如何使用 Tensorflow（python）为 keras model.fit（）创建我自己的数据集？答案

【问题标题】：How to create my own dataset for keras model.fit() using Tensorflow(python)?如何使用 Tensorflow（python）为 keras model.fit（）创建我自己的数据集？
【发布时间】：2019-05-25 13:53:07
【问题描述】：

我想训练一个简单的分类神经网络，可以将数据分为两种类型，即真或假。

我有 29 个数据以及可用的相应标签。我想解析这些数据以形成一个数据集，该数据集可以输入 model.fit() 以训练神经网络。

请建议我如何使用各自的标签排列数据。用什么，列表、字典、数组？

有2个指纹的值由'$'符号分隔，它们是否匹配（即真或假）由另一个'$'符号分隔。一个指纹有 63 个特征，用 ','（逗号）符号分隔。

所以，每一行都有2个指纹的数据和真假数据。我有以下格式的数据：

文件名：thumb_and_index.txt

239,1,255,255,255,255,2,0,130,3,1,105,24,152,0,192,126,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,128,0,192,0,192,0,0,0,0,0,0,0,147,18,19,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,101,22,154,0,240,30,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,128,0,0,0,0,0,0,0,0,0,0,0,0,71,150,212,$true
239,1,255,255,255,255,2,0,130,3,1,82,23,146,0,128,126,0,14,0,6,0,6,0,2,0,0,0,0,0,2,0,2,0,2,0,2,0,2,0,6,128,6,192,14,224,30,255,254,0,0,0,0,0,0,207,91,180,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,81,28,138,0,241,254,128,6,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,128,0,128,2,128,2,192,6,224,6,224,62,0,0,0,0,0,0,0,0,0,0,0,0,13,62,$true
239,1,255,255,255,255,2,0,130,3,1,92,29,147,0,224,0,192,0,192,0,128,0,128,0,128,0,128,0,128,0,128,0,128,0,192,0,192,0,224,0,224,2,240,2,248,6,255,14,76,16,0,0,0,0,19,235,73,181,0,0,0,0,$239,192,255,255,255,255,2,0,130,3,1,0,0,0,0,248,30,240,14,224,0,224,0,128,0,128,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,6,128,14,192,14,252,30,0,0,0,0,0,0,0,0,0,0,0,0,158,46,$false
239,1,255,255,255,255,2,0,130,3,1,0,0,0,0,128,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,128,0,0,0,0,0,0,0,217,85,88,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,90,27,135,0,252,254,224,126,128,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,190,148,$false
239,1,255,255,255,255,2,0,130,3,1,89,22,129,0,129,254,128,254,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,2,0,6,0,6,128,14,192,14,224,14,0,0,0,0,0,0,20,20,43,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,91,17,134,0,0,126,0,30,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,2,0,6,0,6,0,30,192,62,224,126,224,254,0,0,0,0,0,0,0,0,0,0,0,0,138,217,$true
239,1,255,255,255,255,2,0,130,3,1,71,36,143,0,128,254,0,14,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,2,0,2,0,6,80,18,0,0,0,0,153,213,11,95,83,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,94,30,140,0,129,254,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,192,6,0,0,0,0,0,0,0,0,0,0,0,0,54,13,$true
239,1,255,255,255,255,2,0,130,3,1,66,42,135,0,255,254,1,254,0,14,0,6,0,6,0,6,0,6,0,6,0,2,0,2,0,2,0,2,0,2,0,2,0,6,0,6,0,6,0,0,0,0,0,0,225,165,64,152,172,88,0,0,$239,1,255,255,255,255,2,0,130,3,1,62,29,137,0,255,254,249,254,240,6,224,2,224,0,224,0,224,0,224,0,224,0,224,0,224,0,240,0,240,0,240,0,240,0,240,0,240,2,0,0,0,0,0,0,0,0,0,0,0,0,0,98,$true
239,1,255,255,255,255,2,0,130,3,1,83,31,142,0,255,254,128,254,0,30,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,128,2,192,2,192,2,192,2,192,6,0,0,0,0,0,0,146,89,117,12,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,84,14,154,0,0,2,0,2,0,2,0,2,0,2,0,6,0,14,128,30,192,62,255,254,255,254,255,254,255,254,255,254,255,254,255,254,255,254,0,0,0,0,0,0,0,0,0,0,0,0,0,31,$false
