tf.raw_ops.Conv3DBackpropFilter Computes the gradients of 3-D convolution with respect to the filter.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Conv3DBackpropFilter
tf.raw_ops.Conv3DBackpropFilter(
    input, filter, out_backprop, strides, padding, dilations=[1, 1, 1, 1, 1],
    name=None
)

 Args
  input   A Tensor. Must be one of the following types: half, float32, float64. Shape [batch, depth, rows, cols, in_channels].
  filter   A Tensor. Must have the same type as input. Shape [depth, rows, cols, in_channels, out_channels]. in_channels must match between input and filter.
  out_backprop   A Tensor. Must have the same type as input. Backprop signal of shape [batch, out_depth, out_rows, out_cols, out_channels].
  strides   A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1, 1].
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.Conv3DBackpropFilterV2 Computes the gradients of 3-D convolution with respect to the filter.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Conv3DBackpropFilterV2
tf.raw_ops.Conv3DBackpropFilterV2(
    input, filter_sizes, out_backprop, strides, padding,
    data_format='NDHWC', dilations=[1, 1, 1, 1, 1], name=None
)

 Args
  input   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. Shape [batch, depth, rows, cols, in_channels].
  filter_sizes   A Tensor of type int32. An integer vector representing the tensor shape of filter, where filter is a 5-D [filter_depth, filter_height, filter_width, in_channels, out_channels] tensor.
  out_backprop   A Tensor. Must have the same type as input. Backprop signal of shape [batch, out_depth, out_rows, out_cols, out_channels].
  strides   A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.
  data_format   An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1, 1]. 1-D tensor of length 5. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.Conv3DBackpropInput Computes the gradients of 3-D convolution with respect to the input.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Conv3DBackpropInput
tf.raw_ops.Conv3DBackpropInput(
    input, filter, out_backprop, strides, padding, dilations=[1, 1, 1, 1, 1],
    name=None
)

 Args
  input   A Tensor. Must be one of the following types: half, float32, float64. Shape [batch, depth, rows, cols, in_channels].
  filter   A Tensor. Must have the same type as input. Shape [depth, rows, cols, in_channels, out_channels]. in_channels must match between input and filter.
  out_backprop   A Tensor. Must have the same type as input. Backprop signal of shape [batch, out_depth, out_rows, out_cols, out_channels].
  strides   A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1, 1].
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.Conv3DBackpropInputV2 Computes the gradients of 3-D convolution with respect to the input.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Conv3DBackpropInputV2
tf.raw_ops.Conv3DBackpropInputV2(
    input_sizes, filter, out_backprop, strides, padding,
    data_format='NDHWC', dilations=[1, 1, 1, 1, 1], name=None
)

 Args
  input_sizes   A Tensor. Must be one of the following types: int32, int64. An integer vector representing the tensor shape of input, where input is a 5-D [batch, depth, rows, cols, in_channels] tensor.
  filter   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. Shape [depth, rows, cols, in_channels, out_channels]. in_channels must match between input and filter.
  out_backprop   A Tensor. Must have the same type as filter. Backprop signal of shape [batch, out_depth, out_rows, out_cols, out_channels].
  strides   A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.
  data_format   An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1, 1]. 1-D tensor of length 5. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as filter.


tf.raw_ops.Copy Copy a tensor from CPU-to-CPU or GPU-to-GPU.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Copy
tf.raw_ops.Copy(
    input, tensor_name='', debug_ops_spec=[], name=None
)
 Performs CPU-to-CPU or GPU-to-GPU deep-copying of tensor, depending on the device on which the tensor is allocated. N.B.: If the all downstream attached debug ops are disabled given the current gRPC gating status, the output will simply forward the input tensor without deep-copying. See the documentation of Debug* ops for more details. Unlike the CopyHost Op, this op does not have HostMemory constraint on its input or output.

 Args
  input   A Tensor. Input tensor.
  tensor_name   An optional string. Defaults to "". The name of the input tensor.
  debug_ops_spec   An optional list of strings. Defaults to []. A list of debug op spec (op, url, gated_grpc) for attached debug ops. Each element of the list has the format ;;, wherein gated_grpc is boolean represented as 0/1. E.g., "DebugIdentity;grpc://foo:3333;1", "DebugIdentity;file:///tmp/tfdbg_1;0".

  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.CopyHost Copy a tensor to host.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CopyHost
tf.raw_ops.CopyHost(
    input, tensor_name='', debug_ops_spec=[], name=None
)
 Performs CPU-to-CPU deep-copying of tensor. N.B.: If the all downstream attached debug ops are disabled given the current gRPC gating status, the output will simply forward the input tensor without deep-copying. See the documentation of Debug* ops for more details. Unlike the Copy Op, this op has HostMemory constraint on its input or output.

 Args
  input   A Tensor. Input tensor.
  tensor_name   An optional string. Defaults to "". The name of the input tensor.
  debug_ops_spec   An optional list of strings. Defaults to []. A list of debug op spec (op, url, gated_grpc) for attached debug ops. Each element of the list has the format ;;, wherein gated_grpc is boolean represented as 0/1. E.g., "DebugIdentity;grpc://foo:3333;1", "DebugIdentity;file:///tmp/tfdbg_1;0".

  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.Cos Computes cos of x element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Cos
tf.raw_ops.Cos(
    x, name=None
)
 Given an input tensor, this function computes cosine of every element in the tensor. Input range is (-inf, inf) and output range is [-1,1]. If input lies outside the boundary, nan is returned. x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10000, float("inf")])
tf.math.cos(x) ==> [nan -0.91113025 0.87758255 0.5403023 0.36235774 0.48718765 -0.95215535 nan]

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

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.Cosh Computes hyperbolic cosine of x element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Cosh
tf.raw_ops.Cosh(
    x, name=None
)
 Given an input tensor, this function computes hyperbolic cosine of every element in the tensor. Input range is [-inf, inf] and output range is [1, inf]. x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 2, 10, float("inf")])
tf.math.cosh(x) ==> [inf 4.0515420e+03 1.1276259e+00 1.5430807e+00 1.8106556e+00 3.7621956e+00 1.1013233e+04 inf]

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

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.CountUpTo Increments 'ref' until it reaches 'limit'.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CountUpTo
tf.raw_ops.CountUpTo(
    ref, limit, name=None
)

 Args
  ref   A mutable Tensor. Must be one of the following types: int32, int64. Should be from a scalar Variable node.
  limit   An int. If incrementing ref would bring it above limit, instead generates an 'OutOfRange' error.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as ref.


tf.raw_ops.CreateSummaryDbWriter  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CreateSummaryDbWriter
tf.raw_ops.CreateSummaryDbWriter(
    writer, db_uri, experiment_name, run_name, user_name, name=None
)

 Args
  writer   A Tensor of type resource.
  db_uri   A Tensor of type string.
  experiment_name   A Tensor of type string.
  run_name   A Tensor of type string.
  user_name   A Tensor of type string.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.CreateSummaryFileWriter  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CreateSummaryFileWriter
tf.raw_ops.CreateSummaryFileWriter(
    writer, logdir, max_queue, flush_millis, filename_suffix, name=None
)

 Args
  writer   A Tensor of type resource.
  logdir   A Tensor of type string.
  max_queue   A Tensor of type int32.
  flush_millis   A Tensor of type int32.
  filename_suffix   A Tensor of type string.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.CropAndResize Extracts crops from the input image tensor and resizes them.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CropAndResize
tf.raw_ops.CropAndResize(
    image, boxes, box_ind, crop_size, method='bilinear',
    extrapolation_value=0, name=None
)
 Extracts crops from the input image tensor and resizes them using bilinear sampling or nearest neighbor sampling (possibly with aspect ratio change) to a common output size specified by crop_size. This is more general than the crop_to_bounding_box op which extracts a fixed size slice from the input image and does not allow resizing or aspect ratio change. Returns a tensor with crops from the input image at positions defined at the bounding box locations in boxes. The cropped boxes are all resized (with bilinear or nearest neighbor interpolation) to a fixed size = [crop_height, crop_width]. The result is a 4-D tensor [num_boxes, crop_height, crop_width, depth]. The resizing is corner aligned. In particular, if boxes = [[0, 0, 1, 1]], the method will give identical results to using tf.image.resize_bilinear() or tf.image.resize_nearest_neighbor()(depends on the method argument) with align_corners=True.

