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+# TensorFlow Lite Converter command-line examples
+
+This page shows how to use the TensorFlow Lite Converter in the command line.
+
+[TOC]
+
+## Command-line tools <a name="tools"></a>
+
+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` 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 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>
+
+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>
+
+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))
+into a TensorFlow Lite FlatBuffer to perform floating-point inference. Frozen
+graphs contain the variables stored in Checkpoint files as Const ops.
+
+```
+curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.50_128_frozen.tgz \
+ | tar xzv -C /tmp
+tflite_convert \
+ --output_file=/tmp/foo.tflite \
+ --graph_def_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \
+ --input_arrays=input \
+ --output_arrays=MobilenetV1/Predictions/Reshape_1
+```
+
+The value for `input_shapes` is automatically determined whenever possible.
+
+### 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.
+
+```
+tflite_convert \
+ --output_file=/tmp/foo.tflite \
+ --saved_model_dir=/tmp/saved_model
+```
+
+[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
+contained within the SavedModel. The values for `--input_arrays` and
+`--output_arrays` are an aggregated, alphabetized list of the inputs and outputs
+in the [SignatureDefs](https://www.tensorflow.org/serving/signature_defs) within
+the
+[MetaGraphDef](https://www.tensorflow.org/guide/saved_model#apis_to_build_and_load_a_savedmodel)
+specified by `--saved_model_tag_set`. As with the GraphDef, the value for
+`input_shapes` is automatically determined whenever possible.
+
+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>
+
+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
+quantization behavior that was used during training.
+
+The following command generates a quantized TensorFlow Lite FlatBuffer from a
+"quantized" TensorFlow GraphDef.
+
+```
+tflite_convert \
+ --output_file=/tmp/foo.tflite \
+ --graph_def_file=/tmp/some_quantized_graph.pb \
+ --inference_type=QUANTIZED_UINT8 \
+ --input_arrays=input \
+ --output_arrays=MobilenetV1/Predictions/Reshape_1 \
+ --mean_values=128 \
+ --std_dev_values=127
+```
+
+### 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, 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].
+
+```
+curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.50_128_frozen.tgz \
+ | tar xzv -C /tmp
+tflite_convert \
+ --output_file=/tmp/foo.cc \
+ --graph_def_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \
+ --inference_type=QUANTIZED_UINT8 \
+ --input_arrays=input \
+ --output_arrays=MobilenetV1/Predictions/Reshape_1 \
+ --default_ranges_min=0 \
+ --default_ranges_max=6 \
+ --mean_values=128 \
+ --std_dev_values=127
+```
+
+## Specifying input and output arrays
+
+### Multiple input arrays
+
+The flag `input_arrays` takes in a comma-separated list of input arrays as seen
+in the example below. This is useful for models or subgraphs with multiple
+inputs.
+
+```
+curl https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_2016_08_28_frozen.pb.tar.gz \
+ | tar xzv -C /tmp
+tflite_convert \
+ --graph_def_file=/tmp/inception_v1_2016_08_28_frozen.pb \
+ --output_file=/tmp/foo.tflite \
+ --input_shapes=1,28,28,96:1,28,28,16:1,28,28,192:1,28,28,64 \
+ --input_arrays=InceptionV1/InceptionV1/Mixed_3b/Branch_1/Conv2d_0a_1x1/Relu,InceptionV1/InceptionV1/Mixed_3b/Branch_2/Conv2d_0a_1x1/Relu,InceptionV1/InceptionV1/Mixed_3b/Branch_3/MaxPool_0a_3x3/MaxPool,InceptionV1/InceptionV1/Mixed_3b/Branch_0/Conv2d_0a_1x1/Relu \
+ --output_arrays=InceptionV1/Logits/Predictions/Reshape_1
+```
+
+Note that `input_shapes` is provided as a colon-separated list. Each input shape
+corresponds to the input array at the same position in the respective list.
+
+### Multiple output arrays
+
+The flag `output_arrays` takes in a comma-separated list of output arrays as
+seen in the example below. This is useful for models or subgraphs with multiple
+outputs.
