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Keras - 正しいクラス予測を取得する際の問題

2017-09-06 12 views
1

Kerasを使用して2つの異なるカテゴリの画像を分類するためにCNNを構築しました。私が抱えている問題は、トレーニング後に正しい予測を得ることができないということです。Keras - 正しいクラス予測を取得する際の問題

ちょっとした背景... データセットは、78750の大きな例（約95％のCat 1と5％のCat 2）です。これは、Catのオーバーフィッティングが発生していると想定している可能性があります。 1（これは問題だと思うが、データセットのサイズを変更するのは他の多くの理由で難しい）

これを解決するために、私は各畳み込みレイヤーに正規化を追加しましたが、役に立たない。

私の質問はこれです...私は絶対に私のカテゴリのサイズを変更する必要がありますか、または私がCatの過仕掛けと戦うために何かできることはありますか？ 1？ここで

はCNNのためのコードである：ここで

model = Sequential() 
model.add(Conv2D(filters=25, 
       kernel_size=(10, 10), 
       strides=(1, 1), 
       activation='relu', 
       input_shape=input_shape, 
       padding="VALID", 
       kernel_initializer=random_normal(mean=0, stddev=.1), 
       kernel_regularizer=l2(.001))) 
model.add(MaxPooling2D(pool_size=(2, 2), 
         strides=(2, 2))) 

model.add(Conv2D(filters=25, 
       kernel_size=(7, 7), 
       strides=(1, 1), 
       activation='relu', 
       padding="VALID", 
       kernel_initializer=random_normal(mean=0, stddev=.1), 
       kernel_regularizer=l2(.001))) 
model.add(MaxPooling2D(pool_size=(2, 2), 
         strides=(2, 2))) 

model.add(Conv2D(filters=25, 
       kernel_size=(5, 5), 
       strides=(2, 2), 
       activation='relu', 
       padding="VALID", 
       kernel_initializer=random_normal(mean=0, stddev=.1), 
       kernel_regularizer=l2(.001))) 
model.add(MaxPooling2D(pool_size=(2, 2), 
         strides=(1, 1))) 

model.add(Conv2D(filters=25, 
       kernel_size=(5, 5), 
       strides=(2, 2), 
       activation='relu', 
       padding="VALID", 
       kernel_initializer=random_normal(mean=0, stddev=.1), 
       kernel_regularizer=l2(.001))) 

model.add(Flatten()) 
model.add(Dense(2, activation='relu', kernel_initializer=random_normal(mean=0, stddev=.1), kernel_regularizer=l2(.001))) 
model.add(Dense(2, activation='softmax')) 

model.compile(loss=keras.losses.categorical_crossentropy, 
       optimizer=keras.optimizers.sgd(lr=.001, momentum=0.9), 
       metrics=['accuracy'])

