tf.raw_ops.ReaderReadUpTo Returns up to num_records (key, value) pairs produced by a Reader.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ReaderReadUpTo
tf.raw_ops.ReaderReadUpTo(
    reader_handle, queue_handle, num_records, name=None
)
 Will dequeue from the input queue if necessary (e.g. when the Reader needs to start reading from a new file since it has finished with the previous file). It may return less than num_records even before the last batch.

 Args
  reader_handle   A Tensor of type mutable string. Handle to a Reader.
  queue_handle   A Tensor of type mutable string. Handle to a Queue, with string work items.
  num_records   A Tensor of type int64. number of records to read from Reader.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (keys, values).     keys   A Tensor of type string.
  values   A Tensor of type string.


tf.raw_ops.ReaderReadUpToV2 Returns up to num_records (key, value) pairs produced by a Reader.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ReaderReadUpToV2
tf.raw_ops.ReaderReadUpToV2(
    reader_handle, queue_handle, num_records, name=None
)
 Will dequeue from the input queue if necessary (e.g. when the Reader needs to start reading from a new file since it has finished with the previous file). It may return less than num_records even before the last batch.

 Args
  reader_handle   A Tensor of type resource. Handle to a Reader.
  queue_handle   A Tensor of type resource. Handle to a Queue, with string work items.
  num_records   A Tensor of type int64. number of records to read from Reader.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (keys, values).     keys   A Tensor of type string.
  values   A Tensor of type string.


tf.raw_ops.ReaderReadV2 Returns the next record (key, value pair) produced by a Reader.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ReaderReadV2
tf.raw_ops.ReaderReadV2(
    reader_handle, queue_handle, name=None
)
 Will dequeue from the input queue if necessary (e.g. when the Reader needs to start reading from a new file since it has finished with the previous file).

 Args
  reader_handle   A Tensor of type resource. Handle to a Reader.
  queue_handle   A Tensor of type resource. Handle to a Queue, with string work items.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (key, value).     key   A Tensor of type string.
  value   A Tensor of type string.


tf.raw_ops.ReaderReset Restore a Reader to its initial clean state.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ReaderReset
tf.raw_ops.ReaderReset(
    reader_handle, name=None
)

 Args
  reader_handle   A Tensor of type mutable string. Handle to a Reader.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ReaderResetV2 Restore a Reader to its initial clean state.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ReaderResetV2
tf.raw_ops.ReaderResetV2(
    reader_handle, name=None
)

 Args
  reader_handle   A Tensor of type resource. Handle to a Reader.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ReaderRestoreState Restore a reader to a previously saved state.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ReaderRestoreState
tf.raw_ops.ReaderRestoreState(
    reader_handle, state, name=None
)
 Not all Readers support being restored, so this can produce an Unimplemented error.

 Args
  reader_handle   A Tensor of type mutable string. Handle to a Reader.
  state   A Tensor of type string. Result of a ReaderSerializeState of a Reader with type matching reader_handle.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ReaderRestoreStateV2 Restore a reader to a previously saved state.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ReaderRestoreStateV2
tf.raw_ops.ReaderRestoreStateV2(
    reader_handle, state, name=None
)
 Not all Readers support being restored, so this can produce an Unimplemented error.

 Args
  reader_handle   A Tensor of type resource. Handle to a Reader.
  state   A Tensor of type string. Result of a ReaderSerializeState of a Reader with type matching reader_handle.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ReaderSerializeState Produce a string tensor that encodes the state of a Reader.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ReaderSerializeState
tf.raw_ops.ReaderSerializeState(
    reader_handle, name=None
)
 Not all Readers support being serialized, so this can produce an Unimplemented error.

 Args
  reader_handle   A Tensor of type mutable string. Handle to a Reader.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.ReaderSerializeStateV2 Produce a string tensor that encodes the state of a Reader.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ReaderSerializeStateV2
tf.raw_ops.ReaderSerializeStateV2(
    reader_handle, name=None
)
 Not all Readers support being serialized, so this can produce an Unimplemented error.

 Args
  reader_handle   A Tensor of type resource. Handle to a Reader.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.ReadFile Reads and outputs the entire contents of the input filename.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ReadFile
tf.raw_ops.ReadFile(
    filename, name=None
)

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

 Returns   A Tensor of type string.


tf.raw_ops.ReadVariableOp Reads the value of a variable.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ReadVariableOp
tf.raw_ops.ReadVariableOp(
    resource, dtype, name=None
)
 The tensor returned by this operation is immutable. The value returned by this operation is guaranteed to be influenced by all the writes on which this operation depends directly or indirectly, and to not be influenced by any of the writes which depend directly or indirectly on this operation.

 Args
  resource   A Tensor of type resource. handle to the resource in which to store the variable.
  dtype   A tf.DType. the dtype of the value.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.Real   Returns the real part of a complex number.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Real
tf.raw_ops.Real(
    input, Tout=tf.dtypes.float32, name=None
)
 Given a tensor input of complex numbers, this operation returns a tensor of type float that is the real part of each element in input. All elements in input must be complex numbers of the form \(a + bj\), where a is the real part returned by this operation and b is the imaginary part. For example: # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
tf.real(input) ==> [-2.25, 3.25]

 Args
  input   A Tensor. Must be one of the following types: complex64, complex128.
  Tout   An optional tf.DType from: tf.float32, tf.float64. Defaults to tf.float32.
  name   A name for the operation (optional).

 Returns   A Tensor of type Tout.


tf.raw_ops.RealDiv Returns x / y element-wise for real types.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.RealDiv
tf.raw_ops.RealDiv(
    x, y, name=None
)
 If x and y are reals, this will return the floating-point division.
Note: 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.RebatchDataset 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.RebatchDataset
tf.raw_ops.RebatchDataset(
    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_workers.

 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.RebatchDatasetV2 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.RebatchDatasetV2
tf.raw_ops.RebatchDatasetV2(
    input_dataset, batch_sizes, drop_remainder, output_types, output_shapes,
    name=None
)
 Creates a dataset that rebatches elements from input_dataset into new batch sizes.

 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset.
  batch_sizes   A Tensor of type int64. A vector of integers representing the size of batches to produce. These values are cycled through in order.
  drop_remainder   A Tensor of type bool.
  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.Reciprocal   Computes the reciprocal of x element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Reciprocal
tf.raw_ops.Reciprocal(
    x, name=None
)
 I.e., \(y = 1 / x\).

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

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.ReciprocalGrad Computes the gradient for the inverse of x wrt its input.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ReciprocalGrad
tf.raw_ops.ReciprocalGrad(
    y, dy, name=None
)
 Specifically, grad = -dy * y*y, where y = 1/x, and dy is the corresponding input gradient.

 Args
  y   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.
  dy   A Tensor. Must have the same type as y.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as y.


tf.raw_ops.RecordInput Emits randomized records.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.RecordInput
tf.raw_ops.RecordInput(
    file_pattern, file_random_seed=301, file_shuffle_shift_ratio=0,
    file_buffer_size=10000, file_parallelism=16, batch_size=32,
    compression_type='', name=None
)

 Args
  file_pattern   A string. Glob pattern for the data files.
  file_random_seed   An optional int. Defaults to 301. Random seeds used to produce randomized records.
  file_shuffle_shift_ratio   An optional float. Defaults to 0. Shifts the list of files after the list is randomly shuffled.
  file_buffer_size   An optional int. Defaults to 10000. The randomization shuffling buffer.
  file_parallelism   An optional int. Defaults to 16. How many sstables are opened and concurrently iterated over.
  batch_size   An optional int. Defaults to 32. The batch size.
  compression_type   An optional string. Defaults to "". The type of compression for the file. Currently ZLIB and GZIP are supported. Defaults to none.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.Recv Receives the named tensor from send_device on recv_device.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Recv
tf.raw_ops.Recv(
    tensor_type, tensor_name, send_device, send_device_incarnation, recv_device,
    client_terminated=False, name=None
)

 Args
  tensor_type   A tf.DType.
  tensor_name   A string. The name of the tensor to receive.
  send_device   A string. The name of the device sending the tensor.
  send_device_incarnation   An int. The current incarnation of send_device.
  recv_device   A string. The name of the device receiving the tensor.
  client_terminated   An optional bool. Defaults to False. If set to true, this indicates that the node was added to the graph as a result of a client-side feed or fetch of Tensor data, in which case the corresponding send or recv is expected to be managed locally by the caller.
  name   A name for the operation (optional).

