Score-CAMを使ってcifar10とラブライブのキャラを識別した ネットワークはVGG-16っぽいやつを使用した。
μ's
, Aqours
, 虹ヶ咲
のメンバーの画像をクロールして教師あり学習して分類。
ニューラルネットは同じものを使用。
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 64, 64, 64) 1792
_________________________________________________________________
conv2d_2 (Conv2D) (None, 64, 64, 64) 36928
_________________________________________________________________
conv2d_3 (Conv2D) (None, 64, 64, 64) 36928
_________________________________________________________________
batch_normalization_1 (Batch (None, 64, 64, 64) 256
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 32, 32, 64) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 32, 32, 64) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 32, 32, 64) 36928
_________________________________________________________________
conv2d_5 (Conv2D) (None, 32, 32, 64) 36928
_________________________________________________________________
conv2d_6 (Conv2D) (None, 32, 32, 64) 36928
_________________________________________________________________
batch_normalization_2 (Batch (None, 32, 32, 64) 256
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 16, 16, 64) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 16, 16, 64) 0
_________________________________________________________________
conv2d_7 (Conv2D) (None, 16, 16, 64) 36928
_________________________________________________________________
conv2d_8 (Conv2D) (None, 16, 16, 64) 36928
_________________________________________________________________
conv2d_9 (Conv2D) (None, 16, 16, 64) 36928
_________________________________________________________________
batch_normalization_3 (Batch (None, 16, 16, 64) 256
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 8, 8, 64) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 8, 8, 64) 0
_________________________________________________________________
conv2d_10 (Conv2D) (None, 8, 8, 64) 36928
_________________________________________________________________
conv2d_11 (Conv2D) (None, 8, 8, 64) 36928
_________________________________________________________________
conv2d_12 (Conv2D) (None, 8, 8, 64) 36928
_________________________________________________________________
batch_normalization_4 (Batch (None, 8, 8, 64) 256
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 4, 4, 64) 0
_________________________________________________________________
dropout_4 (Dropout) (None, 4, 4, 64) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 1024) 0
_________________________________________________________________
dense_1 (Dense) (None, 512) 524800
_________________________________________________________________
activation_1 (Activation) (None, 512) 0
_________________________________________________________________
dropout_5 (Dropout) (None, 512) 0
_________________________________________________________________
dense_2 (Dense) (None, 128) 65664
_________________________________________________________________
activation_2 (Activation) (None, 128) 0
_________________________________________________________________
dropout_6 (Dropout) (None, 128) 0
_________________________________________________________________
dense_3 (Dense) (None, 64) 8256
_________________________________________________________________
activation_3 (Activation) (None, 64) 0
_________________________________________________________________
dropout_7 (Dropout) (None, 64) 0
_________________________________________________________________
dense_4 (Dense) (None, 27) 1755
_________________________________________________________________
activation_4 (Activation) (None, 27) 0
=================================================================