hidden_init=args.hidden_init,
dropout=args.dropout,
gpu=args.cuda)
gan_gen = MLP_G(ninput=args.z_size, noutput=args.nhidden, layers=args.arch_g)
gan_disc = MLP_D(ninput=args.nhidden, noutput=1, layers=args.arch_d)
print(autoencoder)
print(gan_gen)
print(gan_disc)
optimizer_ae = optim.SGD(autoencoder.parameters(), lr=args.lr_ae)
optimizer_gan_g = optim.Adam(gan_gen.parameters(),
lr=args.lr_gan_g,
betas=(args.beta1, 0.999))
optimizer_gan_d = optim.Adam(gan_disc.parameters(),
lr=args.lr_gan_d,
betas=(args.beta1, 0.999))
criterion_ce = nn.CrossEntropyLoss()
if args.cuda:
autoencoder = autoencoder.cuda()
gan_gen = gan_gen.cuda()
gan_disc = gan_disc.cuda()
criterion_ce = criterion_ce.cuda()
###############################################################################
# Training code
###############################################################################
hidden_init=args.hidden_init,
dropout=args.dropout,
gpu=args.cuda)
gan_gen = MLP_G(ninput=args.z_size, noutput=args.nhidden, layers=args.arch_g)
gan_disc = MLP_D(ninput=args.nhidden, noutput=1, layers=args.arch_d)
print(autoencoder)
print(gan_gen)
print(gan_disc)
optimizer_ae = optim.SGD(autoencoder.parameters(), lr=args.lr_ae)
optimizer_gan_g = optim.Adam(gan_gen.parameters(),
lr=args.lr_gan_g,
betas=(args.beta1, 0.999))
optimizer_gan_d = optim.Adam(gan_disc.parameters(),
lr=args.lr_gan_d,
betas=(args.beta1, 0.999))
criterion_ce = nn.CrossEntropyLoss()
if args.cuda:
autoencoder = autoencoder.cuda()
gan_gen = gan_gen.cuda()
gan_disc = gan_disc.cuda()
criterion_ce = criterion_ce.cuda()
###############################################################################
# Training code
###############################################################################
ntokens=word_args.ntokens,
nlayers=word_args.nlayers,
noise_r=word_args.noise_r,
hidden_init=word_args.hidden_init,
dropout=word_args.dropout)
word_ae.load_state_dict(word_ae_params)
D = MLP_D(input_dim=args.nhidden, output_dim=1, arch_layers=args.arch_d)
G = MLP_G(input_dim=args.nhidden,
output_dim=args.nhidden,
noise_dim=args.z_size,
arch_layers=args.arch_g)
if args.finetune_ae:
logger.info("AE will be fine-tuned")
optimizer_D = optim.Adam(list(D.parameters()) +
list(char_ae.parameters()) +
list(word_ae.parameters()),
lr=args.lr_gan_d,
betas=(args.beta1, 0.999))
optimizer_G = optim.Adam(list(G.parameters()) +
list(char_ae.parameters()) +
list(word_ae.parameters()),
lr=args.lr_gan_g,
betas=(args.beta1, 0.999))
else:
logger.info("AE will not be fine-tuned")
optimizer_D = optim.Adam(D.parameters(),
lr=args.lr_gan_d,
betas=(args.beta1, 0.999))
optimizer_G = optim.Adam(G.parameters(),
