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"""Functional test for GradientDescent."""
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
class GradientDescentOptimizerTest(tf.test.TestCase):
def testBasic(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])
sgd_op = tf.train.GradientDescentOptimizer(3.0).apply_gradients(
zip([grads0, grads1], [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
sgd_op.run()
# Validate updated params
self.assertAllClose([1.0 - 3.0 * 0.1, 2.0 - 3.0 * 0.1], var0.eval())
self.assertAllClose([3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01], var1.eval())
def testFloat64(self):
with self.test_session():
opt = tf.train.GradientDescentOptimizer(3.0)
# compute_gradients.
values = [1.0, 3.0]
good_vars = [tf.Variable([v]) for v in values]
bad_loss = tf.constant(2.0, tf.float64, name="bad_loss")
self.assertRaisesRegexp(
ValueError, r"Invalid type.*float64.*bad_loss.*expected.*float32",
opt.compute_gradients, bad_loss, good_vars)
bad_vars = [
tf.Variable(np.array([v], np.float64), name="bad_var")
for v in values]
self.assertRaisesRegexp(
ValueError, r"Invalid type.*float64.*bad_var.*expected.*float32",
opt.compute_gradients, tf.cast(bad_vars[0] + bad_vars[1], tf.float32),
bad_vars)
opt.compute_gradients(good_vars[0] + good_vars[1], good_vars)
# apply_gradients.
bad_grads = [
tf.constant([0.1], dtype=np.float64, name="bad_grad"),
tf.constant([0.01])]
self.assertRaisesRegexp(
ValueError, r"Invalid type.*float64.*bad_grad.*expected.*float32",
opt.apply_gradients, zip(bad_grads, good_vars))
good_grads = [tf.constant([0.01]), tf.constant([0.02])]
self.assertRaisesRegexp(
ValueError, r"Invalid type.*float64.*bad_var.*expected.*float32",
opt.apply_gradients, zip(good_grads, bad_vars))
opt.apply_gradients(zip(good_grads, good_vars))
def testWithGlobalStep(self):
with self.test_session():
global_step = tf.Variable(0, trainable=False)
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])
sgd_op = tf.train.GradientDescentOptimizer(3.0).apply_gradients(
zip([grads0, grads1], [var0, var1]), global_step=global_step)
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
sgd_op.run()
# Validate updated params and global_step
self.assertAllClose([1.0 - 3.0 * 0.1, 2.0 - 3.0 * 0.1], var0.eval())
self.assertAllClose([3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01], var1.eval())
self.assertAllClose(1, global_step.eval())
def testSparseBasic(self):
with self.test_session():
var0 = tf.Variable([[1.0], [2.0]])
var1 = tf.Variable([[3.0], [4.0]])
grads0 = tf.IndexedSlices(tf.constant([0.1], shape=[1, 1]),
tf.constant([0]),
tf.constant([2, 1]))
grads1 = tf.IndexedSlices(tf.constant([0.01], shape=[1, 1]),
tf.constant([1]),
tf.constant([2, 1]))
sgd_op = tf.train.GradientDescentOptimizer(3.0).apply_gradients(
zip([grads0, grads1], [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
sgd_op.run()
# Validate updated params
self.assertAllClose([[1.0 - 3.0 * 0.1], [2.0]], var0.eval())
self.assertAllClose([[3.0], [4.0 - 3.0 * 0.01]], var1.eval())
if __name__ == "__main__":
tf.test.main()
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