def run_svgd(key, lr, full_data=False, progress_bar=False):
key, subkey = random.split(key)
init_particles = ravel(*sample_from_prior(subkey, n_particles))
svgd_opt = optax.sgd(lr)
svgd_grad = models.KernelGradient(
get_target_logp=lambda batch: get_minibatch_logp(*batch), scaled=True)
particles = models.Particles(key,
svgd_grad.gradient,
init_particles,
custom_optimizer=svgd_opt)
test_batches = get_batches(x_test, y_test, 2 *
NUM_VALS) if full_data else get_batches(
x_val, y_val, 2 * NUM_VALS)
train_batches = get_batches(xx, yy, NUM_STEPS +
1) if full_data else get_batches(
x_train, y_train, NUM_STEPS + 1)
for i, batch in tqdm(enumerate(train_batches),
total=NUM_STEPS,
disable=not progress_bar):
particles.step(batch)
if i % (NUM_STEPS // NUM_VALS) == 0:
test_logp = get_minibatch_logp(*next(test_batches))
stepdata = {
"accuracy":
compute_test_accuracy(unravel(particles.particles)[0]),
"test_logp": test_logp(particles.particles),
}
metrics.append_to_log(particles.rundata, stepdata)
particles.done()
return particles
def run_sgld(key, lr, full_data=False, progress_bar=False):
key, subkey = random.split(key)
init_particles = ravel(*sample_from_prior(subkey, n_particles))
key, subkey = random.split(key)
# sgld_opt = utils.scaled_sgld(subkey, lr, schedule)
sgld_opt = utils.sgld(lr, 0)
def energy_gradient(data, particles, aux=True):
"""data = [batch_x, batch_y]"""
xbatch, ybatch = data
logp = get_minibatch_logp(xbatch, ybatch)
logprob, grads = value_and_grad(logp)(particles)
if aux:
return -grads, {"logp": logprob}
else:
return -grads
particles = models.Particles(key,
energy_gradient,
init_particles,
custom_optimizer=sgld_opt)
test_batches = get_batches(x_test, y_test, 2 *
NUM_VALS) if full_data else get_batches(
x_val, y_val, 2 * NUM_VALS)
train_batches = get_batches(xx, yy, NUM_STEPS +
1) if full_data else get_batches(
x_train, y_train, NUM_STEPS + 1)
for i, batch_xy in tqdm(enumerate(train_batches),
total=NUM_STEPS,
disable=not progress_bar):
particles.step(batch_xy)
if i % (NUM_STEPS // NUM_VALS) == 0:
test_logp = get_minibatch_logp(*next(test_batches))
stepdata = {
"accuracy":
compute_test_accuracy(unravel(particles.particles)[0]),
"train_accuracy":
compute_train_accuracy(unravel(particles.particles)[0]),
"test_logp":
np.mean(test_logp(particles.particles))
}
metrics.append_to_log(particles.rundata, stepdata)
particles.done()
return particles
def run_neural_svgd(key, plr, full_data=False, progress_bar=False):
key, subkey = random.split(key)
init_particles = ravel(*sample_from_prior(subkey, n_particles))
nsvgd_opt = optax.sgd(plr)
key1, key2 = random.split(key)
neural_grad = models.SteinNetwork(
target_dim=init_particles.shape[1],
#get_target_logp=lambda batch: get_minibatch_logp(*batch),
learning_rate=neural_lr,
key=key1,
aux=False,
lambda_reg=lambda_reg)
particles = models.Particles(key2,
neural_grad.gradient,
init_particles,
custom_optimizer=nsvgd_opt)
test_batches = get_batches(x_test, y_test, 2 *
NUM_VALS) if full_data else get_batches(
x_val, y_val, 2 * NUM_VALS)
train_batches = get_batches(xx, yy, NUM_STEPS +
1) if full_data else get_batches(
x_train, y_train, NUM_STEPS + 1)
@jit
def v_dlogp(particles, batch):
logp = get_minibatch_logp(*batch)
return vmap(grad(logp))(particles)
# Warmup on first batch
# key, subkey = random.split(key)
# neural_grad.warmup(key=subkey,
# sample_split_particles=sample_tv,
# next_data=lambda: next(get_batches(x_train, y_train, n_steps=100+1)), # note: lambda always returns first batch
# n_iter=3)
first_batch = next(get_batches(x_train, y_train, n_steps=100 + 1))
for _ in range(3):
key, subkey = random.split(key)