+
+```
+curl https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_2016_08_28_frozen.pb.tar.gz \
+ | tar xzv -C /tmp
+tflite_convert \
+ --graph_def_file=/tmp/inception_v1_2016_08_28_frozen.pb \
+ --output_file=/tmp/foo.tflite \
+ --input_arrays=input \
+ --output_arrays=InceptionV1/InceptionV1/Mixed_3b/Branch_1/Conv2d_0a_1x1/Relu,InceptionV1/InceptionV1/Mixed_3b/Branch_2/Conv2d_0a_1x1/Relu
+```
+
+### 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. 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.
+
+```
+curl https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_2016_08_28_frozen.pb.tar.gz \
+ | tar xzv -C /tmp
+tflite_convert \
+ --graph_def_file=/tmp/inception_v1_2016_08_28_frozen.pb \
+ --output_file=/tmp/foo.pb \
+ --input_shapes=1,28,28,96:1,28,28,16:1,28,28,192:1,28,28,64 \
+ --input_arrays=InceptionV1/InceptionV1/Mixed_3b/Branch_1/Conv2d_0a_1x1/Relu,InceptionV1/InceptionV1/Mixed_3b/Branch_2/Conv2d_0a_1x1/Relu,InceptionV1/InceptionV1/Mixed_3b/Branch_3/MaxPool_0a_3x3/MaxPool,InceptionV1/InceptionV1/Mixed_3b/Branch_0/Conv2d_0a_1x1/Relu \
+ --output_arrays=InceptionV1/InceptionV1/Mixed_3b/concat_v2
+```
+
+Note that the final representation in TensorFlow Lite FlatBuffers tends to have
+coarser granularity than the very fine granularity of the TensorFlow GraphDef
+representation. For example, while a fully-connected layer is typically
+represented as at least four separate ops in TensorFlow GraphDef (Reshape,
+MatMul, BiasAdd, Relu...), it is typically represented as a single "fused" op
+(FullyConnected) in the converter's optimized representation and in the final
+on-device representation. As the level of granularity gets coarser, some
+intermediate arrays (say, the array between the MatMul and the BiasAdd in the
+TensorFlow GraphDef) are dropped.
+
+When specifying intermediate arrays as `--input_arrays` and `--output_arrays`,
+it is desirable (and often required) to specify arrays that are meant to survive
+in the final form of the graph, after fusing. These are typically the outputs of
+activation functions (since everything in each layer until the activation
+function tends to get fused).
+
+## Logging
+
+
+## Graph visualizations
+
+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>
+
+The first way to get a Graphviz rendering is to pass `GRAPHVIZ_DOT` into
+`--output_format`. This results in a plausible visualization of the graph. This
+reduces the requirements that exist during conversion from a TensorFlow GraphDef
+to a TensorFlow Lite FlatBuffer. This may be useful if the conversion to TFLite
+is failing.
+
+```
+curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.50_128_frozen.tgz \
+ | tar xzv -C /tmp
+tflite_convert \
+ --graph_def_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \
+ --output_file=/tmp/foo.dot \
+ --output_format=GRAPHVIZ_DOT \
+ --input_shape=1,128,128,3 \
+ --input_arrays=input \
+ --output_arrays=MobilenetV1/Predictions/Reshape_1
+```
+
+The resulting `.dot` file can be rendered into a PDF as follows:
+
+```
+dot -Tpdf -O /tmp/foo.dot
+```
+
+And the resulting `.dot.pdf` can be viewed in any PDF viewer, but we suggest one
+with a good ability to pan and zoom across a very large page. Google Chrome does
+well in that respect.
+
+```
+google-chrome /tmp/foo.dot.pdf
+```
+
+Example PDF files are viewable online in the next section.
+
+### Using `--dump_graphviz_dir`
+
+The second way to get a Graphviz rendering is to pass the `--dump_graphviz_dir`
+flag, specifying a destination directory to dump Graphviz rendering to. Unlike
+the previous approach, this one retains the original output format. This
+provides a visualization of the actual graph resulting from a specific
+conversion process.
