make sure can work with hdf5 models

bob/learn/tensorflow/network/SequenceNetwork.py

@@ -120,11 +120,11 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
 
         # Feeding the placeholder
         if self.inference_placeholder is None:
-            self.compute_inference_placeholder(data.shape[1:])
+            self.compute_inference_placeholder([None] + list(data.shape[1:]))
         feed_dict = {self.inference_placeholder: data}
 
         if self.inference_graph is None:
-            self.compute_inference_graph(self.inference_placeholder, feature_layer)
+            self.compute_inference_graph(feature_layer)
 
         embedding = session.run([self.inference_graph], feed_dict=feed_dict)[0]
 
@@ -266,7 +266,8 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
         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):
+            if not isinstance(self.sequence_net[k], MaxPooling) and \
+                    not isinstance(self.sequence_net[k], LogSoftMax):
                 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)
@@ -300,11 +301,11 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
 
         # Loading architecture
         self.sequence_net = pickle.loads(hdf5.read('architecture'))
-        self.deployment_shape = hdf5.read('deployment_shape')
 
         self.turn_gpu_onoff(use_gpu)
 
         if shape is None:
+            self.deployment_shape = hdf5.read('deployment_shape')
             shape = self.deployment_shape
             shape[0] = batch
 
@@ -312,7 +313,7 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
         place_holder = tf.placeholder(tf.float32, shape=shape, name="load")
         self.compute_graph(place_holder)
         tf.initialize_all_variables().run(session=session)
-        self.load_variables_only(hdf5, session)
+        self.load_variables_only(hdf5)
 
     def save(self, saver, path):