split_particles = sample_tv(subkey)
split_dlogp = [v_dlogp(x, first_batch) for x in split_particles]
neural_grad.train(split_particles,
split_dlogp,
n_steps=30,
early_stopping=True)
for i, data_batch in tqdm(enumerate(train_batches),
total=NUM_STEPS,
disable=not progress_bar):
key, subkey = random.split(key)
split_particles = particles.next_batch(subkey)
split_dlogp = [v_dlogp(x, data_batch) for x in split_particles]
neural_grad.train(split_particles, split_dlogp, n_steps=10)
particles.step(neural_grad.get_params())
if i % (NUM_STEPS // NUM_VALS) == 0:
test_logp = get_minibatch_logp(*next(test_batches))
train_logp = get_minibatch_logp(*data_batch)
stepdata = {
"accuracy":
compute_test_accuracy(unravel(particles.particles)[0]),
"test_logp": test_logp(particles.particles),
"training_logp": train_logp(particles.particles),
}
metrics.append_to_log(particles.rundata, stepdata)
neural_grad.done()
particles.done()
return particles, neural_grad
def train(key,
particle_stepsize: float = 1e-3,
evaluate_every: int = 10,
n_iter: int = 400,
n_samples: int = 100,
results_file: str = cfg.results_path + 'svgd-bnn.csv',
overwrite_file: bool = False,
optimizer="sgd"):
"""
Initialize model; warmup; training; evaluation.
Returns a dictionary of metrics.
Args:
particle_stepsize: learning rate of BNN
evaluate_every: compute metrics every `evaluate_every` steps
n_iter: number of train-update iterations
write_results_to_file: whether to save accuracy in csv file
"""
csv_string = f"{particle_stepsize}"
# initialize particles and the dynamics model
key, subkey = random.split(key)
init_particles = vmap(bnn.init_flat_params)(random.split(
subkey, n_samples))
if optimizer == "sgd":
opt = optax.sgd(particle_stepsize)
elif optimizer == "adam":
opt = optax.adam(particle_stepsize)
else:
raise ValueError("must be adam or sgd")
key, subkey1, subkey2 = random.split(key, 3)
svgd_grad = models.KernelGradient(get_target_logp=bnn.get_minibatch_logp,
scaled=False,
lambda_reg=LAMBDA_REG)
particles = models.Particles(key=subkey2,
gradient=svgd_grad.gradient,
init_samples=init_particles,
custom_optimizer=opt)
def evaluate(step_counter, ps):
stepdata = {
"accuracy": bnn.compute_acc_from_flat(ps),
"step_counter": step_counter,
}
with open(results_file, "a") as file:
file.write(csv_string + f"{step_counter},{stepdata['accuracy']}\n")
return stepdata
if not os.path.isfile(results_file) or overwrite_file:
with open(results_file, "w") as file:
file.write("meta_lr,particle_stepsize,patience,"
"max_train_steps,step,accuracy\n")
print("Training...")
for step_counter in tqdm(range(n_iter), disable=on_cluster):
train_batch = next(data.train_batches)
particles.step(train_batch)
if (step_counter + 1) % evaluate_every == 0:
metrics.append_to_log(particles.rundata,
evaluate(step_counter, particles.particles))
if step_counter % data.steps_per_epoch == 0:
print(f"Starting epoch {step_counter // data.steps_per_epoch + 1}")
# final eval
final_eval = evaluate(-1, particles.particles)
particles.done()
return final_eval['accuracy']
def train(
key,
meta_lr: float = DEFAULT_META_LR,
particle_stepsize: float = 1e-3,
evaluate_every: int = 10,
n_iter: int = 200,
n_samples: int = cfg.n_samples +
1, # add 1 to account for dummy val set
particle_steps_per_iter: int = 1,
max_train_steps_per_iter: int = DEFAULT_MAX_TRAIN_STEPS,
patience: int = DEFAULT_PATIENCE,
dropout: bool = True,
results_file: str = cfg.results_path + 'nvgd-bnn.csv',
overwrite_file: bool = False,
early_stopping: bool = True,
optimizer: str = "sgd",
hidden_sizes=[DEFAULT_LAYER_SIZE] * 3,
use_hypernetwork: bool = False):
"""
Initialize model; warmup; training; evaluation.