239,1,255,255,255,255,2,0,130,3,1,66,41,135,0,255,254,248,62,128,30,0,14,0,14,0,14,0,14,0,14,0,14,0,6,0,6,0,6,0,14,0,14,0,14,192,14,224,14,0,0,0,0,0,0,105,213,155,107,95,23,0,0,$239,1,255,255,255,255,2,0,130,3,1,61,33,133,0,255,254,255,254,224,62,192,6,192,6,192,6,192,6,192,6,192,6,224,6,224,6,224,6,224,6,224,6,224,6,224,6,224,6,0,0,0,0,0,0,0,0,0,0,0,0,0,62,$false
239,1,255,255,255,255,2,0,130,3,1,88,31,119,0,0,14,0,14,0,6,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,100,133,59,150,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,97,21,137,0,128,14,0,6,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,6,0,0,0,0,0,0,0,0,0,0,0,80,147,210,$true
239,1,255,255,255,255,2,0,130,3,1,85,21,137,0,224,14,192,6,192,6,128,6,0,6,0,6,0,6,0,6,0,6,0,6,0,6,0,6,0,6,128,14,192,30,224,126,224,254,0,0,0,0,0,0,79,158,178,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,89,25,134,0,240,6,128,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,128,2,128,2,192,2,192,6,224,6,240,14,240,30,0,0,0,0,0,0,0,0,0,0,0,0,72,31,$true
239,1,255,255,255,255,2,0,130,3,1,90,25,128,0,241,254,0,30,0,6,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,6,0,6,192,14,0,0,0,0,0,0,225,153,189,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,96,12,153,0,192,14,128,6,128,6,128,6,0,6,128,2,128,2,128,2,128,6,128,6,192,14,240,30,255,254,255,254,255,254,255,254,255,254,0,0,0,0,0,0,0,0,0,0,0,0,0,18,$false
239,1,255,255,255,255,2,0,130,3,1,96,22,142,0,255,254,254,14,128,2,128,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,192,2,0,0,0,0,0,0,18,25,100,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,76,24,145,0,224,2,192,0,128,0,128,0,128,0,128,0,128,0,128,0,128,0,224,2,240,126,255,254,255,254,255,254,255,254,255,254,255,254,0,0,0,0,0,0,0,0,0,0,0,0,0,145,$false
239,1,255,255,255,255,2,0,130,3,1,71,33,117,0,129,254,0,30,0,14,0,14,0,6,0,6,0,2,0,2,0,6,0,6,0,6,0,6,0,6,128,14,192,14,240,30,240,254,0,0,0,0,0,0,235,85,221,57,17,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,76,31,112,0,255,254,0,62,0,62,0,62,0,14,0,6,0,6,0,6,0,6,0,6,0,6,0,6,0,6,0,6,0,6,128,14,224,62,0,0,0,0,0,0,0,0,0,0,0,0,30,170,$true
239,1,255,255,255,255,2,0,130,3,1,64,29,117,0,128,30,0,30,0,30,0,14,0,6,0,6,0,6,0,6,0,6,0,14,0,14,0,14,128,30,192,30,224,62,240,254,255,254,0,0,0,0,0,0,99,80,119,149,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,72,18,132,0,128,2,0,0,0,0,128,0,128,0,128,0,128,0,192,2,224,2,240,14,252,14,255,254,255,254,255,254,255,254,255,254,255,254,0,0,0,0,0,0,0,0,0,0,0,0,0,14,$false
239,1,255,255,255,255,2,0,130,3,1,82,16,132,0,255,254,255,254,255,254,240,30,224,14,224,14,192,6,192,6,192,2,192,2,192,2,192,2,192,2,192,2,192,1,224,2,240,6,0,0,0,0,0,0,215,21,0,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,85,23,130,0,240,30,192,14,128,14,128,6,128,2,128,2,128,2,128,2,128,2,128,0,192,0,192,2,192,2,224,2,224,6,240,6,248,30,0,0,0,0,0,0,0,0,0,0,0,0,0,62,$true
239,1,255,255,255,255,2,0,130,3,1,100,28,141,0,255,254,255,254,224,14,192,14,192,6,192,2,128,2,128,2,128,2,0,2,0,2,0,2,0,2,0,6,0,6,0,6,192,14,0,0,0,0,0,0,42,88,87,169,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,95,31,134,0,255,254,240,254,224,0,192,0,192,0,192,0,128,0,128,0,128,0,128,0,128,0,128,0,128,0,128,0,128,0,192,2,192,6,0,0,0,0,0,0,0,0,0,0,0,0,0,182,$true
