Python tensorflow.python.ops.clip_ops.clip_by_global_norm() Examples

def get_train_step(self, loss):
    """Returns the ops to run to perform a training step on this estimator.

    Args:
      loss: The loss to use when calculating gradients.

    Returns:
      The ops to run to perform a training step.
    """
    my_vars = self._get_vars()
    if not (self._get_feature_columns() or my_vars):
      return []

    grads = gradients.gradients(loss, my_vars)
    if self._gradient_clip_norm:
      grads, _ = clip_ops.clip_by_global_norm(grads, self._gradient_clip_norm)
    return [self._get_optimizer().apply_gradients(zip(grads, my_vars))] 

def get_train_step(self, loss):
    """Returns the ops to run to perform a training step on this estimator.

    Args:
      loss: The loss to use when calculating gradients.

    Returns:
      The ops to run to perform a training step.
    """
    my_vars = self._get_vars()
    if not (self._get_feature_columns() or my_vars):
      return []

    grads = gradients.gradients(loss, my_vars)
    if self._gradient_clip_norm:
      grads, _ = clip_ops.clip_by_global_norm(grads, self._gradient_clip_norm)
    return [self._get_optimizer().apply_gradients(zip(grads, my_vars))] 

def get_train_step(self, loss):
    """Returns the ops to run to perform a training step on this estimator.

    Args:
      loss: The loss to use when calculating gradients.

    Returns:
      The ops to run to perform a training step.
    """
    my_vars = self._get_vars()
    if not (self._get_feature_columns() or my_vars):
      return []

    grads = gradients.gradients(loss, my_vars)
    if self._gradient_clip_norm:
      grads, _ = clip_ops.clip_by_global_norm(grads, self._gradient_clip_norm)
    return [self._get_optimizer().apply_gradients(zip(grads, my_vars))] 

def get_train_step(self, loss):
    """Returns the ops to run to perform a training step on this estimator.

    Args:
      loss: The loss to use when calculating gradients.

    Returns:
      The ops to run to perform a training step.
    """
    my_vars = self._get_vars()
    if not (self._get_feature_columns() or my_vars):
      return []

    grads = gradients.gradients(loss, my_vars)
    if self._gradient_clip_norm:
      grads, _ = clip_ops.clip_by_global_norm(grads, self._gradient_clip_norm)
    return [self._get_optimizer().apply_gradients(zip(grads, my_vars))] 

def __init__(self,
               num_label_columns,
               optimizer,
               gradient_clip_norm,
               num_ps_replicas,
               scope):
    """Common initialization for all _ComposableModel objects.

    Args:
      num_label_columns: The number of label columns.
      optimizer: An instance of `tf.Optimizer` used to apply gradients to
        the model. If `None`, will use a FTRL optimizer.
      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.
      num_ps_replicas: The number of parameter server replicas.
      scope: Scope for variables created in this model.
    """
    self._num_label_columns = num_label_columns
    self._optimizer = optimizer
    self._gradient_clip_norm = gradient_clip_norm
    self._num_ps_replicas = num_ps_replicas
    self._scope = scope
    self._feature_columns = None 

def _clip_gradients_by_norm(grads_and_vars, clip_gradients):
  """Clips gradients by global norm."""
  gradients, variables = zip(*grads_and_vars)
  clipped_gradients, _ = clip_ops.clip_by_global_norm(gradients, clip_gradients)
  return list(zip(clipped_gradients, variables)) 

def _clip_gradients_by_norm(grads_and_vars, clip_gradients):
  """Clips gradients by global norm."""
  gradients, variables = zip(*grads_and_vars)
  clipped_gradients, _ = clip_ops.clip_by_global_norm(gradients, clip_gradients)
  return list(zip(clipped_gradients, variables)) 

def __init__(self,
               num_label_columns,
               optimizer=None,
               _joint_weights=False,
               gradient_clip_norm=None,
               num_ps_replicas=0,
               scope=None,
               trainable=True):
    """Initializes LinearComposableModel objects.

    Args:
      num_label_columns: The number of label columns.
      optimizer: An instance of `tf.Optimizer` used to apply gradients to
        the model. If `None`, will use a FTRL optimizer.
      _joint_weights: If True use a single (possibly partitioned) variable
        to store all weights in this model. Faster, but requires that all
        feature columns are sparse and have the 'sum' combiner.
      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.
      num_ps_replicas: The number of parameter server replicas.
      scope: Optional scope for variables created in this model. If scope
        is not supplied, it will default to 'linear'.
      trainable: True if this model contains variables that can be trained.
        False otherwise (in cases where the variables are used strictly for
        transforming input labels for training).
    """
    scope = "linear" if not scope else scope
    super(LinearComposableModel, self).__init__(
        num_label_columns=num_label_columns,
        optimizer=optimizer,
        gradient_clip_norm=gradient_clip_norm,
        num_ps_replicas=num_ps_replicas,
        scope=scope,
        trainable=trainable)
    self._joint_weights = _joint_weights 

def __init__(self,
               num_label_columns,
               optimizer,
               gradient_clip_norm,
               num_ps_replicas,
               scope,
               trainable=True):
    """Common initialization for all _ComposableModel objects.

