diff --git a/exp/kerokero/train.py b/exp/kerokero/train.py
--- a/exp/kerokero/train.py
+++ b/exp/kerokero/train.py
@@ -5,8 +5,8 @@ import argparse
 import logging as log
 
 import numpy as np
-from keras.layers import Conv2D,Dropout,Dense,Flatten,MaxPooling2D,BatchNormalization,GlobalAveragePooling2D,Reshape,concatenate
-from keras.models import Sequential,load_model,Model,Input
+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.metrics
 
@@ -73,41 +73,10 @@ def createCNN():
 	return model
 
 
-def createHinted():
-	input=Input((224,224,1))
-	base=load_model(args.load_hints)
-	for layer in base.layers:
-		layer.trainable=False
-	hints=base(input)
-
-	x=BatchNormalization()(input)
-	x=Conv2D(24,(5,5),padding="same",kernel_initializer="he_normal",activation="relu",input_shape=(224,224,1),data_format="channels_last")(x)
-	x=MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid")(x)
-	x=Conv2D(36,(5,5),activation="relu")(x)
-	x=MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid")(x)
-	x=Conv2D(48,(5,5),activation="relu")(x)
-	x=MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid")(x)
-	x=Conv2D(64,(3,3),activation="relu")(x)
-	x=MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid")(x)
-	x=Conv2D(64,(3,3),activation="relu")(x)
-	x=GlobalAveragePooling2D()(x)
-
-	x=concatenate([x,Flatten()(hints)])
-	x=Dense(500,activation="relu")(x)
-	x=Dense(90,activation="relu")(x)
-
-	predictions=Reshape((4,2))(Dense(8)(x))
-
-	model=Model(inputs=input,outputs=predictions)
-
-	model.compile(optimizer='rmsprop',loss=averageDistance,metrics=['mae','accuracy'])
-	return model
-
-
 if args.load_model:
 	model=load_model(args.load_model)
 else:
-	model=createHinted()
+	model=createCNN()
 	model.summary()
 
 log.info("loading data...")