cnn-without-any-downsampling - プロジェクトは、cnnのダウンサンプリング（アップサミング）が必須ではないことを示しています
(A project demonstrate that downsampling(upsaming) in cnn are not nesscessary)
Created at: 2019-11-30 22:19:52
Language: Python
License: MIT
このコードの使用方法
このリポジトリには、tensorflow-gpu-1.5.0または他の互換性のあるバージョンのtensorflowが必要です。
タイプ別に簡単に実行できます
python cifar10.py
CNNは本当にダウンサンプリング（アップサンプリング）が必要ですか？
一般的な畳み込みニューラルネットワークでは、サンプリングはほぼユビキタスであり、以前はmax_poolingでしたが、現在は畳み込みにまたがっています。例としてvggネットワークを取り上げます。これは、非常に多くのmax_poolingを使用します。
Input-->Conv-->DownSample_x_2-->Conv-->DownSample_x_2-->Conv-->DownSample_x_2-->GAP-->Conv1x1-->Softmax-->Output
そして、セマンティックセグメンテーションネットワークのパラダイムは次のように動作します。
Input-->Conv-->DownSample_x_2-->Conv-->DownSample_x_2-->Conv-->DownSample_x_2-->Deconv_x_2-->Deconv_x_2-->Deconv_x_2-->Softmax-->Output
しかし、私たちはそれについて考えなければなりません。ダウンサンプリングとアップサンプリングは本当に必要ですか？取り外すことはできませんか？
cifar10の分類タスクで、ダウンサンプリングを削除し、畳み込みを拡張畳み込みに変更しようとしました。ダイヤルレートはそれぞれ増加しました。モデル構造を以下に示します。
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 32, 32, 16) 448
_________________________________________________________________
batch_normalization (BatchNo (None, 32, 32, 16) 64
_________________________________________________________________
activation (Activation) (None, 32, 32, 16) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 32, 32, 24) 3480
_________________________________________________________________
batch_normalization_1 (Batch (None, 32, 32, 24) 96
_________________________________________________________________
activation_1 (Activation) (None, 32, 32, 24) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 32, 32, 32) 6944
_________________________________________________________________
batch_normalization_2 (Batch (None, 32, 32, 32) 128
_________________________________________________________________
activation_2 (Activation) (None, 32, 32, 32) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 32, 32, 48) 13872
_________________________________________________________________
batch_normalization_3 (Batch (None, 32, 32, 48) 192
_________________________________________________________________
activation_3 (Activation) (None, 32, 32, 48) 0
_________________________________________________________________
global_average_pooling2d (Gl (None, 48) 0
_________________________________________________________________
dense (Dense) (None, 10) 490
_________________________________________________________________
activation_4 (Activation) (None, 10) 0
=================================================================80回のトレーニングの後、最終的に次の分類結果が得られました。
| 時代 | 損失 | val_accuracy |
|---|---|---|
| 10 | 0.9200 | 0.6346 |
| 20 | 0.7925 | 0.6769 |
| 30 | 0.7293 | 0.7193 |
| 40 | 0.6737 | 0.7479 |
| 50 | 0.6516 | 0.7470 |
| 60 | 0.6311 | 0.7678 |
| 70 | 0.6085 | 0.7478 |
| 80 | 0.5865 | 0.7665 |
検証データセットの精度曲線を以下に示します。
最終的な正解率は76％に達しました。同じパラメータを持つvgg構造を持つ畳み込みネットワークの正解率は基本的にこれに近いです。
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 32, 32, 3)] 0
__________________________________________________________________________________________________
conv2d (Conv2D) (None, 32, 32, 16) 448 input_1[0][0]
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 32, 32, 16) 64 conv2d[0][0]
__________________________________________________________________________________________________
activation (Activation) (None, 32, 32, 16) 0 batch_normalization[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 32, 32, 16) 2320 activation[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 32, 32, 16) 64 conv2d_1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 32, 32, 16) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 32, 32, 16) 2320 activation_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 32, 32, 16) 64 conv2d_2[0][0]
__________________________________________________________________________________________________
add (Add) (None, 32, 32, 16) 0 activation[0][0]
batch_normalization_2[0][0]
__________________________________________________________________________________________________
activation_2 (Activation) (None, 32, 32, 16) 0 add[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 32, 32, 16) 2320 activation_2[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 32, 32, 16) 64 conv2d_3[0][0]
__________________________________________________________________________________________________
activation_3 (Activation) (None, 32, 32, 16) 0 batch_normalization_3[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 32, 32, 16) 2320 activation_3[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 32, 32, 16) 64 conv2d_4[0][0]
