Use backtick format for API symbols.
Fixes: #32162
PiperOrigin-RevId: 268100465
diff --git a/tensorflow/lite/g3doc/guide/ops_compatibility.md b/tensorflow/lite/g3doc/guide/ops_compatibility.md
index 8d7e3ba..1c5fc9b 100644
--- a/tensorflow/lite/g3doc/guide/ops_compatibility.md
+++ b/tensorflow/lite/g3doc/guide/ops_compatibility.md
@@ -16,7 +16,7 @@
TensorFlow Lite is to carefully consider how operations are converted and
optimized, along with the limitations imposed by this process.
-## Supported Types
+## Supported types
Most TensorFlow Lite operations target both floating-point (float32) and
quantized (uint8, int8) inference, but many ops do not yet for other types like
@@ -29,60 +29,51 @@
via a calibration data set, or doing "on-the-fly" range estimation. See
[quantization](../performance/model_optimization.md).
-## Data Format and Broadcasting
+## Data format and broadcasting
At the moment TensorFlow Lite supports only TensorFlow's "NHWC" format, and
broadcasting is only support in a limited number of ops (tf.add, tf.mul, tf.sub,
and tf.div).
-## Compatible Operations
+## Compatible operations
The following TensorFlow operations are usually mapped to their TensorFlow Lite
counterparts:
-* [tf.batch_to_space_nd](https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd) -
- *as long as the input tensor is 4D (1 batch + 2 spatial + 1 other) and the
- crops attribute is not used*
-* [tf.exp](https://www.tensorflow.org/api_docs/python/tf/exp)
-* [tf.fake_quant*](https://www.tensorflow.org/api_docs/python/tf/fake_quant_with_min_max_args)
-* [tf.matmul](https://www.tensorflow.org/api_docs/python/tf/matmul) - *as long
- as the second argument is constant and transposition is not used*
-* [tf.nn.avg_pool](https://www.tensorflow.org/api_docs/python/tf/nn/avg_pool)
-* [tf.nn.conv2d](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d) -
- *as long as the filter is constant*
-* [tf.nn.depthwise_conv2d](https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d) -
- *as long as the filter is constant and rate is [1,1]*
-* [tf.nn.l2_normalize](https://www.tensorflow.org/api_docs/python/tf/nn/l2_normalize) -
- *as long as normalization is done along the last dimension*
-* [tf.nn.local_response_normalization](https://www.tensorflow.org/api_docs/python/tf/nn/local_response_normalization)
-* [tf.nn.log_softmax](https://www.tensorflow.org/api_docs/python/tf/nn/log_softmax) -
- *as long as axis is not provided*
-* [tf.nn.max_pool](https://www.tensorflow.org/api_docs/python/tf/nn/max_pool)
-* [tf.nn.softmax](https://www.tensorflow.org/api_docs/python/tf/nn/softmax) -
- *as long as tensors are 2D and axis is the last dimension*
-* [tf.nn.top_k](https://www.tensorflow.org/api_docs/python/tf/nn/top_k)
-* [tf.one_hot](https://www.tensorflow.org/api_docs/python/tf/one_hot)
-* [tf.pad](https://www.tensorflow.org/api_docs/python/tf/pad) - *as long as
- mode and constant_values are not used*
-* [tf.reduce_mean](https://www.tensorflow.org/api_docs/python/tf/reduce_mean) -
- *as long as the reduction_indices attribute is not used*
-* [tf.reshape](https://www.tensorflow.org/api_docs/python/tf/reshape)
-* [tf.sigmoid](https://www.tensorflow.org/api_docs/python/tf/sigmoid)
-* [tf.space_to_batch_nd](https://www.tensorflow.org/api_docs/python/tf/space_to_batch_nd) -
- *as long as the input tensor is 4D (1 batch + 2 spatial + 1 other)*
-* [tf.space_to_depth](https://www.tensorflow.org/api_docs/python/tf/space_to_depth)
-* [tf.split](https://www.tensorflow.org/api_docs/python/tf/split) - *as long
- as num is not provided and num_or_size_split contains number of splits as a
- 0D tensor*
-* [tf.squeeze](https://www.tensorflow.org/api_docs/python/tf/squeeze) - *as
- long as axis is not provided*
-* [tf.squared_difference](https://www.tensorflow.org/versions/master/api_docs/python/tf/squared_difference)
-* [tf.strided_slice](https://www.tensorflow.org/api_docs/python/tf/strided_slice) -
- *as long as ellipsis_mask and new_axis_mask are not used*
-* [tf.transpose](https://www.tensorflow.org/versions/master/api_docs/python/tf/transpose) -
- *as long as conjugate is not used*
+* `tf.batch_to_space_nd` —As long as the input tensor is 4D (1 batch + 2
+ spatial + 1 other) and the crops attribute is not used.
