From 557d0fa74de74b8dbd8618a972725a7a9926e452 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=C3=89tienne=20Simon?= <esimon@esimon.eu>
Date: Fri, 12 Jun 2015 02:45:14 -0400
Subject: Fix RNN validation

---
 model/rnn.py | 27 ++++++++++++++++++++++-----
 train.py     | 12 +++++++++---
 2 files changed, 31 insertions(+), 8 deletions(-)

diff --git a/model/rnn.py b/model/rnn.py
index af35414..be17a95 100644
--- a/model/rnn.py
+++ b/model/rnn.py
@@ -93,8 +93,8 @@ class Model(Initializable):
     def predict_inputs(self):
         return self.inputs
 
-    @application(outputs=['cost'])
-    def cost(self, latitude, longitude, latitude_mask, **kwargs):
+    @application(outputs=['cost_matrix'])
+    def cost_matrix(self, latitude, longitude, latitude_mask, **kwargs):
         latitude = latitude.T
         longitude = longitude.T
         latitude_mask = latitude_mask.T
@@ -106,13 +106,30 @@ class Model(Initializable):
                 axis=2)
         target = target.repeat(latitude.shape[0], axis=0)
         ce = error.erdist(target.reshape((-1, 2)), res.reshape((-1, 2)))
-        ce *= latitude_mask.flatten()
-        return ce.sum() / latitude_mask.sum()
+        ce = ce.reshape(latitude.shape)
+        return ce * latitude_mask
+
+    @cost_matrix.property('inputs')
+    def cost_matrix_inputs(self):
+        return self.inputs + ['destination_latitude', 'destination_longitude']
+
+    @application(outputs=['cost'])
+    def cost(self, latitude_mask, **kwargs):
+        return self.cost_matrix(latitude_mask=latitude_mask, **kwargs).sum() / latitude_mask.sum()
 
     @cost.property('inputs')
     def cost_inputs(self):
         return self.inputs + ['destination_latitude', 'destination_longitude']
 
+    @application(outputs=['cost'])
+    def valid_cost(self, **kwargs):
+        # Only works when batch_size is 1.
+        return self.cost_matrix(**kwargs)[-1,0]
+
+    @valid_cost.property('inputs')
+    def valid_cost_inputs(self):
+        return self.inputs + ['destination_latitude', 'destination_longitude']
+
 
 class Stream(object):
     def __init__(self, config):
@@ -141,7 +158,7 @@ class Stream(object):
         stream = transformers.add_destination(stream)
         stream = transformers.Select(stream, tuple(v for v in req_vars if not v.endswith('_mask')))
 
-        stream = Batch(stream, iteration_scheme=ConstantScheme(1000))
+        stream = Batch(stream, iteration_scheme=ConstantScheme(1))
         stream = Padding(stream, mask_sources=['latitude', 'longitude'])
         stream = transformers.Select(stream, req_vars)
         return stream
diff --git a/train.py b/train.py
index 876fcba..94c00d2 100755
--- a/train.py
+++ b/train.py
@@ -100,6 +100,12 @@ if __name__ == "__main__":
     cg = ComputationGraph(cost)
     monitored = set([cost] + VariableFilter(roles=[roles.COST])(cg.variables))
 
+    valid_monitored = monitored
+    if hasattr(model, 'valid_cost'):
+        valid_cost = model.valid_cost(**inputs)
+        valid_cg = ComputationGraph(valid_cost)
+        valid_monitored = set([valid_cost] + VariableFilter(roles=[roles.COST])(valid_cg.variables))
+
     if hasattr(config, 'dropout') and config.dropout < 1.0:
         cg = apply_dropout(cg, config.dropout_inputs(cg), config.dropout)
     if hasattr(config, 'noise') and config.noise > 0.0:
@@ -124,7 +130,7 @@ if __name__ == "__main__":
             ]),
         params=params)
     
-    plot_vars = [['valid_' + x.name for x in monitored]]
+    plot_vars = [['valid_' + x.name for x in valid_monitored]]
     logger.info('Plotted variables: %s' % str(plot_vars))
 
     dump_path = os.path.join('model_data', model_name)
@@ -136,13 +142,13 @@ if __name__ == "__main__":
     dump_ext.manager = CustomDumpManager(dump_path)
 
     extensions=[TrainingDataMonitoring(monitored, prefix='train', every_n_batches=1000),
-                DataStreamMonitoring(monitored, valid_stream,
+                DataStreamMonitoring(valid_monitored, valid_stream,
                                      prefix='valid',
                                      every_n_batches=1000),
                 Printing(every_n_batches=1000),
                 Plot(model_name, channels=plot_vars, every_n_batches=500),
                 load_dump_ext,
-                dump_ext 
+                dump_ext
                 ]
 
     main_loop = MainLoop(
-- 
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