tensorflow 中的 Mean_iou 未更新/导致正确值

Question

我在 tensorflow 实现了一个 U.NET 版本，试图从卫星图像中识别建筑物。 该实施正在发挥作用，并在分类方面取得了可喜的成果。 除 mean_iou 外，所有指标似乎都正常工作。 无论超参数和从数据集中选择的图像如何，mean_iou 始终相同。 该值类似于每个纪元后的 15 个小数点。

与 mean_iou 相比，准确率和召回率值要高得多，而且应该是预期的，所以似乎有些东西没有按预期工作。

由于我对 tensorflow 比较陌生，所以错误可能完全不同，但我是来学习的。 所有反馈将不胜感激。

这是 model 培训的相关代码和打印输出。

import numpy as np
import tensorflow as tf
from unet_model import build_unet
from data import load_dataset, tf_dataset
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, CSVLogger, EarlyStopping

model_types = ['segnet-master', 'unet-master', 'simpler', 'even-simpler']

if __name__ == "__main__":
    """ Hyperparamaters """
    dataset_path = "building-segmentation"
    input_shape = (64, 64, 3)
    batch_size = 20
    model = 3
    epochs = 5
    res = 64
    lr = 1e-3
    model_path = f"unet_models/unet_{epochs}_epochs_{res}.h5"
    csv_path = f"csv/data_unet_{epochs}_{res}.csv"

    """ Load the dataset """
    (train_images, train_masks), (val_images, val_masks) = load_dataset(dataset_path)

    train_dataset = tf_dataset(train_images, train_masks, batch=batch_size)
    val_dataset = tf_dataset(val_images, val_masks, batch=batch_size)


    model = build_unet(input_shape)

    model.compile(
        loss="binary_crossentropy",
        optimizer=tf.keras.optimizers.Adam(lr),
        metrics=[
            tf.keras.metrics.MeanIoU(num_classes=2),
            tf.keras.metrics.IoU(num_classes=2, target_class_ids=[0]),
            tf.keras.metrics.Recall(),
            tf.keras.metrics.Precision()
        ]
    )


    callbacks = [
        ModelCheckpoint(model_path, monitor="val_loss", verbose=1),
        ReduceLROnPlateau(monitor="val_loss", patience=10, factor=0.1, verbose=1),
        CSVLogger(csv_path),
        EarlyStopping(monitor="val_loss", patience=10)
    ]

    train_steps = len(train_images)//batch_size
    if len(train_images) % batch_size != 0:
        train_steps += 1

    test_steps = len(val_images)//batch_size
    if len(val_images) % batch_size != 0:
        test_steps += 1

    model.fit(
        train_dataset,
        validation_data=val_dataset,
        epochs=epochs,
        steps_per_epoch=train_steps,
        validation_steps=test_steps,
        callbacks=callbacks
    )