EDIT 1

は... 1つのエポックのためのトレーニングを実行しているの出力である

Epoch 1/2 
    500/78750 [..............................] - ETA: 664s - loss: 1.3999 - acc: 0.9460 
1000/78750 [..............................] - ETA: 652s - loss: 1.3713 - acc: 0.9500 
1500/78750 [..............................] - ETA: 648s - loss: 1.3897 - acc: 0.9460 
2000/78750 [..............................] - ETA: 648s - loss: 1.3970 - acc: 0.9420 
2500/78750 [..............................] - ETA: 646s - loss: 1.3965 - acc: 0.9376 
3000/78750 [>.............................] - ETA: 640s - loss: 1.3972 - acc: 0.9373 
3500/78750 [>.............................] - ETA: 636s - loss: 1.3886 - acc: 0.9377 
4000/78750 [>.............................] - ETA: 628s - loss: 1.3886 - acc: 0.9403 
4500/78750 [>.............................] - ETA: 625s - loss: 1.3857 - acc: 0.9400 
5000/78750 [>.............................] - ETA: 619s - loss: 1.3813 - acc: 0.9416 
5500/78750 [=>............................] - ETA: 612s - loss: 1.3773 - acc: 0.9436 
6000/78750 [=>............................] - ETA: 608s - loss: 1.3756 - acc: 0.9447 
6500/78750 [=>............................] - ETA: 606s - loss: 1.3735 - acc: 0.9454 
7000/78750 [=>............................] - ETA: 602s - loss: 1.3733 - acc: 0.9466 
7500/78750 [=>............................] - ETA: 597s - loss: 1.3709 - acc: 0.9481 
8000/78750 [==>...........................] - ETA: 594s - loss: 1.3688 - acc: 0.9480 
8500/78750 [==>...........................] - ETA: 589s - loss: 1.3672 - acc: 0.9485 
9000/78750 [==>...........................] - ETA: 584s - loss: 1.3656 - acc: 0.9491 
9500/78750 [==>...........................] - ETA: 580s - loss: 1.3642 - acc: 0.9491 
10000/78750 [==>...........................] - ETA: 576s - loss: 1.3629 - acc: 0.9497 
10500/78750 [===>..........................] - ETA: 571s - loss: 1.3625 - acc: 0.9494 
11000/78750 [===>..........................] - ETA: 567s - loss: 1.3615 - acc: 0.9495 
11500/78750 [===>..........................] - ETA: 562s - loss: 1.3604 - acc: 0.9496 
12000/78750 [===>..........................] - ETA: 558s - loss: 1.3596 - acc: 0.9496 
12500/78750 [===>..........................] - ETA: 554s - loss: 1.3599 - acc: 0.9496 
13000/78750 [===>..........................] - ETA: 549s - loss: 1.3591 - acc: 0.9494 
13500/78750 [====>.........................] - ETA: 545s - loss: 1.3588 - acc: 0.9496 
14000/78750 [====>.........................] - ETA: 541s - loss: 1.3588 - acc: 0.9496 
14500/78750 [====>.........................] - ETA: 537s - loss: 1.3581 - acc: 0.9497 
15000/78750 [====>.........................] - ETA: 533s - loss: 1.3577 - acc: 0.9497 
15500/78750 [====>.........................] - ETA: 529s - loss: 1.3571 - acc: 0.9503 
16000/78750 [=====>........................] - ETA: 525s - loss: 1.3568 - acc: 0.9502 
16500/78750 [=====>........................] - ETA: 520s - loss: 1.3563 - acc: 0.9498 
17000/78750 [=====>........................] - ETA: 515s - loss: 1.3557 - acc: 0.9500 
17500/78750 [=====>........................] - ETA: 510s - loss: 1.3552 - acc: 0.9501 
18000/78750 [=====>........................] - ETA: 506s - loss: 1.3547 - acc: 0.9504 
18500/78750 [======>.......................] - ETA: 502s - loss: 1.3544 - acc: 0.9504 
19000/78750 [======>.......................] - ETA: 497s - loss: 1.3540 - acc: 0.9502 
19500/78750 [======>.......................] - ETA: 492s - loss: 1.3537 - acc: 0.9502 
20000/78750 [======>.......................] - ETA: 488s - loss: 1.3533 - acc: 0.9501 
20500/78750 [======>.......................] - ETA: 483s - loss: 1.3529 - acc: 0.9497 
21000/78750 [=======>......................] - ETA: 479s - loss: 1.3525 - acc: 0.9496 
21500/78750 [=======>......................] - ETA: 475s - loss: 1.3522 - acc: 0.9500 
22000/78750 [=======>......................] - ETA: 471s - loss: 1.3518 - acc: 0.9498 
22500/78750 [=======>......................] - ETA: 466s - loss: 1.3515 - acc: 0.9497 
23000/78750 [=======>......................] - ETA: 462s - loss: 1.3512 - acc: 0.9499 
23500/78750 [=======>......................] - ETA: 458s - loss: 1.3509 - acc: 0.9496 
24000/78750 [========>.....................] - ETA: 454s - loss: 1.3506 - acc: 0.9495 
24500/78750 [========>.....................] - ETA: 450s - loss: 1.3503 - acc: 0.9499 
25000/78750 [========>.....................] - ETA: 445s - loss: 1.3501 - acc: 0.9501 
25500/78750 [========>.....................] - ETA: 441s - loss: 1.3498 - acc: 0.9500 
26000/78750 [========>.....................] - ETA: 437s - loss: 1.3496 - acc: 0.9501 
