of `input_fn` and returns features and
        labels which will be fed into the model.
      dnn_optimizer: string, `Optimizer` object, or callable that defines the
        optimizer to use for training the DNN. If `None`, will use the Adagrad
        optimizer with default learning rate.
      dnn_activation_fn: Activation function applied to each layer of the DNN.
        If `None`, will use `tf.nn.relu`.
      dnn_dropout: When not `None`, the probability to drop out a given
        unit in the DNN.
      dnn_input_layer_partitioner: Partitioner for input layer of the DNN.
        Defaults to `min_max_variable_partitioner` with `min_slice_size`
        64 << 20.
      dnn_input_layer_to_tree: Whether to provide the DNN's input layer
      as a feature to the tree.
      dnn_steps_to_train: Number of steps to train dnn for before switching
        to gbdt.
      predict_with_tree_only: Whether to use only the tree model output as the
        final prediction.
      tree_feature_columns: An iterable containing all the feature columns
        used by the model's boosted trees. If dnn_input_layer_to_tree is
        set to True, these features are in addition to dnn_feature_columns.
      tree_center_bias: Whether a separate tree should be created for
        first fitting the bias.
      dnn_to_tree_distillation_param: A Tuple of (float, loss_fn), where the
        float defines the weight of the distillation loss, and the loss_fn, for
        computing distillation loss, takes dnn_logits, tree_logits and weight
        tensor. If the entire tuple is None, no distillation will be applied. If
        only the loss_fn is None, we will take the sigmoid/softmax cross entropy
        loss be default. When distillation is applied, `predict_with_tree_only`
        will be set to True.
      use_core_versions: Whether feature columns and loss are from the core (as
        opposed to contrib) version of tensorflow.
    """
    head = head_lib.regression_head(
        label_name=label_name,
        label_dimension=label_dimension,
        weight_column_name=weight_column_name,
        enable_centered_bias=False)

    # num_classes needed for GradientBoostedDecisionTreeModel
    if label_dimension == 1:
      tree_learner_config.num_classes = 2
    else:
      tree_learner_config.num_classes = label_dimension

    def _model_fn(features, labels, mode, config):
      return _dnn_tree_combined_model_fn(
          features=features,
          labels=labels,
          mode=mode,
          head=head,
          dnn_hidden_units=dnn_hidden_units,
          dnn_feature_columns=dnn_feature_columns,
          tree_learner_config=tree_learner_config,
#.........这里部分代码省略.........