blockimmune, Bureaucrats, Administrators
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* first thing to become nans seems to be actor loss and scores. after that, everything becomes nans
* fixed entropy epsilon. hope this works now.
== MNIST Training Example ==
<syntaxhighlight lang="python">
"""Trains a simple MNIST model using Equinox."""
import logging
import equinox as eqx
import jax
import jax.numpy as jnp
import optax
from jax import random
from tensorflow.keras.datasets import mnist
logging.basicConfig(
level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S"
)
logger = logging.getLogger(__name__)
def load_mnist() -> tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray, jnp.ndarray]:
"""Load and preprocess the MNIST dataset."""
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = jnp.float32(x_train) / 255.0
x_test = jnp.float32(x_test) / 255.0
x_train = x_train.reshape(-1, 28 * 28)
x_test = x_test.reshape(-1, 28 * 28)
y_train = jax.nn.one_hot(y_train, 10)
y_test = jax.nn.one_hot(y_test, 10)
return x_train, y_train, x_test, y_test
class DenseModel(eqx.Module):
"""Define a simple dense neural network model."""
layers: list
def __init__(self, key: jnp.ndarray) -> None:
keys = random.split(key, 3)
self.layers = [
eqx.nn.Linear(28 * 28, 128, key=keys[0]),
eqx.nn.Linear(128, 64, key=keys[1]),
eqx.nn.Linear(64, 10, key=keys[2]),
]
def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
for layer in self.layers[:-1]:
x = jax.nn.relu(layer(x))
return self.layers[-1](x)
@eqx.filter_value_and_grad
def loss_fn(model: DenseModel, x_b: jnp.ndarray, y_b: jnp.ndarray) -> jnp.ndarray:
"""Define the loss function (cross-entropy loss). Vecotrized across batch with vmap."""
pred_b = jax.vmap(model)(x_b)
return -jnp.mean(jnp.sum(y_b * jax.nn.log_softmax(pred_b), axis=-1))
@eqx.filter_jit
def make_step(
model: DenseModel,
optimizer: optax.GradientTransformation,
opt_state: optax.OptState,
x_b: jnp.ndarray,
y_b: jnp.ndarray,
) -> tuple[jnp.ndarray, DenseModel, optax.OptState]:
"""Perform a single optimization step."""
loss, grads = loss_fn(model, x_b, y_b)
updates, opt_state = optimizer.update(grads, opt_state)
model = eqx.apply_updates(model, updates)
return loss, model, opt_state
def train(
model: DenseModel,
optimizer: optax.GradientTransformation,
x_train: jnp.ndarray,
y_train: jnp.ndarray,
batch_size: int,
num_epochs: int,
) -> DenseModel:
"""Train the model using the given data."""
opt_state = optimizer.init(eqx.filter(model, eqx.is_array))
for epoch in range(num_epochs):
for i in range(0, len(x_train), batch_size):
x_batch = x_train[i : i + batch_size]
y_batch = y_train[i : i + batch_size]
loss, model, opt_state = make_step(model, optimizer, opt_state, x_batch, y_batch)
logger.info(f"Epoch {epoch+1}/{num_epochs}, Loss: {loss:.4f}")
return model
@eqx.filter_jit
def accuracy(model: DenseModel, x_b: jnp.ndarray, y_b: jnp.ndarray) -> jnp.ndarray:
"""Takes in batch oftest images/label pairing with model and returns accuracy."""
pred = jax.vmap(model)(x_b)
return jnp.mean(jnp.argmax(pred, axis=-1) == jnp.argmax(y_b, axis=-1))
def main() -> None:
# Load data
x_train, y_train, x_test, y_test = load_mnist()
# Initialize model and optimizer
key = random.PRNGKey(0)
model = DenseModel(key)
optimizer = optax.adam(learning_rate=1e-3)
# Train the model
batch_size = 32
num_epochs = 10
trained_model = train(model, optimizer, x_train, y_train, batch_size, num_epochs)
test_accuracy = accuracy(trained_model, x_test, y_test)
logger.info(f"Test accuracy: {test_accuracy:.4f}")
if __name__ == "__main__":
main()
</syntaxhighlight>
