title: How to have parallel convolutional layers in keras?
body: <p>I am a little new to neural networks and keras. I have some images with size 6*7 and the size of the filter is 15. I want to have several filters and train a convolutional layer separately on each and then combine them. I have looked at one example here:</p>
  
  <pre><code>model = Sequential()
  model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],
                      border_mode='valid',
                      input_shape=input_shape))
  model.add(Activation('relu'))
  model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=pool_size))
  model.add(Dropout(0.25))
  model.add(Flatten(input_shape=input_shape))
  model.add(Dense(128))
  model.add(Activation('relu'))
  model.add(Dense(128))
  model.add(Activation('relu'))
  model.add(Dropout(0.5))
  model.add(Dense(nb_classes))
  model.add(Activation('tanh'))
  </code></pre>
  
  <p>This model works with one filter. Can anybody give me some hints on how to modify the model to work with parallel convolutional layers.</p>
  
  <p>Thanks</p>