title: How to predict using LinearRegressor in TensorFlow (without being forced to enter the solution)?
body: <p>I'm trying to learn some TensorFlow (Python) basics from Google's Crash Course online. However, I'm finding the explanations a little light and am confused how to use this beyond the examples / tasks they set.</p>
  
  <p>For example, there is a basic task to predict housing prices in California using Linear Regression with Gradient Descent (see: <a href="https://colab.research.google.com/notebooks/mlcc/validation.ipynb" rel="nofollow noreferrer">https://colab.research.google.com/notebooks/mlcc/validation.ipynb</a>)</p>
  
  <p>The tasks are all about training and then testing a model using an existing dataset. This works well, but I'm struggling to use the code to predict house prices for arbitrary input values (i.e. not from the test/training set).</p>
  
  <p>The problem arises because the input function to the LinearRegressor requires you to enter the target values, which of course are unknown if you want to make predictions:</p>
  
  <p>e.g.</p>
  
  <pre><code>predict_test_input_fn = lambda: my_input_fn(
    test_examples, 
    test_targets["median_house_value"], 
    num_epochs=1, 
    shuffle=False)
  </code></pre>
  
  <p>where the my_input_func is defined within the Crash Course notebook (see link above).</p>
  
  <p>If anyone is familiar with TensorFlow / the Google ML Crash Course, could you perhaps please explain what should be entered as a target value, when the point of using a trained model is to find unknown values?</p>
  
  <p>Thank you very much for any help!</p>