张量流中的eval()和run()

Question

张量流中的eval()和run()

Ram*_*h-X 14 python python-2.7 tensorflow

我指的是由tensorflow给出的Deep MNIST for Experts教程.我在培训和评估该教程的一部分时遇到了问题.他们在那里给出了如下示例代码.

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv),reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess.run(tf.initialize_all_variables())
for i in range(20000):
  batch = mnist.train.next_batch(50)
  if i%100 == 0:
    train_accuracy = accuracy.eval(feed_dict={x:batch[0], y_: batch[1], keep_prob: 1.0})
    print("step %d, training accuracy %g"%(i, train_accuracy))
  train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

print("test accuracy %g"%accuracy.eval(feed_dict={x: mnist.test.images,
                       y_: mnist.test.labels, keep_prob: 1.0}))

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所以在这些代码段中他们曾经一次使用accuracy.eval()过.还有其他时间train_step.run().据我所知,它们都是张量变量.

在某些情况下,我看到过