497 lines
20 KiB
Python
497 lines
20 KiB
Python
# Copyright 2015 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.
|
|
# ==============================================================================
|
|
"""Functional tests for Ftrl operations."""
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import numpy as np
|
|
|
|
from tensorflow.python.framework import constant_op
|
|
from tensorflow.python.framework import dtypes
|
|
from tensorflow.python.framework import ops
|
|
from tensorflow.python.keras.optimizer_v2 import ftrl
|
|
from tensorflow.python.ops import embedding_ops
|
|
from tensorflow.python.ops import math_ops
|
|
from tensorflow.python.ops import variables
|
|
from tensorflow.python.platform import test
|
|
from tensorflow.python.training import adagrad
|
|
from tensorflow.python.training import gradient_descent
|
|
|
|
|
|
class FtrlOptimizerTest(test.TestCase):
|
|
|
|
def doTestFtrlwithoutRegularization(self, use_resource=False):
|
|
# TODO(tanzheny, omalleyt): Fix test in eager mode.
|
|
for dtype in [dtypes.float32]:
|
|
with ops.Graph().as_default(), self.cached_session(use_gpu=True):
|
|
if use_resource:
|
|
var0 = variables.Variable([0.0, 0.0], dtype=dtype)
|
|
var1 = variables.Variable([0.0, 0.0], dtype=dtype)
|
|
else:
|
|
var0 = variables.Variable([0.0, 0.0], dtype=dtype)
|
|
var1 = variables.Variable([0.0, 0.0], dtype=dtype)
|
|
grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
|
|
grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
|
|
opt = ftrl.Ftrl(
|
|
3.0,
|
|
initial_accumulator_value=0.1,
|
|
l1_regularization_strength=0.0,
|
|
l2_regularization_strength=0.0)
|
|
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
|
|
self.evaluate(variables.global_variables_initializer())
|
|
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
self.assertAllClose([0.0, 0.0], v0_val)
|
|
self.assertAllClose([0.0, 0.0], v1_val)
|
|
|
|
# Run 3 steps FTRL
|
|
for _ in range(3):
|
|
update.run()
|
|
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
self.assertAllCloseAccordingToType(
|
|
np.array([-2.60260963, -4.29698515]), v0_val)
|
|
self.assertAllCloseAccordingToType(
|
|
np.array([-0.28432083, -0.56694895]), v1_val)
|
|
|
|
def testFtrlWithoutRegularization(self):
|
|
self.doTestFtrlwithoutRegularization(use_resource=False)
|
|
|
|
def testResourceFtrlWithoutRegularization(self):
|
|
self.doTestFtrlwithoutRegularization(use_resource=True)
|
|
|
|
def testFtrlwithoutRegularization2(self):
|
|
# TODO(tanzheny, omalleyt): Fix test in eager mode.
|
|
for dtype in [dtypes.half, dtypes.float32]:
|
|
with ops.Graph().as_default(), self.cached_session(use_gpu=True):
|
|
var0 = variables.Variable([1.0, 2.0], dtype=dtype)
|
|
var1 = variables.Variable([4.0, 3.0], dtype=dtype)
|
|
grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
|
|
grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
|
|
|
|
opt = ftrl.Ftrl(
|
|
3.0,
|
|
initial_accumulator_value=0.1,
|
|
l1_regularization_strength=0.0,
|
|
l2_regularization_strength=0.0)
|
|
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
|
|
self.evaluate(variables.global_variables_initializer())
|
|
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
self.assertAllCloseAccordingToType([1.0, 2.0], v0_val)
|
|
self.assertAllCloseAccordingToType([4.0, 3.0], v1_val)
|
|
|
|
# Run 3 steps FTRL
|
|
for _ in range(3):
|
|
update.run()
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
self.assertAllCloseAccordingToType(
|
|
np.array([-2.55607247, -3.98729396]), v0_val)
|
|
self.assertAllCloseAccordingToType(
|
|
np.array([-0.28232238, -0.56096673]), v1_val)
|
|
|
|
def testMinimizeSparseResourceVariable(self):
