1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
|
# Copyright 2017 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 tf.contrib.kfac.optimizer."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.kfac.python.ops import layer_collection as lc
from tensorflow.contrib.kfac.python.ops import optimizer
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables as tf_variables
from tensorflow.python.platform import test
def dummy_layer_collection():
lcoll = lc.LayerCollection()
dummy = array_ops.constant([1., 2.])
lcoll.register_categorical_predictive_distribution(logits=dummy)
return lcoll
class OptimizerTest(test.TestCase):
def testOptimizerInitInvalidMomentumRegistration(self):
with self.assertRaises(ValueError):
optimizer.KfacOptimizer(
0.1, 0.2, 0.3, lc.LayerCollection(), momentum_type='foo')
def testOptimizerInit(self):
with ops.Graph().as_default():
layer_collection = lc.LayerCollection()
inputs = array_ops.ones((2, 1)) * 2
weights_val = np.ones((1, 1), dtype=np.float32) * 3.
weights = variable_scope.get_variable(
'w', initializer=array_ops.constant(weights_val))
bias = variable_scope.get_variable(
'b', initializer=init_ops.zeros_initializer(), shape=(1, 1))
output = math_ops.matmul(inputs, weights) + bias
layer_collection.register_fully_connected((weights, bias), inputs, output)
logits = math_ops.tanh(output)
targets = array_ops.constant([[0.], [1.]])
output = math_ops.reduce_mean(
nn.softmax_cross_entropy_with_logits(logits=logits, labels=targets))
layer_collection.register_categorical_predictive_distribution(logits)
optimizer.KfacOptimizer(
0.1,
0.2,
0.3,
layer_collection,
momentum=0.5,
momentum_type='regular')
def testSquaredFisherNorm(self):
with ops.Graph().as_default(), self.test_session() as sess:
grads_and_vars = [(array_ops.constant([[1., 2.], [3., 4.]]), None),
(array_ops.constant([[2., 3.], [4., 5.]]), None)]
pgrads_and_vars = [(array_ops.constant([[3., 4.], [5., 6.]]), None),
(array_ops.constant([[7., 8.], [9., 10.]]), None)]
opt = optimizer.KfacOptimizer(0.1, 0.2, 0.3, dummy_layer_collection())
sq_norm = opt._squared_fisher_norm(grads_and_vars, pgrads_and_vars)
self.assertAlmostEqual(174., sess.run(sq_norm), places=5)
def testUpdateClipCoeff(self):
with ops.Graph().as_default(), self.test_session() as sess:
grads_and_vars = [(array_ops.constant([[1., 2.], [3., 4.]]), None),
(array_ops.constant([[2., 3.], [4., 5.]]), None)]
pgrads_and_vars = [(array_ops.constant([[3., 4.], [5., 6.]]), None),
(array_ops.constant([[7., 8.], [9., 10.]]), None)]
lrate = 0.1
# Note: without rescaling, the squared Fisher norm of the update
# is 1.74
# If the update already satisfies the norm constraint, there should
# be no rescaling.
opt = optimizer.KfacOptimizer(
lrate, 0.2, 0.3, dummy_layer_collection(), norm_constraint=10.)
coeff = opt._update_clip_coeff(grads_and_vars, pgrads_and_vars)
self.assertAlmostEqual(1., sess.run(coeff), places=5)
# If the update violates the constraint, it should be rescaled to
# be on the constraint boundary.
opt = optimizer.KfacOptimizer(
lrate, 0.2, 0.3, dummy_layer_collection(), norm_constraint=0.5)
coeff = opt._update_clip_coeff(grads_and_vars, pgrads_and_vars)
sq_norm_pgrad = opt._squared_fisher_norm(grads_and_vars, pgrads_and_vars)
sq_norm_update = lrate**2 * coeff**2 * sq_norm_pgrad
self.assertAlmostEqual(0.5, sess.run(sq_norm_update), places=5)
def testComputeUpdateStepsRegular(self):
# TODO(olganw): implement this.
pass
def testComputeUpdateStepsAdam(self):
# TODO(olganw): implement this.
pass
def testUpdateVelocities(self):
with ops.Graph().as_default(), self.test_session() as sess:
layers = lc.LayerCollection()
layers.register_categorical_predictive_distribution(
array_ops.constant([1.0]))
opt = optimizer.KfacOptimizer(
0.1, 0.2, 0.3, layers, momentum=0.5, momentum_type='regular')
x = variable_scope.get_variable('x', initializer=array_ops.ones((2, 2)))
y = variable_scope.get_variable(
'y', initializer=array_ops.ones((2, 2)) * 2)
vec1 = array_ops.ones((2, 2)) * 3
vec2 = array_ops.ones((2, 2)) * 4
model_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
update_op = opt._update_velocities([(vec1, x), (vec2, y)], 0.5)
opt_vars = [
v for v in ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
if v not in model_vars
]
sess.run(tf_variables.global_variables_initializer())
old_opt_vars = sess.run(opt_vars)
# Optimizer vars start out at 0.
for opt_var in old_opt_vars:
self.assertAllEqual(sess.run(array_ops.zeros_like(opt_var)), opt_var)
sess.run(update_op)
new_opt_vars = sess.run(opt_vars)
# After one update, the velocities are equal to the vectors.
for vec, opt_var in zip([vec1, vec2], new_opt_vars):
self.assertAllEqual(sess.run(vec), opt_var)
sess.run(update_op)
final_opt_vars = sess.run(opt_vars)
for first, second in zip(new_opt_vars, final_opt_vars):
self.assertFalse(np.equal(first, second).all())
def testApplyGradients(self):
with ops.Graph().as_default(), self.test_session() as sess:
layer_collection = lc.LayerCollection()
inputs = array_ops.ones((2, 1)) * 2
weights_val = np.ones((1, 1), dtype=np.float32) * 3.
weights = variable_scope.get_variable(
'w', initializer=array_ops.constant(weights_val))
bias = variable_scope.get_variable(
'b', initializer=init_ops.zeros_initializer(), shape=(1, 1))
output = math_ops.matmul(inputs, weights) + bias
layer_collection.register_fully_connected((weights, bias), inputs, output)
logits = math_ops.tanh(output)
targets = array_ops.constant([[0.], [1.]])
output = math_ops.reduce_mean(
nn.softmax_cross_entropy_with_logits(logits=logits, labels=targets))
layer_collection.register_categorical_predictive_distribution(logits)
opt = optimizer.KfacOptimizer(
0.1,
0.2,
0.3,
layer_collection,
momentum=0.5,
momentum_type='regular')
grads_and_vars = opt.compute_gradients(output, [weights, bias])
all_vars = [grad_and_var[1] for grad_and_var in grads_and_vars]
op = opt.apply_gradients(grads_and_vars)
sess.run(tf_variables.global_variables_initializer())
old_vars = sess.run(all_vars)
sess.run(op)
new_vars = sess.run(all_vars)
for old_var, new_var in zip(old_vars, new_vars):
self.assertNotEqual(old_var, new_var)
if __name__ == '__main__':
test.main()
|
