diff --git a/unsupervised/models.py b/unsupervised/models.py
index 4ef2331..fe5f603 100644
--- a/unsupervised/models.py
+++ b/unsupervised/models.py
@@ -19,7 +19,7 @@ def wrap_mobilenet_nnconv5_for_utrain(model):
 
 
 def res_layer(inputs, out_channels, down_sample=None, stride=1, normalisation=layers.BatchNormalization,
-              activation=layers.ReLU):
+              activation=layers.ReLU, name=None):
     x = layers.Conv2D(out_channels, 3, padding='same', strides=stride)(inputs)
     x = normalisation()(x)
     x = activation()(x)
@@ -29,12 +29,12 @@ def res_layer(inputs, out_channels, down_sample=None, stride=1, normalisation=la
     if down_sample is not None:
         inputs = down_sample
     x = layers.Add()([x, inputs])
-    x = activation()(x)
+    x = activation(name=name)(x)
     return x
 
 
 def res_block(inputs, out_channels, num_blocks=1, stride=1, normalisation=layers.BatchNormalization,
-              activation=layers.ReLU):
+              activation=layers.ReLU, name=None):
     down_sample = None
     if stride != 1 or inputs.shape[-1] != out_channels:
         down_sample = layers.Conv2D(out_channels, 1, stride, padding='same')(inputs)
@@ -42,7 +42,7 @@ def res_block(inputs, out_channels, num_blocks=1, stride=1, normalisation=layers
 
     x = res_layer(inputs, out_channels, down_sample, stride, normalisation, activation)
     for i in range(1, num_blocks):
-        x = res_layer(x, out_channels, None, 1, normalisation, activation)
+        x = res_layer(x, out_channels, None, 1, normalisation, activation, name)
     return x
 
 
@@ -55,18 +55,35 @@ def resnet_18(shape=(224, 224, 6)):
     inputs = layers.Input(shape)
     x = keras.layers.Conv2D(64, 7, 2, padding='same')(inputs)
     x = layers.BatchNormalization()(x)
-    x = layers.ReLU()(x)
-    x = layers.MaxPooling2D(3, 2, 'same')(x)
-    x = res_block(x, 64, 2)
-    x = res_block(x, 128, 2, 2)
-    x = res_block(x, 256, 2, 2)
-    x = res_block(x, 512, 2, 2)
-    return keras.Model(inputs=inputs, outputs=x)
+    skip_1 = layers.ReLU(name="res_1")(x)
+    x = layers.MaxPooling2D(3, 2, 'same')(skip_1)
+    skip_2 = res_block(x, 64, 2, name="res_2")
+    skip_3 = res_block(skip_2, 128, 2, 2, name="res_3")
+    skip_4 = res_block(skip_3, 256, 2, 2, name="res_4")
+    skip_5 = res_block(skip_4, 512, 2, 2, name="res_5")
+    return keras.Model(inputs=inputs, outputs=[skip_1, skip_2, skip_3, skip_4, skip_5])
 
 
+# TODO Monodepth and sfm learner both solve the posenet on all source images. So for the case of monodepth, it would need
+#   9 as the input (3 images - target and 2 source images) and would produce 2 6DOF poses
 def pose_net(shape=(224, 224, 6), encoder=resnet_18):
     resnet = encoder(shape=shape)
 
+    for layer in resnet.layers:
+        layer.trainable = True
+
+    # Concatenate every skip connection
+    cat_skips = [layers.ReLU()(layers.Conv2D(256, 1)(encode_output[-1])) for encode_output in resnet.outputs]
+    cat_skips = layers.Concatenate(1)(cat_skips)
+
+    x = layers.Conv2D(256, 3, padding='same')(cat_skips)
+    x = layers.ReLU()(x)
+
+    x = layers.Conv2D(256, 3, padding='same')
+    x = layers.ReLU()(x)
+
+    x = layers.Conv2D(256, 12, 1)(x)
+
     # Decoder
 
     pass