From af6fd843a1a4afff3ea1ec29b3b4a972a03c411b Mon Sep 17 00:00:00 2001
From: Elie Khoury <elie.khoury@idiap.ch>
Date: Wed, 6 May 2015 09:29:17 +0200
Subject: [PATCH] added logging info for the EM training
---
bob/learn/em/test/test_em.py | 3 +++
bob/learn/em/train.py | 13 ++++++++++---
2 files changed, 13 insertions(+), 3 deletions(-)
diff --git a/bob/learn/em/test/test_em.py b/bob/learn/em/test/test_em.py
index 0dc6281..97f1a2b 100644
--- a/bob/learn/em/test/test_em.py
+++ b/bob/learn/em/test/test_em.py
@@ -18,6 +18,9 @@ from bob.learn.em import KMeansMachine, GMMMachine, KMeansTrainer, \
import bob.learn.em
+import bob.core
+bob.core.log.setup("bob.learn.em")
+
#, MAP_GMMTrainer
def loadGMM():
diff --git a/bob/learn/em/train.py b/bob/learn/em/train.py
index ee2ecd4..6bb18e3 100644
--- a/bob/learn/em/train.py
+++ b/bob/learn/em/train.py
@@ -6,6 +6,8 @@
# Copyright (C) 2011-2015 Idiap Research Institute, Martigny, Switzerland
import numpy
import bob.learn.em
+import logging
+logger = logging.getLogger('bob.learn.em')
def train(trainer, machine, data, max_iterations = 50, convergence_threshold=None, initialize=True, rng=None):
"""
@@ -42,16 +44,21 @@ def train(trainer, machine, data, max_iterations = 50, convergence_threshold=Non
average_output = trainer.compute_likelihood(machine)
for i in range(max_iterations):
+
average_output_previous = average_output
trainer.m_step(machine, data)
trainer.e_step(machine, data)
if convergence_threshold!=None and hasattr(trainer,"compute_likelihood"):
average_output = trainer.compute_likelihood(machine)
-
+ else:
+ logger.info("Iteration = %d ", i)
#Terminates if converged (and likelihood computation is set)
- if convergence_threshold!=None and abs((average_output_previous - average_output)/average_output_previous) <= convergence_threshold:
- break
+ if convergence_threshold!=None:
+ convergence_value = abs((average_output_previous - average_output)/average_output_previous)
+ logger.info("Iteration = %d \t convergence value = %f ", i, convergence_value)
+ if convergence_value <= convergence_threshold:
+ break
if hasattr(trainer,"finalize"):
trainer.finalize(machine, data)
--
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