corpus.dictionary.weights_matrix = to_gpu(args.cuda,
corpus.dictionary.weights_matrix)
autoencoder = Seq2Seq2Decoder(emsize=args.emsize,
nhidden=args.nhidden,
ntokens=ntokens,
nlayers=args.nlayers,
noise_r=args.noise_r,
hidden_init=args.hidden_init,
dropout=args.dropout,
gpu=args.cuda,
weights_matrix=corpus.dictionary.weights_matrix)
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)
classifier = MLP_Classify(ninput=args.nhidden,
noutput=1,
layers=args.arch_classify)
g_factor = None
print(autoencoder)
print(gan_gen)
print(gan_disc)
print(classifier)
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))
###############################################################################
ntokens = len(corpus.dictionary.word2idx)
autoencoder = Seq2Seq2Decoder(emsize=args.emsize,
nhidden=args.nhidden,
ntokens=ntokens,
nlayers=args.nlayers,
noise_r=args.noise_r,
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)
classifier = MLP_Classify(ninput=args.nhidden,
noutput=1,
layers=args.arch_classify)
g_factor = None
print(autoencoder)
print(gan_gen)
print(gan_disc)
print(classifier)
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))
pooling_enc=args.pooling_enc,
gpu=args.cuda)
else:
autoencoder = Seq2Seq2Decoder(emsize=args.emsize,
nhidden=args.nhidden,
ntokens=ntokens,
nlayers=args.nlayers,
noise_r=args.noise_r,
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)
classifier = MLP_Classify(ninput=args.nhidden,
noutput=1,
layers=args.arch_classify)
g_factor = None
print(autoencoder)
print(gan_gen)
print(gan_disc)
print(classifier)
if args.cuda:
autoencoder = autoencoder.cuda()
gan_gen = gan_gen.cuda()
gan_disc = gan_disc.cuda()
classifier = classifier.cuda()