__________________________________________________________________________________________________
add_1 (Add) (None, 32, 32, 16) 0 activation_2[0][0]
batch_normalization_4[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, 32, 32, 16) 0 add_1[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 32, 32, 16) 2320 activation_4[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 32, 32, 16) 64 conv2d_5[0][0]
__________________________________________________________________________________________________
activation_5 (Activation) (None, 32, 32, 16) 0 batch_normalization_5[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 32, 32, 16) 2320 activation_5[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 32, 32, 16) 64 conv2d_6[0][0]
__________________________________________________________________________________________________
add_2 (Add) (None, 32, 32, 16) 0 activation_4[0][0]
batch_normalization_6[0][0]
__________________________________________________________________________________________________
activation_6 (Activation) (None, 32, 32, 16) 0 add_2[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 32, 32, 16) 2320 activation_6[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 32, 32, 16) 64 conv2d_7[0][0]
__________________________________________________________________________________________________
activation_7 (Activation) (None, 32, 32, 16) 0 batch_normalization_7[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 32, 32, 16) 2320 activation_7[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 32, 32, 16) 64 conv2d_8[0][0]
__________________________________________________________________________________________________
add_3 (Add) (None, 32, 32, 16) 0 activation_6[0][0]
batch_normalization_8[0][0]
__________________________________________________________________________________________________
activation_8 (Activation) (None, 32, 32, 16) 0 add_3[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 32, 32, 16) 2320 activation_8[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 32, 32, 16) 64 conv2d_9[0][0]
__________________________________________________________________________________________________
activation_9 (Activation) (None, 32, 32, 16) 0 batch_normalization_9[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 32, 32, 16) 2320 activation_9[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 32, 32, 16) 64 conv2d_10[0][0]
__________________________________________________________________________________________________
add_4 (Add) (None, 32, 32, 16) 0 activation_8[0][0]
batch_normalization_10[0][0]
__________________________________________________________________________________________________
activation_10 (Activation) (None, 32, 32, 16) 0 add_4[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 32, 32, 16) 2320 activation_10[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 32, 32, 16) 64 conv2d_11[0][0]
__________________________________________________________________________________________________
activation_11 (Activation) (None, 32, 32, 16) 0 batch_normalization_11[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 32, 32, 16) 2320 activation_11[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 32, 32, 16) 64 conv2d_12[0][0]
__________________________________________________________________________________________________
add_5 (Add) (None, 32, 32, 16) 0 activation_10[0][0]
batch_normalization_12[0][0]
__________________________________________________________________________________________________
activation_12 (Activation) (None, 32, 32, 16) 0 add_5[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 32, 32, 32) 4640 activation_12[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 32, 32, 32) 128 conv2d_13[0][0]
__________________________________________________________________________________________________
activation_13 (Activation) (None, 32, 32, 32) 0 batch_normalization_13[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 32, 32, 32) 9248 activation_13[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 32, 32, 32) 4640 activation_12[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 32, 32, 32) 128 conv2d_14[0][0]
