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Expand Up @@ -12,73 +12,50 @@ welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg)](CO

[Tensor2Tensor](https://github.com/tensorflow/tensor2tensor), or
[T2T](https://github.com/tensorflow/tensor2tensor) for short, is a library
of deep learning models and datasets. It has binaries to train the models and
to download and prepare the data for you. T2T is modular and extensible and can
be used in [notebooks](https://goo.gl/wkHexj) for prototyping your own models
or running existing ones on your data. It is actively used and maintained by
researchers and engineers within
the [Google Brain team](https://research.google.com/teams/brain/) and was used
to develop state-of-the-art models for translation (see
[Attention Is All You Need](https://arxiv.org/abs/1706.03762)), summarization,
image generation and other tasks. You can read
more about T2T in the [Google Research Blog post introducing
it](https://research.googleblog.com/2017/06/accelerating-deep-learning-research.html).

We're eager to collaborate with you on extending T2T, so please feel
free to [open an issue on
GitHub](https://github.com/tensorflow/tensor2tensor/issues) or
send along a pull request to add your dataset or model.
See [our contribution
doc](CONTRIBUTING.md) for details and our [open
issues](https://github.com/tensorflow/tensor2tensor/issues).
You can chat with us and other users on
[Gitter](https://gitter.im/tensor2tensor/Lobby) and please join our
[Google Group](https://groups.google.com/forum/#!forum/tensor2tensor) to keep up
with T2T announcements.
of deep learning models and datasets designed to [accelerate deep learning
research](https://research.googleblog.com/2017/06/accelerating-deep-learning-research.html) and make it more accessible.

T2T is actively used and maintained by researchers and engineers within the
[Google Brain team](https://research.google.com/teams/brain/) and a community
of users. We're eager to collaborate with you too, so feel free to
[open an issue on GitHub](https://github.com/tensorflow/tensor2tensor/issues)
or send along a pull request (see [our contribution doc](CONTRIBUTING.md)).
You can chat with us on
[Gitter](https://gitter.im/tensor2tensor/Lobby) and join the
[T2T Google Group](https://groups.google.com/forum/#!forum/tensor2tensor).

### Quick Start

[This iPython notebook](https://goo.gl/wkHexj) explains T2T and runs in your
browser using a free VM from Google, no installation needed.

Alternatively, here is a one-command version that installs T2T, downloads data,
trains an English-German translation model, and evaluates it:
Alternatively, here is a one-command version that installs T2T, downloads MNIST,
trains a model and evaluates it:

```
pip install tensor2tensor && t2t-trainer \
--generate_data \
--data_dir=~/t2t_data \
--problems=translate_ende_wmt32k \
--model=transformer \
--hparams_set=transformer_base_single_gpu \
--output_dir=~/t2t_train/base
```

You can decode from the model interactively:

```
t2t-decoder \
--data_dir=~/t2t_data \
--problems=translate_ende_wmt32k \
--model=transformer \
--hparams_set=transformer_base_single_gpu \
--output_dir=~/t2t_train/base \
--decode_interactive
--output_dir=~/t2t_train/mnist \
--problems=image_mnist \
--model=shake_shake \
--hparams_set=shake_shake_quick \
--train_steps=1000 \
--eval_steps=100
```

See the [Walkthrough](#walkthrough) below for more details on each step
and [Suggested Models](#suggested-models) for well performing models
on common tasks.

### Contents

* [Walkthrough](#walkthrough)
* [Suggested Models](#suggested-models)
* [Translation](#translation)
* [Summarization](#summarization)
* [Suggested Datasets and Models](#suggested-datasets-and-models)
* [Image Classification](#image-classification)
* [Installation](#installation)
* [Features](#features)
* [Language Modeling](#language-modeling)
* [Sentiment Analysis](#sentiment-analysis)
* [Speech Recognition](#speech-recognition)
* [Summarization](#summarization)
* [Translation](#translation)
* [Basics](#basics)
* [Walkthrough](#walkthrough)
* [Installation](#installation)
* [Features](#features)
* [T2T Overview](#t2t-overview)
* [Datasets](#datasets)
* [Problems and Modalities](#problems-and-modalities)
Expand All @@ -87,10 +64,102 @@ on common tasks.
* [Trainer](#trainer)
* [Adding your own components](#adding-your-own-components)
* [Adding a dataset](#adding-a-dataset)
* [Papers](#papers)

## Suggested Datasets and Models

---
Below we list a number of tasks that can be solved with T2T when
you train the appropriate model on the appropriate problem.
We give the problem and model below and we suggest a setting of
hyperparameters that we know works well in our setup. We usually
run either on Cloud TPUs or on 8-GPU machines; you might need
to modify the hyperparameters if you run on a different setup.

