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"""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()
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