 Args
  image   A Tensor. Must be one of the following types: uint8, uint16, int8, int16, int32, int64, half, float32, float64. A 4-D tensor of shape [batch, image_height, image_width, depth]. Both image_height and image_width need to be positive.
  boxes   A Tensor of type float32. A 2-D tensor of shape [num_boxes, 4]. The i-th row of the tensor specifies the coordinates of a box in the box_ind[i] image and is specified in normalized coordinates [y1, x1, y2, x2]. A normalized coordinate value of y is mapped to the image coordinate at y * (image_height - 1), so as the [0, 1] interval of normalized image height is mapped to [0, image_height - 1] in image height coordinates. We do allow y1 > y2, in which case the sampled crop is an up-down flipped version of the original image. The width dimension is treated similarly. Normalized coordinates outside the [0, 1] range are allowed, in which case we use extrapolation_value to extrapolate the input image values.
  box_ind   A Tensor of type int32. A 1-D tensor of shape [num_boxes] with int32 values in [0, batch). The value of box_ind[i] specifies the image that the i-th box refers to.
  crop_size   A Tensor of type int32. A 1-D tensor of 2 elements, size = [crop_height, crop_width]. All cropped image patches are resized to this size. The aspect ratio of the image content is not preserved. Both crop_height and crop_width need to be positive.
  method   An optional string from: "bilinear", "nearest". Defaults to "bilinear". A string specifying the sampling method for resizing. It can be either "bilinear" or "nearest" and default to "bilinear". Currently two sampling methods are supported: Bilinear and Nearest Neighbor.
  extrapolation_value   An optional float. Defaults to 0. Value used for extrapolation, when applicable.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.CropAndResizeGradBoxes Computes the gradient of the crop_and_resize op wrt the input boxes tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CropAndResizeGradBoxes
tf.raw_ops.CropAndResizeGradBoxes(
    grads, image, boxes, box_ind, method='bilinear', name=None
)

 Args
  grads   A Tensor of type float32. A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth].
  image   A Tensor. Must be one of the following types: uint8, uint16, int8, int16, int32, int64, half, float32, float64. A 4-D tensor of shape [batch, image_height, image_width, depth]. Both image_height and image_width need to be positive.
  boxes   A Tensor of type float32. A 2-D tensor of shape [num_boxes, 4]. The i-th row of the tensor specifies the coordinates of a box in the box_ind[i] image and is specified in normalized coordinates [y1, x1, y2, x2]. A normalized coordinate value of y is mapped to the image coordinate at y * (image_height - 1), so as the [0, 1] interval of normalized image height is mapped to [0, image_height - 1] in image height coordinates. We do allow y1 > y2, in which case the sampled crop is an up-down flipped version of the original image. The width dimension is treated similarly. Normalized coordinates outside the[0, 1]range are allowed, in which case we useextrapolation_valueto extrapolate the input image values.

 box_ind	  ATensorof typeint32. A 1-D tensor of shape[num_boxes]with int32 values in[0, batch). The value ofbox_ind[i]specifies the image that thei-th box refers to.

 method	  An optionalstringfrom:"bilinear". Defaults to"bilinear". A string specifying the interpolation method. Only 'bilinear' is supported for now.

 name`   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.CropAndResizeGradImage Computes the gradient of the crop_and_resize op wrt the input image tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CropAndResizeGradImage
tf.raw_ops.CropAndResizeGradImage(
    grads, boxes, box_ind, image_size, T, method='bilinear', name=None
)

 Args
  grads   A Tensor of type float32. A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth].
  boxes   A Tensor of type float32. A 2-D tensor of shape [num_boxes, 4]. The i-th row of the tensor specifies the coordinates of a box in the box_ind[i] image and is specified in normalized coordinates [y1, x1, y2, x2]. A normalized coordinate value of y is mapped to the image coordinate at y * (image_height - 1), so as the [0, 1] interval of normalized image height is mapped to [0, image_height - 1] in image height coordinates. We do allow y1 > y2, in which case the sampled crop is an up-down flipped version of the original image. The width dimension is treated similarly. Normalized coordinates outside the[0, 1]range are allowed, in which case we useextrapolation_valueto extrapolate the input image values.

 box_ind	  ATensorof typeint32. A 1-D tensor of shape[num_boxes]with int32 values in[0, batch). The value ofbox_ind[i]specifies the image that thei-th box refers to.

 image_size	  ATensorof typeint32. A 1-D tensor with value[batch, image_height, image_width, depth]containing the original image size. Bothimage_heightandimage_widthneed to be positive.

 T	  A tf.DType from:tf.float32, tf.half, tf.float64.

 method	  An optionalstringfrom:"bilinear", "nearest". Defaults to"bilinear". A string specifying the interpolation method. Only 'bilinear' is supported for now.

 name`   A name for the operation (optional).

 Returns   A Tensor of type T.


tf.raw_ops.Cross Compute the pairwise cross product.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Cross
tf.raw_ops.Cross(
    a, b, name=None
)
 a and b must be the same shape; they can either be simple 3-element vectors, or any shape where the innermost dimension is 3. In the latter case, each pair of corresponding 3-element vectors is cross-multiplied independently.

 Args
  a   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. A tensor containing 3-element vectors.
  b   A Tensor. Must have the same type as a. Another tensor, of same type and shape as a.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as a.


tf.raw_ops.CrossReplicaSum An Op to sum inputs across replicated TPU instances.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CrossReplicaSum
tf.raw_ops.CrossReplicaSum(
    input, group_assignment, name=None
)
 Each instance supplies its own input. For example, suppose there are 8 TPU instances: [A, B, C, D, E, F, G, H]. Passing group_assignment=[[0,2,4,6],[1,3,5,7]] sets A, C, E, G as group 0, and B, D, F, H as group 1. Thus we get the outputs: [A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H].

 Args
  input   A Tensor. Must be one of the following types: half, bfloat16, float32, int32, uint32. The local input to the sum.
  group_assignment   A Tensor of type int32. An int32 tensor with shape [num_groups, num_replicas_per_group]. group_assignment[i] represents the replica ids in the ith subgroup.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.CSRSparseMatrixComponents Reads out the CSR components at batch index.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CSRSparseMatrixComponents
tf.raw_ops.CSRSparseMatrixComponents(
    csr_sparse_matrix, index, type, name=None
)
 This op is meant only for debugging / testing, and its interface is not expected to be stable.

 Args
  csr_sparse_matrix   A Tensor of type variant. A batched CSRSparseMatrix.
  index   A Tensor of type int32. The index in csr_sparse_matrix's batch.
  type   A tf.DType from: tf.float32, tf.float64, tf.complex64, tf.complex128.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (row_ptrs, col_inds, values).     row_ptrs   A Tensor of type int32.
  col_inds   A Tensor of type int32.
  values   A Tensor of type type.


tf.raw_ops.CSRSparseMatrixToDense Convert a (possibly batched) CSRSparseMatrix to dense.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CSRSparseMatrixToDense
tf.raw_ops.CSRSparseMatrixToDense(
    sparse_input, type, name=None
)

 Args
  sparse_input   A Tensor of type variant. A batched CSRSparseMatrix.
  type   A tf.DType from: tf.float32, tf.float64, tf.complex64, tf.complex128.
  name   A name for the operation (optional).

 Returns   A Tensor of type type.


tf.raw_ops.CSRSparseMatrixToSparseTensor Converts a (possibly batched) CSRSparesMatrix to a SparseTensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CSRSparseMatrixToSparseTensor
tf.raw_ops.CSRSparseMatrixToSparseTensor(
    sparse_matrix, type, name=None
)

 Args
  sparse_matrix   A Tensor of type variant. A (possibly batched) CSRSparseMatrix.
  type   A tf.DType from: tf.float32, tf.float64, tf.complex64, tf.complex128.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (indices, values, dense_shape).     indices   A Tensor of type int64.
  values   A Tensor of type type.
  dense_shape   A Tensor of type int64.


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

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

 Returns   A Tensor of type variant.


tf.raw_ops.CSVDatasetV2  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CSVDatasetV2
tf.raw_ops.CSVDatasetV2(
    filenames, compression_type, buffer_size, header, field_delim, use_quote_delim,
    na_value, select_cols, record_defaults, exclude_cols, output_shapes, name=None
)

 Args
  filenames   A Tensor of type string.
  compression_type   A Tensor of type string.
  buffer_size   A Tensor of type int64.
  header   A Tensor of type bool.
  field_delim   A Tensor of type string.
  use_quote_delim   A Tensor of type bool.
  na_value   A Tensor of type string.
  select_cols   A Tensor of type int64.
  record_defaults   A list of Tensor objects with types from: float32, float64, int32, int64, string.
  exclude_cols   A Tensor of type int64.
  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.CTCBeamSearchDecoder Performs beam search decoding on the logits given in input.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CTCBeamSearchDecoder
tf.raw_ops.CTCBeamSearchDecoder(
    inputs, sequence_length, beam_width, top_paths, merge_repeated=True, name=None
)
 A note about the attribute merge_repeated: For the beam search decoder, this means that if consecutive entries in a beam are the same, only the first of these is emitted. That is, when the top path is "A B B B B", "A B" is returned if merge_repeated = True but "A B B B B" is returned if merge_repeated = False.

