Changeset - 9483b964f560
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Laman - 6 years ago 2019-05-05 13:03:27

saving and loading prepared data
3 files changed with 33 insertions and 14 deletions:
exp/kerokero/prepare_data.py
8
3
exp/kerokero/test.py
6
2
exp/kerokero/train.py
19
9
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exp/kerokero/prepare_data.py
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@@ -113,6 +113,11 @@ def show(img,filename="x"):
 

			




	
	
	
		
 

			




	
	
	
		
 
if __name__=="__main__":

			




	
	
	
		
 
	root=sys.argv[1]

			




	
	
	
		
 
	for d in traverseDirs(root):

			




	
	
	
		
 
		harvestDir(d)

			




	
	
	
		
 
	((trainImages,trainLabels),(testImages,testLabels))=loadDataset(sys.argv[1])

			




	
	
	
		
 
	np.savez_compressed(

			




	
	
	
		
 
		sys.argv[2],

			




	
	
	
		
 
		trainImages=trainImages,

			




	
	
	
		
 
		trainLabels=trainLabels,

			




	
	
	
		
 
		testImages=testImages,

			




	
	
	
		
 
		testLabels=testLabels

			




	
	
	
		
 
	)

			




        

    


    
    

    

        

                

                    exp/kerokero/test.py
                

                

                  
                      
                        

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@@ -10,13 +10,17 @@ from analyzer.corners import Corners

			




	
	
	
		
 

			




	
	
	
		
 
parser=argparse.ArgumentParser()

			




	
	
	
		
 
parser.add_argument("model")

			




	
	
	
		
 
parser.add_argument("data_dir")

			




	
	
	
		
 
parser.add_argument("data")

			




	
	
	
		
 
args=parser.parse_args()

			




	
	
	
		
 

			




	
	
	
		
 
model=load_model(args.model)

			




	
	
	
		
 

			




	
	
	
		
 
print("loading data...")

			




	
	
	
		
 
((trainImages,trainLabels),(testImages,testLabels))=loadDataset(args.data_dir)

			




	
	
	
		
 
with np.load(args.data) as data:

			




	
	
	
		
 
	trainImages=data["trainImages"]

			




	
	
	
		
 
	trainLabels=data["trainLabels"]

			




	
	
	
		
 
	testImages=data["testImages"]

			




	
	
	
		
 
	testLabels=data["testLabels"]

			




	
	
	
		
 
print("done")

			




	
	
	
		
 

			




	
	
	
		
 
for img in testImages:

			




        

    


    
    

    

        

                

                    exp/kerokero/train.py
                

                

                  
                      
                        

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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 prepare_data import loadDataset

			




	
	
	
		
 
import ftp

			




	
	
	
		
 

			




	
	
	
		
 
log.basicConfig(level=log.INFO,format="%(asctime)s %(levelname)s: %(message)s")

			




	
	
	
		
 

			




	
	
	
		
 
parser=argparse.ArgumentParser()

			




	
	
	
		
 
parser.add_argument("data_dir")

			




	
	
	
		
 
parser.add_argument("data")

			




	
	
	
		
 
parser.add_argument("--load_model")

			




	
	
	
		
 
parser.add_argument("--save_model",default="/tmp/gogo-{0:03}.h5")

			




	
	
	
		
 
parser.add_argument("--epochs",type=int,default=100)

			




	
	
		
 
@@ -55,10 +58,10 @@ def createCNN():

			




	
	
	
		
 
	

			




	
	
	
		
 
	model.add(Flatten())

			




	
	
	
		
 
	

			




	
	
	
		
 
	model.add(Dense(500, activation="relu"))

			




	
	
	
		
 
	model.add(Dense(500,activation="relu"))

			




	
	
	
		
 
	model.add(Dropout(0.1))

			




	
	
	
		
 
	

			




	
	
	
		
 
	model.add(Dense(128, activation="relu"))

			




	
	
	
		
 
	model.add(Dense(128,activation="relu"))

			




	
	
	
		
 
	model.add(Dropout(0.1))

			




	
	
	
		
 
	

			




	
	
	
		
 
	model.add(Dense(8))

			




	
	
		
 
@@ -71,11 +74,18 @@ model=createCNN()

			




	
	
	
		
 
if args.load_model:

			




	
	
	
		
 
	model=load_model(args.load_model)

			




	
	
	
		
 

			




	
	
	
		
 
print("loading data...")

			




	
	
	
		
 
((trainImages,trainLabels),(testImages,testLabels))=loadDataset(args.data_dir)

			




	
	
	
		
 
print("done")

			




	
	
	
		
 
log.info("loading data...")

			




	
	
	
		
 
with np.load(args.data) as data:

			




	
	
	
		
 
	trainImages=data["trainImages"]

			




	
	
	
		
 
	trainLabels=data["trainLabels"]

			




	
	
	
		
 
	testImages=data["testImages"]

			




	
	
	
		
 
	testLabels=data["testLabels"]

			




	
	
	
		
 
log.info("done")

			




	
	
	
		
 

			




	
	
	
		
 
for i in range(args.initial_epoch,args.epochs//10):

			




	
	
	
		
 
	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))

			




	
	
	
		
 
	path=args.save_model.format(i+1)

			




	
	
	
		
 
	log.info("saving model...")

			




	
	
	
		
 
	model.save(path)

			




	
	
	
		
 
	ftp.push(path)

			




	
	
	
		
 
log.info(model.evaluate(testImages,testLabels))

			




        

    



    


    



    



      

      





    
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