mcoric96/learning-gaussian-distributions-in-tensorflow

Template TensorFlow code for feed-forward neural networks - learning Gaussian distributions

Learning-Gaussian-distributions-in-TensorFlow

Template TensorFlow code for feed-forward neural networks - learning Gaussian distributions.

Model architecture illustration:

• Input values: vector of numbers $x$.
  
• Output values: $\mu$, $\sigma$ (parameters of Gaussian distrbution conditioned on input $x$).
  
• Loss function: standard negative log-likelihood of target value $y$ under model output distribution: $-\log p(y; \mu, \sigma)$, where $\mu$, $\sigma = f(x)$ and $f$ is neural network model.

For dataset $X = \{x_1, x_2, ..., x_n\}$ and target values $Y = \{y_1, y_2, ..., y_3\}$ loss function negative log-likelihood is defined as:

$$L(\theta ; X) = \frac{1}{n} \sum_{i=1}^n -\log p(y_i; \mu_i, \sigma_i)$$

where $\mu_i$ and $\sigma_i$ are model outputs for $x_i$, $\theta$ are model parameters and $p$ is probability density function defined with model outputs $\mu_i$ and $\sigma_i$.

Model architecture:

• feed-forward neural network (model can be extended for recurrent architectures),
• base layers learn joint representations of inputs,
• parameter layers ( $\mu$ - layer and $\sigma$ - layer) learn specific representations important for each output parameter,
• alternative for regression models with single numerical output,
• $\mu$ - layer output activation function: can be any function, linear or any other that restricts output range,
• $\sigma$ - layer output activation function: should be function with only positive value outputs, like softplus.

Model output $\sigma$ represents aleatoric model uncertainty (illustration example below).