tensorflow cifar10教程失败

2023年2月22日 237次阅读

参见英文答案 >
Tensorflow.strided_slice missing argument ‘strides’?                                    2个

我已经从教程
here中的链接下载了CIFAR10代码,并且我正在尝试运行该教程.我用命令运行它

python cifar10_train.py

它启动正常并按预期下载数据文件.当它尝试打开输入文件时,它失败并带有以下跟踪：

Traceback (most recent call last):
  File "cifar10_train.py", line 120, in <module>
    tf.app.run()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 43, in run
    sys.exit(main(sys.argv[:1] + flags_passthrough))
  File "cifar10_train.py", line 116, in main
    train()
  File "cifar10_train.py", line 63, in train
    images, labels = cifar10.distorted_inputs()
  File "/notebooks/Python Scripts/tensorflowModels/tutorials/image/cifar10/cifar10.py", line 157, in distorted_inputs
    batch_size=FLAGS.batch_size)
  File "/notebooks/Python Scripts/tensorflowModels/tutorials/image/cifar10/cifar10_input.py", line 161, in distorted_inputs
    read_input = read_cifar10(filename_queue)
  File "/notebooks/Python Scripts/tensorflowModels/tutorials/image/cifar10/cifar10_input.py", line 87, in read_cifar10
    tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32)
TypeError: strided_slice() takes at least 4 arguments (3 given)

果然,当我调查代码时,在cifar10_input.py中调用strided_slice()只有3个参数：

tf.strided_slice(record_bytes, [0], [label_bytes])

而tensorflow文档确实表明必须至少有4个参数.

出了什么问题？我已经下载了最新的张量流(0.12),我正在运行cifar代码的主分支.

最佳答案 在
github的一些讨论后,我已经进行了以下更改,似乎使它工作：

在cifar10_input.py中

-  result.label = tf.cast(tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32)
+  result.label = tf.cast(tf.slice(record_bytes, [0], [label_bytes]), tf.int32)



-  depth_major = tf.reshape( tf.strided_slice(record_bytes, [label_bytes], [label_bytes + image_bytes]),      [result.depth, result.height, result.width])
+  depth_major = tf.reshape(tf.slice(record_bytes, [label_bytes], [image_bytes]), [result.depth, result.height, result.width])

然后在cifar10_input.py和cifar10.py中我不得不搜索“不赞成”,无论我在哪里找到它,都要根据我在api指南中读到的内容替换它(希望正确).这方面的例子：

-  tf.contrib.deprecated.image_summary('images', images)
+  tf.summary.image('images', images)

和

 - tf.contrib.deprecated.histogram_summary(tensor_name + '/activations', x)
 - tf.contrib.deprecated.scalar_summary(tensor_name + '/sparsity',
 + tf.summary.histogram(tensor_name + '/activations', x)
 + tf.summary.scalar(tensor_name + '/sparsity',

现在好像很开心.我会看看它是否完成正常,如果我在上面输入的更改给出了所需的诊断输出.

我仍然希望听到更接近代码的人的确切答案.

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