Merged commit includes the following changes:

175703479 by yifeif: Internal change. -- 175695370 by A. Unique TensorFlower: Implements _shared_embedding_columns and adds some tests. -- 175695349 by A. Unique TensorFlower: Implements tf.metrics.true_negatives, adds missing tests and does some cleanup in tf.contrib.metrics. -- PiperOrigin-RevId: 175703479
author: A. Unique TensorFlower <gardener@tensorflow.org> 2017-11-14 10:54:28 -0800
committer: TensorFlower Gardener <gardener@tensorflow.org> 2017-11-14 10:57:46 -0800
commit: c674e27bfd68a6c990e694b6afd901bfeeaa006d (patch)
tree: bf5bcf0f0432db68a2c951347d01ee03f69a4ca7 /tensorflow/contrib/lite/models
parent: f5669d905a28893c71ff44245da6ed5e13d55d1c (diff)
7 files changed, 314 insertions, 0 deletions
diff --git a/tensorflow/contrib/lite/models/smartreply/g3doc/README.md b/tensorflow/contrib/lite/models/smartreply/g3doc/README.md
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+# Smart Reply Model
+
+## What is On-Device Smart Reply Model?
+
+Smart Replies are contextually relevant, one-touch responses that help the user
+to reply to an incoming text message (or email) efficiently and effortlessly.
+Smart Replies have been highly successful across several Google products
+including
+[Gmail](https://www.blog.google/products/gmail/save-time-with-smart-reply-in-gmail/),
+[Inbox](https://www.blog.google/products/gmail/computer-respond-to-this-email/)
+and
+[Allo](https://blog.google/products/allo/google-allo-smarter-messaging-app/).
+
+The On-device Smart Reply model is targeted towards text chat use cases. It has
+a completely different architecture from its cloud-based counterparts, and is
+built specifically for memory constraints devices such as phones & watches. It
+has been successfully used to provide [Smart Replies on Android
+Wear](https://research.googleblog.com/2017/02/on-device-machine-intelligence.html)
+to all first- & third-party apps.
+
+The on-device model comes with several benefits. It is:
+
+*   **Faster**: The model resides on the device and does not require internet
+    connectivity. Thus, the inference is very fast and has an average latency of
+    only a few milliseconds.
+*   **Resource efficient**: The model has a small memory footprint on
+    the device.
+*   **Privacy-friendly**: The user data never leaves the device and this
+    eliminates any privacy restrictions.
+
+A caveat, though, is that the on-device model has lower triggering rate than its
+cloud counterparts (triggering rate is the percentage of times the model
+suggests a response for an incoming message).
+
+## When to use this Model?
+
+The On-Device Smart Reply model is aimed towards improving the messaging
+experience for day-to-day conversational chat messages. We recommend using this
+model for similar use cases. Some sample messages on which the model does well
+are provided in this [tsv
+file](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/models/testdata/smartreply_samples.tsv)
+for reference. The file format is:
+
+```
+   {incoming_message  smart_reply1   [smart_reply2]   [smart_reply3]}
+```
+
+For the current model, we see a triggering rate of about 30-40% for messages
+which are similar to those provided in the tsv file above.
+
+In case the model does not trigger any response, the system falls back to
+suggesting replies from a fixed back-off set that was compiled from popular
+response intents observed in chat conversations. Some of the fallback responses
+are `Ok, Yes, No, 👍, ☺`.
+
+The model can only be used for inference at this time (i.e. it cannot be custom
+trained). If you are interested to know how the model was trained, please refer
+to this [blog
+post](https://research.googleblog.com/2017/02/on-device-machine-intelligence.html)
+and [research paper](https://arxiv.org/pdf/1708.00630).
+
+## How to use this Model?
+
+We have provided a pre-built demo APK that you can download, install and test on
+your phone ([demo APK
+here](http://download.tensorflow.org/deps/tflite/SmartReplyDemo.apk)).
+
+The On-Device Smart Reply demo App works in the following way:
+
+1.  Android app links to the JNI binary with a predictor library.
+
+2.  In the predictor library, `GetSegmentPredictions` is called with a list of input
+    strings.
+
+    2.1 The input string can be 1-3 most recent messages of the conversations in
+    form of string vector. The model will run on these input sentences and
+    provide Smart Replies corresponding to them.
+
+    2.2 The function performs some preprocessing on input data which includes:
+
+    *   Sentence splitting: The input message will be split into sentences if
+        message has more than one sentence. Eg: a message like “How are you?
+        Want to grab lunch?” will be broken down into 2 different sentences.
+    *   Normalization: The individual sentences will be normalized by converting
+        them into lower cases, removing unnecessary punctuations, etc. Eg: “how
+        are you????” will be converted to “how are you?” (refer for NORMALIZE op
+        for more details).
+
+        The input string content will be converted to tensors.
+
+    2.3 The function then runs the prediction model on the input tensors.
