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PiperOrigin-RevId: 236802220
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Billy Lamberta 2019-03-05 00:55:10 -08:00 committed by TensorFlower Gardener
parent f2b72d031c
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6
tensorflow/lite/g3doc/convert/cmdline_examples.md
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@ -64,19 +64,19 @@ tflite_convert \
--saved_model_dir=/tmp/saved_model --saved_model_dir=/tmp/saved_model
``` ```
[SavedModel](https://www.tensorflow.org/guide/saved_model.md#using_savedmodel_with_estimators) [SavedModel](https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators)
has fewer required flags than frozen graphs due to access to additional data has fewer required flags than frozen graphs due to access to additional data
contained within the SavedModel. The values for `--input_arrays` and contained within the SavedModel. The values for `--input_arrays` and
`--output_arrays` are an aggregated, alphabetized list of the inputs and outputs `--output_arrays` are an aggregated, alphabetized list of the inputs and outputs
in the [SignatureDefs](../../serving/signature_defs.md) within in the [SignatureDefs](../../serving/signature_defs.md) within
the the
[MetaGraphDef](https://www.tensorflow.org/saved_model.md#apis_to_build_and_load_a_savedmodel) [MetaGraphDef](https://www.tensorflow.org/saved_model#apis_to_build_and_load_a_savedmodel)
specified by `--saved_model_tag_set`. As with the GraphDef, the value for specified by `--saved_model_tag_set`. As with the GraphDef, the value for
`input_shapes` is automatically determined whenever possible. `input_shapes` is automatically determined whenever possible.
There is currently no support for MetaGraphDefs without a SignatureDef or for There is currently no support for MetaGraphDefs without a SignatureDef or for
MetaGraphDefs that use the [`assets/` MetaGraphDefs that use the [`assets/`
directory](https://www.tensorflow.org/guide/saved_model.md#structure_of_a_savedmodel_directory). directory](https://www.tensorflow.org/guide/saved_model#structure_of_a_savedmodel_directory).
### Convert a tf.Keras model <a name="keras"></a> ### Convert a tf.Keras model <a name="keras"></a>

4
tensorflow/lite/g3doc/convert/python_api.md
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@ -241,8 +241,8 @@ interpreter.allocate_tensors()
In order to run the latest version of the TensorFlow Lite Converter Python API, In order to run the latest version of the TensorFlow Lite Converter Python API,
either install the nightly build with either install the nightly build with
[pip](https://www.tensorflow.org/install/pip) (recommended) or [pip](https://www.tensorflow.org/install/pip) (recommended) or
[Docker](https://www.tensorflow.org/install/docker.md), or [Docker](https://www.tensorflow.org/install/docker), or
[build the pip package from source](https://www.tensorflow.org/install/source.md). [build the pip package from source](https://www.tensorflow.org/install/source).
### Converting models from TensorFlow 1.12 <a name="pre_tensorflow_1.12"></a> ### Converting models from TensorFlow 1.12 <a name="pre_tensorflow_1.12"></a>

2
tensorflow/lite/g3doc/guide/ops_select.md
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@ -34,7 +34,7 @@ choice. It also discusses some [known limitations](#known-limitations), the
To convert a TensorFlow model to a TensorFlow Lite model with TensorFlow ops, To convert a TensorFlow model to a TensorFlow Lite model with TensorFlow ops,
use the `target_ops` argument in the use the `target_ops` argument in the
[TensorFlow Lite converter](../convert/index.md). The [TensorFlow Lite converter](../convert/). The
following values are valid options for `target_ops`: following values are valid options for `target_ops`:
* `TFLITE_BUILTINS` - Converts models using TensorFlow Lite builtin ops. * `TFLITE_BUILTINS` - Converts models using TensorFlow Lite builtin ops.

2
tensorflow/lite/g3doc/performance/post_training_quantization.md
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@ -3,7 +3,7 @@
Post-training quantization is a general technique to reduce model size while also Post-training quantization is a general technique to reduce model size while also
providing up to 3x lower latency with little degradation in model accuracy. Post-training providing up to 3x lower latency with little degradation in model accuracy. Post-training
quantization quantizes weights from floating point to 8-bits of precision. This technique quantization quantizes weights from floating point to 8-bits of precision. This technique
is enabled as an option in the [TensorFlow Lite converter](../convert/index.md): is enabled as an option in the [TensorFlow Lite converter](../convert/):
``` ```
import tensorflow as tf import tensorflow as tf