## Walkthrough
### Image Classification

For image classification, we have a number of standard data-sets:
* ImageNet (a large data-set): `--problems=image_imagenet`, or one
of the re-scaled versions (`image_imagenet224`, `image_imagenet64`,
`image_imagenet32`)
* CIFAR-10: `--problems=image_cifar10` (or
`--problems=image_cifar10_plain` to turn off data augmentation)
* CIFAR-100: `--problems=image_cifar100`
* MNIST: `--problems=image_mnist`

For ImageNet, we suggest to use the ResNet or Xception, i.e.,
use `--model=resnet --hparams_set=resnet_50` or
`--model=xception --hparams_set=xception_base`.
Resnet should get to above 76% top-1 accuracy on ImageNet.

For CIFAR and MNIST, we suggest to try the shake-shake model:
`--model=shake_shake --hparams_set=shakeshake_big`.
This setting trained for `--train_steps=700000` should yield
close to 97% accuracy on CIFAR-10.

### Language Modeling

For language modeling, we have these data-sets in T2T:
* PTB (a small data-set): `--problems=languagemodel_ptb10k` for
word-level modeling and `--problems=languagemodel_ptb_characters`
for character-level modeling.
* LM1B (a billion-word corpus): `--problems=languagemodel_lm1b32k` for
subword-level modeling and `--problems=languagemodel_lm1b_characters`
for character-level modeling.

We suggest to start with `--model=transformer` on this task and use
`--hparams_set=transformer_small` for PTB and
`--hparams_set=transformer_base` for LM1B.

### Sentiment Analysis

For the task of recognizing the sentiment of a sentence, use
* the IMDB data-set: `--problems=sentiment_imdb`

We suggest to use `--model=transformer_encoder` here and since it is
a small data-set, try `--hparams_set=transformer_tiny` and train for
few steps (e.g., `--train_steps=2000`).

### Speech Recognition

For speech-to-text, we have these data-sets in T2T:
* Librispeech (English speech to text): `--problems=librispeech` for
the whole set and `--problems=librispeech_clean` for a smaller
but nicely filtered part.

### Summarization

For summarizing longer text into shorter one we have these data-sets:
* CNN/DailyMail articles summarized into a few sentences:
`--problems=summarize_cnn_dailymail32k`

We suggest to use `--model=transformer` and
`--hparams_set=transformer_prepend` for this task.
This yields good ROUGE scores.

### Translation

There are a number of translation data-sets in T2T:
* English-German: `--problems=translate_ende_wmt32k`
* English-French: `--problems=translate_enfr_wmt32k`
* English-Czech: `--problems=translate_encs_wmt32k`
* English-Chinese: `--problems=translate_enzh_wmt32k`

You can get translations in the other direction by appending `_rev` to
the problem name, e.g., for German-English use
`--problems=translate_ende_wmt32k_rev`.

For all translation problems, we suggest to try the Transformer model:
`--model=transformer`. At first it is best to try the base setting,
`--hparams_set=transformer_base`. When trained on 8 GPUs for 300K steps
this should reach a BLEU score of about 28 on the English-German data-set,
which is close to state-of-the art. If training on a single GPU, try the
`--hparams_set=transformer_base_single_gpu` setting. For very good results
or larger data-sets (e.g., for English-French)m, try the big model
with `--hparams_set=transformer_big`.

## Basics

### Walkthrough

Here's a walkthrough training a good English-to-German translation
model using the Transformer model from [*Attention Is All You
Expand Down Expand Up @@ -156,36 +225,8 @@ cat translation.en
t2t-bleu --translation=translation.en --reference=ref-translation.de
```

---

## Suggested Models

Here are some combinations of models, hparams and problems that we found
work well, so we suggest to use them if you're interested in that problem.

### Translation

For translation, esp. English-German and English-French, we suggest to use
the Transformer model in base or big configurations, i.e.
for `--problems=translate_ende_wmt32k` use `--model=transformer` and
`--hparams_set=transformer_base`. When trained on 8 GPUs for 300K steps
this should reach a BLEU score of about 28.
### Installation

### Summarization

For summarization suggest to use the Transformer model in prepend mode, i.e.
for `--problems=summarize_cnn_dailymail32k` use `--model=transformer` and
`--hparams_set=transformer_prepend`.

### Image Classification

For image classification suggest to use the ResNet or Xception, i.e.
for `--problems=image_imagenet` use `--model=resnet50` and
`--hparams_set=resnet_base` or `--model=xception` and
`--hparams_set=xception_base`.


## Installation

