gen_init=gen_init,
theta_y=theta_y,
disc_init=netD_state,
experiment=e)
e.log.info(model)
model.train()
model.get_theta()
model.save()
pickle.dump([
theta_y, model.all_theta, model.all_delta, model.all_weights,
model.all_grad_norm, model.all_loss, model.all_tolerance
], open(e.experiment_dir + "/bayes_all+thetas+weights.pkl", "wb+"))
if __name__ == '__main__':
args = config.get_base_parser().parse_args()
with train_helper.experiment(args, args.save_prefix) as e:
np.random.seed(e.config.random_seed)
torch.manual_seed(e.config.random_seed)
e.log.info("*" * 25 + " ARGS " + "*" * 25)
e.log.info(args)
e.log.info("*" * 25 + " ARGS " + "*" * 25)
run(e)
v1_norm = (v1**2).sum(-1)**0.5
v2_norm = (v2**2).sum(-1)**0.5
return prod / (v1_norm * v2_norm)
save_dict = torch.load(args.save_file,
map_location=lambda storage, loc: storage)
config = save_dict['config']
checkpoint = save_dict['state_dict']
config.debug = True
with open(args.vocab_file, "rb") as fp:
W, vocab = pickle.load(fp)
with train_helper.experiment(config, config.save_prefix) as e:
e.log.info("vocab loaded from: {}".format(args.vocab_file))
model = models.vgvae(vocab_size=len(vocab),
embed_dim=e.config.edim if W is None else W.shape[1],
embed_init=W,
experiment=e)
model.eval()
model.load(checkpointed_state_dict=checkpoint)
e.log.info(model)
def encode(d):
global vocab, batch_size
new_d = [[vocab.get(w, 0) for w in s.split(" ")] for s in d]
all_y_vecs = []
all_z_vecs = []