def test_influences_from(self):
ig = InfluenceGraph()
ig.add_node(1, activation=12)
ig.add_node(2, activation=13)
ig.add_node(3, activation=0)
ig.add_influence(1, 2, thing=1)
ig.add_influence(2, 3, thing=2)
self.assertEqual(ig.influences_from(1), [(1, 2, {'thing': 1})])
def test_typed_nodes(self):
ig = InfluenceGraph()
ig.add_node(1, activation=12)
ig.add_node(2, activation=13)
ig.add_node(3, activation=0)
ig.add_influence(1, 2, thing=1)
ig.add_influence(2, 3, thing=2)
start, mid, terminal = ig.get_typed_nodes()
self.assertEqual(start, [1])
self.assertEqual(mid, [2])
self.assertEqual(terminal, [3])
def test_add_node(self):
ig = InfluenceGraph()
ig.add_node(1, activation=12)
ig.add_node(2, activation=13)
ig.add_node(3, activation=0)
self.assertEqual(ig.nodes(), {
1: {
'activation': 12
},
2: {
'activation': 13
},
3: {
'activation': 0
}
})
def apply(self, model, x):
influences = InfluenceGraph()
x_tensor = tf.convert_to_tensor([x], dtype=tf.float32)
# Calculate d output / d x_tensor
model_headless = Sequential(model.layers[:-1])
with tf.GradientTape() as g:
g.watch(x_tensor)
output = tf.squeeze(model_headless(x_tensor))
gradients = tf.squeeze(g.gradient(output, x_tensor))
pred_label = f"Predicted {tf.math.argmax(output).numpy()}"
influences.add_node(pred_label)
for i, feature in enumerate(x):
influences.add_node(f"Feature {i}",
grad=gradients[i].numpy(),
value=feature)
influences.add_influence(f"Feature {i}", pred_label)
return influences, int(tf.math.argmax(output).numpy()), 0.7
def apply(self, model, x):
influences = InfluenceGraph()
x_tensor = tf.convert_to_tensor([x], dtype=tf.float32)
# Calculate d output / d x_tensor
model_headless = Sequential(model.layers[:-1])
with tf.GradientTape() as g:
g.watch(x_tensor)
output = tf.squeeze(model_headless(x_tensor))
gradients = tf.squeeze(g.gradient(output, x_tensor))
pred_label = tf.math.argmax(output).numpy()
confidence = max(keras.backend.softmax(output.numpy()))
influences.add_node('Prediction')
influences.add_node('Input', grad=0)
for i, feature in enumerate(x):
influences.add_node(f'Feature {i}',
grad=gradients[i].numpy(),
value=feature,
idx=i)
influences.add_influence(f'Feature {i}', 'Prediction')
influences.add_influence('Input', f'Feature {i}')
return influences, str(pred_label), confidence
def apply(self, model, x):
influences = InfluenceGraph()
influences.add_node('has leaves', value=1)
influences.add_node('orange plant', value=2)
influences.add_node('carrot')
influences.add_influence('has leaves', 'orange plant')
influences.add_influence('orange plant', 'carrot')
return influences, 'carrot', 0.9
def test_remove_node(self):
ig = InfluenceGraph()
ig.add_node(1, activation=12)
ig.add_node(2, activation=13)
ig.remove_node(2)
self.assertEqual(ig.nodes(), {1: {'activation': 12}})
def apply(self, model, x):
influences = InfluenceGraph()
layers = copy.copy(model.layers)
layers.reverse()
classifier_layers = []
last_conv_layer = None
for layer in layers:
if not (isinstance(layer, keras.layers.Conv2D)):
classifier_layers.append(layer)
else:
last_conv_layer = layer
break
classifier_layers.reverse()
if last_conv_layer is None:
raise Exception('Could not detect any convolutional layers')
relevant_features = self.get_relevant(model, x, last_conv_layer,
classifier_layers)
preds = model.predict(x)
influences.add_node('Prediction')
if self.decoder_function is not None:
_, predicted_class, confidence = decode_predictions(preds,
top=1)[0][0]
else:
predicted_class, confidence = default_decoder_function(
preds, model, x)
influences.add_node('Input', grad=0)
for layer, idx, grad in relevant_features:
influences.add_node(f'Filter {idx}',
layer=layer,
filter_idx=idx,
grad=grad)
influences.add_influence(f'Filter {idx}', 'Prediction')
influences.add_influence('Input', f'Filter {idx}')
return influences, predicted_class, confidence