__________________________________________________________________________________________________
add_6 (Add) (None, 32, 32, 32) 0 conv2d_15[0][0]
batch_normalization_14[0][0]
__________________________________________________________________________________________________
activation_14 (Activation) (None, 32, 32, 32) 0 add_6[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 32, 32, 32) 9248 activation_14[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 32, 32, 32) 128 conv2d_16[0][0]
__________________________________________________________________________________________________
activation_15 (Activation) (None, 32, 32, 32) 0 batch_normalization_15[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 32, 32, 32) 9248 activation_15[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 32, 32, 32) 128 conv2d_17[0][0]
__________________________________________________________________________________________________
add_7 (Add) (None, 32, 32, 32) 0 activation_14[0][0]
batch_normalization_16[0][0]
__________________________________________________________________________________________________
activation_16 (Activation) (None, 32, 32, 32) 0 add_7[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 32, 32, 32) 9248 activation_16[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 32, 32, 32) 128 conv2d_18[0][0]
__________________________________________________________________________________________________
activation_17 (Activation) (None, 32, 32, 32) 0 batch_normalization_17[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, 32, 32, 32) 9248 activation_17[0][0]
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 32, 32, 32) 128 conv2d_19[0][0]
__________________________________________________________________________________________________
add_8 (Add) (None, 32, 32, 32) 0 activation_16[0][0]
batch_normalization_18[0][0]
__________________________________________________________________________________________________
activation_18 (Activation) (None, 32, 32, 32) 0 add_8[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 32, 32, 32) 9248 activation_18[0][0]
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 32, 32, 32) 128 conv2d_20[0][0]
__________________________________________________________________________________________________
activation_19 (Activation) (None, 32, 32, 32) 0 batch_normalization_19[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, 32, 32, 32) 9248 activation_19[0][0]
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 32, 32, 32) 128 conv2d_21[0][0]
__________________________________________________________________________________________________
add_9 (Add) (None, 32, 32, 32) 0 activation_18[0][0]
batch_normalization_20[0][0]
__________________________________________________________________________________________________
activation_20 (Activation) (None, 32, 32, 32) 0 add_9[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, 32, 32, 32) 9248 activation_20[0][0]
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 32, 32, 32) 128 conv2d_22[0][0]
__________________________________________________________________________________________________
activation_21 (Activation) (None, 32, 32, 32) 0 batch_normalization_21[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D) (None, 32, 32, 32) 9248 activation_21[0][0]
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 32, 32, 32) 128 conv2d_23[0][0]
__________________________________________________________________________________________________
add_10 (Add) (None, 32, 32, 32) 0 activation_20[0][0]
batch_normalization_22[0][0]
__________________________________________________________________________________________________
activation_22 (Activation) (None, 32, 32, 32) 0 add_10[0][0]
__________________________________________________________________________________________________
conv2d_24 (Conv2D) (None, 32, 32, 32) 9248 activation_22[0][0]
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 32, 32, 32) 128 conv2d_24[0][0]
__________________________________________________________________________________________________
activation_23 (Activation) (None, 32, 32, 32) 0 batch_normalization_23[0][0]
__________________________________________________________________________________________________
conv2d_25 (Conv2D) (None, 32, 32, 32) 9248 activation_23[0][0]
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 32, 32, 32) 128 conv2d_25[0][0]
