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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 was developed by researchers and engineers in the Google Brain team and a community of users. It is now deprecated — we keep it running and welcome bug-fixes, but encourage users to use the successor library Trax.

Quick Start

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 \
  --problem=image_mnist \
  --model=shake_shake \
  --hparams_set=shake_shake_quick \
  --train_steps=1000 \


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.

Mathematical Language Understanding

For evaluating mathematical expressions at the character level involving addition, subtraction and multiplication of both positive and negative decimal numbers with variable digits assigned to symbolic variables, use

You can try solving the problem with different transformer models and hyperparameters as described in the paper:

Story, Question and Answer

For answering questions based on a story, use

You can choose the bAbi task from the range [1,20] and the subset from 1k or 10k. To combine test data from all tasks into a single test set, use --problem=babi_qa_concat_all_tasks_10k

Image Classification

For image classification, we have a number of standard data-sets:

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.

Image Generation

For (un)conditional image generation, we have a number of standard data-sets:

We suggest to use the Image Transformer, i.e., --model=imagetransformer, or the Image Transformer Plus, i.e., --model=imagetransformerpp that uses discretized mixture of logistics, or variational auto-encoder, i.e., --model=transformer_ae. For CIFAR-10, using --hparams_set=imagetransformer_cifar10_base or --hparams_set=imagetransformer_cifar10_base_dmol yields 2.90 bits per dimension. For Imagenet-32, using --hparams_set=imagetransformer_imagenet32_base yields 3.77 bits per dimension.

Language Modeling

For language modeling, we have these data-sets in T2T:

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

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:


For summarizing longer text into shorter one we have these data-sets:

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:

You can get translations in the other direction by appending _rev to the problem name, e.g., for German-English use --problem=translate_ende_wmt32k_rev (note that you still need to download the original data with t2t-datagen --problem=translate_ende_wmt32k).

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.

See this example to know how the translation works.



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




# Generate data
t2t-datagen \
  --data_dir=$DATA_DIR \
  --tmp_dir=$TMP_DIR \

# Train
# *  If you run out of memory, add --hparams='batch_size=1024'.
t2t-trainer \
  --data_dir=$DATA_DIR \
  --problem=$PROBLEM \
  --model=$MODEL \
  --hparams_set=$HPARAMS \

# Decode

echo "Hello world" >> $DECODE_FILE
echo "Goodbye world" >> $DECODE_FILE
echo -e 'Hallo Welt\nAuf Wiedersehen Welt' > ref-translation.de


t2t-decoder \
  --data_dir=$DATA_DIR \
  --problem=$PROBLEM \
  --model=$MODEL \
  --hparams_set=$HPARAMS \
  --output_dir=$TRAIN_DIR \
  --decode_hparams="beam_size=$BEAM_SIZE,alpha=$ALPHA" \
  --decode_from_file=$DECODE_FILE \

# 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]


# Data generator

# Trainer
t2t-trainer --registry_help

Library usage:

python -c "from tensor2tensor.models.transformer import Transformer"


T2T overview


Problems consist of features such as inputs and targets, and metadata such as each feature's modality (e.g. symbol, image, audio) and vocabularies. Problem features are given by a dataset, which is stored as a TFRecord file with tensorflow.Example protocol buffers. All problems are imported in all_problems.py or are registered with @registry.register_problem. Run t2t-datagen to see the list of available problems and download them.


T2TModels define the core tensor-to-tensor computation. They apply a default transformation to each input and output so that models may deal with modality-independent tensors (e.g. embeddings at the input; and a linear transform at the output to produce logits for a softmax over classes). All models are imported in the models subpackage, inherit from T2TModel, and are registered with @registry.register_model.

Hyperparameter Sets

Hyperparameter sets are encoded in HParams objects, and are registered with @registry.register_hparams. Every model and problem has a HParams. 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 entrypoint for training, evaluation, and inference. Users can easily switch between problems, models, and hyperparameter sets by using the --model, --problem, 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).

Adding your own components

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.

Adding a dataset

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.

Run on FloydHub

Run on FloydHub

Click this button to open a Workspace on FloydHub. You can use the workspace to develop and test your code on a fully configured cloud GPU machine.

Tensor2Tensor comes preinstalled in the environment, you can simply open a Terminal and run your code.

# Test the quick-start on a Workspace's Terminal with this command
t2t-trainer \
  --generate_data \
  --data_dir=./t2t_data \
  --output_dir=./t2t_train/mnist \
  --problem=image_mnist \
  --model=shake_shake \
  --hparams_set=shake_shake_quick \
  --train_steps=1000 \

Note: Ensure compliance with the FloydHub Terms of Service.


When referencing Tensor2Tensor, please cite this paper.

  author    = {Ashish Vaswani and Samy Bengio and Eugene Brevdo and
    Francois Chollet and Aidan N. Gomez and Stephan Gouws and Llion Jones and
    \L{}ukasz Kaiser and Nal Kalchbrenner and Niki Parmar and Ryan Sepassi and
    Noam Shazeer and Jakob Uszkoreit},
  title     = {Tensor2Tensor for Neural Machine Translation},
  journal   = {CoRR},
  volume    = {abs/1803.07416},
  year      = {2018},
  url       = {http://arxiv.org/abs/1803.07416},

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.

NOTE: This is not an official Google product.