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  /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)
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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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