 Args
  inputs   A Tensor. Must be one of the following types: float32, float64. 3-D, shape: (max_time x batch_size x num_classes), the logits.
  sequence_length   A Tensor of type int32. A vector containing sequence lengths, size (batch).
  beam_width   An int that is >= 1. A scalar >= 0 (beam search beam width).
  top_paths   An int that is >= 1. A scalar >= 0, <= beam_width (controls output size).
  merge_repeated   An optional bool. Defaults to True. If true, merge repeated classes in output.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (decoded_indices, decoded_values, decoded_shape, log_probability).     decoded_indices   A list of top_paths Tensor objects with type int64.
  decoded_values   A list of top_paths Tensor objects with type int64.
  decoded_shape   A list of top_paths Tensor objects with type int64.
  log_probability   A Tensor. Has the same type as inputs.


tf.raw_ops.CTCGreedyDecoder Performs greedy decoding on the logits given in inputs.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CTCGreedyDecoder
tf.raw_ops.CTCGreedyDecoder(
    inputs, sequence_length, merge_repeated=False, name=None
)
 A note about the attribute merge_repeated: if enabled, when consecutive logits' maximum indices are the same, only the first of these is emitted. Labeling the blank '*', the sequence "A B B * B B" becomes "A B B" if merge_repeated = True and "A B B B B" if merge_repeated = False. Regardless of the value of merge_repeated, if the maximum index of a given time and batch corresponds to the blank, index (num_classes - 1), no new element is emitted.

 Args
  inputs   A Tensor. Must be one of the following types: float32, float64. 3-D, shape: (max_time x batch_size x num_classes), the logits.
  sequence_length   A Tensor of type int32. A vector containing sequence lengths, size (batch_size).
  merge_repeated   An optional bool. Defaults to False. If True, merge repeated classes in output.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (decoded_indices, decoded_values, decoded_shape, log_probability).     decoded_indices   A Tensor of type int64.
  decoded_values   A Tensor of type int64.
  decoded_shape   A Tensor of type int64.
  log_probability   A Tensor. Has the same type as inputs.


tf.raw_ops.CTCLoss Calculates the CTC Loss (log probability) for each batch entry. Also calculates  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CTCLoss
tf.raw_ops.CTCLoss(
    inputs, labels_indices, labels_values, sequence_length,
    preprocess_collapse_repeated=False, ctc_merge_repeated=True,
    ignore_longer_outputs_than_inputs=False, name=None
)
 the gradient. This class performs the softmax operation for you, so inputs should be e.g. linear projections of outputs by an LSTM.

 Args
  inputs   A Tensor. Must be one of the following types: float32, float64. 3-D, shape: (max_time x batch_size x num_classes), the logits.
  labels_indices   A Tensor of type int64. The indices of a SparseTensor<int32, 2>. labels_indices(i, :) == [b, t] means labels_values(i) stores the id for (batch b, time t).
  labels_values   A Tensor of type int32. The values (labels) associated with the given batch and time.
  sequence_length   A Tensor of type int32. A vector containing sequence lengths (batch).
  preprocess_collapse_repeated   An optional bool. Defaults to False. Scalar, if true then repeated labels are collapsed prior to the CTC calculation.
  ctc_merge_repeated   An optional bool. Defaults to True. Scalar. If set to false, during CTC calculation repeated non-blank labels will not be merged and are interpreted as individual labels. This is a simplified version of CTC.
  ignore_longer_outputs_than_inputs   An optional bool. Defaults to False. Scalar. If set to true, during CTC calculation, items that have longer output sequences than input sequences are skipped: they don't contribute to the loss term and have zero-gradient.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (loss, gradient).     loss   A Tensor. Has the same type as inputs.
  gradient   A Tensor. Has the same type as inputs.


tf.raw_ops.CTCLossV2 Calculates the CTC Loss (log probability) for each batch entry. Also calculates  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CTCLossV2
tf.raw_ops.CTCLossV2(
    inputs, labels_indices, labels_values, sequence_length,
    preprocess_collapse_repeated=False, ctc_merge_repeated=True,
    ignore_longer_outputs_than_inputs=False, name=None
)
 the gradient. This class performs the softmax operation for you, so inputs should be e.g. linear projections of outputs by an LSTM.

 Args
  inputs   A Tensor of type float32. 3-D, shape: (max_time x batch_size x num_classes), the logits. Default blank label is 0 rather num_classes - 1.
  labels_indices   A Tensor of type int64. The indices of a SparseTensor<int32, 2>. labels_indices(i, :) == [b, t] means labels_values(i) stores the id for (batch b, time t).
  labels_values   A Tensor of type int32. The values (labels) associated with the given batch and time.
  sequence_length   A Tensor of type int32. A vector containing sequence lengths (batch).
  preprocess_collapse_repeated   An optional bool. Defaults to False. Scalar, if true then repeated labels are collapsed prior to the CTC calculation.
  ctc_merge_repeated   An optional bool. Defaults to True. Scalar. If set to false, during CTC calculation repeated non-blank labels will not be merged and are interpreted as individual labels. This is a simplified version of CTC.
  ignore_longer_outputs_than_inputs   An optional bool. Defaults to False. Scalar. If set to true, during CTC calculation, items that have longer output sequences than input sequences are skipped: they don't contribute to the loss term and have zero-gradient.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (loss, gradient).     loss   A Tensor of type float32.
  gradient   A Tensor of type float32.


tf.raw_ops.CudnnRNN A RNN backed by cuDNN.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CudnnRNN
tf.raw_ops.CudnnRNN(
    input, input_h, input_c, params, rnn_mode='lstm',
    input_mode='linear_input', direction='unidirectional',
    dropout=0, seed=0, seed2=0, is_training=True, name=None
)
 Computes the RNN from the input and initial states, with respect to the params buffer. rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and the actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. Should be "unidirectional" or "bidirectional". dropout: Dropout probability. When set to 0., dropout is disabled. seed: The 1st part of a seed to initialize dropout. seed2: The 2nd part of a seed to initialize dropout. input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, num_units]. input_c: For LSTM, a 3-D tensor with the shape of [num_layer * dir, batch, num_units]. For other models, it is ignored. params: A 1-D tensor that contains the weights and biases in an opaque layout. The size must be created through CudnnRNNParamsSize, and initialized separately. Note that they might not be compatible across different generations. So it is a good idea to save and restore output: A 3-D tensor with the shape of [seq_length, batch_size, dir * num_units]. output_h: The same shape has input_h. output_c: The same shape as input_c for LSTM. An empty tensor for other models. is_training: Indicates whether this operation is used for inference or training. reserve_space: An opaque tensor that can be used in backprop calculation. It is only produced if is_training is false.

 Args
  input   A Tensor. Must be one of the following types: half, float32, float64.
  input_h   A Tensor. Must have the same type as input.
  input_c   A Tensor. Must have the same type as input.
  params   A Tensor. Must have the same type as input.
  rnn_mode   An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".
  input_mode   An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".
  direction   An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".
  dropout   An optional float. Defaults to 0.
  seed   An optional int. Defaults to 0.
  seed2   An optional int. Defaults to 0.
  is_training   An optional bool. Defaults to True.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (output, output_h, output_c, reserve_space).     output   A Tensor. Has the same type as input.
  output_h   A Tensor. Has the same type as input.
  output_c   A Tensor. Has the same type as input.
  reserve_space   A Tensor. Has the same type as input.


tf.raw_ops.CudnnRNNBackprop Backprop step of CudnnRNN.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CudnnRNNBackprop
tf.raw_ops.CudnnRNNBackprop(
    input, input_h, input_c, params, output, output_h, output_c, output_backprop,
    output_h_backprop, output_c_backprop, reserve_space, rnn_mode='lstm',
    input_mode='linear_input', direction='unidirectional',
    dropout=0, seed=0, seed2=0, name=None
)
 Compute the backprop of both data and weights in a RNN. rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and the actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. Should be "unidirectional" or "bidirectional". dropout: Dropout probability. When set to 0., dropout is disabled. seed: The 1st part of a seed to initialize dropout. seed2: The 2nd part of a seed to initialize dropout. input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, num_units]. input_c: For LSTM, a 3-D tensor with the shape of [num_layer * dir, batch, num_units]. For other models, it is ignored. params: A 1-D tensor that contains the weights and biases in an opaque layout. The size must be created through CudnnRNNParamsSize, and initialized separately. Note that they might not be compatible across different generations. So it is a good idea to save and restore output: A 3-D tensor with the shape of [seq_length, batch_size, dir * num_units]. output_h: The same shape has input_h. output_c: The same shape as input_c for LSTM. An empty tensor for other models. output_backprop: A 3-D tensor with the same shape as output in the forward pass. output_h_backprop: A 3-D tensor with the same shape as output_h in the forward pass. output_c_backprop: A 3-D tensor with the same shape as output_c in the forward pass. reserve_space: The same reserve_space produced in for forward operation. input_backprop: The backprop to input in the forward pass. Has the same shape as input. input_h_backprop: The backprop to input_h in the forward pass. Has the same shape as input_h. input_c_backprop: The backprop to input_c in the forward pass. Has the same shape as input_c. params_backprop: The backprop to the params buffer in the forward pass. Has the same shape as params.

