Keras：如何将 CNN 模型与决策树连接起来
Keras: How to connect a CNN model with a decision tree
我想训练一个模型来根据物理信号预测一个人的情绪。我有一个物理信号并将其用作输入功能；
ecg(Electrocardiography)
我想用CNN架构从数据中提取特征，然后用这些提取的特征喂一个经典的"Decision Tree Classifier"。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，以便在输出层中使用 决策树 而不是 model.add(Dense(3, activation = 'softmax'))。我试过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；
THE OUTPUT: Tensor("conv1d_56/Relu:0", shape=(?, 8971, 30),
dtype=float32)
我猜，output 变量保存提取的特征。现在，如何使用存储在 output 变量中的数据来为我的 决策树 分类器模型提供数据？这是来自 scikit learn 的决策树；
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier(criterion = 'entropy')
dtc.fit()
fit()方法应该怎么喂？提前致谢。
要提取可以传递给另一个算法的特征向量，您需要在 softmax 层之前有一个完全连接的层。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'))
如果你然后 运行 model.summary() 你可以看到层的名称：
_________________________________________________________________
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
_________________________________________________________________
网络训练完成后，您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
我想训练一个模型来根据物理信号预测一个人的情绪。我有一个物理信号并将其用作输入功能；
ecg(Electrocardiography)
我想用CNN架构从数据中提取特征，然后用这些提取的特征喂一个经典的"Decision Tree Classifier"。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，以便在输出层中使用 决策树 而不是 model.add(Dense(3, activation = 'softmax'))。我试过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；
THE OUTPUT: Tensor("conv1d_56/Relu:0", shape=(?, 8971, 30), dtype=float32)
我猜，output 变量保存提取的特征。现在，如何使用存储在 output 变量中的数据来为我的 决策树 分类器模型提供数据？这是来自 scikit learn 的决策树；
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier(criterion = 'entropy')
dtc.fit()
fit()方法应该怎么喂？提前致谢。
要提取可以传递给另一个算法的特征向量，您需要在 softmax 层之前有一个完全连接的层。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'))
如果你然后 运行 model.summary() 你可以看到层的名称：
_________________________________________________________________
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
_________________________________________________________________
网络训练完成后，您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