2

我正在实施交替培训计划。该图包含两个训练操作。培训应该在这些之间交替进行。

这与像这样或这样的研究有关

下面是一个小例子。但它似乎在每一步都更新了两个操作。我怎样才能明确地在这些之间交替？

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
# Import data
mnist = input_data.read_data_sets('/tmp/tensorflow/mnist/input_data', one_hot=True)

# Create the model
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]), name='weights')
b = tf.Variable(tf.zeros([10]), name='biases')
y = tf.matmul(x, W) + b

# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
global_step = tf.Variable(0, trainable=False)

tvars1 = [b]
train_step1 = tf.train.GradientDescentOptimizer(0.5).apply_gradients(zip(tf.gradients(cross_entropy, tvars1), tvars1), global_step)
tvars2 = [W]
train_step2 = tf.train.GradientDescentOptimizer(0.5).apply_gradients(zip(tf.gradients(cross_entropy, tvars2), tvars2), global_step)
train_step = tf.cond(tf.equal(tf.mod(global_step,2), 0), true_fn= lambda:train_step1, false_fn=lambda : train_step2)


sess = tf.InteractiveSession()
tf.global_variables_initializer().run()


# Train
for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
    if i % 100 == 0:
        print(sess.run([cross_entropy, global_step], feed_dict={x: mnist.test.images,
                                         y_: mnist.test.labels}))

这导致

[2.0890141, 2]
[0.38277805, 202]
[0.33943111, 402]
[0.32314575, 602]
[0.3113254, 802]
[0.3006627, 1002]
[0.2965056, 1202]
[0.29858461, 1402]
[0.29135355, 1602]
[0.29006076, 1802]

全局步骤迭代到 1802，因此每次 train_step调用都会执行两个训练操作。（例如，当永远为假的条件为时，也会发生这种情况tf.equal(global_step,-1)。）

我的问题是如何在执行train_step1和之间交替train_step2？

4

1 回答 1

5

我认为最简单的方法就是

for i in range(1000):
  batch_xs, batch_ys = mnist.train.next_batch(100)
  if i % 2 == 0:
    sess.run(train_step1, feed_dict={x: batch_xs, y_: batch_ys})
  else:
    sess.run(train_step2, feed_dict={x: batch_xs, y_: batch_ys})

但是如果需要通过 tensorflow 条件流进行切换，可以这样操作：

optimizer = tf.train.GradientDescentOptimizer(0.5)
train_step = tf.cond(tf.equal(tf.mod(global_step, 2), 0),
                     true_fn=lambda: optimizer.apply_gradients(zip(tf.gradients(cross_entropy, tvars1), tvars1), global_step),
                     false_fn=lambda: optimizer.apply_gradients(zip(tf.gradients(cross_entropy, tvars2), tvars2), global_step))
于 2017-11-07T11:11:59.130 回答