# DL之Attention：基于ClutteredMNIST手写数字图片数据集分别利用CNN_Init、ST_CNN算法(CNN+SpatialTransformer)实现多分类预测（一）

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## 数据特征工程

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

## 输出结果

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

#(1)、定义模型结构

model = Sequential()

activation='relu',

input_shape=input_shape))

activation='relu'))

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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