Hybrid PC on MNIST¤
This notebook demonstrates how to train a hybrid predictive coding network that can both generate and classify MNIST digits.
%%capture
!pip install torch==2.3.1
!pip install torchvision==0.18.1
!pip install matplotlib==3.0.0
import jpc
import jax
import jax.numpy as jnp
import equinox as eqx
import equinox.nn as nn
import optax
import torch
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import warnings
warnings.simplefilter('ignore') # ignore warnings
Hyperparameters¤
We define some global parameters, including network architecture, learning rate, batch size, etc.
SEED = 0
LAYER_SIZES = [10, 300, 300, 784]
ACT_FN = "relu"
LEARNING_RATE = 1e-3
BATCH_SIZE = 64
MAX_T1 = 50
TEST_EVERY = 100
N_TRAIN_ITERS = 300
Dataset¤
Some utils to fetch MNIST.
#@title data utils
def get_mnist_loaders(batch_size):
train_data = MNIST(train=True, normalise=True)
test_data = MNIST(train=False, normalise=True)
train_loader = DataLoader(
dataset=train_data,
batch_size=batch_size,
shuffle=True,
drop_last=True
)
test_loader = DataLoader(
dataset=test_data,
batch_size=batch_size,
shuffle=True,
drop_last=True
)
return train_loader, test_loader
class MNIST(datasets.MNIST):
def __init__(self, train, normalise=True, save_dir="data"):
if normalise:
transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(
mean=(0.1307), std=(0.3081)
)
]
)
else:
transform = transforms.Compose([transforms.ToTensor()])
super().__init__(save_dir, download=True, train=train, transform=transform)
def __getitem__(self, index):
img, label = super().__getitem__(index)
img = torch.flatten(img)
label = one_hot(label)
return img, label
def one_hot(labels, n_classes=10):
arr = torch.eye(n_classes)
return arr[labels]
def plot_mnist_imgs(imgs, labels, n_imgs=10):
plt.figure(figsize=(20, 2))
for i in range(n_imgs):
plt.subplot(1, n_imgs, i + 1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(imgs[i].reshape(28, 28), cmap=plt.cm.binary_r)
plt.xlabel(jnp.argmax(labels, axis=1)[i])
plt.show()
Train and test¤
Similar to a standard PC network, a hybrid model can be trained in a single line of code with jpc.make_hpc_step() (see the docs for more details). Similarly, we can use jpc.test_hpc() to compute different test metrics (docs here). Note that these functions are already "jitted" for optimised performance. Below we simply wrap each of these functions in training and test loops, respectively.
def evaluate(
key,
layer_sizes,
batch_size,
generator,
amortiser,
test_loader
):
amort_accs, hpc_accs, gen_accs = 0, 0, 0
for batch_id, (img_batch, label_batch) in enumerate(test_loader):
img_batch, label_batch = img_batch.numpy(), label_batch.numpy()
amort_acc, hpc_acc, gen_acc, img_preds = jpc.test_hpc(
key=key,
layer_sizes=layer_sizes,
batch_size=batch_size,
generator=generator,
amortiser=amortiser,
input=label_batch,
output=img_batch
)
amort_accs += amort_acc
hpc_accs += hpc_acc
gen_accs += gen_acc
return (
amort_accs / len(test_loader),
hpc_accs / len(test_loader),
gen_accs / len(test_loader),
label_batch,
img_preds
)
def train(
seed,
layer_sizes,
act_fn,
batch_size,
lr,
max_t1,
test_every,
n_train_iters
):
key = jax.random.PRNGKey(seed)
key, *subkey = jax.random.split(key, 3)
generator = jpc.make_mlp(subkey[0], layer_sizes, act_fn)
amortiser = jpc.make_mlp(subkey[1], layer_sizes[::-1], act_fn)
gen_optim = optax.adam(lr)
amort_optim = optax.adam(lr)
optims = [gen_optim, amort_optim]
gen_opt_state = gen_optim.init(
(eqx.filter(generator, eqx.is_array), None)
)
amort_opt_state = amort_optim.init(eqx.filter(amortiser, eqx.is_array))
opt_states = [gen_opt_state, amort_opt_state]
train_loader, test_loader = get_mnist_loaders(batch_size)
for iter, (img_batch, label_batch) in enumerate(train_loader):
img_batch, label_batch = img_batch.numpy(), label_batch.numpy()
result = jpc.make_hpc_step(
generator=generator,
amortiser=amortiser,
optims=optims,
opt_states=opt_states,
input=label_batch,
output=img_batch,
max_t1=max_t1
)
generator, amortiser = result["generator"], result["amortiser"]
optims, opt_states = result["optims"], result["opt_states"]
gen_loss, amort_loss = result["losses"]
if ((iter+1) % test_every) == 0:
amort_acc, hpc_acc, gen_acc, label_batch, img_preds = evaluate(
key,
layer_sizes,
batch_size,
generator,
amortiser,
test_loader
)
print(
f"Iter {iter+1}, gen loss={gen_loss:4f}, "
f"amort loss={amort_loss:4f}, "
f"avg amort test accuracy={amort_acc:4f}, "
f"avg hpc test accuracy={hpc_acc:4f}, "
f"avg gen test accuracy={gen_acc:4f}, "
)
if (iter+1) >= n_train_iters:
break
plot_mnist_imgs(img_preds, label_batch)
return amortiser, generator
Run¤
network = train(
seed=SEED,
layer_sizes=LAYER_SIZES,
act_fn=ACT_FN,
batch_size=BATCH_SIZE,
lr=LEARNING_RATE,
max_t1=MAX_T1,
test_every=TEST_EVERY,
n_train_iters=N_TRAIN_ITERS
)
