Python tensorflow.python.platform.tf_logging.warn() Examples

def __init__(self, cell, num_proj, input_size=None):
        """Create a cell with input projection.

        Args:
            cell: an RNNCell, a projection of inputs is added before it.
            num_proj: Python integer.    The dimension to project to.
            input_size: Deprecated and unused.

        Raises:
            TypeError: if cell is not an RNNCell.
        """
        if input_size is not None:
            logging.warn("%s: The input_size parameter is deprecated.", self)
        if not isinstance(cell, RNNCell):
            raise TypeError("The parameter cell is not RNNCell.")
        self._cell = cell
        self._num_proj = num_proj 

def __init__(self, num_units, forget_bias=1.0,
               state_is_tuple=True, activation=None, reuse=None):
    """Initialize the basic LSTM cell.

    Args:
      num_units: int, The number of units in the LSTM cell.
      forget_bias: float, The bias added to forget gates (see above).
      state_is_tuple: If True, accepted and returned states are 2-tuples of
        the `c_state` and `m_state`.  If False, they are concatenated
        along the column axis.  The latter behavior will soon be deprecated.
      activation: Activation function of the inner states.  Default: `tanh`.
      reuse: (optional) Python boolean describing whether to reuse variables
        in an existing scope.  If not `True`, and the existing scope already has
        the given variables, an error is raised.
    """
    super(BasicLSTMCell, self).__init__(_reuse=reuse)
    if not state_is_tuple:
      logging.warn("%s: Using a concatenated state is slower and will soon be "
                   "deprecated.  Use state_is_tuple=True.", self)
    self._num_units = num_units
    self._forget_bias = forget_bias
    self._state_is_tuple = state_is_tuple
    self._activation = activation or math_ops.tanh 

def garbage_collect_exports(export_dir_base, exports_to_keep):
  """Deletes older exports, retaining only a given number of the most recent.

  Export subdirectories are assumed to be named with monotonically increasing
  integers; the most recent are taken to be those with the largest values.

  Args:
    export_dir_base: the base directory under which each export is in a
      versioned subdirectory.
    exports_to_keep: the number of recent exports to retain.
  """
  if exports_to_keep is None:
    return

  keep_filter = gc.largest_export_versions(exports_to_keep)
  delete_filter = gc.negation(keep_filter)
  for p in delete_filter(gc.get_paths(export_dir_base,
                                      parser=_export_version_parser)):
    try:
      gfile.DeleteRecursively(p.path)
    except errors_impl.NotFoundError as e:
      logging.warn('Can not delete %s recursively: %s', p.path, e) 

def __init__(self, num_units, is_training, forget_bias=1.0, input_size=None,
                 state_is_tuple=True, reuse=None):
        """Initialize the basic LSTM cell.
        Args:
          num_units: int, The number of units in the LSTM cell.
          is_training: bool, set True when training.
          forget_bias: float, The bias added to forget gates (see above).
          input_size: Deprecated and unused.
          state_is_tuple: If True, accepted and returned states are 2-tuples of
            the `c_state` and `m_state`.  If False, they are concatenated
            along the column axis.  The latter behavior will soon be deprecated.
          reuse: (optional) Python boolean describing whether to reuse variables
            in an existing scope.  If not `True`, and the existing scope already has
            the given variables, an error is raised.
        """
        if not state_is_tuple:
            logging.warn("%s: Using a concatenated state is slower and will soon be "
                         "deprecated.  Use state_is_tuple=True.", self)
        if input_size is not None:
            logging.warn("%s: The input_size parameter is deprecated.", self)
        self._num_units = num_units
        self._forget_bias = forget_bias
        self._state_is_tuple = state_is_tuple
        self._reuse = reuse
        self._is_training = is_training 