|
|
# TODO(tanzheny, omalleyt): Fix test in eager mode.
|
|
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
|
|
with ops.Graph().as_default(), self.cached_session(use_gpu=True):
|
|
var0 = variables.Variable([[1.0, 2.0]], dtype=dtype)
|
|
x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
|
|
|
|
def loss():
|
|
pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) # pylint: disable=cell-var-from-loop
|
|
return pred * pred
|
|
|
|
sgd_op = ftrl.Ftrl(1.0).minimize(loss, var_list=[var0])
|
|
self.evaluate(variables.global_variables_initializer())
|
|
# Fetch params to validate initial values
|
|
self.assertAllCloseAccordingToType([[1.0, 2.0]], self.evaluate(var0))
|
|
# Run 1 step of sgd
|
|
sgd_op.run()
|
|
# Validate updated params
|
|
self.assertAllCloseAccordingToType([[0, 1]],
|
|
self.evaluate(var0),
|
|
atol=0.01)
|
|
|
|
def testFtrlWithL1(self):
|
|
# TODO(tanzheny, omalleyt): Fix test in eager mode.
|
|
for dtype in [dtypes.half, dtypes.float32]:
|
|
with ops.Graph().as_default(), self.cached_session(use_gpu=True):
|
|
var0 = variables.Variable([1.0, 2.0], dtype=dtype)
|
|
var1 = variables.Variable([4.0, 3.0], dtype=dtype)
|
|
grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
|
|
grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
|
|
|
|
opt = ftrl.Ftrl(
|
|
3.0,
|
|
initial_accumulator_value=0.1,
|
|
l1_regularization_strength=0.001,
|
|
l2_regularization_strength=0.0)
|
|
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
|
|
self.evaluate(variables.global_variables_initializer())
|
|
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
self.assertAllCloseAccordingToType([1.0, 2.0], v0_val)
|
|
self.assertAllCloseAccordingToType([4.0, 3.0], v1_val)
|
|
|
|
# Run 10 steps FTRL
|
|
for _ in range(10):
|
|
update.run()
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
self.assertAllCloseAccordingToType(
|
|
np.array([-7.66718769, -10.91273689]), v0_val)
|
|
self.assertAllCloseAccordingToType(
|
|
np.array([-0.93460727, -1.86147261]), v1_val)
|
|
|
|
def testFtrlWithBeta(self):
|
|
# TODO(tanzheny, omalleyt): Fix test in eager mode.
|
|
for dtype in [dtypes.half, dtypes.float32]:
|
|
with ops.Graph().as_default(), self.cached_session(use_gpu=True):
|
|
var0 = variables.Variable([1.0, 2.0], dtype=dtype)
|
|
var1 = variables.Variable([4.0, 3.0], dtype=dtype)
|
|
grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
|
|
grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
|
|
|
|
opt = ftrl.Ftrl(3.0, initial_accumulator_value=0.1, beta=0.1)
|
|
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
|
|
self.evaluate(variables.global_variables_initializer())
|
|
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
self.assertAllCloseAccordingToType([1.0, 2.0], v0_val)
|
|
self.assertAllCloseAccordingToType([4.0, 3.0], v1_val)
|
|
|
|
# Run 10 steps FTRL
|
|
for _ in range(10):
|
|
update.run()
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
self.assertAllCloseAccordingToType(
|
|
np.array([-6.096838, -9.162214]), v0_val)
|
|
self.assertAllCloseAccordingToType(
|
|
np.array([-0.717741, -1.425132]), v1_val)
|
|
|
|
def testFtrlWithL2_Beta(self):
|
|
# TODO(tanzheny, omalleyt): Fix test in eager mode.
|
|
for dtype in [dtypes.half, dtypes.float32]:
|
|
with ops.Graph().as_default(), self.cached_session(use_gpu=True):
|
|
var0 = variables.Variable([1.0, 2.0], dtype=dtype)
|
|
var1 = variables.Variable([4.0, 3.0], dtype=dtype)
|
|
grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
|
|
grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
|
|
|
|
opt = ftrl.Ftrl(
|
|
3.0,
|
|
initial_accumulator_value=0.1,
|
|
l1_regularization_strength=0.0,
|
|
l2_regularization_strength=0.1,
|
|
beta=0.1)
|
|
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
|
|
self.evaluate(variables.global_variables_initializer())
|
|
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
self.assertAllCloseAccordingToType([1.0, 2.0], v0_val)
|
|
self.assertAllCloseAccordingToType([4.0, 3.0], v1_val)
|
|
|
|
# Run 10 steps FTRL
|
|
for _ in range(10):
|
|
update.run()
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
self.assertAllCloseAccordingToType(
|
|
np.array([-2.735487, -4.704625]), v0_val)
|
|
self.assertAllCloseAccordingToType(
|
|
np.array([-0.294335, -0.586556]), v1_val)
|
|
|
|
def testFtrlWithL1_L2(self):
