diff --git a/exp/kerokero/train.py b/exp/kerokero/train.py
--- a/exp/kerokero/train.py
+++ b/exp/kerokero/train.py
@@ -1,6 +1,6 @@
 import argparse
 
-from keras.layers import Conv2D,Dropout,Dense,Flatten
+from keras.layers import Conv2D,Dropout,Dense,Flatten,MaxPooling2D,BatchNormalization
 from keras.models import Sequential,load_model
 
 from prepare_data import loadDataset
@@ -14,19 +14,60 @@ parser.add_argument("--epochs",type=int,
 parser.add_argument("--initial_epoch",type=int,default=0)
 args=parser.parse_args()
 
-model=Sequential([
-	Flatten(input_shape=(224,224)),
-	Dense(128, activation="relu"),
-	Dropout(0.1),
-	Dense(64, activation="relu"),
-	Dense(8)
-])
+
+def createFullyConnected():
+	model=Sequential([
+		Flatten(input_shape=(224,224)),
+		Dense(128, activation="relu"),
+		Dropout(0.1),
+		Dense(64, activation="relu"),
+		Dense(8)
+	])
+
+	model.compile(
+		optimizer='adam',
+		loss='mse',
+		metrics=['mae','accuracy']
+	)
+	return model
 
-model.compile(
-	optimizer='adam',
-	loss='mse',
-	metrics=['mae','accuracy']
-)
+def createCNN():
+	model=Sequential()
+	
+	model.add(Conv2D(filters=16,kernel_size=2,padding="same",activation="relu",input_shape=(224,224,1)))
+	model.add(Dropout(0.1))
+	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))
+	model.add(BatchNormalization())
+	
+	model.add(Conv2D(32,(5,5),activation="relu"))
+	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))
+	model.add(Dropout(0.2))
+	model.add(BatchNormalization())
+	
+	model.add(Conv2D(64,(5,5),activation="relu"))
+	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))
+	model.add(BatchNormalization())
+	
+	model.add(Conv2D(128,(3,3),activation="relu"))
+	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))
+	model.add(Dropout(0.4))
+	model.add(BatchNormalization())
+	
+	model.add(Flatten())
+	
+	model.add(Dense(500, activation="relu"))
+	model.add(Dropout(0.1))
+	
+	model.add(Dense(128, activation="relu"))
+	model.add(Dropout(0.1))
+	
+	model.add(Dense(8))
+
+	model.compile(optimizer='adam',loss='mse',metrics=['mae','accuracy'])
+	return model
+
+
+model=createCNN()
 if args.load_model:
 	model=load_model(args.load_model)
 
@@ -35,6 +76,6 @@ print("loading data...")
 print("done")
 
 for i in range(args.initial_epoch,args.epochs//10):
-	model.fit(trainImages,trainLabels,epochs=(i+1)*10,initial_epoch=i*10,batch_size=128,validation_split=1/9)
+	model.fit(trainImages.reshape((-1,224,224,1)),trainLabels,epochs=(i+1)*10,initial_epoch=i*10,batch_size=128,validation_split=0.2)
 	model.save(args.save_model.format(i+1))
 print(model.evaluate(testImages,testLabels))