Neuron Conductance Tuple Support #602
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| net, net.multi_relu, inp, lambda x: x[0][:, 1], [0.0, 6.0, 0.0] | ||
| ) | ||
| lc = LayerConductance(net, net.multi_relu) | ||
| print(lc.attribute(inp, target=0)) |
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do we need this printout ? Did you wanted to assert with the results from Layer Conductance ?
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Whoops, good catch thanks! Was initially going to do that, but then made it a separate test, forgot to remove this.
| self._conductance_input_test_assert( | ||
| net, net.linear1, inp, lambda x: x[:, 0], [0.0, 90.0, 0.0] | ||
| ) | ||
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If there is a test model that has a custom layer which returns a tuple, could we, please, add a test for that too.
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Yes, I think both the other 2 added tests cover this case. Passing multi_input_module=True uses a custom layer MultiRelu, which returns a tuple.
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| - a callable, which should | ||
| take the target layer as input (single tensor or tuple | ||
| if multiple tensors are in layer) and return a selected | ||
| neuron (output shape should be 1D with length equal to |
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mismatched parens here
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This adds support for Neuron Conductance to allow a callable for selector function, as long as this function is used for selection rather than aggregation. This functionality allows neuron conductance to work with layers which may have input or output as a tuple of tensors.