tf.random.gamma

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Draws shape samples from each of the given Gamma distribution(s).

Aliases:

  • tf.compat.v1.random.gamma
  • tf.compat.v1.random_gamma
  • tf.compat.v2.random.gamma
tf.random.gamma(
    shape,
    alpha,
    beta=None,
    dtype=tf.dtypes.float32,
    seed=None,
    name=None
)

alpha is the shape parameter describing the distribution(s), and beta is the inverse scale parameter(s).

Note: Because internal calculations are done using float64 and casting has floor semantics, we must manually map zero outcomes to the smallest possible positive floating-point value, i.e., np.finfo(dtype).tiny. This means that np.finfo(dtype).tiny occurs more frequently than it otherwise should. This bias can only happen for small values of alpha, i.e., alpha << 1 or large values of beta, i.e., beta >> 1.

The samples are differentiable w.r.t. alpha and beta. The derivatives are computed using the approach described in the paper

Michael Figurnov, Shakir Mohamed, Andriy Mnih. Implicit Reparameterization Gradients, 2018

Example:

samples = tf.random.gamma([10], [0.5, 1.5])
# samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents
# the samples drawn from each distribution

samples = tf.random.gamma([7, 5], [0.5, 1.5])
# samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1]
# represents the 7x5 samples drawn from each of the two distributions

alpha = tf.constant([[1.],[3.],[5.]])
beta = tf.constant([[3., 4.]])
samples = tf.random.gamma([30], alpha=alpha, beta=beta)
# samples has shape [30, 3, 2], with 30 samples each of 3x2 distributions.

loss = tf.reduce_mean(tf.square(samples))
dloss_dalpha, dloss_dbeta = tf.gradients(loss, [alpha, beta])
# unbiased stochastic derivatives of the loss function
alpha.shape == dloss_dalpha.shape  # True
beta.shape == dloss_dbeta.shape  # True

Args:

  • shape: A 1-D integer Tensor or Python array. The shape of the output samples to be drawn per alpha/beta-parameterized distribution.
  • alpha: A Tensor or Python value or N-D array of type dtype. alpha provides the shape parameter(s) describing the gamma distribution(s) to sample. Must be broadcastable with beta.
  • beta: A Tensor or Python value or N-D array of type dtype. Defaults to 1. beta provides the inverse scale parameter(s) of the gamma distribution(s) to sample. Must be broadcastable with alpha.
  • dtype: The type of alpha, beta, and the output: float16, float32, or float64.
  • seed: A Python integer. Used to create a random seed for the distributions. See tf.compat.v1.set_random_seed for behavior.
  • name: Optional name for the operation.

Returns:

  • samples: a Tensor of shape tf.concat([shape, tf.shape(alpha + beta)], axis=0) with values of type dtype.

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