"""Tests for rmsprop."""
import math

import tensorflow.python.platform

import numpy as np
import tensorflow as tf


class RMSPropOptimizerTest(tf.test.TestCase):

  def testWithoutMomentum(self):
    with self.test_session():
      var0 = tf.Variable([1.0, 2.0])
      var1 = tf.Variable([3.0, 4.0])
      grads0 = tf.constant([0.1, 0.1])
      grads1 = tf.constant([0.01, 0.01])
      opt = tf.train.RMSPropOptimizer(learning_rate=2.0, decay=0.9,
                                      momentum=0.0, epsilon=1.0)
      update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
      tf.initialize_all_variables().run()

      rms0 = opt.get_slot(var0, "rms")
      self.assertTrue(rms0 is not None)
      rms1 = opt.get_slot(var1, "rms")
      self.assertTrue(rms1 is not None)
      mom0 = opt.get_slot(var0, "momentum")
      self.assertTrue(mom0 is not None)
      mom1 = opt.get_slot(var1, "momentum")
      self.assertTrue(mom1 is not None)

      # Fetch params to validate initial values
      self.assertAllClose([1.0, 2.0], var0.eval())
      self.assertAllClose([3.0, 4.0], var1.eval())
      # Step 1: the rms accumulators where 1. So we should see a normal
      # update: v -= grad * learning_rate
      update.run()
      # Check the root mean square accumulators.
      self.assertAllClose(np.array([0.901, 0.901]), rms0.eval())
      self.assertAllClose(np.array([0.90001, 0.90001]), rms1.eval())
      # Check the parameters.
      self.assertAllClose(np.array([1.0 - (0.1 * 2.0 / math.sqrt(0.901+1.0)),
                                    2.0 - (0.1 * 2.0 / math.sqrt(0.901+1.0))]),
                          var0.eval())
      self.assertAllClose(np.array([3.0 - (0.01 * 2.0
                                           / math.sqrt(0.90001+1.0)),
                                    4.0 - (0.01 * 2.0
                                           / math.sqrt(0.90001+1.0))]),
                          var1.eval())
      # Step 2: the root mean square accumulators contain the previous update.
      update.run()
      # Check the rms accumulators.
      self.assertAllClose(np.array([0.901*0.9+0.001, 0.901*0.9+0.001]),
                          rms0.eval())
      self.assertAllClose(np.array([0.90001*0.9+1e-5, 0.90001*0.9+1e-5]),
                          rms1.eval())
      # Check the parameters.
      self.assertAllClose(
          np.array([1.0 - (0.1 * 2.0 / math.sqrt(0.901+1.0))
                    - (0.1 * 2.0 / math.sqrt(0.901*0.9+0.001+1.0)),
                    2.0 - (0.1 * 2.0 / math.sqrt(0.901+1.0))
                    - (0.1 * 2.0 / math.sqrt(0.901*0.9+0.001+1.0))]),
          var0.eval())
      self.assertAllClose(np.array([3.0 - (0.01 * 2.0 / math.sqrt(0.90001+1.0))
                                    - (0.01 * 2.0 /
                                       math.sqrt(0.90001*0.9+1e-5+1.0)),
                                    4.0 - (0.01 * 2.0 / math.sqrt(0.90001+1.0))
                                    - (0.01 * 2.0 /
                                       math.sqrt(0.90001*0.9+1e-5+1.0))]),
                          var1.eval())

  def testWithMomentum(self):
    with self.test_session():
      var0 = tf.Variable([1.0, 2.0])
      var1 = tf.Variable([3.0, 4.0])
      grads0 = tf.constant([0.1, 0.1])
      grads1 = tf.constant([0.01, 0.01])

      opt = tf.train.RMSPropOptimizer(learning_rate=2.0, decay=0.9,
                                      momentum=0.5, epsilon=1e-5)
      update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
      tf.initialize_all_variables().run()

      rms0 = opt.get_slot(var0, "rms")
      self.assertTrue(rms0 is not None)
      rms1 = opt.get_slot(var1, "rms")
      self.assertTrue(rms1 is not None)
      mom0 = opt.get_slot(var0, "momentum")
      self.assertTrue(mom0 is not None)
      mom1 = opt.get_slot(var1, "momentum")
      self.assertTrue(mom1 is not None)

