Freeze layers for transfer learning.

training/deepspeech_training/train.py

@@ -322,8 +322,35 @@ def get_tower_results(iterator, optimizer, dropout_rates):
                     # Retain tower's avg losses
                     tower_avg_losses.append(avg_loss)
 
+                    train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
+
+                    # Filter out layers if we want to freeze some
+                    if FLAGS.freeze_source_layers > 0:
+                        filter_vars = []
+                        if FLAGS.freeze_source_layers <= 5:
+                            filter_vars.append("layer_1")
+                        if FLAGS.freeze_source_layers <= 4:
+                            filter_vars.append("layer_2")
+                        if FLAGS.freeze_source_layers <= 3:
+                            filter_vars.append("layer_3")
+                        if FLAGS.freeze_source_layers <= 2:
+                            filter_vars.append("lstm")
+                        if FLAGS.freeze_source_layers <= 1:
+                            filter_vars.append("layer_5")
+
+                        new_train_vars = list(train_vars)
+                        for fv in filter_vars:
+                            for tv in train_vars:
+                                if fv in tv.name:
+                                    new_train_vars.remove(tv)
+                        train_vars = new_train_vars
+                        msg = "Tower {} -  Training only variables: {}"
+                        print(msg.format(i, [v.name for v in train_vars]))
+                    else:
+                        print("Tower {} - Training all layers".format(i))
+
                     # Compute gradients for model parameters using tower's mini-batch
-                    gradients = optimizer.compute_gradients(avg_loss)
+                    gradients = optimizer.compute_gradients(avg_loss, var_list=train_vars)
 
                     # Retain tower's gradients
                     tower_gradients.append(gradients)
@@ -671,7 +698,6 @@ def __call__(self, progress, data, **kwargs):
 
                 print('-' * 80)
 
-
         except KeyboardInterrupt:
             pass
         log_info('FINISHED optimization in {}'.format(datetime.utcnow() - train_start_time))

training/deepspeech_training/util/checkpoints.py

@@ -46,6 +46,21 @@ def _load_checkpoint(session, checkpoint_path, allow_drop_layers, allow_lr_init=
                       'tensors. Missing variables: {}'.format(missing_var_names))
             sys.exit(1)
 
+    if FLAGS.load_frozen_graph:
+        # After training with "freeze_source_layers" the Adam tensors for the frozen layers aren't
+        # existing anymore because they were not used
+        # Therefore we have to initialize them again to continue training on such checkpoints
+        for v in load_vars:
+            if v.op.name not in vars_in_ckpt:
+                if 'Adam' in v.name:
+                    init_vars.add(v)
+                else:
+                    msg = "Tried to load a frozen checkpoint but there was a missing " \
+                          "variable other than the Adam tensors: {}"
+                    log_error(msg.format(v))
+                    sys.exit(1)
+        load_vars -= init_vars
+
     if allow_drop_layers and FLAGS.drop_source_layers > 0:
         # This transfer learning approach requires supplying
         # the layers which we exclude from the source model.

training/deepspeech_training/util/flags.py

@@ -93,6 +93,8 @@ def create_flags():
     # Transfer Learning
 
     f.DEFINE_integer('drop_source_layers', 0, 'single integer for how many layers to drop from source model (to drop just output == 1, drop penultimate and output ==2, etc)')
+    f.DEFINE_integer('freeze_source_layers', 0, 'use same value as above to freeze the other layers')
+    f.DEFINE_boolean('load_frozen_graph', False, 'Needed to load a graph checkpoint which was trained with "freeze_source_layers" flag. Allows initialization of missing optimization tensors.')
 
     # Exporting