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# 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.loss_functions."""
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 loss_functions
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.platform import test
class InsertSliceInZerosTest(test.TestCase):
def testBadShape(self):
bad_shaped_ones = array_ops.ones(shape=[1, 3]) # n.b. shape[1] != 1
with self.assertRaises(ValueError):
loss_functions.insert_slice_in_zeros(bad_shaped_ones, 1, 42, 17)
def test3d(self):
input_tensor = constant_op.constant([[[1, 2]], [[3, 4]]])
expected_output_array = [[[1, 2], [0, 0]], [[3, 4], [0, 0]]]
op = loss_functions.insert_slice_in_zeros(input_tensor, 1, 2, 0)
with self.test_session() as sess:
actual_output_array = sess.run(op)
self.assertAllEqual(expected_output_array, actual_output_array)
class CategoricalLogitsNegativeLogProbLossTest(test.TestCase):
def testSample(self):
"""Ensure samples can be drawn."""
with ops.Graph().as_default(), self.test_session() as sess:
logits = np.asarray([
[0., 0., 0.], #
[1., -1., 0.]
]).astype(np.float32)
loss = loss_functions.CategoricalLogitsNegativeLogProbLoss(
array_ops.constant(logits))
sample = loss.sample(42)
sample = sess.run(sample)
self.assertEqual(sample.shape, (2,))
def testEvaluateOnTargets(self):
"""Ensure log probability can be evaluated correctly."""
with ops.Graph().as_default(), self.test_session() as sess:
logits = np.asarray([
[0., 0., 0.], #
[1., -1., 0.]
]).astype(np.float32)
targets = np.asarray([2, 1]).astype(np.int32)
loss = loss_functions.CategoricalLogitsNegativeLogProbLoss(
array_ops.constant(logits), targets=array_ops.constant(targets))
neg_log_prob = loss.evaluate()
neg_log_prob = sess.run(neg_log_prob)
# Calculate explicit log probability of targets.
probs = np.exp(logits) / np.sum(np.exp(logits), axis=1, keepdims=True)
log_probs = np.log([
probs[0, targets[0]], #
probs[1, targets[1]]
])
expected_log_prob = np.sum(log_probs)
self.assertAllClose(neg_log_prob, -expected_log_prob)
def testEvaluateOnSample(self):
"""Ensure log probability of a sample can be drawn."""
with ops.Graph().as_default(), self.test_session() as sess:
logits = np.asarray([
[0., 0., 0.], #
[1., -1., 0.]
]).astype(np.float32)
loss = loss_functions.CategoricalLogitsNegativeLogProbLoss(
array_ops.constant(logits))
neg_log_prob = loss.evaluate_on_sample(42)
# Simply ensure this doesn't crash. As the output is random, it's
# difficult to say if the output is correct or not...
neg_log_prob = sess.run(neg_log_prob)
def testMultiMinibatchRegistration(self):
"""Ensure this loss function supports registering multiple minibatches."""
with ops.Graph().as_default():
tower_logits = []
loss = None
num_towers = 5
for _ in range(num_towers):
logits = random_ops.random_uniform(shape=[2, 3])
tower_logits.append(logits)
if loss is None:
loss = loss_functions.CategoricalLogitsNegativeLogProbLoss(logits)
else:
loss.register_additional_minibatch(logits)
self.assertListEqual(loss.input_minibatches, tower_logits)
self.assertEqual(loss.num_registered_minibatches, num_towers)
if __name__ == "__main__":
test.main()
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