Returns a dictionary of metrics.
Args:
meta_lr: learning rate of Stein network
particle_stepsize: learning rate of BNN
evaluate_every: compute metrics every `evaluate_every` steps
n_iter: number of train-update iterations
particle_steps_per_iter: num particle updates after training Stein network
max_train_steps_per_iter: cutoff for Stein network training iteration
patience: early stopping criterion
dropout: use dropout during training of the Stein network
write_results_to_file: whether to save accuracy in csv file
use_hypernetwork: whether to use net-to-net hypernetwork to model
the witness function
"""
# csv_string = f"{meta_lr},{particle_stepsize}," \
# f"{patience},{max_train_steps_per_iter},"
# initialize particles and the dynamics model
key, subkey = random.split(key)
init_particles = vmap(bnn.init_flat_params)(random.split(
subkey, n_samples))
if optimizer == "sgd":
opt = optax.sgd(particle_stepsize)
elif optimizer == "adam":
opt = optax.adam(particle_stepsize)
else:
raise ValueError("optimizer must be sgd or adam")
key, subkey1, subkey2 = random.split(key, 3)
neural_grad = models.SteinNetwork(target_dim=init_particles.shape[1],
learning_rate=meta_lr,
key=subkey1,
sizes=hidden_sizes +
[init_particles.shape[1]],
aux=False,
use_hutchinson=True,
lambda_reg=LAMBDA_REG,
patience=patience,
dropout=dropout,
particle_unravel=nets.cnn_unravel,
hypernet=use_hypernetwork)
particles = models.Particles(key=subkey2,
gradient=neural_grad.gradient,
init_samples=init_particles,
custom_optimizer=opt)
minibatch_vdlogp = vmap(value_and_grad(bnn.minibatch_logp), (0, None))
@jit
def split_vdlogp(split_particles, train_batch):
"""returns tuple (split_logp, split_dlogp)"""
train_out, val_out = [
minibatch_vdlogp(x, train_batch) for x in split_particles
]
return tuple(zip(train_out, val_out))
def step(split_particles, split_dlogp):
"""one iteration of the particle trajectory simulation"""
neural_grad.train(split_particles=split_particles,
split_dlogp=split_dlogp,
n_steps=max_train_steps_per_iter,
early_stopping=early_stopping)
for _ in range(particle_steps_per_iter):
particles.step(neural_grad.get_params())
return
def evaluate(step_counter, ps, logp):
ll = logp.mean()
stepdata = {
"accuracy": bnn.compute_acc_from_flat(ps),
"step_counter": step_counter,
"loglikelihood": ll,
}
with open(results_file, "a") as file:
file.write(f"{step_counter},{stepdata['accuracy']},{ll}\n")
return stepdata
if not os.path.isfile(results_file) or overwrite_file:
with open(results_file, "w") as file:
file.write("step_counter,accuracy,loglikelihood\n")
print("Training...")
for step_counter in tqdm(range(n_iter), disable=on_cluster):
key, subkey = random.split(key)
train_batch = next(data.train_batches)
n_train_particles = 3 * n_samples // 4 if early_stopping else n_samples - 1
split_particles = particles.next_batch(
key, n_train_particles=n_train_particles)
split_logp, split_dlogp = split_vdlogp(split_particles, train_batch)
step(split_particles, split_dlogp)
if (step_counter + 1) % evaluate_every == 0:
eval_ps = particles.particles if early_stopping else split_particles[
0]
metrics.append_to_log(
particles.rundata,
evaluate(step_counter, eval_ps, split_logp[0]))
if step_counter % data.steps_per_epoch == 0:
print(f"Starting epoch {step_counter // data.steps_per_epoch + 1}")
neural_grad.done()
particles.done()
final_eval = evaluate(-1, particles.particles, split_particles[0])
return round(float(final_eval['accuracy']), 4), particles.rundata