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"""Functional test for optimizer."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.python.platform
import tensorflow as tf
class OptimizerTest(tf.test.TestCase):
def testBasic(self):
with self.test_session():
var0 = tf.Variable([1.0, 2.0])
var1 = tf.Variable([3.0, 4.0])
cost = 5 * var0 + 3 * var1
global_step = tf.Variable(tf.zeros([], tf.int64), name='global_step')
sgd_op = tf.train.GradientDescentOptimizer(3.0)
opt_op = sgd_op.minimize(cost, global_step, [var0, var1])
tf.initialize_all_variables().run()
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], var0.eval())
self.assertAllClose([3.0, 4.0], var1.eval())
# Run 1 step of sgd through optimizer
opt_op.run()
# Validate updated params
self.assertAllClose([-14., -13.], var0.eval())
self.assertAllClose([-6., -5.], var1.eval())
def testAggregationMethod(self):
with self.test_session():
var0 = tf.Variable([1.0, 2.0])
var1 = tf.Variable([3.0, 4.0])
cost = 5 * var0 + 3 * var1
global_step = tf.Variable(tf.zeros([], tf.int64), name='global_step')
sgd_op = tf.train.GradientDescentOptimizer(3.0)
opt_op = sgd_op.minimize(
cost, global_step, [var0, var1], aggregation_method=
tf.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N)
tf.initialize_all_variables().run()
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], var0.eval())
self.assertAllClose([3.0, 4.0], var1.eval())
# Run 1 step of sgd through optimizer
opt_op.run()
# Validate updated params
self.assertAllClose([-14., -13.], var0.eval())
self.assertAllClose([-6., -5.], var1.eval())
if __name__ == "__main__":
tf.test.main()
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