Keras: How to connect a CNN model with a decision tree
$begingroup$
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.
deep-learning classification keras scikit-learn decision-trees
asked 5 mins ago
Ozan YurtseverOzan Yurtsever
1085
$endgroup$
add a comment |
$begingroup$
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.
deep-learning classification keras scikit-learn decision-trees
asked 5 mins ago
Ozan YurtseverOzan Yurtsever
1085
$endgroup$
add a comment |
$begingroup$
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.
deep-learning classification keras scikit-learn decision-trees
asked 5 mins ago
Ozan YurtseverOzan Yurtsever
1085
$endgroup$
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.
deep-learning classification keras scikit-learn decision-trees
deep-learning classification keras scikit-learn decision-trees
asked 5 mins ago
Ozan YurtseverOzan Yurtsever
1085
asked 5 mins ago
Ozan YurtseverOzan Yurtsever
1085
asked 5 mins ago
Ozan YurtseverOzan Yurtsever
1085
asked 5 mins ago
Ozan YurtseverOzan Yurtsever
1085
asked 5 mins ago
Ozan YurtseverOzan Yurtsever
1085
1085
add a comment |
add a comment |
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