python - Keras：如何将 CNN 模型与决策树连接起来

Question

我想训练一个模型来从物理信号中预测一个人的情绪。我有一个物理信号并将其用作输入功能；

ecg（心电图）

我想使用 CNN 架构从数据中提取特征，然后使用这些提取的特征来提供经典的“决策树分类器”。下面，你可以看到我的 CNN 方法没有决策树；

model = Sequential()
model.add(Conv1D(15,60,padding='valid', activation='relu',input_shape=(18000,1), strides = 1,  kernel_regularizer=regularizers.l1_l2(l1=0.1, l2=0.1)))
model.add(MaxPooling1D(2,data_format='channels_last'))
model.add(Dropout(0.6))
model.add(BatchNormalization())
model.add(Conv1D(30, 60, padding='valid', activation='relu',kernel_regularizer = regularizers.l1_l2(l1=0.1, l2=0.1), strides=1))
model.add(MaxPooling1D(4,data_format='channels_last'))
model.add(Dropout(0.6))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dense(3, activation = 'softmax'))

我想编辑此代码，以便在输出层中将有工作决策树而不是model.add(Dense(3, activation = 'softmax')). 我试图像这样保存最后一个卷积层的输出；

output = model.layers[-6].output

当我打印出output变量时，结果是这样的；

输出：张量("conv1d_56/Relu:0", shape=(?, 8971, 30), dtype=float32)

我猜，该output变量包含提取的特征。现在，我怎样才能用存储在变量中的这些数据来提供我的决策树分类器模型？output这是来自 scikit learn 的决策树；

from sklearn.tree import DecisionTreeClassifier

dtc = DecisionTreeClassifier(criterion = 'entropy')
dtc.fit()

我应该如何喂养该fit()方法？提前致谢。

标签： pythontensorflowkerasscikit-learndecision-tree

Answer 1

要提取可以传递给另一个算法的特征向量，您需要在 softmax 层之前有一个全连接层。像这样的东西会在你的 softmax 层之前添加一个 128 维的层：

model = Sequential()
model.add(Conv1D(15,60,padding='valid', activation='relu',input_shape=(18000,1), strides = 1,  kernel_regularizer=regularizers.l1_l2(l1=0.1, l2=0.1)))
model.add(MaxPooling1D(2,data_format='channels_last'))
model.add(Dropout(0.6))
model.add(BatchNormalization())
model.add(Conv1D(30, 60, padding='valid', activation='relu',kernel_regularizer = regularizers.l1_l2(l1=0.1, l2=0.1), strides=1))
model.add(MaxPooling1D(4,data_format='channels_last'))
model.add(Dropout(0.6))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(3, activation = 'softmax'))

如果然后运行model.summary()，您可以看到图层的名称：

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv1d_9 (Conv1D)            (None, 17941, 15)         915       
_________________________________________________________________
max_pooling1d_9 (MaxPooling1 (None, 8970, 15)          0         
_________________________________________________________________
dropout_10 (Dropout)         (None, 8970, 15)          0         
_________________________________________________________________
batch_normalization_9 (Batch (None, 8970, 15)          60        
_________________________________________________________________
conv1d_10 (Conv1D)           (None, 8911, 30)          27030     
_________________________________________________________________
max_pooling1d_10 (MaxPooling (None, 2227, 30)          0         
_________________________________________________________________
dropout_11 (Dropout)         (None, 2227, 30)          0         
_________________________________________________________________
batch_normalization_10 (Batc (None, 2227, 30)          120       
_________________________________________________________________
flatten_6 (Flatten)          (None, 66810)             0         
_________________________________________________________________
dense_7 (Dense)              (None, 128)               8551808   
_________________________________________________________________
dropout_12 (Dropout)         (None, 128)               0         
_________________________________________________________________
dense_8 (Dense)              (None, 3)                 387       
=================================================================
Total params: 8,580,320
Trainable params: 8,580,230
Non-trainable params: 90
_________________________________________________________________

一旦您的网络经过训练，您就可以创建一个新模型，其中输出层变为“dense_7”，它将生成 128 维特征向量：

feature_vectors_model = Model(model.input, model.get_layer('dense_7').output)
dtc_features = feature_vectors_model.predict(your_X_data)  # fit your decision tree on this data