 Returns   A Tensor of type tensor_type.


tf.raw_ops.RecvTPUEmbeddingActivations An op that receives embedding activations on the TPU.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.RecvTPUEmbeddingActivations
tf.raw_ops.RecvTPUEmbeddingActivations(
    num_outputs, config, name=None
)
 The TPU system performs the embedding lookups and aggregations specified by the arguments to TPUEmbeddingEnqueue(Integer/Sparse/SparseTensor)Batch. The results of these aggregations are visible to the Tensorflow Graph as the outputs of a RecvTPUEmbeddingActivations op. This op returns a list containing one Tensor of activations per table specified in the model. There can be at most one RecvTPUEmbeddingActivations op in the TPU graph.

 Args
  num_outputs   An int that is >= 1. The number of output activation tensors, equal to the number of embedding tables in the model.
  config   A string. Serialized TPUEmbeddingConfiguration proto.
  name   A name for the operation (optional).

 Returns   A list of num_outputs Tensor objects with type float32.


tf.raw_ops.ReduceDataset Reduces the input dataset to a singleton using a reduce function.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ReduceDataset
tf.raw_ops.ReduceDataset(
    input_dataset, initial_state, other_arguments, f, output_types, output_shapes,
    use_inter_op_parallelism=True, name=None
)

 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset.
  initial_state   A list of Tensor objects. A nested structure of tensors, representing the initial state of the transformation.
  other_arguments   A list of Tensor objects.
  f   A function decorated with @Defun. A function that maps (old_state, input_element) to new_state. It must take two arguments and return a nested structures of tensors. The structure of new_state must match the structure of initial_state.
  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.
  name   A name for the operation (optional).

 Returns   A list of Tensor objects of type output_types.


tf.raw_ops.ReduceJoin   Joins a string Tensor across the given dimensions.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ReduceJoin
tf.raw_ops.ReduceJoin(
    inputs, reduction_indices, keep_dims=False, separator='', name=None
)
 Computes the string join across dimensions in the given string Tensor of shape [\\(d_0, d_1, ..., d_{n-1}\\)]. Returns a new Tensor created by joining the input strings with the given separator (default: empty string). Negative indices are counted backwards from the end, with -1 being equivalent to n - 1. If indices are not specified, joins across all dimensions beginning from n - 1 through 0. For example: # tensor `a` is [["a", "b"], ["c", "d"]]
tf.reduce_join(a, 0) ==> ["ac", "bd"]
tf.reduce_join(a, 1) ==> ["ab", "cd"]
tf.reduce_join(a, -2) = tf.reduce_join(a, 0) ==> ["ac", "bd"]
tf.reduce_join(a, -1) = tf.reduce_join(a, 1) ==> ["ab", "cd"]
tf.reduce_join(a, 0, keep_dims=True) ==> [["ac", "bd"]]
tf.reduce_join(a, 1, keep_dims=True) ==> [["ab"], ["cd"]]
tf.reduce_join(a, 0, separator=".") ==> ["a.c", "b.d"]
tf.reduce_join(a, [0, 1]) ==> "acbd"
tf.reduce_join(a, [1, 0]) ==> "abcd"
tf.reduce_join(a, []) ==> [["a", "b"], ["c", "d"]]
tf.reduce_join(a) = tf.reduce_join(a, [1, 0]) ==> "abcd"

 Args
  inputs   A Tensor of type string. The input to be joined. All reduced indices must have non-zero size.
  reduction_indices   A Tensor of type int32. The dimensions to reduce over. Dimensions are reduced in the order specified. Omitting reduction_indices is equivalent to passing [n-1, n-2, ..., 0]. Negative indices from -n to -1 are supported.
  keep_dims   An optional bool. Defaults to False. If True, retain reduced dimensions with length 1.
  separator   An optional string. Defaults to "". The separator to use when joining.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.RefEnter Creates or finds a child frame, and makes data available to the child frame.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.RefEnter
tf.raw_ops.RefEnter(
    data, frame_name, is_constant=False, parallel_iterations=10, name=None
)
 The unique frame_name is used by the Executor to identify frames. If is_constant is true, output is a constant in the child frame; otherwise it may be changed in the child frame. At most parallel_iterations iterations are run in parallel in the child frame.

 Args
  data   A mutable Tensor. The tensor to be made available to the child frame.
  frame_name   A string. The name of the child frame.
  is_constant   An optional bool. Defaults to False. If true, the output is constant within the child frame.
  parallel_iterations   An optional int. Defaults to 10. The number of iterations allowed to run in parallel.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as data.


tf.raw_ops.RefExit Exits the current frame to its parent frame.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.RefExit
tf.raw_ops.RefExit(
    data, name=None
)
 Exit makes its input data available to the parent frame.

 Args
  data   A mutable Tensor. The tensor to be made available to the parent frame.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as data.


tf.raw_ops.RefIdentity Return the same ref tensor as the input ref tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.RefIdentity
tf.raw_ops.RefIdentity(
    input, name=None
)

 Args
  input   A mutable Tensor.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as input.


tf.raw_ops.RefMerge Forwards the value of an available tensor from inputs to output.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.RefMerge
tf.raw_ops.RefMerge(
    inputs, name=None
)
 Merge waits for at least one of the tensors in inputs to become available. It is usually combined with Switch to implement branching. Merge forwards the first tensor for become available to output, and sets value_index to its index in inputs.

 Args
  inputs   A list of at least 1 mutable Tensor objects with the same type. The input tensors, exactly one of which will become available.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (output, value_index).     output   A mutable Tensor. Has the same type as inputs.
  value_index   A Tensor of type int32.


tf.raw_ops.RefNextIteration Makes its input available to the next iteration.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.RefNextIteration
tf.raw_ops.RefNextIteration(
    data, name=None
)

 Args
  data   A mutable Tensor. The tensor to be made available to the next iteration.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as data.


tf.raw_ops.RefSelect Forwards the indexth element of inputs to output.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.RefSelect
tf.raw_ops.RefSelect(
    index, inputs, name=None
)

 Args
  index   A Tensor of type int32. A scalar that determines the input that gets selected.
  inputs   A list of at least 1 mutable Tensor objects with the same type. A list of ref tensors, one of which will be forwarded to output.
  name   A name for the operation (optional).

 Returns   A mutable Tensor. Has the same type as inputs.


tf.raw_ops.RefSwitch Forwards the ref tensor data to the output port determined by pred.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.RefSwitch
tf.raw_ops.RefSwitch(
    data, pred, name=None
)
 If pred is true, the data input is forwarded to output_true. Otherwise, the data goes to output_false. See also Switch and Merge.

 Args
  data   A mutable Tensor. The ref tensor to be forwarded to the appropriate output.
  pred   A Tensor of type bool. A scalar that specifies which output port will receive data.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (output_false, output_true).     output_false   A mutable Tensor. Has the same type as data.
  output_true   A mutable Tensor. Has the same type as data.


tf.raw_ops.RegexFullMatch   Check if the input matches the regex pattern.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.RegexFullMatch
tf.raw_ops.RegexFullMatch(
    input, pattern, name=None
)
 The input is a string tensor of any shape. The pattern is a scalar string tensor which is applied to every element of the input tensor. The boolean values (True or False) of the output tensor indicate if the input matches the regex pattern provided. The pattern follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) Examples:
tf.strings.regex_full_match(["TF lib", "lib TF"], ".*lib$")
<tf.Tensor: shape=(2,), dtype=bool, numpy=array([ True, False])>
tf.strings.regex_full_match(["TF lib", "lib TF"], ".*TF$")
<tf.Tensor: shape=(2,), dtype=bool, numpy=array([False,  True])>

 Args
  input   A Tensor of type string. A string tensor of the text to be processed.
  pattern   A Tensor of type string. A scalar string tensor containing the regular expression to match the input.
  name   A name for the operation (optional).

