diff --git a/exp/kerokero/train.py b/exp/kerokero/train.py
--- a/exp/kerokero/train.py
+++ b/exp/kerokero/train.py
@@ -4,11 +4,9 @@ import argparse
 import logging as log
 
 import numpy as np
-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.layers import Conv2D,Dropout,Dense,Flatten,MaxPooling2D,BatchNormalization,GlobalAveragePooling2D
+from keras.models import Sequential,load_model
 from keras.callbacks import TensorBoard
-from keras.applications.inception_v3 import InceptionV3,preprocess_input
 
 import config as cfg
 import ftp
@@ -41,84 +39,51 @@ def createFullyConnected():
 
 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(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(36,(5,5),activation="relu"))
+	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),border_mode="valid"))
+
+	model.add(Conv2D(48,(5,5),activation="relu"))
+	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),border_mode="valid"))
+
+	model.add(Conv2D(64,(3,3),activation="relu"))
+	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),border_mode="valid"))
+
+	model.add(Conv2D(64,(3,3),activation="relu"))
+
+	model.add(GlobalAveragePooling2D())
+
 	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(90,activation="relu"))
 	model.add(Dense(8))
 
-	model.compile(optimizer='adam',loss='mse',metrics=['mae','accuracy'])
+	model.compile(optimizer="rmsprop",loss="mse",metrics=["mae","accuracy"])
 	return model
 
 
-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
-
-
+model=createCNN()
 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=preprocess_input(data["trainImages"])
+	trainImages=data["trainImages"]
 	trainLabels=data["trainLabels"]
-	testImages=preprocess_input(data["testImages"])
+	testImages=data["testImages"]
 	testLabels=data["testLabels"]
 log.info("done")
 
-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')
-
+tensorboard = TensorBoard(log_dir=os.path.join(cfg.thisDir,"../logs","{}".format(time())))
 BIG_STEP=20
 for i in range(args.initial_epoch//BIG_STEP,args.epochs//BIG_STEP):
-	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])
+	model.fit(trainImages.reshape((-1,224,224,1)),trainLabels,epochs=(i+1)*BIG_STEP,initial_epoch=i*BIG_STEP,batch_size=20,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((-1,224,224,3)),testLabels))
+log.info(model.evaluate(testImages.reshape((-1,224,224,1)),testLabels))