+
+```
+curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.50_128_frozen.tgz \
+ | tar xzv -C /tmp
+tflite_convert \
+ --graph_def_file=/tmp/mobilenet_v1_0.50_128/frozen_graph.pb \
+ --output_file=/tmp/foo.tflite \
+ --input_arrays=input \
+ --output_arrays=MobilenetV1/Predictions/Reshape_1 \
+ --dump_graphviz_dir=/tmp
+```
+
+This generates a few files in the destination directory. The two most important
+files are `toco_AT_IMPORT.dot` and `/tmp/toco_AFTER_TRANSFORMATIONS.dot`.
+`toco_AT_IMPORT.dot` represents the original graph containing only the
+transformations done at import time. This tends to be a complex visualization
+with limited information about each node. It is useful in situations where a
+conversion command fails.
+
+`toco_AFTER_TRANSFORMATIONS.dot` represents the graph after all transformations
+were applied to it, just before it is exported. Typically, this is a much
+smaller graph with more information about each node.
+
+As before, these can be rendered to PDFs:
+
+```
+dot -Tpdf -O /tmp/toco_*.dot
+```
+
+Sample output files can be seen here below. Note that it is the same
+`AveragePool` node in the top right of each image.
+
+<table><tr>
+ <td>
+ <a target="_blank" href="https://storage.googleapis.com/download.tensorflow.org/example_images/toco_AT_IMPORT.dot.pdf">
+ <img src="https://www.tensorflow.org/images/tflite_convert/tflite_convert_before.png"/>
+ </a>
+ </td>
+ <td>
+ <a target="_blank" href="https://storage.googleapis.com/download.tensorflow.org/example_images/toco_AFTER_TRANSFORMATIONS.dot.pdf">
+ <img src="https://www.tensorflow.org/images/tflite_convert/tflite_convert_after.png"/>
+ </a>
+ </td>
+</tr>
+<tr><td>before</td><td>after</td></tr>
+</table>
+
+### Graph "video" logging
+
+When `--dump_graphviz_dir` is used, one may additionally pass
+`--dump_graphviz_video`. This causes a graph visualization to be dumped after
+each individual graph transformation, resulting in thousands of files.
+Typically, one would then bisect into these files to understand when a given
+change was introduced in the graph.
+
+### Legend for the graph visualizations <a name="graphviz-legend"></a>
+
+* Operators are red square boxes with the following hues of red:
+ * Most operators are
+ <span style="background-color:#db4437;color:white;border:1px;border-style:solid;border-color:black;padding:1px">bright
+ red</span>.
+ * Some typically heavy operators (e.g. Conv) are rendered in a
+ <span style="background-color:#c53929;color:white;border:1px;border-style:solid;border-color:black;padding:1px">darker
+ red</span>.
+* Arrays are octagons with the following colors:
+ * Constant arrays are
+ <span style="background-color:#4285f4;color:white;border:1px;border-style:solid;border-color:black;padding:1px">blue</span>.
+ * Activation arrays are gray:
+ * Internal (intermediate) activation arrays are
+ <span style="background-color:#f5f5f5;border:1px;border-style:solid;border-color:black;border:1px;border-style:solid;border-color:black;padding:1px">light
+ gray</span>.
+ * Those activation arrays that are designated as `--input_arrays` or
+ `--output_arrays` are
+ <span style="background-color:#9e9e9e;border:1px;border-style:solid;border-color:black;padding:1px">dark
+ gray</span>.
+ * RNN state arrays are green. Because of the way that the converter
+ represents RNN back-edges explicitly, each RNN state is represented by a
+ pair of green arrays:
+ * The activation array that is the source of the RNN back-edge (i.e.
+ whose contents are copied into the RNN state array after having been
+ computed) is
+ <span style="background-color:#b7e1cd;border:1px;border-style:solid;border-color:black;padding:1px">light
+ green</span>.
