title: Convert sklearn.svm SVC classifier to Keras implementation
body: <p>I'm trying to convert some old code from using sklearn to Keras implementation. Since it is crucial to maintain the same way of operation, I want to understand if I'm doing it correctly.</p>
  
  <p>I've converted most of the code already, however I'm having trouble with sklearn.svm SVC classifier conversion. Here is how it looks right now:</p>
  
  <pre><code>from sklearn.svm import SVC
  model = SVC(kernel='linear', probability=True)
  model.fit(X, Y_labels)
  </code></pre>
  
  <p>Super easy, right. However, I couldn't find the analog of SVC classifier in Keras. So, what I've tried is this:</p>
  
  <pre><code>from keras.models import Sequential
  from keras.layers import Dense
  
  model = Sequential()
  model.add(Dense(64, activation='relu'))
  model.add(Dense(1, activation='softmax'))
  model.compile(loss='squared_hinge',
                optimizer='adadelta',
                metrics=['accuracy'])
  model.fit(X, Y_labels)
  </code></pre>
  
  <p>But, I think that it is not correct by any means. Could you, please, help me find an alternative of the SVC classifier from sklearn in Keras?</p>
  
  <p>Thank you.</p>