View source on GitHub |

Outputs deterministic pseudorandom values from a gamma distribution.

```
tf.random.stateless_gamma(
shape, seed, alpha, beta=None, dtype=tf.dtypes.float32, name=None
)
```

The generated values follow a gamma distribution with specified concentration
(`alpha`

) and inverse scale (`beta`

) parameters.

This is a stateless version of `tf.random.gamma`

: if run twice with the same
seeds, it will produce the same pseudorandom numbers. The output is consistent
across multiple runs on the same hardware (and between CPU and GPU), but may
change between versions of TensorFlow or on non-CPU/GPU hardware.

A slight difference exists in the interpretation of the `shape`

parameter
between `stateless_gamma`

and `gamma`

: in `gamma`

, the `shape`

is always
prepended to the shape of the broadcast of `alpha`

with `beta`

; whereas in
`stateless_gamma`

the `shape`

parameter must always encompass the shapes of
each of `alpha`

and `beta`

(which must broadcast together to match the
trailing dimensions of `shape`

).

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.stateless_gamma([10, 2], seed=[12, 34], alpha=[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.stateless_gamma([7, 5, 2], seed=[12, 34], alpha=[.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.stateless_gamma(
[30, 3, 2], seed=[12, 34], alpha=alpha, beta=beta)
# samples has shape [30, 3, 2], with 30 samples each of 3x2 distributions.
with tf.GradientTape() as tape:
tape.watch([alpha, beta])
loss = tf.reduce_mean(tf.square(tf.random.stateless_gamma(
[30, 3, 2], seed=[12, 34], alpha=alpha, beta=beta)))
dloss_dalpha, dloss_dbeta = tape.gradient(loss, [alpha, beta])
# unbiased stochastic derivatives of the loss function
alpha.shape == dloss_dalpha.shape # True
beta.shape == dloss_dbeta.shape # True
```

#### Args:

: A 1-D integer Tensor or Python array. The shape of the output tensor.`shape`

: A shape [2] Tensor, the seed to the random number generator. Must have dtype`seed`

`int32`

or`int64`

. (When using XLA, only`int32`

is allowed.): Tensor. The concentration parameter of the gamma distribution. Must be broadcastable with`alpha`

`beta`

, and broadcastable with the rightmost dimensions of`shape`

.: Tensor. The inverse scale parameter of the gamma distribution. Must be broadcastable with`beta`

`alpha`

and broadcastable with the rightmost dimensions of`shape`

.: Floating point dtype of`dtype`

`alpha`

,`beta`

, and the output.: A name for the operation (optional).`name`

#### Returns:

: A Tensor of the specified shape filled with random gamma values. For each i, each `samples[..., i] is an independent draw from the gamma distribution with concentration alpha[i] and scale beta[i].`samples`