26500/78750 [=========>....................] - ETA: 433s - loss: 1.3494 - acc: 0.9503 
27000/78750 [=========>....................] - ETA: 428s - loss: 1.3491 - acc: 0.9501 
27500/78750 [=========>....................] - ETA: 424s - loss: 1.3489 - acc: 0.9501 
28000/78750 [=========>....................] - ETA: 419s - loss: 1.3487 - acc: 0.9501 
28500/78750 [=========>....................] - ETA: 415s - loss: 1.3484 - acc: 0.9503 
29000/78750 [==========>...................] - ETA: 411s - loss: 1.3482 - acc: 0.9503 
29500/78750 [==========>...................] - ETA: 407s - loss: 1.3480 - acc: 0.9501 
30000/78750 [==========>...................] - ETA: 403s - loss: 1.3478 - acc: 0.9503 
30500/78750 [==========>...................] - ETA: 399s - loss: 1.3476 - acc: 0.9501 
31000/78750 [==========>...................] - ETA: 395s - loss: 1.3474 - acc: 0.9502 
31500/78750 [===========>..................] - ETA: 391s - loss: 1.3472 - acc: 0.9501 
32000/78750 [===========>..................] - ETA: 387s - loss: 1.3470 - acc: 0.9501 
32500/78750 [===========>..................] - ETA: 383s - loss: 1.3468 - acc: 0.9502 
33000/78750 [===========>..................] - ETA: 379s - loss: 1.3467 - acc: 0.9501 
33500/78750 [===========>..................] - ETA: 375s - loss: 1.3465 - acc: 0.9501 
34000/78750 [===========>..................] - ETA: 371s - loss: 1.3464 - acc: 0.9503 
34500/78750 [============>.................] - ETA: 367s - loss: 1.3462 - acc: 0.9502 
35000/78750 [============>.................] - ETA: 363s - loss: 1.3461 - acc: 0.9503 
35500/78750 [============>.................] - ETA: 358s - loss: 1.3459 - acc: 0.9503 
36000/78750 [============>.................] - ETA: 354s - loss: 1.3458 - acc: 0.9502 
36500/78750 [============>.................] - ETA: 350s - loss: 1.3456 - acc: 0.9504 
37000/78750 [=============>................] - ETA: 346s - loss: 1.3455 - acc: 0.9504 
37500/78750 [=============>................] - ETA: 341s - loss: 1.3454 - acc: 0.9505 
38000/78750 [=============>................] - ETA: 337s - loss: 1.3452 - acc: 0.9506 
38500/78750 [=============>................] - ETA: 333s - loss: 1.3451 - acc: 0.9506 
39000/78750 [=============>................] - ETA: 329s - loss: 1.3450 - acc: 0.9506 
39500/78750 [==============>...............] - ETA: 325s - loss: 1.3449 - acc: 0.9506 
40000/78750 [==============>...............] - ETA: 321s - loss: 1.3448 - acc: 0.9508 
40500/78750 [==============>...............] - ETA: 317s - loss: 1.3447 - acc: 0.9509 
41000/78750 [==============>...............] - ETA: 313s - loss: 1.3445 - acc: 0.9507 
41500/78750 [==============>...............] - ETA: 309s - loss: 1.3444 - acc: 0.9506 
42000/78750 [===============>..............] - ETA: 304s - loss: 1.3443 - acc: 0.9507 
42500/78750 [===============>..............] - ETA: 300s - loss: 1.3442 - acc: 0.9508 
43000/78750 [===============>..............] - ETA: 296s - loss: 1.3441 - acc: 0.9508 
43500/78750 [===============>..............] - ETA: 292s - loss: 1.3440 - acc: 0.9508 
44000/78750 [===============>..............] - ETA: 287s - loss: 1.3439 - acc: 0.9508 
44500/78750 [===============>..............] - ETA: 283s - loss: 1.3438 - acc: 0.9509 
45000/78750 [================>.............] - ETA: 279s - loss: 1.3438 - acc: 0.9509 
45500/78750 [================>.............] - ETA: 275s - loss: 1.3437 - acc: 0.9511 
46000/78750 [================>.............] - ETA: 271s - loss: 1.3436 - acc: 0.9510 
46500/78750 [================>.............] - ETA: 267s - loss: 1.3435 - acc: 0.9512 
47000/78750 [================>.............] - ETA: 263s - loss: 1.3434 - acc: 0.9513 
47500/78750 [=================>............] - ETA: 259s - loss: 1.3433 - acc: 0.9512 
48000/78750 [=================>............] - ETA: 255s - loss: 1.3432 - acc: 0.9513 
48500/78750 [=================>............] - ETA: 250s - loss: 1.3431 - acc: 0.9512 
49000/78750 [=================>............] - ETA: 246s - loss: 1.3430 - acc: 0.9511 
49500/78750 [=================>............] - ETA: 242s - loss: 1.3429 - acc: 0.9511 
50000/78750 [==================>...........] - ETA: 238s - loss: 1.3428 - acc: 0.9513 
50500/78750 [==================>...........] - ETA: 233s - loss: 1.3428 - acc: 0.9514 
51000/78750 [==================>...........] - ETA: 229s - loss: 1.3427 - acc: 0.9514 
51500/78750 [==================>...........] - ETA: 225s - loss: 1.3426 - acc: 0.9514 
52000/78750 [==================>...........] - ETA: 221s - loss: 1.3427 - acc: 0.9515 
52500/78750 [===================>..........] - ETA: 217s - loss: 1.3426 - acc: 0.9515 
