diff --git a/DeepSpeech.ipynb b/DeepSpeech.ipynb
index 12912f4d..5f1cccbc 100644
--- a/DeepSpeech.ipynb
+++ b/DeepSpeech.ipynb
@@ -1395,7 +1395,7 @@
     "        \n",
     "    # Indicate optimization has concluded\n",
     "    print \"Optimization Finished!\"\n",
-    "    return last_train_wer, last_validation_wer, saver"
+    "    return last_train_wer, last_validation_wer"
    ]
   },
   {
@@ -1425,7 +1425,7 @@
     "    time_started = datetime.datetime.utcnow()\n",
     "    \n",
     "    # Train the network\n",
-    "    last_train_wer, last_validation_wer, saver = train(session, data_sets)\n",
+    "    last_train_wer, last_validation_wer = train(session, data_sets)\n",
     "    \n",
     "    # Take final time for time measurement\n",
     "    time_finished = datetime.datetime.utcnow()\n",
@@ -1475,9 +1475,14 @@
     "# Don't export a model if no export directory has been set\n",
     "if export_dir:\n",
     "    with tf.device('/cpu:0'):\n",
+    "        session = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True))\n",
+    "        saver = tf.train.Saver(tf.all_variables())\n",
     "        model_exporter = exporter.Exporter(saver)\n",
     "\n",
     "        # Run inference\n",
+    "        # Replace the dropout placeholder with a constant\n",
+    "        dropout_rate_placeholder = tf.constant(0.0)\n",
+    "\n",
     "        # Input tensor will be of shape [batch_size, n_steps, n_input + 2*n_input*n_context]\n",
     "        input_tensor = tf.placeholder(tf.float32, [None, None, n_input + 2*n_input*n_context])\n",
     "\n",
@@ -1510,8 +1515,7 @@
     "        model_exporter.init(session.graph.as_graph_def(),\n",
     "                            named_graph_signatures = {\n",
     "                                'inputs': exporter.generic_signature(\n",
-    "                                    { 'input': input_tensor,\n",
-    "                                      'dropout_rate': dropout_rate_placeholder }),\n",
+    "                                    { 'input': input_tensor }),\n",
     "                                'outputs': exporter.generic_signature(\n",
     "                                    { 'outputs': decoded})})\n",
     "        model_exporter.export(export_dir, tf.constant(export_version), session)\n",