Gradient \(\odot\) Input¶
_{ }View colab tutorial  _{ }View source  ðŸ“° Paper
Gradient \(\odot\) Input is a visualization techniques based on the gradient of a class score relative to the input, elementwise with the input. This method was introduced by Shrikumar et al., 2016^{1}, in an old version of their DeepLIFT paper^{2}.
Quote
Gradient inputs was at first proposed as a technique to improve the sharpness of the attribution maps. The attribution is computed taking the (signed) partial derivatives of the output with respect to the input and multiplying them with the input itself.
A theoretical analysis conducted by Ancona et al, 2018^{3} showed that Gradient \(\odot\) Input is equivalent to \(\epsilon\)LRP and DeepLift methods under certain conditions: using a baseline of zero, and with all biases to zero.
More precisely, the explanation \(\phi\) for an input \(x\) and a classifier \(f\) is defined as
with \(\odot\) the Hadamard product.
Example¶
from xplique.attributions import GradientInput
# load images, labels and model
# ...
method = GradientInput(model)
explanations = method.explain(images, labels)
Notebooks¶
GradientInput
¶
Used to compute elementwise product between the saliency maps of Simonyan et al. and the
input (Gradient x Input).
__init__(self,
model: keras.src.engine.training.Model,
output_layer: Union[str, int, None] = None,
batch_size: Optional[int] = 64,
operator: Optional[Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float]] = None,
reducer: Optional[str] = 'mean')
¶
model: keras.src.engine.training.Model,
output_layer: Union[str, int, None] = None,
batch_size: Optional[int] = 64,
operator: Optional[Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float]] = 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] = 64
Number of inputs to explain at once, if None compute all at once.

operator : Optional[Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float]] = 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.