def _write_summary_results(output_dir, eval_results, current_global_step):
  """Writes eval results into summary file in given dir."""
  logging.info('Saving evaluation summary for step %d: %s', current_global_step,
               _eval_results_to_str(eval_results))
  summary_writer = get_summary_writer(output_dir)
  summary = summary_pb2.Summary()
  for key in eval_results:
    if eval_results[key] is None:
      continue
    value = summary.value.add()
    value.tag = key
    if (isinstance(eval_results[key], np.float32) or
        isinstance(eval_results[key], float)):
      value.simple_value = float(eval_results[key])
    else:
      logging.warn('Skipping summary for %s, must be a float or np.float32.',
                   key)
  summary_writer.add_summary(summary, current_global_step)
  summary_writer.flush() 

def __new__(cls,
              column_name,
              size,
              dimension,
              hash_key,
              combiner="sqrtn",
              initializer=None):
    if initializer is not None and not callable(initializer):
      raise ValueError("initializer must be callable if specified. "
                       "column_name: {}".format(column_name))
    if initializer is None:
      logging.warn("The default stddev value of initializer will change from "
                   "\"0.1\" to \"1/sqrt(dimension)\" after 2017/02/25.")
      stddev = 0.1
      initializer = init_ops.truncated_normal_initializer(
          mean=0.0, stddev=stddev)
    return super(_ScatteredEmbeddingColumn, cls).__new__(cls, column_name, size,
                                                         dimension, hash_key,
                                                         combiner,
                                                         initializer) 

def variable_op_scope(values,
                      name_or_scope,
                      default_name=None,
                      initializer=None,
                      regularizer=None,
                      caching_device=None,
                      partitioner=None,
                      custom_getter=None,
                      reuse=None,
                      dtype=None):
  """Deprecated: context manager for defining an op that creates variables."""
  logging.warn("tf.variable_op_scope(values, name, default_name) is deprecated,"
               " use tf.variable_scope(name, default_name, values)")
  with variable_scope(name_or_scope,
                      default_name=default_name,
                      values=values,
                      initializer=initializer,
                      regularizer=regularizer,
                      caching_device=caching_device,
                      partitioner=partitioner,
                      custom_getter=custom_getter,
                      reuse=reuse,
                      dtype=dtype) as scope:
    yield scope 

def _ParseFileVersion(file_version):
  """Convert the string file_version in event.proto into a float.

  Args:
    file_version: String file_version from event.proto

  Returns:
    Version number as a float.
  """
  tokens = file_version.split('brain.Event:')
  try:
    return float(tokens[-1])
  except ValueError:
    ## This should never happen according to the definition of file_version
    ## specified in event.proto.
    logging.warn(('Invalid event.proto file_version. Defaulting to use of '
                  'out-of-order event.step logic for purging expired events.'))
    return -1 

def __init__(self, num_units, forget_bias=1.0, input_size=None,
               state_is_tuple=True, activation=tanh):
    """Initialize the basic LSTM cell.

    Args:
      num_units: int, The number of units in the LSTM cell.
      forget_bias: float, The bias added to forget gates (see above).
      input_size: Deprecated and unused.
      state_is_tuple: If True, accepted and returned states are 2-tuples of
        the `c_state` and `m_state`.  If False, they are concatenated
        along the column axis.  The latter behavior will soon be deprecated.
      activation: Activation function of the inner states.
    """
    if not state_is_tuple:
      logging.warn("%s: Using a concatenated state is slower and will soon be "
                   "deprecated.  Use state_is_tuple=True.", self)
    if input_size is not None:
      logging.warn("%s: The input_size parameter is deprecated.", self)
    self._num_units = num_units
    self._forget_bias = forget_bias
    self._state_is_tuple = state_is_tuple
    self._activation = activation 

def __init__(self, cell, num_proj, input_size=None):
    """Create a cell with input projection.

    Args:
      cell: an RNNCell, a projection of inputs is added before it.
      num_proj: Python integer.  The dimension to project to.
      input_size: Deprecated and unused.