 Args
  input   A Tensor. Must be one of the following types: half, float32, float64.
  input_h   A Tensor. Must have the same type as input.
  input_c   A Tensor. Must have the same type as input.
  params   A Tensor. Must have the same type as input.
  output   A Tensor. Must have the same type as input.
  output_h   A Tensor. Must have the same type as input.
  output_c   A Tensor. Must have the same type as input.
  output_backprop   A Tensor. Must have the same type as input.
  output_h_backprop   A Tensor. Must have the same type as input.
  output_c_backprop   A Tensor. Must have the same type as input.
  reserve_space   A Tensor. Must have the same type as input.
  rnn_mode   An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".
  input_mode   An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".
  direction   An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".
  dropout   An optional float. Defaults to 0.
  seed   An optional int. Defaults to 0.
  seed2   An optional int. Defaults to 0.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (input_backprop, input_h_backprop, input_c_backprop, params_backprop).     input_backprop   A Tensor. Has the same type as input.
  input_h_backprop   A Tensor. Has the same type as input.
  input_c_backprop   A Tensor. Has the same type as input.
  params_backprop   A Tensor. Has the same type as input.


tf.raw_ops.CudnnRNNBackpropV2 Backprop step of CudnnRNN.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CudnnRNNBackpropV2
tf.raw_ops.CudnnRNNBackpropV2(
    input, input_h, input_c, params, output, output_h, output_c, output_backprop,
    output_h_backprop, output_c_backprop, reserve_space, host_reserved,
    rnn_mode='lstm', input_mode='linear_input',
    direction='unidirectional', dropout=0, seed=0, seed2=0, name=None
)
 Compute the backprop of both data and weights in a RNN. Takes an extra "host_reserved" inupt than CudnnRNNBackprop, which is used to determine RNN cudnnRNNAlgo_t and cudnnMathType_t. rnn_mode: Indicates the type of the RNN model. input_mode: Indicates whether there is a linear projection between the input and the actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. Should be "unidirectional" or "bidirectional". dropout: Dropout probability. When set to 0., dropout is disabled. seed: The 1st part of a seed to initialize dropout. seed2: The 2nd part of a seed to initialize dropout. input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, num_units]. input_c: For LSTM, a 3-D tensor with the shape of [num_layer * dir, batch, num_units]. For other models, it is ignored. params: A 1-D tensor that contains the weights and biases in an opaque layout. The size must be created through CudnnRNNParamsSize, and initialized separately. Note that they might not be compatible across different generations. So it is a good idea to save and restore output: A 3-D tensor with the shape of [seq_length, batch_size, dir * num_units]. output_h: The same shape has input_h. output_c: The same shape as input_c for LSTM. An empty tensor for other models. output_backprop: A 3-D tensor with the same shape as output in the forward pass. output_h_backprop: A 3-D tensor with the same shape as output_h in the forward pass. output_c_backprop: A 3-D tensor with the same shape as output_c in the forward pass. reserve_space: The same reserve_space produced in the forward operation. host_reserved: The same host_reserved produced in the forward operation. input_backprop: The backprop to input in the forward pass. Has the same shape as input. input_h_backprop: The backprop to input_h in the forward pass. Has the same shape as input_h. input_c_backprop: The backprop to input_c in the forward pass. Has the same shape as input_c. params_backprop: The backprop to the params buffer in the forward pass. Has the same shape as params.

 Args
  input   A Tensor. Must be one of the following types: half, float32, float64.
  input_h   A Tensor. Must have the same type as input.
  input_c   A Tensor. Must have the same type as input.
  params   A Tensor. Must have the same type as input.
  output   A Tensor. Must have the same type as input.
  output_h   A Tensor. Must have the same type as input.
  output_c   A Tensor. Must have the same type as input.
  output_backprop   A Tensor. Must have the same type as input.
  output_h_backprop   A Tensor. Must have the same type as input.
  output_c_backprop   A Tensor. Must have the same type as input.
  reserve_space   A Tensor. Must have the same type as input.
  host_reserved   A Tensor of type int8.
  rnn_mode   An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".
  input_mode   An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".
  direction   An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".
  dropout   An optional float. Defaults to 0.
  seed   An optional int. Defaults to 0.
  seed2   An optional int. Defaults to 0.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (input_backprop, input_h_backprop, input_c_backprop, params_backprop).     input_backprop   A Tensor. Has the same type as input.
  input_h_backprop   A Tensor. Has the same type as input.
  input_c_backprop   A Tensor. Has the same type as input.
  params_backprop   A Tensor. Has the same type as input.


tf.raw_ops.CudnnRNNBackpropV3 Backprop step of CudnnRNNV3.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CudnnRNNBackpropV3
tf.raw_ops.CudnnRNNBackpropV3(
    input, input_h, input_c, params, sequence_lengths, output, output_h, output_c,
    output_backprop, output_h_backprop, output_c_backprop, reserve_space,
    host_reserved, rnn_mode='lstm', input_mode='linear_input',
    direction='unidirectional', dropout=0, seed=0, seed2=0, num_proj=0,
    time_major=True, name=None
)
 Compute the backprop of both data and weights in a RNN. Takes an extra "sequence_lengths" input than CudnnRNNBackprop. rnn_mode: Indicates the type of the RNN model. input_mode: Indicates whether there is a linear projection between the input and the actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. Should be "unidirectional" or "bidirectional". dropout: Dropout probability. When set to 0., dropout is disabled. seed: The 1st part of a seed to initialize dropout. seed2: The 2nd part of a seed to initialize dropout. input: If time_major is true, this is a 3-D tensor with the shape of [seq_length, batch_size, input_size]. If time_major is false, the shape is [batch_size, seq_length, input_size]. input_h: If time_major is true, this is a 3-D tensor with the shape of [num_layer * dir, batch_size, num_units]. If time_major is false, the shape is [batch_size, num_layer * dir, num_units]. input_c: For LSTM, a 3-D tensor with the shape of [num_layer * dir, batch, num_units]. For other models, it is ignored. params: A 1-D tensor that contains the weights and biases in an opaque layout. The size must be created through CudnnRNNParamsSize, and initialized separately. Note that they might not be compatible across different generations. So it is a good idea to save and restore sequence_lengths: a vector of lengths of each input sequence. output: If time_major is true, this is a 3-D tensor with the shape of [seq_length, batch_size, dir * num_units]. If time_major is false, the shape is [batch_size, seq_length, dir * num_units]. output_h: The same shape has input_h. output_c: The same shape as input_c for LSTM. An empty tensor for other models. output_backprop: A 3-D tensor with the same shape as output in the forward pass. output_h_backprop: A 3-D tensor with the same shape as output_h in the forward pass. output_c_backprop: A 3-D tensor with the same shape as output_c in the forward pass. time_major: Indicates whether the input/output format is time major or batch major. reserve_space: The same reserve_space produced in the forward operation. input_backprop: The backprop to input in the forward pass. Has the same shape as input. input_h_backprop: The backprop to input_h in the forward pass. Has the same shape as input_h. input_c_backprop: The backprop to input_c in the forward pass. Has the same shape as input_c. params_backprop: The backprop to the params buffer in the forward pass. Has the same shape as params.

 Args
  input   A Tensor. Must be one of the following types: half, float32, float64.
  input_h   A Tensor. Must have the same type as input.
  input_c   A Tensor. Must have the same type as input.
  params   A Tensor. Must have the same type as input.
  sequence_lengths   A Tensor of type int32.
  output   A Tensor. Must have the same type as input.
  output_h   A Tensor. Must have the same type as input.
  output_c   A Tensor. Must have the same type as input.
  output_backprop   A Tensor. Must have the same type as input.
  output_h_backprop   A Tensor. Must have the same type as input.
  output_c_backprop   A Tensor. Must have the same type as input.
  reserve_space   A Tensor. Must have the same type as input.
  host_reserved   A Tensor of type int8.
  rnn_mode   An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".
  input_mode   An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".
  direction   An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".
  dropout   An optional float. Defaults to 0.
  seed   An optional int. Defaults to 0.
  seed2   An optional int. Defaults to 0.
  num_proj   An optional int. Defaults to 0.
  time_major   An optional bool. Defaults to True.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (input_backprop, input_h_backprop, input_c_backprop, params_backprop).     input_backprop   A Tensor. Has the same type as input.
  input_h_backprop   A Tensor. Has the same type as input.
  input_c_backprop   A Tensor. Has the same type as input.
  params_backprop   A Tensor. Has the same type as input.


tf.raw_ops.CudnnRNNCanonicalToParams Converts CudnnRNN params from canonical form to usable form.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CudnnRNNCanonicalToParams
tf.raw_ops.CudnnRNNCanonicalToParams(
    num_layers, num_units, input_size, weights, biases, rnn_mode='lstm',
    input_mode='linear_input', direction='unidirectional',
    dropout=0, seed=0, seed2=0, name=None
)
 Writes a set of weights into the opaque params buffer so they can be used in upcoming training or inferences. Note that the params buffer may not be compatible across different GPUs. So any save and restoration should be converted to and from the canonical weights and biases. num_layers: Specifies the number of layers in the RNN model. num_units: Specifies the size of the hidden state. input_size: Specifies the size of the input state. weights: the canonical form of weights that can be used for saving and restoration. They are more likely to be compatible across different generations. biases: the canonical form of biases that can be used for saving and restoration. They are more likely to be compatible across different generations. num_params: number of parameter sets for all layers. Each layer may contain multiple parameter sets, with each set consisting of a weight matrix and a bias vector. rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and The actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. dir = (direction == bidirectional) ? 2 : 1 dropout: dropout probability. When set to 0., dropout is disabled. seed: the 1st part of a seed to initialize dropout. seed2: the 2nd part of a seed to initialize dropout.

