# VAE
## VAE + losses
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### log_importance_weight_matrix
``` python
def log_importance_weight_matrix(
batch_size:int, dataset_size:Array
)->Array:
```
*Calculates a log importance weight matrix.*
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### matrix_log_density_gaussian
``` python
def matrix_log_density_gaussian(
x:Array, mu:Array, logvar:Array
)->Array:
```
*Calculates log density of a Gaussian for all batch pairs.*
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### TC_term
``` python
def TC_term(
mean:Array, logvar:Array, z:Array
)->Array:
```
*compute TC term*
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### log_density_gaussian
``` python
def log_density_gaussian(
x:Array, mu:Array, logvar:Array
):
```
*Calculates log density of a Gaussian.*
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source
### log_normal_pdf
``` python
def log_normal_pdf(
mu:Array, logvar:Array, sample:Array
):
```
*Call self as a function.*
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### kl_standard_normal
``` python
def kl_standard_normal(
mean:Array, logvar:Array
)->Array:
```
*KL divergence between diag Gaussian q(z|x) ~ N(mean, exp(logvar)) and
p(z)=N(0,I).*
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### gaussian_negloglik
``` python
def gaussian_negloglik(
recon_mean:Array, recon_logvar:Array, targets:Array, eps:float=1e-08
)->Array:
```
*reconstruction loss for continuous data*
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### categorical_reconstruction_loss
``` python
def categorical_reconstruction_loss(
log_cp_i:Array, targets:Array
)->Array:
```
*reconstruction loss for discrete data*
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### VAEmodel
``` python
def VAEmodel(
encoder:Module, decoder:Module, parent:Union=,
name:Optional=None
)->None:
```
*VAE model, wrapper calling the encoder -\> reparam -\> decoder*
Args:
encoder: nn.Module
decoder: nn.Module
Returns:
mean: (batch, latent_dim)
logvar: (batch, latent_dim)
z: (batch, latent_dim)
cp: (batch, seq_len, local_dimension)
## Trainer
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### VAETrainer
``` python
def VAETrainer(
model:Module, dataset:Dataset, optimizer:GradientTransformation=adabelief
):
```
*Training wrapper for a the cpVAE for quantum*
Args:
model: nn.Module (VAEmodel)
dataset: Dataset
optimizer: optax.GradientTransformation