tf.random.gamma

View source on GitHub

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

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.random.gamma, tf.compat.v1.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 (Figurnov et al., 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.random.set_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.

References:

Implicit Reparameterization Gradients: Figurnov et al., 2018 (pdf)