gnn (version 0.0-2)

VAE_model: Variational Autoencoder

Description

Setup of a variational autoencoder (VAE) model.

Usage

VAE_model(dim, activation = c(rep("relu", length(dim) - 2), "sigmoid"),
          batch.norm = FALSE, dropout.rate = 0,
          sd = 1, loss.type = c("MSE", "binary.cross", "MMD"), nGPU = 0, ...)

Arguments

dim

numeric vector of length at least two, giving the dimensions of the input layer (equal to the dimension of the output layer), the hidden layer(s) (if any) and the latent layer (in this order).

activation

character vector of length length(dim) - 1 specifying the activation functions for all hidden layers and the output layer (in this order); note that the input layer does not have an activation function.

batch.norm

logical indicating whether batch normalization layers are to be added after each hidden layer.

dropout.rate

numeric value in [0,1] specifying the fraction of input to be dropped; see the rate parameter of layer_dropout(). Note that only if positive, dropout layers are added after each hidden layer.

sd

positive numeric value giving the standard deviation of the normal distribution used as prior.

loss.type

character string indicating the type of reconstruction loss. Currently available are the mean squared error ("MSE"), binary cross entropy ("binary.cross") and (kernel) maximum mean discrepancy ("MMD").

nGPU

non-negative integer specifying the number of GPUs available if the GPU version of TensorFlow is installed. If positive, a (special) multiple GPU model for data parallelism is instantiated. Note that for multi-layer perceptrons on a few GPUs, this model does not yet yield any scale-up computational factor (in fact, currently very slightly negative scale-ups are likely due to overhead costs).

…

additional arguments passed to loss().

Value

VAE_model() returns a list with components

model:

VAE model (a keras object inheriting from the classes "keras.engine.training.Model", "keras.engine.network.Network", "keras.engine.base_layer.Layer" and "python.builtin.object").

encoder:

the encoder (a keras object as model).

generator:

the generator (a keras object as model).

type:

character string indicating the type of model ("VAE").

dim:

see above.

activation:

see above.

batch.norm:

see above.

dropout.rate:

see above.

sd:

see above.

loss.type:

see above.

dim.train:

dimension of the training data (NA unless trained).

batch.size:

batch size (NA unless trained).

nepoch:

number of epochs (NA unless trained).

References

Kingma, D. P. and Welling, M. (2014). Stochastic gradient VB and the variational auto-encoder. Second International Conference on Learning Representations (ICLR). See https://keras.rstudio.com/articles/examples/variational_autoencoder.html

See Also

GMMN_model()

Examples

# NOT RUN {
 # to avoid win-builder error "Error: Installation of TensorFlow not found"
## Example model with a 5d input, 300d hidden and 4d output layer
str(VAE_model(c(5, 300, 4)))
# }