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Implementation of triplet loss in TensorFlow
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# Triplet loss in TensorFlow [](https://travis-ci.org/omoindrot/tensorflow-triplet-loss) *Author: Olivier Moindrot* This repository contains a triplet loss implementation in TensorFlow with online triplet mining. Please check the [blog post][blog] for a full description. The code structure is adapted from code I wrote for [CS230](https://cs230.stanford.edu) in [this repository](https://github.com/cs230-stanford/cs230-code-examples) at `tensorflow/vision`. A set of tutorials for this code can be found [here](https://cs230-stanford.github.io). ## Requirements We recommend using python3 and a virtual environment. The default `venv` should be used, or `virtualenv` with `python3`. ```bash python3 -m venv .env source .env/bin/activate pip install -r requirements_cpu.txt ``` If you are using a GPU, you will need to install `tensorflow-gpu` so do: ```bash pip install -r requirements_gpu.txt ``` ## Triplet loss |![triplet-loss-img] | |:--:| | *Triplet loss on two positive faces (Obama) and one negative face (Macron)* | The interesting part, defining triplet loss with triplet mining can be found in [`model/triplet_loss.py`](model/triplet_loss.py). Everything is explained in the [blog post][blog]. To use the "batch all" version, you can do: ```python from model.triplet_loss import batch_all_triplet_loss loss, fraction_positive = batch_all_triplet_loss(labels, embeddings, margin, squared=False) ``` In this case `fraction_positive` is a useful thing to plot in TensorBoard to track the average number of hard and semi-hard triplets. To use the "batch hard" version, you can do: ```python from model.triplet_loss import batch_hard_triplet_loss loss = batch_hard_triplet_loss(labels, embeddings, margin, squared=False) ``` ## Training on MNIST To run a new experiment called `base_model`, do: ```bash python train.py --model_dir experiments/base_model ``` You will first need to create a configuration file like this one: [`params.json`](experiments/batch_all/params.json). This json file specifies all the hyperparameters for the model. All the weights and summaries will be saved in the `model_dir`. Once trained, you can visualize the embeddings by running: ```bash python visualize_embeddings.py --model_dir experiments/base_model ``` And run tensorboard in the experiment directory: ```bash tensorboard --logdir experiments/base_model ``` Here is the result ([link][embeddings-gif] to gif): |![embeddings-img] | |:--:| | *Embeddings of the MNIST test images visualized with T-SNE (perplexity 25)* | ## Test To run all the tests, run this from the project directory: ```bash pytest ``` To run a specific test: ```bash pytest model/tests/test_triplet_loss.py ``` ## Resources - [Blog post][blog] explaining this project. - Source code for the built-in TensorFlow function for semi hard online mining triplet loss: [`tf.contrib.losses.metric_learning.triplet_semihard_loss`][tf-triplet-loss]. - [Facenet paper][facenet] introducing online triplet mining - Detailed explanation of online triplet mining in [*In Defense of the Triplet Loss for Person Re-Identification*][in-defense] - Blog post by Brandom Amos on online triplet mining: [*OpenFace 0.2.0: Higher accuracy and halved execution time*][openface-blog]. - Source code for the built-in TensorFlow function for semi hard online mining triplet loss: [`tf.contrib.losses.metric_learning.triplet_semihard_loss`][tf-triplet-loss]. - The [coursera lecture][coursera] on triplet loss [blog]: https://omoindrot.github.io/triplet-loss [triplet-types-img]: https://omoindrot.github.io/assets/triplet_loss/triplets.png [triplet-loss-img]: https://omoindrot.github.io/assets/triplet_loss/triplet_loss.png [online-triplet-loss-img]: https://omoindrot.github.io/assets/triplet_loss/online_triplet_loss.png [embeddings-img]: https://omoindrot.github.io/assets/triplet_loss/embeddings.png [embeddings-gif]: https://omoindr
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Olivier Moindrot · @checkfragment · France
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9ae78df162604c6a, topic:tensorflow
matched fp:9ae78df162604c6a, topic:embeddings