def main(args):
# load model
if 'full' in args.model:
model = LFADS_full.load(args.model)
else:
model = LFADS.load(args.model)
if args.gpu is not None:
cuda.get_device(args.gpu).use()
model.to_gpu(args.gpu)
xp = cuda.cupy
else:
xp = np
# generate completely new samples
print("generate completely new samples from prior distribution...")
# sample initial condition (g0)
mus = xp.zeros((args.batch_size, model.generator.g_dims), dtype=xp.float32)
sigmas = xp.ones((args.batch_size, model.generator.g_dims),
dtype=xp.float32)
g0_bxd = F.gaussian(Variable(mus), Variable(sigmas))
# inffered inputs are sampled from a Gaussian autoregressive prior
xs = []
for i in range(args.nsample):
print("now generating %d'th sample among %d" % (i, args.nsample))
if i == 0:
u_i_bxd = model.generator.sample_u_1(xp.ndarray(None),
batch_size=args.batch_size,
prior_sample=True)
g_i_bxd = model.generator(u_i_bxd, hx=g0_bxd)
else:
u_i_bxd = model.generator.sample_u_i(xp.ndarray(None),
u_i_bxd,
batch_size=args.batch_size,
prior_sample=True)
g_i_bxd = model.generator(u_i_bxd, hx=g_i_bxd)
f_i = model.generator.l_f(g_i_bxd)
x_i = model.generator.sample_x_hat(f_i, calc_rec_loss=False)
xs.append(cuda.to_cpu(x_i.data))
# save
data_dict = {'xs': xs}
data_fname = args.model + '_prior_sampling.h5'
with h5py.File(data_fname, 'w') as hf:
for k, v in data_dict.items():
hf.create_dataset(k, data=v, compression=False)
def main(args):
# load data
print >> sys.stderr, 'Loading data...'
dataset = util.read_datasets(args.data_path, args.data_fname_stem)
train_data = dataset["train_data"]
test_data = dataset["valid_data"]
# load model
if 'full' in args.model:
model = LFADS_full.load(args.model)
else:
model = LFADS.load(args.model)
if args.gpu is not None:
cuda.get_device(args.gpu).use()
model.to_gpu(args.gpu)
# posterior sampling
# encoder
ndata = train_data.shape[0]
x_hat_all = []
f_all = []
for i in range(ndata):
print("now %d'th data among %d" % (i, ndata))
x_data = train_data[i, :, :].astype(np.float32)
x_data = x_data[np.newaxis, :, :]
x_data = np.tile(x_data, (args.nsample, 1, 1))
# copy data to GPU
if args.gpu is not None:
x_data = cuda.to_gpu(x_data)
# create variable
xs = []
[xs.append(Variable(x.astype(np.float32))) for x in x_data]
# encoder
_, h_bxtxd = model.encoder(xs)
h_bxtxd = F.stack(h_bxtxd, 0)
d_dims = h_bxtxd.data.shape[2]
# generator
g0_bxd, _ = model.generator.sample_g0(
F.concat(
[h_bxtxd[:, 0, -d_dims / 2:], h_bxtxd[:, -1, :d_dims / 2]],
axis=1))
f0_bxd = model.generator.l_f(g0_bxd)
# sampling
x_hat = []
f_trial = []
for j in range(0, h_bxtxd[0].data.shape[0]):
if j == 0:
if 'full' in args.model:
con_i = model.controller(
F.concat((f0_bxd, h_bxtxd[:, j, :d_dims / 2],
h_bxtxd[:, j, d_dims / 2:]),
axis=1))
u_i_bxd, _ = model.generator.sample_u_1(con_i)
g_i_bxd = model.generator(u_i_bxd, hx=g0_bxd)
else:
g_i_bxd = model.generator(F.concat([
h_bxtxd[:, j, :d_dims / 2], h_bxtxd[:, j, d_dims / 2:]
],
axis=1),
hx=g0_bxd)
else:
if 'full' in args.model:
con_i = model.controller(F.concat([
f_i, h_bxtxd[:, j, :d_dims / 2], h_bxtxd[:, j,
d_dims / 2:]
],
axis=1),
hx=con_i)
u_i_bxd, _ = model.generator.sample_u_i(con_i, u_i_bxd)
g_i_bxd = model.generator(u_i_bxd, hx=g_i_bxd)
else:
g_i_bxd = model.generator(F.concat([
h_bxtxd[:, j, :d_dims / 2], h_bxtxd[:, j, d_dims / 2:]
],
axis=1),
hx=g_i_bxd)
f_i = model.generator.l_f(g_i_bxd)
x_hat_i = model.generator.sample_x_hat(f_i,
xs=Variable(x_data[:,
j, :]),
nrep=1,
calc_rec_loss=False)
f_i = F.mean(f_i, axis=0)
x_hat.append(cuda.to_cpu(x_hat_i.data))
f_trial.append(cuda.to_cpu(f_i.data))
x_hat_all.append(x_hat)
f_all.append(f_trial)
# save
data_dict = {'x_hat_all': x_hat_all, 'f_all': f_all}
data_fname = args.model + '_posterior_sampling.h5'
with h5py.File(data_fname, 'w') as hf:
for k, v in data_dict.items():
hf.create_dataset(k, data=v, compression=False)
