OneEye Changeset - 9483b964f560

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 31 insertions and 12 deletions:

exp/kerokero/prepare_data.py

exp/kerokero/test.py

exp/kerokero/train.py

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exp/kerokero/prepare_data.py

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@@ @@ -110,9 +110,14 @@ def show(img,filename="x"): @@
 	cv.imshow(filename,img)
 	cv.waitKey(0)
 	cv.destroyAllWindows()
 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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@@ @@ -7,19 +7,23 @@ from prepare_data import loadDataset,Sam @@
 from analyzer.epoint import EPoint
 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:
 	label=model.predict(np.reshape(img,(1,224,224,1)))
 	print(label)
 	points=[]

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)
 parser.add_argument("--initial_epoch",type=int,default=0)
 args=parser.parse_args()
@@ @@ -68,14 +71,21 @@ def createCNN(): @@
 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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