> For the complete documentation index, see [llms.txt](https://docs.seldon.ai/alibi-explain/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.seldon.ai/alibi-explain/api-reference/models/tensorflow/cfrl_models.md).

# alibi.models.tensorflow\.cfrl\_models

This module contains the Tensorflow implementation of models used for the Counterfactual with Reinforcement Learning experiments for both data modalities (image and tabular).

## `ADULTDecoder`

*Inherits from:* `Model`, `TensorFlowTrainer`, `Trainer`, `Layer`, `TFLayer`, `KerasAutoTrackable`, `AutoTrackable`, `Trackable`, `Operation`, `KerasSaveable`

ADULT decoder used in the Counterfactual with Reinforcement Learning experiments. The model consists of of a fully connected layer with ReLU nonlinearity, and a multiheaded layer, one for each categorical feature and a single head for the rest of numerical features. The hidden dimension used in the paper is 128.

### Constructor

```python
ADULTDecoder(self, hidden_dim: int, output_dims: List[int], **kwargs)
```

| Name          | Type        | Default | Description                |
| ------------- | ----------- | ------- | -------------------------- |
| `hidden_dim`  | `int`       |         | Hidden dimension.          |
| `output_dims` | `List[int]` |         |                            |
| `output_dim`  |             |         | List of output dimensions. |

### Methods

#### `call`

```python
call(x: tensorflow.python.framework.tensor.Tensor, kwargs) -> List[tensorflow.python.framework.tensor.Tensor]
```

| Name       | Type                                        | Default | Description                |
| ---------- | ------------------------------------------- | ------- | -------------------------- |
| `x`        | `tensorflow.python.framework.tensor.Tensor` |         | Input tensor.              |
| `**kwargs` |                                             |         | Other arguments. Not used. |

**Returns**

* Type: `List[tensorflow.python.framework.tensor.Tensor]`

## `ADULTEncoder`

*Inherits from:* `Model`, `TensorFlowTrainer`, `Trainer`, `Layer`, `TFLayer`, `KerasAutoTrackable`, `AutoTrackable`, `Trackable`, `Operation`, `KerasSaveable`

ADULT encoder used in the Counterfactual with Reinforcement Learning experiments. The model consists of two fully connected layers with ReLU and tanh nonlinearities. The tanh nonlinearity clips the embedding in \[-1, 1] as required in the DDPG algorithm (e.g., \[act\_low, act\_high]). The layers' dimensions used in the paper are 128 and 15, although those can vary as they were selected to generalize across many datasets.

### Constructor

```python
ADULTEncoder(self, hidden_dim: int, latent_dim: int, **kwargs)
```

| Name         | Type  | Default | Description       |
| ------------ | ----- | ------- | ----------------- |
| `hidden_dim` | `int` |         | Hidden dimension. |
| `latent_dim` | `int` |         | Latent dimension. |

### Methods

#### `call`

```python
call(x: tensorflow.python.framework.tensor.Tensor, kwargs) -> tensorflow.python.framework.tensor.Tensor
```

| Name       | Type                                        | Default | Description      |
| ---------- | ------------------------------------------- | ------- | ---------------- |
| `x`        | `tensorflow.python.framework.tensor.Tensor` |         | Input tensor.    |
| `**kwargs` |                                             |         | Other arguments. |

**Returns**

* Type: `tensorflow.python.framework.tensor.Tensor`

## `MNISTClassifier`

*Inherits from:* `Model`, `TensorFlowTrainer`, `Trainer`, `Layer`, `TFLayer`, `KerasAutoTrackable`, `AutoTrackable`, `Trackable`, `Operation`, `KerasSaveable`

MNIST classifier used in the experiments for Counterfactual with Reinforcement Learning. The model consists of two convolutional layers having 64 and 32 channels and a kernel size of 2 with ReLU nonlinearities, followed by maxpooling of size 2 and dropout of 0.3. The convolutional block is followed by a fully connected layer of 256 with ReLU nonlinearity, and finally a fully connected layer is used to predict the class logits (10 in MNIST case).

