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

Question

我需要随着时间的推移得到损失历史,以图形方式绘制它.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)

随着append_loss_history定义:

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

当我看到详细的输出时ScipyOptimizerInterface,损失实际上会随着时间的推移而减少.但是当我打印时loss_history,损失几乎是一样的.

请参阅文档:"受优化影响的变量在优化结束时就地更新" https://www.tensorflow.org/api_docs/python/tf/contrib/opt/ScipyOptimizerInterface.这是损失不变的原因吗？

Answer 1

我认为你已经解决了这个问题；变量本身在优化结束之前不会被修改（而不是被馈送到 session.run 调用），并且评估“反向通道”张量会获取未修改的变量。相反，使用fetches参数 tooptimizer.minimize搭载session.run指定提要的调用：

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())

（根据文档中的示例修改）。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]