Skip to content

TerminationCriterion improvements (higher is better) #1552

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

TerminationCriterion improvements (higher is better) #1552

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
25 changes: 18 additions & 7 deletions pylearn2/termination_criteria/__init__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -41,8 +41,8 @@ def continue_learning(self, model):
class MonitorBased(TerminationCriterion):
"""
A termination criterion that pulls out the specified channel in
the model's monitor and checks to see if it has decreased by a
certain proportion of the lowest value in the last N epochs.
the model's monitor and checks to see if it has decreased/increased
by a certain proportion of the lowest value in the last N epochs.

Parameters
----------
Expand All @@ -55,14 +55,23 @@ class MonitorBased(TerminationCriterion):
Name of the channel to examine. If None and the monitor
has only one channel, this channel will be used; otherwise, an
error will be raised.
higher_is_better : bool, optional
Whether a higher value of channel_name indicates a better model.
When True, prop_decrease is treated like prop_increase.
"""

def __init__(self, prop_decrease=.01, N=5, channel_name=None):
def __init__(self, prop_decrease=.01, N=5, channel_name=None,
higher_is_better=False):
self._channel_name = channel_name
self.prop_decrease = prop_decrease
self.N = N
self.countdown = N
self.best_value = np.inf
self.higher_is_better = higher_is_better
if higher_is_better:
self.coeff = -1.
else:
self.coeff = 1.

self.best_value = self.coeff * np.inf

def continue_learning(self, model):
"""
Expand Down Expand Up @@ -94,12 +103,14 @@ def continue_learning(self, model):
# called unless the channel value is lower than the best value times
# the prop_decrease factor, in which case the countdown is reset to N
# and the best value is updated
if v[-1] < (1. - self.prop_decrease) * self.best_value:
if self.coeff * v[-1] < (self.coeff *
(1. - (self.coeff * self.prop_decrease)) *
self.best_value):
self.countdown = self.N
else:
self.countdown = self.countdown - 1

if v[-1] < self.best_value:
if self.coeff * v[-1] < self.coeff * self.best_value:
self.best_value = v[-1]

# The optimization continues until the countdown has reached 0,
Expand Down