如何在tensorflow中输入数据？

我有4个文件：train.txt、trainLabel.txt、test.txt、testLabel.txt

train.txt

1,60,feature_col0,feature_col1,feature_col2,feature_col3,feature_col4,feature_col5,feature_col6,feature_col7,feature_col8,feature_col9,feature_col10,feature_col11,feature_col12,feature_col13,feature_col14,feature_col15,feature_col16,feature_col17,feature_col18,feature_col19,feature_col20,feature_col21,feature_col22,feature_col23,feature_col24,feature_col25,feature_col26,feature_col27,feature_col28,feature_col29,feature_col30,feature_col31,feature_col32,feature_col33,feature_col34,feature_col35,feature_col36,feature_col37,feature_col38,feature_col39,feature_col40,feature_col41,feature_col42,feature_col43,feature_col44,feature_col45,feature_col46,feature_col47,feature_col48,feature_col49,feature_col50,feature_col51,feature_col52,feature_col53,feature_col54,feature_col55,feature_col56,feature_col57,feature_col58,feature_col59
1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1

1,4,feature_col0,feature_col1,feature_col2,feature_col3
1,1,1,0

1,60,feature_col0,feature_col1,feature_col2,feature_col3,feature_col4,feature_col5,feature_col6,feature_col7,feature_col8,feature_col9,feature_col10,feature_col11,feature_col12,feature_col13,feature_col14,feature_col15,feature_col16,feature_col17,feature_col18,feature_col19,feature_col20,feature_col21,feature_col22,feature_col23,feature_col24,feature_col25,feature_col26,feature_col27,feature_col28,feature_col29,feature_col30,feature_col31,feature_col32,feature_col33,feature_col34,feature_col35,feature_col36,feature_col37,feature_col38,feature_col39,feature_col40,feature_col41,feature_col42,feature_col43,feature_col44,feature_col45,feature_col46,feature_col47,feature_col48,feature_col49,feature_col50,feature_col51,feature_col52,feature_col53,feature_col54,feature_col55,feature_col56,feature_col57,feature_col58,feature_col59
0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,1,0,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1

1,4,feature_col0,feature_col1,feature_col2,feature_col3
1,1,0,0

trainLabel.txt

1,60,feature_col0,feature_col1,feature_col2,feature_col3,feature_col4,feature_col5,feature_col6,feature_col7,feature_col8,feature_col9,feature_col10,feature_col11,feature_col12,feature_col13,feature_col14,feature_col15,feature_col16,feature_col17,feature_col18,feature_col19,feature_col20,feature_col21,feature_col22,feature_col23,feature_col24,feature_col25,feature_col26,feature_col27,feature_col28,feature_col29,feature_col30,feature_col31,feature_col32,feature_col33,feature_col34,feature_col35,feature_col36,feature_col37,feature_col38,feature_col39,feature_col40,feature_col41,feature_col42,feature_col43,feature_col44,feature_col45,feature_col46,feature_col47,feature_col48,feature_col49,feature_col50,feature_col51,feature_col52,feature_col53,feature_col54,feature_col55,feature_col56,feature_col57,feature_col58,feature_col59
1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1

1,4,feature_col0,feature_col1,feature_col2,feature_col3
1,1,1,0

1,60,feature_col0,feature_col1,feature_col2,feature_col3,feature_col4,feature_col5,feature_col6,feature_col7,feature_col8,feature_col9,feature_col10,feature_col11,feature_col12,feature_col13,feature_col14,feature_col15,feature_col16,feature_col17,feature_col18,feature_col19,feature_col20,feature_col21,feature_col22,feature_col23,feature_col24,feature_col25,feature_col26,feature_col27,feature_col28,feature_col29,feature_col30,feature_col31,feature_col32,feature_col33,feature_col34,feature_col35,feature_col36,feature_col37,feature_col38,feature_col39,feature_col40,feature_col41,feature_col42,feature_col43,feature_col44,feature_col45,feature_col46,feature_col47,feature_col48,feature_col49,feature_col50,feature_col51,feature_col52,feature_col53,feature_col54,feature_col55,feature_col56,feature_col57,feature_col58,feature_col59
0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,1,0,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1

1,4,feature_col0,feature_col1,feature_col2,feature_col3
1,1,0,0

test.txt

1,60,feature_col0,feature_col1,feature_col2,feature_col3,feature_col4,feature_col5,feature_col6,feature_col7,feature_col8,feature_col9,feature_col10,feature_col11,feature_col12,feature_col13,feature_col14,feature_col15,feature_col16,feature_col17,feature_col18,feature_col19,feature_col20,feature_col21,feature_col22,feature_col23,feature_col24,feature_col25,feature_col26,feature_col27,feature_col28,feature_col29,feature_col30,feature_col31,feature_col32,feature_col33,feature_col34,feature_col35,feature_col36,feature_col37,feature_col38,feature_col39,feature_col40,feature_col41,feature_col42,feature_col43,feature_col44,feature_col45,feature_col46,feature_col47,feature_col48,feature_col49,feature_col50,feature_col51,feature_col52,feature_col53,feature_col54,feature_col55,feature_col56,feature_col57,feature_col58,feature_col59
1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1

