tf.raw_ops.ExperimentalChooseFastestDataset  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalChooseFastestDataset
tf.raw_ops.ExperimentalChooseFastestDataset(
    input_datasets, num_experiments, output_types, output_shapes, name=None
)

 Args
  input_datasets   A list of at least 2 Tensor objects with type variant.
  num_experiments   An int.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalCSVDataset  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalCSVDataset
tf.raw_ops.ExperimentalCSVDataset(
    filenames, compression_type, buffer_size, header, field_delim, use_quote_delim,
    na_value, select_cols, record_defaults, output_shapes, name=None
)

 Args
  filenames   A Tensor of type string.
  compression_type   A Tensor of type string.
  buffer_size   A Tensor of type int64.
  header   A Tensor of type bool.
  field_delim   A Tensor of type string.
  use_quote_delim   A Tensor of type bool.
  na_value   A Tensor of type string.
  select_cols   A Tensor of type int64.
  record_defaults   A list of Tensor objects with types from: float32, float64, int32, int64, string.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalDatasetCardinality Returns the cardinality of input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalDatasetCardinality
tf.raw_ops.ExperimentalDatasetCardinality(
    input_dataset, name=None
)
 Returns the cardinality of input_dataset.

 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the dataset to return cardinality for.
  name   A name for the operation (optional).

 Returns   A Tensor of type int64.


tf.raw_ops.ExperimentalDatasetToTFRecord Writes the given dataset to the given file using the TFRecord format.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalDatasetToTFRecord
tf.raw_ops.ExperimentalDatasetToTFRecord(
    input_dataset, filename, compression_type, name=None
)

 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the dataset to write.
  filename   A Tensor of type string. A scalar string tensor representing the filename to use.
  compression_type   A Tensor of type string. A scalar string tensor containing either (i) the empty string (no compression), (ii) "ZLIB", or (iii) "GZIP".
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ExperimentalDenseToSparseBatchDataset Creates a dataset that batches input elements into a SparseTensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalDenseToSparseBatchDataset
tf.raw_ops.ExperimentalDenseToSparseBatchDataset(
    input_dataset, batch_size, row_shape, output_types, output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant. A handle to an input dataset. Must have a single component.
  batch_size   A Tensor of type int64. A scalar representing the number of elements to accumulate in a batch.
  row_shape   A Tensor of type int64. A vector representing the dense shape of each row in the produced SparseTensor. The shape may be partially specified, using -1 to indicate that a particular dimension should use the maximum size of all batch elements.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalDirectedInterleaveDataset A substitute for InterleaveDataset on a fixed list of N datasets.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalDirectedInterleaveDataset
tf.raw_ops.ExperimentalDirectedInterleaveDataset(
    selector_input_dataset, data_input_datasets, output_types, output_shapes,
    name=None
)

 Args
  selector_input_dataset   A Tensor of type variant. A dataset of scalar DT_INT64 elements that determines which of the N data inputs should produce the next output element.
  data_input_datasets   A list of at least 1 Tensor objects with type variant. N datasets with the same type that will be interleaved according to the values of selector_input_dataset.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalGroupByReducerDataset Creates a dataset that computes a group-by on input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalGroupByReducerDataset
tf.raw_ops.ExperimentalGroupByReducerDataset(
    input_dataset, key_func_other_arguments, init_func_other_arguments,
    reduce_func_other_arguments, finalize_func_other_arguments, key_func, init_func,
    reduce_func, finalize_func, output_types, output_shapes, name=None
)
 Creates a dataset that computes a group-by on input_dataset.

 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset.
  key_func_other_arguments   A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for key_func.
  init_func_other_arguments   A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for init_func.
  reduce_func_other_arguments   A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for reduce_func.
  finalize_func_other_arguments   A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for finalize_func.
  key_func   A function decorated with @Defun. A function mapping an element of input_dataset, concatenated with key_func_other_arguments to a scalar value of type DT_INT64.
  init_func   A function decorated with @Defun. A function mapping a key of type DT_INT64, concatenated with init_func_other_arguments to the initial reducer state.
  reduce_func   A function decorated with @Defun. A function mapping the current reducer state and an element of input_dataset, concatenated with reduce_func_other_arguments to a new reducer state.
  finalize_func   A function decorated with @Defun. A function mapping the final reducer state to an output element.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalGroupByWindowDataset Creates a dataset that computes a windowed group-by on input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalGroupByWindowDataset
tf.raw_ops.ExperimentalGroupByWindowDataset(
    input_dataset, key_func_other_arguments, reduce_func_other_arguments,
    window_size_func_other_arguments, key_func, reduce_func, window_size_func,
    output_types, output_shapes, name=None
)
 //

 Args
  input_dataset   A Tensor of type variant.
  key_func_other_arguments   A list of Tensor objects.
  reduce_func_other_arguments   A list of Tensor objects.
  window_size_func_other_arguments   A list of Tensor objects.
  key_func   A function decorated with @Defun. A function mapping an element of input_dataset, concatenated with key_func_other_arguments to a scalar value of type DT_INT64.
  reduce_func   A function decorated with @Defun.
  window_size_func   A function decorated with @Defun.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalIgnoreErrorsDataset Creates a dataset that contains the elements of input_dataset ignoring errors.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalIgnoreErrorsDataset
tf.raw_ops.ExperimentalIgnoreErrorsDataset(
    input_dataset, output_types, output_shapes, log_warning=False, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  log_warning   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalIteratorGetDevice Returns the name of the device on which resource has been placed.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalIteratorGetDevice
tf.raw_ops.ExperimentalIteratorGetDevice(
    resource, name=None
)

 Args
  resource   A Tensor of type resource.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.ExperimentalLatencyStatsDataset Records the latency of producing input_dataset elements in a StatsAggregator.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalLatencyStatsDataset
tf.raw_ops.ExperimentalLatencyStatsDataset(
    input_dataset, tag, output_types, output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  tag   A Tensor of type string.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalLMDBDataset  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalLMDBDataset
tf.raw_ops.ExperimentalLMDBDataset(
    filenames, output_types, output_shapes, name=None
)

 Args
  filenames   A Tensor of type string.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalMapAndBatchDataset Creates a dataset that fuses mapping with batching.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalMapAndBatchDataset
tf.raw_ops.ExperimentalMapAndBatchDataset(
    input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder,
    f, output_types, output_shapes, preserve_cardinality=False, name=None
)
 Creates a dataset that applies f to the outputs of input_dataset and then batches batch_size of them. Unlike a "MapDataset", which applies f sequentially, this dataset invokes up to batch_size * num_parallel_batches copies of f in parallel.

 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset.
  other_arguments   A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for f.
  batch_size   A Tensor of type int64. A scalar representing the number of elements to accumulate in a batch. It determines the number of concurrent invocations of f that process elements from input_dataset in parallel.
  num_parallel_calls   A Tensor of type int64. A scalar representing the maximum number of parallel invocations of the map_fn function. Applying the map_fn on consecutive input elements in parallel has the potential to improve input pipeline throughput.
  drop_remainder   A Tensor of type bool. A scalar representing whether the last batch should be dropped in case its size is smaller than desired.
  f   A function decorated with @Defun. A function to apply to the outputs of input_dataset.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  preserve_cardinality   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalMapDataset Creates a dataset that applies f to the outputs of input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalMapDataset
tf.raw_ops.ExperimentalMapDataset(
    input_dataset, other_arguments, f, output_types, output_shapes,
    use_inter_op_parallelism=True, preserve_cardinality=False, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  other_arguments   A list of Tensor objects.
  f   A function decorated with @Defun.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  use_inter_op_parallelism   An optional bool. Defaults to True.
  preserve_cardinality   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalMatchingFilesDataset  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalMatchingFilesDataset
tf.raw_ops.ExperimentalMatchingFilesDataset(
    patterns, name=None
)

 Args
  patterns   A Tensor of type string.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalMaxIntraOpParallelismDataset Creates a dataset that overrides the maximum intra-op parallelism.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalMaxIntraOpParallelismDataset
tf.raw_ops.ExperimentalMaxIntraOpParallelismDataset(
    input_dataset, max_intra_op_parallelism, output_types, output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  max_intra_op_parallelism   A Tensor of type int64. Identifies the maximum intra-op parallelism to use.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalNonSerializableDataset  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalNonSerializableDataset
tf.raw_ops.ExperimentalNonSerializableDataset(
    input_dataset, output_types, output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalParallelInterleaveDataset Creates a dataset that applies f to the outputs of input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalParallelInterleaveDataset
tf.raw_ops.ExperimentalParallelInterleaveDataset(
    input_dataset, other_arguments, cycle_length, block_length, sloppy,
    buffer_output_elements, prefetch_input_elements, f, output_types, output_shapes,
    name=None
)
 The resulting dataset is similar to the InterleaveDataset, with the exception that if retrieving the next value from a dataset would cause the requester to block, it will skip that input dataset. This dataset is especially useful when loading data from a variable-latency datastores (e.g. HDFS, GCS), as it allows the training step to proceed so long as some data is available. !! WARNING !! This dataset is not deterministic!

