diff options
Diffstat (limited to 'tensorflow/core/ops/nn_ops.cc')
| -rw-r--r-- | tensorflow/core/ops/nn_ops.cc | 56 |
1 files changed, 36 insertions, 20 deletions
diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc index bf088dc45e..593f986edb 100644 --- a/tensorflow/core/ops/nn_ops.cc +++ b/tensorflow/core/ops/nn_ops.cc @@ -1,3 +1,18 @@ +/* Copyright 2015 Google Inc. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op.h" #include "tensorflow/core/util/padding.h" @@ -498,27 +513,27 @@ REGISTER_OP("InTopK") .Attr("k: int") .Attr("T: {int32, int64} = DT_INT32") .Doc(R"doc( -Says whether the targets are in the top K predictions. +Says whether the targets are in the top `K` predictions. -This outputs a batch_size bool array, an entry out[i] is true if the -prediction for the target class is among the top k predictions among -all predictions for example i. Note that the behavior of InTopK differs -from the TopK op in its handling of ties; if multiple classes have the -same prediction value and straddle the top-k boundary, all of those -classes are considered to be in the top k. +This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the +prediction for the target class is among the top `k` predictions among +all predictions for example `i`. Note that the behavior of `InTopK` differs +from the `TopK` op in its handling of ties; if multiple classes have the +same prediction value and straddle the top-`k` boundary, all of those +classes are considered to be in the top `k`. More formally, let - \\(predictions_i\\) be the predictions for all classes for example i, - \\(targets_i\\) be the target class for example i, - \\(out_i\\) be the output for example i, + \\(predictions_i\\) be the predictions for all classes for example `i`, + \\(targets_i\\) be the target class for example `i`, + \\(out_i\\) be the output for example `i`, $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$ -predictions: A batch_size x classes tensor -targets: A batch_size vector of class ids -k: Number of top elements to look at for computing precision -precision: Computed Precision at k as a bool Tensor +predictions: A `batch_size` x `classes` tensor. +targets: A `batch_size` vector of class ids. +k: Number of top elements to look at for computing precision. +precision: Computed Precision at `k` as a `bool Tensor`. )doc"); @@ -529,7 +544,7 @@ REGISTER_OP("TopK") .Output("indices: int32") .Attr("T: realnumbertype") .Doc(R"doc( -Returns the values and indices of the k largest elements for each row. +Returns the values and indices of the `k` largest elements for each row. \\(values_{i, j}\\) represents the j-th largest element in \\(input_i\\). @@ -537,11 +552,12 @@ Returns the values and indices of the k largest elements for each row. such that \\(input_{i, indices_{i, j}} = values_{i, j}\\). If two elements are equal, the lower-index element appears first. -k: Number of top elements to look for within each row -input: A batch_size x classes tensor -values: A batch_size x k tensor with the k largest elements for each row, - sorted in descending order -indices: A batch_size x k tensor with the index of each value within each row +k: Number of top elements to look for within each row. +input: A `batch_size` x `classes` tensor. +values: A `batch_size` x `k` tensor with the `k` largest elements for + each row, sorted in descending order. +indices: A `batch_size` x `k` tensor with the index of each value within + each row. )doc"); |