      # Fetch params to validate initial values
      self.assertAllClose([1.0, 2.0], var0.eval())
      self.assertAllClose([3.0, 4.0], var1.eval())
      # Step 1: rms = 1, mom = 0. So we should see a normal
      # update: v -= grad * learning_rate
      update.run()
      # Check the root mean square accumulators.
      self.assertAllClose(np.array([0.901, 0.901]), rms0.eval())
      self.assertAllClose(np.array([0.90001, 0.90001]), rms1.eval())
      # Check the momentum accumulators
      self.assertAllClose(np.array([(0.1 * 2.0 / math.sqrt(0.901+1e-5)),
                                    (0.1 * 2.0 / math.sqrt(0.901+1e-5))]),
                          mom0.eval())
      self.assertAllClose(np.array([(0.01 * 2.0/ math.sqrt(0.90001+1e-5)),
                                    (0.01 * 2.0/ math.sqrt(0.90001+1e-5))]),
                          mom1.eval())

      # Check that the parameters.
      self.assertAllClose(np.array([1.0 - (0.1 * 2.0 / math.sqrt(0.901+1e-5)),
                                    2.0 - (0.1 * 2.0 / math.sqrt(0.901+1e-5))]),
                          var0.eval())
      self.assertAllClose(np.array([3.0 - (0.01 * 2.0/ math.sqrt(0.90001+1e-5)),
                                    4.0 - (0.01 * 2.0/ math.sqrt(0.90001+1e-5))]
                                  ),
                          var1.eval())

      # Step 2: the root mean square accumulators contain the previous update.
      update.run()
      # Check the rms accumulators.
      self.assertAllClose(np.array([0.901*0.9+0.001, 0.901*0.9+0.001]),
                          rms0.eval())
      self.assertAllClose(np.array([0.90001*0.9+1e-5, 0.90001*0.9+1e-5]),
                          rms1.eval())
      self.assertAllClose(np.array([0.5 * (0.1 * 2.0 / math.sqrt(0.901+1e-5)) +
                                    (0.1*2.0/math.sqrt(0.901*0.9+0.001+1e-5)),
                                    0.5 * (0.1 * 2.0 / math.sqrt(0.901+1e-5)) +
                                    (0.1*2.0/math.sqrt(0.901*0.9+0.001+1e-5))
                                   ]), mom0.eval())
      self.assertAllClose(np.array([0.5 *(0.01 * 2.0/ math.sqrt(0.90001+1e-5))+
                                    (0.01 * 2.0 /math.sqrt(0.90001*0.9+2e-5)),
                                    0.5 *(0.01 * 2.0/ math.sqrt(0.90001+1e-5))+
                                    (0.01 * 2.0 / math.sqrt(0.90001*0.9+2e-5))
                                   ]), mom1.eval())

      # Check the parameters.
      self.assertAllClose(
          np.array([1.0 - (0.1 * 2.0 / math.sqrt(0.901+1e-5)) - (0.5 * (
              0.1 * 2.0 / math.sqrt(0.901+1e-5)) +(
                  0.1 * 2.0 / math.sqrt(0.901*0.9+0.001+1e-5))),
                    2.0 - (0.1 * 2.0 / math.sqrt(0.901+1e-5)) - (0.5 * (
                        0.1 * 2.0 / math.sqrt(0.901+1e-5)) +(
                            0.1 * 2.0 / math.sqrt(0.901*0.9+0.001+1e-5)))
                   ]), var0.eval())

      self.assertAllClose(
          np.array([3.0 - (0.01 * 2.0 / math.sqrt(0.90001+1e-5))
                    - (0.5 *(0.01 * 2.0/ math.sqrt(0.90001+1e-5)) +
                       (0.01 * 2.0 /math.sqrt(0.90001*0.9+2e-5))),
                    4.0 - (0.01 * 2.0 / math.sqrt(0.90001+1e-5))
                    - (0.5 *(0.01 * 2.0/ math.sqrt(0.90001+1e-5)) +
                       (0.01 * 2.0 / math.sqrt(0.90001*0.9+2e-5)))]),
          var1.eval())


if __name__ == "__main__":
  tf.test.main()