 Returns   A Tensor of type bool.


tf.raw_ops.RegexReplace Replaces matches of the pattern regular expression in input with the  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.RegexReplace
tf.raw_ops.RegexReplace(
    input, pattern, rewrite, replace_global=True, name=None
)
 replacement string provided in rewrite. It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) Args: input: A Tensor of type string. The text to be processed. pattern: A Tensor of type string. The regular expression to be matched in the input strings. rewrite: A Tensor of type string. The rewrite string to be substituted for the pattern expression where it is matched in the input strings. replace_global: An optional bool. Defaults to True. If True, the replacement is global (that is, all matches of the pattern regular expression in each input string are rewritten), otherwise the rewrite substitution is only made for the first pattern match. name: A name for the operation (optional). Returns: A Tensor of type string.


tf.raw_ops.RegisterDataset Registers a dataset with the tf.data service.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.RegisterDataset
tf.raw_ops.RegisterDataset(
    dataset, address, protocol, external_state_policy, name=None
)

 Args
  dataset   A Tensor of type variant.
  address   A Tensor of type string.
  protocol   A Tensor of type string.
  external_state_policy   An int.
  name   A name for the operation (optional).

 Returns   A Tensor of type int64.


tf.raw_ops.Relu Computes rectified linear: max(features, 0).  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Relu
tf.raw_ops.Relu(
    features, name=None
)
 See: https://en.wikipedia.org/wiki/Rectifier_(neural_networks) Example usage: >>> tf.nn.relu([-2., 0., -0., 3.]).numpy()
array([ 0.,  0., -0.,  3.], dtype=float32)

 Args
  features   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64, qint8.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as features.


tf.raw_ops.Relu6 Computes rectified linear 6: min(max(features, 0), 6).  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Relu6
tf.raw_ops.Relu6(
    features, name=None
)

 Args
  features   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as features.


tf.raw_ops.Relu6Grad Computes rectified linear 6 gradients for a Relu6 operation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Relu6Grad
tf.raw_ops.Relu6Grad(
    gradients, features, name=None
)

 Args
  gradients   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The backpropagated gradients to the corresponding Relu6 operation.
  features   A Tensor. Must have the same type as gradients. The features passed as input to the corresponding Relu6 operation, or its output; using either one produces the same result.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as gradients.


tf.raw_ops.ReluGrad Computes rectified linear gradients for a Relu operation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ReluGrad
tf.raw_ops.ReluGrad(
    gradients, features, name=None
)

 Args
  gradients   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The backpropagated gradients to the corresponding Relu operation.
  features   A Tensor. Must have the same type as gradients. The features passed as input to the corresponding Relu operation, OR the outputs of that operation (both work equivalently).
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as gradients.


tf.raw_ops.RemoteCall Runs function f on a remote device indicated by target.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.RemoteCall
tf.raw_ops.RemoteCall(
    target, args, Tout, f, name=None
)

 Args
  target   A Tensor of type string. A fully specified device name where we want to run the function.
  args   A list of Tensor objects. A list of arguments for the function.
  Tout   A list of tf.DTypes that has length >= 1. The type list for the return values.
  f   A function decorated with @Defun. The function to run remotely.
  name   A name for the operation (optional).

 Returns   A list of Tensor objects of type Tout.


tf.raw_ops.RepeatDataset Creates a dataset that emits the outputs of input_dataset count times.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.RepeatDataset
tf.raw_ops.RepeatDataset(
    input_dataset, count, output_types, output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  count   A Tensor of type int64. A scalar representing the number of times that input_dataset should be repeated. A value of -1 indicates that it should be repeated infinitely.
  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.RequantizationRange Computes a range that covers the actual values present in a quantized tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.RequantizationRange
tf.raw_ops.RequantizationRange(
    input, input_min, input_max, name=None
)
 Given a quantized tensor described by (input, input_min, input_max), outputs a range that covers the actual values present in that tensor. This op is typically used to produce the requested_output_min and requested_output_max for Requantize.

 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.
  input_min   A Tensor of type float32. The float value that the minimum quantized input value represents.
  input_max   A Tensor of type float32. The float value that the maximum quantized input value represents.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (output_min, output_max).     output_min   A Tensor of type float32.
  output_max   A Tensor of type float32.


tf.raw_ops.RequantizationRangePerChannel Computes requantization range per channel.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.RequantizationRangePerChannel
tf.raw_ops.RequantizationRangePerChannel(
    input, input_min, input_max, clip_value_max, name=None
)

 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. The original input tensor.
  input_min   A Tensor of type float32. The minimum value of the input tensor
  input_max   A Tensor of type float32. The maximum value of the input tensor.
  clip_value_max   A float. The maximum value of the output that needs to be clipped. Example: set this to 6 for Relu6.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (output_min, output_max).     output_min   A Tensor of type float32.
  output_max   A Tensor of type float32.


tf.raw_ops.Requantize Converts the quantized input tensor into a lower-precision output.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Requantize
tf.raw_ops.Requantize(
    input, input_min, input_max, requested_output_min, requested_output_max,
    out_type, name=None
)
 Converts the quantized input tensor into a lower-precision output, using the output range specified with requested_output_min and requested_output_max. [input_min, input_max] are scalar floats that specify the range for the float interpretation of the input data. For example, if input_min is -1.0f and input_max is 1.0f, and we are dealing with quint16 quantized data, then a 0 value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f.

 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.
  input_min   A Tensor of type float32. The float value that the minimum quantized input value represents.
  input_max   A Tensor of type float32. The float value that the maximum quantized input value represents.
  requested_output_min   A Tensor of type float32. The float value that the minimum quantized output value represents.
  requested_output_max   A Tensor of type float32. The float value that the maximum quantized output value represents.
  out_type   A tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. The type of the output. Should be a lower bit depth than Tinput.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (output, output_min, output_max).     output   A Tensor of type out_type.
  output_min   A Tensor of type float32.
  output_max   A Tensor of type float32.


tf.raw_ops.RequantizePerChannel Requantizes input with min and max values known per channel.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.RequantizePerChannel
tf.raw_ops.RequantizePerChannel(
    input, input_min, input_max, requested_output_min, requested_output_max,
    out_type=tf.dtypes.quint8, name=None
)

 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. The original input tensor.
  input_min   A Tensor of type float32. The minimum value of the input tensor
  input_max   A Tensor of type float32. The maximum value of the input tensor.
  requested_output_min   A Tensor of type float32. The minimum value of the output tensor requested.
  requested_output_max   A Tensor of type float32. The maximum value of the output tensor requested.
  out_type   An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.quint8. The quantized type of output tensor that needs to be converted.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (output, output_min, output_max).     output   A Tensor of type out_type.
  output_min   A Tensor of type float32.
  output_max   A Tensor of type float32.


tf.raw_ops.Reshape Reshapes a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Reshape
tf.raw_ops.Reshape(
    tensor, shape, name=None
)
 Given tensor, this operation returns a tensor that has the same values as tensor with shape shape. If one component of 1-D tensor shape is the special value -1, the size of that dimension is computed so that the total size remains constant. In particular, a shape of [-1] flattens into 1-D. At most one component of shape may be unknown. The shape must be 1-D and the operation returns a tensor with shape shape filled with the values of tensor. In this case, the number of elements implied by shape must be the same as the number of elements in tensor. It is an error if shape is not 1-D. For example: # tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9]
# tensor 't' has shape [9]
reshape(t, [3, 3]) ==> [[1, 2, 3],
                        [4, 5, 6],
                        [7, 8, 9]]