+* `tf.exp`
+* `tf.fake_quant`
+* `tf.matmul` —As the second argument is constant and transposition is not
+ used*
+* `tf.nn.avg_pool`
+* `tf.nn.conv2d` —As long as the filter is constant.
+* `tf.nn.depthwise_conv2d` —As long as the filter is constant and rate is `[1,
+ 1]`.
+* `tf.nn.l2_normalize` —As long as normalization is done along the last
+ dimension.
+* `tf.nn.local_response_normalization`
+* `tf.nn.log_softmax` —As long as axis is not provided.
+* `tf.nn.max_pool`
+* `tf.nn.softmax` —As long as tensors are 2D and axis is the last dimension.
+* `tf.nn.top_k`
+* `tf.one_hot`
+* `tf.pad` —As long as mode and constant_values are not used.
+* `tf.reduce_mean` —As long as the reduction_indices attribute is not used.
+* `tf.reshape`
+* `tf.sigmoid`
+* `tf.space_to_batch_nd` —As long as the input tensor is 4D (1 batch + 2
+ spatial + 1 other).
+* `tf.space_to_depth`
+* `tf.split` —As long as num is not provided and `num_or_size_split` contains
+ number of splits as a 0D tensor.
+* `tf.squeeze` —As long as axis is not provided.
+* `tf.squared_difference`
+* `tf.strided_slice` —As long as `ellipsis_mask and new_axis_mask` are not
+ used.
+* `tf.transpose` —As long as conjugate is not used.
-## Straightforward Conversions, Constant-Folding and Fusing
+## Straight-forward conversions, constant-folding and fusing
A number of TensorFlow operations can be processed by TensorFlow Lite even
though they have no direct equivalent. This is the case for operations that can
@@ -94,48 +85,47 @@
Here is a non-exhaustive list of TensorFlow operations that are usually removed
from the graph:
-* [tf.add](https://www.tensorflow.org/api_docs/python/tf/add)
-* [tf.check_numerics](https://www.tensorflow.org/api_docs/python/tf/check_numerics)
-* [tf.constant](https://www.tensorflow.org/api_docs/python/tf/constant)
-* [tf.div](https://www.tensorflow.org/api_docs/python/tf/div)
-* [tf.divide](https://www.tensorflow.org/api_docs/python/tf/divide)
-* [tf.fake_quant_with_min_max_args](https://www.tensorflow.org/api_docs/python/tf/fake_quant_with_min_max_args)
-* [tf.fake_quant_with_min_max_vars](https://www.tensorflow.org/api_docs/python/tf/fake_quant_with_min_max_vars)
-* [tf.identity](https://www.tensorflow.org/api_docs/python/tf/identity)
-* [tf.maximum](https://www.tensorflow.org/api_docs/python/tf/maximum)
-* [tf.minimum](https://www.tensorflow.org/api_docs/python/tf/minimum)
-* [tf.multiply](https://www.tensorflow.org/api_docs/python/tf/multiply)
-* [tf.no_op](https://www.tensorflow.org/api_docs/python/tf/no_op)
-* [tf.placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)
-* [tf.placeholder_with_default](https://www.tensorflow.org/api_docs/python/tf/placeholder_with_default)
-* [tf.realdiv](https://www.tensorflow.org/api_docs/python/tf/realdiv)
-* [tf.reduce_max](https://www.tensorflow.org/api_docs/python/tf/reduce_max)
