# TensorFlow Addons Losses: TripletSemiHardLoss

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|--------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|
| [View on TensorFlow.org](https://www.tensorflow.org/addons/tutorials/losses_triplet) | [Run in Google Colab](https://colab.research.google.com/github/tensorflow/addons/blob/master/docs/tutorials/losses_triplet.ipynb) | [View source on GitHub](https://github.com/tensorflow/addons/blob/master/docs/tutorials/losses_triplet.ipynb) | [Download notebook](https://storage.googleapis.com/tensorflow_docs/addons/docs/tutorials/losses_triplet.ipynb) |

Overview
--------

This notebook will demonstrate how to use the TripletSemiHardLoss function in TensorFlow Addons.

### Resources:

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://arxiv.org/pdf/1503.03832.pdf)
- [Oliver Moindrot's blog does an excellent job of describing the algorithm in detail](https://omoindrot.github.io/triplet-loss)

TripletLoss
-----------

As first introduced in the FaceNet paper, TripletLoss is a loss function that trains a neural network to closely embed features of the same class while maximizing the distance between embeddings of different classes. To do this an anchor is chosen along with one negative and one positive sample.

**The loss function is described as a Euclidean distance function:**

Where A is our anchor input, P is the positive sample input, N is the negative sample input, and alpha is some margin you use to specify when a triplet has become too "easy" and you no longer want to adjust the weights from it.

SemiHard Online Learning
------------------------

As shown in the paper, the best results are from triplets known as "Semi-Hard". These are defined as triplets where the negative is farther from the anchor than the positive, but still produces a positive loss. To efficiently find these triplets you utilize online learning and only train from the Semi-Hard examples in each batch.

Setup
-----

    pip install -U tensorflow-addons

    import io
    import numpy as np

    import tensorflow as tf
    import tensorflow_addons as tfa
    import tensorflow_datasets as tfds

Prepare the Data
----------------

    def _normalize_img(img, label):
        img = tf.cast(img, tf.float32) / 255.
        return (img, label)

    train_dataset, test_dataset = tfds.load(name="mnist", split=['train', 'test'], as_supervised=True)

    # Build your input pipelines
    train_dataset = train_dataset.shuffle(1024).batch(32)
    train_dataset = train_dataset.map(_normalize_img)

    test_dataset = test_dataset.batch(32)
    test_dataset = test_dataset.map(_normalize_img)

Build the Model
---------------

    model = tf.keras.Sequential([
        tf.keras.layers.Conv2D(filters=64, kernel_size=2, padding='same', activation='relu', input_shape=(28,28,1)),
        tf.keras.layers.MaxPooling2D(pool_size=2),
        tf.keras.layers.Dropout(0.3),
        tf.keras.layers.Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'),
        tf.keras.layers.MaxPooling2D(pool_size=2),
        tf.keras.layers.Dropout(0.3),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(256, activation=None), # No activation on final dense layer
        tf.keras.layers.Lambda(lambda x: tf.math.l2_normalize(x, axis=1)) # L2 normalize embeddings

    ])

Train and Evaluate
------------------

    # Compile the model
    model.compile(
        optimizer=tf.keras.optimizers.Adam(0.001),
        loss=tfa.losses.TripletSemiHardLoss())

    # Train the network
    history = model.fit(
        train_dataset,
        epochs=5)

```
Epoch 1/5
1875/1875 [==============================] - 13s 4ms/step - loss: 0.5952
Epoch 2/5
1875/1875 [==============================] - 6s 3ms/step - loss: 0.4562
Epoch 3/5
1875/1875 [==============================] - 7s 4ms/step - loss: 0.4207
Epoch 4/5
1875/1875 [==============================] - 6s 3ms/step - loss: 0.4040
Epoch 5/5
1875/1875 [==============================] - 6s 3ms/step - loss: 0.3964
```  

    # Evaluate the network
    results = model.predict(test_dataset)

```
313/313 [==============================] - 1s 1ms/step
```  

    # Save test embeddings for visualization in projector
    np.savetxt("vecs.tsv", results, delimiter='\t')

    out_m = io.open('meta.tsv', 'w', encoding='utf-8')
    for img, labels in tfds.as_numpy(test_dataset):
        [out_m.write(str(x) + "\n") for x in labels]
    out_m.close()


    try:
      from google.colab import files
      files.download('vecs.tsv')
      files.download('meta.tsv')
    except:
      pass

Embedding Projector
-------------------

The vector and metadata files can be loaded and visualized here: <https://projector.tensorflow.org/>

You can see the results of our embedded test data when visualized with UMAP: