Python partitioned_variables.min_max_variable_partitioner方法代码示例

# 需要导入模块: from tensorflow.python.ops import partitioned_variables [as 别名]
# 或者: from tensorflow.python.ops.partitioned_variables import min_max_variable_partitioner [as 别名]
def build_model(self, features, feature_columns, is_training):
    """See base class."""
    self._feature_columns = feature_columns
    partitioner = partitioned_variables.min_max_variable_partitioner(
        max_partitions=self._num_ps_replicas, min_slice_size=64 << 20)
    with variable_scope.variable_scope(
        self._scope, values=features.values(),
        partitioner=partitioner) as scope:
      if self._joint_weights:
        logits, _, _ = layers.joint_weighted_sum_from_feature_columns(
            columns_to_tensors=features,
            feature_columns=self._get_feature_columns(),
            num_outputs=self._num_label_columns,
            weight_collections=[self._scope],
            trainable=self._trainable,
            scope=scope)
      else:
        logits, _, _ = layers.weighted_sum_from_feature_columns(
            columns_to_tensors=features,
            feature_columns=self._get_feature_columns(),
            num_outputs=self._num_label_columns,
            weight_collections=[self._scope],
            trainable=self._trainable,
            scope=scope)
    return logits 

# 需要导入模块: from tensorflow.python.ops import partitioned_variables [as 别名]
# 或者: from tensorflow.python.ops.partitioned_variables import min_max_variable_partitioner [as 别名]
def build_model(self, features, feature_columns, is_training):
    """See base class."""
    self._feature_columns = feature_columns
    partitioner = partitioned_variables.min_max_variable_partitioner(
        max_partitions=self._num_ps_replicas,
        min_slice_size=64 << 20)
    with variable_scope.variable_scope(
        self._scope,
        values=features.values(),
        partitioner=partitioner) as scope:
      if self._joint_weights:
        logits, _, _ = layers.joint_weighted_sum_from_feature_columns(
            columns_to_tensors=features,
            feature_columns=self._get_feature_columns(),
            num_outputs=self._num_label_columns,
            weight_collections=[self._scope],
            scope=scope)
      else:
        logits, _, _ = layers.weighted_sum_from_feature_columns(
            columns_to_tensors=features,
            feature_columns=self._get_feature_columns(),
            num_outputs=self._num_label_columns,
            weight_collections=[self._scope],
            scope=scope)
    return logits 

# 需要导入模块: from tensorflow.python.ops import partitioned_variables [as 别名]
# 或者: from tensorflow.python.ops.partitioned_variables import min_max_variable_partitioner [as 别名]
def _linear_model_fn(features, labels, mode, head, feature_columns, optimizer,
                     partitioner, config):
  """A model_fn for linear models that use a gradient-based optimizer.

  Args:
    features: dict of `Tensor`.
    labels: `Tensor` of shape `[batch_size, logits_dimension]`.
    mode: Defines whether this is training, evaluation or prediction.
      See `ModeKeys`.
    head: A `Head` instance.
    feature_columns: An iterable containing all the feature columns used by
      the model.
    optimizer: string, `Optimizer` object, or callable that defines the
      optimizer to use for training. If `None`, will use a FTRL optimizer.
    partitioner: Partitioner for variables.
    config: `RunConfig` object to configure the runtime settings.

  Returns:
    An `EstimatorSpec` instance.

  Raises:
    ValueError: mode or params are invalid, or features has the wrong type.
  """
  if not isinstance(features, dict):
    raise ValueError('features should be a dictionary of `Tensor`s. '
                     'Given type: {}'.format(type(features)))
  optimizer = optimizers.get_optimizer_instance(
      optimizer or _get_default_optimizer(feature_columns),
      learning_rate=_LEARNING_RATE)
  num_ps_replicas = config.num_ps_replicas if config else 0

  partitioner = partitioner or (
      partitioned_variables.min_max_variable_partitioner(
          max_partitions=num_ps_replicas,
          min_slice_size=64 << 20))

  with variable_scope.variable_scope(
      'linear',
      values=tuple(six.itervalues(features)),
      partitioner=partitioner):

    logit_fn = _linear_logit_fn_builder(
        units=head.logits_dimension, feature_columns=feature_columns)
    logits = logit_fn(features=features)

    def _train_op_fn(loss):
      """Returns the op to optimize the loss."""
      return optimizer.minimize(
          loss,
          global_step=training_util.get_global_step())

    return head.create_estimator_spec(
        features=features,
        mode=mode,
        labels=labels,
        train_op_fn=_train_op_fn,
        logits=logits) 

# 需要导入模块: from tensorflow.python.ops import partitioned_variables [as 别名]
# 或者: from tensorflow.python.ops.partitioned_variables import min_max_variable_partitioner [as 别名]
def __init__(self,
               hidden_units,
               feature_columns,
               model_dir=None,
               label_dimension=1,
               weight_column=None,
               optimizer='Adagrad',
               activation_fn=nn.relu,
               dropout=None,
               input_layer_partitioner=None,
               config=None):
    """Initializes a `DNNRegressor` instance.

    Args:
      hidden_units: Iterable of number 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.
      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: A string or a `_NumericColumn` created by
        `tf.feature_column.numeric_column` defining feature column representing
        weights. It is used to down weight or boost examples during training. It
        will be multiplied by the loss of the example. If it is a string, it is
        used as a key to fetch weight tensor from the `features`. If it is a
        `_NumericColumn`, raw tensor is fetched by key `weight_column.key`,
        then weight_column.normalizer_fn is applied on it to get weight tensor.
      optimizer: An instance of `tf.Optimizer` used to train the model. Defaults
        to 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.
      input_layer_partitioner: Optional. Partitioner for input layer. Defaults
        to `min_max_variable_partitioner` with `min_slice_size` 64 << 20.
      config: `RunConfig` object to configure the runtime settings.
    """
    def _model_fn(features, labels, mode, config):
      return _dnn_model_fn(
          features=features,
          labels=labels,
          mode=mode,
          head=head_lib.  # pylint: disable=protected-access
          _regression_head_with_mean_squared_error_loss(
              label_dimension=label_dimension, weight_column=weight_column),
          hidden_units=hidden_units,
          feature_columns=tuple(feature_columns or []),
          optimizer=optimizer,
          activation_fn=activation_fn,
          dropout=dropout,
          input_layer_partitioner=input_layer_partitioner,
          config=config)
    super(DNNRegressor, self).__init__(
        model_fn=_model_fn, model_dir=model_dir, config=config)