 Args
  input_dataset   A Tensor of type variant.
  other_arguments   A list of Tensor objects.
  cycle_length   A Tensor of type int64.
  block_length   A Tensor of type int64.
  sloppy   A Tensor of type bool.
  buffer_output_elements   A Tensor of type int64.
  prefetch_input_elements   A Tensor of type int64.
  f   A function decorated with @Defun. A function mapping elements of input_dataset, concatenated with other_arguments, to a Dataset variant that contains elements matching output_types and output_shapes.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalParseExampleDataset Transforms input_dataset containing Example protos as vectors of DT_STRING into a dataset of Tensor or SparseTensor objects representing the parsed features.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalParseExampleDataset
tf.raw_ops.ExperimentalParseExampleDataset(
    input_dataset, num_parallel_calls, dense_defaults, sparse_keys, dense_keys,
    sparse_types, dense_shapes, output_types, output_shapes, sloppy=False, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  num_parallel_calls   A Tensor of type int64.
  dense_defaults   A list of Tensor objects with types from: float32, int64, string. A dict mapping string keys to Tensors. The keys of the dict must match the dense_keys of the feature.
  sparse_keys   A list of strings. A list of string keys in the examples features. The results for these keys will be returned as SparseTensor objects.
  dense_keys   A list of strings. A list of Ndense string Tensors (scalars). The keys expected in the Examples features associated with dense values.
  sparse_types   A list of tf.DTypes from: tf.float32, tf.int64, tf.string. A list of DTypes of the same length as sparse_keys. Only tf.float32 (FloatList), tf.int64 (Int64List), and tf.string (BytesList) are supported.
  dense_shapes   A list of shapes (each a tf.TensorShape or list of ints). List of tuples with the same length as dense_keys. The shape of the data for each dense feature referenced by dense_keys. Required for any input tensors identified by dense_keys. Must be either fully defined, or may contain an unknown first dimension. An unknown first dimension means the feature is treated as having a variable number of blocks, and the output shape along this dimension is considered unknown at graph build time. Padding is applied for minibatch elements smaller than the maximum number of blocks for the given feature along this dimension.
  output_types   A list of tf.DTypes that has length >= 1. The type list for the return values.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1. The list of shapes being produced.
  sloppy   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalPrivateThreadPoolDataset Creates a dataset that uses a custom thread pool to compute input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalPrivateThreadPoolDataset
tf.raw_ops.ExperimentalPrivateThreadPoolDataset(
    input_dataset, num_threads, output_types, output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  num_threads   A Tensor of type int64. Identifies the number of threads to use for the private threadpool.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalRandomDataset Creates a Dataset that returns pseudorandom numbers.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalRandomDataset
tf.raw_ops.ExperimentalRandomDataset(
    seed, seed2, output_types, output_shapes, name=None
)

 Args
  seed   A Tensor of type int64. A scalar seed for the random number generator. If either seed or seed2 is set to be non-zero, the random number generator is seeded by the given seed. Otherwise, a random seed is used.
  seed2   A Tensor of type int64. A second scalar seed to avoid seed collision.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalRebatchDataset Creates a dataset that changes the batch size.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalRebatchDataset
tf.raw_ops.ExperimentalRebatchDataset(
    input_dataset, num_replicas, output_types, output_shapes, use_fallback=True,
    name=None
)
 Creates a dataset that changes the batch size of the dataset to current batch size // num_replicas.

 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset.
  num_replicas   A Tensor of type int64. A scalar representing the number of replicas to distribute this batch across. As a result of this transformation the current batch size would end up being divided by this parameter.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  use_fallback   An optional bool. Defaults to True.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalScanDataset Creates a dataset successively reduces f over the elements of input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalScanDataset
tf.raw_ops.ExperimentalScanDataset(
    input_dataset, initial_state, other_arguments, f, output_types, output_shapes,
    preserve_cardinality=False, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  initial_state   A list of Tensor objects.
  other_arguments   A list of Tensor objects.
  f   A function decorated with @Defun.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  preserve_cardinality   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalSetStatsAggregatorDataset  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalSetStatsAggregatorDataset
tf.raw_ops.ExperimentalSetStatsAggregatorDataset(
    input_dataset, stats_aggregator, tag, counter_prefix, output_types,
    output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  stats_aggregator   A Tensor of type resource.
  tag   A Tensor of type string.
  counter_prefix   A Tensor of type string.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalSleepDataset  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalSleepDataset
tf.raw_ops.ExperimentalSleepDataset(
    input_dataset, sleep_microseconds, output_types, output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  sleep_microseconds   A Tensor of type int64.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalSlidingWindowDataset Creates a dataset that passes a sliding window over input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalSlidingWindowDataset
tf.raw_ops.ExperimentalSlidingWindowDataset(
    input_dataset, window_size, window_shift, window_stride, output_types,
    output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  window_size   A Tensor of type int64. A scalar representing the number of elements in the sliding window.
  window_shift   A Tensor of type int64. A scalar representing the steps moving the sliding window forward in one iteration. It must be positive.
  window_stride   A Tensor of type int64. A scalar representing the stride of the input elements of the sliding window. It must be positive.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalSqlDataset Creates a dataset that executes a SQL query and emits rows of the result set.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalSqlDataset
tf.raw_ops.ExperimentalSqlDataset(
    driver_name, data_source_name, query, output_types, output_shapes, name=None
)

 Args
  driver_name   A Tensor of type string. The database type. Currently, the only supported type is 'sqlite'.
  data_source_name   A Tensor of type string. A connection string to connect to the database.
  query   A Tensor of type string. A SQL query to execute.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalStatsAggregatorHandle Creates a statistics manager resource.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalStatsAggregatorHandle
tf.raw_ops.ExperimentalStatsAggregatorHandle(
    container='', shared_name='', name=None
)

 Args
  container   An optional string. Defaults to "".
  shared_name   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.ExperimentalStatsAggregatorSummary Produces a summary of any statistics recorded by the given statistics manager.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalStatsAggregatorSummary
tf.raw_ops.ExperimentalStatsAggregatorSummary(
    iterator, name=None
)

 Args
  iterator   A Tensor of type resource.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.ExperimentalTakeWhileDataset Creates a dataset that stops iteration when predicate` is false.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalTakeWhileDataset
tf.raw_ops.ExperimentalTakeWhileDataset(
    input_dataset, other_arguments, predicate, output_types, output_shapes,
    name=None
)
 The predicate function must return a scalar boolean and accept the following arguments:  One tensor for each component of an element of input_dataset. One tensor for each value in other_arguments.

