View source on GitHub |
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).
Note: Because internal calculations are done usingfloat64
and casting hasfloor
semantics, we must manually map zero outcomes to the smallest possible positive floating-point value, i.e.,np.finfo(dtype).tiny
. This means thatnp.finfo(dtype).tiny
occurs more frequently than it otherwise should. This bias can only happen for small values ofalpha
, i.e.,alpha << 1
or large values ofbeta
, 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).
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 . |
Implicit Reparameterization Gradients: Figurnov et al., 2018 (pdf)
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/random/gamma