我面临着一个受约束、等式和不等式影响的数值优化问题。看起来对于这个任务,一切都在张量流中,阅读 https://www.tensorflow.org/api_docs/python/tf/contrib/constrained_optimization 等文档。
虽然我错过了一个最小的工作示例。我已经做了广泛的谷歌搜索,但没有结果。任何人都可以与我分享一些有用的资源吗?最好在急切模式下运行。
编辑:
我现在已经找到了 https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/constrained_optimization
我仍然欢迎任何额外的资源。
您可以使用TF> 1.4可用的TFCO。
下面是一个我们想要最小化的具体示例:
(x - 2) ^ 2 + y
S.T.
- x + y = 1
- x> 0
- y> 0
import tensorflow as tf
# Use the GitHub version of TFCO
# !pip install git+https://github.com/google-research/tensorflow_constrained_optimization
import tensorflow_constrained_optimization as tfco
class SampleProblem(tfco.ConstrainedMinimizationProblem):
def __init__(self, loss_fn, weights):
self._loss_fn = loss_fn
self._weights = weights
@property
def num_constraints(self):
return 4
def objective(self):
return loss_fn()
def constraints(self):
x, y = self._weights
sum_weights = x + y
lt_or_eq_one = sum_weights - 1
gt_or_eq_one = 1 - sum_weights
constraints = tf.stack([lt_or_eq_one, gt_or_eq_one, -x, -y])
return constraints
x = tf.Variable(0.0, dtype=tf.float32, name='x')
y = tf.Variable(0.0, dtype=tf.float32, name='y')
def loss_fn():
return (x - 2) ** 2 + y
problem = SampleProblem(loss_fn, [x, y])
optimizer = tfco.LagrangianOptimizer(
optimizer=tf.optimizers.Adagrad(learning_rate=0.1),
num_constraints=problem.num_constraints
)
var_list = [x, y] + problem.trainable_variables + optimizer.trainable_variables()
for i in range(10000):
optimizer.minimize(problem, var_list=var_list)
if i % 1000 == 0:
print(f'step = {i}')
print(f'loss = {loss_fn()}')
print(f'constraint = {(x + y).numpy()}')
print(f'x = {x.numpy()}, y = {y.numpy()}')
自从我问这个问题以来,已经有一些赞成票,所以我想更多的人正在寻找解决方案。就我而言,对于我的特定问题,我决定从tensorflow
移动到pyomo
以运行约束优化。也许这可以帮助其他人。