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Searched
refs:Categorical
(Results
1 - 14
of
14
) sorted by null
/external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/
mixture_same_family_test.py
29
from tensorflow.python.ops.distributions import
categorical
as categorical_lib
40
mixture_distribution=categorical_lib.
Categorical
(probs=[0.3, 0.7]),
51
mixture_distribution=categorical_lib.
Categorical
(probs=[[0.3, 0.7]]),
64
mixture_distribution=categorical_lib.
Categorical
(probs=mix_probs),
77
mixture_distribution=categorical_lib.
Categorical
(probs=[0.3, 0.7]),
88
mixture_distribution=categorical_lib.
Categorical
(probs=[0.3, 0.7]),
103
mixture_distribution=categorical_lib.
Categorical
(probs=[0.3, 0.7]),
116
mixture_distribution=categorical_lib.
Categorical
(probs=[0.3, 0.7]),
133
mixture_distribution=categorical_lib.
Categorical
(probs=[0.3, 0.7]),
141
mixture_distribution=categorical_lib.
Categorical
(probs=[0.3, 0.7])
[
all
...]
mixture_test.py
121
cat = ds.
Categorical
(logits, dtype=dtypes.int32)
147
cat = ds.
Categorical
(logits, dtype=dtypes.int32)
177
ds.
Categorical
([0.1, 0.5]), # 2 classes
186
ds.
Categorical
([-0.5, 0.5]), # scalar batch
197
ds.
Categorical
(cat_logits),
207
ds.
Categorical
([0.1, 0.2]), [
226
with self.assertRaisesWithPredicateMatch(TypeError, "
Categorical
"):
228
cat = ds.
Categorical
([0.3, 0.2])
241
ds.Mixture(ds.
Categorical
([0.3, 0.2]), None,
386
cat=ds.
Categorical
(probs=cat_probs)
[
all
...]
distribution_util_test.py
33
from tensorflow.python.ops.distributions import
categorical
408
cat=
categorical
.
Categorical
(probs=[[0.3, 0.7]]),
425
mixture_distribution=
categorical
.
Categorical
(probs=[0.3, 0.7]),
/external/tensorflow/tensorflow/python/kernel_tests/distributions/
categorical_test.py
15
"""Tests for
Categorical
distribution."""
31
from tensorflow.python.ops.distributions import
categorical
40
return
categorical
.
Categorical
(logits, dtype=dtype)
47
dist =
categorical
.
Categorical
(probs=p)
55
dist =
categorical
.
Categorical
(logits=logits)
111
dist =
categorical
.
Categorical
(logits
[
all
...]
/external/tensorflow/tensorflow/python/ops/distributions/
categorical.py
15
"""The
Categorical
distribution class."""
62
@tf_export("distributions.
Categorical
")
63
class
Categorical
(distribution.Distribution):
64
"""
Categorical
distribution.
66
The
Categorical
distribution is parameterized by either probabilities or
70
The
Categorical
distribution is closely related to the `OneHotCategorical` and
71
`Multinomial` distributions. The
Categorical
distribution can be intuited as
106
dist =
Categorical
(probs=[0.1, 0.5, 0.4])
122
dist =
Categorical
(logits=np.log([0.1, 0.5, 0.4])
139
dist =
Categorical
(probs=p
[
all
...]
distributions.py
25
from tensorflow.python.ops.distributions.
categorical
import
Categorical
46
"
Categorical
",
/external/tensorflow/tensorflow/contrib/gan/python/
train_test.py
36
from tensorflow.python.ops.distributions import
categorical
77
[
categorical
.
Categorical
([1.0])])
149
predicted_distributions=[
categorical
.
Categorical
([1.0])],
157
predicted_distributions=[
categorical
.
Categorical
([1.0])],
[
all
...]
/external/tensorflow/tensorflow/contrib/distributions/python/ops/
poisson_lognormal.py
32
from tensorflow.python.ops.distributions import
categorical
as categorical_lib
279
self._mixture_distribution = categorical_lib.
Categorical
(
mixture.py
32
from tensorflow.python.ops.distributions import
categorical
41
The mixture model is defined by a `
Categorical
` distribution (the mixture)
55
cat=tfd.
Categorical
(probs=[mix, 1.-mix]),
78
A `Mixture` is defined by a `
Categorical
` (`cat`, representing the
87
cat: A `
Categorical
` distribution instance, representing the probabilities
108
TypeError: If cat is not a `
Categorical
`, or `components` is not
120
if not isinstance(cat,
categorical
.
Categorical
):
121
raise TypeError("cat must be a
Categorical
distribution, but saw: %s" %
vector_diffeomixture.py
36
from tensorflow.python.ops.distributions import
categorical
as categorical_lib
452
self._mixture_distribution = categorical_lib.
Categorical
(
[
all
...]
/external/tensorflow/tensorflow/contrib/seq2seq/python/ops/
helper.py
37
from tensorflow.python.ops.distributions import
categorical
314
sample_id_sampler =
categorical
.
Categorical
(logits=outputs)
613
sample_id_sampler =
categorical
.
Categorical
(logits=logits)
/external/tensorflow/tensorflow/contrib/kfac/python/ops/
loss_functions.py
30
from tensorflow.python.ops.distributions import
categorical
588
"""Neg log prob loss for a
categorical
distribution parameterized by logits.
591
Note that the Fisher (for a single case) of a
categorical
distribution, with
647
return
categorical
.
Categorical
(logits=self._logits)
794
"""Neg log prob loss for a
categorical
distribution with onehot targets.
797
distribution is OneHotCategorical as opposed to
Categorical
.
/external/tensorflow/tensorflow/contrib/gan/python/losses/python/
losses_impl_test.py
32
from tensorflow.python.ops.distributions import
categorical
542
self._predicted_distributions = [
categorical
.
Categorical
(logits=[1.0, 2.0]),
/external/tensorflow/tensorflow/contrib/seq2seq/python/kernel_tests/
basic_decoder_test.py
38
from tensorflow.python.ops.distributions import
categorical
520
sample_fn = lambda x:
categorical
.
Categorical
(logits=x).sample()
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