Python tensorflow.python.platform.tf_logging.warn() Examples
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Example #1
Source File: rnn_cell.py From ecm with Apache License 2.0 | 6 votes |
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
Example #2
Source File: rnn_cell_impl.py From lambda-packs with MIT License | 6 votes |
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
Example #3
Source File: saved_model_export_utils.py From lambda-packs with MIT License | 6 votes |
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)
Example #4
Source File: bn_basic_lstm.py From tensorflow_end2end_speech_recognition with MIT License | 6 votes |
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
Example #5
Source File: graph_actions.py From lambda-packs with MIT License | 6 votes |
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()
Example #6
Source File: feature_column.py From lambda-packs with MIT License | 6 votes |
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)
Example #7
Source File: variable_scope.py From auto-alt-text-lambda-api with MIT License | 6 votes |
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
Example #8
Source File: event_accumulator.py From auto-alt-text-lambda-api with MIT License | 6 votes |
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
Example #9
Source File: core_rnn_cell_impl.py From auto-alt-text-lambda-api with MIT License | 6 votes |
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
Example #10
Source File: core_rnn_cell_impl.py From auto-alt-text-lambda-api with MIT License | 6 votes |
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
Example #11
Source File: graph_actions.py From auto-alt-text-lambda-api with MIT License | 6 votes |
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()
Example #12
Source File: tpu_estimator.py From Chinese-XLNet with Apache License 2.0 | 6 votes |
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)
Example #13
Source File: basic_lstm.py From tensorflow_end2end_speech_recognition with MIT License | 6 votes |
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
Example #14
Source File: rnn_cell.py From ecm with Apache License 2.0 | 6 votes |
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
Example #15
Source File: graph_actions.py From deep_image_model with Apache License 2.0 | 6 votes |
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()
Example #16
Source File: event_accumulator.py From deep_image_model with Apache License 2.0 | 6 votes |
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
Example #17
Source File: rnn_cell.py From deep_image_model with Apache License 2.0 | 6 votes |
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
Example #18
Source File: variable_scope.py From deep_image_model with Apache License 2.0 | 6 votes |
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
Example #19
Source File: rnn_cell.py From ROLO with Apache License 2.0 | 6 votes |
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
Example #20
Source File: rnn_cell.py From ROLO with Apache License 2.0 | 6 votes |
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
Example #21
Source File: ConvLSTMCell.py From Conv3D_BICLSTM with MIT License | 6 votes |
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
Example #22
Source File: ops.py From lambda-packs with MIT License | 5 votes |
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
Example #23
Source File: ops.py From deep_image_model with Apache License 2.0 | 5 votes |
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
Example #24
Source File: variable_scope.py From lambda-packs with MIT License | 5 votes |
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
Example #25
Source File: rnn_cell.py From deep_image_model with Apache License 2.0 | 5 votes |
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
Example #26
Source File: core_rnn_cell.py From lambda-packs with MIT License | 5 votes |
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
Example #27
Source File: rnn_cell.py From Multiview2Novelview with MIT License | 5 votes |
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
Example #28
Source File: feature_column.py From lambda-packs with MIT License | 5 votes |
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)
Example #29
Source File: rnn_cell.py From ROLO with Apache License 2.0 | 5 votes |
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
Example #30
Source File: rnn_cell.py From deep_image_model with Apache License 2.0 | 5 votes |
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