# tf.random.gamma

View source

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`.