在每个训练步骤中能否获得目标函数值? [英] Is it possible to get the objective function value during each training step?

问题描述

在通常的TensorFlow训练循环中，例如

In the usual TensorFlow training loop, such as

train_op = tf.train.AdamOptimizer().minimize(cross_entropy)

with tf.Session() as sess:
    for i in range(num_steps):
        # ...
        train_op.run(feed_dict = feed_dict)

train_op.run返回None.

但是，有时收集中间结果(例如物镜的值或准确性)很有用.

However, sometimes it's useful to collect intermediate results, such as the value of the objective or the accuracy.

添加额外的sess.run调用将需要再次进行前向传播，从而增加了运行时间:

Adding extra sess.run calls would require doing the forward propagation again, increasing the run time:

train_op = tf.train.AdamOptimizer().minimize(cross_entropy)

with tf.Session() as sess:
    for i in range(num_steps):
        # ...
        o, a = sess.run([objective, accuracy], feed_dict = feed_dict)
        train_op.run(feed_dict = feed_dict)

是否可以一次性在TensorFlow中做到这一点?

Is it possible to do this in TensorFlow in one go?

有人建议

sess.run([objective, accuracy, train_op], feed_dict = feed_dict)

但是结果取决于列表元素的执行顺序:

but the results depend on the execution order of the list elements:

[objective, accuracy, train_op]

似乎是不确定的-根据是否使用CUDA，您将获得不同的结果.

推荐答案

只需将train_op添加到要评估的节点列表中即可.

Simply add you train_op to the list of nodes to be evaluated.

o, a, _ = sess.run([objective, accuracy, train_op], feed_dict = feed_dict)

关于训练步骤及其在评估中的顺序，我做了以下小实验:

Regarding the training step and its order in the evaluation, I made the following small experiment:

import tensorflow as tf
x = tf.Variable(0, dtype=tf.float32)
loss = tf.nn.l2_loss(x-1)
train_opt = tf.train.GradientDescentOptimizer(1)
train_op = train_opt.minimize(loss)
init_op = tf.global_variables_initializer()

sess = tf.Session()
sess.run(init_op)
x_val, _, loss_val = sess.run([x, train_op, loss])
# returns x_val = 1.0, loss_val = 0.5

这种情况比我最初想象的要混乱.似乎可以肯定的是，访存的执行顺序并不取决于它们在列表中的位置:x_val和loss_val将是相同的，而与它们在列表中的位置无关.

The situation is more confused than I initially thought. What seems to be a given is that the order of execution of the fetches does not depend of their respective position in the list: x_val and loss_val will be the same independently of their position in the list.

但是，正如@MaxB注意到的那样，不能保证它们的执行顺序.在GPU上运行上述代码时，x_val设置为0.0(初始值).但是，在CPU上运行时，x_val为1.0，即从train_op更新后的 值.

However, as @MaxB noticed, their order of execution is not guaranteed. When running the above code on GPU, x_val is set to 0.0, the initial value. However, when running on CPU, x_val is 1.0, that is, the value after the update from train_op.

如上面的实验所示，这种依赖于配置的行为可能仅限于由训练操作更新的变量，但不能保证它们来自tf的文档.

This configuration-dependant behavior could be limited to variables updated by training operations, as the experiment above suggests, but their is no guarantee coming from tf's documentation.

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