Testing the new machine concept

bob/learn/tensorflow/analyzers/Analizer.py

@@ -21,7 +21,7 @@ class Analizer:
 
     """
 
-    def __init__(self, data_shuffler, graph, feature_placeholder, session):
+    def __init__(self, data_shuffler, machine, feature_placeholder, session):
         """
         Use the CNN as feature extractor for a n-class classification
 
@@ -33,7 +33,7 @@ class Analizer:
         """
 
         self.data_shuffler = data_shuffler
-        self.graph = graph
+        self.machine = machine
         self.feature_placeholder = feature_placeholder
         self.session = session
 
@@ -47,7 +47,7 @@ class Analizer:
         data, labels = self.data_shuffler.get_batch(train_dataset=False)
         feed_dict = {self.feature_placeholder: data}
 
-        return self.session.run([self.graph], feed_dict=feed_dict)[0], labels
+        return self.machine(feed_dict, self.session)
 
     def __call__(self):
 

bob/learn/tensorflow/network/SequenceNetwork.py

@@ -26,18 +26,29 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
         Base constructor
 
         **Parameters**
-        input: Place Holder
+        feature_layer:
         """
 
         self.sequence_net = OrderedDict()
         self.feature_layer = feature_layer
 
     def add(self, layer):
+        """
+        Add a layer in the sequence network
+
+        """
         if not isinstance(layer, Layer):
             raise ValueError("Input `layer` must be an instance of `bob.learn.tensorflow.layers.Layer`")
         self.sequence_net[layer.name] = layer
 
     def compute_graph(self, input_data, cut=False):
+        """
+        Given the current network, return the Tensorflow graph
+
+         **Parameter**
+          input_data:
+          cut:
+        """
 
         input_offset = input_data
         for k in self.sequence_net.keys():
@@ -49,3 +60,10 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
                 return input_offset
 
         return input_offset
+
+    def compute_projection_graph(self, placeholder):
+        return self.compute_graph(placeholder, cut=True)
+
+    def __call__(self, feed_dict, session):
+        #placeholder
+        return session.run([self.graph], feed_dict=feed_dict)[0]

bob/learn/tensorflow/script/train_mnist_siamese.py

@@ -44,7 +44,7 @@ def main():
     data_shuffler = PairDataShuffler(data, labels)
 
     # Preparing the architecture
-    lenet = Lenet(feature_layer="fc1")
+    lenet = Lenet(feature_layer="fc2")
 
     loss = ContrastiveLoss()
     trainer = SiameseTrainer(architecture=lenet, loss=loss, iterations=ITERATIONS, base_lr=0.00001)

bob/learn/tensorflow/trainers/SiameseTrainer.py

@@ -63,7 +63,6 @@ class SiameseTrainer(object):
 
         train_left_graph = self.architecture.compute_graph(train_placeholder_left_data)
         train_right_graph = self.architecture.compute_graph(train_placeholder_right_data)
-        feature_graph = self.architecture.compute_graph(feature_placeholder, cut=True)
 
         loss_train, within_class, between_class = self.loss(train_placeholder_labels,
                                                             train_left_graph,
@@ -88,7 +87,7 @@ class SiameseTrainer(object):
         print("Initializing !!")
         # Training
         with tf.Session() as session:
-            analizer = Analizer(data_shuffler, feature_graph, feature_placeholder, session)
+            analizer = Analizer(data_shuffler, self.architecture, feature_placeholder, session)
 
             train_writer = tf.train.SummaryWriter('./LOGS/train',
                                                   session.graph)