时代	损失	lr	mean_io_u	精确	记起	val_loss	val_mean_io_u	val_precision	val_recall
0	0.41137945652008057	0.001	0.37184661626815796	0.695444643497467	0.5243006944656372	0.87176513671875	0.37157535552978516	0.38247567415237427	0.9118495583534241
1个	0.3461640477180481	0.001	0.37182655930519104	0.7579150795936584	0.6075601577758789	0.3907579183578491	0.37157535552978516	0.8406943082809448	0.5024654865264893
2个	0.3203786611557007	0.001	0.37182655930519104	0.7694798707962036	0.6599727272987366	0.3412915766239166	0.37157535552978516	0.6986522674560547	0.7543279528617859
3个	0.2999393939971924	0.001	0.37182655930519104	0.7859976887702942	0.6890525221824646	0.40518054366111755	0.37157535552978516	0.6738141775131226	0.6654454469680786
4个	0.28737708926200867	0.001	0.37182655930519104	0.793653130531311	0.7092126607894897	0.37544798851013184	0.37157535552978516	0.621263325214386	0.768422544002533
5个	0.27629318833351135	0.001	0.37182655930519104	0.8028419613838196	0.72260981798172	0.4055494964122772	0.37157535552978516	0.8477562665939331	0.5473824143409729
6个	0.2665417492389679	0.001	0.37182655930519104	0.809609055519104	0.7353982329368591	0.33294594287872314	0.37157535552978516	0.7307689785957336	0.6933897733688354
7	0.25887876749038696	0.001	0.37182655930519104	0.8132126927375793	0.744954526424408	0.28797024488449097	0.37157535552978516	0.7534120082855225	0.7735632061958313
8个	0.25271594524383545	0.001	0.37182655930519104	0.8179733753204346	0.7538670897483826	0.30249008536338806	0.37157535552978516	0.8644329905509949	0.6237345337867737
9	0.24556593596935272	0.001	0.37182655930519104	0.8207928538322449	0.7622584104537964	0.3576349914073944	0.37157535552978516	0.6576451063156128	0.8346141576766968
10	0.23954670131206512	0.001	0.37182655930519104	0.8256030082702637	0.769091010093689	0.2541409134864807	0.37157535552978516	0.8100516200065613	0.7633218765258789
11	0.2349284589290619	0.001	0.37182655930519104	0.8274455070495605	0.7762861847877502	0.24383187294006348	0.37157535552978516	0.795067310333252	0.8124401569366455
12	0.22480393946170807	0.001	0.37182655930519104	0.8354562520980835	0.787416398525238	0.3778316378593445	0.37157535552978516	0.6533672213554382	0.8588836789131165
13	0.22573505342006683	0.001	0.37182655930519104	0.8342418670654297	0.7852107882499695	0.3342073857784271	0.37157535552978516	0.6768029928207397	0.7917631268501282
14	0.21639415621757507	0.001	0.37182655930519104	0.8411555886268616	0.7972605228424072	0.2792396545410156	0.37157535552978516	0.7611830234527588	0.7955203652381897
15	0.21154287457466125	0.001	0.37182655930519104	0.8441442251205444	0.8019176125526428	0.27426305413246155	0.37157535552978516	0.8764772415161133	0.6708933115005493
16	0.20740143954753876	0.001	0.37182655930519104	0.8469985127449036	0.8068550825119019	0.367437481880188	0.37157535552978516	0.646026611328125	0.8527452945709229
17	0.2005360722541809	0.001	0.37182655930519104	0.8522992134094238	0.8129924535751343	0.22591133415699005	0.37157535552978516	0.8203750252723694	0.8089460730552673
18	0.1976771354675293	0.001	0.37182655930519104	0.853760302066803	0.8163849115371704	0.2331937551498413	0.37157535552978516	0.807687520980835	0.8157453536987305
19	0.19583451747894287	0.001	0.37182655930519104	0.8560215830802917	0.8190248012542725	0.2519392669200897	0.37157535552978516	0.7935053110122681	0.8000433444976807
20	0.1872621327638626	0.001	0.37182655930519104	0.8615736365318298	0.8263705372810364	0.22855037450790405	0.37157535552978516	0.7948822975158691	0.8500961065292358
21	0.1852150857448578	0.001	0.37182655930519104	0.8620718717575073	0.8289932012557983	0.2352440059185028	0.37157535552978516	0.7972174286842346	0.8323403000831604
22	0.17845036089420319	0.001	0.37182655930519104	0.8677510023117065	0.8351714611053467	0.21090157330036163	0.37157535552978516	0.8470866084098816	0.8098670244216919
23	0.1732502579689026	0.001	0.37182655930519104	0.8711428046226501	0.8414102792739868	0.32612740993499756	0.37157535552978516	0.8412857055664062	0.695543646812439
24	0.17396509647369385	0.001	0.37182655930519104	0.8704758882522583	0.840953528881073	0.2149643898010254	0.37157535552978516	0.8315027952194214	0.8180400729179382
25	0.1740695685148239	0.001	0.37182655930519104	0.8702647089958191	0.8410759568214417	0.2138184905052185	0.37157535552978516	0.8604387044906616	0.7878146171569824