    Raises:
      TypeError: if cell is not an RNNCell.
    """
    if input_size is not None:
      logging.warn("%s: The input_size parameter is deprecated.", self)
    if not isinstance(cell, RNNCell):
      raise TypeError("The parameter cell is not RNNCell.")
    self._cell = cell
    self._num_proj = num_proj 

def _write_summary_results(output_dir, eval_results, current_global_step):
  """Writes eval results into summary file in given dir."""
  logging.info('Saving evaluation summary for step %d: %s', current_global_step,
               _eval_results_to_str(eval_results))
  summary_writer = get_summary_writer(output_dir)
  summary = summary_pb2.Summary()
  for key in eval_results:
    if eval_results[key] is None:
      continue
    value = summary.value.add()
    value.tag = key
    if (isinstance(eval_results[key], np.float32) or
        isinstance(eval_results[key], float)):
      value.simple_value = float(eval_results[key])
    else:
      logging.warn('Skipping summary for %s, must be a float or np.float32.',
                   key)
  summary_writer.add_summary(summary, current_global_step)
  summary_writer.flush() 

def _validate_input_pipeline(self):
    """Validates the input pipeline.

    Perform some sanity checks to log user friendly information. We should
    error out to give users better error message. But, if
    _WRAP_INPUT_FN_INTO_WHILE_LOOP is False (legacy behavior), we cannot break
    user code, so, log a warning.

    Raises:
      RuntimeError: If the validation failed.
    """
    if ops.get_default_graph().get_collection(ops.GraphKeys.QUEUE_RUNNERS):
      err_msg = ('Input pipeline contains one or more QueueRunners. '
                 'It could be slow and not scalable. Please consider '
                 'converting your input pipeline to use `tf.data` instead (see '
                 'https://www.tensorflow.org/guide/datasets for '
                 'instructions.')
      if _WRAP_INPUT_FN_INTO_WHILE_LOOP:
        raise RuntimeError(err_msg)
      else:
        logging.warn(err_msg) 

def __init__(self, num_units, forget_bias=1.0, input_size=None,
                 state_is_tuple=True, reuse=None):
        """Initialize the basic LSTM cell.
        Args:
          num_units: int, The number of units in the LSTM cell.
          forget_bias: float, The bias added to forget gates (see above).
          input_size: Deprecated and unused.
          state_is_tuple: If True, accepted and returned states are 2-tuples of
            the `c_state` and `m_state`.  If False, they are concatenated
            along the column axis.  The latter behavior will soon be deprecated.
          reuse: (optional) Python boolean describing whether to reuse variables
            in an existing scope.  If not `True`, and the existing scope already has
            the given variables, an error is raised.
        """
        if not state_is_tuple:
            logging.warn("%s: Using a concatenated state is slower and will soon be "
                         "deprecated.  Use state_is_tuple=True.", self)
        if input_size is not None:
            logging.warn("%s: The input_size parameter is deprecated.", self)
        self._num_units = num_units
        self._forget_bias = forget_bias
        self._state_is_tuple = state_is_tuple
        self._reuse = reuse 

def __init__(self, num_units, forget_bias=1.0, input_size=None,
                             state_is_tuple=True, activation=tanh):
        """Initialize the basic LSTM cell.

        Args:
            num_units: int, The number of units in the LSTM cell.
            forget_bias: float, The bias added to forget gates (see above).
            input_size: Deprecated and unused.
            state_is_tuple: If True, accepted and returned states are 2-tuples of
                the `c_state` and `m_state`.    If False, they are concatenated
                along the column axis.    The latter behavior will soon be deprecated.
            activation: Activation function of the inner states.
        """
        if not state_is_tuple:
            logging.warn("%s: Using a concatenated state is slower and will soon be "
                                     "deprecated.    Use state_is_tuple=True.", self)
        if input_size is not None:
            logging.warn("%s: The input_size parameter is deprecated.", self)
        self._num_units = num_units
        self._forget_bias = forget_bias
        self._state_is_tuple = state_is_tuple
        self._activation = activation 

