OneEye Changeset - 4381957e967e

Changeset - 4381957e967e

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Laman - 6 years ago 2019-05-09 22:25:27

keras checkpoint saving

1 file changed with 14 insertions and 10 deletions:

exp/kerokero/train.py

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exp/kerokero/train.py

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 import os
 from time import time
 import argparse
 import logging as log
 import numpy as np
 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.callbacks import TensorBoard,ModelCheckpoint
 import config as cfg
 import ftp
 parser=argparse.ArgumentParser()
 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)
 parser.add_argument("--log_dir",default="/tmp/tflogs")
 args=parser.parse_args()
 def createFullyConnected():
 	model=Sequential([
 		Flatten(input_shape=(224,224)),
 		Dense(128, activation="relu"),
 		Dropout(0.1),
 		Dense(64, activation="relu"),
 		Dense(8)
 	])
 	model.compile(
 		optimizer='adam',
 		loss='mse',
 		metrics=['mae','accuracy']
+	)
 	return model
 def createCNN():
 	model=Sequential()
 	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(Dense(90,activation="relu"))
 	model.add(Dense(8))
 	model.compile(optimizer="rmsprop",loss="mse",metrics=["mae","accuracy"])
 	return model
 model=createCNN()
 if args.load_model:
 	model=load_model(args.load_model)
 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")
 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((-1,224,224,1)),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)
 tensorboard=TensorBoard(log_dir=os.path.join(args.log_dir,"{}".format(time())))
 checkpoint=ModelCheckpoint(args.save_model,monitor="val_loss",period=10)
 model.fit(
 	trainImages.reshape((-1,224,224,1)),
 	trainLabels,
 	epochs=args.epochs,
 	initial_epoch=args.initial_epoch,
 	batch_size=20,
 	validation_split=0.2,
 	callbacks=[tensorboard,checkpoint]
+)
 log.info(model.evaluate(testImages.reshape((-1,224,224,1)),testLabels))

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