2017-02-09 82 views
0

TF-苗條inceptionv3火車tensorflow TF-苗條inceptionv3訓練損耗曲線奇怪的是從頭

我用纖細/ train_image_classifier.py訓練對我自己的數據集inception_v3模型: 蟒蛇train_image_classifier.py --train_dir = $ {TRAIN_DIR} --dataset_name = mydataset --dataset_split_name =列車--dataset_dir = $ {DATASET_DIR} --model_name = inception_v3 --num_clones = 2

如圖所示tensorboard損失曲線很奇怪,這是一個線性用一個小凸點部分減少直線。 enter image description here

下面是最後輸出,損失降低0.0001每20或30以下步驟:

INFO:tensorflow:global step 34590: loss = 0.5359 (1.17 sec/step) 
INFO:tensorflow:global step 34600: loss = 0.5358 (1.15 sec/step) 
INFO:tensorflow:global step 34590: loss = 0.5359 (1.17 sec/step) 
INFO:tensorflow:global step 34600: loss = 0.5358 (1.15 sec/step) 
INFO:tensorflow:global step 34610: loss = 0.5358 (1.17 sec/step) 
INFO:tensorflow:global step 34620: loss = 0.5357 (1.12 sec/step) 
INFO:tensorflow:global step 34630: loss = 0.5357 (1.16 sec/step) 
INFO:tensorflow:global step 34640: loss = 0.5356 (1.16 sec/step) 
INFO:tensorflow:global step 34650: loss = 0.5356 (1.16 sec/step) 
INFO:tensorflow:global step 34660: loss = 0.5355 (1.15 sec/step) 
INFO:tensorflow:global step 34670: loss = 0.5355 (1.15 sec/step) 
INFO:tensorflow:global step 34680: loss = 0.5355 (1.18 sec/step) 
INFO:tensorflow:global step 34690: loss = 0.5354 (1.17 sec/step) 
INFO:tensorflow:global step 34700: loss = 0.5354 (1.15 sec/step) 
INFO:tensorflow:global step 34710: loss = 0.5353 (1.15 sec/step) 
INFO:tensorflow:global step 34720: loss = 0.5353 (2.25 sec/step) 
INFO:tensorflow:global step 34730: loss = 0.5353 (2.22 sec/step) 
INFO:tensorflow:global step 34740: loss = 0.5352 (1.16 sec/step) 
INFO:tensorflow:global step 34750: loss = 0.5352 (1.16 sec/step) 
INFO:tensorflow:global step 34760: loss = 0.5351 (1.18 sec/step) 
INFO:tensorflow:global step 34770: loss = 0.5351 (1.15 sec/step) 
INFO:tensorflow:global step 34780: loss = 0.5350 (1.17 sec/step) 
INFO:tensorflow:global step 34790: loss = 0.5350 (1.15 sec/step) 
INFO:tensorflow:global step 34800: loss = 0.5349 (1.12 sec/step) 
INFO:tensorflow:global step 34810: loss = 0.5349 (1.12 sec/step) 
INFO:tensorflow:global step 34820: loss = 0.5349 (1.16 sec/step) 
INFO:tensorflow:global step 34830: loss = 0.5348 (1.16 sec/step) 
INFO:tensorflow:global step 34840: loss = 0.5348 (1.18 sec/step) 
INFO:tensorflow:global step 34850: loss = 0.5347 (1.12 sec/step) 
INFO:tensorflow:global step 34860: loss = 0.5347 (1.12 sec/step) 
INFO:tensorflow:global step 34870: loss = 0.5347 (1.18 sec/step) 
INFO:tensorflow:global step 34880: loss = 0.5346 (1.13 sec/step) 
INFO:tensorflow:global step 34890: loss = 0.5346 (1.18 sec/step) 
INFO:tensorflow:global step 34900: loss = 0.5345 (1.16 sec/step) 
INFO:tensorflow:global step 34910: loss = 0.5345 (1.15 sec/step) 
INFO:tensorflow:global step 34920: loss = 0.5344 (1.17 sec/step) 
INFO:tensorflow:global step 34930: loss = 0.5344 (1.14 sec/step) 
INFO:tensorflow:global step 34940: loss = 0.5344 (1.15 sec/step) 
INFO:tensorflow:global step 34950: loss = 0.5343 (1.14 sec/step) 
INFO:tensorflow:global step 34960: loss = 0.5343 (1.17 sec/step) 

mydataset.py是相同,除了flowers.py:

SPLITS_TO_SIZES = {'train': 18000000, 'validation': 400000} 
_NUM_CLASSES = 4 

這是正常的嗎?謝謝你的幫助。

回答

0

您在訓練中繪製n步後的總損失圖(如果您使用tf.contrib.slim訓練方法,這可能是number_of_steps),而記錄的損失是每10步執行一次。 希望這有助於!