diff --git a/exp/kerokero/train.py b/exp/kerokero/train.py
--- a/exp/kerokero/train.py
+++ b/exp/kerokero/train.py
@@ -4,11 +4,17 @@ import argparse
 import logging as log
 
 import numpy as np
-from keras.layers import Conv2D,Dropout,Dense,Flatten,MaxPooling2D,BatchNormalization,GlobalAveragePooling2D
+from keras.layers import Conv2D,Dropout,Dense,Flatten,MaxPooling2D,BatchNormalization,GlobalAveragePooling2D,Reshape
 from keras.models import Sequential,load_model
 from keras.callbacks import TensorBoard,ModelCheckpoint
+import keras.losses
+import keras.metrics
 
 import config as cfg
+from k_util import averageDistance
+
+keras.losses.averageDistance=averageDistance
+keras.metrics.averageDistance=averageDistance
 
 parser=argparse.ArgumentParser()
 parser.add_argument("data")
@@ -41,17 +47,17 @@ def createCNN():
 
 	model.add(BatchNormalization(input_shape=(224,224,1)))
 
-	model.add(Conv2D(24,(5,5),border_mode="same",init="he_normal",activation="relu",input_shape=(224,224,1),dim_ordering="tf"))
-	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),border_mode="valid"))
+	model.add(Conv2D(24,(5,5),padding="same",kernel_initializer="he_normal",activation="relu",input_shape=(224,224,1),data_format="channels_last"))
+	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))
 
 	model.add(Conv2D(36,(5,5),activation="relu"))
-	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),border_mode="valid"))
+	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))
 
 	model.add(Conv2D(48,(5,5),activation="relu"))
-	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),border_mode="valid"))
+	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))
 
 	model.add(Conv2D(64,(3,3),activation="relu"))
-	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),border_mode="valid"))
+	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))
 
 	model.add(Conv2D(64,(3,3),activation="relu"))
 
@@ -60,8 +66,9 @@ def createCNN():
 	model.add(Dense(500,activation="relu"))
 	model.add(Dense(90,activation="relu"))
 	model.add(Dense(8))
+	model.add(Reshape((4,2)))
 
-	model.compile(optimizer="rmsprop",loss="mse",metrics=["mae","accuracy"])
+	model.compile(optimizer="rmsprop",loss=averageDistance,metrics=["mae","accuracy"])
 	return model
 
 
@@ -82,7 +89,7 @@ checkpoint=ModelCheckpoint(args.save_mod
 
 model.fit(
 	trainImages.reshape((-1,224,224,1)),
-	trainLabels,
+	trainLabels.reshape((-1,4,2)),
 	epochs=args.epochs,
 	initial_epoch=args.initial_epoch,
 	batch_size=20,
@@ -90,4 +97,4 @@ model.fit(
 	callbacks=[tensorboard,checkpoint]
 )
 
-log.info(model.evaluate(testImages.reshape((-1,224,224,1)),testLabels))
+log.info(model.evaluate(testImages.reshape((-1,224,224,1)),testLabels.reshape((-1,4,2))))