53000/78750 [===================>..........] - ETA: 213s - loss: 1.3425 - acc: 0.9515 
53500/78750 [===================>..........] - ETA: 209s - loss: 1.3425 - acc: 0.9516 
54000/78750 [===================>..........] - ETA: 204s - loss: 1.3424 - acc: 0.9515 
54500/78750 [===================>..........] - ETA: 200s - loss: 1.3423 - acc: 0.9513 
55000/78750 [===================>..........] - ETA: 196s - loss: 1.3423 - acc: 0.9515 
55500/78750 [====================>.........] - ETA: 192s - loss: 1.3422 - acc: 0.9514 
56000/78750 [====================>.........] - ETA: 188s - loss: 1.3421 - acc: 0.9513 
56500/78750 [====================>.........] - ETA: 184s - loss: 1.3420 - acc: 0.9513 
57000/78750 [====================>.........] - ETA: 179s - loss: 1.3420 - acc: 0.9513 
57500/78750 [====================>.........] - ETA: 175s - loss: 1.3419 - acc: 0.9513 
58000/78750 [=====================>........] - ETA: 171s - loss: 1.3419 - acc: 0.9513 
58500/78750 [=====================>........] - ETA: 167s - loss: 1.3418 - acc: 0.9512 
59000/78750 [=====================>........] - ETA: 163s - loss: 1.3417 - acc: 0.9510 
59500/78750 [=====================>........] - ETA: 159s - loss: 1.3417 - acc: 0.9511 
60000/78750 [=====================>........] - ETA: 155s - loss: 1.3416 - acc: 0.9511 
60500/78750 [======================>.......] - ETA: 150s - loss: 1.3415 - acc: 0.9512 
61000/78750 [======================>.......] - ETA: 146s - loss: 1.3414 - acc: 0.9512 
61500/78750 [======================>.......] - ETA: 142s - loss: 1.3414 - acc: 0.9512 
62000/78750 [======================>.......] - ETA: 138s - loss: 1.3413 - acc: 0.9512 
62500/78750 [======================>.......] - ETA: 134s - loss: 1.3412 - acc: 0.9513 
63000/78750 [=======================>......] - ETA: 130s - loss: 1.3412 - acc: 0.9514 
63500/78750 [=======================>......] - ETA: 126s - loss: 1.3411 - acc: 0.9514 
64000/78750 [=======================>......] - ETA: 121s - loss: 1.3411 - acc: 0.9515 
64500/78750 [=======================>......] - ETA: 117s - loss: 1.3411 - acc: 0.9516 
65000/78750 [=======================>......] - ETA: 113s - loss: 1.3410 - acc: 0.9516 
65500/78750 [=======================>......] - ETA: 109s - loss: 1.3412 - acc: 0.9516 
66000/78750 [========================>.....] - ETA: 105s - loss: 1.3411 - acc: 0.9517 
66500/78750 [========================>.....] - ETA: 101s - loss: 1.3410 - acc: 0.9516 
67000/78750 [========================>.....] - ETA: 97s - loss: 1.3410 - acc: 0.9516 
67500/78750 [========================>.....] - ETA: 92s - loss: 1.3409 - acc: 0.9516 
68000/78750 [========================>.....] - ETA: 88s - loss: 1.3408 - acc: 0.9515 
68500/78750 [=========================>....] - ETA: 84s - loss: 1.3408 - acc: 0.9515 
69000/78750 [=========================>....] - ETA: 80s - loss: 1.3407 - acc: 0.9515 
69500/78750 [=========================>....] - ETA: 76s - loss: 1.3407 - acc: 0.9515 
70000/78750 [=========================>....] - ETA: 72s - loss: 1.3406 - acc: 0.9515 
70500/78750 [=========================>....] - ETA: 68s - loss: 1.3405 - acc: 0.9516 
71000/78750 [==========================>...] - ETA: 64s - loss: 1.3405 - acc: 0.9516 
71500/78750 [==========================>...] - ETA: 59s - loss: 1.3404 - acc: 0.9516 
72000/78750 [==========================>...] - ETA: 55s - loss: 1.3404 - acc: 0.9517 
72500/78750 [==========================>...] - ETA: 51s - loss: 1.3403 - acc: 0.9518 
73000/78750 [==========================>...] - ETA: 47s - loss: 1.3403 - acc: 0.9517 
73500/78750 [===========================>..] - ETA: 43s - loss: 1.3402 - acc: 0.9518 
74000/78750 [===========================>..] - ETA: 39s - loss: 1.3401 - acc: 0.9517 
74500/78750 [===========================>..] - ETA: 35s - loss: 1.3401 - acc: 0.9518 
75000/78750 [===========================>..] - ETA: 31s - loss: 1.3400 - acc: 0.9518 
75500/78750 [===========================>..] - ETA: 26s - loss: 1.3401 - acc: 0.9519 
76000/78750 [===========================>..] - ETA: 22s - loss: 1.3400 - acc: 0.9519 
76500/78750 [============================>.] - ETA: 18s - loss: 1.3400 - acc: 0.9519 
77000/78750 [============================>.] - ETA: 14s - loss: 1.3399 - acc: 0.9519 
77500/78750 [============================>.] - ETA: 10s - loss: 1.3399 - acc: 0.9519 
78000/78750 [============================>.] - ETA: 6s - loss: 1.3398 - acc: 0.9518 
78500/78750 [============================>.] - ETA: 2s - loss: 1.3398 - acc: 0.9518 
78750/78750 [==============================] - 855s - loss: 1.3397 - acc: 0.9518 - val_loss: 1.3321 - val_acc: 0.9523

