我一直在使用我在Github上找到的AlexNet Training的实现 . 我有8个输入类 . 每个类都包含一些图像（Ex class1：Bear，Class2：Tiger; Class3：Horse ......）问题是当我运行以下代码时，我看到训练精度始终等于1除以类的数量（in这种情况训练准确度= 0.125，如果我只有2个等级，训练精度将等于0.5）
我发现这很奇怪，我无法弄清楚以下代码中的错误：
from importData import Dataset
import inference
training = Dataset('wxb_pic/pic', '.jpg')
testing = Dataset('wxb_pic/pic_test', '.jpg')
import tensorflow as tf
# Parameters
learn_rate = 0.001
decay_rate = 0.1
batch_size = 64
display_step = 20
n_classes = training.num_labels # we got mad kanji
dropout = 0.8 # Dropout, probability to keep units
imagesize = 227
img_channel = 3
x = tf.placeholder(tf.float32, [None, imagesize, imagesize, img_channel])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_prob = tf.placeholder(tf.float32) # dropout (keep probability)
pred = inference.alex_net(x, keep_prob, n_classes, imagesize, img_channel)
cost =tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
global_step = tf.Variable(0, trainable=False)
lr = tf.train.exponential_decay(learn_rate, global_step, 1000, decay_rate, staircase=True)
optimizer = tf.train.AdamOptimizer(learning_rate=lr).minimize(cost, global_step=global_step)
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
init = tf.initialize_all_variables()
saver = tf.train.Saver()
tf.add_to_collection("x", x)
tf.add_to_collection("y", y)
tf.add_to_collection("keep_prob", keep_prob)
tf.add_to_collection("pred", pred)
tf.add_to_collection("accuracy", accuracy)
with tf.Session() as sess:
sess.run(init)
step = 1
while step < 3000:
batch_ys, batch_xs = training.nextBatch(batch_size)
sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})
if step % display_step == 0:
acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
rate = sess.run(lr)
print "lr " + str(rate) + " Iter " + str(step) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc)
if step % 1000 == 0:
saver.save(sess, 'save/model.ckpt', global_step=step*batch_size)
step += 1
print "Optimization Finished!"
step_test = 1
while step_test * batch_size < len(testing):
testing_ys, testing_xs = testing.nextBatch(batch_size)
print "Testing Accuracy:", sess.run(accuracy, feed_dict={x: testing_xs, y: testing_ys, keep_prob: 1.})
step_test += 1
我坚持这个，我想训练AlexNet模型来测试我的机器的性能 . 谢谢^^