diff --git a/exp/keras/train.py b/exp/kerokero/train.py
copy from exp/keras/train.py
copy to exp/kerokero/train.py
--- a/exp/keras/train.py
+++ b/exp/kerokero/train.py
@@ -1,13 +1,25 @@
+import argparse
+
 from keras.layers import Conv2D,Dropout,Dense,Flatten
-from keras.models import Sequential
+from keras.models import Sequential,load_model
+
+from prepare_data import loadDataset
 
 
-model = Sequential([
-	Flatten(input_shape=(96,96)),
+parser=argparse.ArgumentParser()
+parser.add_argument("data_dir")
+parser.add_argument("--load_model")
+parser.add_argument("--save_model",default="/tmp/gogo-{0:03}.h5")
+parser.add_argument("--epochs",type=int,default=100)
+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(30)
+	Dense(8)
 ])
 
 model.compile(
@@ -15,5 +27,14 @@ model.compile(
 	loss='mse',
 	metrics=['mae','accuracy']
 )
+if args.load_model:
+	model=load_model(args.load_model)
 
-model.fit(X_train,y_train,epochs = 500,batch_size = 128,validation_split = 0.2)
+print("loading data...")
+((trainImages,trainLabels),(testImages,testLabels))=loadDataset(args.data_dir)
+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.save(args.save_model.format(i+1))
+print(model.evaluate(testImages,testLabels))