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diff --git a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md index aba7536cbd..e3c46eb377 100644 --- a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md +++ b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md @@ -1,7 +1,7 @@ -# TensorFlow Lite Optimizing Converter command-line examples +# TensorFlow Lite Converter command-line examples -This page provides examples on how to use TOCO via command line. It is -complemented by the following documents: +This page shows how to use the TensorFlow Lite Converter in the command line. It +is complemented by the following documents: * [README](../README.md) * [Command-line glossary](cmdline_reference.md) @@ -10,7 +10,7 @@ complemented by the following documents: Table of contents: * [Command-line tools](#tools) - * [Converting models prior to TensorFlow 1.9.](#pre-tensorflow-1.9) + * [Converting models prior to TensorFlow 1.9](#pre-tensorflow-1.9) * [Basic examples](#basic) * [Convert a TensorFlow GraphDef](#graphdef) * [Convert a TensorFlow SavedModel](#savedmodel) @@ -31,27 +31,28 @@ Table of contents: ## Command-line tools <a name="tools"></a> -There are two approaches to running TOCO via command line. +There are two approaches to running the converter in the command line. * `tflite_convert`: Starting from TensorFlow 1.9, the command-line tool - `tflite_convert` will be installed as part of the Python package. All of the + `tflite_convert` is installed as part of the Python package. All of the examples below use `tflite_convert` for simplicity. * Example: `tflite_convert --output_file=...` -* `bazel`: In order to run the latest version of TOCO, [clone the TensorFlow - repository](https://www.tensorflow.org/install/source) - and use `bazel`. This is the recommended approach for converting models that - utilize new features that were not supported by TOCO in TensorFlow 1.9. +* `bazel`: In order to run the latest version of the TensorFlow Lite Converter + either install the nightly build using + [pip](https://www.tensorflow.org/install/pip) or + [clone the TensorFlow repository](https://www.tensorflow.org/install/source) + and use `bazel`. * Example: `bazel run //tensorflow/contrib/lite/python:tflite_convert -- --output_file=...` -### Converting models prior to TensorFlow 1.9. <a name="pre-tensorflow-1.9"></a> +### Converting models prior to TensorFlow 1.9 <a name="pre-tensorflow-1.9"></a> -The recommended approach for using TOCO prior to TensorFlow 1.9 is the [Python -API](python_api.md#pre-tensorflow-1.9). If a command line tool is desired, the -`toco` command line tool was available in TensorFlow 1.7. Enter `toco --help` in -Terminal for additional details on the command-line flags available. There were -no command line tools in TensorFlow 1.8. +The recommended approach for using the converter prior to TensorFlow 1.9 is the +[Python API](python_api.md#pre-tensorflow-1.9). If a command line tool is +desired, the `toco` command line tool was available in TensorFlow 1.7. Enter +`toco --help` in Terminal for additional details on the command-line flags +available. There were no command line tools in TensorFlow 1.8. ## Basic examples <a name="basic"></a> @@ -117,9 +118,9 @@ tflite_convert \ ### Convert a TensorFlow GraphDef for quantized inference <a name="graphdef-quant"></a> -TOCO is compatible with fixed point quantization models described -[here](https://www.tensorflow.org/performance/quantization). These are float -models with +The TensorFlow Lite Converter is compatible with fixed point quantization models +described [here](https://www.tensorflow.org/performance/quantization). These are +float models with [`FakeQuant*`](https://www.tensorflow.org/api_guides/python/array_ops#Fake_quantization) ops inserted at the boundaries of fused layers to record min-max range information. This generates a quantized inference workload that reproduces the @@ -141,12 +142,12 @@ tflite_convert \ ### Use \"dummy-quantization\" to try out quantized inference on a float graph <a name="dummy-quant"></a> -In order to evaluate the possible benefit of generating a quantized graph, TOCO -allows "dummy-quantization" on float graphs. The flags `--default_ranges_min` -and `--default_ranges_max` accept plausible values for the min-max ranges of the -values in all arrays that do not have min-max information. "Dummy-quantization" -will produce lower accuracy but will emulate the performance of a correctly -quantized model. +In order to evaluate the possible benefit of generating a quantized graph, the +converter allows "dummy-quantization" on float graphs. The flags +`--default_ranges_min` and `--default_ranges_max` accept plausible values for +the min-max ranges of the values in all arrays that do not have min-max +information. "Dummy-quantization" will produce lower accuracy but will emulate +the performance of a correctly quantized model. The example below contains a model using Relu6 activation functions. Therefore, a reasonable guess is that most activation ranges should be contained in [0, 6]. @@ -207,10 +208,10 @@ tflite_convert \ ### Specifying subgraphs Any array in the input file can be specified as an input or output array in -order to extract subgraphs out of an input graph file. TOCO discards the parts -of the graph outside of the specific subgraph. Use [graph -visualizations](#graph-visualizations) to identify the input and output arrays -that make up the desired subgraph. +order to extract subgraphs out of an input graph file. The TensorFlow Lite +Converter discards the parts of the graph outside of the specific subgraph. Use +[graph visualizations](#graph-visualizations) to identify the input and output +arrays that make up the desired subgraph. The follow command shows how to extract a single fused layer out of a TensorFlow GraphDef. @@ -247,9 +248,10 @@ function tends to get fused). ## Graph visualizations -TOCO can export a graph to the Graphviz Dot format for easy visualization via -either the `--output_format` flag or the `--dump_graphviz_dir` flag. The -subsections below outline the use cases for each. +The converter can export a graph to the Graphviz Dot format for easy +visualization using either the `--output_format` flag or the +`--dump_graphviz_dir` flag. The subsections below outline the use cases for +each. ### Using `--output_format=GRAPHVIZ_DOT` <a name="using-output-format-graphviz-dot"></a> |