我正在按照"Deep MNIST for Experts"教程,https: //www.tensorflow.org/versions/r0.11/tutorials/mnist/pros/index.html#deep-mnist-for-experts
使用卷积神经网络,我得到93.49%的准确率.这实际上很低,我正在努力改进它,但我有一个疑问.根据教程,
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})
在每100次迭代之后记录列车精度并且看到准确性,它保持振荡,如增加然后减少.
step 100, training accuracy 0.1
step 200, training accuracy 0.13
step 300, training accuracy 0.12
step 400, training accuracy 0.08
step 500, training accuracy 0.12
step 600, training accuracy 0.05
step 700, training accuracy 0.09
step 800, training accuracy 0.1
step 900, …