title: How can combine two tensors so they are in one dataset?
body: <p>I'm working with the <code>Titanic</code> data set from the TensorFlow API.</p>
  <p>I don't know how to make the features tensors model friendly.</p>
  <p>Here's the best I got, but it's for one tensor at a time. How do I make it so it can handle all tensors in the features item?</p>
  <pre><code>import tensorflow as tf
  import tensorflow_datasets as tfds
  from tensorflow.keras.optimizers import Adam
      
  data = tfds.load(&quot;titanic&quot;,split='train', as_supervised=True).map(lambda x,y: (x,y)).prefetch(1)
      
  for i in data.batch(1309):
      xx1 = i[0]['age']
      xx2 = i[0]['fare']
      yyy = tf.convert_to_tensor(tf.one_hot(i[1],2))
  
  model = tf.keras.models.Sequential([tf.keras.layers.Dense(1),
  tf.keras.layers.Dense(13, activation='relu'),
  tf.keras.layers.Dense(2, activation='softmax')])
  
  model.compile(
    optimizer=Adam(learning_rate=0.01), 
    loss='categorical_crossentropy', 
    metrics=['accuracy']
  )
  
  model.fit(xx1,yyy,epochs=30)
  </code></pre>
  <p>How do I concat the <code>age</code> and <code>fare</code> tensors so that they're in one data set?</p>
  <p>I tried <code>concat</code> and <code>stack</code> to no avail.</p>