# helia_edge.trainers.distiller

## Distiller Trainer API

This module contains the implementation of a distiller trainer that can be used to train a student model

**Classes**

| Name | Description |
| --- | --- |
| `Distiller` | A trainer for distillation |

## helia_edge.trainers.distiller.Distiller

`class` · `python`

```python
Distiller(student: keras.models.Model, teacher: keras.models.Model)
```

Train a student using target labels and a teacher's softened predictions.

call() returns the student's output. The distillation objective evaluates
the teacher with training=False and applies temperature-scaled softmax
along axis 1, so predictions must have their class dimension on that axis.
Configure the optimizer, two loss functions and mixing weight with compile().
The objective does not apply sample_weight.

**Parameters**

| Name | Type | Default | Description |
| --- | --- | --- | --- |
| student | keras.models.Model | Required | Keras model whose predictions are returned and scored. |
| teacher | keras.models.Model | Required | Keras model providing the reference predictions. |

Source: `helia_edge/trainers/distiller.py:16`

### helia_edge.trainers.distiller.Distiller.teacher

`attribute` · `python`

```python
teacher: keras.models.Model = teacher
```

Source: `helia_edge/trainers/distiller.py:36`

### helia_edge.trainers.distiller.Distiller.student

`attribute` · `python`

```python
student: keras.models.Model = student
```

Source: `helia_edge/trainers/distiller.py:37`

### helia_edge.trainers.distiller.Distiller.compile

`method` · `python`

```python
compile(
    optimizer: keras.optimizers.Optimizer,
    metrics: list[keras.metrics.Metric],
    student_loss_fn: keras.losses.Loss,
    distillation_loss_fn: keras.losses.Loss,
    alpha: float = 0.1,
    temperature: float = 3,
)
```

Configure the distiller.

**Parameters**

| Name | Type | Default | Description |
| --- | --- | --- | --- |
| optimizer | keras.optimizers.Optimizer | Required | Keras optimizer for the student weights |
| metrics | list[keras.metrics.Metric] | Required | Keras metrics for evaluation |
| student_loss_fn | keras.losses.Loss | Required | Loss function of difference between student predictions and ground-truth |
| distillation_loss_fn | keras.losses.Loss | Required | Loss function of difference between soft student predictions and soft teacher predictions |
| alpha | float | 0.1 | weight to student_loss_fn and 1-alpha to distillation_loss_fn. Defaults to 0.1. |
| temperature | float | 3 | Temperature for softening probability distributions. Defaults to 3. |

Source: `helia_edge/trainers/distiller.py:39`

### helia_edge.trainers.distiller.Distiller.compute_loss

`method` · `python`

```python
compute_loss(x=None, y=None, y_pred=None, sample_weight=None, allow_empty=False)
```

Source: `helia_edge/trainers/distiller.py:66`

### helia_edge.trainers.distiller.Distiller.call

`method` · `python`

```python
call(x)
```

Source: `helia_edge/trainers/distiller.py:78`