From 259b000cba65c813b6a87a4e97c447a8430a450c Mon Sep 17 00:00:00 2001
From: Matt Graham <m.m.graham@ed.ac.uk>
Date: Mon, 10 Oct 2016 09:25:33 +0100
Subject: [PATCH] Adding schedulers to optimiser.

---
 mlp/optimisers.py | 26 ++++++++++++++++++--------
 1 file changed, 18 insertions(+), 8 deletions(-)

diff --git a/mlp/optimisers.py b/mlp/optimisers.py
index ec2aefc..0c2001c 100644
--- a/mlp/optimisers.py
+++ b/mlp/optimisers.py
@@ -18,7 +18,7 @@ class Optimiser(object):
     """Basic model optimiser."""
 
     def __init__(self, model, error, learning_rule, train_dataset,
-                 valid_dataset=None, data_monitors=None):
+                 valid_dataset=None, data_monitors=None, schedulers=[]):
         """Create a new optimiser instance.
 
         Args:
@@ -43,6 +43,7 @@ class Optimiser(object):
         self.data_monitors = OrderedDict([('error', error)])
         if data_monitors is not None:
             self.data_monitors.update(data_monitors)
+        self.schedulers = schedulers
 
     def do_training_epoch(self):
         """Do a single training epoch.
@@ -103,7 +104,7 @@ class Optimiser(object):
             epoch_time: Time taken in seconds for the epoch to complete.
             stats: Monitored stats for the epoch.
         """
-        logger.info('Epoch {0}: {1:.1f}s to complete\n    {2}'.format(
+        logger.info('Epoch {0}: {1:.2f}s to complete\n    {2}'.format(
             epoch, epoch_time,
             ', '.join(['{0}={1:.2e}'.format(k, v) for (k, v) in stats.items()])
         ))
@@ -118,17 +119,26 @@ class Optimiser(object):
                 every `stats_interval` epochs.
 
         Returns:
-            Tuple with first value being an array of training run statistics
-            and the second being a dict mapping the labels for the statistics
-            recorded to their column index in the array.
+            Tuple with first value being an array of training run statistics,
+            the second being a dict mapping the labels for the statistics
+            recorded to their column index in the array and the final value
+            being the total time elapsed in seconds during the training run.
         """
         run_stats = [self.get_epoch_stats().values()]
+        run_start_time = time.time()
         for epoch in range(1, num_epochs + 1):
-            start_time = time.clock()
+            for scheduler in self.schedulers:
+                scheduler.update_learning_rule(self.learning_rule, epoch - 1)
+            start_time = time.time()
             self.do_training_epoch()
-            epoch_time = time.clock() - start_time
+            epoch_time = time.time() - start_time
             if epoch % stats_interval == 0:
                 stats = self.get_epoch_stats()
                 self.log_stats(epoch, epoch_time, stats)
                 run_stats.append(stats.values())
-        return np.array(run_stats), {k: i for i, k in enumerate(stats.keys())}
+        run_time = time.time() - run_start_time
+        return (
+            np.array(run_stats),
+            {k: i for i, k in enumerate(stats.keys())},
+            run_time
+        )