title: tf.keras: Feeding extra values to model.fit
body: <p>Say you have e.g. a boolean <code>tf.placeholder</code>, and you want to feed it when you call <code>Model.fit</code>. How would you do it? Below is some runnable dummy code that illustrates the problem.</p>
  
  <pre><code>import tensorflow as tf
  from tensorflow.keras.layers import Dense, Input, Flatten
  from tensorflow.keras.models import Model
  # A boolean value that should have some effect of something
  do_stuff = tf.placeholder(tf.bool)
  # If do_stuff is true, return tf.ones else tf.zeros, and a 1 or 0 label
  if_dostuff = lambda: [tf.ones((5, 5)), tf.constant(1)]
  if_not_dostuff = lambda: [tf.zeros((5, 5)), tf.constant(0)]
  X, Y_true = tf.cond(do_stuff, if_dostuff, if_not_dostuff)
  # Make some dummy labels
  # Do some random model operation
  X_input = Input(shape=(5, 5))
  layer_mod = Flatten()(X_input)
  layer_mod = Dense(1)(layer_mod)
  out_model = Model(inputs=[X_input], outputs=[layer_mod])
  # Compile model
  out_model.compile(
      optimizer=tf.keras.optimizers.Adam(),
      loss=tf.keras.metrics.binary_crossentropy
  )
  ### Other ops with other models and summaries etc. ###
  out_model.fit(...) # What do I do at this point?
  </code></pre>
  
  <p>Keep in mind that the boolean is just to keep things simple. In reality I have strings that are iterator handles that needs to be fed (based on what dataset I want to train).</p>
  
  <p>How can I do keras' amazing <code>model.fit</code> interface with this sort of layout? </p>
  
  <p>An alternative would be what I ask in <a href="https://stackoverflow.com/questions/55687011/access-training-operation-in-a-tf-keras-model">this question</a></p>