在TensorFlow中动态更改权重 [英] Dynamically changing weights in TensorFlow

问题描述

在TensorFlow中，我正在尝试在训练期间更改权重，但结果没有变化.我试图破坏权重(设置为零)，但是它似乎什么也没做(除了需要更长的时间才能完成).我想念什么?有没有一种方法可以在会话期间像常规矩阵/张量那样操纵W?

In TensorFlow, I'm trying to change weights during training, but get no change in the results. I've tried to disrupt the weights (set to zero), but it seems to do nothing (other than take longer to complete). What am I missing? Is there a way to manipulate W like a regular matrix/tensor during session?

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

import tensorflow as tf
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
W = tf.Variable(tf.zeros([784,10]), trainable=True)
W2 = tf.Variable(tf.zeros([784,10]), trainable=False)
b = tf.Variable(tf.zeros([10]))

sess.run(tf.initialize_all_variables())

y = tf.nn.softmax(tf.matmul(x,W) + b)
loss = tf.reduce_mean(tf.square(y_ - y))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)

for i in range(1000):
#try to change W during training
  W = W2

  W = tf.Variable(tf.zeros([784,10]))

  W.assign(tf.Variable(tf.zeros([784,10])))

  batch = mnist.train.next_batch(1)

  train_step.run(feed_dict={x: batch[0], y_: batch[1]})

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

print(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

准确性保持不变(0.82).

Accuracy remains the same (0.82).

推荐答案

使用tf.assign()时，需要为此操作指定名称:

When you use tf.assign(), you need to give a name for this operation:

W = W.assign(tf.Variable(tf.zeros([784,10])))

W= W.assign(tf.Variable(tf.zeros([784,10])))

然后再次使用W时，将执行分配操作.

Then when you use W again, the assign operation will be executed.

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