def _write_summary_results(output_dir, eval_results, current_global_step):
  """Writes eval results into summary file in given dir."""
  logging.info('Saving evaluation summary for step %d: %s', current_global_step,
               _eval_results_to_str(eval_results))
  summary_writer = get_summary_writer(output_dir)
  summary = summary_pb2.Summary()
  for key in eval_results:
    if eval_results[key] is None:
      continue
    value = summary.value.add()
    value.tag = key
    if (isinstance(eval_results[key], np.float32) or
        isinstance(eval_results[key], float)):
      value.simple_value = float(eval_results[key])
    else:
      logging.warn('Skipping summary for %s, must be a float or np.float32.',
                   key)
  summary_writer.add_summary(summary, current_global_step)
  summary_writer.flush() 

def _ParseFileVersion(file_version):
  """Convert the string file_version in event.proto into a float.

  Args:
    file_version: String file_version from event.proto

  Returns:
    Version number as a float.
  """
  tokens = file_version.split('brain.Event:')
  try:
    return float(tokens[-1])
  except ValueError:
    ## This should never happen according to the definition of file_version
    ## specified in event.proto.
    logging.warn(('Invalid event.proto file_version. Defaulting to use of '
                  'out-of-order event.step logic for purging expired events.'))
    return -1 

def __init__(self, num_units, forget_bias=1.0, input_size=None,
               state_is_tuple=True, activation=tanh):
    """Initialize the basic LSTM cell.

    Args:
      num_units: int, The number of units in the LSTM cell.
      forget_bias: float, The bias added to forget gates (see above).
      input_size: Deprecated and unused.
      state_is_tuple: If True, accepted and returned states are 2-tuples of
        the `c_state` and `m_state`.  If False, they are concatenated
        along the column axis.  The latter behavior will soon be deprecated.
      activation: Activation function of the inner states.
    """
    if not state_is_tuple:
      logging.warn("%s: Using a concatenated state is slower and will soon be "
                   "deprecated.  Use state_is_tuple=True.", self)
    if input_size is not None:
      logging.warn("%s: The input_size parameter is deprecated.", self)
    self._num_units = num_units
    self._forget_bias = forget_bias
    self._state_is_tuple = state_is_tuple
    self._activation = activation 

def variable_op_scope(values,
                      name_or_scope,
                      default_name=None,
                      initializer=None,
                      regularizer=None,
                      caching_device=None,
                      partitioner=None,
                      custom_getter=None,
                      reuse=None,
                      dtype=None):
  """Deprecated: context manager for defining an op that creates variables."""
  logging.warn("tf.variable_op_scope(values, name, default_name) is deprecated,"
               " use tf.variable_scope(name, default_name, values)")
  with variable_scope(name_or_scope,
                      default_name=default_name,
                      values=values,
                      initializer=initializer,
                      regularizer=regularizer,
                      caching_device=caching_device,
                      partitioner=partitioner,
                      custom_getter=custom_getter,
                      reuse=reuse,
                      dtype=dtype) as scope:
    yield scope 

def __init__(self, cell, num_proj, input_size=None):
    """Create a cell with input projection.

    Args:
      cell: an RNNCell, a projection of inputs is added before it.
      num_proj: Python integer.  The dimension to project to.
      input_size: Deprecated and unused.

    Raises:
      TypeError: if cell is not an RNNCell.
    """
    if input_size is not None:
      logging.warn("%s: The input_size parameter is deprecated.", self)
    if not isinstance(cell, RNNCell):
      raise TypeError("The parameter cell is not RNNCell.")
    self._cell = cell
    self._num_proj = num_proj 

def __init__(self, num_units, forget_bias=1.0, input_size=None,
               state_is_tuple=True, activation=tanh):
    """Initialize the basic LSTM cell.

