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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 0ab024c618..4bf47aa3c4 100644 --- a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md +++ b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md @@ -11,8 +11,10 @@ Table of contents: * [Command-line tools](#tools) * [Converting models prior to TensorFlow 1.9.](#pre-tensorflow-1.9) -* [Convert a TensorFlow GraphDef](#graphdef) -* [Convert a TensorFlow SavedModel](#savedmodel) +* [Basic examples](#basic) + * [Convert a TensorFlow GraphDef](#graphdef) + * [Convert a TensorFlow SavedModel](#savedmodel) + * [Convert a tf.keras model](#keras) * [Quantization](#quantization) * [Convert a TensorFlow GraphDef for quantized inference](#graphdef-quant) * [Use "dummy-quantization" to try out quantized inference on a float @@ -34,7 +36,7 @@ There are two approaches to running TOCO via 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 examples below use `tflite_convert` for simplicity. - * Example: `tflite --output_file=...` + * Example: `tflite_convert --output_file=...` * `bazel`: In order to run the latest version of TOCO, [clone the TensorFlow repository](https://www.tensorflow.org/install/install_sources#clone_the_tensorflow_repository) and use `bazel`. This is the recommended approach for converting models that @@ -51,7 +53,12 @@ API](python_api.md#pre-tensorflow-1.9). If a command line tool is desired, the Terminal for additional details on the command-line flags available. There were no command line tools in TensorFlow 1.8. -## Convert a TensorFlow GraphDef <a name="graphdef"></a> +## Basic examples <a name="basic"></a> + +The following section shows examples of how to convert a basic float-point model +from each of the supported data formats into a TensorFlow Lite FlatBuffers. + +### Convert a TensorFlow GraphDef <a name="graphdef"></a> The follow example converts a basic TensorFlow GraphDef (frozen by [freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py)) @@ -70,7 +77,7 @@ tflite_convert \ The value for `input_shapes` is automatically determined whenever possible. -## Convert a TensorFlow SavedModel <a name="savedmodel"></a> +### Convert a TensorFlow SavedModel <a name="savedmodel"></a> The follow example converts a basic TensorFlow SavedModel into a Tensorflow Lite FlatBuffer to perform floating-point inference. @@ -95,6 +102,17 @@ There is currently no support for MetaGraphDefs without a SignatureDef or for MetaGraphDefs that use the [`assets/` directory](https://www.tensorflow.org/guide/saved_model#structure_of_a_savedmodel_directory). +### Convert a tf.Keras model <a name="keras"></a> + +The following example converts a `tf.keras` model into a TensorFlow Lite +Flatbuffer. The `tf.keras` file must contain both the model and the weights. + +``` +tflite_convert \ + --output_file=/tmp/foo.tflite \ + --keras_model_file=/tmp/keras_model.h5 +``` + ## Quantization ### Convert a TensorFlow GraphDef for quantized inference <a name="graphdef-quant"></a> |