 Args
  num_layers   A Tensor of type int32.
  num_units   A Tensor of type int32.
  input_size   A Tensor of type int32.
  weights   A list of at least 1 Tensor objects with the same type in: half, float32, float64.
  biases   A list with the same length as weights of Tensor objects with the same type as weights.
  rnn_mode   An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".
  input_mode   An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".
  direction   An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".
  dropout   An optional float. Defaults to 0.
  seed   An optional int. Defaults to 0.
  seed2   An optional int. Defaults to 0.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as weights.


tf.raw_ops.CudnnRNNCanonicalToParamsV2 Converts CudnnRNN params from canonical form to usable form. It supports the projection in LSTM.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CudnnRNNCanonicalToParamsV2
tf.raw_ops.CudnnRNNCanonicalToParamsV2(
    num_layers, num_units, input_size, weights, biases, rnn_mode='lstm',
    input_mode='linear_input', direction='unidirectional',
    dropout=0, seed=0, seed2=0, num_proj=0, name=None
)
 Writes a set of weights into the opaque params buffer so they can be used in upcoming training or inferences. Note that the params buffer may not be compatible across different GPUs. So any save and restoration should be converted to and from the canonical weights and biases. num_layers: Specifies the number of layers in the RNN model. num_units: Specifies the size of the hidden state. input_size: Specifies the size of the input state. weights: the canonical form of weights that can be used for saving and restoration. They are more likely to be compatible across different generations. biases: the canonical form of biases that can be used for saving and restoration. They are more likely to be compatible across different generations. num_params_weights: number of weight parameter matrix for all layers. num_params_biases: number of bias parameter vector for all layers. rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and The actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. dir = (direction == bidirectional) ? 2 : 1 dropout: dropout probability. When set to 0., dropout is disabled. seed: the 1st part of a seed to initialize dropout. seed2: the 2nd part of a seed to initialize dropout. num_proj: The output dimensionality for the projection matrices. If None or 0, no projection is performed.

 Args
  num_layers   A Tensor of type int32.
  num_units   A Tensor of type int32.
  input_size   A Tensor of type int32.
  weights   A list of at least 1 Tensor objects with the same type in: half, float32, float64.
  biases   A list of at least 1 Tensor objects with the same type as weights.
  rnn_mode   An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".
  input_mode   An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".
  direction   An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".
  dropout   An optional float. Defaults to 0.
  seed   An optional int. Defaults to 0.
  seed2   An optional int. Defaults to 0.
  num_proj   An optional int. Defaults to 0.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as weights.


tf.raw_ops.CudnnRNNParamsSize Computes size of weights that can be used by a Cudnn RNN model.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CudnnRNNParamsSize
tf.raw_ops.CudnnRNNParamsSize(
    num_layers, num_units, input_size, T, S, rnn_mode='lstm',
    input_mode='linear_input', direction='unidirectional',
    dropout=0, seed=0, seed2=0, num_proj=0, name=None
)
 Return the params size that can be used by the Cudnn RNN model. Subsequent weight allocation and initialization should use this size. num_layers: Specifies the number of layers in the RNN model. num_units: Specifies the size of the hidden state. input_size: Specifies the size of the input state. rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and The actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. dir = (direction == bidirectional) ? 2 : 1 dropout: dropout probability. When set to 0., dropout is disabled. seed: the 1st part of a seed to initialize dropout. seed2: the 2nd part of a seed to initialize dropout. params_size: The size of the params buffer that should be allocated and initialized for this RNN model. Note that this params buffer may not be compatible across GPUs. Please use CudnnRNNParamsWeights and CudnnRNNParamsBiases to save and restore them in a way that is compatible across different runs.

 Args
  num_layers   A Tensor of type int32.
  num_units   A Tensor of type int32.
  input_size   A Tensor of type int32.
  T   A tf.DType from: tf.half, tf.float32, tf.float64.
  S   A tf.DType from: tf.int32, tf.int64.
  rnn_mode   An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".
  input_mode   An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".
  direction   An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".
  dropout   An optional float. Defaults to 0.
  seed   An optional int. Defaults to 0.
  seed2   An optional int. Defaults to 0.
  num_proj   An optional int. Defaults to 0.
  name   A name for the operation (optional).

 Returns   A Tensor of type S.


tf.raw_ops.CudnnRNNParamsToCanonical Retrieves CudnnRNN params in canonical form.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CudnnRNNParamsToCanonical
tf.raw_ops.CudnnRNNParamsToCanonical(
    num_layers, num_units, input_size, params, num_params,
    rnn_mode='lstm', input_mode='linear_input',
    direction='unidirectional', dropout=0, seed=0, seed2=0, name=None
)
 Retrieves a set of weights from the opaque params buffer that can be saved and restored in a way compatible with future runs. Note that the params buffer may not be compatible across different GPUs. So any save and restoration should be converted to and from the canonical weights and biases. num_layers: Specifies the number of layers in the RNN model. num_units: Specifies the size of the hidden state. input_size: Specifies the size of the input state. num_params: number of parameter sets for all layers. Each layer may contain multiple parameter sets, with each set consisting of a weight matrix and a bias vector. weights: the canonical form of weights that can be used for saving and restoration. They are more likely to be compatible across different generations. biases: the canonical form of biases that can be used for saving and restoration. They are more likely to be compatible across different generations. rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and The actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. dir = (direction == bidirectional) ? 2 : 1 dropout: dropout probability. When set to 0., dropout is disabled. seed: the 1st part of a seed to initialize dropout. seed2: the 2nd part of a seed to initialize dropout.

 Args
  num_layers   A Tensor of type int32.
  num_units   A Tensor of type int32.
  input_size   A Tensor of type int32.
  params   A Tensor. Must be one of the following types: half, float32, float64.
  num_params   An int that is >= 1.
  rnn_mode   An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".
  input_mode   An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".
  direction   An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".
  dropout   An optional float. Defaults to 0.
  seed   An optional int. Defaults to 0.
  seed2   An optional int. Defaults to 0.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (weights, biases).     weights   A list of num_params Tensor objects with the same type as params.
  biases   A list of num_params Tensor objects with the same type as params.


tf.raw_ops.CudnnRNNParamsToCanonicalV2 Retrieves CudnnRNN params in canonical form. It supports the projection in LSTM.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CudnnRNNParamsToCanonicalV2
tf.raw_ops.CudnnRNNParamsToCanonicalV2(
    num_layers, num_units, input_size, params, num_params_weights,
    num_params_biases, rnn_mode='lstm',
    input_mode='linear_input', direction='unidirectional',
    dropout=0, seed=0, seed2=0, num_proj=0, name=None
)
 Retrieves a set of weights from the opaque params buffer that can be saved and restored in a way compatible with future runs. Note that the params buffer may not be compatible across different GPUs. So any save and restoration should be converted to and from the canonical weights and biases. num_layers: Specifies the number of layers in the RNN model. num_units: Specifies the size of the hidden state. input_size: Specifies the size of the input state. num_params_weights: number of weight parameter matrix for all layers. num_params_biases: number of bias parameter vector for all layers. weights: the canonical form of weights that can be used for saving and restoration. They are more likely to be compatible across different generations. biases: the canonical form of biases that can be used for saving and restoration. They are more likely to be compatible across different generations. rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and The actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. dir = (direction == bidirectional) ? 2 : 1 dropout: dropout probability. When set to 0., dropout is disabled. seed: the 1st part of a seed to initialize dropout. seed2: the 2nd part of a seed to initialize dropout. num_proj: The output dimensionality for the projection matrices. If None or 0, no projection is performed.

 Args
  num_layers   A Tensor of type int32.
  num_units   A Tensor of type int32.
  input_size   A Tensor of type int32.
  params   A Tensor. Must be one of the following types: half, float32, float64.
  num_params_weights   An int that is >= 1.
  num_params_biases   An int that is >= 1.
  rnn_mode   An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".
  input_mode   An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".
  direction   An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".
  dropout   An optional float. Defaults to 0.
  seed   An optional int. Defaults to 0.
  seed2   An optional int. Defaults to 0.
  num_proj   An optional int. Defaults to 0.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (weights, biases).     weights   A list of num_params_weights Tensor objects with the same type as params.
  biases   A list of num_params_biases Tensor objects with the same type as params.


tf.raw_ops.CudnnRNNV2 A RNN backed by cuDNN.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CudnnRNNV2
tf.raw_ops.CudnnRNNV2(
    input, input_h, input_c, params, rnn_mode='lstm',
    input_mode='linear_input', direction='unidirectional',
    dropout=0, seed=0, seed2=0, is_training=True, name=None
)
 Computes the RNN from the input and initial states, with respect to the params buffer. Produces one extra output "host_reserved" than CudnnRNN. rnn_mode: Indicates the type of the RNN model. input_mode: Indicates whether there is a linear projection between the input and the actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. Should be "unidirectional" or "bidirectional". dropout: Dropout probability. When set to 0., dropout is disabled. seed: The 1st part of a seed to initialize dropout. seed2: The 2nd part of a seed to initialize dropout. input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, num_units]. input_c: For LSTM, a 3-D tensor with the shape of [num_layer * dir, batch, num_units]. For other models, it is ignored. params: A 1-D tensor that contains the weights and biases in an opaque layout. The size must be created through CudnnRNNParamsSize, and initialized separately. Note that they might not be compatible across different generations. So it is a good idea to save and restore output: A 3-D tensor with the shape of [seq_length, batch_size, dir * num_units]. output_h: The same shape has input_h. output_c: The same shape as input_c for LSTM. An empty tensor for other models. is_training: Indicates whether this operation is used for inference or training. reserve_space: An opaque tensor that can be used in backprop calculation. It is only produced if is_training is true. host_reserved: An opaque tensor that can be used in backprop calculation. It is only produced if is_training is true. It is output on host memory rather than device memory.