__________________________________________________________________________________________________
add_11 (Add) (None, 32, 32, 32) 0 activation_22[0][0]
batch_normalization_24[0][0]
__________________________________________________________________________________________________
activation_24 (Activation) (None, 32, 32, 32) 0 add_11[0][0]
__________________________________________________________________________________________________
conv2d_26 (Conv2D) (None, 32, 32, 64) 18496 activation_24[0][0]
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, 32, 32, 64) 256 conv2d_26[0][0]
__________________________________________________________________________________________________
activation_25 (Activation) (None, 32, 32, 64) 0 batch_normalization_25[0][0]
__________________________________________________________________________________________________
conv2d_27 (Conv2D) (None, 32, 32, 64) 36928 activation_25[0][0]
__________________________________________________________________________________________________
conv2d_28 (Conv2D) (None, 32, 32, 64) 18496 activation_24[0][0]
__________________________________________________________________________________________________
batch_normalization_26 (BatchNo (None, 32, 32, 64) 256 conv2d_27[0][0]
__________________________________________________________________________________________________
add_12 (Add) (None, 32, 32, 64) 0 conv2d_28[0][0]
batch_normalization_26[0][0]
__________________________________________________________________________________________________
activation_26 (Activation) (None, 32, 32, 64) 0 add_12[0][0]
__________________________________________________________________________________________________
conv2d_29 (Conv2D) (None, 32, 32, 64) 36928 activation_26[0][0]
__________________________________________________________________________________________________
batch_normalization_27 (BatchNo (None, 32, 32, 64) 256 conv2d_29[0][0]
__________________________________________________________________________________________________
activation_27 (Activation) (None, 32, 32, 64) 0 batch_normalization_27[0][0]
__________________________________________________________________________________________________
conv2d_30 (Conv2D) (None, 32, 32, 64) 36928 activation_27[0][0]
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, 32, 32, 64) 256 conv2d_30[0][0]
__________________________________________________________________________________________________
add_13 (Add) (None, 32, 32, 64) 0 activation_26[0][0]
batch_normalization_28[0][0]
__________________________________________________________________________________________________
activation_28 (Activation) (None, 32, 32, 64) 0 add_13[0][0]
__________________________________________________________________________________________________
conv2d_31 (Conv2D) (None, 32, 32, 64) 36928 activation_28[0][0]
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, 32, 32, 64) 256 conv2d_31[0][0]
__________________________________________________________________________________________________
activation_29 (Activation) (None, 32, 32, 64) 0 batch_normalization_29[0][0]
__________________________________________________________________________________________________
conv2d_32 (Conv2D) (None, 32, 32, 64) 36928 activation_29[0][0]
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, 32, 32, 64) 256 conv2d_32[0][0]
__________________________________________________________________________________________________
add_14 (Add) (None, 32, 32, 64) 0 activation_28[0][0]
batch_normalization_30[0][0]
__________________________________________________________________________________________________
activation_30 (Activation) (None, 32, 32, 64) 0 add_14[0][0]
__________________________________________________________________________________________________
conv2d_33 (Conv2D) (None, 32, 32, 64) 36928 activation_30[0][0]
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, 32, 32, 64) 256 conv2d_33[0][0]
__________________________________________________________________________________________________
activation_31 (Activation) (None, 32, 32, 64) 0 batch_normalization_31[0][0]
__________________________________________________________________________________________________
conv2d_34 (Conv2D) (None, 32, 32, 64) 36928 activation_31[0][0]
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, 32, 32, 64) 256 conv2d_34[0][0]