    Args:
      num_label_columns: The number of label columns.
      optimizer: An instance of `tf.Optimizer` used to apply gradients to
        the model. If `None`, will use a FTRL optimizer.
      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.
      num_ps_replicas: The number of parameter server replicas.
      scope: Scope for variables created in this model.
      trainable: True if this model contains variables that can be trained.
        False otherwise (in cases where the variables are used strictly for
        transforming input labels for training).
    """
    self._num_label_columns = num_label_columns
    self._optimizer = optimizer
    self._gradient_clip_norm = gradient_clip_norm
    self._num_ps_replicas = num_ps_replicas
    self._scope = scope
    self._trainable = trainable
    self._feature_columns = None 

def _process_gradients(self, gradients_vars):
    """Process gradients (e.g. clipping) before applying them to weights."""
    with ops.name_scope('process_gradients'):
      gradients, variables = zip(*gradients_vars)
      if self._gradient_clipping_norm is not None:
        gradients, _ = clip_ops.clip_by_global_norm(
            gradients, self._gradient_clipping_norm)
      return zip(gradients, variables) 

def __init__(self,
               num_label_columns,
               hidden_units,
               optimizer=None,
               activation_fn=nn.relu,
               dropout=None,
               gradient_clip_norm=None,
               num_ps_replicas=0,
               scope=None):
    """Initializes DNNComposableModel objects.

    Args:
      num_label_columns: The number of label columns.
      hidden_units: List of hidden units per layer. All layers are fully
        connected.
      optimizer: An instance of `tf.Optimizer` used to apply gradients to
        the model. If `None`, will use a FTRL 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.
      num_ps_replicas: The number of parameter server replicas.
      scope: Optional scope for variables created in this model. If not scope
        is supplied, one is generated.
    """
    scope = "dnn" if not scope else scope
    super(DNNComposableModel, self).__init__(
        num_label_columns=num_label_columns,
        optimizer=optimizer,
        gradient_clip_norm=gradient_clip_norm,
        num_ps_replicas=num_ps_replicas,
        scope=scope)
    self._hidden_units = hidden_units
    self._activation_fn = activation_fn
    self._dropout = dropout 

def __init__(self,
               num_label_columns,
               optimizer=None,
               _joint_weights=False,
               gradient_clip_norm=None,
               num_ps_replicas=0,
               scope=None):
    """Initializes LinearComposableModel objects.

    Args:
      num_label_columns: The number of label columns.
      optimizer: An instance of `tf.Optimizer` used to apply gradients to
        the model. If `None`, will use a FTRL optimizer.
      _joint_weights: If True use a single (possibly partitioned) variable
        to store all weights in this model. Faster, but requires that all
        feature columns are sparse and have the 'sum' combiner.
      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.
      num_ps_replicas: The number of parameter server replicas.
      scope: Optional scope for variables created in this model. If scope
        is not supplied, it will default to 'linear'.
    """
    scope = "linear" if not scope else scope
    super(LinearComposableModel, self).__init__(
        num_label_columns=num_label_columns,
        optimizer=optimizer,
        gradient_clip_norm=gradient_clip_norm,
        num_ps_replicas=num_ps_replicas,
        scope=scope)
    self._joint_weights = _joint_weights 

def _clip_gradients_by_norm(grads_and_vars, clip_gradients):
  """Clips gradients by global norm."""
  gradients, variables = zip(*grads_and_vars)
  clipped_gradients, _ = clip_ops.clip_by_global_norm(gradients, clip_gradients)
  return list(zip(clipped_gradients, variables)) 

def _clip_gradients_by_norm(grads_and_vars, clip_gradients):
  """Clips gradients by global norm."""
  gradients, variables = zip(*grads_and_vars)
  clipped_gradients, _ = clip_ops.clip_by_global_norm(gradients, clip_gradients)
  return list(zip(clipped_gradients, variables)) 

def __init__(self,
               num_label_columns,
               optimizer=None,
               _joint_weights=False,
               gradient_clip_norm=None,
               num_ps_replicas=0,
               scope=None,
               trainable=True):
    """Initializes LinearComposableModel objects.

