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DL之Attention：基于ClutteredMNIST手寫數字圖片資料集分別利用CNN_Init、ST_CNN演算法(CNN+SpatialTransformer)實作多分類預測

2021-02-28 10:12:00 其他

DL之Attention：基于ClutteredMNIST手寫數字圖片資料集分別利用CNN_Init、ST_CNN演算法(CNN+SpatialTransformer)實作多分類預測

目錄

基于ClutteredMNIST手寫數字圖片資料集分別利用CNN_Init、ST_CNN演算法(CNN+SpatialTransformer)實作多分類預測

資料特征工程

T1、CNN_Init start

輸出結果

核心代碼

T2、ST_CNN start

核心代碼

相關文章
DL之Attention：基于ClutteredMNIST手寫數字圖片資料集分別利用CNN_Init、ST_CNN演算法(CNN+SpatialTransformer)實作多分類預測
DL之Attention：基于ClutteredMNIST手寫數字圖片資料集分別利用CNN_Init、ST_CNN演算法(CNN+SpatialTransformer)實作多分類預測實作

基于ClutteredMNIST手寫數字圖片資料集分別利用CNN_Init、ST_CNN演算法(CNN+SpatialTransformer)實作多分類預測

資料特征工程

Train samples: (50000, 60, 60, 1)
Validation samples: (10000, 60, 60, 1)
Test samples: (10000, 60, 60, 1)
Input shape: (60, 60, 1)

T1、CNN_Init start

輸出結果

T1、CNN_Init start!
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 58, 58, 32)        320       
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 56, 56, 64)        18496     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 26, 26, 64)        36928     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 13, 13, 64)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 11, 11, 64)        36928     
_________________________________________________________________
dropout_1 (Dropout)          (None, 11, 11, 64)        0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 7744)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 128)               991360    
_________________________________________________________________
dropout_2 (Dropout)          (None, 128)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 10)                1290      
=================================================================
Total params: 1,085,322
Trainable params: 1,085,322
Non-trainable params: 0
_________________________________________________________________
None
Train on 50000 samples, validate on 10000 samples
Epoch 1/30

核心代碼

    #(1)、定義模型結構
    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3),
                     activation='relu',
                     input_shape=input_shape))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(64, kernel_size=(3, 3),
                     activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes, activation='softmax'))

