title: Tensorflow, what is the best way to add extra inforamtion to a sequential model?
body: <p>I'm new to machine learning and I'm tring to understand how to make models.</p>
  <p>What I'm tring to do.</p>
  <pre><code>import tensorflow as tf
  import numpy as np
  from tensorflow import keras
  from tensorflow.keras import layers
  
  # this are my X and Y values
  X = np.array([ [x, x+1 ] for x in range(80) ])
  Y = [ x + 2 for x in range(80) ]
  
  # The data given in this case is random but has a pridictable pattern.
  # if i would use a list of [81,82,1,2] 
  # to predict once i fit the model it will give 83.
  
  # I have this extra information that could be helpful for the prediction.
  extra_info = [ 1, 2 ]
  
  # What is the best way to add this extra information?
  
  # This is the only way i konw how to add it:
  X = np.array([ [x, x+1, extra_info[0], extra_info[1] ] for x in range(80) ])
  </code></pre>
  <p>Is this the correct way of doing this?</p>
  <p>Is not going to confuse the model?</p>
  <p>And if the <code>extra_info</code> had more than 1000 fields whould this also be correct?</p>
  <pre><code>model = keras.Sequential([
    layers.Dense( 64, activation='relu', input_shape =[ 4 ] ),
    layers.Dense( 64, activation='relu' ),
    layers.Dense( 1 )
  ])
  
  model.compile(
      loss='mse',
      optimizer = tf.keras.optimizers.RMSprop( 0.001 ),
      metrics=['mae', 'mse']
  )
  
  history = model.fit(
    X, Y, epochs=20, verbose=0,
  )
  
  print(model.predict(np.array([80,81,1,2])))
  </code></pre>