 Args
  input   A Tensor. Must be one of the following types: half, float32, float64.
  input_h   A Tensor. Must have the same type as input.
  input_c   A Tensor. Must have the same type as input.
  params   A Tensor. Must have the same type as input.
  rnn_mode   An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".
  input_mode   An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".
  direction   An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".
  dropout   An optional float. Defaults to 0.
  seed   An optional int. Defaults to 0.
  seed2   An optional int. Defaults to 0.
  is_training   An optional bool. Defaults to True.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (output, output_h, output_c, reserve_space, host_reserved).     output   A Tensor. Has the same type as input.
  output_h   A Tensor. Has the same type as input.
  output_c   A Tensor. Has the same type as input.
  reserve_space   A Tensor. Has the same type as input.
  host_reserved   A Tensor of type int8.


tf.raw_ops.CudnnRNNV3 A RNN backed by cuDNN.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CudnnRNNV3
tf.raw_ops.CudnnRNNV3(
    input, input_h, input_c, params, sequence_lengths, rnn_mode='lstm',
    input_mode='linear_input', direction='unidirectional',
    dropout=0, seed=0, seed2=0, num_proj=0, is_training=True, time_major=True,
    name=None
)
 Computes the RNN from the input and initial states, with respect to the params buffer. Accepts one extra input "sequence_lengths" than CudnnRNN. rnn_mode: Indicates the type of the RNN model. input_mode: Indicates whether there is a linear projection between the input and the actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. Should be "unidirectional" or "bidirectional". dropout: Dropout probability. When set to 0., dropout is disabled. seed: The 1st part of a seed to initialize dropout. seed2: The 2nd part of a seed to initialize dropout. input: If time_major is true, this is a 3-D tensor with the shape of [seq_length, batch_size, input_size]. If time_major is false, the shape is [batch_size, seq_length, input_size]. input_h: If time_major is true, this is a 3-D tensor with the shape of [num_layer * dir, batch_size, num_units]. If time_major is false, the shape is [batch_size, num_layer * dir, num_units]. input_c: For LSTM, a 3-D tensor with the shape of [num_layer * dir, batch, num_units]. For other models, it is ignored. params: A 1-D tensor that contains the weights and biases in an opaque layout. The size must be created through CudnnRNNParamsSize, and initialized separately. Note that they might not be compatible across different generations. So it is a good idea to save and restore sequence_lengths: a vector of lengths of each input sequence. output: If time_major is true, this is a 3-D tensor with the shape of [seq_length, batch_size, dir * num_units]. If time_major is false, the shape is [batch_size, seq_length, dir * num_units]. output_h: The same shape has input_h. output_c: The same shape as input_c for LSTM. An empty tensor for other models. is_training: Indicates whether this operation is used for inference or training. time_major: Indicates whether the input/output format is time major or batch major. reserve_space: An opaque tensor that can be used in backprop calculation. It is only produced if is_training is true.

 Args
  input   A Tensor. Must be one of the following types: half, float32, float64.
  input_h   A Tensor. Must have the same type as input.
  input_c   A Tensor. Must have the same type as input.
  params   A Tensor. Must have the same type as input.
  sequence_lengths   A Tensor of type int32.
  rnn_mode   An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".
  input_mode   An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".
  direction   An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".
  dropout   An optional float. Defaults to 0.
  seed   An optional int. Defaults to 0.
  seed2   An optional int. Defaults to 0.
  num_proj   An optional int. Defaults to 0.
  is_training   An optional bool. Defaults to True.
  time_major   An optional bool. Defaults to True.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (output, output_h, output_c, reserve_space, host_reserved).     output   A Tensor. Has the same type as input.
  output_h   A Tensor. Has the same type as input.
  output_c   A Tensor. Has the same type as input.
  reserve_space   A Tensor. Has the same type as input.
  host_reserved   A Tensor of type int8.


tf.raw_ops.Cumprod Compute the cumulative product of the tensor x along axis.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Cumprod
tf.raw_ops.Cumprod(
    x, axis, exclusive=False, reverse=False, name=None
)
 By default, this op performs an inclusive cumprod, which means that the first element of the input is identical to the first element of the output: tf.cumprod([a, b, c])  # => [a, a * b, a * b * c]
 By setting the exclusive kwarg to True, an exclusive cumprod is performed instead: tf.cumprod([a, b, c], exclusive=True)  # => [1, a, a * b]
 By setting the reverse kwarg to True, the cumprod is performed in the opposite direction: tf.cumprod([a, b, c], reverse=True)  # => [a * b * c, b * c, c]
 This is more efficient than using separate tf.reverse ops. The reverse and exclusive kwargs can also be combined: tf.cumprod([a, b, c], exclusive=True, reverse=True)  # => [b * c, c, 1]

 Args
  x   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. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half.
  axis   A Tensor. Must be one of the following types: int32, int64. A Tensor of type int32 (default: 0). Must be in the range [-rank(x), rank(x)).
  exclusive   An optional bool. Defaults to False. If True, perform exclusive cumprod.
  reverse   An optional bool. Defaults to False. A bool (default: False).
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.Cumsum Compute the cumulative sum of the tensor x along axis.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Cumsum
tf.raw_ops.Cumsum(
    x, axis, exclusive=False, reverse=False, name=None
)
 By default, this op performs an inclusive cumsum, which means that the first element of the input is identical to the first element of the output: tf.cumsum([a, b, c])  # => [a, a + b, a + b + c]
 By setting the exclusive kwarg to True, an exclusive cumsum is performed instead: tf.cumsum([a, b, c], exclusive=True)  # => [0, a, a + b]
 By setting the reverse kwarg to True, the cumsum is performed in the opposite direction: tf.cumsum([a, b, c], reverse=True)  # => [a + b + c, b + c, c]
 This is more efficient than using separate tf.reverse ops. The reverse and exclusive kwargs can also be combined: tf.cumsum([a, b, c], exclusive=True, reverse=True)  # => [b + c, c, 0]

 Args
  x   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. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half.
  axis   A Tensor. Must be one of the following types: int32, int64. A Tensor of type int32 (default: 0). Must be in the range [-rank(x), rank(x)).
  exclusive   An optional bool. Defaults to False. If True, perform exclusive cumsum.
  reverse   An optional bool. Defaults to False. A bool (default: False).
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.CumulativeLogsumexp Compute the cumulative product of the tensor x along axis.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.CumulativeLogsumexp
tf.raw_ops.CumulativeLogsumexp(
    x, axis, exclusive=False, reverse=False, name=None
)
 By default, this op performs an inclusive cumulative log-sum-exp, which means that the first element of the input is identical to the first element of the output: tf.math.cumulative_logsumexp([a, b, c])  # => [a, log(exp(a) + exp(b)), log(exp(a) + exp(b) + exp(c))]
 By setting the exclusive kwarg to True, an exclusive cumulative log-sum-exp is performed instead: tf.cumulative_logsumexp([a, b, c], exclusive=True)  # => [-inf, a, log(exp(a) * exp(b))]
 Note that the neutral element of the log-sum-exp operation is -inf, however, for performance reasons, the minimal value representable by the floating point type is used instead. By setting the reverse kwarg to True, the cumulative log-sum-exp is performed in the opposite direction.

 Args
  x   A Tensor. Must be one of the following types: half, float32, float64. A Tensor. Must be one of the following types: float16, float32, float64.
  axis   A Tensor. Must be one of the following types: int32, int64. A Tensor of type int32 (default: 0). Must be in the range [-rank(x), rank(x)).
  exclusive   An optional bool. Defaults to False. If True, perform exclusive cumulative log-sum-exp.
  reverse   An optional bool. Defaults to False. A bool (default: False).
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.DataFormatDimMap Returns the dimension index in the destination data format given the one in  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DataFormatDimMap
tf.raw_ops.DataFormatDimMap(
    x, src_format='NHWC', dst_format='NCHW', name=None
)
 the source data format.

