示例：Adam 优化器

import argparse

import torch

import funsor
import funsor.ops as ops
from funsor.adam import Adam
from funsor.domains import Real, Reals
from funsor.tensor import Tensor
from funsor.terms import Variable


def main(args):
    funsor.set_backend("torch")

    # Problem definition.
    N = 100
    P = 10
    data = Tensor(torch.randn(N, P))["n"]
    true_weight = Tensor(torch.randn(P))
    true_bias = Tensor(torch.randn(()))
    truth = true_bias + true_weight @ data

    # Model.
    weight = Variable("weight", Reals[P])
    bias = Variable("bias", Real)
    pred = bias + weight @ data
    loss = (pred - truth).abs().reduce(ops.add, "n")

    # Inference.
    with Adam(args.num_steps, lr=args.learning_rate, log_every=args.log_every) as optim:
        loss.reduce(ops.min, {"weight", "bias"})

    print(f"True bias\n{true_bias}")
    print("Learned bias\n{}".format(optim.param("bias")))
    print(f"True weight\n{true_weight}")
    print("Learned weight\n{}".format(optim.param("weight")))


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Linear regression example using Adam interpretation"
    )
    parser.add_argument("-P", "--num-features", type=int, default=10)
    parser.add_argument("-N", "--num-data", type=int, default=100)
    parser.add_argument("-n", "--num-steps", type=int, default=201)
    parser.add_argument("-lr", "--learning-rate", type=float, default=0.05)
    parser.add_argument("--log-every", type=int, default=20)
    args = parser.parse_args()
    main(args)