title: calling predict on the loaded model in keras returns the probaility value of each action not the prediction
body: <p>I am writing an RL program with keras.
  The program builds a NN model and then uses that model to predict the situation.
  ```def _build_model(self):</p>
  <pre><code>    model = Sequential()
      model.add(Dense(24,input_dim=self.state_size, activation='relu'))
      model.add(Dense(18, activation='relu'))
      model.add(Dense(self.action_size, activation ='linear'))
      model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
  
      return model```
  </code></pre>
  <p>I use action = model.predict(state) in each episode.
  In the end I use keras.save(name) to save the model before exiting the program.
  As I mentioned, I use model.predict() in each episode of the game to select an action and it returns an action out of 8 actions[0 to 7]</p>
  <p>But if I write a piece of code and use keras.models.load_model(name) and then call
  action = model.predict(state), it returns an array of 8 elements which are probability of each action, not the selected action that  expect.</p>
  <p>I need to add that if I don't want to save the model to deploy and use it later, it is working fine. All the results and outputs sound logical but if I decide to save it for later to just put it in the new environment and it takes action, I cannot as I explained above.
  How can I resolve this issue?
  Thanks in advance for your help.</p>