diff --git a/fast_depth_functional.py b/fast_depth_functional.py
index 4da5289..b27aceb 100644
--- a/fast_depth_functional.py
+++ b/fast_depth_functional.py
@@ -1,7 +1,8 @@
+import tensorflow as tf
 import tensorflow.keras as keras
 
 '''
-Functional version of fastdepth model. Note that this doesn't work at the moment
+Functional version of fastdepth model
 '''
 
 
@@ -16,11 +17,11 @@ def FDDepthwiseBlock(inputs,
     return keras.layers.ReLU(6., name='conv_pw_%d_relu' % block_id)(x)
 
 
-def make_fastdepth_functional():
+def make_fastdepth_functional(weights=None):
     # This doesn't work, at least right now...
     input = keras.layers.Input(shape=(224, 224, 3))
     x = input
-    mobilenet = keras.applications.MobileNet(include_top=False, weights=None)
+    mobilenet = keras.applications.MobileNet(include_top=False, weights=weights)
     for layer in mobilenet.layers:
         x = layer(x)
         if layer.name == 'conv_pw_5_relu':
@@ -30,6 +31,7 @@ def make_fastdepth_functional():
         elif layer.name == 'conv_pw_1_relu':
             conv1 = x
 
+    # Fast depth decoder
     x = FDDepthwiseBlock(x, 512, block_id=14)
     # TODO: Bilinear interpolation
     #     x = keras.layers.experimental.preprocessing.Resizing(14, 14)
@@ -53,5 +55,43 @@ def make_fastdepth_functional():
     return keras.Model(inputs=input, outputs=x, name="fast_depth")
 
 
+def delta1_metric(y_true, y_pred):
+    maxRatio = tf.maximum(y_pred / y_true, y_true / y_pred)
+    return float((maxRatio < 1.25).float().mean())
+
+
+def delta2_metric(y_true, y_pred):
+    maxRatio = tf.maximum(y_pred / y_true, y_true / y_pred)
+    return float((maxRatio < 1.2 ** 25).float().mean())
+
+
+def delta3_metric(y_true, y_pred):
+    maxRatio = tf.maximum(y_pred / y_true, y_true / y_pred)
+    return float((maxRatio < 1.25 ** 3).float().mean())
+
+
+def fastdepth_for_training():
+    # Pretrained mobilenet on imagenet dataset
+    model = make_fastdepth_functional('imagenet')
+    return model.compile(optimizer=keras.optimizers.SGD(momentum=0.9),
+                         loss=keras.losses.MSE(),
+                         metrics=[keras.metrics.RootMeanSquaredError(),
+                                  keras.metrics.MeanSquaredError(),
+                                  delta1_metric,
+                                  delta2_metric,
+                                  delta3_metric])
+
+
+def train_compiled_model(compiled_model, dataset):
+    """
+
+    :param compiled_model: Compiled model to train on
+    :param dataset: Dataset to train on (must be compatible with model
+    :return:
+    """
+    # TODO: Use tf nyu_v2 dataset to train.
+    pass
+
+
 if __name__ == '__main__':
     make_fastdepth_functional().summary()
diff --git a/main.py b/main.py
index 62b1885..2bdb6ca 100644
--- a/main.py
+++ b/main.py
@@ -7,13 +7,5 @@ def load_nyu():
     return builder.as_dataset(split='train', shuffle_files=True)
 
 
-def print_hi(name):
-    # Use a breakpoint in the code line below to debug your script.
-    print(f'Hi, {name}')  # Press Ctrl+F8 to toggle the breakpoint.
-
-
-# Press the green button in the gutter to run the script.
 if __name__ == '__main__':
     load_nyu()
-
-# See PyCharm help at https://www.jetbrains.com/help/pycharm/