Tensorflow仅对变量的某些元素进行最小化 [英] Tensorflow minimise with respect to only some elements of a variable
问题描述
是否可以通过仅更改变量的某些元素来使损失函数最小化?换句话说,如果我的变量X
的长度为2,如何通过更改X[0]
并保持X[1]
不变来最小化损失函数?
Is it possible to minimise a loss function by changing only some elements of a variable? In other words, if I have a variable X
of length 2, how can I minimise my loss function by changing X[0]
and keeping X[1]
constant?
希望我尝试过的这段代码将描述我的问题:
Hopefully this code I have attempted will describe my problem:
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()))
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: [3.]
我想在其中找到X0 = 2
的最佳值,从而找到X = [2, 2]
where I would like to to find the optimal value of X0 = 2
and thus X = [2, 2]
修改
这样做的动机:我想导入训练有素的图形/模型,然后根据我拥有的一些新数据来调整某些变量的各个元素.
Motivation for doing this: I would like to import a trained graph/model and then tweak various elements of some of the variables depending on some new data I have.
推荐答案
您可以使用此技巧将梯度计算限制为一个索引:
You can use this trick to restrict the gradient calculation to one index:
import tensorflow as tf
import tensorflow.contrib.opt as opt
X = tf.Variable([1.0, 2.0])
part_X = tf.scatter_nd([[0]], [X[0]], [2])
X_2 = part_X + tf.stop_gradient(-part_X + X)
Y = tf.constant([2.0, -3.0])
loss = tf.reduce_sum(tf.squared_difference(X_2, Y))
opt = opt.ScipyOptimizerInterface(loss, [X])
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
opt.minimize(sess)
print("X: {}".format(X.eval()))
part_X
变为要在与X形状相同的单热矢量中更改的值.由于part_X - part_X
为0,因此part_X + tf.stop_gradient(-part_X + X)
与正向传递中的X相同.通过tf.stop_gradient
可以防止所有不必要的梯度计算.
part_X
becomes the value you want to change in a one-hot vector of the same shape as X. part_X + tf.stop_gradient(-part_X + X)
is the same as X in the forward pass, since part_X - part_X
is 0. However in the backward pass the tf.stop_gradient
prevents all unnecessary gradient calculations.
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