如何使用 tf.contrib.opt.ScipyOptimizerInterface 获取损失函数历史记录

Question

我需要获取随时间变化的损失历史记录以将其绘制成图表。这是我的代码框架：

optimizer = tf.contrib.opt.ScipyOptimizerInterface(loss, method='L-BFGS-B', 
options={'maxiter': args.max_iterations, 'disp': print_iterations})
optimizer.minimize(sess, loss_callback=append_loss_history)

用append_loss_history定义：

def append_loss_history(**kwargs):
    global step
    if step % 50 == 0:
        loss_history.append(loss.eval())
    step += 1

当我看到 ScipyOptimizerInterface 的详细输出时，损失实际上随着时间的推移而减少。但是当我打印 loss_history 时，随着时间的推移损失几乎相同。

参考文档： "Variables subject to optimization are updated in-place AT THE END OF OPTIMIZATION" https://www.tensorflow.org/api_docs/python/tf/contrib/opt/ScipyOptimizerInterface。这是损失不变的原因吗？

Answer 1

我认为你有问题；直到优化结束（而不是 being fed to session.run calls），变量本身才被修改，并且评估 "back channel" 张量得到未修改的变量。相反，使用 optimizer.minimize 的 fetches 参数来搭载指定提要的 session.run 调用：

import tensorflow as tf

def print_loss(loss_evaled, vector_evaled):
  print(loss_evaled, vector_evaled)

vector = tf.Variable([7., 7.], 'vector')
loss = tf.reduce_sum(tf.square(vector))

optimizer = tf.contrib.opt.ScipyOptimizerInterface(
    loss, method='L-BFGS-B',
    options={'maxiter': 100})

with tf.Session() as session:
  tf.global_variables_initializer().run()
  optimizer.minimize(session,
                     loss_callback=print_loss,
                     fetches=[loss, vector])
  print(vector.eval())

（修改自example in the documentation）。这将打印具有更新值的张量：

98.0 [ 7.  7.]
79.201 [ 6.29289341  6.29289341]
7.14396e-12 [ -1.88996808e-06  -1.88996808e-06]
[ -1.88996808e-06  -1.88996808e-06]

如何使用 tf.contrib.opt.ScipyOptimizerInterface 获取损失函数历史记录

How to get loss function history using tf.contrib.opt.ScipyOptimizerInterface

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