tf.random.gamma

Aliases:

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

Defined in tensorflow/python/ops/random_ops.py.

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

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

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.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.