1,4,feature_col0,feature_col1,feature_col2,feature_col3
1,1,1,0

1,60,feature_col0,feature_col1,feature_col2,feature_col3,feature_col4,feature_col5,feature_col6,feature_col7,feature_col8,feature_col9,feature_col10,feature_col11,feature_col12,feature_col13,feature_col14,feature_col15,feature_col16,feature_col17,feature_col18,feature_col19,feature_col20,feature_col21,feature_col22,feature_col23,feature_col24,feature_col25,feature_col26,feature_col27,feature_col28,feature_col29,feature_col30,feature_col31,feature_col32,feature_col33,feature_col34,feature_col35,feature_col36,feature_col37,feature_col38,feature_col39,feature_col40,feature_col41,feature_col42,feature_col43,feature_col44,feature_col45,feature_col46,feature_col47,feature_col48,feature_col49,feature_col50,feature_col51,feature_col52,feature_col53,feature_col54,feature_col55,feature_col56,feature_col57,feature_col58,feature_col59
0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,1,0,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1

1,4,feature_col0,feature_col1,feature_col2,feature_col3
1,1,0,0

testLabel.txt

1,60,feature_col0,feature_col1,feature_col2,feature_col3,feature_col4,feature_col5,feature_col6,feature_col7,feature_col8,feature_col9,feature_col10,feature_col11,feature_col12,feature_col13,feature_col14,feature_col15,feature_col16,feature_col17,feature_col18,feature_col19,feature_col20,feature_col21,feature_col22,feature_col23,feature_col24,feature_col25,feature_col26,feature_col27,feature_col28,feature_col29,feature_col30,feature_col31,feature_col32,feature_col33,feature_col34,feature_col35,feature_col36,feature_col37,feature_col38,feature_col39,feature_col40,feature_col41,feature_col42,feature_col43,feature_col44,feature_col45,feature_col46,feature_col47,feature_col48,feature_col49,feature_col50,feature_col51,feature_col52,feature_col53,feature_col54,feature_col55,feature_col56,feature_col57,feature_col58,feature_col59
1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1

1,4,feature_col0,feature_col1,feature_col2,feature_col3
1,1,1,0

1,60,feature_col0,feature_col1,feature_col2,feature_col3,feature_col4,feature_col5,feature_col6,feature_col7,feature_col8,feature_col9,feature_col10,feature_col11,feature_col12,feature_col13,feature_col14,feature_col15,feature_col16,feature_col17,feature_col18,feature_col19,feature_col20,feature_col21,feature_col22,feature_col23,feature_col24,feature_col25,feature_col26,feature_col27,feature_col28,feature_col29,feature_col30,feature_col31,feature_col32,feature_col33,feature_col34,feature_col35,feature_col36,feature_col37,feature_col38,feature_col39,feature_col40,feature_col41,feature_col42,feature_col43,feature_col44,feature_col45,feature_col46,feature_col47,feature_col48,feature_col49,feature_col50,feature_col51,feature_col52,feature_col53,feature_col54,feature_col55,feature_col56,feature_col57,feature_col58,feature_col59
0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,1,0,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1

1,4,feature_col0,feature_col1,feature_col2,feature_col3
1,1,0,0

dpNum表示特征值

我想输入一些数据，比如train.txt

[1,0………..，1]#秩1张量；这是一个形状为[60]的向量

预测

[1,0,0,1]#秩1张量；这是一个形状为[4]
的向量，来自页面：

也就是说，您可以通过调用
training\u set.target
访问目标，这将为您提供每个数据点的标签

此外，我不确定您是否对某些术语感到困惑：您说训练数据集有15000个数据点，但只有1000个标签，这（至少对于Iris数据集而言）没有多大意义，因为我认为整个数据集都有标签。你是说你有15000个训练样本和1000个测试样本吗

所以，不确定你是否已经明白了下面的所有内容，但如果不是，希望它能帮你把事情弄清楚。假设Iris数据集看起来像这样（取自）：

现在通常使用以下术语：


表中的每一行都是一个数据点或样本
在这种情况下，数据点的维数为4（4个特征为萼片长度、萼片宽度、花瓣长度和花瓣宽度）
标签或目标是上表中的最后一列（
I.setosa
或
I.versicolor
）。通常，标签是以某种方式编码的，例如，标签是
I.setosa
的
0
和
1
，否则，正如您在问题中所暗示的那样。不过，可能不止这两个标签。例如，在Iris数据集中，通常还有第三朵花，名为
I.virginica
训练集和测试集看起来完全相同，只是测试集通常较小（除了评估分类器最终输出的分数外，您不使用测试集的标签）
您能再具体一点吗？
train.csv
真的有15000个维度，还是说15000个数据点？“目标维度”到底是什么？您的代码中的
IRIS\u TRAINING
是什么？你能发布一个csv文件的小摘录吗？我生成了一些虚假数据，并编辑了我的问题。非常感谢。谢谢你的术语。在我的老问题train.txt中，确实有15000个维度，我想预测1000个维度的结果。