-* [tf.reduce_min](https://www.tensorflow.org/api_docs/python/tf/reduce_min)
-* [tf.reduce_sum](https://www.tensorflow.org/api_docs/python/tf/reduce_sum)
-* [tf.rsqrt](https://www.tensorflow.org/api_docs/python/tf/rsqrt)
-* [tf.shape](https://www.tensorflow.org/api_docs/python/tf/shape)
-* [tf.sqrt](https://www.tensorflow.org/api_docs/python/tf/sqrt)
-* [tf.square](https://www.tensorflow.org/api_docs/python/tf/square)
-* [tf.subtract](https://www.tensorflow.org/api_docs/python/tf/subtract)
-* [tf.tile](https://www.tensorflow.org/api_docs/python/tf/tile)
-* [tf.nn.batch_norm_with_global_normalization](https://www.tensorflow.org/api_docs/python/tf/nn/batch_norm_with_global_normalization)
-* [tf.nn.bias_add](https://www.tensorflow.org/api_docs/python/tf/nn/bias_add)
-* [tf.nn.fused_batch_norm](https://www.tensorflow.org/api_docs/python/tf/nn/fused_batch_norm)
-* [tf.nn.relu](https://www.tensorflow.org/api_docs/python/tf/nn/relu)
-* [tf.nn.relu6](https://www.tensorflow.org/api_docs/python/tf/nn/relu6)
+* `tf.add`
+* `tf.check_numerics`
+* `tf.constant`
+* `tf.div`
+* `tf.divide`
+* `tf.fake_quant_with_min_max_args`
+* `tf.fake_quant_with_min_max_vars`
+* `tf.identity`
+* `tf.maximum`
+* `tf.minimum`
+* `tf.multiply`
+* `tf.no_op`
+* `tf.placeholder`
+* `tf.placeholder_with_default`
+* `tf.realdiv`
+* `tf.reduce_max`
+* `tf.reduce_min`
+* `tf.reduce_sum`
+* `tf.rsqrt`
+* `tf.shape`
+* `tf.sqrt`
+* `tf.square`
+* `tf.subtract`
+* `tf.tile`
+* `tf.nn.batch_norm_with_global_normalization`
+* `tf.nn.bias_add`
+* `tf.nn.fused_batch_norm`
+* `tf.nn.relu`
+* `tf.nn.relu6`
-Note that many of those operations don't have TensorFlow Lite equivalents and
-the corresponding model will not be convertible if they can't be elided or
-fused.
+Note: Many of those operations don't have TensorFlow Lite equivalents and the
+corresponding model will not be convertible if they can't be elided or fused.
-## Unsupported Operations
+## Unsupported operations
TensorFlow operation not listed above are likely unsupported. Notably, the
following common ops are not supported at the moment:
-* [tf.depth_to_space](https://www.tensorflow.org/api_docs/python/tf/depth_to_space)
+* `tf.depth_to_space`
-## TensorFlow Lite Operations
+## TensorFlow Lite operations
The following TensorFlow Lite operations are fully supported and used in place
of the TensorFlow operations listed above:
@@ -1154,14 +1144,14 @@
And these are TensorFlow Lite operations that are present but not ready for
custom models yet:
-* CALL
-* CONCAT_EMBEDDINGS
-* CUSTOM
-* EMBEDDING_LOOKUP
-* EMBEDDING_LOOKUP_SPARSE
-* HASHTABLE_LOOKUP
-* LSH_PROJECTION
-* LSTM
-* RNN
-* SKIP_GRAM
-* SVDF
+* `CALL`
+* `CONCAT_EMBEDDINGS`
+* `CUSTOM`
+* `EMBEDDING_LOOKUP`
+* `EMBEDDING_LOOKUP_SPARSE`
+* `HASHTABLE_LOOKUP`
+* `LSH_PROJECTION`
+* `LSTM`
+* `RNN`
+* `SKIP_GRAM`
+* `SVDF`