### Constructor

```python
MNISTClassifier(self, output_dim: int = 10, **kwargs) -> None
```

| Name         | Type  | Default | Description      |
| ------------ | ----- | ------- | ---------------- |
| `output_dim` | `int` | `10`    | Output dimension |

### Methods

#### `call`

```python
call(x: tensorflow.python.framework.tensor.Tensor, training: bool = True, kwargs) -> tensorflow.python.framework.tensor.Tensor
```

| Name       | Type                                        | Default | Description                |
| ---------- | ------------------------------------------- | ------- | -------------------------- |
| `x`        | `tensorflow.python.framework.tensor.Tensor` |         | Input tensor.              |
| `training` | `bool`                                      | `True`  | Training flag.             |
| `**kwargs` |                                             |         | Other arguments. Not used. |

**Returns**

* Type: `tensorflow.python.framework.tensor.Tensor`

## `MNISTDecoder`

*Inherits from:* `Model`, `TensorFlowTrainer`, `Trainer`, `Layer`, `TFLayer`, `KerasAutoTrackable`, `AutoTrackable`, `Trackable`, `Operation`, `KerasSaveable`

MNIST decoder used in the Counterfactual with Reinforcement Learning experiments. The model consists of a fully connected layer of 128 units with ReLU activation followed by a convolutional block. The convolutional block consists fo 4 convolutional layers having 8, 8, 8 and 1 channels and a kernel size of 3. Each convolutional layer, except the last one, has ReLU nonlinearities and is followed by an up-sampling layer of size 2. The final layers uses a sigmoid activation to clip the output values in \[0, 1].

### Constructor

```python
MNISTDecoder(self, **kwargs) -> None
```

### Methods

#### `call`

```python
call(x: tensorflow.python.framework.tensor.Tensor, kwargs) -> tensorflow.python.framework.tensor.Tensor
```

| Name       | Type                                        | Default | Description                |
| ---------- | ------------------------------------------- | ------- | -------------------------- |
| `x`        | `tensorflow.python.framework.tensor.Tensor` |         | Input tensor               |
| `**kwargs` |                                             |         | Other arguments. Not used. |

**Returns**

* Type: `tensorflow.python.framework.tensor.Tensor`

## `MNISTEncoder`

*Inherits from:* `Model`, `TensorFlowTrainer`, `Trainer`, `Layer`, `TFLayer`, `KerasAutoTrackable`, `AutoTrackable`, `Trackable`, `Operation`, `KerasSaveable`

MNIST encoder used in the experiments for the Counterfactual with Reinforcement Learning. The model consists of 3 convolutional layers having 16, 8 and 8 channels and a kernel size of 3, with ReLU nonlinearities. Each convolutional layer is followed by a maxpooling layer of size 2. Finally, a fully connected layer follows the convolutional block with a tanh nonlinearity. The tanh clips the output between \[-1, 1], required in the DDPG algorithm (e.g., \[act\_low, act\_high]). The embedding dimension used in the paper is 32, although this can vary.

### Constructor

```python
MNISTEncoder(self, latent_dim: int, **kwargs) -> None
```

| Name         | Type  | Default | Description       |
| ------------ | ----- | ------- | ----------------- |
| `latent_dim` | `int` |         | Latent dimension. |

### Methods

#### `call`

```python
call(x: tensorflow.python.framework.tensor.Tensor, kwargs) -> tensorflow.python.framework.tensor.Tensor
```

| Name       | Type                                        | Default | Description                |
| ---------- | ------------------------------------------- | ------- | -------------------------- |
| `x`        | `tensorflow.python.framework.tensor.Tensor` |         | Input tensor.              |
| `**kwargs` |                                             |         | Other arguments. Not used. |

**Returns**

* Type: `tensorflow.python.framework.tensor.Tensor`