    Args:
      num_label_columns: The number of label columns.
      optimizer: An instance of `tf.Optimizer` used to apply gradients to
        the model. If `None`, will use a FTRL optimizer.
      _joint_weights: If True use a single (possibly partitioned) variable
        to store all weights in this model. Faster, but requires that all
        feature columns are sparse and have the 'sum' combiner.
      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.
      num_ps_replicas: The number of parameter server replicas.
      scope: Optional scope for variables created in this model. If scope
        is not supplied, it will default to 'linear'.
      trainable: True if this model contains variables that can be trained.
        False otherwise (in cases where the variables are used strictly for
        transforming input labels for training).
    """
    scope = "linear" if not scope else scope
    super(LinearComposableModel, self).__init__(
        num_label_columns=num_label_columns,
        optimizer=optimizer,
        gradient_clip_norm=gradient_clip_norm,
        num_ps_replicas=num_ps_replicas,
        scope=scope,
        trainable=trainable)
    self._joint_weights = _joint_weights 

def __init__(self,
               num_label_columns,
               optimizer,
               gradient_clip_norm,
               num_ps_replicas,
               scope,
               trainable=True):
    """Common initialization for all _ComposableModel objects.

    Args:
      num_label_columns: The number of label columns.
      optimizer: An instance of `tf.Optimizer` used to apply gradients to
        the model. If `None`, will use a FTRL optimizer.
      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.
      num_ps_replicas: The number of parameter server replicas.
      scope: Scope for variables created in this model.
      trainable: True if this model contains variables that can be trained.
        False otherwise (in cases where the variables are used strictly for
        transforming input labels for training).
    """
    self._num_label_columns = num_label_columns
    self._optimizer = optimizer
    self._gradient_clip_norm = gradient_clip_norm
    self._num_ps_replicas = num_ps_replicas
    self._scope = scope
    self._trainable = trainable
    self._feature_columns = None 

def __init__(self,
               num_label_columns,
               optimizer=None,
               _joint_weights=False,
               gradient_clip_norm=None,
               num_ps_replicas=0,
               scope=None,
               trainable=True):
    """Initializes LinearComposableModel objects.

    Args:
      num_label_columns: The number of label columns.
      optimizer: An instance of `tf.Optimizer` used to apply gradients to
        the model. If `None`, will use a FTRL optimizer.
      _joint_weights: If True use a single (possibly partitioned) variable
        to store all weights in this model. Faster, but requires that all
        feature columns are sparse and have the 'sum' combiner.
      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.
      num_ps_replicas: The number of parameter server replicas.
      scope: Optional scope for variables created in this model. If scope
        is not supplied, it will default to 'linear'.
      trainable: True if this model contains variables that can be trained.
        False otherwise (in cases where the variables are used strictly for
        transforming input labels for training).
    """
    scope = "linear" if not scope else scope
    super(LinearComposableModel, self).__init__(
        num_label_columns=num_label_columns,
        optimizer=optimizer,
        gradient_clip_norm=gradient_clip_norm,
        num_ps_replicas=num_ps_replicas,
        scope=scope,
        trainable=trainable)
    self._joint_weights = _joint_weights 

def __init__(self,
               num_label_columns,
               optimizer,
               gradient_clip_norm,
               num_ps_replicas,
               scope,
               trainable=True):
    """Common initialization for all _ComposableModel objects.

    Args:
      num_label_columns: The number of label columns.
      optimizer: An instance of `tf.Optimizer` used to apply gradients to
        the model. If `None`, will use a FTRL optimizer.
      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.
      num_ps_replicas: The number of parameter server replicas.
      scope: Scope for variables created in this model.
      trainable: True if this model contains variables that can be trained.
        False otherwise (in cases where the variables are used strictly for
        transforming input labels for training).
    """
    self._num_label_columns = num_label_columns
    self._optimizer = optimizer
    self._gradient_clip_norm = gradient_clip_norm
    self._num_ps_replicas = num_ps_replicas
    self._scope = scope
    self._trainable = trainable
    self._feature_columns = None 

def __init__(self,
               num_label_columns,
               hidden_units,
               optimizer=None,
               activation_fn=nn.relu,
               dropout=None,
               gradient_clip_norm=None,
               num_ps_replicas=0,
               scope=None,
               trainable=True):
    """Initializes DNNComposableModel objects.

