diff --git a/spkeras/models.py b/spkeras/models.py
index 1b3b579..a7ade21 100644
--- a/spkeras/models.py
+++ b/spkeras/models.py
@@ -169,7 +169,7 @@ def convert(self, mdl,x_train,thresholding=0.5,scaling_factor=1,method=0,timeste
         new_mdl['config']['output_layers'] =  [[inbound_nodes, 0, 0]]
         new_mdl = json.dumps(new_mdl)
         new_model = model_from_json(new_mdl,
-                                     custom_objects={'SpikeActivation':SpikeActivation})
+                                    custom_objects={'SpikeActivation':SpikeActivation})
         input_shape = model.layers[0].input_shape
 
 
@@ -408,6 +408,22 @@ def evaluate(self,x_test,y_test,timesteps=256,thresholding=0.5,scaling_factor=1,
         _x_test = x_test*fix if fix > 0 else np.floor(x_test*self.timesteps)
         return self.model.evaluate(_x_test,y_test)
 
+    def predict(self, features, timesteps=256, thresholding=0.5, scaling_factor=1,
+                 spike_ext=0, noneloss=False, sf=None, fix=0):
+        import numpy as np
+        self.timesteps = timesteps
+        self.thresholding = thresholding
+        self.scaling_factor = scaling_factor
+        self.spike_ext = spike_ext
+        self.noneloss = noneloss
+        self.model = self.chts_model(timesteps=timesteps,thresholding=thresholding,
+                                     scaling_factor=scaling_factor,
+                                     spike_ext=spike_ext,noneloss=noneloss,sf=sf)
+        
+        self.get_config()
+        _features = features*fix if fix > 0 else np.floor(features*self.timesteps)
+        return self.model.predict(features)
+    
     def chts_model(self,timesteps=256,thresholding=0.5,scaling_factor=1,spike_ext=0,noneloss=False,sf=None):
         #method: 0:threshold norm 1:weight norm 
         from tensorflow.keras.models import Sequential, model_from_json