tf.random.gamma

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

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).

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

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.

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

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