+
+    2.4 The function also performs some post-processing which includes
+    aggregating the model predictions for the input sentences from 2.2 and
+    returning the appropriate responses.
+
+3.  Finally, it gets response(s) from `std::vector<PredictorResponse>`, and
+    returns back to Android app. Responses are sorted in descending order of
+    confidence score.
+
+## Ops and Functionality Supported
+
+Following are the ops supported for using On-Device Smart Reply model:
+
+*   **NORMALIZE**
+
+    This is a custom op which normalizes the sentences by:
+
+    *   Converting all sentences into lower case.
+    *   Removing unnecessary punctuations (eg: “how are you????” → “how are
+        you?”).
+    *   Expanding sentences wherever necessary (eg: “ I’m home” → “I am home”).
+
+*   **SKIP_GRAM**
+
+    This is an op inside TensorFlow Lite that converts sentences into a list of
+    skip grams. The configurable parameters are `ngram_size` and
+    `max_skip_size`. For the model provided, the values for these parameters are
+    set to 3 & 2 respectively.
+
+*   **EXTRACT_FEATURES**
+
+    This is a custom op that hashes skip grams to features represented as
+    integers. Longer skip-grams are allocated higher weights.
+
+*   **LSH_PROJECTION**
+
+    This is an op inside TensorFlow Lite that projects input features to a
+    corresponding bit vector space using Locality Sensitive Hashing (LSH).
+
+*   **PREDICT**
+
+    This is a custom op that runs the input features through the projection
+    model (details [here](https://arxiv.org/pdf/1708.00630.pdf)), computes the
+    appropriate response labels along with weights for the projected features,
+    and aggregates the response labels and weights together.
+
+*   **HASHTABLE_LOOKUP**
+
+    This is a custom op that uses label id from predict op and looks up the
+    response text from the given label id.
+
+## Further Information
+
+*   Open source code
+    [here](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/models/smartreply/).
diff --git a/tensorflow/contrib/lite/models/testdata/g3doc/README.md b/tensorflow/contrib/lite/models/testdata/g3doc/README.md
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+## Speech Model Tests
+
+Sample test data has been provided for speech related models in Tensorflow Lite
+to help users working with speech models to verify and test their models.
+
+For the hotword, speaker-id and automatic speech recognition sample models, the
+architecture assumes that the models receive their input from a speech
+pre-processing module. The speech pre-processing module receives the audio
+signal and produces features for the encoder neural network and uses some
+typical signal processing algorithms, like FFT and spectral subtraction, and
+ultimately produces a log-mel filterbank (the log of the triangular mel filters
+applied to the power spectra). The text-to-speech model assumes that the inputs
+are linguistic features describing characteristics of phonemes, syllables,
+words, phrases, and sentence. The outputs are acoustic features including
+mel-cepstral coefficients, log fundamental frequency, and band aperiodicity.
+The pre-processing modules for these models are not provided in the open source
+version of TensorFlow Lite.
+
+The following sections describe the architecture of the sample models at a high
+level:
+
+### Hotword Model
+
+The hotword model is the neural network model we use for keyphrase/hotword
+spotting (i.e. "okgoogle" detection). It is the entry point for voice
+interaction (e.g. Google search app on Android devices or Google Home, etc.).
+The speech hotword model block diagram is shown in Figure below. It has an input
+size of 40 (float), an output size of 7 (float), one Svdf layer, and four fully
+connected layers with the corresponding parameters as shown in figure below.
+
+![hotword_model](hotword.svg "Hotword model")
+
+### Speaker-id Model
+
+The speaker-id model is the neural network model we use for speaker
+verification. It runs after the hotword triggers. The speech speaker-id model
+block diagram is shown in Figure below. It has an input size of 80 (float), an
+output size of 64 (float), three Lstm layers, and one fully connected layers
+with the corresponding parameters as shown in figure below.
+
+![speakerid_model](speakerid.svg "Speaker-id model")
+
+### Text-to-speech (TTS) Model
+
+The text-to-speech model is the neural network model used to generate speech
+from text. The speech text-to-speech model’s block diagram is shown
+in Figure below. It has and input size of 334 (float), an output size of 196
+(float), two fully connected layers, three Lstm layers, and one recurrent layer
+with the corresponding parameters as shown in the figure.
+
+![tts_model](tts.svg "TTS model")
+
+### Automatic Speech Recognizer (ASR) Acoustic Model (AM)
+
+The acoustic model for automatic speech recognition is the neural network model
+for matching phonemes to the input autio features. It generates posterior
+probabilities of phonemes from speech frontend features (log-mel filterbanks).
+It has an input size of 320 (float), an output size of 42 (float), five LSTM
+layers and one fully connected layers with a Softmax activation function, with
+the corresponding parameters as shown in the figure.