+ * The actual RNN state array is
+ <span style="background-color:#0f9d58;color:white;border:1px;border-style:solid;border-color:black;padding:1px">dark
+ green</span>. It is the destination of the RNN back-edge updating
+ it.
diff --git a/tensorflow/contrib/lite/g3doc/tflite_convert/cmdline_reference.md b/tensorflow/contrib/lite/g3doc/tflite_convert/cmdline_reference.md
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+# TensorFlow Lite Converter command-line glossary
+
+This page is complete reference of command-line flags used by the TensorFlow
+Lite Converter's command line starting from TensorFlow 1.9 up until the most
+recent build of TensorFlow.
+
+[TOC]
+
+## High-level flags
+
+The following high level flags specify the details of the input and output
+files. The flag `--output_file` is always required. Additionally, either
+`--graph_def_file`, `--saved_model_dir` or `--keras_model_file` is required.
+
+* `--output_file`. Type: string. Specifies the full path of the output file.
+* `--graph_def_file`. Type: string. Specifies the full path of the input
+ GraphDef file frozen using
+ [freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py).
+* `--saved_model_dir`. Type: string. Specifies the full path to the directory
+ containing the SavedModel.
+* `--keras_model_file`. Type: string. Specifies the full path of the HDF5 file
+ containing the tf.keras model.
+* `--output_format`. Type: string. Default: `TFLITE`. Specifies the format of
+ the output file. Allowed values:
+ * `TFLITE`: TensorFlow Lite FlatBuffer format.
+ * `GRAPHVIZ_DOT`: GraphViz `.dot` format containing a visualization of the
+ graph after graph transformations.
+ * Note that passing `GRAPHVIZ_DOT` to `--output_format` leads to loss
+ of TFLite specific transformations. Therefore, the resulting
+ visualization may not reflect the final set of graph
+ transformations. To get a final visualization with all graph
+ transformations use `--dump_graphviz_dir` instead.
+
+The following flags specify optional parameters when using SavedModels.
+
+* `--saved_model_tag_set`. Type: string. Default:
+ [kSavedModelTagServe](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/saved_model/tag_constants.h).
+ Specifies a comma-separated set of tags identifying the MetaGraphDef within
+ the SavedModel to analyze. All tags in the tag set must be specified.
+* `--saved_model_signature_key`. Type: string. Default:
+ [DEFAULT_SERVING_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants).
+ Specifies the key identifying the SignatureDef containing inputs and
+ outputs.
+
+## Model flags
+
+*Model flags* provide additional information about the model stored in the input
+file.
+
+* `--input_arrays`. Type: comma-separated list of strings. Specifies the list
+ of names of input activation tensors.
+* `--output_arrays`. Type: comma-separated list of strings. Specifies the list
+ of names of output activation tensors.
+
+The following flags define properties of the input tensors. Each item in the
+`--input_arrays` flag should correspond to each item in the following flags
+based on index.
+
+* `--input_shapes`. Type: colon-separated list of comma-separated lists of
+ integers. Each comma-separated list of integers gives the shape of one of
+ the input arrays specified in
+ [TensorFlow convention](https://www.tensorflow.org/guide/dims_types#shape).
+ * Example: `--input_shapes=1,60,80,3` for a typical vision model means a
+ batch size of 1, an input image height of 60, an input image width of
+ 80, and an input image depth of 3 (representing RGB channels).
+ * Example: `--input_arrays=foo,bar --input_shapes=2,3:4,5,6` means "foo"
+ has a shape of [2, 3] and "bar" has a shape of [4, 5, 6].
+* `--std_dev_values`, `--mean_values`. Type: comma-separated list of floats.
+ These specify the (de-)quantization parameters of the input array, when it
+ is quantized. This is only needed if `inference_input_type` is
+ `QUANTIZED_UINT8`.
+ * The meaning of `mean_values` and `std_dev_values` is as follows: each
+ quantized value in the quantized input array will be interpreted as a
+ mathematical real number (i.e. as an input activation value) according
+ to the following formula:
+ * `real_value = (quantized_input_value - mean_value) / std_dev_value`.
