Searched refs:concentration1_b (Results 1 – 3 of 3) sorted by relevance
| /third_party/mindspore/mindspore/nn/probability/distribution/ |
| D | beta.py | 264 …def _cross_entropy(self, dist, concentration1_b, concentration0_b, concentration1_a=None, concentr… argument
277 … + self._kl_loss(dist, concentration1_b, concentration0_b, concentration1_a, concentration0_a)
298 …def _kl_loss(self, dist, concentration1_b, concentration0_b, concentration1_a=None, concentration0… argument
316 concentration1_b = self._check_value(concentration1_b, 'concentration1_b')
318 concentration1_b = self.cast(concentration1_b, self.parameter_type)
322 total_concentration_b = concentration1_b + concentration0_b
324 log_normalization_b = self.lbeta(concentration1_b, concentration0_b)
326 - (self.digamma(concentration1_a) * (concentration1_b - concentration1_a)) \
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| /third_party/mindspore/tests/ut/python/nn/probability/distribution/ |
| D | test_beta.py | 129 def construct(self, concentration1_b, concentration0_b, concentration1_a, concentration0_a): argument
130 kl1 = self.g1.kl_loss('Gamma', concentration1_b, concentration0_b)
131 …kl2 = self.g2.kl_loss('Gamma', concentration1_b, concentration0_b, concentration1_a, concentration…
139 concentration1_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
143 ans = net(concentration1_b, concentration0_b, concentration1_a, concentration0_a)
155 def construct(self, concentration1_b, concentration0_b, concentration1_a, concentration0_a): argument
156 h1 = self.g1.cross_entropy('Gamma', concentration1_b, concentration0_b)
157 …h2 = self.g2.cross_entropy('Gamma', concentration1_b, concentration0_b, concentration1_a, concentr…
165 concentration1_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
169 ans = net(concentration1_b, concentration0_b, concentration1_a, concentration0_a)
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| /third_party/mindspore/tests/st/probability/distribution/ |
| D | test_beta.py | 89 concentration1_b = np.array([1.0]).astype(np.float32)
93 total_concentration_b = concentration1_b + concentration0_b
95 log_normalization_b = np.log(special.beta(concentration1_b, concentration0_b))
97 - (special.digamma(concentration1_a) * (concentration1_b - concentration1_a)) \
102 concentration1 = Tensor(concentration1_b, dtype=dtype.float32)
228 concentration1_b = np.array([2.0]).astype(np.float32)
230 ans = prob(Tensor(concentration1_b), Tensor(concentration0_b))
233 total_concentration_b = concentration1_b + concentration0_b
235 log_normalization_b = np.log(special.beta(concentration1_b, concentration0_b))
237 - (special.digamma(concentration1_a) * (concentration1_b - concentration1_a)) \
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