__________________________________________________________________________________________________
add_15 (Add) (None, 32, 32, 64) 0 activation_30[0][0]
batch_normalization_32[0][0]
__________________________________________________________________________________________________
activation_32 (Activation) (None, 32, 32, 64) 0 add_15[0][0]
__________________________________________________________________________________________________
conv2d_35 (Conv2D) (None, 32, 32, 64) 36928 activation_32[0][0]
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, 32, 32, 64) 256 conv2d_35[0][0]
__________________________________________________________________________________________________
activation_33 (Activation) (None, 32, 32, 64) 0 batch_normalization_33[0][0]
__________________________________________________________________________________________________
conv2d_36 (Conv2D) (None, 32, 32, 64) 36928 activation_33[0][0]
__________________________________________________________________________________________________
batch_normalization_34 (BatchNo (None, 32, 32, 64) 256 conv2d_36[0][0]
__________________________________________________________________________________________________
add_16 (Add) (None, 32, 32, 64) 0 activation_32[0][0]
batch_normalization_34[0][0]
__________________________________________________________________________________________________
activation_34 (Activation) (None, 32, 32, 64) 0 add_16[0][0]
__________________________________________________________________________________________________
conv2d_37 (Conv2D) (None, 32, 32, 64) 36928 activation_34[0][0]
__________________________________________________________________________________________________
batch_normalization_35 (BatchNo (None, 32, 32, 64) 256 conv2d_37[0][0]
__________________________________________________________________________________________________
activation_35 (Activation) (None, 32, 32, 64) 0 batch_normalization_35[0][0]
__________________________________________________________________________________________________
conv2d_38 (Conv2D) (None, 32, 32, 64) 36928 activation_35[0][0]
__________________________________________________________________________________________________
batch_normalization_36 (BatchNo (None, 32, 32, 64) 256 conv2d_38[0][0]
__________________________________________________________________________________________________
add_17 (Add) (None, 32, 32, 64) 0 activation_34[0][0]
batch_normalization_36[0][0]
__________________________________________________________________________________________________
activation_36 (Activation) (None, 32, 32, 64) 0 add_17[0][0]
__________________________________________________________________________________________________
global_average_pooling2d (Globa (None, 64) 0 activation_36[0][0]
__________________________________________________________________________________________________
flatten (Flatten) (None, 64) 0 global_average_pooling2d[0][0]
__________________________________________________________________________________________________
dense (Dense) (None, 10) 650 flatten[0][0]
==================================================================================================これは私たちに考えさせました、サンプリングは本当に必要ですか？もちろん、エンジニアリングの観点からは、サンプリングによってフィーチャマップのサイズを大幅に削減できるため、計算量を大幅に削減できます。ただし、この実験面では、サンプリングは畳み込みニューラルネットワークのパフォーマンスの向上には役立ちません。最大プーリングにはノイズを抑制する効果があるので便利ですが、最大プーリングは、従来のメディアンファイラーと同じように、ダウンサンプリングなしで実装することもできます。
これは、各畳み込み層が空間相関のエンコードに使用され、浅い特徴が短距離相関をエンコードし、より深い畳み込み層が長距離空間相関をエンコードすることも示しています。特定のレベルでは、統計的な意味での空間相関はなくなります（これは、画像内の意味のあるオブジェクトのサイズによって異なります）。このレイヤーでは、GAPを使用して空間フィーチャを集約できます。
サンプリング層がないと、分類ネットワークのパラダイムは次のようになります。
Input-->Conv(dilate_rate=1)-->Conv(dilate_rate=2)-->Conv(dilate_rate=4)-->Conv(dilate_rate=8)-->GAP-->Conv1x1-->Softmax-->Output
セマンティックセグメンテーションネットワークのパラダイムは次のようになります。
Input-->Conv(dilate_rate=1)-->Conv(dilate_rate=2)-->Conv(dilate_rate=4)-->Conv(dilate_rate=8)-->Conv(dilate_rate=4)-->Conv(dilate_rate=2)-->Conv(dilate_rate=1)-->Softmax-->Output
私の知る限り、画像の分類とセグメンテーションにグローバル平均プールと組み合わせた拡張畳み込みを使用した最初の人でした。パフォーマンスの向上がない場合でも（ただし、基本的に問題はありません）。拡張畳み込みは必要ないことに注意してください。より大きなカーネルサイズの畳み込みはそれを置き換えることができますが、これは必然的により多くのパラメーターを導入し、過剰適合につながる可能性があります。同様のアイデアがdeeplabの論文[セマンティック画像セグメンテーションのためのAtrousConvolutionの再考]に最初に登場しました：https：//arxiv.org/abs/1706.05587
この記事では、拡張畳み込みは主に、ネットワークの最後の数層のダウンサンプリング操作と対応するフィルターカーネルのアップサンプリング操作を削除することで、新しい学習パラメーターを追加せずに、よりコンパクトな特徴を抽出するために使用されます。