 Args
  input_dataset   A Tensor of type variant.
  other_arguments   A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for predicate.
  predicate   A function decorated with @Defun. A function returning a scalar boolean.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalThreadPoolDataset Creates a dataset that uses a custom thread pool to compute input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalThreadPoolDataset
tf.raw_ops.ExperimentalThreadPoolDataset(
    input_dataset, thread_pool, output_types, output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  thread_pool   A Tensor of type resource. A resource produced by the ThreadPoolHandle op.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalThreadPoolHandle Creates a dataset that uses a custom thread pool to compute input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalThreadPoolHandle
tf.raw_ops.ExperimentalThreadPoolHandle(
    num_threads, display_name, max_intra_op_parallelism=1, container='',
    shared_name='', name=None
)

 Args
  num_threads   An int. The number of threads in the thread pool.
  display_name   A string. A human-readable name for the threads that may be visible in some visualizations. threadpool.
  max_intra_op_parallelism   An optional int. Defaults to 1. The maximum degree of parallelism to use within operations that execute on this threadpool.
  container   An optional string. Defaults to "".
  shared_name   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.ExperimentalUnbatchDataset A dataset that splits the elements of its input into multiple elements.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalUnbatchDataset
tf.raw_ops.ExperimentalUnbatchDataset(
    input_dataset, output_types, output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.ExperimentalUniqueDataset Creates a dataset that contains the unique elements of input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalUniqueDataset
tf.raw_ops.ExperimentalUniqueDataset(
    input_dataset, output_types, output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.Expint  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Expint
tf.raw_ops.Expint(
    x, name=None
)

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.Expm1 Computes exp(x) - 1 element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Expm1
tf.raw_ops.Expm1(
    x, name=None
)
 i.e. exp(x) - 1 or e^(x) - 1, where x is the input tensor. e denotes Euler's number and is approximately equal to 2.718281. x = tf.constant(2.0)
tf.math.expm1(x) ==> 6.389056

x = tf.constant([2.0, 8.0])
tf.math.expm1(x) ==> array([6.389056, 2979.958], dtype=float32)

x = tf.constant(1 + 1j)
tf.math.expm1(x) ==> (0.46869393991588515+2.2873552871788423j)

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.ExtractGlimpse Extracts a glimpse from the input tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExtractGlimpse
tf.raw_ops.ExtractGlimpse(
    input, size, offsets, centered=True, normalized=True, uniform_noise=True,
    noise='uniform', name=None
)
 Returns a set of windows called glimpses extracted at location offsets from the input tensor. If the windows only partially overlaps the inputs, the non overlapping areas will be filled with random noise. The result is a 4-D tensor of shape [batch_size, glimpse_height, glimpse_width, channels]. The channels and batch dimensions are the same as that of the input tensor. The height and width of the output windows are specified in the size parameter. The argument normalized and centered controls how the windows are built:  If the coordinates are normalized but not centered, 0.0 and 1.0 correspond to the minimum and maximum of each height and width dimension. If the coordinates are both normalized and centered, they range from -1.0 to 1.0. The coordinates (-1.0, -1.0) correspond to the upper left corner, the lower right corner is located at (1.0, 1.0) and the center is at (0, 0). If the coordinates are not normalized they are interpreted as numbers of pixels.

 Args
  input   A Tensor of type float32. A 4-D float tensor of shape [batch_size, height, width, channels].
  size   A Tensor of type int32. A 1-D tensor of 2 elements containing the size of the glimpses to extract. The glimpse height must be specified first, following by the glimpse width.
  offsets   A Tensor of type float32. A 2-D integer tensor of shape [batch_size, 2] containing the y, x locations of the center of each window.
  centered   An optional bool. Defaults to True. indicates if the offset coordinates are centered relative to the image, in which case the (0, 0) offset is relative to the center of the input images. If false, the (0,0) offset corresponds to the upper left corner of the input images.
  normalized   An optional bool. Defaults to True. indicates if the offset coordinates are normalized.
  uniform_noise   An optional bool. Defaults to True. indicates if the noise should be generated using a uniform distribution or a Gaussian distribution.
  noise   An optional string. Defaults to "uniform". indicates if the noise should uniform, gaussian, or zero. The default is uniform which means the the noise type will be decided by uniform_noise.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.ExtractGlimpseV2 Extracts a glimpse from the input tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExtractGlimpseV2
tf.raw_ops.ExtractGlimpseV2(
    input, size, offsets, centered=True, normalized=True, uniform_noise=True,
    noise='uniform', name=None
)
 Returns a set of windows called glimpses extracted at location offsets from the input tensor. If the windows only partially overlaps the inputs, the non overlapping areas will be filled with random noise. The result is a 4-D tensor of shape [batch_size, glimpse_height, glimpse_width, channels]. The channels and batch dimensions are the same as that of the input tensor. The height and width of the output windows are specified in the size parameter. The argument normalized and centered controls how the windows are built:  If the coordinates are normalized but not centered, 0.0 and 1.0 correspond to the minimum and maximum of each height and width dimension. If the coordinates are both normalized and centered, they range from -1.0 to 1.0. The coordinates (-1.0, -1.0) correspond to the upper left corner, the lower right corner is located at (1.0, 1.0) and the center is at (0, 0). If the coordinates are not normalized they are interpreted as numbers of pixels.

 Args
  input   A Tensor of type float32. A 4-D float tensor of shape [batch_size, height, width, channels].
  size   A Tensor of type int32. A 1-D tensor of 2 elements containing the size of the glimpses to extract. The glimpse height must be specified first, following by the glimpse width.
  offsets   A Tensor of type float32. A 2-D integer tensor of shape [batch_size, 2] containing the y, x locations of the center of each window.
  centered   An optional bool. Defaults to True. indicates if the offset coordinates are centered relative to the image, in which case the (0, 0) offset is relative to the center of the input images. If false, the (0,0) offset corresponds to the upper left corner of the input images.
  normalized   An optional bool. Defaults to True. indicates if the offset coordinates are normalized.
  uniform_noise   An optional bool. Defaults to True. indicates if the noise should be generated using a uniform distribution or a Gaussian distribution.
  noise   An optional string. Defaults to "uniform". indicates if the noise should uniform, gaussian, or zero. The default is uniform which means the the noise type will be decided by uniform_noise.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.ExtractImagePatches Extract patches from images and put them in the "depth" output dimension.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExtractImagePatches
tf.raw_ops.ExtractImagePatches(
    images, ksizes, strides, rates, padding, name=None
)

 Args
  images   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, int16, int32, int64, uint8, uint16, uint32, uint64, complex64, complex128, bool. 4-D Tensor with shape [batch, in_rows, in_cols, depth].
  ksizes   A list of ints that has length >= 4. The size of the sliding window for each dimension of images.
  strides   A list of ints that has length >= 4. How far the centers of two consecutive patches are in the images. Must be: [1, stride_rows, stride_cols, 1].
  rates   A list of ints that has length >= 4. Must be: [1, rate_rows, rate_cols, 1]. This is the input stride, specifying how far two consecutive patch samples are in the input. Equivalent to extracting patches with patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1), followed by subsampling them spatially by a factor of rates. This is equivalent to rate in dilated (a.k.a. Atrous) convolutions.
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as images.


tf.raw_ops.ExtractJpegShape Extract the shape information of a JPEG-encoded image.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExtractJpegShape
tf.raw_ops.ExtractJpegShape(
    contents, output_type=tf.dtypes.int32, name=None
)
 This op only parses the image header, so it is much faster than DecodeJpeg.

 Args
  contents   A Tensor of type string. 0-D. The JPEG-encoded image.
  output_type   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32. (Optional) The output type of the operation (int32 or int64). Defaults to int32.
  name   A name for the operation (optional).

 Returns   A Tensor of type output_type.


tf.raw_ops.ExtractVolumePatches Extract patches from input and put them in the "depth" output dimension. 3D extension of extract_image_patches.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExtractVolumePatches
tf.raw_ops.ExtractVolumePatches(
    input, ksizes, strides, padding, name=None
)

 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. 5-D Tensor with shape [batch, in_planes, in_rows, in_cols, depth].
  ksizes   A list of ints that has length >= 5. The size of the sliding window for each dimension of input.
  strides   A list of ints that has length >= 5. 1-D of length 5. How far the centers of two consecutive patches are in input. Must be: [1, stride_planes, stride_rows, stride_cols, 1].
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use. The size-related attributes are specified as follows: ksizes = [1, ksize_planes, ksize_rows, ksize_cols, 1]
strides = [1, stride_planes, strides_rows, strides_cols, 1]

  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.Fact Output a fact about factorials.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Fact
tf.raw_ops.Fact(
    name=None
)

 Args
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.FakeParam This op is used as a placeholder in If branch functions. It doesn't provide a  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FakeParam
tf.raw_ops.FakeParam(
    dtype, shape, name=None
)
 valid output when run, so must either be removed (e.g. replaced with a function input) or guaranteed not to be used (e.g. if mirroring an intermediate output needed for the gradient computation of the other branch).

