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Diffstat (limited to 'tensorflow/docs_src/extend/new_data_formats.md')
-rw-r--r-- | tensorflow/docs_src/extend/new_data_formats.md | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/tensorflow/docs_src/extend/new_data_formats.md b/tensorflow/docs_src/extend/new_data_formats.md index 47a8344b70..7ca50c9c76 100644 --- a/tensorflow/docs_src/extend/new_data_formats.md +++ b/tensorflow/docs_src/extend/new_data_formats.md @@ -4,7 +4,7 @@ PREREQUISITES: * Some familiarity with C++. * Must have - @{$install_sources$downloaded TensorFlow source}, and be + [downloaded TensorFlow source](../install/install_sources.md), and be able to build it. We divide the task of supporting a file format into two pieces: @@ -67,7 +67,7 @@ need to: You can put all the C++ code in a single file, such as `my_reader_dataset_op.cc`. It will help if you are -familiar with @{$adding_an_op$the adding an op how-to}. The following skeleton +familiar with [the adding an op how-to](../extend/adding_an_op.md). The following skeleton can be used as a starting point for your implementation: ```c++ @@ -227,8 +227,8 @@ REGISTER_KERNEL_BUILDER(Name("MyReaderDataset").Device(tensorflow::DEVICE_CPU), ``` The last step is to build the C++ code and add a Python wrapper. The easiest way -to do this is by @{$adding_an_op#build_the_op_library$compiling a dynamic -library} (e.g. called `"my_reader_dataset_op.so"`), and adding a Python class +to do this is by [compiling a dynamic +library](../extend/adding_an_op.md#build_the_op_library) (e.g. called `"my_reader_dataset_op.so"`), and adding a Python class that subclasses `tf.data.Dataset` to wrap it. An example Python program is given here: @@ -285,7 +285,7 @@ You can see some examples of `Dataset` wrapper classes in ## Writing an Op for a record format Generally this is an ordinary op that takes a scalar string record as input, and -so follow @{$adding_an_op$the instructions to add an Op}. +so follow [the instructions to add an Op](../extend/adding_an_op.md). You may optionally take a scalar string key as input, and include that in error messages reporting improperly formatted data. That way users can more easily track down where the bad data came from. |