# tf.random_gamma(shape, alpha, beta=None, dtype=tf.float32, seed=None, name=None)

### tf.random_gamma(shape, alpha, beta=None, dtype=tf.float32, seed=None, name=None)

See the guide: Constants, Sequences, and Random Values > Random Tensors

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).

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

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

Note that for small alpha values, there is a chance you will draw a value of exactly 0, which gets worse for lower-precision dtypes, even though zero is not in the support of the gamma distribution.

Relevant cdfs (~chance you will draw a exactly-0 value):

  stats.gamma(.01).cdf(np.finfo(np.float16).tiny)
0.91269738769897879
stats.gamma(.01).cdf(np.finfo(np.float32).tiny)
0.41992668622045726
stats.gamma(.01).cdf(np.finfo(np.float64).tiny)
0.00084322740680686662
stats.gamma(.35).cdf(np.finfo(np.float16).tiny)
0.037583276135263931
stats.gamma(.35).cdf(np.finfo(np.float32).tiny)
5.9514895726818067e-14
stats.gamma(.35).cdf(np.finfo(np.float64).tiny)
2.3529843400647272e-108


#### 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)) with values of type dtype.