 Args
  dtype   A tf.DType. The type of the output.
  shape   A tf.TensorShape or list of ints. The purported shape of the output. This is only used for shape inference; the output will not necessarily have this shape. Can be a partial shape.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.FakeQuantWithMinMaxArgs Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQuantWithMinMaxArgs
tf.raw_ops.FakeQuantWithMinMaxArgs(
    inputs, min=-6, max=6, num_bits=8, narrow_range=False, name=None
)
 Attributes
[min; max] define the clamping range for the inputs data.
inputs values are quantized into the quantization range ( [0; 2^num_bits - 1] when narrow_range is false and [1; 2^num_bits - 1] when it is true) and then de-quantized and output as floats in [min; max] interval.
num_bits is the bitwidth of the quantization; between 2 and 16, inclusive.  Before quantization, min and max values are adjusted with the following logic. It is suggested to have min <= 0 <= max. If 0 is not in the range of values, the behavior can be unexpected:  If 0 < min < max: min_adj = 0 and max_adj = max - min. If min < max < 0: min_adj = min - max and max_adj = 0. If min <= 0 <= max: scale = (max - min) / (2^num_bits - 1), min_adj = scale * round(min / scale) and max_adj = max + min_adj - min.  Quantization is called fake since the output is still in floating point.

 Args
  inputs   A Tensor of type float32.
  min   An optional float. Defaults to -6.
  max   An optional float. Defaults to 6.
  num_bits   An optional int. Defaults to 8.
  narrow_range   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.FakeQuantWithMinMaxArgsGradient Compute gradients for a FakeQuantWithMinMaxArgs operation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQuantWithMinMaxArgsGradient
tf.raw_ops.FakeQuantWithMinMaxArgsGradient(
    gradients, inputs, min=-6, max=6, num_bits=8, narrow_range=False, name=None
)

 Args
  gradients   A Tensor of type float32. Backpropagated gradients above the FakeQuantWithMinMaxArgs operation.
  inputs   A Tensor of type float32. Values passed as inputs to the FakeQuantWithMinMaxArgs operation.
  min   An optional float. Defaults to -6.
  max   An optional float. Defaults to 6.
  num_bits   An optional int. Defaults to 8.
  narrow_range   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.FakeQuantWithMinMaxVars Fake-quantize the 'inputs' tensor of type float via global float scalars  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQuantWithMinMaxVars
tf.raw_ops.FakeQuantWithMinMaxVars(
    inputs, min, max, num_bits=8, narrow_range=False, name=None
)
 Fake-quantize the inputs tensor of type float via global float scalars min and max to outputs tensor of same shape as inputs. Attributes
[min; max] define the clamping range for the inputs data.
inputs values are quantized into the quantization range ( [0; 2^num_bits - 1] when narrow_range is false and [1; 2^num_bits - 1] when it is true) and then de-quantized and output as floats in [min; max] interval.
num_bits is the bitwidth of the quantization; between 2 and 16, inclusive.  Before quantization, min and max values are adjusted with the following logic. It is suggested to have min <= 0 <= max. If 0 is not in the range of values, the behavior can be unexpected:  If 0 < min < max: min_adj = 0 and max_adj = max - min. If min < max < 0: min_adj = min - max and max_adj = 0. If min <= 0 <= max: scale = (max - min) / (2^num_bits - 1), min_adj = scale * round(min / scale) and max_adj = max + min_adj - min.  This operation has a gradient and thus allows for training min and max values.

 Args
  inputs   A Tensor of type float32.
  min   A Tensor of type float32.
  max   A Tensor of type float32.
  num_bits   An optional int. Defaults to 8.
  narrow_range   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.FakeQuantWithMinMaxVarsGradient Compute gradients for a FakeQuantWithMinMaxVars operation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQuantWithMinMaxVarsGradient
tf.raw_ops.FakeQuantWithMinMaxVarsGradient(
    gradients, inputs, min, max, num_bits=8, narrow_range=False, name=None
)

 Args
  gradients   A Tensor of type float32. Backpropagated gradients above the FakeQuantWithMinMaxVars operation.
  inputs   A Tensor of type float32. Values passed as inputs to the FakeQuantWithMinMaxVars operation. min, max: Quantization interval, scalar floats.
  min   A Tensor of type float32.
  max   A Tensor of type float32.
  num_bits   An optional int. Defaults to 8. The bitwidth of the quantization; between 2 and 8, inclusive.
  narrow_range   An optional bool. Defaults to False. Whether to quantize into 2^num_bits - 1 distinct values.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (backprops_wrt_input, backprop_wrt_min, backprop_wrt_max).     backprops_wrt_input   A Tensor of type float32.
  backprop_wrt_min   A Tensor of type float32.
  backprop_wrt_max   A Tensor of type float32.


tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel Fake-quantize the 'inputs' tensor of type float via per-channel floats  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQuantWithMinMaxVarsPerChannel
tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel(
    inputs, min, max, num_bits=8, narrow_range=False, name=None
)
 Fake-quantize the inputs tensor of type float per-channel and one of the shapes: [d], [b, d] [b, h, w, d] via per-channel floats min and max of shape [d] to outputs tensor of same shape as inputs. Attributes
[min; max] define the clamping range for the inputs data.
inputs values are quantized into the quantization range ( [0; 2^num_bits - 1] when narrow_range is false and [1; 2^num_bits - 1] when it is true) and then de-quantized and output as floats in [min; max] interval.
num_bits is the bitwidth of the quantization; between 2 and 16, inclusive.  Before quantization, min and max values are adjusted with the following logic. It is suggested to have min <= 0 <= max. If 0 is not in the range of values, the behavior can be unexpected:  If 0 < min < max: min_adj = 0 and max_adj = max - min. If min < max < 0: min_adj = min - max and max_adj = 0. If min <= 0 <= max: scale = (max - min) / (2^num_bits - 1), min_adj = scale * round(min / scale) and max_adj = max + min_adj - min.  This operation has a gradient and thus allows for training min and max values.

 Args
  inputs   A Tensor of type float32.
  min   A Tensor of type float32.
  max   A Tensor of type float32.
  num_bits   An optional int. Defaults to 8.
  narrow_range   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.FakeQuantWithMinMaxVarsPerChannelGradient Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQuantWithMinMaxVarsPerChannelGradient
tf.raw_ops.FakeQuantWithMinMaxVarsPerChannelGradient(
    gradients, inputs, min, max, num_bits=8, narrow_range=False, name=None
)

 Args
  gradients   A Tensor of type float32. Backpropagated gradients above the FakeQuantWithMinMaxVars operation, shape one of: [d], [b, d], [b, h, w, d].
  inputs   A Tensor of type float32. Values passed as inputs to the FakeQuantWithMinMaxVars operation, shape same as gradients. min, max: Quantization interval, floats of shape [d].
  min   A Tensor of type float32.
  max   A Tensor of type float32.
  num_bits   An optional int. Defaults to 8. The bitwidth of the quantization; between 2 and 16, inclusive.
  narrow_range   An optional bool. Defaults to False. Whether to quantize into 2^num_bits - 1 distinct values.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (backprops_wrt_input, backprop_wrt_min, backprop_wrt_max).     backprops_wrt_input   A Tensor of type float32.
  backprop_wrt_min   A Tensor of type float32.
  backprop_wrt_max   A Tensor of type float32.


tf.raw_ops.FakeQueue Deprecated. Do not use.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQueue
tf.raw_ops.FakeQueue(
    resource, name=None
)

 Args
  resource   A Tensor of type resource.
  name   A name for the operation (optional).

 Returns   A Tensor of type mutable string.


tf.raw_ops.FFT Fast Fourier transform.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FFT
tf.raw_ops.FFT(
    input, name=None
)
 Computes the 1-dimensional discrete Fourier transform over the inner-most dimension of input.

 Args
  input   A Tensor. Must be one of the following types: complex64, complex128. A complex tensor.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.FFT2D 2D fast Fourier transform.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FFT2D
tf.raw_ops.FFT2D(
    input, name=None
)
 Computes the 2-dimensional discrete Fourier transform over the inner-most 2 dimensions of input.

 Args
  input   A Tensor. Must be one of the following types: complex64, complex128. A complex tensor.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.FFT3D 3D fast Fourier transform.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FFT3D
tf.raw_ops.FFT3D(
    input, name=None
)
 Computes the 3-dimensional discrete Fourier transform over the inner-most 3 dimensions of input.

 Args
  input   A Tensor. Must be one of the following types: complex64, complex128. A complex tensor.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.FIFOQueue A queue that produces elements in first-in first-out order.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FIFOQueue
tf.raw_ops.FIFOQueue(
    component_types, shapes=[], capacity=-1, container='',
    shared_name='', name=None
)

 Args
  component_types   A list of tf.DTypes that has length >= 1. The type of each component in a value.
  shapes   An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to []. The shape of each component in a value. The length of this attr must be either 0 or the same as the length of component_types. If the length of this attr is 0, the shapes of queue elements are not constrained, and only one element may be dequeued at a time.
  capacity   An optional int. Defaults to -1. The upper bound on the number of elements in this queue. Negative numbers mean no limit.
  container   An optional string. Defaults to "". If non-empty, this queue is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this queue will be shared under the given name across multiple sessions.
  name   A name for the operation (optional).

