Question

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除以等级数（在这种情况下训练精度= 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

我对此感到困惑，我想训练AlexNet模型来测试我机器的性能。谢谢^^

在训练AlexNet模型时具有恒定的准确度

0 个答案: