上一节，我们参照官方例程，在训练网络时，实时的对比标签值和预测值，并将结果打印到终端。但是，不足的是，使用训练集测试训练结果，并没有什么说服力。本节将使用 CIFAR-10 的测试集，在训练时，实时测试训练网络。

总体思路

实现方法

def ReadImages(filenames):
    # 使用 tensorflow 将文件名 list 转成队列（queue）
    filename_queue = tf.train.string_input_producer(filenames)
    # 标签占一个字节
    label_bytes = 1
    ...
    # 创建固定长度的数据 reader
    reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
    key, value = reader.read(filename_queue)
    ...
    # 将图片从 [深度，高(长)，宽] 转成 [高(长)，宽, 深度] 形状
    uint8image = tf.transpose(depth_major, [1, 2, 0])

    return label, uint8image

def Distorted_inputs(filenames, batch_size):    
    # 读入数据
    label, uint8image = ReadImages(filenames)
...
    # 产生一个图片 batch
    num_preprocess_threads = 16
    images, label_batch = tf.train.batch(
        [float_image, label],
        batch_size=batch_size,
        num_threads=num_preprocess_threads,
        capacity=min_queue_examples + 3 * batch_size)

    # 返回的是加工过的图片和标签batch
    return images, tf.reshape(label_batch, [batch_size])

testFilenames = [os.path.join('cifar10_data/cifar-10-batches-bin', 'test_batch.bin')]
testimages, testLabels = Distorted_inputs(testFilenames, BATCH_SIZE)
testlogits = NetworkRes(testimages)
prediction = tf.nn.in_top_k(testlogits,testLabels,1)

def Train():
    # 创建文件名 list
    for i in range(1,6):
        filenames = [os.path.join('cifar10_data/cifar-10-batches-bin', 'data_batch_%d.bin' % i)]

    images, label_batch = Distorted_inputs(filenames, BATCH_SIZE)
    logits = NetworkRes(images)
    loss = Loss(logits, label_batch)

    saver = tf.train.Saver()

    # 使用测试集测试网络
    testFilenames = [os.path.join('cifar10_data/cifar-10-batches-bin', 'test_batch.bin')]
    testimages, testLabels = Distorted_inputs(testFilenames, BATCH_SIZE)
    testlogits = NetworkRes(testimages)
    prediction = tf.nn.in_top_k(testlogits,testLabels,1)

    train_step = tf.train.AdamOptimizer(5e-5).minimize(loss)
    init = tf.global_variables_initializer()
    with tf.Session() as sess:
        sess.run(init)
        saver.restore(sess, 'net_w_b/save_w_b.ckpt')
        threads = tf.train.start_queue_runners(sess=sess)
        for i in range(500000):
            # print 'times: %d' % i
            sess.run(train_step)
            if i%10 == 0:
                rp = sess.run(prediction)
                print 'i=%d, loss:' % i, sess.run(loss), 'prediction %d/%d' % (np.sum(rp), BATCH_SIZE)
                print rp
            if i%10000 == 0:
                print "save ckpt----------------------"
                saver.save(sess, 'net_w_b/save_w_b.ckpt') 

运行结果

saver.restore(sess, 'net_w_b/save_w_b.ckpt')

...
i=330, loss: 1.1338959 prediction 4/50
[False  True False False False False False  True False False False False
 False False False False False False False False False False False False
 False False  True False False False False False False False False False
 False  True False False False False False False False False False False
 False False]
i=340, loss: 0.8164284 prediction 4/50
[False  True False False False False False False False  True False False
 False False False False False False False False False False False False
 False  True False False False False False False False False False False
 False False False False False False False False  True False False False
 False False]
i=350, loss: 1.2925113 prediction 9/50
[False  True False False  True False False False False False False  True
  True False  True False False False False  True  True False False False
 False False False False False False False False False False False False
 False False False False False  True False False False  True False False
 False False]
i=360, loss: 0.96927905 prediction 1/50
[False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False  True False False False False
 False False False False False False False False False False False False
 False False]
i=370, loss: 1.0052602 prediction 7/50
[False False  True False False False False  True False  True False False
 False False False False False False False False False  True  True False
 False False False False False False False False  True False False False
 False False  True False False False False False False False False False
 False False]
i=380, loss: 1.0849243 prediction 6/50
[False False False False False False False False False False False False
 False False False False False False  True  True  True False False  True
 False False False False False False False False False False False False
 False False False  True False False False False False False False False
 False  True]
i=390, loss: 1.3486145 prediction 4/50
[False False False False False  True  True False False False False False
  True False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False  True False False False False False
 False False]
...