# tensor 't' is [[[1, 1], [2, 2]],
#                [[3, 3], [4, 4]]]
# tensor 't' has shape [2, 2, 2]
reshape(t, [2, 4]) ==> [[1, 1, 2, 2],
                        [3, 3, 4, 4]]

# tensor 't' is [[[1, 1, 1],
#                 [2, 2, 2]],
#                [[3, 3, 3],
#                 [4, 4, 4]],
#                [[5, 5, 5],
#                 [6, 6, 6]]]
# tensor 't' has shape [3, 2, 3]
# pass '[-1]' to flatten 't'
reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]

# -1 can also be used to infer the shape

# -1 is inferred to be 9:
reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
                         [4, 4, 4, 5, 5, 5, 6, 6, 6]]
# -1 is inferred to be 2:
reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
                         [4, 4, 4, 5, 5, 5, 6, 6, 6]]
# -1 is inferred to be 3:
reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1],
                              [2, 2, 2],
                              [3, 3, 3]],
                             [[4, 4, 4],
                              [5, 5, 5],
                              [6, 6, 6]]]

# tensor 't' is [7]
# shape `[]` reshapes to a scalar
reshape(t, []) ==> 7

 Args
  tensor   A Tensor.
  shape   A Tensor. Must be one of the following types: int32, int64. Defines the shape of the output tensor.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as tensor.


tf.raw_ops.ResizeArea Resize images to size using area interpolation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResizeArea
tf.raw_ops.ResizeArea(
    images, size, align_corners=False, name=None
)
 Input images can be of different types but output images are always float. The range of pixel values for the output image might be slightly different from the range for the input image because of limited numerical precision. To guarantee an output range, for example [0.0, 1.0], apply tf.clip_by_value to the output. Each output pixel is computed by first transforming the pixel's footprint into the input tensor and then averaging the pixels that intersect the footprint. An input pixel's contribution to the average is weighted by the fraction of its area that intersects the footprint. This is the same as OpenCV's INTER_AREA.

 Args
  images   A Tensor. Must be one of the following types: int8, uint8, int16, uint16, int32, int64, half, float32, float64, bfloat16. 4-D with shape [batch, height, width, channels].
  size   A 1-D int32 Tensor of 2 elements: new_height, new_width. The new size for the images.
  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 of type float32.


tf.raw_ops.ResizeBicubic Resize images to size using bicubic interpolation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResizeBicubic
tf.raw_ops.ResizeBicubic(
    images, size, align_corners=False, half_pixel_centers=False, name=None
)
 Input images can be of different types but output images are always float.

 Args
  images   A Tensor. Must be one of the following types: int8, uint8, int16, uint16, int32, int64, half, float32, float64, bfloat16. 4-D with shape [batch, height, width, channels].
  size   A 1-D int32 Tensor of 2 elements: new_height, new_width. The new size for the images.
  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.
  half_pixel_centers   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.ResizeBicubicGrad Computes the gradient of bicubic interpolation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResizeBicubicGrad
tf.raw_ops.ResizeBicubicGrad(
    grads, original_image, align_corners=False, half_pixel_centers=False, name=None
)

 Args
  grads   A Tensor of type float32. 4-D with shape [batch, height, width, channels].
  original_image   A Tensor. Must be one of the following types: float32, float64. 4-D with shape [batch, orig_height, orig_width, channels], The image tensor that was resized.
  align_corners   An optional bool. Defaults to False. If true, the centers of the 4 corner pixels of the input and grad tensors are aligned. Defaults to false.
  half_pixel_centers   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as original_image.


tf.raw_ops.ResizeBilinear Resize images to size using bilinear interpolation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResizeBilinear
tf.raw_ops.ResizeBilinear(
    images, size, align_corners=False, half_pixel_centers=False, name=None
)
 Input images can be of different types but output images are always float.

 Args
  images   A Tensor. Must be one of the following types: int8, uint8, int16, uint16, int32, int64, bfloat16, half, float32, float64, bfloat16. 4-D with shape [batch, height, width, channels].
  size   A 1-D int32 Tensor of 2 elements: new_height, new_width. The new size for the images.
  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.
  half_pixel_centers   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.ResizeBilinearGrad Computes the gradient of bilinear interpolation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResizeBilinearGrad
tf.raw_ops.ResizeBilinearGrad(
    grads, original_image, align_corners=False, half_pixel_centers=False, name=None
)

 Args
  grads   A Tensor of type float32. 4-D with shape [batch, height, width, channels].
  original_image   A Tensor. Must be one of the following types: float32, bfloat16, half, float64. 4-D with shape [batch, orig_height, orig_width, channels], The image tensor that was resized.
  align_corners   An optional bool. Defaults to False. If true, the centers of the 4 corner pixels of the input and grad tensors are aligned. Defaults to false.
  half_pixel_centers   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as original_image.


tf.raw_ops.ResizeNearestNeighbor Resize images to size using nearest neighbor interpolation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResizeNearestNeighbor
tf.raw_ops.ResizeNearestNeighbor(
    images, size, align_corners=False, half_pixel_centers=False, name=None
)

 Args
  images   A Tensor. Must be one of the following types: int8, uint8, int16, uint16, int32, int64, half, float32, float64, bfloat16. 4-D with shape [batch, height, width, channels].
  size   A 1-D int32 Tensor of 2 elements: new_height, new_width. The new size for the images.
  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.
  half_pixel_centers   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as images.


tf.raw_ops.ResizeNearestNeighborGrad Computes the gradient of nearest neighbor interpolation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResizeNearestNeighborGrad
tf.raw_ops.ResizeNearestNeighborGrad(
    grads, size, align_corners=False, half_pixel_centers=False, name=None
)

 Args
  grads   A Tensor. Must be one of the following types: uint8, int8, int32, half, float32, float64. 4-D with shape [batch, height, width, channels].
  size   A 1-D int32 Tensor of 2 elements: orig_height, orig_width. The original input size.
  align_corners   An optional bool. Defaults to False. If true, the centers of the 4 corner pixels of the input and grad tensors are aligned. Defaults to false.
  half_pixel_centers   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as grads.


tf.raw_ops.ResourceAccumulatorApplyGradient Applies a gradient to a given accumulator.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceAccumulatorApplyGradient
tf.raw_ops.ResourceAccumulatorApplyGradient(
    handle, local_step, gradient, name=None
)
 Does not add if local_step is lesser than the accumulator's global_step.

 Args
  handle   A Tensor of type resource. The handle to a accumulator.
  local_step   A Tensor of type int64. The local_step value at which the gradient was computed.
  gradient   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. A tensor of the gradient to be accumulated.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceAccumulatorNumAccumulated Returns the number of gradients aggregated in the given accumulators.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceAccumulatorNumAccumulated
tf.raw_ops.ResourceAccumulatorNumAccumulated(
    handle, name=None
)

 Args
  handle   A Tensor of type resource. The handle to an accumulator.
  name   A name for the operation (optional).

 Returns   A Tensor of type int32.


tf.raw_ops.ResourceAccumulatorSetGlobalStep Updates the accumulator with a new value for global_step.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceAccumulatorSetGlobalStep
tf.raw_ops.ResourceAccumulatorSetGlobalStep(
    handle, new_global_step, name=None
)
 Logs warning if the accumulator's value is already higher than new_global_step.

