d_lr = 1e-3
n_sample = 1 # Number of Monte Calro Search
generate_samples = 2000 # Number of generated sentences
# Pretraining parameters
g_pre_lr = 1e-3
d_pre_lr = 1e-3
g_pre_epochs = 60
d_pre_epochs = 1
trainer = Trainer(rf_system,
B,
T,
n_authorized,
g_H,
d_dropout,
g_lr=g_lr,
d_lr=d_lr,
n_sample=n_sample,
generate_samples=generate_samples)
sig_rd = np.concatenate([sig_auth, sig_impersonate_ad])
txid_rd = np.concatenate([
txid_auth,
np.ones((sig_impersonate_ad.shape[0], )) * n_authorized
])
sig_rd, txid_rd = shuffle(sig_rd, txid_rd)
txid_disc = txid_rd == n_authorized
txid_disc = np.invert(txid_disc)
txid_disc = txid_disc.astype(int)
d_dropout = 0.5 # dropout ratio
d_lr = 1e-3
n_sample=1 # Number of Monte Calro Search
generate_samples = 2000 # Number of generated sentences
# Pretraining parameters
g_pre_lr = 1e-3
d_pre_lr = 1e-3
g_pre_epochs= 60
d_pre_epochs = 1
d_load_path = rf_system.full_sess_dir+"/discriminator%s.json"%suffix
trainer = Trainer(rf_system, B, T, n_authorized, g_H, d_dropout, g_lr=g_lr, d_lr=d_lr, n_sample=n_sample, generate_samples=generate_samples, d_load_path=d_load_path)
sig_rd = np.concatenate([sig_auth,sig_impersonate_ad])
txid_rd = np.concatenate([txid_auth,np.ones((sig_impersonate_ad.shape[0],))*n_authorized])
sig_rd, txid_rd = shuffle(sig_rd, txid_rd)
txid_disc = txid_rd == n_authorized
txid_disc = np.invert(txid_disc)
txid_disc = txid_disc.astype(int)
test_frac = 0.1
valid_frac = 0.2
n_samples = sig_rd.shape[0]
n_test = int(test_frac*n_samples)
d_lr = 1e-3
n_sample = 1 # Number of Monte Calro Search
generate_samples = 2000 # Number of generated sentences
# Pretraining parameters
g_pre_lr = 1e-3
d_pre_lr = 1e-3
g_pre_epochs = 60
d_pre_epochs = 1
trainer = Trainer(rf_system,
B,
T,
n_authorized,
g_H,
d_dropout,
g_lr=g_lr,
d_lr=d_lr,
n_sample=n_sample,
generate_samples=generate_samples)
sig_rd = np.concatenate([sig_auth, sig_impersonate_ad])
txid_rd = np.concatenate([
txid_auth,
np.ones((sig_impersonate_ad.shape[0], )) * n_authorized
])
sig_rd, txid_rd = shuffle(sig_rd, txid_rd)
txid_disc = txid_rd == n_authorized
txid_disc = np.invert(txid_disc)
txid_disc = txid_disc.astype(int)