 Returns   A Tensor of type mutable string.


tf.raw_ops.FIFOQueueV2 A queue that produces elements in first-in first-out order.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FIFOQueueV2
tf.raw_ops.FIFOQueueV2(
    component_types, shapes=[], capacity=-1, container='',
    shared_name='', name=None
)

 Args
  component_types   A list of tf.DTypes that has length >= 1. The type of each component in a value.
  shapes   An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to []. The shape of each component in a value. The length of this attr must be either 0 or the same as the length of component_types. If the length of this attr is 0, the shapes of queue elements are not constrained, and only one element may be dequeued at a time.
  capacity   An optional int. Defaults to -1. The upper bound on the number of elements in this queue. Negative numbers mean no limit.
  container   An optional string. Defaults to "". If non-empty, this queue is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this queue will be shared under the given name across multiple sessions.
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.Fill Creates a tensor filled with a scalar value.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Fill
tf.raw_ops.Fill(
    dims, value, name=None
)
 This operation creates a tensor of shape dims and fills it with value. For example: # Output tensor has shape [2, 3].
fill([2, 3], 9) ==> [[9, 9, 9]
                     [9, 9, 9]]
 tf.fill differs from tf.constant in a few ways:
tf.fill only supports scalar contents, whereas tf.constant supports Tensor values.
tf.fill creates an Op in the computation graph that constructs the actual Tensor value at runtime. This is in contrast to tf.constant which embeds the entire Tensor into the graph with a Const node. Because tf.fill evaluates at graph runtime, it supports dynamic shapes based on other runtime Tensors, unlike tf.constant.

 Args
  dims   A Tensor. Must be one of the following types: int32, int64. 1-D. Represents the shape of the output tensor.
  value   A Tensor. 0-D (scalar). Value to fill the returned tensor.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as value.
 Numpy Compatibility Equivalent to np.full


tf.raw_ops.FilterByLastComponentDataset Creates a dataset containing elements of first component of input_dataset having true in the last component.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FilterByLastComponentDataset
tf.raw_ops.FilterByLastComponentDataset(
    input_dataset, output_types, output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.FilterDataset Creates a dataset containing elements of input_dataset matching predicate.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FilterDataset
tf.raw_ops.FilterDataset(
    input_dataset, other_arguments, predicate, output_types, output_shapes,
    name=None
)
 The predicate function must return a scalar boolean and accept the following arguments:  One tensor for each component of an element of input_dataset. One tensor for each value in other_arguments.

 Args
  input_dataset   A Tensor of type variant.
  other_arguments   A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for predicate.
  predicate   A function decorated with @Defun. A function returning a scalar boolean.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.Fingerprint Generates fingerprint values.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Fingerprint
tf.raw_ops.Fingerprint(
    data, method, name=None
)
 Generates fingerprint values of data. Fingerprint op considers the first dimension of data as the batch dimension, and output[i] contains the fingerprint value generated from contents in data[i, ...] for all i. Fingerprint op writes fingerprint values as byte arrays. For example, the default method farmhash64 generates a 64-bit fingerprint value at a time. This 8-byte value is written out as an uint8 array of size 8, in little-endian order. For example, suppose that data has data type DT_INT32 and shape (2, 3, 4), and that the fingerprint method is farmhash64. In this case, the output shape is (2, 8), where 2 is the batch dimension size of data, and 8 is the size of each fingerprint value in bytes. output[0, :] is generated from 12 integers in data[0, :, :] and similarly output[1, :] is generated from other 12 integers in data[1, :, :]. Note that this op fingerprints the raw underlying buffer, and it does not fingerprint Tensor's metadata such as data type and/or shape. For example, the fingerprint values are invariant under reshapes and bitcasts as long as the batch dimension remain the same: Fingerprint(data) == Fingerprint(Reshape(data, ...))
Fingerprint(data) == Fingerprint(Bitcast(data, ...))
 For string data, one should expect Fingerprint(data) != Fingerprint(ReduceJoin(data)) in general.

 Args
  data   A Tensor. Must have rank 1 or higher.
  method   A Tensor of type string. Fingerprint method used by this op. Currently available method is farmhash::fingerprint64.
  name   A name for the operation (optional).

 Returns   A Tensor of type uint8.


tf.raw_ops.FixedLengthRecordDataset Creates a dataset that emits the records from one or more binary files.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FixedLengthRecordDataset
tf.raw_ops.FixedLengthRecordDataset(
    filenames, header_bytes, record_bytes, footer_bytes, buffer_size, name=None
)

 Args
  filenames   A Tensor of type string. A scalar or a vector containing the name(s) of the file(s) to be read.
  header_bytes   A Tensor of type int64. A scalar representing the number of bytes to skip at the beginning of a file.
  record_bytes   A Tensor of type int64. A scalar representing the number of bytes in each record.
  footer_bytes   A Tensor of type int64. A scalar representing the number of bytes to skip at the end of a file.
  buffer_size   A Tensor of type int64. A scalar representing the number of bytes to buffer. Must be > 0.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.FixedLengthRecordDatasetV2  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FixedLengthRecordDatasetV2
tf.raw_ops.FixedLengthRecordDatasetV2(
    filenames, header_bytes, record_bytes, footer_bytes, buffer_size,
    compression_type, name=None
)

 Args
  filenames   A Tensor of type string.
  header_bytes   A Tensor of type int64.
  record_bytes   A Tensor of type int64.
  footer_bytes   A Tensor of type int64.
  buffer_size   A Tensor of type int64.
  compression_type   A Tensor of type string.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.FixedLengthRecordReader A Reader that outputs fixed-length records from a file.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FixedLengthRecordReader
tf.raw_ops.FixedLengthRecordReader(
    record_bytes, header_bytes=0, footer_bytes=0, hop_bytes=0,
    container='', shared_name='', name=None
)

 Args
  record_bytes   An int. Number of bytes in the record.
  header_bytes   An optional int. Defaults to 0. Number of bytes in the header, defaults to 0.
  footer_bytes   An optional int. Defaults to 0. Number of bytes in the footer, defaults to 0.
  hop_bytes   An optional int. Defaults to 0. Number of bytes to hop before each read. Default of 0 means using record_bytes.
  container   An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.
  name   A name for the operation (optional).

 Returns   A Tensor of type mutable string.


tf.raw_ops.FixedLengthRecordReaderV2 A Reader that outputs fixed-length records from a file.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FixedLengthRecordReaderV2
tf.raw_ops.FixedLengthRecordReaderV2(
    record_bytes, header_bytes=0, footer_bytes=0, hop_bytes=0,
    container='', shared_name='', encoding='',
    name=None
)

 Args
  record_bytes   An int. Number of bytes in the record.
  header_bytes   An optional int. Defaults to 0. Number of bytes in the header, defaults to 0.
  footer_bytes   An optional int. Defaults to 0. Number of bytes in the footer, defaults to 0.
  hop_bytes   An optional int. Defaults to 0. Number of bytes to hop before each read. Default of 0 means using record_bytes.
  container   An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.
  encoding   An optional string. Defaults to "". The type of encoding for the file. Currently ZLIB and GZIP are supported. Defaults to none.
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.FixedUnigramCandidateSampler Generates labels for candidate sampling with a learned unigram distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FixedUnigramCandidateSampler
tf.raw_ops.FixedUnigramCandidateSampler(
    true_classes, num_true, num_sampled, unique, range_max, vocab_file='',
    distortion=1, num_reserved_ids=0, num_shards=1, shard=0, unigrams=[], seed=0,
    seed2=0, name=None
)
 A unigram sampler could use a fixed unigram distribution read from a file or passed in as an in-memory array instead of building up the distribution from data on the fly. There is also an option to skew the distribution by applying a distortion power to the weights. The vocabulary file should be in CSV-like format, with the last field being the weight associated with the word. For each batch, this op picks a single set of sampled candidate labels. The advantages of sampling candidates per-batch are simplicity and the possibility of efficient dense matrix multiplication. The disadvantage is that the sampled candidates must be chosen independently of the context and of the true labels.