 Args
  handle   A Tensor of type resource. The handle to an accumulator.
  new_global_step   A Tensor of type int64. The new global_step value to set.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceAccumulatorTakeGradient Extracts the average gradient in the given ConditionalAccumulator.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceAccumulatorTakeGradient
tf.raw_ops.ResourceAccumulatorTakeGradient(
    handle, num_required, dtype, name=None
)
 The op blocks until sufficient (i.e., more than num_required) gradients have been accumulated. If the accumulator has already aggregated more than num_required gradients, it returns the average of the accumulated gradients. Also automatically increments the recorded global_step in the accumulator by 1, and resets the aggregate to 0.

 Args
  handle   A Tensor of type resource. The handle to an accumulator.
  num_required   A Tensor of type int32. Number of gradients required before we return an aggregate.
  dtype   A tf.DType from: tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8, tf.complex64, tf.int64, tf.qint8, tf.quint8, tf.qint32, tf.bfloat16, tf.uint16, tf.complex128, tf.half, tf.uint32, tf.uint64. The data type of accumulated gradients. Needs to correspond to the type of the accumulator.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.ResourceApplyAdadelta Update '*var' according to the adadelta scheme.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyAdadelta
tf.raw_ops.ResourceApplyAdadelta(
    var, accum, accum_update, lr, rho, epsilon, grad, use_locking=False, name=None
)
 accum = rho() * accum + (1 - rho()) * grad.square(); update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad; update_accum = rho() * update_accum + (1 - rho()) * update.square(); var -= update;

 Args
  var   A Tensor of type resource. Should be from a Variable().
  accum   A Tensor of type resource. Should be from a Variable().
  accum_update   A Tensor of type resource. Should be from a Variable().
  lr   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Scaling factor. Must be a scalar.
  rho   A Tensor. Must have the same type as lr. Decay factor. Must be a scalar.
  epsilon   A Tensor. Must have the same type as lr. Constant factor. Must be a scalar.
  grad   A Tensor. Must have the same type as lr. The gradient.
  use_locking   An optional bool. Defaults to False. If True, updating of the var, accum and update_accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceApplyAdagrad Update '*var' according to the adagrad scheme.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyAdagrad
tf.raw_ops.ResourceApplyAdagrad(
    var, accum, lr, grad, use_locking=False, update_slots=True, name=None
)
 accum += grad * grad var -= lr * grad * (1 / sqrt(accum))

 Args
  var   A Tensor of type resource. Should be from a Variable().
  accum   A Tensor of type resource. Should be from a Variable().
  lr   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Scaling factor. Must be a scalar.
  grad   A Tensor. Must have the same type as lr. The gradient.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  update_slots   An optional bool. Defaults to True.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceApplyAdagradDA Update '*var' according to the proximal adagrad scheme.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyAdagradDA
tf.raw_ops.ResourceApplyAdagradDA(
    var, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2,
    global_step, use_locking=False, name=None
)

 Args
  var   A Tensor of type resource. Should be from a Variable().
  gradient_accumulator   A Tensor of type resource. Should be from a Variable().
  gradient_squared_accumulator   A Tensor of type resource. Should be from a Variable().
  grad   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. The gradient.
  lr   A Tensor. Must have the same type as grad. Scaling factor. Must be a scalar.
  l1   A Tensor. Must have the same type as grad. L1 regularization. Must be a scalar.
  l2   A Tensor. Must have the same type as grad. L2 regularization. Must be a scalar.
  global_step   A Tensor of type int64. Training step number. Must be a scalar.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceApplyAdagradV2 Update '*var' according to the adagrad scheme.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyAdagradV2
tf.raw_ops.ResourceApplyAdagradV2(
    var, accum, lr, epsilon, grad, use_locking=False, update_slots=True, name=None
)
 accum += grad * grad var -= lr * grad * (1 / (sqrt(accum) + epsilon))

 Args
  var   A Tensor of type resource. Should be from a Variable().
  accum   A Tensor of type resource. Should be from a Variable().
  lr   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Scaling factor. Must be a scalar.
  epsilon   A Tensor. Must have the same type as lr. Constant factor. Must be a scalar.
  grad   A Tensor. Must have the same type as lr. The gradient.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  update_slots   An optional bool. Defaults to True.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceApplyAdam   Update '*var' according to the Adam algorithm.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyAdam
tf.raw_ops.ResourceApplyAdam(
    var, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad,
    use_locking=False, use_nesterov=False, name=None
)
  $$\text{lr}_t := \mathrm{learning_rate} * \sqrt{1 - \beta_2^t} / (1 - \beta_1^t)$$   $$m_t := \beta_1 * m_{t-1} + (1 - \beta_1) * g$$   $$v_t := \beta_2 * v_{t-1} + (1 - \beta_2) * g * g$$   $$\text{variable} := \text{variable} - \text{lr}_t * m_t / (\sqrt{v_t} + \epsilon)$$

 Args
  var   A Tensor of type resource. Should be from a Variable().
  m   A Tensor of type resource. Should be from a Variable().
  v   A Tensor of type resource. Should be from a Variable().
  beta1_power   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Must be a scalar.
  beta2_power   A Tensor. Must have the same type as beta1_power. Must be a scalar.
  lr   A Tensor. Must have the same type as beta1_power. Scaling factor. Must be a scalar.
  beta1   A Tensor. Must have the same type as beta1_power. Momentum factor. Must be a scalar.
  beta2   A Tensor. Must have the same type as beta1_power. Momentum factor. Must be a scalar.
  epsilon   A Tensor. Must have the same type as beta1_power. Ridge term. Must be a scalar.
  grad   A Tensor. Must have the same type as beta1_power. The gradient.
  use_locking   An optional bool. Defaults to False. If True, updating of the var, m, and v tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  use_nesterov   An optional bool. Defaults to False. If True, uses the nesterov update.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceApplyAdaMax Update '*var' according to the AdaMax algorithm.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyAdaMax
tf.raw_ops.ResourceApplyAdaMax(
    var, m, v, beta1_power, lr, beta1, beta2, epsilon, grad, use_locking=False,
    name=None
)
 mt <- beta1 * m{t-1} + (1 - beta1) * g vt <- max(beta2 * v{t-1}, abs(g)) variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon)

 Args
  var   A Tensor of type resource. Should be from a Variable().
  m   A Tensor of type resource. Should be from a Variable().
  v   A Tensor of type resource. Should be from a Variable().
  beta1_power   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Must be a scalar.
  lr   A Tensor. Must have the same type as beta1_power. Scaling factor. Must be a scalar.
  beta1   A Tensor. Must have the same type as beta1_power. Momentum factor. Must be a scalar.
  beta2   A Tensor. Must have the same type as beta1_power. Momentum factor. Must be a scalar.
  epsilon   A Tensor. Must have the same type as beta1_power. Ridge term. Must be a scalar.
  grad   A Tensor. Must have the same type as beta1_power. The gradient.
  use_locking   An optional bool. Defaults to False. If True, updating of the var, m, and v tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceApplyAdamWithAmsgrad   Update '*var' according to the Adam algorithm.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyAdamWithAmsgrad
tf.raw_ops.ResourceApplyAdamWithAmsgrad(
    var, m, v, vhat, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad,
    use_locking=False, name=None
)
  $$\text{lr}_t := \mathrm{learning_rate} * \sqrt{1 - \beta_2^t} / (1 - \beta_1^t)$$   $$m_t := \beta_1 * m_{t-1} + (1 - \beta_1) * g$$   $$v_t := \beta_2 * v_{t-1} + (1 - \beta_2) * g * g$$   $$\hat{v}_t := max{\hat{v}_{t-1}, v_t}$$   $$\text{variable} := \text{variable} - \text{lr}_t * m_t / (\sqrt{\hat{v}_t} + \epsilon)$$