    Args:
      num_label_columns: The number of label columns.
      hidden_units: List of hidden units per layer. All layers are fully
        connected.
      optimizer: An instance of `tf.Optimizer` used to apply gradients to
        the model. If `None`, will use a FTRL 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.
      num_ps_replicas: The number of parameter server replicas.
      scope: Optional scope for variables created in this model. If not scope
        is supplied, one is generated.
      trainable: True if this model contains variables that can be trained.
        False otherwise (in cases where the variables are used strictly for
        transforming input labels for training).
    """
    scope = "dnn" if not scope else scope
    super(DNNComposableModel, self).__init__(
        num_label_columns=num_label_columns,
        optimizer=optimizer,
        gradient_clip_norm=gradient_clip_norm,
        num_ps_replicas=num_ps_replicas,
        scope=scope,
        trainable=trainable)
    self._hidden_units = hidden_units
    self._activation_fn = activation_fn
    self._dropout = dropout 

def __init__(self,
               num_label_columns,
               hidden_units,
               optimizer=None,
               activation_fn=nn.relu,
               dropout=None,
               gradient_clip_norm=None,
               num_ps_replicas=0,
               scope=None,
               trainable=True):
    """Initializes DNNComposableModel objects.

    Args:
      num_label_columns: The number of label columns.
      hidden_units: List of hidden units per layer. All layers are fully
        connected.
      optimizer: An instance of `tf.Optimizer` used to apply gradients to
        the model. If `None`, will use a FTRL 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.
      num_ps_replicas: The number of parameter server replicas.
      scope: Optional scope for variables created in this model. If not scope
        is supplied, one is generated.
      trainable: True if this model contains variables that can be trained.
        False otherwise (in cases where the variables are used strictly for
        transforming input labels for training).
    """
    scope = "dnn" if not scope else scope
    super(DNNComposableModel, self).__init__(
        num_label_columns=num_label_columns,
        optimizer=optimizer,
        gradient_clip_norm=gradient_clip_norm,
        num_ps_replicas=num_ps_replicas,
        scope=scope,
        trainable=trainable)
    self._hidden_units = hidden_units
    self._activation_fn = activation_fn
    self._dropout = dropout 

def __init__(self,
               num_label_columns,
               hidden_units,
               optimizer=None,
               activation_fn=nn.relu,
               dropout=None,
               gradient_clip_norm=None,
               num_ps_replicas=0,
               scope=None,
               trainable=True):
    """Initializes DNNComposableModel objects.

    Args:
      num_label_columns: The number of label columns.
      hidden_units: List of hidden units per layer. All layers are fully
        connected.
      optimizer: An instance of `tf.Optimizer` used to apply gradients to
        the model. If `None`, will use a FTRL 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.
      num_ps_replicas: The number of parameter server replicas.
      scope: Optional scope for variables created in this model. If not scope
        is supplied, one is generated.
      trainable: True if this model contains variables that can be trained.
        False otherwise (in cases where the variables are used strictly for
        transforming input labels for training).
    """
    scope = "dnn" if not scope else scope
    super(DNNComposableModel, self).__init__(
        num_label_columns=num_label_columns,
        optimizer=optimizer,
        gradient_clip_norm=gradient_clip_norm,
        num_ps_replicas=num_ps_replicas,
        scope=scope,
        trainable=trainable)
    self._hidden_units = hidden_units
    self._activation_fn = activation_fn
    self._dropout = dropout 

def __init__(self,  # _joint_weights: pylint: disable=invalid-name
               feature_columns,
               head,
               model_dir=None,
               weight_column_name=None,
               optimizer=None,
               gradient_clip_norm=None,
               _joint_weights=False,
               config=None,
               feature_engineering_fn=None):
    """Construct a `LinearEstimator` object.

    Args:
      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`.
      head: An instance of _Head class.
      model_dir: Directory to save model parameters, graph, 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 Ftrl optimizer.
      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.
      _joint_weights: If True use a single (possibly partitioned) variable to
        store the weights. It's faster, but requires all feature columns are
        sparse and have the 'sum' combiner. Incompatible with 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.

    Returns:
      A `LinearEstimator` estimator.

    Raises:
      ValueError: if optimizer is not supported, e.g., SDCAOptimizer
    """
    assert feature_columns
    if isinstance(optimizer, sdca_optimizer.SDCAOptimizer):
      raise ValueError("LinearEstimator does not support SDCA optimizer.")

    params = {
        "head": head,
        "feature_columns": feature_columns,
        "optimizer": optimizer,
        "gradient_clip_norm": gradient_clip_norm,
        "joint_weights": _joint_weights,
    }
    super(LinearEstimator, self).__init__(
        model_fn=_linear_model_fn,
        model_dir=model_dir,
        config=config,
        params=params,
        feature_engineering_fn=feature_engineering_fn)