diff --git a/iss/models/SimpleConvAutoEncoder.py b/iss/models/SimpleConvAutoEncoder.py
index 9d72b93..dad9ccf 100644
--- a/iss/models/SimpleConvAutoEncoder.py
+++ b/iss/models/SimpleConvAutoEncoder.py
@@ -3,7 +3,7 @@
 from iss.models import AbstractAutoEncoderModel
 from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Reshape, Flatten, BatchNormalization, Activation
 from keras.optimizers import Adadelta, Adam
-from keras.models import Model
+from keras.models import Model, load_model
 from keras import backend as K
 import numpy as np
 
@@ -22,6 +22,9 @@ class SimpleConvAutoEncoder(AbstractAutoEncoderModel):
 			self.lr = config['learning_rate']
 			self.build_model()
 
+		def load(self, which = 'final_model'):
+			self.model = load_model('{}/{}.hdf5'.format(self.save_directory, which), custom_objects= {'my_loss':self.my_loss})
+
 		def build_model(self):
 			input_shape = self.input_shape
 			latent_shape = self.latent_shape
@@ -77,9 +80,11 @@ class SimpleConvAutoEncoder(AbstractAutoEncoderModel):
 			
 			optimizer = Adam(lr = self.lr, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
 			
-			def my_loss(picture, picture_dec):
-				loss = K.mean(K.binary_crossentropy(picture, picture_dec))
-				return loss
+
 
 			# self.model.compile(optimizer = optimizer, loss = 'binary_crossentropy')
-			self.model.compile(optimizer = optimizer, loss = my_loss)
+			self.model.compile(optimizer = optimizer, loss = self.my_loss)
+
+		def my_loss(self, picture, picture_dec):
+			loss = K.mean(K.binary_crossentropy(picture, picture_dec))
+			return loss
\ No newline at end of file