|
|
# TODO(tanzheny, omalleyt): Fix test in eager mode.
|
|
for dtype in [dtypes.half, dtypes.float32]:
|
|
with ops.Graph().as_default(), self.cached_session(use_gpu=True):
|
|
var0 = variables.Variable([1.0, 2.0], dtype=dtype)
|
|
var1 = variables.Variable([4.0, 3.0], dtype=dtype)
|
|
grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
|
|
grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
|
|
|
|
opt = ftrl.Ftrl(
|
|
3.0,
|
|
initial_accumulator_value=0.1,
|
|
l1_regularization_strength=0.001,
|
|
l2_regularization_strength=2.0)
|
|
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
|
|
self.evaluate(variables.global_variables_initializer())
|
|
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
self.assertAllCloseAccordingToType([1.0, 2.0], v0_val)
|
|
self.assertAllCloseAccordingToType([4.0, 3.0], v1_val)
|
|
|
|
# Run 10 steps FTRL
|
|
for _ in range(10):
|
|
update.run()
|
|
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
self.assertAllCloseAccordingToType(
|
|
np.array([-0.24059935, -0.46829352]), v0_val)
|
|
self.assertAllCloseAccordingToType(
|
|
np.array([-0.02406147, -0.04830509]), v1_val)
|
|
|
|
def testFtrlWithL1_L2_L2Shrinkage(self):
|
|
"""Test the new FTRL op with support for l2 shrinkage.
|
|
|
|
The addition of this parameter which places a constant pressure on weights
|
|
towards the origin causes the gradient descent trajectory to differ. The
|
|
weights will tend to have smaller magnitudes with this parameter set.
|
|
"""
|
|
# TODO(tanzheny, omalleyt): Fix test in eager mode.
|
|
for dtype in [dtypes.half, dtypes.float32]:
|
|
with ops.Graph().as_default(), self.cached_session(use_gpu=True):
|
|
var0 = variables.Variable([1.0, 2.0], dtype=dtype)
|
|
var1 = variables.Variable([4.0, 3.0], dtype=dtype)
|
|
grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
|
|
grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
|
|
|
|
opt = ftrl.Ftrl(
|
|
3.0,
|
|
initial_accumulator_value=0.1,
|
|
l1_regularization_strength=0.001,
|
|
l2_regularization_strength=2.0,
|
|
l2_shrinkage_regularization_strength=0.1)
|
|
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
|
|
self.evaluate(variables.global_variables_initializer())
|
|
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
self.assertAllCloseAccordingToType([1.0, 2.0], v0_val)
|
|
self.assertAllCloseAccordingToType([4.0, 3.0], v1_val)
|
|
|
|
# Run 10 steps FTRL
|
|
for _ in range(10):
|
|
update.run()
|
|
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
self.assertAllCloseAccordingToType(
|
|
np.array([-0.22578995, -0.44345796]), v0_val)
|
|
self.assertAllCloseAccordingToType(
|
|
np.array([-0.14378493, -0.13229476]), v1_val)
|
|
|
|
def testFtrlWithL1_L2_L2ShrinkageSparse(self):
|
|
"""Tests the new FTRL op with support for l2 shrinkage on sparse grads."""
|
|
# TODO(tanzheny, omalleyt): Fix test in eager mode.
|
|
for dtype in [dtypes.half, dtypes.float32]:
|
|
with ops.Graph().as_default(), self.cached_session(use_gpu=True):
|
|
var0 = variables.Variable([[1.0], [2.0]], dtype=dtype)
|
|
var1 = variables.Variable([[4.0], [3.0]], dtype=dtype)
|
|
grads0 = ops.IndexedSlices(
|
|
constant_op.constant([0.1], shape=[1, 1], dtype=dtype),
|
|
constant_op.constant([0]), constant_op.constant([2, 1]))
|
|
grads1 = ops.IndexedSlices(
|
|
constant_op.constant([0.02], shape=[1, 1], dtype=dtype),
|
|
constant_op.constant([1]), constant_op.constant([2, 1]))
|
|
|
|
opt = ftrl.Ftrl(
|
|
3.0,
|
|
initial_accumulator_value=0.1,
|
|
l1_regularization_strength=0.001,
|
|
l2_regularization_strength=2.0,
|
|
l2_shrinkage_regularization_strength=0.1)
|
|
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
|
|
self.evaluate(variables.global_variables_initializer())
|
|
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
self.assertAllCloseAccordingToType([[1.0], [2.0]], v0_val)
|
|
self.assertAllCloseAccordingToType([[4.0], [3.0]], v1_val)
|
|
|
|
# Run 10 steps FTRL
|
|
for _ in range(10):
|
|
update.run()
|
|
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
self.assertAllCloseAccordingToType([[-0.22578995], [2.]], v0_val)
|
|
self.assertAllCloseAccordingToType([[4.], [-0.13229476]], v1_val)
|
|
|
|
def testFtrlWithL2ShrinkageDoesNotChangeLrSchedule(self):
|
|
"""Verifies that l2 shrinkage in FTRL does not change lr schedule."""
