The TensorFlow Model Garden is a repository with a number of different implementations of state-of-the-art (SOTA) models and modeling solutions for TensorFlow users. We aim to demonstrate the best practices for modeling so that TensorFlow users can take full advantage of TensorFlow for their research and product development.
The TensorFlow Model Garden is a repository with a number of different implementations of state-of-the-art (SOTA) models and modeling solutions for TensorFlow users. We aim to demonstrate the best practices for modeling so that TensorFlow users
can take full advantage of TensorFlow for their research and product development.
| Directory | Description |
|-----------|-------------|
| [official](official) | • A collection of example implementations for SOTA models using the latest TensorFlow 2's high-level APIs<br/>• Officially maintained, supported, and kept up to date with the latest TensorFlow 2 APIs by TensorFlow<br/>• Reasonably optimized for fast performance while still being easy to read |
| [research](research) | • A collection of research model implementations in TensorFlow 1 or 2 by researchers<br/>• Maintained and supported by researchers |
| [community](community) | • A curated list of the GitHub repositories with machine learning models and implementations powered by TensorFlow 2 |
| [orbit](orbit) | • A flexible and lightweight library that users can easily use or fork when writing customized training loop code in TensorFlow 2.x. It seamlessly integrates with `tf.distribute` and supports running on different device types (CPU, GPU, and TPU). |
| May 21, 2020 | [Unifying Deep Local and Global Features for Image Search (DELG)](https://github.com/tensorflow/models/tree/master/research/delf#delg) code released
| May 7, 2020 | [MnasFPN with MobileNet-V2 backbone](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#mobile-models) released for object detection
| May 1, 2020 | [DELF: DEep Local Features](https://github.com/tensorflow/models/tree/master/research/delf) updated to support TensorFlow 2.1
| July 10, 2020 | TensorFlow 2 meets the [Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection)([Blog](https://blog.tensorflow.org/2020/07/tensorflow-2-meets-object-detection-api.html)) |
| June 30, 2020 | [SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization](https://github.com/tensorflow/models/tree/master/official/vision/detection#train-a-spinenet-49-based-mask-r-cnn) released ([Tweet](https://twitter.com/GoogleAI/status/1278016712978264064)) |
| June 17, 2020 | [Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection](https://github.com/tensorflow/models/tree/master/research/object_detection#june-17th-2020) released ([Tweet](https://twitter.com/GoogleAI/status/1276571419422253057)) |
| May 21, 2020 | [Unifying Deep Local and Global Features for Image Search (DELG)](https://github.com/tensorflow/models/tree/master/research/delf#delg) code released |
| May 19, 2020 | [MobileDets: Searching for Object Detection Architectures for Mobile Accelerators](https://github.com/tensorflow/models/tree/master/research/object_detection#may-19th-2020) released |
| May 7, 2020 | [MnasFPN with MobileNet-V2 backbone](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#mobile-models) released for object detection |
| May 1, 2020 | [DELF: DEep Local Features](https://github.com/tensorflow/models/tree/master/research/delf) updated to support TensorFlow 2.1 |
| March 31, 2020 | [Introducing the Model Garden for TensorFlow 2](https://blog.tensorflow.org/2020/03/introducing-model-garden-for-tensorflow-2.html)([Tweet](https://twitter.com/TensorFlow/status/1245029834633297921)) |
" \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/official_models/nlp/customize_encoder\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
" \u003c/td\u003e\n",
" \u003ctd\u003e\n",
" \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/models/blob/master/official/colab/nlp/customize_encoder.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
" \u003c/td\u003e\n",
" \u003ctd\u003e\n",
" \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/official/colab/nlp/customize_encoder.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
"The [TensorFlow Models NLP library](https://github.com/tensorflow/models/tree/master/official/nlp/modeling) is a collection of tools for building and training modern high performance natural language models.\n",
"\n",
"The [TransformEncoder](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/networks/encoder_scaffold.py) is the core of this library, and lots of new network architectures are proposed to improve the encoder. In this Colab notebook, we will learn how to customize the encoder to employ new network architectures."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "YYxdyoWgsl8t"
},
"source": [
"## Install and import"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "fEJSFutUsn_h"
},
"source": [
"### Install the TensorFlow Model Garden pip package\n",
"\n",
"* `tf-models-nightly` is the nightly Model Garden package created daily automatically.\n",
"* `pip` will install all models and dependencies automatically."
