相关疑难解决方法(0)

是否可以通过仅更改变量的某些元素来最小化损失函数？换句话说,如果我有一个X长度为2 的变量,我如何通过改变X[0]和保持X[1]常数来最小化我的损失函数？

希望我试过的这段代码能描述我的问题:

import tensorflow as tf
import tensorflow.contrib.opt as opt

X = tf.Variable([1.0, 2.0])
X0 = tf.Variable([3.0])

Y = tf.constant([2.0, -3.0])

scatter = tf.scatter_update(X, [0], X0)

with tf.control_dependencies([scatter]):
    loss = tf.reduce_sum(tf.squared_difference(X, Y))

opt = opt.ScipyOptimizerInterface(loss, [X0])

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    opt.minimize(sess)

    print("X: {}".format(X.eval()))
    print("X0: {}".format(X0.eval()))

哪个输出:

INFO:tensorflow:Optimization terminated with:
  Message: b'CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL'
  Objective function value: 26.000000
  Number of iterations: 0
  Number of functions evaluations: 1
X: [3. 2.]
X0: …