def setup_networks(self):
z_dim = self.z_dim
hidden_sizes = self.hidden_layers
# define the neural networks used later in the model and the guide.
# these networks are MLPs (multi-layered perceptrons or simple feed-forward networks)
# where the provided activation parameter is used on every linear layer except
# for the output layer where we use the provided output_activation parameter
self.encoder_y = MLP(
[self.input_size] + hidden_sizes + [self.output_size],
activation=nn.Softplus,
output_activation=nn.Softmax,
allow_broadcast=self.allow_broadcast,
use_cuda=self.use_cuda,
)
# a split in the final layer's size is used for multiple outputs
# and potentially applying separate activation functions on them
# e.g. in this network the final output is of size [z_dim,z_dim]
# to produce loc and scale, and apply different activations [None,Exp] on them
self.encoder_z = MLP(
[self.input_size + self.output_size] + hidden_sizes +
[[z_dim, z_dim]],
activation=nn.Softplus,
output_activation=[None, Exp],
allow_broadcast=self.allow_broadcast,
use_cuda=self.use_cuda,
)
self.decoder = MLP(
[z_dim + self.output_size] + hidden_sizes + [self.input_size],
activation=nn.Softplus,
output_activation=nn.Sigmoid,
allow_broadcast=self.allow_broadcast,
use_cuda=self.use_cuda,
)
# using GPUs for faster training of the networks
if self.use_cuda:
self.cuda()
def setup_networks(self):
z_dim = self.z_dim
hidden_sizes = self.hidden_layers
# define the neural networks used later in the model and the guide.
# these networks are MLPs (multi-layered perceptrons or simple feed-forward networks)
# where the provided activation parameter is used on every linear layer except
# for the output layer where we use the provided output_activation parameter
# NOTE: we use a customized epsilon-scaled versions of Softmax and
# Sigmoid operations for numerical stability
self.encoder_y = MLP([self.input_size] + hidden_sizes +
[self.output_size],
activation=nn.Softplus,
output_activation=ClippedSoftmax,
epsilon_scale=self.epsilon_scale,
use_cuda=self.use_cuda)
# a split in the final layer's size is used for multiple outputs
# and potentially applying separate activation functions on them
# e.g. in this network the final output is of size [z_dim,z_dim]
# to produce mu and sigma, and apply different activations [None,Exp] on them
self.encoder_z = MLP([self.input_size + self.output_size] +
hidden_sizes + [[z_dim, z_dim]],
activation=nn.Softplus,
output_activation=[None, Exp],
use_cuda=self.use_cuda)
self.decoder = MLP([z_dim + self.output_size] + hidden_sizes +
[self.input_size],
activation=nn.Softplus,
output_activation=ClippedSigmoid,
epsilon_scale=self.epsilon_scale,
use_cuda=self.use_cuda)
# using GPUs for faster training of the networks
if self.use_cuda:
self.cuda()