 Args
  var   A Tensor of type resource. Should be from a Variable().
  m   A Tensor of type resource. Should be from a Variable().
  v   A Tensor of type resource. Should be from a Variable().
  vhat   A Tensor of type resource. Should be from a Variable().
  beta1_power   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Must be a scalar.
  beta2_power   A Tensor. Must have the same type as beta1_power. Must be a scalar.
  lr   A Tensor. Must have the same type as beta1_power. Scaling factor. Must be a scalar.
  beta1   A Tensor. Must have the same type as beta1_power. Momentum factor. Must be a scalar.
  beta2   A Tensor. Must have the same type as beta1_power. Momentum factor. Must be a scalar.
  epsilon   A Tensor. Must have the same type as beta1_power. Ridge term. Must be a scalar.
  grad   A Tensor. Must have the same type as beta1_power. The gradient.
  use_locking   An optional bool. Defaults to False. If True, updating of the var, m, and v tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceApplyAddSign Update '*var' according to the AddSign update.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyAddSign
tf.raw_ops.ResourceApplyAddSign(
    var, m, lr, alpha, sign_decay, beta, grad, use_locking=False, name=None
)
 mt <- beta1 * m{t-1} + (1 - beta1) * g update <- (alpha + sign_decay * sign(g) *sign(m)) * g variable <- variable - lr_t * update

 Args
  var   A Tensor of type resource. Should be from a Variable().
  m   A Tensor of type resource. Should be from a Variable().
  lr   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Scaling factor. Must be a scalar.
  alpha   A Tensor. Must have the same type as lr. Must be a scalar.
  sign_decay   A Tensor. Must have the same type as lr. Must be a scalar.
  beta   A Tensor. Must have the same type as lr. Must be a scalar.
  grad   A Tensor. Must have the same type as lr. The gradient.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and m tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceApplyCenteredRMSProp Update '*var' according to the centered RMSProp algorithm.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyCenteredRMSProp
tf.raw_ops.ResourceApplyCenteredRMSProp(
    var, mg, ms, mom, lr, rho, momentum, epsilon, grad, use_locking=False, name=None
)
 The centered RMSProp algorithm uses an estimate of the centered second moment (i.e., the variance) for normalization, as opposed to regular RMSProp, which uses the (uncentered) second moment. This often helps with training, but is slightly more expensive in terms of computation and memory. Note that in dense implementation of this algorithm, mg, ms, and mom will update even if the grad is zero, but in this sparse implementation, mg, ms, and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 mean_grad = decay * mean_grad + (1-decay) * gradient Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) mg <- rho * mg{t-1} + (1-rho) * grad ms <- rho * ms{t-1} + (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms - mg * mg + epsilon) var <- var - mom

 Args
  var   A Tensor of type resource. Should be from a Variable().
  mg   A Tensor of type resource. Should be from a Variable().
  ms   A Tensor of type resource. Should be from a Variable().
  mom   A Tensor of type resource. Should be from a Variable().
  lr   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Scaling factor. Must be a scalar.
  rho   A Tensor. Must have the same type as lr. Decay rate. Must be a scalar.
  momentum   A Tensor. Must have the same type as lr. Momentum Scale. Must be a scalar.
  epsilon   A Tensor. Must have the same type as lr. Ridge term. Must be a scalar.
  grad   A Tensor. Must have the same type as lr. The gradient.
  use_locking   An optional bool. Defaults to False. If True, updating of the var, mg, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceApplyFtrl Update '*var' according to the Ftrl-proximal scheme.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyFtrl
tf.raw_ops.ResourceApplyFtrl(
    var, accum, linear, grad, lr, l1, l2, lr_power, use_locking=False,
    multiply_linear_by_lr=False, name=None
)
 accum_new = accum + grad * grad linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new

 Args
  var   A Tensor of type resource. Should be from a Variable().
  accum   A Tensor of type resource. Should be from a Variable().
  linear   A Tensor of type resource. Should be from a Variable().
  grad   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. The gradient.
  lr   A Tensor. Must have the same type as grad. Scaling factor. Must be a scalar.
  l1   A Tensor. Must have the same type as grad. L1 regularization. Must be a scalar.
  l2   A Tensor. Must have the same type as grad. L2 regularization. Must be a scalar.
  lr_power   A Tensor. Must have the same type as grad. Scaling factor. Must be a scalar.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  multiply_linear_by_lr   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceApplyFtrlV2 Update '*var' according to the Ftrl-proximal scheme.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyFtrlV2
tf.raw_ops.ResourceApplyFtrlV2(
    var, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power, use_locking=False,
    multiply_linear_by_lr=False, name=None
)
 grad_with_shrinkage = grad + 2 * l2_shrinkage * var accum_new = accum + grad_with_shrinkage * grad_with_shrinkage linear += grad_with_shrinkage + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new

 Args
  var   A Tensor of type resource. Should be from a Variable().
  accum   A Tensor of type resource. Should be from a Variable().
  linear   A Tensor of type resource. Should be from a Variable().
  grad   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. The gradient.
  lr   A Tensor. Must have the same type as grad. Scaling factor. Must be a scalar.
  l1   A Tensor. Must have the same type as grad. L1 regularization. Must be a scalar.
  l2   A Tensor. Must have the same type as grad. L2 shrinkage regularization. Must be a scalar.
  l2_shrinkage   A Tensor. Must have the same type as grad.
  lr_power   A Tensor. Must have the same type as grad. Scaling factor. Must be a scalar.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  multiply_linear_by_lr   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceApplyGradientDescent Update '*var' by subtracting 'alpha' * 'delta' from it.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyGradientDescent
tf.raw_ops.ResourceApplyGradientDescent(
    var, alpha, delta, use_locking=False, name=None
)

 Args
  var   A Tensor of type resource. Should be from a Variable().
  alpha   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Scaling factor. Must be a scalar.
  delta   A Tensor. Must have the same type as alpha. The change.
  use_locking   An optional bool. Defaults to False. If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceApplyKerasMomentum Update '*var' according to the momentum scheme.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyKerasMomentum
tf.raw_ops.ResourceApplyKerasMomentum(
    var, accum, lr, grad, momentum, use_locking=False, use_nesterov=False, name=None
)
 Set use_nesterov = True if you want to use Nesterov momentum. accum = accum * momentum - lr * grad var += accum

 Args
  var   A Tensor of type resource. Should be from a Variable().
  accum   A Tensor of type resource. Should be from a Variable().
  lr   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Scaling factor. Must be a scalar.
  grad   A Tensor. Must have the same type as lr. The gradient.
  momentum   A Tensor. Must have the same type as lr. Momentum. Must be a scalar.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  use_nesterov   An optional bool. Defaults to False. If True, the tensor passed to compute grad will be var + momentum * accum, so in the end, the var you get is actually var + momentum * accum.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceApplyMomentum Update '*var' according to the momentum scheme.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyMomentum
tf.raw_ops.ResourceApplyMomentum(
    var, accum, lr, grad, momentum, use_locking=False, use_nesterov=False, name=None
)
 Set use_nesterov = True if you want to use Nesterov momentum. accum = accum * momentum + grad var -= lr * accum

 Args
  var   A Tensor of type resource. Should be from a Variable().
  accum   A Tensor of type resource. Should be from a Variable().
  lr   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Scaling factor. Must be a scalar.
  grad   A Tensor. Must have the same type as lr. The gradient.
  momentum   A Tensor. Must have the same type as lr. Momentum. Must be a scalar.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  use_nesterov   An optional bool. Defaults to False. If True, the tensor passed to compute grad will be var - lr * momentum * accum, so in the end, the var you get is actually var - lr * momentum * accum.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceApplyPowerSign Update '*var' according to the AddSign update.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyPowerSign
tf.raw_ops.ResourceApplyPowerSign(
    var, m, lr, logbase, sign_decay, beta, grad, use_locking=False, name=None
)
 mt <- beta1 * m{t-1} + (1 - beta1) * g update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g variable <- variable - lr_t * update

