5e854f25098ca40a4ac80197607bf883feaeb375,research/object_detection/utils/learning_schedules_test.py,LearningSchedulesTest,testExponentialDecayWithBurnin,#LearningSchedulesTest#,24

Before Change

class LearningSchedulesTest(tf.test.TestCase):

  def testExponentialDecayWithBurnin(self):
    global_step = tf.placeholder(tf.int32, [])
    learning_rate_base = 1.0
    learning_rate_decay_steps = 3
    learning_rate_decay_factor = .1
    burnin_learning_rate = .5
    burnin_steps = 2
    exp_rates = [.5, .5, 1, .1, .1, .1, .01, .01]
    learning_rate = learning_schedules.exponential_decay_with_burnin(
        global_step, learning_rate_base, learning_rate_decay_steps,
        learning_rate_decay_factor, burnin_learning_rate, burnin_steps)
    with self.test_session() as sess:
      output_rates = []
      for input_global_step in range(8):
        output_rate = sess.run(learning_rate,
                               feed_dict={global_step: input_global_step})
        output_rates.append(output_rate)
      self.assertAllClose(output_rates, exp_rates)

  def testCosineDecayWithWarmup(self):
    global_step = tf.placeholder(tf.int32, [])
    learning_rate_base = 1.0
    total_steps = 100

After Change

          learning_rate_decay_factor, burnin_learning_rate, burnin_steps)
      return (learning_rate,)

    output_rates = [
        self.execute(graph_fn, [np.array(i).astype(np.int64)]) for i in range(8)
    ]

    exp_rates = [.5, .5, 1, .1, .1, .1, .01, .01]
    self.assertAllClose(output_rates, exp_rates, rtol=1e-4)

Instances