T2、ST_CNN start

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 56, 56, 32)        832       
_________________________________________________________________
activation_1 (Activation)    (None, 56, 56, 32)        0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 28, 28, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 24, 24, 64)        51264     
_________________________________________________________________
activation_2 (Activation)    (None, 24, 24, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 22, 22, 64)        36928     
_________________________________________________________________
activation_3 (Activation)    (None, 22, 22, 64)        0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 11, 11, 64)        0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 7744)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 50)                387250    
_________________________________________________________________
activation_4 (Activation)    (None, 50)                0         
_________________________________________________________________
dense_2 (Dense)              (None, 6)                 306       
=================================================================
Total params: 476,580
Trainable params: 476,580
Non-trainable params: 0
_________________________________________________________________
None
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
spatial_transformer_1 (Spati (None, 30, 30, 1)         476580    
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 28, 28, 32)        320       
_________________________________________________________________
dropout_1 (Dropout)          (None, 28, 28, 32)        0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 26, 26, 64)        18496     
_________________________________________________________________
dropout_2 (Dropout)          (None, 26, 26, 64)        0         
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 13, 13, 64)        0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 11, 11, 64)        36928     
_________________________________________________________________
dropout_3 (Dropout)          (None, 11, 11, 64)        0         
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 5, 5, 64)          0         
_________________________________________________________________
flatten_2 (Flatten)          (None, 1600)              0         
_________________________________________________________________
dense_3 (Dense)              (None, 256)               409856    
_________________________________________________________________
dropout_4 (Dropout)          (None, 256)               0         
_________________________________________________________________
activation_5 (Activation)    (None, 256)               0         
_________________________________________________________________
dense_4 (Dense)              (None, 10)                2570      
_________________________________________________________________
activation_6 (Activation)    (None, 10)                0         
=================================================================
Total params: 944,750
Trainable params: 944,750
Non-trainable params: 0
_________________________________________________________________
None
Train on 50000 samples, validate on 10000 samples
Epoch 1/30
 - 974s - loss: 2.0926 - categorical_accuracy: 0.2345 - val_loss: 1.6258 - val_categorical_accuracy: 0.5949
Epoch 2/30
 - 1007s - loss: 1.0926 - categorical_accuracy: 0.6387 - val_loss: 0.7963 - val_categorical_accuracy: 0.8433
Epoch 3/30
 - 844s - loss: 0.6038 - categorical_accuracy: 0.8118 - val_loss: 0.4906 - val_categorical_accuracy: 0.8977
Epoch 4/30
 - 851s - loss: 0.4351 - categorical_accuracy: 0.8648 - val_loss: 0.3909 - val_categorical_accuracy: 0.9160
Epoch 5/30
 - 864s - loss: 0.3483 - categorical_accuracy: 0.8914 - val_loss: 0.3046 - val_categorical_accuracy: 0.9367
Epoch 6/30
 - 872s - loss: 0.3158 - categorical_accuracy: 0.9027 - val_loss: 0.2826 - val_categorical_accuracy: 0.9349
Epoch 7/30
 - 861s - loss: 0.2772 - categorical_accuracy: 0.9136 - val_loss: 0.3244 - val_categorical_accuracy: 0.9243
Epoch 8/30
 - 862s - loss: 0.2414 - categorical_accuracy: 0.9251 - val_loss: 0.2228 - val_categorical_accuracy: 0.9600
Epoch 9/30
 - 858s - loss: 0.2278 - categorical_accuracy: 0.9287 - val_loss: 0.2305 - val_categorical_accuracy: 0.9556
Epoch 10/30
 - 860s - loss: 0.2150 - categorical_accuracy: 0.9328 - val_loss: 0.2119 - val_categorical_accuracy: 0.9600
Epoch 11/30
 - 862s - loss: 0.2130 - categorical_accuracy: 0.9334 - val_loss: 0.1949 - val_categorical_accuracy: 0.9583
Epoch 12/30
 - 855s - loss: 0.1917 - categorical_accuracy: 0.9410 - val_loss: 0.1841 - val_categorical_accuracy: 0.9595
Epoch 13/30
 - 857s - loss: 0.1891 - categorical_accuracy: 0.9414 - val_loss: 0.2455 - val_categorical_accuracy: 0.9613
Epoch 14/30
 - 862s - loss: 0.1865 - categorical_accuracy: 0.9423 - val_loss: 0.2044 - val_categorical_accuracy: 0.9629
Epoch 15/30
 - 863s - loss: 0.1789 - categorical_accuracy: 0.9446 - val_loss: 0.2147 - val_categorical_accuracy: 0.9647
Epoch 16/30
 - 855s - loss: 0.1708 - categorical_accuracy: 0.9460 - val_loss: 0.1748 - val_categorical_accuracy: 0.9692
Epoch 17/30
 - 859s - loss: 0.1615 - categorical_accuracy: 0.9509 - val_loss: 0.1870 - val_categorical_accuracy: 0.9707
Epoch 18/30
 - 862s - loss: 0.1538 - categorical_accuracy: 0.9514 - val_loss: 0.1906 - val_categorical_accuracy: 0.9689
Epoch 19/30
 - 866s - loss: 0.1494 - categorical_accuracy: 0.9537 - val_loss: 0.1596 - val_categorical_accuracy: 0.9728
Epoch 20/30
 - 864s - loss: 0.1490 - categorical_accuracy: 0.9537 - val_loss: 0.1821 - val_categorical_accuracy: 0.9692
Epoch 21/30
 - 860s - loss: 0.1517 - categorical_accuracy: 0.9524 - val_loss: 0.1579 - val_categorical_accuracy: 0.9701
Epoch 22/30
 - 859s - loss: 0.1506 - categorical_accuracy: 0.9539 - val_loss: 0.1595 - val_categorical_accuracy: 0.9712
Epoch 23/30
 - 859s - loss: 0.1407 - categorical_accuracy: 0.9567 - val_loss: 0.1590 - val_categorical_accuracy: 0.9712
Epoch 24/30
 - 856s - loss: 0.1361 - categorical_accuracy: 0.9569 - val_loss: 0.2160 - val_categorical_accuracy: 0.9723
Epoch 25/30
 - 856s - loss: 0.1348 - categorical_accuracy: 0.9583 - val_loss: 0.1678 - val_categorical_accuracy: 0.9741
Epoch 26/30
 - 856s - loss: 0.1298 - categorical_accuracy: 0.9596 - val_loss: 0.1820 - val_categorical_accuracy: 0.9707
Epoch 27/30
 - 856s - loss: 0.1317 - categorical_accuracy: 0.9597 - val_loss: 0.1998 - val_categorical_accuracy: 0.9738
Epoch 28/30
 - 855s - loss: 0.1325 - categorical_accuracy: 0.9594 - val_loss: 0.1991 - val_categorical_accuracy: 0.9674
Epoch 29/30
 - 856s - loss: 0.1230 - categorical_accuracy: 0.9621 - val_loss: 0.1848 - val_categorical_accuracy: 0.9720
Epoch 30/30
 - 856s - loss: 0.1246 - categorical_accuracy: 0.9611 - val_loss: 0.1754 - val_categorical_accuracy: 0.9755

