diff --git a/exp/kerokero/train.py b/exp/kerokero/train.py
--- a/exp/kerokero/train.py
+++ b/exp/kerokero/train.py
@@ -4,9 +4,11 @@ import argparse
 import logging as log
 
 import numpy as np
-from keras.layers import Conv2D,Dropout,Dense,Flatten,MaxPooling2D,BatchNormalization
-from keras.models import Sequential,load_model
+from keras.layers import Conv2D,Dropout,Dense,Flatten,MaxPooling2D,GlobalAveragePooling2D,BatchNormalization
+from keras.models import Sequential,load_model,Model
+from keras.optimizers import SGD
 from keras.callbacks import TensorBoard
+from keras.applications.inception_v3 import InceptionV3,preprocess_input
 
 import config as cfg
 import ftp
@@ -17,6 +19,7 @@ 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)
+parser.add_argument("--log_dir",default="/tmp/tflogs")
 args=parser.parse_args()
 
 
@@ -72,24 +75,50 @@ def createCNN():
 	return model
 
 
-model=createCNN()
+def createPretrained():
+	base=InceptionV3(weights="imagenet",include_top=False,input_shape=(224,224,3))
+
+	x=base.output
+	x=GlobalAveragePooling2D()(x)
+	x=Dense(1024,activation="relu")(x)
+	predictions=Dense(8)(x)
+
+	model=Model(inputs=base.input,outputs=predictions)
+	for layer in base.layers:
+		layer.trainable=False
+
+	model.compile(optimizer='adam',loss='mse',metrics=['mae','accuracy'])
+	return model
+
+
 if args.load_model:
 	model=load_model(args.load_model)
+else:
+	model=createPretrained()
 
 log.info("loading data...")
 with np.load(args.data) as data:
-	trainImages=data["trainImages"]
+	trainImages=preprocess_input(data["trainImages"])
 	trainLabels=data["trainLabels"]
-	testImages=data["testImages"]
+	testImages=preprocess_input(data["testImages"])
 	testLabels=data["testLabels"]
 log.info("done")
 
-tensorboard = TensorBoard(log_dir=os.path.join(cfg.thisDir,"../logs","{}".format(time())))
-BIG_STEP=50
+tensorboard = TensorBoard(log_dir=os.path.join(args.log_dir,"{}".format(time())))
+
+if not args.load_model:
+	model.fit(trainImages.reshape((-1,224,224,3)),trainLabels,epochs=10,batch_size=128,validation_split=0.2,callbacks=[tensorboard])
+for layer in model.layers[:249]:
+	layer.trainable = False
+for layer in model.layers[249:]:
+	layer.trainable = True
+model.compile(optimizer=SGD(lr=0.0001,momentum=0.9),loss='mse')
+
+BIG_STEP=20
 for i in range(args.initial_epoch//BIG_STEP,args.epochs//BIG_STEP):
-	model.fit(trainImages.reshape((-1,224,224,1)),trainLabels,epochs=(i+1)*BIG_STEP,initial_epoch=i*BIG_STEP,batch_size=128,validation_split=0.2,callbacks=[tensorboard])
+	model.fit(trainImages.reshape((-1,224,224,3)),trainLabels,epochs=(i+1)*BIG_STEP,initial_epoch=i*BIG_STEP,batch_size=128,validation_split=0.2,callbacks=[tensorboard])
 	path=args.save_model.format((i+1)*BIG_STEP)
 	log.info("saving model...")
 	model.save(path)
 	# ftp.push(path)
-log.info(model.evaluate(testImages.reshape((-1,224,224,1)),testLabels))
+log.info(model.evaluate(testImages.reshape((-1,224,224,3)),testLabels))