def test_filter_activations_by_layers_last_layer(self):
"Filter activations by layer (Last layer)"
selected_layer = self.num_layers - 1
filtered_activations = ablation.filter_activations_by_layers(
self.activations, [selected_layer], self.num_layers)
np.testing.assert_array_almost_equal(
filtered_activations, self.layer_activations[selected_layer])
def test_filter_activations_by_layers_multiple(self):
"Filter activations by layers (Multiple layers)"
selected_layers = [1, 3]
filtered_activations = ablation.filter_activations_by_layers(
self.activations, selected_layers, self.num_layers)
expected_output = np.concatenate(
[self.layer_activations[s_l] for s_l in selected_layers], axis=1)
np.testing.assert_array_almost_equal(filtered_activations,
expected_output)
def test_filter_activations_by_layers_bidi_backward(self):
"Filter activations by layer (Bi-directional backward)"
selected_layer = 2
filtered_activations = ablation.filter_activations_by_layers(
self.activations, [selected_layer],
self.num_layers,
bidirectional_filtering="backward")
np.testing.assert_array_almost_equal(
filtered_activations, self.layer_activations[selected_layer]
[:, self.num_neurons_per_layer // 2:])
def test_filter_activations_by_layers_bidi_backward_multiple(self):
"Filter activations by layers (Bi-directional backward)"
selected_layers = [1, 3]
filtered_activations = ablation.filter_activations_by_layers(
self.activations,
selected_layers,
self.num_layers,
bidirectional_filtering="backward")
expected_output = np.concatenate([
self.layer_activations[s_l][:, self.num_neurons_per_layer // 2:]
for s_l in selected_layers
],
axis=1)
np.testing.assert_array_almost_equal(filtered_activations,
expected_output)
