Source code for

.. module:: CNormalizerDNN
   :synopsis: Normalizer which returns the deepfeatures at a specified neural network layer.

.. moduleauthor:: Marco Melis <>
.. moduleauthor:: Angelo Sotgiu

from secml import _NoValue
from secml.array import CArray
from import CNormalizer
from secml.core.exceptions import NotFittedError

[docs]class CNormalizerDNN(CNormalizer): """Normalized features are the DNN deepfeatures Parameters ---------- net : CClassifierDNN DNN to be used for extracting deepfeatures. This must be already trained. out_layer : str or None, optional Identifier of the layer at which the features must be retrieved. If None, the output of last layer will be returned. Attributes ---------- class_type : 'dnn' Notes ----- Any additional inner preprocess should not be passed as the `preprocess` parameter but to the DNN instead. """ __class_type = 'dnn' def __init__(self, net, out_layer=None, preprocess=_NoValue): self._net = net self.out_layer = out_layer if not raise NotFittedError( "the DNN should be already trained.") if preprocess is not _NoValue: raise ValueError( "any additional `preprocess` should be passed to the DNN.") # No preprocess should be passed to super super(CNormalizerDNN, self).__init__(preprocess=None) @property def net(self): """The DNN.""" return self._net def _check_is_fitted(self): """Check if the preprocessor is trained (fitted). Raises ------ NotFittedError If the preprocessor is not fitted. """ pass # This preprocessor does not require training def _fit(self, x, y=None): """Fit normalization algorithm using data. This fit function is just a placeholder and simply returns the normalizer itself. Parameters ---------- x : CArray Array to be used for training normalization algorithm. Shape of input array depends on the algorithm itself. y : CArray or None, optional Flat array with the label of each pattern. Not Used. Returns ------- CNormalizer Instance of the trained normalizer. """ return self
[docs] def fit(self, x, y): # The inner preprocessor is managed by the inner DNN return self._fit(x, y)
fit.__doc__ = _fit.__doc__ # Same doc of the protected method def _forward(self, x): """Apply the transformation algorithm on data. This extracts the deepfeatures at the specified layer of the DNN. Parameters ---------- x : CArray Array to be transformed. Returns ------- CArray Deepfeatures at the specified DNN layer. Shape depends on the neural network layer shape. """ return, self.out_layer) def _backward(self, w=None): # return the gradient at desired layer return, w=w, layer=self.out_layer)