 Args
  x   A Tensor. Must be one of the following types: int32, int64. A Tensor with each element as a dimension index in source data format. Must be in the range [-4, 4).
  src_format   An optional string. Defaults to "NHWC". source data format.
  dst_format   An optional string. Defaults to "NCHW". destination data format.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.DataFormatVecPermute Permute input tensor from src_format to dst_format.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DataFormatVecPermute
tf.raw_ops.DataFormatVecPermute(
    x, src_format='NHWC', dst_format='NCHW', name=None
)
 Input tensor must be a vector of size 4, or a 4x2 tensor. For example, with src_format of NHWC, dst_format of NCHW, and inputs: [1, 2, 3, 4]
 and [[1, 2, 3, 4],
 [5, 6, 7, 8]]
 , the outputs will be (respectively): [1, 4, 2, 3]
 and [[1, 4, 2, 3],
 [5, 8, 6, 7]]

 Args
  x   A Tensor. Must be one of the following types: int32, int64. Vector of size 4 or Tensor of shape (4, 2) in source data format.
  src_format   An optional string. Defaults to "NHWC". source data format.
  dst_format   An optional string. Defaults to "NCHW". destination data format.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.DataServiceDataset Creates a dataset that reads data from the tf.data service.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DataServiceDataset
tf.raw_ops.DataServiceDataset(
    dataset_id, processing_mode, address, protocol, job_name,
    max_outstanding_requests, iteration_counter, output_types, output_shapes,
    task_refresh_interval_hint_ms=-1, name=None
)

 Args
  dataset_id   A Tensor of type int64.
  processing_mode   A Tensor of type string.
  address   A Tensor of type string.
  protocol   A Tensor of type string.
  job_name   A Tensor of type string.
  max_outstanding_requests   A Tensor of type int64.
  iteration_counter   A Tensor of type resource.
  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.
  task_refresh_interval_hint_ms   An optional int. Defaults to -1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


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

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

 Returns   A Tensor of type int64.


tf.raw_ops.DatasetFromGraph Creates a dataset from the given graph_def.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DatasetFromGraph
tf.raw_ops.DatasetFromGraph(
    graph_def, name=None
)
 Creates a dataset from the provided graph_def.

 Args
  graph_def   A Tensor of type string. The graph representation of the dataset (as serialized GraphDef).
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.DatasetToGraph Returns a serialized GraphDef representing input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DatasetToGraph
tf.raw_ops.DatasetToGraph(
    input_dataset, stateful_whitelist=[], allow_stateful=False,
    strip_device_assignment=False, name=None
)
 Returns a graph representation for input_dataset.

 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the dataset to return the graph representation for.
  stateful_whitelist   An optional list of strings. Defaults to [].
  allow_stateful   An optional bool. Defaults to False.
  strip_device_assignment   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.DatasetToGraphV2 Returns a serialized GraphDef representing input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DatasetToGraphV2
tf.raw_ops.DatasetToGraphV2(
    input_dataset, external_state_policy=0, strip_device_assignment=False, name=None
)
 Returns a graph representation for input_dataset.

 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the dataset to return the graph representation for.
  external_state_policy   An optional int. Defaults to 0.
  strip_device_assignment   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.DatasetToSingleElement Outputs the single element from the given dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DatasetToSingleElement
tf.raw_ops.DatasetToSingleElement(
    dataset, output_types, output_shapes, name=None
)

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

 Returns   A list of Tensor objects of type output_types.


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

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

 Returns   The created Operation.


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

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

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.DebugGradientIdentity Identity op for gradient debugging.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DebugGradientIdentity
tf.raw_ops.DebugGradientIdentity(
    input, name=None
)
 This op is hidden from public in Python. It is used by TensorFlow Debugger to register gradient tensors for gradient debugging. This op operates on non-reference-type tensors.

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

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.DebugGradientRefIdentity Identity op for gradient debugging.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DebugGradientRefIdentity
tf.raw_ops.DebugGradientRefIdentity(
    input, name=None
)
 This op is hidden from public in Python. It is used by TensorFlow Debugger to register gradient tensors for gradient debugging. This op operates on reference-type tensors.

 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.DebugIdentity Provides an identity mapping of the non-Ref type input tensor for debugging.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DebugIdentity
tf.raw_ops.DebugIdentity(
    input, device_name='', tensor_name='', debug_urls=[],
    gated_grpc=False, name=None
)
 Provides an identity mapping of the non-Ref type input tensor for debugging.

 Args
  input   A Tensor. Input tensor, non-Reference type
  device_name   An optional string. Defaults to "". Name of the device on which the tensor resides.
  tensor_name   An optional string. Defaults to "". Name of the input tensor.
  debug_urls   An optional list of strings. Defaults to []. List of URLs to debug targets, e.g., file:///foo/tfdbg_dump, grpc:://localhost:11011
  gated_grpc   An optional bool. Defaults to False. Whether this op will be gated. If any of the debug_urls of this debug node is of the grpc:// scheme, when the value of this attribute is set to True, the data will not actually be sent via the grpc stream unless this debug op has been enabled at the debug_url. If all of the debug_urls of this debug node are of the grpc:// scheme and the debug op is enabled at none of them, the output will be an empty Tensor.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.DebugIdentityV2 Debug Identity V2 Op.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DebugIdentityV2
tf.raw_ops.DebugIdentityV2(
    input, tfdbg_context_id='', op_name='', output_slot=-1,
    tensor_debug_mode=-1, debug_urls=[], circular_buffer_size=1000,
    tfdbg_run_id='', name=None
)
 Provides an identity mapping from input to output, while writing the content of the input tensor by calling DebugEventsWriter. The semantics of the input tensor depends on tensor_debug_mode. In typical usage, the input tensor comes directly from the user computation only when graph_debug_mode is FULL_TENSOR (see protobuf/debug_event.proto for a list of all the possible values of graph_debug_mode). For the other debug modes, the input tensor should be produced by an additional op or subgraph that computes summary information about one or more tensors.

 Args
  input   A Tensor. Input tensor, non-Reference type
  tfdbg_context_id   An optional string. Defaults to "". A tfdbg-generated ID for the context that the op belongs to, e.g., a concrete compiled tf.function.
  op_name   An optional string. Defaults to "". Optional. Name of the op that the debug op is concerned with. Used only for single-tensor trace.
  output_slot   An optional int. Defaults to -1. Optional. Output slot index of the tensor that the debug op is concerned with. Used only for single-tensor trace.
  tensor_debug_mode   An optional int. Defaults to -1. TensorDebugMode enum value. See debug_event.proto for details.
  debug_urls   An optional list of strings. Defaults to []. List of URLs to debug targets, e.g., file:///foo/tfdbg_dump.
  circular_buffer_size   An optional int. Defaults to 1000.
  tfdbg_run_id   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.DebugNanCount Debug NaN Value Counter Op.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DebugNanCount
tf.raw_ops.DebugNanCount(
    input, device_name='', tensor_name='', debug_urls=[],
    gated_grpc=False, name=None
)
 Counts number of NaNs in the input tensor, for debugging.

 Args
  input   A Tensor. Input tensor, non-Reference type.
  device_name   An optional string. Defaults to "".
  tensor_name   An optional string. Defaults to "". Name of the input tensor.
  debug_urls   An optional list of strings. Defaults to []. List of URLs to debug targets, e.g., file:///foo/tfdbg_dump, grpc:://localhost:11011.
  gated_grpc   An optional bool. Defaults to False. Whether this op will be gated. If any of the debug_urls of this debug node is of the grpc:// scheme, when the value of this attribute is set to True, the data will not actually be sent via the grpc stream unless this debug op has been enabled at the debug_url. If all of the debug_urls of this debug node are of the grpc:// scheme and the debug op is enabled at none of them, the output will be an empty Tensor.
  name   A name for the operation (optional).

 Returns   A Tensor of type int64.


tf.raw_ops.DebugNumericSummary Debug Numeric Summary Op.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DebugNumericSummary
tf.raw_ops.DebugNumericSummary(
    input, device_name='', tensor_name='', debug_urls=[],
    lower_bound=float('-inf'), upper_bound=float('inf'),
    mute_if_healthy=False, gated_grpc=False, name=None
)
 Provide a basic summary of numeric value types, range and distribution. output: A double tensor of shape [14 + nDimensions], where nDimensions is the number of dimensions of the tensor's shape. The elements of output are: [0]: is initialized (1.0) or not (0.0). [1]: total number of elements [2]: NaN element count [3]: generalized -inf count: elements <= lower_bound. lower_bound is -inf by default. [4]: negative element count (excluding -inf), if lower_bound is the default -inf. Otherwise, this is the count of elements > lower_bound and < 0. [5]: zero element count [6]: positive element count (excluding +inf), if upper_bound is the default +inf. Otherwise, this is the count of elements < upper_bound and > 0. [7]: generalized +inf count, elements >= upper_bound. upper_bound is +inf by default. Output elements [1:8] are all zero, if the tensor is uninitialized. [8]: minimum of all non-inf and non-NaN elements. If uninitialized or no such element exists: +inf. [9]: maximum of all non-inf and non-NaN elements. If uninitialized or no such element exists: -inf. [10]: mean of all non-inf and non-NaN elements. If uninitialized or no such element exists: NaN. [11]: variance of all non-inf and non-NaN elements. If uninitialized or no such element exists: NaN. [12]: Data type of the tensor encoded as an enum integer. See the DataType proto for more details. [13]: Number of dimensions of the tensor (ndims). [14+]: Sizes of the dimensions.