+ * When performing float inference (`--inference_type=FLOAT`) on a
+ quantized input, the quantized input would be immediately dequantized by
+ the inference code according to the above formula, before proceeding
+ with float inference.
+ * When performing quantized inference
+ (`--inference_type=QUANTIZED_UINT8`), no dequantization is performed by
+ the inference code. However, the quantization parameters of all arrays,
+ including those of the input arrays as specified by `mean_value` and
+ `std_dev_value`, determine the fixed-point multipliers used in the
+ quantized inference code. `mean_value` must be an integer when
+ performing quantized inference.
+
+## Transformation flags
+
+*Transformation flags* specify options of the transformations to be applied to
+the graph, i.e. they specify requested properties that the output file should
+have.
+
+* `--inference_type`. Type: string. Default: `FLOAT`. Data type of all
+ real-number arrays in the output file except for input arrays (defined by
+ `--inference_input_type`). Must be `{FLOAT, QUANTIZED_UINT8}`.
+
+ This flag only impacts real-number arrays including float and quantized
+ arrays. This excludes all other data types including plain integer arrays
+ and string arrays. Specifically:
+
+ * If `FLOAT`, then real-numbers arrays will be of type float in the output
+ file. If they were quantized in the input file, then they get
+ dequantized.
+ * If `QUANTIZED_UINT8`, then real-numbers arrays will be quantized as
+ uint8 in the output file. If they were float in the input file, then
+ they get quantized.
+
+* `--inference_input_type`. Type: string. Data type of a real-number input
+ array in the output file. By default the `--inference_type` is used as type
+ of all of the input arrays. Flag is primarily intended for generating a
+ float-point graph with a quantized input array. A Dequantized operator is
+ added immediately after the input array. Must be `{FLOAT, QUANTIZED_UINT8}`.
+
+ The flag is typically used for vision models taking a bitmap as input but
+ requiring floating-point inference. For such image models, the uint8 input
+ is quantized and the quantization parameters used for such input arrays are
+ their `mean_value` and `std_dev_value` parameters.
+
+* `--default_ranges_min`, `--default_ranges_max`. Type: floating-point.
+ Default value for the (min, max) range values used for all arrays without a
+ specified range. Allows user to proceed with quantization of non-quantized
+ or incorrectly-quantized input files. These flags produce models with low
+ accuracy. They are intended for easy experimentation with quantization via
+ "dummy quantization".
+
+* `--drop_control_dependency`. Type: boolean. Default: True. Indicates whether
+ to drop control dependencies silently. This is due to TensorFlow Lite not
+ supporting control dependencies.
+
+* `--reorder_across_fake_quant`. Type: boolean. Default: False. Indicates
+ whether to reorder FakeQuant nodes in unexpected locations. Used when the
+ location of the FakeQuant nodes is preventing graph transformations
+ necessary to convert the graph. Results in a graph that differs from the
+ quantized training graph, potentially causing differing arithmetic behavior.
+
+* `--allow_custom_ops`. Type: string. Default: False. Indicates whether to
+ allow custom operations. When false, any unknown operation is an error. When
+ true, custom ops are created for any op that is unknown. The developer will
+ need to provide these to the TensorFlow Lite runtime with a custom resolver.
+
+* `--post_training_quantize`. Type: boolean. Default: False. Boolean
+ indicating whether to quantize the weights of the converted float model.
+ Model size will be reduced and there will be latency improvements (at the
+ cost of accuracy).
+
+## Logging flags
+
+The following flags generate graph visualizations of the graph as
+[GraphViz](https://www.graphviz.org/) `.dot` files at various points during
+graph transformations:
+
+* `--dump_graphviz_dir`. Type: string. Specifies the full path of the
+ directory to output GraphViz `.dot` files. Outputs the graph immediately
+ after reading in the graph and after all of the transformations have been
+ completed.
