title: How to apply regularization when fine tuning on the imagenet extracted models
body: <p>One way of fine tuning is extracting a model ( like VGG16 trained on Imagenet) , adding a layer or so and then train the model.</p>
  <p>Is it possible to apply regularization to the model layers apart from the added layer using Tensorflow.Keras. I don't think adding regularization to only one layer effects the outcome much.</p>
  <p>I know we can apply the regularization for the added layer as:</p>
  <pre><code>x = Dense(classes, kernel_regularizer=l2(reg), name=&quot;labels&quot;)(x)
  </code></pre>
  <p>But is it possible to apply regularization for other layers as well in Keras.
  It could be easily done in mxnet.</p>
  <p>Would be grateful for any help.</p>