From 448e848796757ad9f0a2f681886f868b8f22e81f Mon Sep 17 00:00:00 2001
From: Alex Auvolat <alex.auvolat@ens.fr>
Date: Fri, 22 May 2015 15:51:02 -0400
Subject: Add ElementwiseRemoveNotFinite step rule.

---
 train.py | 37 +++++++++++++++++++++++++++++++++++--
 1 file changed, 35 insertions(+), 2 deletions(-)

diff --git a/train.py b/train.py
index 17fa612..9f636c0 100755
--- a/train.py
+++ b/train.py
@@ -7,8 +7,10 @@ import os
 import sys
 from functools import reduce
 
+from theano import tensor
+
 from blocks import roles
-from blocks.algorithms import AdaDelta, CompositeRule, GradientDescent, RemoveNotFinite
+from blocks.algorithms import AdaDelta, CompositeRule, GradientDescent, RemoveNotFinite, StepRule
 from blocks.extensions import Printing, FinishAfter
 from blocks.extensions.monitoring import DataStreamMonitoring, TrainingDataMonitoring
 from blocks.extensions.plot import Plot
@@ -21,6 +23,37 @@ from blocks.model import Model
 
 logger = logging.getLogger(__name__)
 
+
+class ElementwiseRemoveNotFinite(StepRule):
+    """A step rule that replaces non-finite coefficients by zeros.
+
+    Replaces non-finite elements (such as ``inf`` or ``NaN``) in a step
+    (the parameter update of a single shared variable)
+    with a scaled version of the parameters being updated instead.
+
+    Parameters
+    ----------
+    scaler : float, optional
+        The scaling applied to the parameter in case the step contains
+        non-finite elements. Defaults to 0.1.
+
+    Notes
+    -----
+    This trick was originally used in the GroundHog_ framework.
+
+    .. _GroundHog: https://github.com/lisa-groundhog/GroundHog
+
+    """
+    def __init__(self, scaler=0.1):
+        self.scaler = scaler
+
+    def compute_step(self, param, previous_step):
+        not_finite = tensor.isnan(previous_step) + tensor.isinf(previous_step)
+        step = tensor.switch(not_finite, self.scaler * param, previous_step)
+
+        return step, []
+
+
 if __name__ == "__main__":
     if len(sys.argv) != 2:
         print >> sys.stderr, 'Usage: %s config' % sys.argv[0]
@@ -66,7 +99,7 @@ if __name__ == "__main__":
     algorithm = GradientDescent(
         cost=cost,
         step_rule=CompositeRule([
-                RemoveNotFinite(),
+                ElementwiseRemoveNotFinite(),
                 AdaDelta(),
                 #Momentum(learning_rate=config.learning_rate, momentum=config.momentum),
             ]),
-- 
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