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Tensor2Tensor, or T2T for short, is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. T2T is actively used and maintained by researchers and engineers within the Google Brain team and a community of users. We're eager to collaborate with you too, so feel free to open an issue on GitHub or send along a pull request (see our contribution doc). You can chat with us on Gitter and join the T2T Google Group.
This iPython notebook 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 MNIST, trains a model and evaluates it:
pip install tensor2tensor && t2t-trainer \
--generate_data \
--data_dir=~/t2t_data \
--output_dir=~/t2t_train/mnist \
--problems=image_mnist \
--model=shake_shake \
--hparams_set=shake_shake_quick \
--train_steps=1000 \
--eval_steps=100
- Suggested Datasets and Models
- Basics
- T2T Overview
- Adding your own components
- Adding a dataset
- Papers
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.
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.
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.
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
).
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.
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.
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), try the big model
with --hparams_set=transformer_big
.
Here's a walkthrough training a good English-to-German translation model using the Transformer model from Attention Is All You Need on WMT data.
pip install tensor2tensor
# See what problems, models, and hyperparameter sets are available.
# You can easily swap between them (and add new ones).
t2t-trainer --registry_help
PROBLEM=translate_ende_wmt32k
MODEL=transformer
HPARAMS=transformer_base_single_gpu
DATA_DIR=$HOME/t2t_data
TMP_DIR=/tmp/t2t_datagen
TRAIN_DIR=$HOME/t2t_train/$PROBLEM/$MODEL-$HPARAMS
mkdir -p $DATA_DIR $TMP_DIR $TRAIN_DIR
# Generate data
t2t-datagen \
--data_dir=$DATA_DIR \
--tmp_dir=$TMP_DIR \
--problem=$PROBLEM
# Train
# * If you run out of memory, add --hparams='batch_size=1024'.
t2t-trainer \
--data_dir=$DATA_DIR \
--problems=$PROBLEM \
--model=$MODEL \
--hparams_set=$HPARAMS \
--output_dir=$TRAIN_DIR
# Decode
DECODE_FILE=$DATA_DIR/decode_this.txt
echo "Hello world" >> $DECODE_FILE
echo "Goodbye world" >> $DECODE_FILE
echo -e 'Hallo Welt\nAuf Wiedersehen Welt' > ref-translation.de
BEAM_SIZE=4
ALPHA=0.6
t2t-decoder \
--data_dir=$DATA_DIR \
--problems=$PROBLEM \
--model=$MODEL \
--hparams_set=$HPARAMS \
--output_dir=$TRAIN_DIR \
--decode_hparams="beam_size=$BEAM_SIZE,alpha=$ALPHA" \
--decode_from_file=$DECODE_FILE \
--decode_to_file=translation.en
# See the translations
cat translation.en
# Evaluate the BLEU score
# Note: Report this BLEU score in papers, not the internal approx_bleu metric.
t2t-bleu --translation=translation.en --reference=ref-translation.de
# Assumes tensorflow or tensorflow-gpu installed
pip install tensor2tensor
# Installs with tensorflow-gpu requirement
pip install tensor2tensor[tensorflow_gpu]
# Installs with tensorflow (cpu) requirement
pip install tensor2tensor[tensorflow]
Binaries:
# Data generator
t2t-datagen
# Trainer
t2t-trainer --registry_help
Library usage:
python -c "from tensor2tensor.models.transformer import Transformer"
- Many state of the art and baseline models are built-in and new models can be added easily (open an issue or pull request!).
- Many datasets across modalities - text, audio, image - available for generation and use, and new ones can be added easily (open an issue or pull request for public datasets!).
- Models can be used with any dataset and input mode (or even multiple); all
modality-specific processing (e.g. embedding lookups for text tokens) is done
with
Modality
objects, which are specified per-feature in the dataset/task specification. - Support for multi-GPU machines and synchronous (1 master, many workers) and asynchronous (independent workers synchronizing through a parameter server) distributed training.
- Easily swap amongst datasets and models by command-line flag with the data
generation script
t2t-datagen
and the training scriptt2t-trainer
. - Train on Google Cloud ML and Cloud TPUs.
Datasets are all standardized on TFRecord
files with tensorflow.Example
protocol buffers. All datasets are registered and generated with the
data
generator
and many common sequence datasets are already available for generation and use.
Problems define training-time hyperparameters for the dataset and task,
mainly by setting input and output modalities (e.g. symbol, image, audio,
label) and vocabularies, if applicable. All problems are defined either in
problem_hparams.py
or are registered with @registry.register_problem
(run t2t-datagen
to see
the list of all available problems).
Modalities, defined in
modality.py
,
abstract away the input and output data types so that models may deal with
modality-independent tensors.
T2TModel
s define the core tensor-to-tensor transformation, independent of
input/output modality or task. Models take dense tensors in and produce dense
tensors that may then be transformed in a final step by a modality depending
on the task (e.g. fed through a final linear transform to produce logits for a
softmax over classes). All models are imported in the
models
subpackage,
inherit from T2TModel
- defined in
t2t_model.py
-
and are registered with
@registry.register_model
.
Hyperparameter sets are defined and registered in code with
@registry.register_hparams
and are encoded in
tf.contrib.training.HParams
objects. The HParams
are available to both the problem specification and the
model. A basic set of hyperparameters are defined in
common_hparams.py
and hyperparameter set functions can compose other hyperparameter set functions.
The trainer binary is the main entrypoint for training, evaluation, and
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).
T2T's components are registered using a central registration mechanism that
enables easily adding new ones and easily swapping amongst them by command-line
flag. You can add your own components without editing the T2T codebase by
specifying the --t2t_usr_dir
flag in t2t-trainer
.
You can do so for models, hyperparameter sets, modalities, and problems. Please do submit a pull request if your component might be useful to others.
See the example_usr_dir
for an example user directory.
To add a new dataset, subclass
Problem
and register it with @registry.register_problem
. See
TranslateEndeWmt8k
for an example.
Also see the data generators README.
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.
- Attention Is All You Need
- Depthwise Separable Convolutions for Neural Machine Translation
- One Model To Learn Them All
- Discrete Autoencoders for Sequence Models
- Generating Wikipedia by Summarizing Long Sequences
- Image Transformer
- Training Tips for the Transformer Model
Note: This is not an official Google product.