とここにありますモデル概要（）...

_________________________________________________________________ 
Layer (type)     Output Shape    Param # 
================================================================= 
conv2d_1 (Conv2D)   (None, 72, 72, 25)  2525  
_________________________________________________________________ 
max_pooling2d_1 (MaxPooling2 (None, 36, 36, 25)  0   
_________________________________________________________________ 
conv2d_2 (Conv2D)   (None, 30, 30, 25)  30650  
_________________________________________________________________ 
max_pooling2d_2 (MaxPooling2 (None, 15, 15, 25)  0   
_________________________________________________________________ 
conv2d_3 (Conv2D)   (None, 6, 6, 25)   15650  
_________________________________________________________________ 
max_pooling2d_3 (MaxPooling2 (None, 5, 5, 25)   0   
_________________________________________________________________ 
conv2d_4 (Conv2D)   (None, 1, 1, 25)   15650  
_________________________________________________________________ 
flatten_1 (Flatten)   (None, 25)    0   
_________________________________________________________________ 
dense_1 (Dense)    (None, 2)     52   
_________________________________________________________________ 
dense_2 (Dense)    (None, 2)     6   
================================================================= 
Total params: 64,533 
Trainable params: 64,533 
Non-trainable params: 0

出典

2017-09-06 Drew Swinney

+0

非常に大きなカーネルサイズを使用していることに気付きました。これにより、モデルのトレーニングを改善するかもしれない貴重な情報をたくさん捨てることができます。通常の3x3よりも高い値を選んだのはなぜですか？ – petezurich

+0

Sergiiの提案に加えて、fit（）関数で 'class_weight'パラメータを設定しようとしましたか？また、層にBatchnormalizationを追加することを検討する必要があります。 – petezurich

答えて

1

1または2エポックで学習率を1e-5に下げて、精度が上がるかどうかを確認してください。動作しない場合は、model.summary（）を指定してください。

出典

2017-09-06 23:23:55 Stidgen

+0

私はトレーニングレートとエポックを1トン計算したが、何も変わっていない。知覚精度に問題はありません（私の編集を確認してください、それは約95％を保持しています）。しかし、それはCatのオーバーフィットのためだと思います。 1.すべての予測はCatに対して100％を出力します。 1の場合は0％、Catの場合は0％。 2。 –

2

データセットのバランスが非常に悪いため、モデルでは2番目のカテゴリがノイズとして扱われ、すべてがカテゴリ1として分類されます。データセットのバランスをとる最も簡単な方法は、2番目のクラスの例をオーバーサンプルし、より頻繁に。

これはおそらくクラス出力の問題を解決しますが、そのようなモデルの一般化は不十分です。一般化能力を向上させるために、data augmentationを試してみてください。ランダムな変換が画像に適用されます。

出典

2017-09-07 07:18:39

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