+* `--dump_graphviz_video`. Type: boolean. Outputs GraphViz after every graph
+ transformation. Requires `--dump_graphviz_dir` to be specified.
diff --git a/tensorflow/contrib/lite/g3doc/tflite_convert/index.md b/tensorflow/contrib/lite/g3doc/tflite_convert/index.md
new file mode 100644
index 0000000000..12ba0225f6
--- /dev/null
+++ b/tensorflow/contrib/lite/g3doc/tflite_convert/index.md
@@ -0,0 +1,22 @@
+# TensorFlow Lite Converter
+
+The TensorFlow Lite Converter converts TensorFlow graphs into
+TensorFlow Lite graphs. There are additional usages that are also detailed in
+the usage documentation.
+
+
+## Where the converter fits in the TensorFlow landscape
+
+Once an application developer has a trained TensorFlow model, the TensorFlow
+Lite Converter will accept
+that model and generate a TensorFlow Lite
+[FlatBuffer](https://google.github.io/flatbuffers/) file. The converter currently supports
+[SavedModels](https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators),
+frozen graphs (models generated via
+[freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py)),
+and `tf.Keras` model files. The TensorFlow Lite FlatBuffer file can be shipped
+to client devices, generally mobile devices, where the TensorFlow Lite
+interpreter handles them on-device. This flow is represented in the diagram
+below.
+
+![drawing](toco_landscape.svg)
diff --git a/tensorflow/contrib/lite/g3doc/tflite_convert/python_api.md b/tensorflow/contrib/lite/g3doc/tflite_convert/python_api.md
new file mode 100644
index 0000000000..e1c0e0c240
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+++ b/tensorflow/contrib/lite/g3doc/tflite_convert/python_api.md
@@ -0,0 +1,258 @@
+# TensorFlow Lite Converter & Interpreter Python API reference
+
+This page provides examples on how to use the TensorFlow Lite Converter and the
+TensorFlow Lite interpreter using the Python API.
+
+[TOC]
+
+
+## High-level overview
+
+While the TensorFlow Lite Converter can be used from the command line, it is
+often convenient to use in a Python script as part of the model development
+pipeline. This allows you to know early that you are designing a model that can
+be targeted to devices with mobile.
+
+## API
+
+The API for converting TensorFlow models to TensorFlow Lite as of TensorFlow 1.9
+is `tf.contrib.lite.TFLiteConverter`. The API for calling the Python intepreter
+is `tf.contrib.lite.Interpreter`.
+
+Note: Reference "Additional Instructions" sections for converting TensorFlow
+models to TensorFlow Lite
+[in TensorFlow 1.9 to TensorFlow 1.11](#pre-tensorflow-1.11) and
+[prior to TensorFlow 1.9](#pre-tensorflow-1.9)
+
+`TFLiteConverter` provides class methods based on the original format of the
+model. `TFLiteConverter.from_session()` is available for GraphDefs.
+`TFLiteConverter.from_saved_model()` is available for SavedModels.
+`TFLiteConverter.from_keras_model_file()` is available for `tf.Keras` files.
+Example usages for simple float-point models are shown in
+[Basic Examples](#basic). Examples usages for more complex models is shown in
+[Complex Examples](#complex).
+
+## 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.
+
+### Exporting a GraphDef from tf.Session <a name="basic-graphdef-sess"></a>
+
+The following example shows how to convert a TensorFlow GraphDef into a
+TensorFlow Lite FlatBuffer from a `tf.Session` object.
+
+```python
+import tensorflow as tf
+
+img = tf.placeholder(name="img", dtype=tf.float32, shape=(1, 64, 64, 3))
+var = tf.get_variable("weights", dtype=tf.float32, shape=(1, 64, 64, 3))
+val = img + var
+out = tf.identity(val, name="out")
+
+with tf.Session() as sess:
+ sess.run(tf.global_variables_initializer())
+ converter = tf.contrib.lite.TFLiteConverter.from_session(sess, [img], [out])
+ tflite_model = converter.convert()
+ open("converted_model.tflite", "wb").write(tflite_model)
+```
+
+### Exporting a GraphDef from file <a name="basic-graphdef-file"></a>
+
+The following example shows how to convert a TensorFlow GraphDef into a
+TensorFlow Lite FlatBuffer when the GraphDef is stored in a file. Both `.pb` and
+`.pbtxt` files are accepted.
