我从这个 TensorFlow 的 csv 阅读器中遗漏了什么? [英] What am I missing from this csv reader for TensorFlow?

问题描述

它主要是从网站上的教程复制粘贴.我收到一个错误:

It is mostly a copy paste from the tutorial, on the website. I am getting an error:

无效参数:ConcatOp:在范围 [0, 0), 但得到 0 [[Node: concat = Concat[N=4, T=DT_INT32,_device="/job:localhost/replica:0/task:0/cpu:0"](concat/concat_dim, DecodeCSV, DecodeCSV:1, DecodeCSV:2, DecodeCSV:3)]]

Invalid argument: ConcatOp : Expected concatenating dimensions in the range [0, 0), but got 0 [[Node: concat = Concat[N=4, T=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"](concat/concat_dim, DecodeCSV, DecodeCSV:1, DecodeCSV:2, DecodeCSV:3)]]

我的 csv 文件的内容是:

the contents of my csv file is:

3,4,1,8,4

 import tensorflow as tf


filename_queue = tf.train.string_input_producer(["test2.csv"])

reader = tf.TextLineReader()
key, value = reader.read(filename_queue)

# Default values, in case of empty columns. Also specifies the type of the
# decoded result.
record_defaults = [[1], [1], [1], [1], [1]]
col1, col2, col3, col4, col5 = tf.decode_csv(
    value, record_defaults=record_defaults)
# print tf.shape(col1)

features = tf.concat(0, [col1, col2, col3, col4])
with tf.Session() as sess:
  # Start populating the filename queue.
  coord = tf.train.Coordinator()
  threads = tf.train.start_queue_runners(coord=coord)

  for i in range(1200):
    # Retrieve a single instance:
    example, label = sess.run([features, col5])

  coord.request_stop()
  coord.join(threads)

推荐答案

问题是由程序中张量的形状引起的.TL;DR 你应该使用 tf.pack() 而不是 tf.concat()，它会转换将四个标量 col 张量转换为长度为 4 的一维张量.

The issue arises due to the shape of the tensors in your program. TL;DR Instead of tf.concat() you should use tf.pack(), which will transform the four scalar col tensors into a 1-D tensor of length 4.

在我们开始之前，请注意您可以在任何 Tensor 对象上使用 get_shape() 方法来获取有关该张量的静态形状信息.例如，代码中注释掉的行可能是:

Before we start, note that you can use the get_shape() method on any Tensor object to get static shape information about that tensor. For example, the commented-out line in your code could be:

print col1.get_shape()
# ==> 'TensorShape([])' - i.e. `col1` is a scalar.

reader.read() 返回的 value 张量是一个标量字符串.tf.decode_csv(value, record_defaults=[...]) 为 record_defaults 的每个元素生成一个与 value 形状相同的张量>，即本例中的标量.标量是具有单个元素的 0 维张量.tf.concat(i, xs) 不是在标量上定义:它将 N 维张量列表 (xs) 连接成一个新的 N 维张量，沿着维度 i，其中 0 <=我＜N，如果 N = 0，则没有有效的 i.

The value tensor returned by reader.read() is a scalar string. tf.decode_csv(value, record_defaults=[...]) produces, for each element of record_defaults, a tensor of the same shape as value, i.e. a scalar in this case. A scalar is a 0-dimensional tensor with a single element. tf.concat(i, xs) is not defined on scalars: it concatenates a list of N-dimensional tensors (xs) into a new N-dimensional tensor, along dimension i, where 0 <= i < N, and there is no valid i if N = 0.

tf.pack(xs)运算符旨在简单地解决这个问题.它需要一个 k N 维张量列表(具有相同的形状)，并将它们打包成一个 N+1 维张量，其大小为 k 在第 0 维.如果您将 tf.concat() 替换为 tf.pack()，您的程序将可以运行:

The tf.pack(xs) operator is designed to solve this problem simply. It takes a list of k N-dimensional tensors (with the same shape) and packs them into an N+1-dimensional tensor with size k in the 0th dimension. If you replace the tf.concat() with tf.pack(), your program will work:

# features = tf.concat(0, [col1, col2, col3, col4])
features = tf.pack([col1, col2, col3, col4])

with tf.Session() as sess:
  # Start populating the filename queue.
  # ...

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