Redefined the projector

bob/learn/tensorflow/analyzers/Analizer.py

@@ -46,12 +46,12 @@ class Analizer:
 
         # Extracting features for enrollment
         enroll_data, enroll_labels = self.data_shuffler.get_batch(train_dataset=False)
-        enroll_features = self.machine(enroll_data, self.session)
+        enroll_features = self.machine(enroll_data, session=self.session)
         del enroll_data
 
         # Extracting features for probing
         probe_data, probe_labels = self.data_shuffler.get_batch(train_dataset=False)
-        probe_features = self.machine(probe_data, self.session)
+        probe_features = self.machine(probe_data, session=self.session)
         del probe_data
 
         # Creating models

bob/learn/tensorflow/network/Lenet.py

@@ -24,7 +24,7 @@ class Lenet(SequenceNetwork):
 
                  fc1_output=400,
                  n_classes=10,
-                 feature_layer="fc2",
+                 default_feature_layer="fc2",
 
                  seed=10, use_gpu = False):
         """
@@ -42,7 +42,7 @@ class Lenet(SequenceNetwork):
 
             seed = 10
         """
-        super(Lenet, self).__init__(feature_layer=feature_layer)
+        super(Lenet, self).__init__(default_feature_layer=default_feature_layer)
 
         self.add(Conv2D(name="conv1", kernel_size=conv1_kernel_size, filters=conv1_output, activation=tf.nn.tanh))
         self.add(MaxPooling(name="pooling1"))

bob/learn/tensorflow/network/SequenceNetwork.py

@@ -21,7 +21,7 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
     Base class to create architectures using TensorFlow
     """
 
-    def __init__(self, feature_layer=None):
+    def __init__(self, default_feature_layer=None):
         """
         Base constructor
 
@@ -30,7 +30,7 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
         """
 
         self.sequence_net = OrderedDict()
-        self.feature_layer = feature_layer
+        self.default_feature_layer = default_feature_layer
         self.input_divide = 1.
         self.input_subtract = 0.
         #self.saver = None
@@ -44,7 +44,7 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
             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):
+    def compute_graph(self, input_data, feature_layer=None):
         """
         Given the current network, return the Tensorflow graph
 
@@ -59,15 +59,15 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
             current_layer.create_variables(input_offset)
             input_offset = current_layer.get_graph()
 
-            if cut and k == self.feature_layer:
+            if feature_layer is not None and k == feature_layer:
                 return input_offset
 
         return input_offset
 
     def compute_projection_graph(self, placeholder):
-        return self.compute_graph(placeholder, cut=True)
+        return self.compute_graph(placeholder)
 
-    def __call__(self, data, session=None):
+    def __call__(self, data, session=None, feature_layer=None):
 
         if session is None:
             session = tf.Session()
@@ -81,7 +81,10 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
         feature_placeholder = tf.placeholder(tf.float32, shape=(batch_size, width, height, channels), name="feature")
         feed_dict = {feature_placeholder: data}
 
-        return session.run([self.compute_projection_graph(feature_placeholder)], feed_dict=feed_dict)[0]
+        if feature_layer is None:
+            feature_layer = self.default_feature_layer
+
+        return session.run([self.compute_graph(feature_placeholder, feature_layer)], feed_dict=feed_dict)[0]
 
     def dump_variables(self):
 
@@ -97,11 +100,6 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
     def save(self, hdf5, step=None):
         """
         Save the state of the network in HDF5 format
-
-        :param session:
-        :param hdf5:
-        :param step:
-        :return:
         """
 
         # Directory that stores the tensorflow variables

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="fc2")
+    lenet = Lenet(default_feature_layer="fc2")
 
     loss = ContrastiveLoss()
     trainer = SiameseTrainer(architecture=lenet,