+
+![asr_am_model](asr_am.svg "ASR AM model")
+
+## Speech models test input/output generation
+
+As mentioned above the input to models are generated from a pre-processing
+module (output of a log-mel filterbank, or linguistic features), and the outputs
+are generated by running the equivalent TensorFlow model by feeding them the
+same input.
+
+## Link to the open source code
+
+### Models:
+
+[Speech hotword model (Svdf rank=1)] (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/models/testdata/speech_hotword_model_rank1.tflite)
+
+[Speech hotword model (Svdf rank=2)] (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/models/testdata/speech_hotword_model_rank2.tflite)
+
+[Speaker-id model] (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/models/testdata/speech_speakerid_model.tflite)
+
+[TTS model] (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/models/testdata/speech_tts_model.tflite)
+
+[ASR AM model] (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/models/testdata/speech_terse_am_model.tflite)
+
+### Test benches
+
+[Speech hotword model test] (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/models/speech_hotword_model_test.cc)
+
+[Speaker-id model test] (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/models/speech_speakerid_model_test.cc)
+
+[TTS model test] (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/models/speech_tts_model_test.cc)
+
+[ASR AM model test] (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/models/speech_terse_am_model_test.cc)
+
+## Android Support
+The models have been tested on Android phones, using the following tests:
+
+[Hotword] (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/android/BUILD?rcl=172930882&l=25)
+
+[Speaker-id] (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/android/BUILD?rcl=172930882&l=36)
+
+
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+
diff --git a/tensorflow/contrib/lite/models/testdata/smartreply_samples.tsv b/tensorflow/contrib/lite/models/testdata/smartreply_samples.tsv
new file mode 100644
index 0000000000..dfdc783106
--- /dev/null
+++ b/tensorflow/contrib/lite/models/testdata/smartreply_samples.tsv
@@ -0,0 +1,50 @@
+any chance ur free tonight	Maybe not
+any updates?	No update yet
+anything i can do to help?	No, but thanks	No, but thank you	No, but thanks for asking
+be safe.	I will be	Will do my best	Thanks, I will
+congratulations	Thanks thanks	Congratulations
+cool, let me know when you have time	Cool	Yes very cool	Yeah, cool
+drive safe	Thank you, I will	Home now	I will thanks
+hang in there, you'll be okay	Doing my best	Of course we will
+happy birthday!	Hey, thanks
+happy new year!	Wish you the same	Thanks and same to you
+have a safe flight	Thanks, love you too	Safe travels
+hey	What is up?	How it going?	Can I help you?
+hey, got a sec?	What is up?	How it going?	Can I help you?
+how are you doing?	Great and you?	I am doing great
+how are you feeling	Feeling okay	A little better	Much much better
+how was your weekend?	It was real good
+how you doing	Okay and you
+hugs.	So sweet	Thanks sweetie	Take care of yourself
+i'm bored	Sorry to hear that	Join the club	No you are not
+i'm planning on coming next week. let me know if that works.	Works	Perfect, thanks
+i'm sick	Sorry to hear that
+i'm so happy for you	Thanks me too
+i'm so hungry	Haha me too
+i'm sorry	No I am sorry	Why sorry?	No worries love
+i'm sorry, i'm going to have to cancel.	No I am sorry	Why sorry?	No worries love
+is there anything i can do to help?	No, but thanks	No, but thanks for asking
+lunch?	Yes coming
+okay. lemme know as soon as you find out.	Any more questions?	It is done
+omg amazing	So amazing
+on my way	Okay see you soon	Cool, see you soon	Oh wow, ok
+oops, mistexted.	Oops	Haha, oh well	That was funny
+safe travels.	Thanks, love you too	Safe travels
+so sorry	So sorry
+sorry, i can't.	No worries at all	Sorry what?
+sorry, i can't do saturday	No worries at all
+thank you so much.	You are so welcome	You are so very welcome	You are most welcome
+thanks for coming	It was my pleasure
+thanks, this has been great.	Glad to help	So happy for you
+tomorrow would be ideal.	Yes it would
+tried calling	Try again?
+ugh, my flight is delayed.	Ugh indeed
+what are you guys up to tonight?	Nothing planned
+what day works best for you	Any day
+what do you want for dinner	Your call	Whatever is fine
+what time will you be home?	Not sure why
+where are you?!?	At my house
+wish you were here.	I wish the same	Me too honey
+you're amazing	You are too	You are amazing	I am
+you're marvelous	You are too
+you're the best.	I do my best	You are the best	Well, I try
+\ No newline at end of file
author	A. Unique TensorFlower <gardener@tensorflow.org>	2017-11-14 10:54:28 -0800
committer	TensorFlower Gardener <gardener@tensorflow.org>	2017-11-14 10:57:46 -0800
commit	c674e27bfd68a6c990e694b6afd901bfeeaa006d (patch)
tree	bf5bcf0f0432db68a2c951347d01ee03f69a4ca7 /tensorflow/contrib/lite/models
parent	f5669d905a28893c71ff44245da6ed5e13d55d1c (diff)