 Args
  true_classes   A Tensor of type int64. A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label.
  num_true   An int that is >= 1. Number of true labels per context.
  num_sampled   An int that is >= 1. Number of candidates to randomly sample.
  unique   A bool. If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities.
  range_max   An int that is >= 1. The sampler will sample integers from the interval [0, range_max).
  vocab_file   An optional string. Defaults to "". Each valid line in this file (which should have a CSV-like format) corresponds to a valid word ID. IDs are in sequential order, starting from num_reserved_ids. The last entry in each line is expected to be a value corresponding to the count or relative probability. Exactly one of vocab_file and unigrams needs to be passed to this op.
  distortion   An optional float. Defaults to 1. The distortion is used to skew the unigram probability distribution. Each weight is first raised to the distortion's power before adding to the internal unigram distribution. As a result, distortion = 1.0 gives regular unigram sampling (as defined by the vocab file), and distortion = 0.0 gives a uniform distribution.
  num_reserved_ids   An optional int. Defaults to 0. Optionally some reserved IDs can be added in the range [0, ..., num_reserved_ids) by the users. One use case is that a special unknown word token is used as ID 0. These IDs will have a sampling probability of 0.
  num_shards   An optional int that is >= 1. Defaults to 1. A sampler can be used to sample from a subset of the original range in order to speed up the whole computation through parallelism. This parameter (together with 'shard') indicates the number of partitions that are being used in the overall computation.
  shard   An optional int that is >= 0. Defaults to 0. A sampler can be used to sample from a subset of the original range in order to speed up the whole computation through parallelism. This parameter (together with 'num_shards') indicates the particular partition number of a sampler op, when partitioning is being used.
  unigrams   An optional list of floats. Defaults to []. A list of unigram counts or probabilities, one per ID in sequential order. Exactly one of vocab_file and unigrams should be passed to this op.
  seed   An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
  seed2   An optional int. Defaults to 0. An second seed to avoid seed collision.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (sampled_candidates, true_expected_count, sampled_expected_count).     sampled_candidates   A Tensor of type int64.
  true_expected_count   A Tensor of type float32.
  sampled_expected_count   A Tensor of type float32.


tf.raw_ops.FlatMapDataset Creates a dataset that applies f to the outputs of input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FlatMapDataset
tf.raw_ops.FlatMapDataset(
    input_dataset, other_arguments, f, output_types, output_shapes, name=None
)
 Unlike MapDataset, the f in FlatMapDataset is expected to return a Dataset variant, and FlatMapDataset will flatten successive results into a single Dataset.

 Args
  input_dataset   A Tensor of type variant.
  other_arguments   A list of Tensor objects.
  f   A function decorated with @Defun. A function mapping elements of input_dataset, concatenated with other_arguments, to a Dataset variant that contains elements matching output_types and output_shapes.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.Floor Returns element-wise largest integer not greater than x.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Floor
tf.raw_ops.Floor(
    x, name=None
)

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.FloorDiv Returns x // y element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FloorDiv
tf.raw_ops.FloorDiv(
    x, y, name=None
)

Note: floor_div supports broadcasting. More about broadcasting here

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128.
  y   A Tensor. Must have the same type as x.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.FloorMod Returns element-wise remainder of division. When x < 0 xor y < 0 is  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FloorMod
tf.raw_ops.FloorMod(
    x, y, name=None
)
 true, this follows Python semantics in that the result here is consistent with a flooring divide. E.g. floor(x / y) * y + mod(x, y) = x.
Note: math.floormod supports broadcasting. More about broadcasting here

 Args
  x   A Tensor. Must be one of the following types: int32, int64, uint64, bfloat16, half, float32, float64.
  y   A Tensor. Must have the same type as x.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.FlushSummaryWriter  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FlushSummaryWriter
tf.raw_ops.FlushSummaryWriter(
    writer, name=None
)

 Args
  writer   A Tensor of type resource.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.For

View aliases

Compat aliases for migration

See
Migration guide for
more details.

`tf.compat.v1.raw_ops.For`

tf.raw_ops.For(
    start, limit, delta, input, body, name=None
)

 output = input;
 for i in range(start, limit, delta)
   output = body(i, output);

 Args
  start   A Tensor of type int32. The lower bound. An int32
  limit   A Tensor of type int32. The upper bound. An int32
  delta   A Tensor of type int32. The increment. An int32
  input   A list of Tensor objects. A list of input tensors whose types are T.
  body   A function decorated with @Defun. A function that takes a list of tensors (int32, T) and returns another list of tensors (T).
  name   A name for the operation (optional).

 Returns   A list of Tensor objects. Has the same type as input.


tf.raw_ops.FractionalAvgPool Performs fractional average pooling on the input.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FractionalAvgPool
tf.raw_ops.FractionalAvgPool(
    value, pooling_ratio, pseudo_random=False, overlapping=False,
    deterministic=False, seed=0, seed2=0, name=None
)
 Fractional average pooling is similar to Fractional max pooling in the pooling region generation step. The only difference is that after pooling regions are generated, a mean operation is performed instead of a max operation in each pooling region.

 Args
  value   A Tensor. Must be one of the following types: float32, float64, int32, int64. 4-D with shape [batch, height, width, channels].
  pooling_ratio   A list of floats that has length >= 4. Pooling ratio for each dimension of value, currently only supports row and col dimension and should be >= 1.0. For example, a valid pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't allow pooling on batch and channels dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions respectively.
  pseudo_random   An optional bool. Defaults to False. When set to True, generates the pooling sequence in a pseudorandom fashion, otherwise, in a random fashion. Check paper Benjamin Graham, Fractional Max-Pooling for difference between pseudorandom and random.
  overlapping   An optional bool. Defaults to False. When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: index 0 1 2 3 4 value 20 5 16 3 7 If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [41/3, 26/3] for fractional avg pooling.

  deterministic   An optional bool. Defaults to False. When set to True, a fixed pooling region will be used when iterating over a FractionalAvgPool node in the computation graph. Mainly used in unit test to make FractionalAvgPool deterministic.
  seed   An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
  seed2   An optional int. Defaults to 0. An second seed to avoid seed collision.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (output, row_pooling_sequence, col_pooling_sequence).     output   A Tensor. Has the same type as value.
  row_pooling_sequence   A Tensor of type int64.
  col_pooling_sequence   A Tensor of type int64.


tf.raw_ops.FractionalAvgPoolGrad Computes gradient of the FractionalAvgPool function.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FractionalAvgPoolGrad
tf.raw_ops.FractionalAvgPoolGrad(
    orig_input_tensor_shape, out_backprop, row_pooling_sequence,
    col_pooling_sequence, overlapping=False, name=None
)
 Unlike FractionalMaxPoolGrad, we don't need to find arg_max for FractionalAvgPoolGrad, we just need to evenly back-propagate each element of out_backprop to those indices that form the same pooling cell. Therefore, we just need to know the shape of original input tensor, instead of the whole tensor.

 Args
  orig_input_tensor_shape   A Tensor of type int64. Original input tensor shape for fractional_avg_pool
  out_backprop   A Tensor. Must be one of the following types: float32, float64, int32, int64. 4-D with shape [batch, height, width, channels]. Gradients w.r.t. the output of fractional_avg_pool.
  row_pooling_sequence   A Tensor of type int64. row pooling sequence, form pooling region with col_pooling_sequence.
  col_pooling_sequence   A Tensor of type int64. column pooling sequence, form pooling region with row_pooling sequence.
  overlapping   An optional bool. Defaults to False. When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: index 0 1 2 3 4 value 20 5 16 3 7 If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [41/3, 26/3] for fractional avg pooling.

