Defined a load and save functions

bob/learn/tensorflow/network/SequenceNetwork.py

@@ -31,7 +31,9 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
 
         self.sequence_net = OrderedDict()
         self.feature_layer = feature_layer
-        self.saver = None
+        self.input_divide = 1.
+        self.input_subtract = 0.
+        #self.saver = None
 
     def add(self, layer):
         """
@@ -92,16 +94,87 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
 
         return variables
 
+    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
+        hdf5.create_group('/tensor_flow')
+        hdf5.cd('/tensor_flow')
+
+        if step is not None:
+            group_name = '/step_{0}'.format(step)
+            hdf5.create_group(group_name)
+            hdf5.cd(group_name)
+
+        # Iterating the variables of the model
+        for v in self.dump_variables().keys():
+            hdf5.set(v, self.dump_variables()[v].eval())
+
+        hdf5.cd('..')
+        if step is not None:
+            hdf5.cd('..')
+
+        hdf5.set('input_divide', self.input_divide)
+        hdf5.set('input_subtract', self.input_subtract)
+
+    def load(self, hdf5, shape, session=None):
+
+        if session is None:
+            session = tf.Session()
+
+        # Loading the normalization parameters
+        self.input_divide = hdf5.read('input_divide')
+        self.input_subtract = hdf5.read('input_subtract')
+
+        # Loading variables
+        place_holder = tf.placeholder(tf.float32, shape=shape, name="load")
+        self.compute_graph(place_holder)
+        tf.initialize_all_variables().run(session=session)
+
+        hdf5.cd('/tensor_flow')
+        for k in self.sequence_net:
+            # TODO: IT IS NOT SMART TESTING ALONG THIS PAGE
+            if not isinstance(self.sequence_net[k], MaxPooling):
+                #self.sequence_net[k].W.assign(hdf5.read(self.sequence_net[k].W.name))
+                self.sequence_net[k].W.assign(hdf5.read(self.sequence_net[k].W.name)).eval(session=session)
+                session.run(self.sequence_net[k].W)
+                self.sequence_net[k].b.assign(hdf5.read(self.sequence_net[k].b.name)).eval(session=session)
+                session.run(self.sequence_net[k].b)
+
+
+        #if self.saver is None:
+        #    variables = self.dump_variables()
+        #    variables['input_divide'] = self.input_divide
+        #    variables['input_subtract'] = self.input_subtract
+        #    self.saver = tf.train.Saver(variables)
+        #self.saver.restore(session, path)
+
+
+
+    """
     def save(self, session, path, step=None):
 
         if self.saver is None:
-            self.saver = tf.train.Saver(self.dump_variables())
+            variables = self.dump_variables()
+            variables['mean'] = tf.Variable(10.0)
+            #import ipdb; ipdb.set_trace()
+
+            tf.initialize_all_variables().run()
+            self.saver = tf.train.Saver(variables)
 
         if step is None:
             return self.saver.save(session, os.path.join(path, "model.ckpt"))
         else:
             return self.saver.save(session, os.path.join(path, "model" + str(step) + ".ckpt"))
 
+
     def load(self, path, shape, session=None):
 
         if session is None:
@@ -113,6 +186,10 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
         tf.initialize_all_variables().run(session=session)
 
         if self.saver is None:
-            self.saver = tf.train.Saver(self.dump_variables())
+            variables = self.dump_variables()
+            variables['input_divide'] = self.input_divide
+            variables['input_subtract'] = self.input_subtract
+            self.saver = tf.train.Saver(variables)
 
         self.saver.restore(session, path)
+    """

bob/learn/tensorflow/trainers/SiameseTrainer.py

@@ -92,6 +92,7 @@ class SiameseTrainer(object):
 
         print("Initializing !!")
         # Training
+        hdf5 = bob.io.base.HDF5File(os.path.join(self.temp_dir, 'model.hdf5'), 'w')
         with tf.Session() as session:
             analizer = Analizer(data_shuffler, self.architecture, session)
 
@@ -119,9 +120,9 @@ class SiameseTrainer(object):
                 if step % self.snapshot == 0:
                     analizer()
                     if self.save_intermediate:
-                        self.architecture.save(session, os.path.join(self.temp_dir, 'OUTPUT'), step)
-
+                        self.architecture.save(hdf5, step)
                     print str(step) + " - " + str(analizer.eer[-1])
 
-            self.architecture.save(session, os.path.join(self.temp_dir, 'OUTPUT'))
+            self.architecture.save(hdf5)
+            del hdf5
             train_writer.close()