Guided Backpropagation¶
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Guidedbackprop is one of the first attribution method and was proposed in 2014. Its operation is similar to Saliency: it consists in backpropagating the output score with respect to the input, however, at each nonlinearity (the ReLUs), only the positive gradient of positive activations are backpropagated. We can see this as a filter on the backprop.
More precisely, with \(f\) our classifier and \(f_l(x)\) the activation at layer \(l\), we usually have:
with \(\mathbb{1}(.)\) the indicator function. With Guidedbackprop, the backpropagation is modified such that :
Example¶
from xplique.attributions import GuidedBackprop
# load images, labels and model
# ...
method = GuidedBackprop(model)
explanations = method.explain(images, labels)
Notebooks¶
GuidedBackprop
¶
Used to compute the Guided Backpropagation, which modifies the classic Saliency procedure on
ReLU's non linearities, allowing only the positive gradients from positive activations to pass
through.
__init__(self,
model: keras.src.engine.training.Model,
output_layer: Union[str, int, None] = None,
batch_size: Optional[int] = 32,
operator: Union[xplique.commons.operators_operations.Tasks, str,
Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None,
reducer: Optional[str] = 'mean')
¶
model: keras.src.engine.training.Model,
output_layer: Union[str, int, None] = None,
batch_size: Optional[int] = 32,
operator: Union[xplique.commons.operators_operations.Tasks, str,
Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None,
reducer: Optional[str] = 'mean')
Parameters

model : keras.src.engine.training.Model
The model from which we want to obtain explanations

output_layer : Union[str, int, None] = None
Layer to target for the outputs (e.g logits or after softmax).
If an
int
is provided it will be interpreted as a layer index.If a
string
is provided it will look for the layer name.Default to the last layer.
It is recommended to use the layer before Softmax.

batch_size : Optional[int] = 32
Number of inputs to explain at once, if None compute all at once.

operator : Union[xplique.commons.operators_operations.Tasks, str, Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None
Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

reducer : Optional[str] = 'mean'
String, name of the reducer to use. Either "min", "mean", "max", "sum", or
None
to ignore.Used only for images to obtain explanation with shape (n, h, w, 1).
explain(self,
inputs: Union[tf.Dataset, tf.Tensor, ] ,
targets: Union[tf.Tensor, , None] = None) > tf.Tensor
¶
inputs: Union[tf.Dataset, tf.Tensor,
targets: Union[tf.Tensor,
Compute the explanations of the given inputs.
Accept Tensor, numpy array or tf.data.Dataset (in that case targets is None)
Parameters

inputs : Union[tf.Dataset, tf.Tensor,
] Dataset, Tensor or Array. Input samples to be explained.
If Dataset, targets should not be provided (included in Dataset).
Expected shape among (N, W), (N, T, W), (N, H, W, C).
More information in the documentation.

targets : Union[tf.Tensor,
, None] = None Tensor or Array. Onehot encoding of the model's output from which an explanation is desired. One encoding per input and only one output at a time. Therefore, the expected shape is (N, output_size).
More information in the documentation.
Return

explanations : tf.Tensor
Explanation generated by the method.