art.estimators.gan
¶
GAN Estimator API.
TensorFlowV2 GAN¶
- class art.estimators.gan.TensorFlowV2GAN(generator: GENERATOR_TYPE, discriminator: CLASSIFIER_TYPE, generator_loss=None, discriminator_loss=None, generator_optimizer_fct=None, discriminator_optimizer_fct=None)¶
This class implements a GAN with the TensorFlow v2 framework.
- __init__(generator: GENERATOR_TYPE, discriminator: CLASSIFIER_TYPE, generator_loss=None, discriminator_loss=None, generator_optimizer_fct=None, discriminator_optimizer_fct=None)¶
Initialization of a test TensorFlow v2 GAN
- Parameters:
generator – a TensorFlow2 generator
discriminator – a TensorFlow v2 discriminator
generator_loss – the loss function to use for the generator
discriminator_loss – the loss function to use for the discriminator
generator_optimizer_fct – the optimizer function to use for the generator
discriminator_optimizer_fct – the optimizer function to use for the discriminator
- property channels_first: bool¶
- Returns:
Boolean to indicate index of the color channels in the sample x.
- property clip_values: Optional[CLIP_VALUES_TYPE]¶
Return the clip values of the input samples.
- Returns:
Clip values (min, max).
- clone_for_refitting() ESTIMATOR_TYPE ¶
Clone estimator for refitting.
- compute_loss(x: ndarray, y: Any, **kwargs) ndarray ¶
Compute the loss of the estimator for samples x.
- Parameters:
x (
ndarray
) – Input samples.y – Target values.
- Returns:
Loss values.
- Return type:
Format as expected by the model
- compute_loss_from_predictions(pred: ndarray, y: ndarray, **kwargs) ndarray ¶
Compute the loss of the estimator for predictions pred.
- Return type:
ndarray
- Parameters:
pred (
ndarray
) – Model predictions.y (
ndarray
) – Target values.
- Returns:
Loss values.
- property discriminator: CLASSIFIER_TYPE¶
- Returns:
the discriminator
- property discriminator_loss: tf.Tensor¶
- Returns:
the loss fct used for the discriminator
- property discriminator_optimizer_fct: tf.Tensor¶
- Returns:
the optimizer function for the discriminator
- fit(x: ndarray, y: ndarray, batch_size: int = 128, nb_epochs: int = 20, **kwargs) None ¶
Creates a generative model
- Parameters:
x (
ndarray
) – the secret backdoor trigger that will produce the targety (
ndarray
) – the target to produce when using the triggerbatch_size (
int
) – batch_size of images used to train generatornb_epochs (
int
) – total number of iterations for performing the attack
- fit_generator(generator: DataGenerator, nb_epochs: int = 20, **kwargs) None ¶
Fit the estimator using a generator yielding training batches. Implementations can provide framework-specific versions of this function to speed-up computation.
- Parameters:
generator – Batch generator providing (x, y) for each epoch.
nb_epochs (
int
) – Number of training epochs.
- property generator: GENERATOR_TYPE¶
- Returns:
the generator
- property generator_loss: tf.Tensor¶
- Returns:
the loss fct used for the generator
- property generator_optimizer_fct: tf.Tensor¶
- Returns:
the optimizer function for the generator
- get_activations(x: ndarray, layer: Union[int, str], batch_size: int, framework: bool = False) ndarray ¶
Return the output of a specific layer for samples x where layer is the index of the layer between 0 and nb_layers - 1 or the name of the layer. The number of layers can be determined by counting the results returned by calling `layer_names.
- Return type:
ndarray
- Parameters:
x (
ndarray
) – Sampleslayer – Index or name of the layer.
batch_size (
int
) – Batch size.framework (
bool
) – If true, return the intermediate tensor representation of the activation.
- Returns:
The output of layer, where the first dimension is the batch size corresponding to x.
- get_params() Dict[str, Any] ¶
Get all parameters and their values of this estimator.
- Returns:
A dictionary of string parameter names to their value.
- property input_shape: Tuple[int, int]¶
Return the shape of one input sample.
- Returns:
Shape of one input sample.
- property layer_names: Optional[List[str]]¶
Return the names of the hidden layers in the model, if applicable.
- Returns:
The names of the hidden layers in the model, input and output layers are ignored.
Warning
layer_names tries to infer the internal structure of the model. This feature comes with no guarantees on the correctness of the result. The intended order of the layers tries to match their order in the model, but this is not guaranteed either.
- loss_gradient(x, y, **kwargs)¶
Compute the gradient of the loss function w.r.t. x.
- Parameters:
x (Format as expected by the model) – Samples.
y (Format as expected by the model) – Target values.
- Returns:
Loss gradients w.r.t. x in the same format as x.
- Return type:
Format as expected by the model
- property model¶
Return the model.
- Returns:
The model.
- predict(x: ndarray, batch_size: int = 128, **kwargs) ndarray ¶
Generates a sample.
- Return type:
ndarray
- Parameters:
x (
ndarray
) – A input seed.batch_size (
int
) – The batch size for predictions.
- Returns:
The generated sample.
- set_params(**kwargs) None ¶
Take a dictionary of parameters and apply checks before setting them as attributes.
- Parameters:
kwargs – A dictionary of attributes.