diff --git a/answer_classifier_cached_features/train.py b/answer_classifier_cached_features/train.py
index 03b406832208af20ccca9425a4f612c44906730f..1db0b33e29492b948d8fa9f1a57756d11daaf193 100644
--- a/answer_classifier_cached_features/train.py
+++ b/answer_classifier_cached_features/train.py
@@ -498,16 +498,20 @@ def create_vgenome_batch_generator():
         constants.object_labels_json,
         constants.attribute_labels_json,
         constants.regions_json,
+        constants.genome_train_subset_region_ids,
         constants.image_size,
         channels=3,
         resnet_feat_dim=constants.resnet_feat_dim,
         mean_image_filename=None)
+
+    num_train_subset_regions = len(data_mgr.region_ids)
+    print num_train_subset_regions
     
     index_generator = tftools.data.random(
         constants.num_regions_with_labels, 
-        constants.region_num_samples, 
+        num_train_subset_regions,
         constants.region_num_epochs, 
-        constants.region_offset)
+        0)
     
     batch_generator = tftools.data.async_batch_generator(
         data_mgr, 
@@ -550,7 +554,7 @@ class attach_optimizer():
 
             self.optimizer.add_variables(
                 self.graph.object_attribute_vars + self.graph.word_vec_vars,
-                learning_rate = 0.1*self.lr)
+                learning_rate = 1.0*self.lr)
 
             
             self.optimizer.add_variables(
diff --git a/constants_crunchy.py b/constants_crunchy.py
index 13c0c2321bf9474f6423ab4533c6c17320a388b6..099855102918f545a806a1f3ef10f2ee28cce5d1 100644
--- a/constants_crunchy.py
+++ b/constants_crunchy.py
@@ -5,7 +5,7 @@ def mkdir_if_not_exists(dir_name):
     if not os.path.exists(dir_name):
         os.mkdir(dir_name)
         
-experiment_name = 'object_attribute_classifier_large_images_vqa_split'
+experiment_name = 'QA_joint_pretrain_genome_split'
 
 # Global output directory (all subexperiments will be saved here)
 global_output_dir = '/home/tanmay/Code/GenVQA/Exp_Results/VQA'
@@ -189,13 +189,13 @@ vqa_answer_vocab_json = os.path.join(
 
 # Answer classifier training params
 answer_batch_size = 50
-answer_num_epochs = 6
+answer_num_epochs = 4
 answer_offset = 0
 answer_obj_atr_loss_wt = 0.1
 answer_regularization_coeff = 1e-5
 answer_queue_size = 500
 answer_embedding_dim = 600
-answer_lr = 1e-4
+answer_lr = 1e-3
 answer_log_every_n_iter = 500
 answer_output_dir = os.path.join(
     global_experiment_dir,
@@ -203,9 +203,9 @@ answer_output_dir = os.path.join(
     
 mkdir_if_not_exists(answer_output_dir)
 
-pretrained_model = '/home/tanmay/Code/GenVQA/Exp_Results/VisualGenome/' + \
-    'object_attribute_classifier_large_images/' + \
-    'object_attribute_classifiers/model-77500'
+pretrained_model = '/home/tanmay/Code/GenVQA/Exp_Results/VQA/' + \
+    'object_attribute_classifier_large_images_vqa_split/' + \
+    'object_attribute_classifiers/model-80000'
 
 answer_model = os.path.join(
     answer_output_dir,