Fix RNN validation

1 files changed, 9 insertions, 3 deletions

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(