 Args
  var   A Tensor of type resource. Should be from a Variable().
  m   A Tensor of type resource. Should be from a Variable().
  lr   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Scaling factor. Must be a scalar.
  logbase   A Tensor. Must have the same type as lr. Must be a scalar.
  sign_decay   A Tensor. Must have the same type as lr. Must be a scalar.
  beta   A Tensor. Must have the same type as lr. Must be a scalar.
  grad   A Tensor. Must have the same type as lr. The gradient.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and m tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceApplyProximalAdagrad Update 'var' and 'accum' according to FOBOS with Adagrad learning rate.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyProximalAdagrad
tf.raw_ops.ResourceApplyProximalAdagrad(
    var, accum, lr, l1, l2, grad, use_locking=False, name=None
)
 accum += grad * grad prox_v = var - lr * grad * (1 / sqrt(accum)) var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}

 Args
  var   A Tensor of type resource. Should be from a Variable().
  accum   A Tensor of type resource. Should be from a Variable().
  lr   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Scaling factor. Must be a scalar.
  l1   A Tensor. Must have the same type as lr. L1 regularization. Must be a scalar.
  l2   A Tensor. Must have the same type as lr. L2 regularization. Must be a scalar.
  grad   A Tensor. Must have the same type as lr. The gradient.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceApplyProximalGradientDescent Update '*var' as FOBOS algorithm with fixed learning rate.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyProximalGradientDescent
tf.raw_ops.ResourceApplyProximalGradientDescent(
    var, alpha, l1, l2, delta, use_locking=False, name=None
)
 prox_v = var - alpha * delta var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}

 Args
  var   A Tensor of type resource. Should be from a Variable().
  alpha   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Scaling factor. Must be a scalar.
  l1   A Tensor. Must have the same type as alpha. L1 regularization. Must be a scalar.
  l2   A Tensor. Must have the same type as alpha. L2 regularization. Must be a scalar.
  delta   A Tensor. Must have the same type as alpha. The change.
  use_locking   An optional bool. Defaults to False. If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceApplyRMSProp Update '*var' according to the RMSProp algorithm.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyRMSProp
tf.raw_ops.ResourceApplyRMSProp(
    var, ms, mom, lr, rho, momentum, epsilon, grad, use_locking=False, name=None
)
 Note that in dense implementation of this algorithm, ms and mom will update even if the grad is zero, but in this sparse implementation, ms and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 Delta = learning_rate * gradient / sqrt(mean_square + epsilon) ms <- rho * ms{t-1} + (1-rho) * grad * grad mom <- momentum * mom{t-1} + lr * grad / sqrt(ms + epsilon) var <- var - mom

 Args
  var   A Tensor of type resource. Should be from a Variable().
  ms   A Tensor of type resource. Should be from a Variable().
  mom   A Tensor of type resource. Should be from a Variable().
  lr   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Scaling factor. Must be a scalar.
  rho   A Tensor. Must have the same type as lr. Decay rate. Must be a scalar.
  momentum   A Tensor. Must have the same type as lr.
  epsilon   A Tensor. Must have the same type as lr. Ridge term. Must be a scalar.
  grad   A Tensor. Must have the same type as lr. The gradient.
  use_locking   An optional bool. Defaults to False. If True, updating of the var, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceConditionalAccumulator A conditional accumulator for aggregating gradients.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceConditionalAccumulator
tf.raw_ops.ResourceConditionalAccumulator(
    dtype, shape, container='', shared_name='',
    reduction_type='MEAN', name=None
)
 The accumulator accepts gradients marked with local_step greater or equal to the most recent global_step known to the accumulator. The average can be extracted from the accumulator, provided sufficient gradients have been accumulated. Extracting the average automatically resets the aggregate to 0, and increments the global_step recorded by the accumulator. This is a resource version of ConditionalAccumulator that will work in TF2.0 with tf.cond version 2.

 Args
  dtype   A tf.DType from: tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8, tf.complex64, tf.int64, tf.qint8, tf.quint8, tf.qint32, tf.bfloat16, tf.uint16, tf.complex128, tf.half, tf.uint32, tf.uint64. The type of the value being accumulated.
  shape   A tf.TensorShape or list of ints. The shape of the values, can be [], in which case shape is unknown.
  container   An optional string. Defaults to "". If non-empty, this accumulator is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this accumulator will be shared under the given name across multiple sessions.
  reduction_type   An optional string from: "MEAN", "SUM". Defaults to "MEAN".
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.ResourceCountUpTo Increments variable pointed to by 'resource' until it reaches 'limit'.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceCountUpTo
tf.raw_ops.ResourceCountUpTo(
    resource, limit, T, name=None
)

 Args
  resource   A Tensor of type resource. Should be from a scalar Variable node.
  limit   An int. If incrementing ref would bring it above limit, instead generates an 'OutOfRange' error.
  T   A tf.DType from: tf.int32, tf.int64.
  name   A name for the operation (optional).

 Returns   A Tensor of type T.


tf.raw_ops.ResourceGather Gather slices from the variable pointed to by resource according to indices.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceGather
tf.raw_ops.ResourceGather(
    resource, indices, dtype, batch_dims=0, validate_indices=True, name=None
)
 indices must be an integer tensor of any dimension (usually 0-D or 1-D). Produces an output tensor with shape indices.shape + params.shape[1:] where: # Scalar indices
output[:, ..., :] = params[indices, :, ... :]

# Vector indices
output[i, :, ..., :] = params[indices[i], :, ... :]

# Higher rank indices
output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]

 Args
  resource   A Tensor of type resource.
  indices   A Tensor. Must be one of the following types: int32, int64.
  dtype   A tf.DType.
  batch_dims   An optional int. Defaults to 0.
  validate_indices   An optional bool. Defaults to True.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.ResourceGatherNd  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceGatherNd
tf.raw_ops.ResourceGatherNd(
    resource, indices, dtype, name=None
)

 Args
  resource   A Tensor of type resource.
  indices   A Tensor. Must be one of the following types: int32, int64.
  dtype   A tf.DType.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.ResourceScatterAdd Adds sparse updates to the variable referenced by resource.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterAdd
tf.raw_ops.ResourceScatterAdd(
    resource, indices, updates, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] += updates[...]

# Vector indices (for each i)
ref[indices[i], ...] += updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] += updates[i, ..., j, ...]
 Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions add. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].

 Args
  resource   A Tensor of type resource. Should be from a Variable node.
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.
  updates   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. A tensor of updated values to add to ref.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceScatterDiv Divides sparse updates into the variable referenced by resource.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterDiv
tf.raw_ops.ResourceScatterDiv(
    resource, indices, updates, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] /= updates[...]

# Vector indices (for each i)
ref[indices[i], ...] /= updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...]
 Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions multiply. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].

 Args
  resource   A Tensor of type resource. Should be from a Variable node.
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.
  updates   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. A tensor of updated values to add to ref.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceScatterMax Reduces sparse updates into the variable referenced by resource using the max operation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterMax
tf.raw_ops.ResourceScatterMax(
    resource, indices, updates, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] = max(ref[indices, ...], updates[...])

# Vector indices (for each i)
ref[indices[i], ...] = max(ref[indices[i], ...], updates[i, ...])

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] = max(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
 Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions are combined. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].

 Args
  resource   A Tensor of type resource. Should be from a Variable node.
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.
  updates   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. A tensor of updated values to add to ref.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceScatterMin Reduces sparse updates into the variable referenced by resource using the min operation.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterMin
tf.raw_ops.ResourceScatterMin(
    resource, indices, updates, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] = min(ref[indices, ...], updates[...])

# Vector indices (for each i)
ref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...])

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] = min(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
 Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions are combined. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].

 Args
  resource   A Tensor of type resource. Should be from a Variable node.
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.
  updates   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. A tensor of updated values to add to ref.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceScatterMul Multiplies sparse updates into the variable referenced by resource.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterMul
tf.raw_ops.ResourceScatterMul(
    resource, indices, updates, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] *= updates[...]