|
|
# TODO(tanzheny, omalleyt): Fix test in eager mode.
|
|
for dtype in [dtypes.half, dtypes.float32]:
|
|
with ops.Graph().as_default(), self.cached_session(use_gpu=True) as sess:
|
|
var0 = variables.Variable([1.0, 2.0], dtype=dtype)
|
|
var1 = variables.Variable([1.0, 2.0], dtype=dtype)
|
|
grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
|
|
grads1 = constant_op.constant([0.1, 0.2], dtype=dtype)
|
|
|
|
opt0 = ftrl.Ftrl(
|
|
3.0,
|
|
initial_accumulator_value=0.1,
|
|
l1_regularization_strength=0.001,
|
|
l2_regularization_strength=2.0,
|
|
l2_shrinkage_regularization_strength=0.1)
|
|
opt1 = ftrl.Ftrl(
|
|
3.0,
|
|
initial_accumulator_value=0.1,
|
|
l1_regularization_strength=0.001,
|
|
l2_regularization_strength=2.0)
|
|
update0 = opt0.apply_gradients([(grads0, var0)])
|
|
update1 = opt1.apply_gradients([(grads1, var1)])
|
|
self.evaluate(variables.global_variables_initializer())
|
|
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
self.assertAllCloseAccordingToType([1.0, 2.0], v0_val)
|
|
self.assertAllCloseAccordingToType([1.0, 2.0], v1_val)
|
|
|
|
# Run 10 steps FTRL
|
|
for _ in range(10):
|
|
update0.run()
|
|
update1.run()
|
|
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
# var0 is experiencing L2 shrinkage so it should be smaller than var1
|
|
# in magnitude.
|
|
self.assertTrue((v0_val**2 < v1_val**2).all())
|
|
accum0 = sess.run(opt0.get_slot(var0, "accumulator"))
|
|
accum1 = sess.run(opt1.get_slot(var1, "accumulator"))
|
|
# L2 shrinkage should not change how we update grad accumulator.
|
|
self.assertAllCloseAccordingToType(accum0, accum1)
|
|
|
|
def applyOptimizer(self, opt, dtype, steps=5, is_sparse=False):
|
|
if is_sparse:
|
|
var0 = variables.Variable([[0.0], [0.0]], dtype=dtype)
|
|
var1 = variables.Variable([[0.0], [0.0]], dtype=dtype)
|
|
grads0 = ops.IndexedSlices(
|
|
constant_op.constant([0.1], shape=[1, 1], dtype=dtype),
|
|
constant_op.constant([0]), constant_op.constant([2, 1]))
|
|
grads1 = ops.IndexedSlices(
|
|
constant_op.constant([0.02], shape=[1, 1], dtype=dtype),
|
|
constant_op.constant([1]), constant_op.constant([2, 1]))
|
|
else:
|
|
var0 = variables.Variable([0.0, 0.0], dtype=dtype)
|
|
var1 = variables.Variable([0.0, 0.0], dtype=dtype)
|
|
grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
|
|
grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
|
|
|
|
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
|
|
self.evaluate(variables.global_variables_initializer())
|
|
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
if is_sparse:
|
|
self.assertAllCloseAccordingToType([[0.0], [0.0]], v0_val)
|
|
self.assertAllCloseAccordingToType([[0.0], [0.0]], v1_val)
|
|
else:
|
|
self.assertAllCloseAccordingToType([0.0, 0.0], v0_val)
|
|
self.assertAllCloseAccordingToType([0.0, 0.0], v1_val)
|
|
|
|
# Run Ftrl for a few steps
|
|
for _ in range(steps):
|
|
update.run()
|
|
|
|
v0_val, v1_val = self.evaluate([var0, var1])
