Searched refs:multinomial (Results 1 – 25 of 26) sorted by relevance
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| /external/tensorflow/tensorflow/python/kernel_tests/distributions/ |
| D | multinomial_test.py | 28 from tensorflow.python.ops.distributions import multinomial
41 dist = multinomial.Multinomial(total_count=1., probs=p)
52 dist = multinomial.Multinomial(total_count=n, probs=p)
63 dist = multinomial.Multinomial(total_count=n, probs=p)
71 dist = multinomial.Multinomial(total_count=3., probs=p)
81 multinom = multinomial.Multinomial(total_count=3., logits=logits)
91 dist = multinomial.Multinomial(total_count=1., logits=logits)
100 dist = multinomial.Multinomial(total_count=n, probs=p, validate_args=True)
114 multinom = multinomial.Multinomial(
127 multinom = multinomial.Multinomial(
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| /external/tensorflow/tensorflow/python/kernel_tests/random/ |
| D | multinomial_op_test.py | 52 native_sampler = random_ops.multinomial
65 samples = self.evaluate(random_ops.multinomial(
105 samples = self.evaluate(random_ops.multinomial(logits, 10))
142 sample_op1 = random_ops.multinomial(logits, num_samples, seed)
143 sample_op2 = random_ops.multinomial(logits, num_samples, seed)
195 random_ops.multinomial(
202 x = random_ops.multinomial(array_ops.zeros([5, 0]), 7)
211 samples = self.evaluate(random_ops.multinomial(logits, num_samples))
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| D | multinomial_op_big_test.py | 38 samples = random_ops.multinomial(
56 samples = random_ops.multinomial(
76 samples = random_ops.multinomial(
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| D | stateless_random_ops_test.py | 130 functools.partial(random_ops.multinomial, **kwds))
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| /external/tensorflow/tensorflow/compiler/tests/ |
| D | categorical_op_test.py | 62 op = random_ops.multinomial(logits, num_samples,
101 return random_ops.multinomial(np.array([[1., 1., 1.]], dtype=dtype), 10,
113 x = random_ops.multinomial(
175 x = random_ops.multinomial(
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| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_StatelessMultinomial.pbtxt | 29 summary: "Draws samples from a multinomial distribution."
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| D | api_def_Multinomial.pbtxt | 36 summary: "Draws samples from a multinomial distribution."
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| /external/tensorflow/tensorflow/python/ops/distributions/ |
| D | distributions.py | 35 from tensorflow.python.ops.distributions.multinomial import Multinomial
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| D | multinomial.py | 263 x = random_ops.multinomial(logits[array_ops.newaxis, ...], n_draw,
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| D | dirichlet_multinomial.py | 271 draws = random_ops.multinomial(
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| D | categorical.py | 277 draws = random_ops.multinomial(
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| /external/tensorflow/tensorflow/python/ops/ |
| D | random_ops.py | 333 def multinomial(logits, num_samples, seed=None, name=None, output_dtype=None): function
392 return gen_random_ops.multinomial(
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| /external/tensorflow/tensorflow/contrib/kernel_methods/ |
| D | README.md | 32 kernels) and (multinomial) logistic regression (with and without kernels).
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| /external/tensorflow/tensorflow/contrib/autograph/examples/benchmarks/ |
| D | cartpole_benchmark.py | 100 actions = tf.multinomial(tf.log(action_probs), 1)
304 actions = tf.multinomial(tf.log(action_probs), 1)
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| /external/tensorflow/tensorflow/contrib/distributions/ |
| D | __init__.py | 85 from tensorflow.python.ops.distributions.multinomial import *
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| /external/tensorflow/tensorflow/tools/api/golden/v1/ |
| D | tensorflow.random.pbtxt | 36 name: "multinomial"
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| D | tensorflow.distributions.-multinomial.pbtxt | 3 is_instance: "<class \'tensorflow.python.ops.distributions.multinomial.Multinomial\'>"
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| D | tensorflow.pbtxt | 1648 name: "multinomial"
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| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| D | onehot_categorical.py | 190 samples = random_ops.multinomial(logits_2d, n, seed=seed)
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| /external/tensorflow/tensorflow/python/grappler/ |
| D | hierarchical_controller.py | 574 actions = random_ops.multinomial(logits, 1, seed=self.hparams.seed)
920 next_y = random_ops.multinomial(logits, 1, seed=self.hparams.seed)
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| /external/tensorflow/tensorflow/contrib/seq2seq/python/ops/ |
| D | sampler.py | 751 draws = random_ops.multinomial(
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| D | helper.py | 106 draws = random_ops.multinomial(
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| /external/tensorflow/tensorflow/examples/udacity/ |
| D | 2_fullyconnected.ipynb | 214 … "We're first going to train a multinomial logistic regression using simple gradient descent.\n",
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| /external/tensorflow/ |
| D | RELEASE.md | 1086 * Fix incorrect sampling of small probabilities in CPU/GPU multinomial.
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| /external/cldr/tools/java/org/unicode/cldr/util/data/transforms/ |
| D | internal_raw_IPA.txt | 111835 multinomial %36853 mˌəltɪnˈomiəl
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