diff --git a/precise/model.py b/precise/model.py
index 696dc85..6ee90ca 100644
--- a/precise/model.py
+++ b/precise/model.py
@@ -36,6 +36,7 @@ class ModelParams:
     extra_metrics = attr.ib(False)  # type: bool
     skip_acc = attr.ib(False)  # type: bool
     loss_bias = attr.ib(0.7)  # type: float
+    freeze_till = attr.ib(0)  # type: bool
 
 
 def load_precise_model(model_name: str) -> Any:
@@ -76,5 +77,7 @@ def create_model(model_name: Optional[str], params: ModelParams) -> 'Sequential'
     load_keras()
     metrics = ['accuracy'] + params.extra_metrics * [false_pos, false_neg]
     set_loss_bias(params.loss_bias)
+    for i in model.layers[:params.freeze_till]:
+        i.trainable = False
     model.compile('rmsprop', weighted_log_loss, metrics=(not params.skip_acc) * metrics)
     return model
diff --git a/precise/scripts/train.py b/precise/scripts/train.py
index af964c1..c35b438 100755
--- a/precise/scripts/train.py
+++ b/precise/scripts/train.py
@@ -60,6 +60,10 @@ class Trainer:
 
         :-em --extra-metrics
             Add extra metrics during training
+        
+        :-f --freeze-till int 0
+            Freeze all weights up to this index (non-inclusive).
+            Can be negative to wrap from end
 
         ...
     '''
@@ -80,7 +84,7 @@ class Trainer:
         inject_params(args.model)
         save_params(args.model)
         params = ModelParams(skip_acc=args.no_validation, extra_metrics=args.extra_metrics,
-                             loss_bias=1.0 - args.sensitivity)
+                             loss_bias=1.0 - args.sensitivity, freeze_till=args.freeze_till)
         self.model = create_model(args.model, params)
         self.train, self.test = self.load_data(self.args)