怎样将CNN提取的特征输入SVM模型（tensorflow）

Question

CNN框架如上图所示，模型summary如下:

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 223, 223, 64)      4864      
_________________________________________________________________
batch_normalization_1 (Batch (None, 223, 223, 64)      256       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 111, 111, 64)      0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 107, 107, 64)      102464    
_________________________________________________________________
batch_normalization_2 (Batch (None, 107, 107, 64)      256       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 53, 53, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 49, 49, 128)       204928    
_________________________________________________________________
batch_normalization_3 (Batch (None, 49, 49, 128)       512       
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 24, 24, 128)       0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 22, 22, 256)       295168    
_________________________________________________________________
batch_normalization_4 (Batch (None, 22, 22, 256)       1024      
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 11, 11, 256)       0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 9, 9, 512)         1180160   
_________________________________________________________________
batch_normalization_5 (Batch (None, 9, 9, 512)         2048      
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 4, 4, 512)         0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 4, 4, 1024)        525312    
_________________________________________________________________
batch_normalization_6 (Batch (None, 4, 4, 1024)        4096      
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 2, 2, 1024)        0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 2, 2, 1024)        1049600   
_________________________________________________________________
batch_normalization_7 (Batch (None, 2, 2, 1024)        4096      
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 1, 1, 1024)        0         
_________________________________________________________________
flatten (Flatten)            (None, 1024)              0         
_________________________________________________________________
dropout (Dropout)            (None, 1024)              0         
_________________________________________________________________
dense (Dense)                (None, 1024)              1049600   
_________________________________________________________________
dense_1 (Dense)              (None, 32)                32800     
=================================================================
Total params: 4,457,184
Trainable params: 4,451,040
Non-trainable params: 6,144

接下来我想使用SVM进行分类，请问应该如何操作

Answer 1

仅供参考

model = keras.Sequential([
tf.keras.Input(shape = (227, 227, 3)),
layers.Conv2D (64, (5,5), strides=(1, 1), padding='same', kernel_regularizer = regularizers.l2(0.001), 
               bias_regularizer = regularizers.l2(0.001)),
layers.ReLU(negative_slope= 1),
layers.BatchNormalization(),
layers.MaxPooling2D(pool_size=(2, 2), padding="same"),

layers.Conv2D (64, (5,5), strides=(1, 1), padding='same', kernel_regularizer = regularizers.l2(0.001), 
               bias_regularizer = regularizers.l2(0.001)),
layers.ReLU(negative_slope = 1),
layers.BatchNormalization(),
layers.MaxPooling2D(pool_size=(2, 2)),

layers.Conv2D (128, (5,5), strides=(1, 1), padding='same', kernel_regularizer = regularizers.l2(0.001), 
               bias_regularizer = regularizers.l2(0.001)),
layers.ReLU(negative_slope = 1),
layers.BatchNormalization(),
layers.MaxPooling2D(pool_size=(2, 2)),

layers.Conv2D (256, (3,3), strides=(1, 1), padding='same', kernel_regularizer = regularizers.l2(0.001), 
               bias_regularizer = regularizers.l2(0.001)),
layers.ReLU(negative_slope = 1),
layers.BatchNormalization(),
layers.MaxPooling2D(pool_size=(2, 2), padding="same"),

layers.Conv2D (512, (3,3), strides=(1, 1), padding='same', kernel_regularizer = regularizers.l2(0.001), 
               bias_regularizer = regularizers.l2(0.001)),
layers.ReLU(negative_slope = 1), #negative slope 1 is good
layers.BatchNormalization(),
layers.MaxPooling2D(pool_size=(2, 2), padding="same"),

layers.Conv2D (1024, (1,1), strides=(1, 1), padding='same', kernel_regularizer = regularizers.l2(0.001), 
               bias_regularizer = regularizers.l2(0.001)),
layers.ReLU(negative_slope = 1),
layers.BatchNormalization(),
layers.MaxPooling2D(pool_size=(2, 2), padding="same"),
layers.MaxPooling2D(pool_size=(3, 3), padding="same"),
layers.Reshape((1, 4096)),
layers.Dense(1024, kernel_regularizer = regularizers.l2(0.001), bias_regularizer = regularizers.l2(0.001)),
layers.ReLU(negative_slope = 1),
layers.Dropout(0.5),
layers.Dense(32, name="my_intermediate_layer", kernel_regularizer = regularizers.l2(0.001), 
             bias_regularizer = regularizers.l2(0.001)),
layers.ReLU(negative_slope = 1),
layers.Dense(5, activation='softmax', name="my_last_layer"),
])

model.summary()
model.compile(optimizer=Optimizer.Adam(lr=0.00005),loss='sparse_categorical_crossentropy',metrics=['accuracy'])

trained = model.fit(train_data,train_labels,epochs=30,validation_split=0.10)

model_feat = Model(inputs=model.input,outputs=model.get_layer('my_intermediate_layer').output)
feat_train = model_feat.predict(train_data)
print(feat_train.shape)

feat_train = feat_train.reshape(413,32)

from sklearn.svm import SVC

svm = SVC(kernel='rbf')
svm.fit(feat_train,train_labels)
svm.score(feat_train,train_labels)