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May 25, 2021
Merged

[pull] master from altosaar:master #4

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27 changes: 14 additions & 13 deletions train_variational_autoencoder_jax.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,8 +26,8 @@


def add_args(parser):
parser.add_argument("--latent_size", type=int, default=128)
parser.add_argument("--hidden_size", type=int, default=256)
parser.add_argument("--latent_size", type=int, default=10)
parser.add_argument("--hidden_size", type=int, default=512)
parser.add_argument("--variational", choices=["flow", "mean-field"])
parser.add_argument("--flow_depth", type=int, default=2)
parser.add_argument("--learning_rate", type=float, default=0.001)
Expand All @@ -39,7 +39,7 @@ def add_args(parser):
parser.add_argument(
"--use_gpu", default=False, action=argparse.BooleanOptionalAction
)
parser.add_argument("--random_seed", type=int, default=582838)
parser.add_argument("--random_seed", type=int, default=42)
parser.add_argument("--train_dir", type=pathlib.Path, default="/tmp")
parser.add_argument("--data_dir", type=pathlib.Path, default="/tmp")

Expand Down Expand Up @@ -78,8 +78,8 @@ def __init__(
[
hk.Linear(self._hidden_size),
jax.nn.relu,
hk.Linear(self._hidden_size),
jax.nn.relu,
# hk.Linear(self._hidden_size),
# jax.nn.relu,
hk.Linear(np.prod(self._output_shape)),
hk.Reshape(self._output_shape, preserve_dims=2),
]
Expand All @@ -106,8 +106,8 @@ def __init__(self, latent_size: int, hidden_size: int):
hk.Flatten(),
hk.Linear(self._hidden_size),
jax.nn.relu,
hk.Linear(self._hidden_size),
jax.nn.relu,
# hk.Linear(self._hidden_size),
# jax.nn.relu,
hk.Linear(self._latent_size * 2),
]
)
Expand All @@ -121,6 +121,7 @@ def condition(self, inputs):

def __call__(self, x: jnp.ndarray) -> Tuple[jnp.ndarray, jnp.ndarray]:
loc, scale = self.condition(x)
# IMPORTANT: need to check in source code that reparameterization_type=tfd.FULLY_REPARAMETERIZED for this class
q_z = tfd.Normal(loc=loc, scale=scale)
return q_z

Expand Down Expand Up @@ -164,8 +165,8 @@ def main():
)(x)
)

@jax.jit
def objective_fn(params: hk.Params, rng_key: PRNGKey, batch: Batch,) -> jnp.ndarray:
# @jax.jit
def objective_fn(params: hk.Params, rng_key: PRNGKey, batch: Batch) -> jnp.ndarray:
x = batch["image"]
out: ModelAndVariationalOutput = model_and_variational.apply(params, rng_key, x)
log_q_z = out.q_z.log_prob(out.z).sum(axis=-1)
Expand All @@ -186,12 +187,12 @@ def objective_fn(params: hk.Params, rng_key: PRNGKey, batch: Batch,) -> jnp.ndar
params = model_and_variational.init(
next(rng_seq), np.zeros((1, *MNIST_IMAGE_SHAPE))
)
optimizer = optax.rmsprop(args.learning_rate, centered=True)
optimizer = optax.adam(args.learning_rate)
opt_state = optimizer.init(params)

@jax.jit
# @jax.jit
def train_step(
params: hk.Params, rng_key: PRNGKey, state: optax.OptState, batch: Batch
params: hk.Params, rng_key: PRNGKey, opt_state: optax.OptState, batch: Batch
) -> Tuple[hk.Params, optax.OptState]:
"""Single update step to maximize the ELBO."""
grads = jax.grad(objective_fn)(params, rng_key, batch)
Expand Down Expand Up @@ -256,7 +257,7 @@ def evaluate(
)
print(
f"Step {step:<10d}\t"
f"Train ELBO estimate: {train_elbo:<5.3f}"
f"Train ELBO estimate: {train_elbo:<5.3f}\t"
f"Validation ELBO estimate: {elbo:<5.3f}\t"
f"Validation log p(x) estimate: {log_p_x:<5.3f}"
)
Expand Down