# Vector indices (for each i)
ref[indices[i], ...] *= updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] *= updates[i, ..., j, ...]
 Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions multiply. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].

 Args
  resource   A Tensor of type resource. Should be from a Variable node.
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.
  updates   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. A tensor of updated values to add to ref.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceScatterNdAdd Applies sparse addition to individual values or slices in a Variable.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterNdAdd
tf.raw_ops.ResourceScatterNdAdd(
    ref, indices, updates, use_locking=True, name=None
)
 ref is a Tensor with rank P and indices is a Tensor of rank Q. indices must be integer tensor, containing indices into ref. It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P. The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the Kth dimension of ref. updates is Tensor of rank Q-1+P-K with shape: [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]
 For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that addition would look like this: ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8], use_resource=True)
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
add = tf.scatter_nd_add(ref, indices, updates)
with tf.Session() as sess:
  print sess.run(add)
 The resulting update to ref would look like this: [1, 13, 3, 14, 14, 6, 7, 20]
 See tf.scatter_nd for more details about how to make updates to slices.

 Args
  ref   A Tensor of type resource. A resource handle. Must be from a VarHandleOp.
  indices   A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.
  updates   A Tensor. A Tensor. Must have the same type as ref. A tensor of values to add to ref.
  use_locking   An optional bool. Defaults to True. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceScatterNdMax  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterNdMax
tf.raw_ops.ResourceScatterNdMax(
    ref, indices, updates, use_locking=True, name=None
)

 Args
  ref   A Tensor of type resource. A resource handle. Must be from a VarHandleOp.
  indices   A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.
  updates   A Tensor. A Tensor. Must have the same type as ref. A tensor of values whose element wise max is taken with ref
  use_locking   An optional bool. Defaults to True. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceScatterNdMin  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterNdMin
tf.raw_ops.ResourceScatterNdMin(
    ref, indices, updates, use_locking=True, name=None
)

 Args
  ref   A Tensor of type resource. A resource handle. Must be from a VarHandleOp.
  indices   A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.
  updates   A Tensor. A Tensor. Must have the same type as ref. A tensor of values whose element wise min is taken with ref.
  use_locking   An optional bool. Defaults to True. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceScatterNdSub Applies sparse subtraction to individual values or slices in a Variable.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterNdSub
tf.raw_ops.ResourceScatterNdSub(
    ref, indices, updates, use_locking=True, name=None
)
 ref is a Tensor with rank P and indices is a Tensor of rank Q. indices must be integer tensor, containing indices into ref. It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P. The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the Kth dimension of ref. updates is Tensor of rank Q-1+P-K with shape: [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]
 For example, say we want to subtract 4 scattered elements from a rank-1 tensor with 8 elements. In Python, that subtraction would look like this: ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8], use_resource=True)
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
sub = tf.scatter_nd_sub(ref, indices, updates)
with tf.Session() as sess:
  print sess.run(sub)
 The resulting update to ref would look like this: [1, -9, 3, -6, -4, 6, 7, -4]
 See tf.scatter_nd for more details about how to make updates to slices.

 Args
  ref   A Tensor of type resource. A resource handle. Must be from a VarHandleOp.
  indices   A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.
  updates   A Tensor. A Tensor. Must have the same type as ref. A tensor of values to add to ref.
  use_locking   An optional bool. Defaults to True. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceScatterNdUpdate Applies sparse updates to individual values or slices within a given  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterNdUpdate
tf.raw_ops.ResourceScatterNdUpdate(
    ref, indices, updates, use_locking=True, name=None
)
 variable according to indices. ref is a Tensor with rank P and indices is a Tensor of rank Q. indices must be integer tensor, containing indices into ref. It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P. The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the Kth dimension of ref. updates is Tensor of rank Q-1+P-K with shape: [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
 For example, say we want to update 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this: ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
update = tf.scatter_nd_update(ref, indices, updates)
with tf.Session() as sess:
  print sess.run(update)
 The resulting update to ref would look like this: [1, 11, 3, 10, 9, 6, 7, 12]
 See tf.scatter_nd for more details about how to make updates to slices.

 Args
  ref   A Tensor of type resource. A resource handle. Must be from a VarHandleOp.
  indices   A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.
  updates   A Tensor. A Tensor. Must have the same type as ref. A tensor of updated values to add to ref.
  use_locking   An optional bool. Defaults to True. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceScatterSub Subtracts sparse updates from the variable referenced by resource.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterSub
tf.raw_ops.ResourceScatterSub(
    resource, indices, updates, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] -= updates[...]

# Vector indices (for each i)
ref[indices[i], ...] -= updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] -= updates[i, ..., j, ...]
 Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions add. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].

 Args
  resource   A Tensor of type resource. Should be from a Variable node.
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.
  updates   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. A tensor of updated values to add to ref.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceScatterUpdate Assigns sparse updates to the variable referenced by resource.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceScatterUpdate
tf.raw_ops.ResourceScatterUpdate(
    resource, indices, updates, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] = updates[...]

# Vector indices (for each i)
ref[indices[i], ...] = updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] = updates[i, ..., j, ...]

 Args
  resource   A Tensor of type resource. Should be from a Variable node.
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.
  updates   A Tensor. A tensor of updated values to add to ref.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceSparseApplyAdadelta var: Should be from a Variable().  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceSparseApplyAdadelta
tf.raw_ops.ResourceSparseApplyAdadelta(
    var, accum, accum_update, lr, rho, epsilon, grad, indices, use_locking=False,
    name=None
)

 Args
  var
  A Tensor of type resource.

  accum
  A Tensor of type resource. Should be from a Variable().

  accum_update

 A Tensor of type resource. Should be from a Variable().

  lr   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Learning rate. Must be a scalar.
  rho   A Tensor. Must have the same type as lr. Decay factor. Must be a scalar.
  epsilon   A Tensor. Must have the same type as lr. Constant factor. Must be a scalar.
  grad   A Tensor. Must have the same type as lr. The gradient.
  indices   A Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var and accum.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceSparseApplyAdagrad Update relevant entries in 'var' and 'accum' according to the adagrad scheme.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceSparseApplyAdagrad
tf.raw_ops.ResourceSparseApplyAdagrad(
    var, accum, lr, grad, indices, use_locking=False, update_slots=True, name=None
)
 That is for rows we have grad for, we update var and accum as follows: accum += grad * grad var -= lr * grad * (1 / sqrt(accum))

 Args
  var   A Tensor of type resource. Should be from a Variable().
  accum   A Tensor of type resource. Should be from a Variable().
  lr   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Learning rate. Must be a scalar.
  grad   A Tensor. Must have the same type as lr. The gradient.
  indices   A Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var and accum.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  update_slots   An optional bool. Defaults to True.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.ResourceSparseApplyAdagradDA Update entries in 'var' and 'accum' according to the proximal adagrad scheme.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceSparseApplyAdagradDA
tf.raw_ops.ResourceSparseApplyAdagradDA(
    var, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1,
    l2, global_step, use_locking=False, name=None
)

 Args
  var   A Tensor of type resource. Should be from a Variable().
  gradient_accumulator   A Tensor of type resource. Should be from a Variable().
  gradient_squared_accumulator   A Tensor of type resource. Should be from a Variable().
  grad   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. The gradient.
  indices   A Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var and accum.
  lr   A Tensor. Must have the same type as grad. Learning rate. Must be a scalar.
  l1   A Tensor. Must have the same type as grad. L1 regularization. Must be a scalar.
  l2   A Tensor. Must have the same type as grad. L2 regularization. Must be a scalar.
  global_step   A Tensor of type int64. Training step number. Must be a scalar.
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  name   A name for the operation (optional).

 Returns   The created Operation.