From 5119a79dbb47dd4b3517d8cd9309f52175de50c1 Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <Nikolai.Hartmann@physik.uni-muenchen.de>
Date: Wed, 22 Aug 2018 11:40:14 +0200
Subject: [PATCH] adjust max activation functions to use masking
---
utils.py | 17 ++++++++++++-----
1 file changed, 12 insertions(+), 5 deletions(-)
diff --git a/utils.py b/utils.py
index 029f76e..ea98793 100644
--- a/utils.py
+++ b/utils.py
@@ -38,6 +38,8 @@ def create_random_event(ranges, mask_probs=None, mask_value=None):
random_event = random_event.reshape(-1, len(random_event))
# if given, mask values with a certain probability
if mask_probs is not None:
+ if mask_value is None:
+ raise ValueError("Need to provide mask_value if random events should be masked")
for var_index, mask_prob in enumerate(mask_probs):
random_event[:,var_index][np.random.rand(len(random_event)) < mask_prob] = mask_value
return random_event
@@ -122,7 +124,7 @@ def get_grad_function(model, layer, neuron):
],
ignoreKwargs=["input_transform", "input_inverse_transform"],
)
-def get_max_activation_events(model, ranges, ntries, layer, neuron, seed=42, **kwargs):
+def get_max_activation_events(model, ranges, ntries, layer, neuron, seed=42, mask_probs=None, mask_value=None, **kwargs):
gradient_function = get_grad_function(model, layer, neuron)
@@ -132,9 +134,15 @@ def get_max_activation_events(model, ranges, ntries, layer, neuron, seed=42, **k
for i in range(ntries):
if not (i%100):
logger.info(i)
- res = max_activation_wrt_input(gradient_function,
- create_random_event(ranges),
- **kwargs)
+ res = max_activation_wrt_input(
+ gradient_function,
+ create_random_event(
+ ranges,
+ mask_probs=mask_probs,
+ mask_value=mask_value
+ ),
+ **kwargs
+ )
if res is not None:
loss, event = res
loss = np.array([loss])
@@ -195,7 +203,6 @@ class WeightedRobustScaler(RobustScaler):
self.center_ = wqs[:,1]
self.scale_ = wqs[:,2]-wqs[:,0]
self.scale_ = _handle_zeros_in_scale(self.scale_, copy=False)
- print(self.scale_)
return self
--
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