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Diffstat (limited to 'tensorflow/docs_src/extend/adding_an_op.md')
| -rw-r--r-- | tensorflow/docs_src/extend/adding_an_op.md | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/tensorflow/docs_src/extend/adding_an_op.md b/tensorflow/docs_src/extend/adding_an_op.md index fbf5c0b90d..cc25ab9b45 100644 --- a/tensorflow/docs_src/extend/adding_an_op.md +++ b/tensorflow/docs_src/extend/adding_an_op.md @@ -56,8 +56,8 @@ PREREQUISITES: * Some familiarity with C++. * Must have installed the - @{$install$TensorFlow binary}, or must have - @{$install_sources$downloaded TensorFlow source}, + [TensorFlow binary](../install/index.md), or must have + [downloaded TensorFlow source](../install/install_sources.md), and be able to build it. [TOC] @@ -1140,7 +1140,7 @@ In general, changes to existing, checked-in specifications must be backwards-compatible: changing the specification of an op must not break prior serialized `GraphDef` protocol buffers constructed from older specifications. The details of `GraphDef` compatibility are -@{$version_compat#compatibility_of_graphs_and_checkpoints$described here}. +[described here](../guide/version_compat.md#compatibility_of_graphs_and_checkpoints). There are several ways to preserve backwards-compatibility. @@ -1190,7 +1190,7 @@ callers. The Python API may be kept compatible by careful changes in a hand-written Python wrapper, by keeping the old signature except possibly adding new optional arguments to the end. Generally incompatible changes may only be made when TensorFlow's changes major versions, and must conform to the -@{$version_compat#compatibility_of_graphs_and_checkpoints$`GraphDef` version semantics}. +[`GraphDef` version semantics](../guide/version_compat.md#compatibility_of_graphs_and_checkpoints). ### GPU Support @@ -1262,7 +1262,7 @@ For example, add `-L /usr/local/cuda-8.0/lib64/` if your CUDA is installed in Given a graph of ops, TensorFlow uses automatic differentiation (backpropagation) to add new ops representing gradients with respect to the existing ops (see -@{$python/train#gradient_computation$Gradient Computation}). +[Gradient Computation](../api_guides/python/train.md#gradient_computation)). To make automatic differentiation work for new ops, you must register a gradient function which computes gradients with respect to the ops' inputs given gradients with respect to the ops' outputs. |