"Before learning how to customize the encoder, let's firstly create a canonical BERT enoder and use it to instantiate a `BertClassifier` for classification task."
"`canonical_classifier_model` can be trained using the training data. For details about how to train the model, please see the colab [fine_tuning_bert.ipynb](https://github.com/tensorflow/models/blob/master/official/colab/fine_tuning_bert.ipynb). We skip the code that trains the model here.\n",
"\n",
"After training, we can apply the model to do prediction.\n"
"One BERT encoder consists of an embedding network and multiple transformer blocks, and each transformer block contains an attention layer and a feedforward layer."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "rmwQfhj6fmKz"
},
"source": [
"We provide easy ways to customize each of those components via (1)\n",
"[EncoderScaffold](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/networks/encoder_scaffold.py) and (2) [TransformerScaffold](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/transformer_scaffold.py)."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "xsMgEVHAui11"
},
"source": [
"### Use EncoderScaffold\n",
"\n",
"`EncoderScaffold` allows users to provide a custom embedding subnetwork\n",
" (which will replace the standard embedding logic) and/or a custom hidden layer class (which will replace the `Transformer` instantiation in the encoder)."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "-JBabpa2AOz8"
},
"source": [
"#### Without Customization\n",
"\n",
"Without any customization, `EncoderScaffold` behaves the same the canonical `TransformerEncoder`.\n",
"\n",
"As shown in the following example, `EncoderScaffold` can load `TransformerEncoder`'s weights and output the same values:"
"Next, we show how to use a customized embedding network.\n",
"\n",
"We firstly build an embedding network that will replace the default network. This one will have 2 inputs (`mask` and `word_ids`) instead of 3, and won't use positional embeddings."
"User can also override the [hidden_cls](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/networks/encoder_scaffold.py#L103) argument in `EncoderScaffold`'s constructor to employ a customized Transformer layer.\n",
"\n",
"See [ReZeroTransformer](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/rezero_transformer.py) for how to implement a customized Transformer layer.\n",
"\n",
"Following is an example of using `ReZeroTransformer`:\n"
"# Assert that the variable `rezero_alpha` from ReZeroTransformer exists.\n",
"assert 'rezero_alpha' in ''.join([x.name for x in classifier_model.trainable_weights])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "6PMHFdvnxvR0"
},
"source": [
"### Use [TransformerScaffold](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/transformer_scaffold.py)\n",
"\n",
"The above method of customizing `Transformer` requires rewriting the whole `Transformer` layer, while sometimes you may only want to customize either attention layer or feedforward block. In this case, [TransformerScaffold](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/transformer_scaffold.py) can be used.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "D6FejlgwyAy_"
},
"source": [
"#### Customize Attention Layer\n",
"\n",
"User can also override the [attention_cls](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/transformer_scaffold.py#L45) argument in `TransformerScaffold`'s constructor to employ a customized Attention layer.\n",
"\n",
"See [TalkingHeadsAttention](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/talking_heads_attention.py) for how to implement a customized `Attention` layer.\n",
"\n",
"Following is an example of using [TalkingHeadsAttention](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/talking_heads_attention.py):"
"# Assert that the variable `pre_softmax_weight` from TalkingHeadsAttention exists.\n",
"assert 'pre_softmax_weight' in ''.join([x.name for x in classifier_model.trainable_weights])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "kuEJcTyByVvI"
},
"source": [
"#### Customize Feedforward Layer\n",
"\n",
"Similiarly, one could also customize the feedforward layer.\n",
"\n",
"See [GatedFeedforward](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/gated_feedforward.py) for how to implement a customized feedforward layer.\n",
"\n",
"Following is an example of using [GatedFeedforward](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/gated_feedforward.py)."
