Note
Click here to download the full example code
Example: Variational Autoencoder¶
import argparse
import inspect
import os
import time
import matplotlib.pyplot as plt
from jax import jit, lax, random
from jax.experimental import stax
import jax.numpy as jnp
from jax.random import PRNGKey
import numpyro
from numpyro import optim
import numpyro.distributions as dist
from numpyro.examples.datasets import MNIST, load_dataset
from numpyro.infer import SVI, Trace_ELBO
RESULTS_DIR = os.path.abspath(
os.path.join(os.path.dirname(inspect.getfile(lambda: None)), ".results")
)
os.makedirs(RESULTS_DIR, exist_ok=True)
def encoder(hidden_dim, z_dim):
return stax.serial(
stax.Dense(hidden_dim, W_init=stax.randn()),
stax.Softplus,
stax.FanOut(2),
stax.parallel(
stax.Dense(z_dim, W_init=stax.randn()),
stax.serial(stax.Dense(z_dim, W_init=stax.randn()), stax.Exp),
),
)
def decoder(hidden_dim, out_dim):
return stax.serial(
stax.Dense(hidden_dim, W_init=stax.randn()),
stax.Softplus,
stax.Dense(out_dim, W_init=stax.randn()),
stax.Sigmoid,
)
def model(batch, hidden_dim=400, z_dim=100):
batch = jnp.reshape(batch, (batch.shape[0], -1))
batch_dim, out_dim = jnp.shape(batch)
decode = numpyro.module("decoder", decoder(hidden_dim, out_dim), (batch_dim, z_dim))
z = numpyro.sample("z", dist.Normal(jnp.zeros((z_dim,)), jnp.ones((z_dim,))))
img_loc = decode(z)
return numpyro.sample("obs", dist.Bernoulli(img_loc), obs=batch)
def guide(batch, hidden_dim=400, z_dim=100):
batch = jnp.reshape(batch, (batch.shape[0], -1))
batch_dim, out_dim = jnp.shape(batch)
encode = numpyro.module("encoder", encoder(hidden_dim, z_dim), (batch_dim, out_dim))
z_loc, z_std = encode(batch)
z = numpyro.sample("z", dist.Normal(z_loc, z_std))
return z
@jit
def binarize(rng_key, batch):
return random.bernoulli(rng_key, batch).astype(batch.dtype)
def main(args):
encoder_nn = encoder(args.hidden_dim, args.z_dim)
decoder_nn = decoder(args.hidden_dim, 28 * 28)
adam = optim.Adam(args.learning_rate)
svi = SVI(
model, guide, adam, Trace_ELBO(), hidden_dim=args.hidden_dim, z_dim=args.z_dim
)
rng_key = PRNGKey(0)
train_init, train_fetch = load_dataset(
MNIST, batch_size=args.batch_size, split="train"
)
test_init, test_fetch = load_dataset(
MNIST, batch_size=args.batch_size, split="test"
)
num_train, train_idx = train_init()
rng_key, rng_key_binarize, rng_key_init = random.split(rng_key, 3)
sample_batch = binarize(rng_key_binarize, train_fetch(0, train_idx)[0])
svi_state = svi.init(rng_key_init, sample_batch)
@jit
def epoch_train(svi_state, rng_key, train_idx):
def body_fn(i, val):
loss_sum, svi_state = val
rng_key_binarize = random.fold_in(rng_key, i)
batch = binarize(rng_key_binarize, train_fetch(i, train_idx)[0])
svi_state, loss = svi.update(svi_state, batch)
loss_sum += loss
return loss_sum, svi_state
return lax.fori_loop(0, num_train, body_fn, (0.0, svi_state))
@jit
def eval_test(svi_state, rng_key, test_idx):
def body_fun(i, loss_sum):
rng_key_binarize = random.fold_in(rng_key, i)
batch = binarize(rng_key_binarize, test_fetch(i, test_idx)[0])
# FIXME: does this lead to a requirement for an rng_key arg in svi_eval?
loss = svi.evaluate(svi_state, batch) / len(batch)
loss_sum += loss
return loss_sum
loss = lax.fori_loop(0, num_test, body_fun, 0.0)
loss = loss / num_test
return loss
def reconstruct_img(epoch, rng_key):
img = test_fetch(0, test_idx)[0][0]
plt.imsave(
os.path.join(RESULTS_DIR, "original_epoch={}.png".format(epoch)),
img,
cmap="gray",
)
rng_key_binarize, rng_key_sample = random.split(rng_key)
test_sample = binarize(rng_key_binarize, img)
params = svi.get_params(svi_state)
z_mean, z_var = encoder_nn[1](
params["encoder$params"], test_sample.reshape([1, -1])
)
z = dist.Normal(z_mean, z_var).sample(rng_key_sample)
img_loc = decoder_nn[1](params["decoder$params"], z).reshape([28, 28])
plt.imsave(
os.path.join(RESULTS_DIR, "recons_epoch={}.png".format(epoch)),
img_loc,
cmap="gray",
)
for i in range(args.num_epochs):
rng_key, rng_key_train, rng_key_test, rng_key_reconstruct = random.split(
rng_key, 4
)
t_start = time.time()
num_train, train_idx = train_init()
_, svi_state = epoch_train(svi_state, rng_key_train, train_idx)
rng_key, rng_key_test, rng_key_reconstruct = random.split(rng_key, 3)
num_test, test_idx = test_init()
test_loss = eval_test(svi_state, rng_key_test, test_idx)
reconstruct_img(i, rng_key_reconstruct)
print(
"Epoch {}: loss = {} ({:.2f} s.)".format(
i, test_loss, time.time() - t_start
)
)
if __name__ == "__main__":
assert numpyro.__version__.startswith("0.8.0")
parser = argparse.ArgumentParser(description="parse args")
parser.add_argument(
"-n", "--num-epochs", default=15, type=int, help="number of training epochs"
)
parser.add_argument(
"-lr", "--learning-rate", default=1.0e-3, type=float, help="learning rate"
)
parser.add_argument("-batch-size", default=128, type=int, help="batch size")
parser.add_argument("-z-dim", default=50, type=int, help="size of latent")
parser.add_argument(
"-hidden-dim",
default=400,
type=int,
help="size of hidden layer in encoder/decoder networks",
)
args = parser.parse_args()
main(args)