  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as out_backprop.


tf.raw_ops.FractionalMaxPool Performs fractional max pooling on the input.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FractionalMaxPool
tf.raw_ops.FractionalMaxPool(
    value, pooling_ratio, pseudo_random=False, overlapping=False,
    deterministic=False, seed=0, seed2=0, name=None
)
 Fractional max pooling is slightly different than regular max pooling. In regular max pooling, you downsize an input set by taking the maximum value of smaller N x N subsections of the set (often 2x2), and try to reduce the set by a factor of N, where N is an integer. Fractional max pooling, as you might expect from the word "fractional", means that the overall reduction ratio N does not have to be an integer. The sizes of the pooling regions are generated randomly but are fairly uniform. For example, let's look at the height dimension, and the constraints on the list of rows that will be pool boundaries. First we define the following:  input_row_length : the number of rows from the input set output_row_length : which will be smaller than the input alpha = input_row_length / output_row_length : our reduction ratio K = floor(alpha) row_pooling_sequence : this is the result list of pool boundary rows  Then, row_pooling_sequence should satisfy:  a[0] = 0 : the first value of the sequence is 0 a[end] = input_row_length : the last value of the sequence is the size K <= (a[i+1] - a[i]) <= K+1 : all intervals are K or K+1 size length(row_pooling_sequence) = output_row_length+1  For more details on fractional max pooling, see this paper: Benjamin Graham, Fractional Max-Pooling

 Args
  value   A Tensor. Must be one of the following types: float32, float64, int32, int64. 4-D with shape [batch, height, width, channels].
  pooling_ratio   A list of floats that has length >= 4. Pooling ratio for each dimension of value, currently only supports row and col dimension and should be >= 1.0. For example, a valid pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't allow pooling on batch and channels dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions respectively.
  pseudo_random   An optional bool. Defaults to False. When set to True, generates the pooling sequence in a pseudorandom fashion, otherwise, in a random fashion. Check paper Benjamin Graham, Fractional Max-Pooling for difference between pseudorandom and random.
  overlapping   An optional bool. Defaults to False. When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: index 0 1 2 3 4 value 20 5 16 3 7 If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [20, 16] for fractional max pooling.

  deterministic   An optional bool. Defaults to False. When set to True, a fixed pooling region will be used when iterating over a FractionalMaxPool node in the computation graph. Mainly used in unit test to make FractionalMaxPool deterministic.
  seed   An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
  seed2   An optional int. Defaults to 0. An second seed to avoid seed collision.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (output, row_pooling_sequence, col_pooling_sequence).     output   A Tensor. Has the same type as value.
  row_pooling_sequence   A Tensor of type int64.
  col_pooling_sequence   A Tensor of type int64.


tf.raw_ops.FractionalMaxPoolGrad Computes gradient of the FractionalMaxPool function.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FractionalMaxPoolGrad
tf.raw_ops.FractionalMaxPoolGrad(
    orig_input, orig_output, out_backprop, row_pooling_sequence,
    col_pooling_sequence, overlapping=False, name=None
)

 Args
  orig_input   A Tensor. Must be one of the following types: float32, float64, int32, int64. Original input for fractional_max_pool
  orig_output   A Tensor. Must have the same type as orig_input. Original output for fractional_max_pool
  out_backprop   A Tensor. Must have the same type as orig_input. 4-D with shape [batch, height, width, channels]. Gradients w.r.t. the output of fractional_max_pool.
  row_pooling_sequence   A Tensor of type int64. row pooling sequence, form pooling region with col_pooling_sequence.
  col_pooling_sequence   A Tensor of type int64. column pooling sequence, form pooling region with row_pooling sequence.
  overlapping   An optional bool. Defaults to False. When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: index 0 1 2 3 4 value 20 5 16 3 7 If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [20, 16] for fractional max pooling.

  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as orig_input.


tf.raw_ops.FresnelCos  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FresnelCos
tf.raw_ops.FresnelCos(
    x, name=None
)

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.FresnelSin  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FresnelSin
tf.raw_ops.FresnelSin(
    x, name=None
)

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.FusedBatchNorm Batch normalization.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FusedBatchNorm
tf.raw_ops.FusedBatchNorm(
    x, scale, offset, mean, variance, epsilon=0.0001, exponential_avg_factor=1,
    data_format='NHWC', is_training=True, name=None
)
 Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.

 Args
  x   A Tensor. Must be one of the following types: float32. A 4D Tensor for input data.
  scale   A Tensor. Must have the same type as x. A 1D Tensor for scaling factor, to scale the normalized x.
  offset   A Tensor. Must have the same type as x. A 1D Tensor for offset, to shift to the normalized x.
  mean   A Tensor. Must have the same type as x. A 1D Tensor for population mean. Used for inference only; must be empty for training.
  variance   A Tensor. Must have the same type as x. A 1D Tensor for population variance. Used for inference only; must be empty for training.
  epsilon   An optional float. Defaults to 0.0001. A small float number added to the variance of x.
  exponential_avg_factor   An optional float. Defaults to 1.
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". The data format for x and y. Either "NHWC" (default) or "NCHW".
  is_training   An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (y, batch_mean, batch_variance, reserve_space_1, reserve_space_2).     y   A Tensor. Has the same type as x.
  batch_mean   A Tensor. Has the same type as x.
  batch_variance   A Tensor. Has the same type as x.
  reserve_space_1   A Tensor. Has the same type as x.
  reserve_space_2   A Tensor. Has the same type as x.


tf.raw_ops.FusedBatchNormGrad Gradient for batch normalization.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FusedBatchNormGrad
tf.raw_ops.FusedBatchNormGrad(
    y_backprop, x, scale, reserve_space_1, reserve_space_2, epsilon=0.0001,
    data_format='NHWC', is_training=True, name=None
)
 Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.

 Args
  y_backprop   A Tensor. Must be one of the following types: float32. A 4D Tensor for the gradient with respect to y.
  x   A Tensor. Must have the same type as y_backprop. A 4D Tensor for input data.
  scale   A Tensor. Must have the same type as y_backprop. A 1D Tensor for scaling factor, to scale the normalized x.
  reserve_space_1   A Tensor. Must have the same type as y_backprop. When is_training is True, a 1D Tensor for the computed batch mean to be reused in gradient computation. When is_training is False, a 1D Tensor for the population mean to be reused in both 1st and 2nd order gradient computation.
  reserve_space_2   A Tensor. Must have the same type as y_backprop. When is_training is True, a 1D Tensor for the computed batch variance (inverted variance in the cuDNN case) to be reused in gradient computation. When is_training is False, a 1D Tensor for the population variance to be reused in both 1st and 2nd order gradient computation.
  epsilon   An optional float. Defaults to 0.0001. A small float number added to the variance of x.
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". The data format for y_backprop, x, x_backprop. Either "NHWC" (default) or "NCHW".
  is_training   An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (x_backprop, scale_backprop, offset_backprop, reserve_space_3, reserve_space_4).     x_backprop   A Tensor. Has the same type as y_backprop.
  scale_backprop   A Tensor. Has the same type as y_backprop.
  offset_backprop   A Tensor. Has the same type as y_backprop.
  reserve_space_3   A Tensor. Has the same type as y_backprop.
  reserve_space_4   A Tensor. Has the same type as y_backprop.


tf.raw_ops.FusedBatchNormGradV2 Gradient for batch normalization.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FusedBatchNormGradV2
tf.raw_ops.FusedBatchNormGradV2(
    y_backprop, x, scale, reserve_space_1, reserve_space_2, epsilon=0.0001,
    data_format='NHWC', is_training=True, name=None
)
 Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.

 Args
  y_backprop   A Tensor. Must be one of the following types: half, bfloat16, float32. A 4D Tensor for the gradient with respect to y.
  x   A Tensor. Must have the same type as y_backprop. A 4D Tensor for input data.
  scale   A Tensor of type float32. A 1D Tensor for scaling factor, to scale the normalized x.
  reserve_space_1   A Tensor. Must be one of the following types: float32. When is_training is True, a 1D Tensor for the computed batch mean to be reused in gradient computation. When is_training is False, a 1D Tensor for the population mean to be reused in both 1st and 2nd order gradient computation.
  reserve_space_2   A Tensor. Must have the same type as reserve_space_1. When is_training is True, a 1D Tensor for the computed batch variance (inverted variance in the cuDNN case) to be reused in gradient computation. When is_training is False, a 1D Tensor for the population variance to be reused in both 1st and 2nd order gradient computation.
  epsilon   An optional float. Defaults to 0.0001. A small float number added to the variance of x.
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". The data format for y_backprop, x, x_backprop. Either "NHWC" (default) or "NCHW".
  is_training   An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (x_backprop, scale_backprop, offset_backprop, reserve_space_3, reserve_space_4).     x_backprop   A Tensor. Has the same type as y_backprop.
  scale_backprop   A Tensor. Has the same type as reserve_space_1.
  offset_backprop   A Tensor. Has the same type as reserve_space_1.
  reserve_space_3   A Tensor. Has the same type as reserve_space_1.
  reserve_space_4   A Tensor. Has the same type as reserve_space_1.


tf.raw_ops.FusedBatchNormGradV3 Gradient for batch normalization.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FusedBatchNormGradV3
tf.raw_ops.FusedBatchNormGradV3(
    y_backprop, x, scale, reserve_space_1, reserve_space_2, reserve_space_3,
    epsilon=0.0001, data_format='NHWC', is_training=True, name=None
)
 Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.

