[FIXED] Keras: How to connect a CNN model with a decision tree

Issue

I want to train a model to predict one's emotion from the physical signals. I have a physical signal and using it as input feature;

ecg(Electrocardiography)

I want to use the CNN architecture to extract features from the data, and then use these extracted features to feed a classical "Decision Tree Classifier". Below, you can see my CNN aproach without the decision tree;

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'))

I want to edit this code so that, in the output layer there will be working decision tree instead of model.add(Dense(3, activation = 'softmax')). I have tried to save the outputs of the last convolutional layer like this;

output = model.layers[-6].output

And when I printed out the output variable, result was this;

THE OUTPUT: Tensor("conv1d_56/Relu:0", shape=(?, 8971, 30), dtype=float32)

I guess, the output variable holds the extracted features. Now, how can I feed my decision tree classifier model with this data which is stored in the output variable? Here is the decision tree from scikit learn;

from sklearn.tree import DecisionTreeClassifier

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

How should I feed the fit() method? Thanks in advance.

Solution

To extract a vector of features that you can pass on to another algorithm, you need a fully connected layer before your softmax layer. Something like this will add in a 128 dimensional layer just before your softmax layer:

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'))

If you then run model.summary() you can see the name of the layers:

_________________________________________________________________
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
_________________________________________________________________

Once your network has been trained you can create a new model where the output layer becomes 'dense_7' and it'll generate 128 dimensional feature vectors:

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

Answered By - Matt