def model_fn(features, labels, mode, params):
      del params
      x = core_layers.dense(features, 100)
      x = core_layers.dense(x, 100)
      x = core_layers.dense(x, 100)
      x = core_layers.dense(x, 100)
      y = core_layers.dense(x, 1)
      loss = losses.mean_squared_error(labels, y)
      opt = adam.AdamOptimizer(learning_rate=0.1)
      train_op = opt.minimize(
          loss, global_step=training_util.get_or_create_global_step())

      return EstimatorSpec(mode=mode, loss=loss, train_op=train_op)