|
|
return v0_val, v1_val
|
|
|
|
# When variables are initialized with Zero, FTRL-Proximal has two properties:
|
|
# 1. Without L1&L2 but with fixed learning rate, FTRL-Proximal is identical
|
|
# with GradientDescent.
|
|
# 2. Without L1&L2 but with adaptive learning rate, FTRL-Proximal is identical
|
|
# with Adagrad.
|
|
# So, basing on these two properties, we test if our implementation of
|
|
# FTRL-Proximal performs same updates as Adagrad or GradientDescent.
|
|
def testEquivAdagradwithoutRegularization(self):
|
|
# TODO(tanzheny, omalleyt): Fix test in eager mode.
|
|
for dtype in [dtypes.half, dtypes.float32]:
|
|
with ops.Graph().as_default(), self.cached_session(use_gpu=True):
|
|
val0, val1 = self.applyOptimizer(
|
|
ftrl.Ftrl(
|
|
3.0,
|
|
# Adagrad learning rate
|
|
learning_rate_power=-0.5,
|
|
initial_accumulator_value=0.1,
|
|
l1_regularization_strength=0.0,
|
|
l2_regularization_strength=0.0),
|
|
dtype)
|
|
|
|
with ops.Graph().as_default(), self.cached_session(use_gpu=True):
|
|
val2, val3 = self.applyOptimizer(
|
|
adagrad.AdagradOptimizer(3.0, initial_accumulator_value=0.1), dtype)
|
|
|
|
self.assertAllCloseAccordingToType(val0, val2)
|
|
self.assertAllCloseAccordingToType(val1, val3)
|
|
|
|
def testEquivSparseAdagradwithoutRegularization(self):
|
|
# TODO(tanzheny, omalleyt): Fix test in eager mode.
|
|
for dtype in [dtypes.half, dtypes.float32]:
|
|
with ops.Graph().as_default(), self.cached_session():
|
|
val0, val1 = self.applyOptimizer(
|
|
ftrl.Ftrl(
|
|
3.0,
|
|
# Adagrad learning rate
|
|
learning_rate_power=-0.5,
|
|
initial_accumulator_value=0.1,
|
|
l1_regularization_strength=0.0,
|
|
l2_regularization_strength=0.0),
|
|
dtype,
|
|
is_sparse=True)
|
|
|
|
with ops.Graph().as_default(), self.cached_session():
|
|
val2, val3 = self.applyOptimizer(
|
|
adagrad.AdagradOptimizer(3.0, initial_accumulator_value=0.1),
|
|
dtype,
|
|
is_sparse=True)
|
|
|
|
self.assertAllCloseAccordingToType(val0, val2)
|
|
self.assertAllCloseAccordingToType(val1, val3)
|
|
|
|
def testEquivSparseGradientDescentwithoutRegularization(self):
|
|
# TODO(tanzheny, omalleyt): Fix test in eager mode.
|
|
for dtype in [dtypes.half, dtypes.float32]:
|
|
with ops.Graph().as_default(), self.cached_session(use_gpu=True):
|
|
val0, val1 = self.applyOptimizer(
|
|
ftrl.Ftrl(
|
|
3.0,
|
|
# Fixed learning rate
|
|
learning_rate_power=-0.0,
|
|
initial_accumulator_value=0.1,
|
|
l1_regularization_strength=0.0,
|
|
l2_regularization_strength=0.0),
|
|
dtype,
|
|
is_sparse=True)
|
|
|
|
with ops.Graph().as_default(), self.cached_session(use_gpu=True):
|
|
val2, val3 = self.applyOptimizer(
|
|
gradient_descent.GradientDescentOptimizer(3.0),
|
|
dtype,
|
|
is_sparse=True)
|
|
|
|
self.assertAllCloseAccordingToType(val0, val2)
|
|
self.assertAllCloseAccordingToType(val1, val3)
|
|
|
|
def testEquivGradientDescentwithoutRegularization(self):
|
|
# TODO(tanzheny, omalleyt): Fix test in eager mode.
|
|
for dtype in [dtypes.half, dtypes.float32]:
|
|
with ops.Graph().as_default(), self.cached_session(use_gpu=True):
|
|
val0, val1 = self.applyOptimizer(
|
|
ftrl.Ftrl(
|
|
3.0,
|
|
# Fixed learning rate
|
|
learning_rate_power=-0.0,
|
|
initial_accumulator_value=0.1,
|
|
l1_regularization_strength=0.0,
|
|
l2_regularization_strength=0.0),
|
|
dtype)
|
|
|
|
with ops.Graph().as_default(), self.cached_session(use_gpu=True):
|
|
val2, val3 = self.applyOptimizer(
|
|
gradient_descent.GradientDescentOptimizer(3.0), dtype)
|
|
|
|
self.assertAllCloseAccordingToType(val0, val2)
|
|
self.assertAllCloseAccordingToType(val1, val3)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test.main()
|