diff --git a/iss/models/AbstractModel.py b/iss/models/AbstractModel.py
index 35c8325..0af2c5f 100644
--- a/iss/models/AbstractModel.py
+++ b/iss/models/AbstractModel.py
@@ -32,4 +32,5 @@ class AbstractAutoEncoderModel(AbstractModel):
 
 	def __init__(self, save_directory, model_name):
 		super().__init__(save_directory, model_name)
-		self.encoded_layer = None
+		self.encoder_model = None
+		self.decoder_model = None
diff --git a/iss/models/SimpleConvAutoEncoder.py b/iss/models/SimpleConvAutoEncoder.py
index ea25771..c184475 100644
--- a/iss/models/SimpleConvAutoEncoder.py
+++ b/iss/models/SimpleConvAutoEncoder.py
@@ -4,6 +4,7 @@ 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 import backend as K
 import numpy as np
 
 class SimpleConvAutoEncoder(AbstractAutoEncoderModel):
@@ -17,11 +18,13 @@ class SimpleConvAutoEncoder(AbstractAutoEncoderModel):
 
 			self.activation = config['activation']
 			self.input_shape = (config['input_height'], config['input_width'], config['input_channel'])
+			self.latent_shape = (config['latent_height'], config['latent_width'], config['latent_channel'])
 			self.lr = config['learning_rate']
 			self.build_model()
 
 		def build_model(self):
 			input_shape = self.input_shape
+			latent_shape = self.latent_shape
 
 			picture = Input(shape = input_shape)
 
@@ -41,8 +44,12 @@ class SimpleConvAutoEncoder(AbstractAutoEncoderModel):
 			x = Activation('relu')(x)
 			encoded = MaxPooling2D((2, 2))(x)
 
+			self.encoder_model = Model(picture, encoded, name = "encoder")
+
 			# decoded network
-			x = Conv2D(16, (3, 3), padding = 'same', name = 'dec_conv_1')(encoded)
+			latent_input = Input(shape = latent_shape)
+
+			x = Conv2D(16, (3, 3), padding = 'same', name = 'dec_conv_1')(latent_input)
 			x = BatchNormalization()(x)
 			x = Activation('relu')(x)
 			x = UpSampling2D((2, 2))(x)
@@ -63,9 +70,11 @@ class SimpleConvAutoEncoder(AbstractAutoEncoderModel):
 			x = Dense(np.prod(input_shape), activation = self.activation)(x)
 			decoded = Reshape((input_shape))(x)
 
-			self.model = Model(picture, decoded)
+			self.decoder_model = Model(latent_input, decoded, name = "decoder")
+
+			picture_dec = self.decoder_model(self.encoder_model(picture))
+			self.model = Model(picture, picture_dec)
 			
-			# optimizer = Adadelta(lr = self.lr, rho = 0.95, epsilon = None, decay = 0.0)
 			optimizer = Adam(lr = 0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
 			
 			self.model.compile(optimizer = optimizer, loss = 'binary_crossentropy')