def _testCompareWithNN(self, weights, biases, partition_strategy):
with ops.Graph().as_default():
loss = sampling_ops.rank_sampled_softmax_loss(
weights=weights(),
biases=biases(),
labels=self._labels(),
inputs=self._inputs(),
num_sampled=self._num_sampled,
num_resampled=self._num_resampled,
num_classes=self._num_classes,
num_true=self._num_true,
sampled_values=self._sampled_values,
resampling_temperature=1.,
remove_accidental_hits=self._remove_accidental_hits,
partition_strategy=partition_strategy)
loss_nn = nn.sampled_softmax_loss(
weights=weights(),
biases=biases(),
labels=self._labels(),
inputs=self._inputs(),
num_sampled=self._num_resampled,
num_classes=self._num_classes,
num_true=self._num_true,
sampled_values=self._resampled_values,
remove_accidental_hits=self._remove_accidental_hits,
partition_strategy=partition_strategy)
with self.test_session() as sess:
loss_val = sess.run(loss)
loss_nn_val = sess.run(loss_nn)
self.assertAllClose(loss_val, loss_nn_val)
def testMissingPartitionStrategy(self):
with ops.Graph().as_default():
with self.assertRaisesRegexp(ValueError,
r'unsupported partition_strategy \(None\)'):
sampling_ops.rank_sampled_softmax_loss(
weights=self._weights(),
biases=self._biases(),
labels=self._labels(),
inputs=self._inputs(),
num_sampled=2,
num_resampled=1,
num_classes=self._num_classes,
num_true=self._num_true,
sampled_values=None,
resampling_temperature=1.,
remove_accidental_hits=True,
partition_strategy=None)
def testInvalidNumSampled1(self):
with ops.Graph().as_default():
with self.assertRaisesRegexp(
ValueError,
r'num_resampled \(3\) must be less than num_sampled \(2\)'):
sampling_ops.rank_sampled_softmax_loss(
weights=self._weights(),
biases=self._biases(),
labels=self._labels(),
inputs=self._inputs(),
num_sampled=2,
num_resampled=3,
num_classes=self._num_classes,
num_true=self._num_true,
sampled_values=None,
resampling_temperature=1.,
remove_accidental_hits=True,
partition_strategy='div')
def _testCompareWithNNTemperature(self, temperature, resampled):
weights = [[1., 2.], [3., 4.]] # two sampled classes
inputs = [[6., -5. / 2.], [-11., 21. / 2.]]
# Let w0, w1 = weights of sampled classes (biases set to 0 for simplicity)
# Let x0, x1 = inputs
# logits:
# w0.x0 = 1
# w0.x1 = 10
# w1.x0 = 8
# w1.x1 = 9
# Resampling 1 class with temperature = t will pick the larger of:
# exp(1/t) + exp(10/t) ==> w0, for values of t < 2.12
# exp(8/t) + exp(9/t) ==> w1, for values of t > 2.13
num_sampled = 2
num_resampled = 1
num_classes = 2
num_true = 1
sampled_values = [0, 1], [[1.], [1.]], [1., 1.]
resampled_values = [resampled], [[1.], [1.]], [1.]
remove_accidental_hits = False
with ops.Graph().as_default():
weights = constant_op.constant(weights)
biases = constant_op.constant([0., 0.])
labels = constant_op.constant([[0], [1]], dtype=dtypes.int64)
inputs = constant_op.constant(inputs)
loss = sampling_ops.rank_sampled_softmax_loss(
weights=weights,
biases=biases,
labels=labels,
inputs=inputs,
num_sampled=num_sampled,
num_resampled=num_resampled,
num_classes=num_classes,
num_true=num_true,
sampled_values=sampled_values,
resampling_temperature=constant_op.constant(temperature),
remove_accidental_hits=remove_accidental_hits,
partition_strategy='div')
loss_nn = nn.sampled_softmax_loss(
weights=weights,
biases=biases,
labels=labels,
inputs=inputs,
num_sampled=num_resampled,
num_classes=num_classes,
num_true=num_true,
sampled_values=resampled_values,
remove_accidental_hits=remove_accidental_hits,
partition_strategy='div')
with self.test_session() as sess:
loss_val = sess.run(loss)
loss_nn_val = sess.run(loss_nn)
self.assertAllClose(loss_val, loss_nn_val)