 Args
  input   A Tensor. Input tensor, non-Reference type.
  device_name   An optional string. Defaults to "".
  tensor_name   An optional string. Defaults to "". Name of the input tensor.
  debug_urls   An optional list of strings. Defaults to []. List of URLs to debug targets, e.g., file:///foo/tfdbg_dump, grpc:://localhost:11011.
  lower_bound   An optional float. Defaults to float('-inf'). (float) The lower bound <= which values will be included in the generalized -inf count. Default: -inf.
  upper_bound   An optional float. Defaults to float('inf'). (float) The upper bound >= which values will be included in the generalized +inf count. Default: +inf.
  mute_if_healthy   An optional bool. Defaults to False. (bool) Do not send data to the debug URLs unless at least one of elements [2], [3] and 7 is non-zero.
  gated_grpc   An optional bool. Defaults to False. Whether this op will be gated. If any of the debug_urls of this debug node is of the grpc:// scheme, when the value of this attribute is set to True, the data will not actually be sent via the grpc stream unless this debug op has been enabled at the debug_url. If all of the debug_urls of this debug node are of the grpc:// scheme and the debug op is enabled at none of them, the output will be an empty Tensor.
  name   A name for the operation (optional).

 Returns   A Tensor of type float64.


tf.raw_ops.DebugNumericSummaryV2 Debug Numeric Summary V2 Op.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DebugNumericSummaryV2
tf.raw_ops.DebugNumericSummaryV2(
    input, output_dtype=tf.dtypes.float32, tensor_debug_mode=-1, tensor_id=-1,
    name=None
)
 Computes a numeric summary of the input tensor. The shape of the output depends on the tensor_debug_mode attribute. This op is used internally by TensorFlow Debugger (tfdbg) v2.

 Args
  input   A Tensor. Input tensor, to be summarized by the op.
  output_dtype   An optional tf.DType from: tf.float32, tf.float64. Defaults to tf.float32. Optional. The type of the output. Can be float32 or float64 (default: float32).
  tensor_debug_mode   An optional int. Defaults to -1. Tensor debug mode: the mode in which the input tensor is summarized by the op. See the TensorDebugMode enum in tensorflow/core/protobuf/debug_event.proto for details. Supported values: 2 (CURT_HEALTH): Output a float32/64 tensor of shape [2]. The 1st element is the tensor_id, if provided, and -1 otherwise. The 2nd element is a bit which is set to 1 if the input tensor has an infinity or nan value, or zero otherwise. 3 (CONCISE_HEALTH): Output a float32/64 tensor of shape [5]. The 1st element is the tensor_id, if provided, and -1 otherwise. The remaining four slots are the total number of elements, -infs, +infs, and nans in the input tensor respectively. 4 (FULL_HEALTH): Output a float32/64 tensor of shape [11]. The 1st element is the tensor_id, if provided, and -1 otherwise. The 2nd element is the device_id, if provided, and -1 otherwise. The 3rd element holds the datatype value of the input tensor as according to the enumerated type in tensorflow/core/framework/types.proto. The remaining elements hold the total number of elements, -infs, +infs, nans, negative finite numbers, zeros, and positive finite numbers in the input tensor respectively. 5 (SHAPE): Output a float32/64 tensor of shape [10]. The 1st element is the tensor_id, if provided, and -1 otherwise. The 2nd element holds the datatype value of the input tensor as according to the enumerated type in tensorflow/core/framework/types.proto. The 3rd element holds the rank of the tensor. The 4th element holds the number of elements within the tensor. Finally the remaining 6 elements hold the shape of the tensor. If the rank of the tensor is lower than 6, the shape is right padded with zeros. If the rank is greater than 6, the head of the shape is truncated. 6 (FULL_NUMERICS): Output a float32/64 tensor of shape [22]. The 1st element is the tensor_id, if provided, and -1 otherwise. The 2nd element is the device_id, if provided, and -1 otherwise. The 3rd element holds the datatype value of the input tensor as according to the enumerated type in tensorflow/core/framework/types.proto. The 4th element holds the rank of the tensor. The 5th to 11th elements hold the shape of the tensor. If the rank of the tensor is lower than 6, the shape is right padded with zeros. If the rank is greater than 6, the head of the shape is truncated. The 12th to 18th elements hold the number of elements, -infs, +infs, nans, denormal floats, negative finite numbers, zeros, and positive finite numbers in the input tensor respectively. The final four elements hold the min value, max value, mean, and variance of the input tensor. 8 (REDUCE_INF_NAN_THREE_SLOTS): Output a float32/64 tensor of shape [3]. The 1st element is -inf if any elements of the input tensor is -inf, or zero otherwise. The 2nd element is +inf if any elements of the input tensor is +inf, or zero otherwise. The 3rd element is nan if any element of the input tensor is nan, or zero otherwise.

  tensor_id   An optional int. Defaults to -1. Optional. An integer identifier for the tensor being summarized by this op.
  name   A name for the operation (optional).

 Returns   A Tensor of type output_dtype.


tf.raw_ops.DecodeAndCropJpeg Decode and Crop a JPEG-encoded image to a uint8 tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DecodeAndCropJpeg
tf.raw_ops.DecodeAndCropJpeg(
    contents, crop_window, channels=0, ratio=1, fancy_upscaling=True,
    try_recover_truncated=False, acceptable_fraction=1, dct_method='',
    name=None
)
 The attr channels indicates the desired number of color channels for the decoded image. Accepted values are:  0: Use the number of channels in the JPEG-encoded image. 1: output a grayscale image. 3: output an RGB image.  If needed, the JPEG-encoded image is transformed to match the requested number of color channels. The attr ratio allows downscaling the image by an integer factor during decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than downscaling the image later. It is equivalent to a combination of decode and crop, but much faster by only decoding partial jpeg image.

 Args
  contents   A Tensor of type string. 0-D. The JPEG-encoded image.
  crop_window   A Tensor of type int32. 1-D. The crop window: [crop_y, crop_x, crop_height, crop_width].
  channels   An optional int. Defaults to 0. Number of color channels for the decoded image.
  ratio   An optional int. Defaults to 1. Downscaling ratio.
  fancy_upscaling   An optional bool. Defaults to True. If true use a slower but nicer upscaling of the chroma planes (yuv420/422 only).
  try_recover_truncated   An optional bool. Defaults to False. If true try to recover an image from truncated input.
  acceptable_fraction   An optional float. Defaults to 1. The minimum required fraction of lines before a truncated input is accepted.
  dct_method   An optional string. Defaults to "". string specifying a hint about the algorithm used for decompression. Defaults to "" which maps to a system-specific default. Currently valid values are ["INTEGER_FAST", "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal jpeg library changes to a version that does not have that specific option.)
  name   A name for the operation (optional).

 Returns   A Tensor of type uint8.


tf.raw_ops.DecodeBase64 Decode web-safe base64-encoded strings.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DecodeBase64
tf.raw_ops.DecodeBase64(
    input, name=None
)
 Input may or may not have padding at the end. See EncodeBase64 for padding. Web-safe means that input must use - and _ instead of + and /.

 Args
  input   A Tensor of type string. Base64 strings to decode.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.DecodeBmp Decode the first frame of a BMP-encoded image to a uint8 tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DecodeBmp
tf.raw_ops.DecodeBmp(
    contents, channels=0, name=None
)
 The attr channels indicates the desired number of color channels for the decoded image. Accepted values are:  0: Use the number of channels in the BMP-encoded image. 3: output an RGB image. 4: output an RGBA image.

 Args
  contents   A Tensor of type string. 0-D. The BMP-encoded image.
  channels   An optional int. Defaults to 0.
  name   A name for the operation (optional).

 Returns   A Tensor of type uint8.


tf.raw_ops.DecodeCompressed Decompress strings.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DecodeCompressed
tf.raw_ops.DecodeCompressed(
    bytes, compression_type='', name=None
)
 This op decompresses each element of the bytes input Tensor, which is assumed to be compressed using the given compression_type. The output is a string Tensor of the same shape as bytes, each element containing the decompressed data from the corresponding element in bytes.

 Args
  bytes   A Tensor of type string. A Tensor of string which is compressed.
  compression_type   An optional string. Defaults to "". A scalar containing either (i) the empty string (no compression), (ii) "ZLIB", or (iii) "GZIP".
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.DecodeCSV Convert CSV records to tensors. Each column maps to one tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.DecodeCSV
tf.raw_ops.DecodeCSV(
    records, record_defaults, field_delim=',', use_quote_delim=True,
    na_value='', select_cols=[], name=None
)
 RFC 4180 format is expected for the CSV records. (https://tools.ietf.org/html/rfc4180) Note that we allow leading and trailing spaces with int or float field.

 Args
  records   A Tensor of type string. Each string is a record/row in the csv and all records should have the same format.
  record_defaults   A list of Tensor objects with types from: float32, float64, int32, int64, string. One tensor per column of the input record, with either a scalar default value for that column or an empty vector if the column is required.
  field_delim   An optional string. Defaults to ",". char delimiter to separate fields in a record.
  use_quote_delim   An optional bool. Defaults to True. If false, treats double quotation marks as regular characters inside of the string fields (ignoring RFC 4180, Section 2, Bullet 5).
  na_value   An optional string. Defaults to "". Additional string to recognize as NA/NaN.
  select_cols   An optional list of ints. Defaults to [].
  name   A name for the operation (optional).

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