"# Assert that the variable `gate` from GatedFeedforward exists.\n",
"assert 'gate' in ''.join([x.name for x in classifier_model.trainable_weights])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "a_8NWUhkzeAq"
},
"source": [
"### Build a new Encoder using building blocks from KerasBERT.\n",
"\n",
"Finally, you could also build a new encoder using building blocks in the modeling library.\n",
"\n",
"See [AlbertTransformerEncoder](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/networks/albert_transformer_encoder.py) as an example:\n"
" \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/official_models/nlp/nlp_modeling_library_intro\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
" \u003c/td\u003e\n",
" \u003ctd\u003e\n",
" \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/models/blob/master/official/colab/nlp/nlp_modeling_library_intro.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
" \u003c/td\u003e\n",
" \u003ctd\u003e\n",
" \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/official/colab/nlp/nlp_modeling_library_intro.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
"In this Colab notebook, you will learn how to build transformer-based models for common NLP tasks including pretraining, span labelling and classification using the building blocks from [NLP modeling library](https://github.com/tensorflow/models/tree/master/official/nlp/modeling)."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "2N97-dps_nUk"
},
"source": [
"## Install and import"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "459ygAVl_rg0"
},
"source": [
"### Install the TensorFlow Model Garden pip package\n",
"\n",
"* `tf-models-nightly` is the nightly Model Garden package created daily automatically.\n",
"* `pip` will install all models and dependencies automatically."
"BERT ([Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)) introduced the method of pre-training language representations on a large text corpus and then using that model for downstream NLP tasks.\n",
"\n",
"In this section, we will learn how to build a model to pretrain BERT on the masked language modeling task and next sentence prediction task. For simplicity, we only show the minimum example and use dummy data."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "MKuHVlsCHmiq"
},
"source": [
"### Build a `BertPretrainer` model wrapping `TransformerEncoder`\n",
"\n",
"The [TransformerEncoder](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/networks/transformer_encoder.py) implements the Transformer-based encoder as described in [BERT paper](https://arxiv.org/abs/1810.04805). It includes the embedding lookups and transformer layers, but not the masked language model or classification task networks.\n",
"\n",
"The [BertPretrainer](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/models/bert_pretrainer.py) allows a user to pass in a transformer stack, and instantiates the masked language model and classification networks that are used to create the training objectives."
"After training, we can save the weights of TransformerEncoder for the downstream fine-tuning tasks. Please see [run_pretraining.py](https://github.com/tensorflow/models/blob/master/official/nlp/bert/run_pretraining.py) for the full example.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "k8cQVFvBCV4s"
},
"source": [
"## Span labeling model\n",
"\n",
"Span labeling is the task to assign labels to a span of the text, for example, label a span of text as the answer of a given question.\n",
"\n",
"In this section, we will learn how to build a span labeling model. Again, we use dummy data for simplicity."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "xrLLEWpfknUW"
},
"source": [
"### Build a BertSpanLabeler wrapping TransformerEncoder\n",
"\n",
"[BertSpanLabeler](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/models/bert_span_labeler.py) implements a simple single-span start-end predictor (that is, a model that predicts two values: a start token index and an end token index), suitable for SQuAD-style tasks.\n",
"\n",
"Note that `BertSpanLabeler` wraps a `TransformerEncoder`, the weights of which can be restored from the above pretraining model.\n"
"With the `loss`, you can optimize the model. Please see [run_squad.py](https://github.com/tensorflow/models/blob/master/official/nlp/bert/run_squad.py) for the full example."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "0A1XnGSTChg9"
},
"source": [
"## Classification model\n",
"\n",
"In the last section, we show how to build a text classification model.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "MSK8OpZgnQa9"
},
"source": [
"### Build a BertClassifier model wrapping TransformerEncoder\n",
"\n",
"[BertClassifier](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/models/bert_classifier.py) implements a [CLS] token classification model containing a single classification head."
"With the `loss`, you can optimize the model. Please see [run_classifier.py](https://github.com/tensorflow/models/blob/master/official/nlp/bert/run_classifier.py) or the colab [fine_tuning_bert.ipynb](https://github.com/tensorflow/models/blob/master/official/colab/fine_tuning_bert.ipynb) for the full example."
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "Introduction to the TensorFlow Models NLP library",