26	0.16104143857955933	0.001	0.37182655930519104	0.8794053196907043	0.8530260324478149	0.23256370425224304	0.37157535552978516	0.8179659843444824	0.8145195841789246
27	0.15866029262542725	0.001	0.37182655930519104	0.8813797831535339	0.8556373119354248	0.21111807227134705	0.37157535552978516	0.8566364049911499	0.805817723274231
28	0.15867507457733154	0.001	0.37182655930519104	0.8811318874359131	0.8551875352859497	0.2091868668794632	0.37157535552978516	0.8498891592025757	0.8088852763175964
29	0.15372247993946075	0.001	0.37182655930519104	0.884833574295044	0.8602938055992126	0.2100905030965805	0.37157535552978516	0.8543928265571594	0.8121073246002197
30	0.1550114005804062	0.001	0.37182655930519104	0.8840479850769043	0.85946124792099	0.21207265555858612	0.37157535552978516	0.8512551784515381	0.814805269241333
31	0.14192143082618713	0.001	0.37182655930519104	0.8927850127220154	0.8717316389083862	0.21726688742637634	0.37157535552978516	0.8147332072257996	0.8602878451347351
32	0.1401694267988205	0.001	0.37182655930519104	0.8940809965133667	0.8732201457023621	0.21714988350868225	0.37157535552978516	0.8370103240013123	0.8307888507843018
33	0.13880570232868195	0.001	0.37182655930519104	0.8950505256652832	0.8743049502372742	0.23316830396652222	0.37157535552978516	0.8291308283805847	0.8264546990394592
34	0.14308543503284454	0.001	0.37182655930519104	0.892676830291748	0.8704872131347656	0.2735193967819214	0.37157535552978516	0.7545790076255798	0.8698106408119202
35	0.14015090465545654	0.001	0.37182655930519104	0.8939213752746582	0.8743175864219666	0.20235474407672882	0.37157535552978516	0.8535885810852051	0.8286886215209961
36	0.1288939267396927	0.001	0.37182655930519104	0.9015076756477356	0.8844809532165527	0.22387968003749847	0.37157535552978516	0.8760555982589722	0.7937673926353455
37	0.12568938732147217	0.001	0.37182655930519104	0.9041174054145813	0.8872519731521606	0.21494744718074799	0.37157535552978516	0.8468613028526306	0.8249993324279785
38	0.12176792323589325	0.001	0.37182655930519104	0.9065613746643066	0.8911336064338684	0.23827765882015228	0.37157535552978516	0.8391880989074707	0.8176671862602234
39	0.11993639171123505	0.001	0.37182655930519104	0.9084023237228394	0.8925207257270813	0.22297391295433044	0.37157535552978516	0.8404833674430847	0.8346469402313232
40	0.11878598481416702	0.001	0.37182655930519104	0.9090615510940552	0.8941413164138794	0.22415445744991302	0.37157535552978516	0.8580552339553833	0.8152300715446472
41	0.1256236732006073	0.001	0.37182655930519104	0.9046309590339661	0.8880045413970947	0.20100584626197815	0.37157535552978516	0.8520526885986328	0.8423823714256287
42	0.10843898355960846	0.001	0.37182655930519104	0.9163806438446045	0.903978168964386	0.21887923777103424	0.37157535552978516	0.86836838722229	0.8237167596817017
43	0.10670299828052521	0.001	0.37182655930519104	0.9178842902183533	0.9054436683654785	0.21005834639072418	0.37157535552978516	0.8679876327514648	0.8253417611122131
44	0.10276217758655548	0.001	0.37182655930519104	0.9207708239555359	0.909300684928894	0.2151617556810379	0.37157535552978516	0.8735089302062988	0.8225894570350647
45	0.10141195356845856	0.001	0.3718271255493164	0.9218501448631287	0.9108821749687195	0.22106514871120453	0.37157535552978516	0.8555923700332642	0.8328163623809814
46	0.09918847680091858	0.001	0.37182655930519104	0.9235833883285522	0.9129346609115601	0.23230132460594177	0.37157535552978516	0.8555824756622314	0.8224022388458252
47	0.10588783025741577	0.001	0.37182655930519104	0.9191931486129761	0.9068878293037415	0.22423967719078064	0.37157535552978516	0.8427634239196777	0.825032114982605
48岁	0.103585384786129	0.001	0.37182655930519104	0.9209527969360352	0.9087461233139038	0.2110774666070938	0.37157535552978516	0.8639764785766602	0.8252225518226624
49	0.09157560020685196	0.001	0.37182655930519104	0.9292182922363281	0.9203035831451416	0.22161123156547546	0.37157535552978516	0.8649827837944031	0.8406093120574951
50	0.08616402745246887	0.001	0.37182655930519104	0.9334553480148315	0.9252204298973083	0.2387685328722	0.37157535552978516	0.8806527256965637	0.811405599117279
51	0.0846954956650734	0.001	0.37182655930519104	0.9345796704292297	0.9265674352645874	0.22581790387630463	0.37157535552978516	0.8756505846977234	0.8313769698143005

Answer 1

对于二元问题，还有一个名为 tf.keras.metrics.BinaryIoU(name='IoU') 的 IOU。 这可能会解决问题。

我在从 tf.keras.metrics.MeanIoU 移动到 tf.keras.metrics.OneHotMeanIoU 后解决了多 class 分段的相同问题，因为我使用的是一个热编码标签。