Patched the train.py script to accept tensors as input

bob/learn/tensorflow/script/train.py

@@ -87,22 +87,34 @@ def main():
         trainer.create_network_from_file(output_dir)
     else:
 
-        # Preparing the architecture
-        input_pl = config.train_data_shuffler("data", from_queue=False)
-        if isinstance(trainer, bob.learn.tensorflow.trainers.SiameseTrainer):
-            graph = dict()
-            graph['left'] = config.architecture(input_pl['left'])
-            graph['right'] = config.architecture(input_pl['right'], reuse=True)
-
-        elif isinstance(trainer, bob.learn.tensorflow.trainers.TripletTrainer):
-            graph = dict()
-            graph['anchor'] = config.architecture(input_pl['anchor'])
-            graph['positive'] = config.architecture(input_pl['positive'], reuse=True)
-            graph['negative'] = config.architecture(input_pl['negative'], reuse=True)
+        # Either bootstrap from scratch or take the pointer directly from the config script
+        train_graph = None
+        validation_graph = None
+        
+        if hasattr(config, 'train_graph'):
+            train_graph = config.train_graph
+            if hasattr(config, 'validation_graph'):
+                validation_graph = config.validation_graph
+            
         else:
-            graph = config.architecture(input_pl)
-
-        trainer.create_network_from_scratch(graph, loss=config.loss,
+            # Preparing the architecture
+            input_pl = config.train_data_shuffler("data", from_queue=False)
+            if isinstance(trainer, bob.learn.tensorflow.trainers.SiameseTrainer):
+                train_graph = dict()
+                train_graph['left'] = config.architecture(input_pl['left'])
+                train_graph['right'] = config.architecture(input_pl['right'], reuse=True)
+
+            elif isinstance(trainer, bob.learn.tensorflow.trainers.TripletTrainer):
+                train_graph = dict()
+                train_graph['anchor'] = config.architecture(input_pl['anchor'])
+                train_graph['positive'] = config.architecture(input_pl['positive'], reuse=True)
+                train_graph['negative'] = config.architecture(input_pl['negative'], reuse=True)
+            else:
+                train_graph = config.architecture(input_pl)
+
+        trainer.create_network_from_scratch(train_graph,
+                                            validation_graph=validation_graph,
+                                            loss=config.loss,
                                             learning_rate=config.learning_rate,
                                             optimizer=config.optimizer)
     trainer.train()

bob/learn/tensorflow/trainers/SiameseTrainer.py

@@ -121,6 +121,7 @@ class SiameseTrainer(Trainer):
 
     def create_network_from_scratch(self,
                                     graph,
+                                    validation_graph=None,
                                     optimizer=tf.train.AdamOptimizer(),
                                     loss=None,
 

bob/learn/tensorflow/trainers/Trainer.py

@@ -59,8 +59,8 @@ class Trainer(object):
 
                  ###### training options ##########
                  iterations=5000,
-                 snapshot=500,
-                 validation_snapshot=100,
+                 snapshot=1000,
+                 validation_snapshot=2000,
                  keep_checkpoint_every_n_hours=2,
 
                  ## Analizer

bob/learn/tensorflow/trainers/TripletTrainer.py

@@ -122,6 +122,7 @@ class TripletTrainer(Trainer):
 
     def create_network_from_scratch(self,
                                     graph,
+                                    validation_graph=None,
                                     optimizer=tf.train.AdamOptimizer(),
                                     loss=None,