TFGENZOO.flows.utils.gaussianize module

TFGENZOO.flows.utils.gaussianize.gaussian_likelihood(mean: tensorflow.python.framework.ops.Tensor, logsd: tensorflow.python.framework.ops.Tensor, x: tensorflow.python.framework.ops.Tensor)

calculate negative log likelihood of Gaussian Distribution.

Parameters

• mean (tf.Tensor) – mean [B, …]

• logsd (tf.Tensor) – log standard deviation [B, …]

• x (tf.Tensor) – tensor [B, …]

Returns

log likelihood [B, …]

Return type

ll (tf.Tensor)

Note

\begin{align} ll &= - \cfrac{1}{2} (k\log(2 \pi) + \log |Var| \\ &+ (x - Mu)^T (Var ^ {-1}) (x - Mu))\\ ,\ where & \\ & k = 1\ (Independent)\\ & Var\ is\ a\ variance = exp(2 logsd) \end{align}

TFGENZOO.flows.utils.gaussianize.gaussian_sample(mean: tensorflow.python.framework.ops.Tensor, logsd: tensorflow.python.framework.ops.Tensor, temparature: float = 1.0)

sampling from mean, logsd * temparature

Parameters

• mean (tf.Tensor) – mean [B, …]

• logsd (tf.Tensor) – log standard deviation [B, …]

• temparature (float) – temparature

Returns

sampled latent variable [B, …]

Return type

new_z(tf.Tensor)

Noto:

I cann’t gurantee it’s correctness. Please open the tensorflow probability’s Issue.