    Args:
      num_units: int, The number of units in the LSTM cell.
      forget_bias: float, The bias added to forget gates (see above).
      input_size: Deprecated and unused.
      state_is_tuple: If True, accepted and returned states are 2-tuples of
        the `c_state` and `m_state`.  If False, they are concatenated
        along the column axis.  The latter behavior will soon be deprecated.
      activation: Activation function of the inner states.
    """
    if not state_is_tuple:
      logging.warn("%s: Using a concatenated state is slower and will soon be "
                   "deprecated.  Use state_is_tuple=True.", self)
    if input_size is not None:
      logging.warn("%s: The input_size parameter is deprecated.", self)
    self._num_units = num_units
    self._forget_bias = forget_bias
    self._state_is_tuple = state_is_tuple
    self._activation = activation 

def __init__(self, num_units, input_size=None,
               use_peepholes=False, cell_clip=None,
               initializer=None, num_proj=None, proj_clip=None,
               num_unit_shards=1, num_proj_shards=1,
               forget_bias=1.0, state_is_tuple=False,
               activation=tanh):

#    if not state_is_tuple:
#      logging.warn(
#          "%s: Using a concatenated state is slower and will soon be "
#          "deprecated.  Use state_is_tuple=True." % self)
    if input_size is not None:
      logging.warn("%s: The input_size parameter is deprecated." % self)

    #self._use_peepholes = use_peepholes
    #self._cell_clip = cell_clip
    #self._initializer = initializer
    #self._num_proj = num_proj
    #self._num_unit_shards = num_unit_shards
    #self._num_proj_shards = num_proj_shards

    self._num_units = num_units
    self._forget_bias = forget_bias
    self._state_is_tuple = state_is_tuple
    self._activation = activation 

def op_scope(values, name, default_name=None):
  """DEPRECATED. Same as name_scope above, just different argument order."""
  logging.warn("tf.op_scope(values, name, default_name) is deprecated,"
               " use tf.name_scope(name, default_name, values)")
  with name_scope(name, default_name=default_name, values=values) as scope:
    yield scope 

def op_scope(values, name, default_name=None):
  """DEPRECATED. Same as name_scope above, just different argument order."""
  logging.warn("tf.op_scope(values, name, default_name) is deprecated,"
               " use tf.name_scope(name, default_name, values)")
  with name_scope(name, default_name=default_name, values=values) as scope:
    yield scope 

def variable_op_scope(values,
                      name_or_scope,
                      default_name=None,
                      initializer=None,
                      regularizer=None,
                      caching_device=None,
                      partitioner=None,
                      custom_getter=None,
                      reuse=None,
                      dtype=None,
                      use_resource=None):
  """Deprecated: context manager for defining an op that creates variables."""
  logging.warn("tf.variable_op_scope(values, name, default_name) is deprecated,"
               " use tf.variable_scope(name, default_name, values)")
  with variable_scope(name_or_scope,
                      default_name=default_name,
                      values=values,
                      initializer=initializer,
                      regularizer=regularizer,
                      caching_device=caching_device,
                      partitioner=partitioner,
                      custom_getter=custom_getter,
                      reuse=reuse,
                      dtype=dtype,
                      use_resource=use_resource) as scope:
    yield scope 

def __init__(self, num_units, input_size=None, activation=tanh):
    if input_size is not None:
      logging.warn("%s: The input_size parameter is deprecated.", self)
    self._num_units = num_units
    self._activation = activation 

def __init__(self,
               cell,
               num_proj,
               activation=None,
               input_size=None,
               reuse=None):
    """Create a cell with input projection.

    Args:
      cell: an RNNCell, a projection of inputs is added before it.
      num_proj: Python integer.  The dimension to project to.
      activation: (optional) an optional activation function.
      input_size: Deprecated and unused.
      reuse: (optional) Python boolean describing whether to reuse variables
        in an existing scope.  If not `True`, and the existing scope already has
        the given variables, an error is raised.