 Args
  y_backprop   A Tensor. Must be one of the following types: half, bfloat16, float32. A 4D Tensor for the gradient with respect to y.
  x   A Tensor. Must have the same type as y_backprop. A 4D Tensor for input data.
  scale   A Tensor of type float32. A 1D Tensor for scaling factor, to scale the normalized x.
  reserve_space_1   A Tensor. Must be one of the following types: float32. When is_training is True, a 1D Tensor for the computed batch mean to be reused in gradient computation. When is_training is False, a 1D Tensor for the population mean to be reused in both 1st and 2nd order gradient computation.
  reserve_space_2   A Tensor. Must have the same type as reserve_space_1. When is_training is True, a 1D Tensor for the computed batch variance (inverted variance in the cuDNN case) to be reused in gradient computation. When is_training is False, a 1D Tensor for the population variance to be reused in both 1st and 2nd order gradient computation.
  reserve_space_3   A Tensor. Must have the same type as reserve_space_1. When is_training is True, a 1D Tensor for some intermediate results to be reused in gradient computation. When is_training is False, a dummy empty Tensor will be created.
  epsilon   An optional float. Defaults to 0.0001. A small float number added to the variance of x.
  data_format   An optional string from: "NHWC", "NCHW", "NDHWC", "NCDHW". Defaults to "NHWC". The data format for y_backprop, x, x_backprop. Either "NHWC" (default) or "NCHW".
  is_training   An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (x_backprop, scale_backprop, offset_backprop, reserve_space_4, reserve_space_5).     x_backprop   A Tensor. Has the same type as y_backprop.
  scale_backprop   A Tensor. Has the same type as reserve_space_1.
  offset_backprop   A Tensor. Has the same type as reserve_space_1.
  reserve_space_4   A Tensor. Has the same type as reserve_space_1.
  reserve_space_5   A Tensor. Has the same type as reserve_space_1.


tf.raw_ops.FusedBatchNormV2 Batch normalization.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FusedBatchNormV2
tf.raw_ops.FusedBatchNormV2(
    x, scale, offset, mean, variance, epsilon=0.0001, exponential_avg_factor=1,
    data_format='NHWC', is_training=True, name=None
)
 Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.

 Args
  x   A Tensor. Must be one of the following types: half, bfloat16, float32. A 4D Tensor for input data.
  scale   A Tensor. Must be one of the following types: float32. A 1D Tensor for scaling factor, to scale the normalized x.
  offset   A Tensor. Must have the same type as scale. A 1D Tensor for offset, to shift to the normalized x.
  mean   A Tensor. Must have the same type as scale. A 1D Tensor for population mean. Used for inference only; must be empty for training.
  variance   A Tensor. Must have the same type as scale. A 1D Tensor for population variance. Used for inference only; must be empty for training.
  epsilon   An optional float. Defaults to 0.0001. A small float number added to the variance of x.
  exponential_avg_factor   An optional float. Defaults to 1.
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". The data format for x and y. Either "NHWC" (default) or "NCHW".
  is_training   An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (y, batch_mean, batch_variance, reserve_space_1, reserve_space_2).     y   A Tensor. Has the same type as x.
  batch_mean   A Tensor. Has the same type as scale.
  batch_variance   A Tensor. Has the same type as scale.
  reserve_space_1   A Tensor. Has the same type as scale.
  reserve_space_2   A Tensor. Has the same type as scale.


tf.raw_ops.FusedBatchNormV3 Batch normalization.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FusedBatchNormV3
tf.raw_ops.FusedBatchNormV3(
    x, scale, offset, mean, variance, epsilon=0.0001, exponential_avg_factor=1,
    data_format='NHWC', is_training=True, name=None
)
 Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.

 Args
  x   A Tensor. Must be one of the following types: half, bfloat16, float32. A 4D Tensor for input data.
  scale   A Tensor. Must be one of the following types: float32. A 1D Tensor for scaling factor, to scale the normalized x.
  offset   A Tensor. Must have the same type as scale. A 1D Tensor for offset, to shift to the normalized x.
  mean   A Tensor. Must have the same type as scale. A 1D Tensor for population mean. Used for inference only; must be empty for training.
  variance   A Tensor. Must have the same type as scale. A 1D Tensor for population variance. Used for inference only; must be empty for training.
  epsilon   An optional float. Defaults to 0.0001. A small float number added to the variance of x.
  exponential_avg_factor   An optional float. Defaults to 1.
  data_format   An optional string from: "NHWC", "NCHW", "NDHWC", "NCDHW". Defaults to "NHWC". The data format for x and y. Either "NHWC" (default) or "NCHW".
  is_training   An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (y, batch_mean, batch_variance, reserve_space_1, reserve_space_2, reserve_space_3).     y   A Tensor. Has the same type as x.
  batch_mean   A Tensor. Has the same type as scale.
  batch_variance   A Tensor. Has the same type as scale.
  reserve_space_1   A Tensor. Has the same type as scale.
  reserve_space_2   A Tensor. Has the same type as scale.
  reserve_space_3   A Tensor. Has the same type as scale.


tf.raw_ops.FusedPadConv2D Performs a padding as a preprocess during a convolution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FusedPadConv2D
tf.raw_ops.FusedPadConv2D(
    input, paddings, filter, mode, strides, padding, name=None
)
 Similar to FusedResizeAndPadConv2d, this op allows for an optimized implementation where the spatial padding transformation stage is fused with the im2col lookup, but in this case without the bilinear filtering required for resizing. Fusing the padding prevents the need to write out the intermediate results as whole tensors, reducing memory pressure, and we can get some latency gains by merging the transformation calculations. The data_format attribute for Conv2D isn't supported by this op, and 'NHWC' order is used instead. Internally this op uses a single per-graph scratch buffer, which means that it will block if multiple versions are being run in parallel. This is because this operator is primarily an optimization to minimize memory usage.

 Args
  input   A Tensor. Must be one of the following types: half, float32, float64. 4-D with shape [batch, in_height, in_width, in_channels].
  paddings   A Tensor of type int32. A two-column matrix specifying the padding sizes. The number of rows must be the same as the rank of input.
  filter   A Tensor. Must have the same type as input. 4-D with shape [filter_height, filter_width, in_channels, out_channels].
  mode   A string from: "REFLECT", "SYMMETRIC".
  strides   A list of ints. 1-D of length 4. The stride of the sliding window for each dimension of input. Must be in the same order as the dimension specified with format.
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.FusedResizeAndPadConv2D Performs a resize and padding as a preprocess during a convolution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FusedResizeAndPadConv2D
tf.raw_ops.FusedResizeAndPadConv2D(
    input, size, paddings, filter, mode, strides, padding,
    resize_align_corners=False, name=None
)
 It's often possible to do spatial transformations more efficiently as part of the packing stage of a convolution, so this op allows for an optimized implementation where these stages are fused together. This prevents the need to write out the intermediate results as whole tensors, reducing memory pressure, and we can get some latency gains by merging the transformation calculations. The data_format attribute for Conv2D isn't supported by this op, and defaults to 'NHWC' order. Internally this op uses a single per-graph scratch buffer, which means that it will block if multiple versions are being run in parallel. This is because this operator is primarily an optimization to minimize memory usage.

 Args
  input   A Tensor. Must be one of the following types: half, float32, float64. 4-D with shape [batch, in_height, in_width, in_channels].
  size   A Tensor of type int32. A 1-D int32 Tensor of 2 elements: new_height, new_width. The new size for the images.
  paddings   A Tensor of type int32. A two-column matrix specifying the padding sizes. The number of rows must be the same as the rank of input.
  filter   A Tensor. Must have the same type as input. 4-D with shape [filter_height, filter_width, in_channels, out_channels].
  mode   A string from: "REFLECT", "SYMMETRIC".
  strides   A list of ints. 1-D of length 4. The stride of the sliding window for each dimension of input. Must be in the same order as the dimension specified with format.
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.
  resize_align_corners   An optional bool. Defaults to False. If true, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. Defaults to false.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.