title: Combine outputs of two Pre Trained models (trained on different dataset) and use some form of binary classifier to predict images
body: <p>I have two Pre-Trained models.</p>
  
  <p><code>Model_1 = Inception Model with Imagenet Dataset (1000 classes)</code></p>
  
  <p><code>My_Model = Inception Model trained with a custom dataset (20 classes)</code> via Transfer Learning and Fine-Tuning</p>
  
  <p>I would like to combine the outputs of both models (<code>Model_1</code> and <code>My_Model</code>) in a new layer. </p>
  
  <p>The new layer should use some binary classifier to tell whether to use <code>Model_1</code> or <code>My_Model</code> for prediction based on the input image.</p>
  
  <p>For Example: </p>
  
  <p>If I try to predict a "Dog" image, the binary classifier which combines both models should say that I need to use <code>Model_1</code> to predict the Dog image (since <code>My_Model</code> dataset is not trained with Dog image) whereas <code>Model_1</code> is trained with Dog images.</p>
  
  <p>Can anyone tell me how to achieve this? Some example implementation or code snippet will be helpful.</p>
  
  <p>Thanks</p>