bf9c196f37
We do not support complex with certain optimizers such as Ftrl, FtrlV2, AdamWithAmsgrad, AdaMax, AddSign & PowerSign since they may use missing operations on complex values such as sqrt. Fixes #32774 PiperOrigin-RevId: 277953548 Change-Id: Ia075aa5c3f944de932d71b9741d626f7ebe5416f
143 lines
5.6 KiB
Python
143 lines
5.6 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
"""Tests for Adadelta Optimizer."""
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import numpy as np
|
|
|
|
from tensorflow.compiler.tests import xla_test
|
|
from tensorflow.python.framework import constant_op
|
|
from tensorflow.python.ops import resource_variable_ops
|
|
from tensorflow.python.ops import variables
|
|
from tensorflow.python.platform import test
|
|
from tensorflow.python.training import adadelta
|
|
|
|
|
|
class AdadeltaOptimizerTest(xla_test.XLATestCase):
|
|
|
|
def testBasic(self):
|
|
num_updates = 4 # number of ADADELTA steps to perform
|
|
if "CPU" in self.device:
|
|
# To avoid timeout on CPU.
|
|
all_grad = [0.2, 0.01]
|
|
all_lr = [1.0, 0.1]
|
|
else:
|
|
all_grad = [0.2, 0.1, 0.01]
|
|
all_lr = [1.0, 0.5, 0.1]
|
|
|
|
for dtype in self.float_types | self.complex_types:
|
|
with self.session(), self.test_scope():
|
|
for grad in all_grad:
|
|
for lr in all_lr:
|
|
var0_init = [1.0, 2.0]
|
|
var1_init = [3.0, 4.0]
|
|
var0 = resource_variable_ops.ResourceVariable(
|
|
var0_init, dtype=dtype)
|
|
var1 = resource_variable_ops.ResourceVariable(
|
|
var1_init, dtype=dtype)
|
|
|
|
grads = constant_op.constant([grad, grad], dtype=dtype)
|
|
|
|
accum = 0.0
|
|
accum_update = 0.0
|
|
|
|
# ADADELTA gradient optimizer
|
|
rho = 0.95
|
|
epsilon = 1e-8
|
|
adadelta_opt = adadelta.AdadeltaOptimizer(
|
|
learning_rate=lr, rho=rho, epsilon=epsilon)
|
|
adadelta_update = adadelta_opt.apply_gradients(
|
|
zip([grads, grads], [var0, var1]))
|
|
self.evaluate(variables.global_variables_initializer())
|
|
opt_vars = adadelta_opt.variables()
|
|
self.assertStartsWith(opt_vars[0].name, var0._shared_name)
|
|
self.assertStartsWith(opt_vars[1].name, var0._shared_name)
|
|
self.assertStartsWith(opt_vars[2].name, var1._shared_name)
|
|
self.assertStartsWith(opt_vars[3].name, var1._shared_name)
|
|
self.assertEqual(4, len(opt_vars))
|
|
# Assign slots
|
|
slot = [None] * 2
|
|
slot_update = [None] * 2
|
|
self.assertEqual(["accum", "accum_update"],
|
|
adadelta_opt.get_slot_names())
|
|
slot[0] = adadelta_opt.get_slot(var0, "accum")
|
|
self.assertEqual(slot[0].get_shape(), var0.get_shape())
|
|
self.assertNotIn(slot[0], variables.trainable_variables())
|
|
|
|
slot_update[0] = adadelta_opt.get_slot(var0, "accum_update")
|
|
self.assertEqual(slot_update[0].get_shape(), var0.get_shape())
|
|
self.assertNotIn(slot_update[0], variables.trainable_variables())
|
|
|
|
slot[1] = adadelta_opt.get_slot(var1, "accum")
|
|
self.assertEqual(slot[1].get_shape(), var1.get_shape())
|
|
self.assertNotIn(slot[1], variables.trainable_variables())
|
|
|
|
slot_update[1] = adadelta_opt.get_slot(var1, "accum_update")
|
|
self.assertEqual(slot_update[1].get_shape(), var1.get_shape())
|
|
self.assertNotIn(slot_update[1], variables.trainable_variables())
|
|
|
|
# Fetch params to validate initial values
|
|
self.assertAllClose(var0_init, self.evaluate(var0))
|
|
self.assertAllClose(var1_init, self.evaluate(var1))
|
|
|
|
update = [None] * num_updates
|
|
tot_update = 0
|
|
for step in range(num_updates):
|
|
# Run adadelta update for comparison
|
|
self.evaluate(adadelta_update)
|
|
|
|
# Perform initial update without previous accum values
|
|
accum = accum * rho + (grad**2) * (1 - rho)
|
|
update[step] = (
|
|
np.sqrt(accum_update + epsilon) *
|
|
(1. / np.sqrt(accum + epsilon)) * grad)
|
|
accum_update = (
|
|
accum_update * rho + (update[step]**2) * (1.0 - rho))
|
|
tot_update += update[step] * lr
|
|
|
|
# Check that the accumulators have been updated
|
|
for slot_idx in range(2):
|
|
self.assertAllCloseAccordingToType(
|
|
np.array([accum, accum], dtype=dtype),
|
|
self.evaluate(slot[slot_idx]),
|
|
rtol=1e-5)
|
|
|
|
self.assertAllCloseAccordingToType(
|
|
np.array([accum_update, accum_update], dtype=dtype),
|
|
self.evaluate(slot_update[slot_idx]),
|
|
rtol=1e-5)
|
|
|
|
# Check that the parameters have been updated
|
|
self.assertAllCloseAccordingToType(
|
|
np.array(
|
|
[var0_init[0] - tot_update, var0_init[1] - tot_update],
|
|
dtype=dtype),
|
|
self.evaluate(var0),
|
|
rtol=1e-5)
|
|
|
|
self.assertAllCloseAccordingToType(
|
|
np.array(
|
|
[var1_init[0] - tot_update, var1_init[1] - tot_update],
|
|
dtype=dtype),
|
|
self.evaluate(var1),
|
|
rtol=1e-5)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test.main()
|