+
+The example uses
+[Mobilenet_1.0_224](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_1.0_224_frozen.tgz).
+The function only supports GraphDefs frozen using
+[freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py).
+
+```python
+import tensorflow as tf
+
+graph_def_file = "/path/to/Downloads/mobilenet_v1_1.0_224/frozen_graph.pb"
+input_arrays = ["input"]
+output_arrays = ["MobilenetV1/Predictions/Softmax"]
+
+converter = tf.contrib.lite.TFLiteConverter.from_frozen_graph(
+ graph_def_file, input_arrays, output_arrays)
+tflite_model = converter.convert()
+open("converted_model.tflite", "wb").write(tflite_model)
+```
+
+### Exporting a SavedModel <a name="basic-savedmodel"></a>
+
+The following example shows how to convert a SavedModel into a TensorFlow Lite
+FlatBuffer.
+
+```python
+import tensorflow as tf
+
+converter = tf.contrib.lite.TFLiteConverter.from_saved_model(saved_model_dir)
+tflite_model = converter.convert()
+open("converted_model.tflite", "wb").write(tflite_model)
+```
+
+For more complex SavedModels, the optional parameters that can be passed into
+`TFLiteConverter.from_saved_model()` are `input_arrays`, `input_shapes`,
+`output_arrays`, `tag_set` and `signature_key`. Details of each parameter are
+available by running `help(tf.contrib.lite.TFLiteConverter)`.
+
+### Exporting a tf.keras File <a name="basic-keras-file"></a>
+
+The following example shows how to convert a `tf.keras` model into a TensorFlow
+Lite FlatBuffer. This example requires
+[`h5py`](http://docs.h5py.org/en/latest/build.html) to be installed.
+
+```python
+import tensorflow as tf
+
+converter = tf.contrib.lite.TFLiteConverter.from_keras_model_file("keras_model.h5")
+tflite_model = converter.convert()
+open("converted_model.tflite", "wb").write(tflite_model)
+```
+
+The `tf.keras` file must contain both the model and the weights. A comprehensive
+example including model construction can be seen below.
+
+```python
+import numpy as np
+import tensorflow as tf
+
+# Generate tf.keras model.
+model = tf.keras.models.Sequential()
+model.add(tf.keras.layers.Dense(2, input_shape=(3,)))
+model.add(tf.keras.layers.RepeatVector(3))
+model.add(tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(3)))
+model.compile(loss=tf.keras.losses.MSE,
+ optimizer=tf.keras.optimizers.RMSprop(lr=0.0001),
+ metrics=[tf.keras.metrics.categorical_accuracy],
+ sample_weight_mode='temporal')
+
+x = np.random.random((1, 3))
+y = np.random.random((1, 3, 3))
+model.train_on_batch(x, y)
+model.predict(x)
+
+# Save tf.keras model in HDF5 format.
+keras_file = "keras_model.h5"
+tf.keras.models.save_model(model, keras_file)
+
+# Convert to TensorFlow Lite model.
+converter = tf.contrib.lite.TFLiteConverter.from_keras_model_file(keras_file)
+tflite_model = converter.convert()
+open("converted_model.tflite", "wb").write(tflite_model)
+```
+
+## Complex examples <a name="complex"></a>
+
+For models where the default value of the attributes is not sufficient, the
+attribute's values should be set before calling `convert()`. In order to call
+any constants use `tf.contrib.lite.constants.<CONSTANT_NAME>` as seen below with
+`QUANTIZED_UINT8`. Run `help(tf.contrib.lite.TFLiteConverter)` in the Python
+terminal for detailed documentation on the attributes.
+
+Although the examples are demonstrated on GraphDefs containing only constants.
+The same logic can be applied irrespective of the input data format.
+
+### Exporting a quantized GraphDef <a name="complex-quant"></a>
+
+The following example shows how to convert a quantized model into a TensorFlow
+Lite FlatBuffer.