    Raises:
      TypeError: if cell is not an RNNCell.
    """
    super(InputProjectionWrapper, self).__init__(_reuse=reuse)
    if input_size is not None:
      logging.warn("%s: The input_size parameter is deprecated.", self)
    if not _like_rnncell(cell):
      raise TypeError("The parameter cell is not RNNCell.")
    self._cell = cell
    self._num_proj = num_proj
    self._activation = activation 

def __init__(self, num_units, forget_bias=1.0,
               input_size=None, activation=math_ops.tanh,
               layer_norm=True, norm_gain=1.0, norm_shift=0.0,
               dropout_keep_prob=1.0, dropout_prob_seed=None,
               reuse=None):
    """Initializes the basic LSTM cell.
    Args:
      num_units: int, The number of units in the LSTM cell.
      forget_bias: float, The bias added to forget gates (see above).
      input_size: Deprecated and unused.
      activation: Activation function of the inner states.
      layer_norm: If `True`, layer normalization will be applied.
      norm_gain: float, The layer normalization gain initial value. If
        `layer_norm` has been set to `False`, this argument will be ignored.
      norm_shift: float, The layer normalization shift initial value. If
        `layer_norm` has been set to `False`, this argument will be ignored.
      dropout_keep_prob: unit Tensor or float between 0 and 1 representing the
        recurrent dropout probability value. If float and 1.0, no dropout will
        be applied.
      dropout_prob_seed: (optional) integer, the randomness seed.
      reuse: (optional) Python boolean describing whether to reuse variables
        in an existing scope.  If not `True`, and the existing scope already has
        the given variables, an error is raised.
    """
    super(LayerNormBasicLSTMCell, self).__init__(_reuse=reuse)

    if input_size is not None:
      logging.warn("%s: The input_size parameter is deprecated.", self)

    self._num_units = num_units
    self._activation = activation
    self._forget_bias = forget_bias
    self._keep_prob = dropout_keep_prob
    self._seed = dropout_prob_seed
    self._layer_norm = layer_norm
    self._norm_gain = norm_gain
    self._norm_shift = norm_shift
    self._reuse = reuse 

def __new__(cls,
              sparse_id_column,
              dimension,
              combiner="mean",
              initializer=None,
              ckpt_to_load_from=None,
              tensor_name_in_ckpt=None,
              shared_embedding_name=None,
              shared_vocab_size=None,
              max_norm=None,
              trainable=True):
    if initializer is not None and not callable(initializer):
      raise ValueError("initializer must be callable if specified. "
                       "Embedding of column_name: {}".format(
                           sparse_id_column.name))

    if (ckpt_to_load_from is None) != (tensor_name_in_ckpt is None):
      raise ValueError("Must specify both `ckpt_to_load_from` and "
                       "`tensor_name_in_ckpt` or none of them.")
    if initializer is None:
      logging.warn("The default stddev value of initializer will change from "
                   "\"1/sqrt(vocab_size)\" to \"1/sqrt(dimension)\" after "
                   "2017/02/25.")
      stddev = 1 / math.sqrt(sparse_id_column.length)
      initializer = init_ops.truncated_normal_initializer(
          mean=0.0, stddev=stddev)
    return super(_EmbeddingColumn, cls).__new__(cls, sparse_id_column,
                                                dimension, combiner,
                                                initializer, ckpt_to_load_from,
                                                tensor_name_in_ckpt,
                                                shared_embedding_name,
                                                shared_vocab_size,
                                                max_norm,
                                                trainable) 

def __init__(self, num_units, input_size=None, activation=tanh):
    if input_size is not None:
      logging.warn("%s: The input_size parameter is deprecated.", self)
    self._num_units = num_units
    self._activation = activation 

def __init__(self, num_units, input_size=None, activation=tanh):
    if input_size is not None:
      logging.warn("%s: The input_size parameter is deprecated.", self)
    self._num_units = num_units
    self._activation = activation