Removing doc strings from REGISTER_OP calls in core/ops.

PiperOrigin-RevId: 180670333

1 files changed, 3 insertions, 77 deletions

diff --git a/tensorflow/core/ops/ctc_ops.cc b/tensorflow/core/ops/ctc_ops.cc
index 1a69106d80..f2322c730b 100644
--- a/tensorflow/core/ops/ctc_ops.cc
+++ b/tensorflow/core/ops/ctc_ops.cc
@@ -59,30 +59,7 @@ REGISTER_OP("CTCLoss")
       c->set_output(0, c->Vector(batch_size));
       c->set_output(1, inputs);
       return Status::OK();
-    })
-    .Doc(R"doc(
-Calculates the CTC Loss (log probability) for each batch entry.  Also calculates
-the gradient.  This class performs the softmax operation for you, so inputs
-should be e.g. linear projections of outputs by an LSTM.
-
-inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits.
-labels_indices: 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: The values (labels) associated with the given batch and time.
-sequence_length: A vector containing sequence lengths (batch).
-preprocess_collapse_repeated: Scalar, if true then repeated labels are
-  collapsed prior to the CTC calculation.
-ctc_merge_repeated: 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: 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.
-loss: A vector (batch) containing log-probabilities.
-gradient: The gradient of `loss`.  3-D, shape:
-  `(max_time x batch_size x num_classes)`.
-)doc");
+    });
 
 REGISTER_OP("CTCGreedyDecoder")
     .Input("inputs: float")
@@ -110,32 +87,7 @@ REGISTER_OP("CTCGreedyDecoder")
       c->set_output(2, c->Vector(2));
       c->set_output(3, c->Matrix(batch_size, 1));
       return Status::OK();
-    })
-    .Doc(R"doc(
-Performs greedy decoding on the logits given in inputs.
-
-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.
-
-inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits.
-sequence_length: A vector containing sequence lengths, size `(batch_size)`.
-merge_repeated: If True, merge repeated classes in output.
-decoded_indices: Indices matrix, size `(total_decoded_outputs x 2)`,
-  of a `SparseTensor<int64, 2>`.  The rows store: [batch, time].
-decoded_values: Values vector, size: `(total_decoded_outputs)`,
-  of a `SparseTensor<int64, 2>`.  The vector stores the decoded classes.
-decoded_shape: Shape vector, size `(2)`, of the decoded SparseTensor.
-  Values are: `[batch_size, max_decoded_length]`.
-log_probability: Matrix, size `(batch_size x 1)`, containing sequence
-  log-probabilities.
-)doc");
+    });
 
 REGISTER_OP("CTCBeamSearchDecoder")
     .Input("inputs: float")
@@ -176,32 +128,6 @@ REGISTER_OP("CTCBeamSearchDecoder")
       }
       c->set_output(out_idx++, c->Matrix(batch_size, top_paths));
       return Status::OK();
-    })
-    .Doc(R"doc(
-Performs beam search decoding on the logits given in input.
-
-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.
-
-inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits.
-sequence_length: A vector containing sequence lengths, size `(batch)`.
-beam_width: A scalar >= 0 (beam search beam width).
-top_paths: A scalar >= 0, <= beam_width (controls output size).
-merge_repeated: If true, merge repeated classes in output.
-decoded_indices: A list (length: top_paths) of indices matrices.  Matrix j,
-  size `(total_decoded_outputs[j] x 2)`, has indices of a
-  `SparseTensor<int64, 2>`.  The rows store: [batch, time].
-decoded_values: A list (length: top_paths) of values vectors.  Vector j,
-  size `(length total_decoded_outputs[j])`, has the values of a
-  `SparseTensor<int64, 2>`.  The vector stores the decoded classes for beam j.
-decoded_shape: A list (length: top_paths) of shape vector.  Vector j,
-  size `(2)`, stores the shape of the decoded `SparseTensor[j]`.
-  Its values are: `[batch_size, max_decoded_length[j]]`.
-log_probability: A matrix, shaped: `(batch_size x top_paths)`.  The
-  sequence log-probabilities.
-)doc");
+    });
 
 }  // namespace tensorflow