```
# Assumes tensorflow or tensorflow-gpu installed
Expand Down Expand Up @@ -214,9 +255,7 @@ Library usage:
python -c "from tensor2tensor.models.transformer import Transformer"
```

---

## Features
### Features

* Many state of the art and baseline models are built-in and new models can be
added easily (open an issue or pull request!).
Expand All @@ -229,11 +268,10 @@ python -c "from tensor2tensor.models.transformer import Transformer"
specification.
* Support for multi-GPU machines and synchronous (1 master, many workers) and
asynchronous (independent workers synchronizing through a parameter server)
[distributed training](https://github.com/tensorflow/tensor2tensor/tree/master/docs/distributed_training.md).
[distributed training](https://tensorflow.github.io/tensor2tensor/distributed_training.html).
* Easily swap amongst datasets and models by command-line flag with the data
generation script `t2t-datagen` and the training script `t2t-trainer`.

---
* Train on [Google Cloud ML](https://tensorflow.github.io/tensor2tensor/cloud_mlengine.html) and [Cloud TPUs](https://tensorflow.github.io/tensor2tensor/cloud_tpu.html).

## T2T overview

Expand Down Expand Up @@ -289,9 +327,7 @@ inference. Users can easily switch between problems, models, and hyperparameter
sets by using the `--model`, `--problems`, and `--hparams_set` flags. Specific
hyperparameters can be overridden with the `--hparams` flag. `--schedule` and
related flags control local and distributed training/evaluation
([distributed training documentation](https://github.com/tensorflow/tensor2tensor/tree/master/docs/distributed_training.md)).

---
([distributed training documentation](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/g3doc/distributed_training.md)).

## Adding your own components

Expand All @@ -317,6 +353,21 @@ for an example.
Also see the [data generators
README](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/README.md).

---
## Papers

Tensor2Tensor was used to develop a number of state-of-the-art models
and deep learning methods. Here we list some papers that were based on T2T
from the start and benefited from its features and architecture in ways
described in the [Google Research Blog post introducing
T2T](https://research.googleblog.com/2017/06/accelerating-deep-learning-research.html).

* [Attention Is All You Need](https://arxiv.org/abs/1706.03762)
* [Depthwise Separable Convolutions for Neural Machine
Translation](https://arxiv.org/abs/1706.03059)
* [One Model To Learn Them All](https://arxiv.org/abs/1706.05137)
* [Discrete Autoencoders for Sequence Models](https://arxiv.org/abs/1801.09797)
* [Generating Wikipedia by Summarizing Long
Sequences](https://arxiv.org/abs/1801.10198)
* [Image Transformer](https://openreview.net/forum?id=r16Vyf-0-)

*Note: This is not an official Google product.*
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# Running on Cloud TPUs

Tensor2Tensor supports running on Google Cloud Platforms TPUs, chips specialized
for ML training.
Tensor2Tensor supports running on Google Cloud Platforms TPUs, chips
specialized for ML training. See the official tutorial for [running Transfomer
on Cloud TPUs](https://cloud.google.com/tpu/docs/tutorials/transformer) or
read on for more T2T models on TPUs.

Models and hparams that are known to work on TPU:
* `transformer` with `transformer_tpu`
* `transformer_encoder` with `transformer_tpu`
* `transformer_decoder` with `transformer_tpu`
* `resnet50` with `resnet_base`
* `revnet104` with `revnet_base`
## Models and hparams for TPU:

TPU access is currently limited, but access will expand soon, so get excited for
your future ML supercomputers in the cloud.
Transformer:
* `transformer` with `transformer_tpu` (or `transformer_packed_tpu`,
`transformer_tiny_tpu`, `transformer_big_tpu`)
* `transformer_encoder` with `transformer_tpu` (and the above ones)

You can run the Transformer model on a number of problems,
from translation through language modeling to sentiment analysis.
See the official tutorial for [running Transfomer
on Cloud TPUs](https://cloud.google.com/tpu/docs/tutorials/transformer)
for some examples and try out your own problems.

Residual networks:
* `resnet` with `resnet_50` (or `resnet_18` or `resnet_34`)
* `revnet` with `revnet_104` (or `revnet_38_cifar`)
* `shake_shake` with `shakeshake_tpu` (or `shakeshake_small`)

We run residual networks on MNIST, CIFAR and ImageNet, but they should
work on any image classification data-set.

## Tutorial: Transformer En-De translation on TPU

Expand Down Expand Up @@ -75,3 +88,24 @@ switch between hardware at will.
* `--cloud_tpu_name`: The name of the TPU instance to use or create. If you want
to launch multiple jobs on TPU, provide different names here for each one.
Each TPU instance can only be training one model at a time.

## Other T2T models on TPU

To run other models on TPU, proceed exactly as in the tutorial above,
just with different model, problem and hparams_set (and directories).
For example, to train a shake-shake model on CIFAR you can run this command.
```
t2t-trainer \
--model=shake_shake \
--hparams_set=shakeshake_tpu \
--problems=image_cifar10 \
--train_steps=180000 \
--eval_steps=9 \
--local_eval_frequency=100 \
--data_dir=$DATA_DIR \
--output_dir=$OUT_DIR \
--cloud_tpu \
--cloud_delete_on_done
```
Note that `eval_steps` should not be too high so as not to run out
of evaluation data.
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