tf.contrib.distributions.Uniform.dtype
  • References/Big Data/TensorFlow/TensorFlow Python/Statistical distributions

tf.contrib.distributions.Uniform.dtype The DType of Tensors handled by this Distribution

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tf.contrib.distributions.Normal.validate_args
  • References/Big Data/TensorFlow/TensorFlow Python/Statistical distributions

tf.contrib.distributions.Normal.validate_args Python boolean indicated possibly expensive checks are enabled.

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tf.contrib.distributions.GammaWithSoftplusAlphaBeta.alpha
  • References/Big Data/TensorFlow/TensorFlow Python/Statistical distributions

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.alpha Shape parameter.

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tf.contrib.distributions.Poisson.entropy()
  • References/Big Data/TensorFlow/TensorFlow Python/Statistical distributions

tf.contrib.distributions.Poisson.entropy(name='entropy') Shanon entropy in nats.

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tf.contrib.distributions.Poisson.name
  • References/Big Data/TensorFlow/TensorFlow Python/Statistical distributions

tf.contrib.distributions.Poisson.name Name prepended to all ops created by this Distribution.

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tf.contrib.distributions.Bernoulli.mean()
  • References/Big Data/TensorFlow/TensorFlow Python/Statistical distributions

tf.contrib.distributions.Bernoulli.mean(name='mean') Mean.

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tf.contrib.distributions.Uniform.a
  • References/Big Data/TensorFlow/TensorFlow Python/Statistical distributions

tf.contrib.distributions.Uniform.a

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tf.contrib.distributions.Exponential.is_reparameterized
  • References/Big Data/TensorFlow/TensorFlow Python/Statistical distributions

tf.contrib.distributions.Exponential.is_reparameterized

2025-01-10 15:47:30
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.name
  • References/Big Data/TensorFlow/TensorFlow Python/Statistical distributions

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.name Name prepended to all ops created by this Distribution

2025-01-10 15:47:30
tf.contrib.distributions.Distribution.sample()
  • References/Big Data/TensorFlow/TensorFlow Python/Statistical distributions

tf.contrib.distributions.Distribution.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape

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