+
+```python
+import tensorflow as tf
+
+img = tf.placeholder(name="img", dtype=tf.float32, shape=(1, 64, 64, 3))
+const = tf.constant([1., 2., 3.]) + tf.constant([1., 4., 4.])
+val = img + const
+out = tf.fake_quant_with_min_max_args(val, min=0., max=1., name="output")
+
+with tf.Session() as sess:
+ converter = tf.contrib.lite.TFLiteConverter.from_session(sess, [img], [out])
+ converter.inference_type = tf.contrib.lite.constants.QUANTIZED_UINT8
+ input_arrays = converter.get_input_arrays()
+ converter.quantized_input_stats = {input_arrays[0] : (0., 1.)} # mean, std_dev
+ tflite_model = converter.convert()
+ open("converted_model.tflite", "wb").write(tflite_model)
+```
+
+## TensorFlow Lite Python interpreter <a name="interpreter"></a>
+
+### Using the interpreter from a model file <a name="interpreter-file"></a>
+
+The following example shows how to use the TensorFlow Lite Python interpreter
+when provided a TensorFlow Lite FlatBuffer file. The example also demonstrates
+how to run inference on random input data. Run
+`help(tf.contrib.lite.Interpreter)` in the Python terminal to get detailed
+documentation on the interpreter.
+
+```python
+import numpy as np
+import tensorflow as tf
+
+# Load TFLite model and allocate tensors.
+interpreter = tf.contrib.lite.Interpreter(model_path="converted_model.tflite")
+interpreter.allocate_tensors()
+
+# Get input and output tensors.
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+# Test model on random input data.
+input_shape = input_details[0]['shape']
+input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
+interpreter.set_tensor(input_details[0]['index'], input_data)
+
+interpreter.invoke()
+output_data = interpreter.get_tensor(output_details[0]['index'])
+print(output_data)
+```
+
+### Using the interpreter from model data <a name="interpreter-data"></a>
+
+The following example shows how to use the TensorFlow Lite Python interpreter
+when starting with the TensorFlow Lite Flatbuffer model previously loaded. This
+example shows an end-to-end use case, starting from building the TensorFlow
+model.
+
+```python
+import numpy as np
+import tensorflow as tf
+
+img = tf.placeholder(name="img", dtype=tf.float32, shape=(1, 64, 64, 3))
+const = tf.constant([1., 2., 3.]) + tf.constant([1., 4., 4.])
+val = img + const
+out = tf.identity(val, name="out")
+
+with tf.Session() as sess:
+ converter = tf.contrib.lite.TFLiteConverter.from_session(sess, [img], [out])
+ tflite_model = converter.convert()
+
+# Load TFLite model and allocate tensors.
+interpreter = tf.contrib.lite.Interpreter(model_content=tflite_model)
+interpreter.allocate_tensors()
+```
+
+## Additional instructions
+
+### Build from source code <a name="latest-package"></a>
+
+In order to run the latest version of the TensorFlow Lite Converter Python API,
+either install the nightly build with
+[pip](https://www.tensorflow.org/install/pip) (recommended) or
+[Docker](https://www.tensorflow.org/install/docker), or
+[build the pip package from source](https://www.tensorflow.org/install/source).
+
+### Converting models in TensorFlow 1.9 to TensorFlow 1.11 <a name="#pre-tensorflow-1.11"></a>
+
+To convert TensorFlow models to TensorFlow Lite in TensorFlow 1.9 through
+TensorFlow 1.11, use `TocoConverter`. `TocoConverter` is semantically
+identically to `TFLiteConverter`.
+
+### Converting models prior to TensorFlow 1.9 <a name="pre-tensorflow-1.9"></a>
+
+To convert TensorFlow models to TensorFlow Lite in TensorFlow 1.7 and TensorFlow
+1.8, use the `toco_convert` function. Run `help(tf.contrib.lite.toco_convert)`
+to get details about accepted parameters.
diff --git a/tensorflow/contrib/lite/g3doc/tflite_convert/toco_landscape.svg b/tensorflow/contrib/lite/g3doc/tflite_convert/toco_landscape.svg
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