trw.train.guided_back_propagation¶
Module Contents¶
Classes¶
Produces gradients generated with guided back propagation from the given image |
Functions¶
|
|
Postptocess the output to be suitable for gradient attribution. |
Attributes¶
- trw.train.guided_back_propagation.logger¶
- trw.train.guided_back_propagation.post_process_output_id(output)¶
- trw.train.guided_back_propagation.post_process_output_for_gradient_attribution(output)¶
Postptocess the output to be suitable for gradient attribution.
In particular, if we have a
trw.train.OutputClassification, we need to apply a softmax operation so that we can backpropagate the loss of a particular class with the appropriate value (1.0).- Parameters
output – a
trw.train.OutputClassification- Returns
a
torch.Tensor
- class trw.train.guided_back_propagation.GuidedBackprop(model, unguided_gradient=False, post_process_output=post_process_output_id)¶
Produces gradients generated with guided back propagation from the given image
- update_relus(self)¶
- Updates relu activation functions so that
1- stores output in forward pass 2- imputes zero for gradient values that are less than zero
- static get_floating_inputs_with_gradients(inputs)¶
Extract inputs that have a gradient
- Parameters
inputs – a tensor of dictionary of tensors
- Returns
Return a list of tuple (name, input) for the input that have a gradient
- __call__(self, inputs, target_class, target_class_name)¶
Generate the guided back-propagation gradient
- Parameters
inputs – a tensor or dictionary of tensors
target_class – the target class to be explained
target_class_name – the name of the output class if multiple outputs
- Returns
a tuple (output_name, dictionary (input, gradient))
- static get_positive_negative_saliency(gradient)¶
Generates positive and negative saliency maps based on the gradient
- Parameters
gradient (numpy arr) – Gradient of the operation to visualize
- Returns
pos_saliency ( )