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10000/10000 [==============================] - 56s 6ms/step

核心代碼

    #(2)、建立ST定位網路：嘗試更多的conv層，并分別在X軸和y軸上做最大池化
    # localization net. TODO: try more conv layers, and do max pooling on X- and Y-axes respectively
    locnet = Sequential()
    # locnet.add(MaxPooling2D(pool_size=(2,2), input_shape=input_shape))
    # locnet.add(Convolution2D(32, (5, 5)))
    locnet.add(Convolution2D(32, (5, 5), input_shape=input_shape))
    locnet.add(Activation('relu'))
    # locnet.add(Dropout(0.2)) # 0.2
    locnet.add(MaxPooling2D(pool_size=(2,2)))
    locnet.add(Convolution2D(64, (5, 5)))
    locnet.add(Activation('relu'))
    # locnet.add(Dropout(0.2)) # 0.3
    locnet.add(Convolution2D(64, (3, 3)))
    locnet.add(Activation('relu'))
    locnet.add(MaxPooling2D(pool_size=(2,2)))
    
    locnet.add(Flatten())
    locnet.add(Dense(50))
    locnet.add(Activation('relu'))
    locnet.add(Dense(6, weights=weights))
    print(locnet.summary())
    
    
    #(3)、建立CNN網路
    model = Sequential()
    model.add(SpatialTransformer(localization_net=locnet,
                                 output_size=(30,30), input_shape=input_shape))
    # model.add(Convolution2D(32, (3, 3), padding='same'))
    # model.add(Activation('relu'))
    # model.add(MaxPooling2D(pool_size=(2, 2)))
    # model.add(Convolution2D(64, (3, 3)))
    # model.add(Activation('relu'))
    # model.add(MaxPooling2D(pool_size=(2, 2)))
    # model.add(Dropout(0.5)) # 0.25
    
    # E: removed first 3 dropout layers
    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))
    model.add(Dropout(0.5)) # 0.5
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(Dropout(0.5)) # 0.5
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(64, kernel_size=(3, 3),
                     activation='relu'))
    model.add(Dropout(0.5)) # 0.5
    model.add(MaxPooling2D(pool_size=(2, 2)))
    # model.add(Conv2D(64, (3, 3), activation='relu'))
    # model.add(Dropout(0.5))
    model.add(Flatten())
    model.add(Dense(256)) # 256
    model.add(Dropout(0.5)) # 0.5
    model.add(Activation('relu'))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))

一個處女座的程式猿 CSDN認證博客專家 華為杯研電賽一等 華為研數模一等獎 國內外AI競十
人工智能碩博生，目前兼職國內外多家頭部人工智能公司的AI技術顧問，擁有十多項發明專利(6項)和軟體著作權(9項)，多個國家級證書(2個國三級、3個國四級)，先后獲得國內外“人工智能演算法”競賽(包括國家級、省市級等，一等獎5項、二等獎4項、三等獎2項)相關證書十多個，以上均以第一作者身份，并擁有省市校級個人榮譽證書十多項，正在撰寫《人工智演算法最新實戰》一書，目前已37萬字，

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