@simon-at-fugu Try to enable deeptaylor analyzer. Please review :)

innvestigate/analyzer/__init__.py

@@ -10,12 +10,12 @@
 from .base import NotAnalyzeableModelException
 from .base import ReverseAnalyzerBase
 # from .deeplift import DeepLIFTWrapper
-#from .gradient_based import Gradient
-#from .gradient_based import InputTimesGradient
-#from .gradient_based import GuidedBackprop
-#from .gradient_based import Deconvnet
-#from .gradient_based import IntegratedGradients
-#from .gradient_based import SmoothGrad
+from .gradient_based import Gradient
+from .gradient_based import InputTimesGradient
+from .gradient_based import GuidedBackprop
+from .gradient_based import Deconvnet
+# from .gradient_based import IntegratedGradients
+# from .gradient_based import SmoothGrad
 # from .misc import Input
 # from .misc import Random
 # from .pattern_based import PatternNet
@@ -44,8 +44,8 @@
 from .relevance_based.relevance_analyzer import LRPSequentialCompositeAFlat
 from .relevance_based.relevance_analyzer import LRPSequentialCompositeBFlat
 from .relevance_based.relevance_analyzer import LRPRuleUntilIndex
-# from .deeptaylor import DeepTaylor
-# from .deeptaylor import BoundedDeepTaylor
+from .deeptaylor import DeepTaylor
+from .deeptaylor import BoundedDeepTaylor
 from .wrapper import WrapperBase
 from .wrapper import AugmentReduceBase
 from .wrapper import GaussianSmoother
@@ -108,8 +108,8 @@
     "lrp.rule_until_index": LRPRuleUntilIndex,
 
     # Deep Taylor
-    #"deep_taylor": DeepTaylor,
-    #"deep_taylor.bounded": BoundedDeepTaylor,
+    "deep_taylor": DeepTaylor,
+    "deep_taylor.bounded": BoundedDeepTaylor,
 
     # # DeepLIFT
     # "deep_lift.wrapper": DeepLIFTWrapper,

innvestigate/analyzer/base.py

@@ -349,6 +349,9 @@ def analyze(self, X, neuron_selection="max_activation", explained_layer_names=No
         self._analyzed = True
         ret = self._postprocess_analysis(ret)
 
+        if isinstance(ret, list) and len(ret) == 1:
+            ret = ret[0]
+
         return ret
 
     def _postprocess_analysis(self, hm):

innvestigate/analyzer/deeptaylor.py

@@ -13,7 +13,7 @@
 from tensorflow.python.keras.engine.input_layer import InputLayer
 
 from . import base
-from .relevance_based import relevance_rule as lrp_rules
+from .relevance_based import relevance_rule_base as lrp_rules
 from ..utils.keras import checks as kchecks
 from ..utils.keras import graph as kgraph
 
@@ -71,35 +71,40 @@ def do_nothing(Xs, Ys, As, reverse_state):
         self._add_conditional_reverse_mapping(
             lambda l: (not kchecks.contains_kernel(l) and
                        kchecks.contains_activation(l)),
-            self._gradient_reverse_mapping,
+            self._gradient_reverse_mapping(),
             name="deep_taylor_relu",
         )
 
         # Assume conv layer beforehand -> unbounded
-        bn_mapping = kgraph.apply_mapping_to_fused_bn_layer(
-            lrp_rules.WSquareRule,
-            fuse_mode="one_linear",
-        )
+        # bn_mapping = kgraph.apply_mapping_to_fused_bn_layer(
+        #     lrp_rules.WSquareRule,
+        #     fuse_mode="one_linear",
+        # )
+        # self._add_conditional_reverse_mapping(
+        #     kchecks.is_batch_normalization_layer,
+        #     bn_mapping,
+        #     name="deep_taylor_batch_norm",
+        # )
         self._add_conditional_reverse_mapping(
             kchecks.is_batch_normalization_layer,
-            bn_mapping,
+            self._gradient_reverse_mapping(),
             name="deep_taylor_batch_norm",
         )
         # Special layers.
         self._add_conditional_reverse_mapping(
             kchecks.is_max_pooling,
-            self._gradient_reverse_mapping,
+            self._gradient_reverse_mapping(),
             name="deep_taylor_max_pooling",
         )
         self._add_conditional_reverse_mapping(
             kchecks.is_average_pooling,
-            self._gradient_reverse_mapping,
+            self._gradient_reverse_mapping(),
             name="deep_taylor_average_pooling",
         )
         self._add_conditional_reverse_mapping(
             lambda l: isinstance(l, keras_layers.Add),
             # Ignore scaling with 0.5
-            self._gradient_reverse_mapping,
+            self._gradient_reverse_mapping(),
             name="deep_taylor_add",
         )
         self._add_conditional_reverse_mapping(
@@ -112,7 +117,7 @@ def do_nothing(Xs, Ys, As, reverse_state):
                 keras_layers.SpatialDropout2D,
                 keras_layers.SpatialDropout3D,
             )),
-            self._gradient_reverse_mapping,
+            self._gradient_reverse_mapping(),
             name="deep_taylor_special_layers",
         )
 
@@ -133,19 +138,19 @@ def do_nothing(Xs, Ys, As, reverse_state):
                 keras_layers.RepeatVector,
                 keras_layers.Reshape,
             )),
-            self._gradient_reverse_mapping,
+            self._gradient_reverse_mapping(),
             name="deep_taylor_no_transform",
         )
 
         return super(DeepTaylor, self)._create_analysis(
             *args, **kwargs)
 
-    def _default_reverse_mapping(self, Xs, Ys, reversed_Ys, reverse_state):
+    def _default_reverse_mapping(self, layer):
         """
         Block all default mappings.
         """
         raise NotImplementedError(
-            "Layer %s not supported." % reverse_state["layer"])
+            "Layer %s not supported." % layer)
 
     def _prepare_model(self, model):
         """

innvestigate/utils/keras/functional.py

@@ -615,8 +615,8 @@ def boundedrule_explanation(ins, layer_func, layer_func_pos, layer_func_neg, out
     #print("TRACING bound")
     to_low = keras_layers.Lambda(lambda x: x * 0 + low_param)
     to_high = keras_layers.Lambda(lambda x: x * 0 + high_param)
-    low = [to_low(x) for x in ins]
-    high = [to_high(x) for x in ins]
+    low = tf.map_fn(to_low, ins)
+    high = tf.map_fn(to_high, ins)
 
     A = out_func(ins, layer_func)
     B = out_func(low, layer_func_pos)

setup.py

@@ -10,7 +10,7 @@
     "numpy",
     "pillow",
     "scipy",
-    "tensorflow==2.1",
+    "tensorflow>=2.3",
 ]
 
 setup_requirements = [