239,1,255,255,255,255,2,0,130,3,1,88,35,121,0,255,14,240,6,224,7,192,2,192,2,192,2,192,2,192,2,192,2,192,2,192,2,224,2,224,2,224,2,224,2,224,2,224,6,0,0,0,0,0,0,36,81,48,225,153,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,81,43,112,0,252,62,248,14,224,2,192,2,192,2,192,0,192,0,192,0,192,0,192,0,192,0,192,0,224,0,224,2,224,2,224,2,224,6,0,0,0,0,0,0,0,0,0,0,0,0,0,76,$true
239,1,255,255,255,255,2,0,130,3,1,103,24,144,0,255,254,192,14,192,6,128,2,128,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,6,128,6,128,6,192,30,224,254,0,0,0,0,0,0,19,82,111,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,98,11,149,0,255,2,255,0,252,0,240,0,240,0,240,0,248,0,248,0,248,0,252,0,254,0,254,2,254,30,254,30,254,30,254,30,254,30,0,0,0,0,0,0,0,0,0,0,0,0,0,114,$false
239,1,255,255,255,255,2,0,130,3,1,92,23,123,0,255,254,255,30,252,6,240,2,224,0,192,0,192,0,192,0,224,0,224,0,224,0,224,2,224,2,224,2,224,2,224,6,224,6,0,0,0,0,0,0,35,161,251,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,52,37,125,0,255,254,255,254,224,254,192,30,192,14,128,14,128,14,128,14,128,14,128,14,128,14,128,14,128,6,0,2,0,2,0,2,192,2,0,0,0,0,0,0,0,0,0,0,0,0,0,110,$false
239,1,255,255,255,255,2,0,130,3,1,103,19,143,0,255,254,254,254,0,126,0,126,0,126,0,62,0,62,0,126,0,126,0,126,0,126,0,126,0,126,0,126,0,254,0,254,0,254,0,0,0,0,0,0,38,168,0,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,90,30,141,0,255,254,193,254,128,62,0,6,0,2,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,6,0,254,0,0,0,0,0,0,0,0,0,0,0,0,53,211,$true
239,1,255,255,255,255,2,0,130,3,1,93,34,137,0,255,254,225,254,192,14,192,2,192,2,192,2,192,2,192,0,192,0,192,0,192,0,192,0,192,0,224,2,224,2,240,6,240,14,0,0,0,0,0,0,101,4,252,164,28,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,88,31,140,0,255,254,192,62,192,14,192,14,0,6,0,6,0,6,0,6,0,2,0,2,0,2,0,2,128,2,128,6,192,6,224,14,240,30,0,0,0,0,0,0,0,0,0,0,0,0,10,97,$true
239,1,255,255,255,255,2,0,130,3,1,57,50,107,0,248,2,248,0,248,0,224,0,224,0,192,0,192,0,192,0,128,0,128,0,128,0,128,0,192,0,192,0,192,0,192,2,224,2,0,0,0,0,0,0,34,10,146,27,176,73,73,82,$239,1,255,255,255,255,2,0,130,3,1,54,42,111,0,255,254,255,254,254,126,252,6,240,2,224,2,224,2,224,0,224,0,224,0,224,0,224,0,224,0,224,0,224,0,192,0,192,0,0,0,0,0,0,0,0,0,0,0,0,0,0,225,$true
239,1,255,255,255,255,2,0,130,3,1,103,18,142,0,241,254,224,254,128,126,128,126,0,62,0,30,0,30,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,0,0,0,0,0,209,21,0,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,103,10,139,0,255,254,255,254,255,254,225,254,192,254,192,254,192,126,128,62,0,30,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,163,$true
239,1,255,255,255,255,2,0,130,3,1,85,21,132,0,248,2,248,2,248,0,240,0,240,0,240,0,240,0,240,0,240,0,240,0,248,0,248,0,252,0,252,0,252,0,254,2,255,6,0,0,0,0,0,0,94,23,110,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,76,26,133,0,129,254,128,62,0,62,0,62,0,62,0,62,0,30,0,30,0,30,0,30,0,30,0,30,0,30,0,30,128,30,192,14,224,14,0,0,0,0,0,0,0,0,0,0,0,0,222,36,$true
239,1,255,255,255,255,2,0,130,3,1,87,28,141,0,255,254,255,254,224,254,224,126,224,126,0,14,0,2,0,2,0,2,0,0,0,0,0,0,0,2,0,2,0,2,0,2,0,2,0,0,0,0,0,0,143,231,78,148,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,89,30,139,0,255,254,248,254,240,30,224,14,224,14,192,6,192,2,128,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,26,213,$true
239,1,255,255,255,255,2,0,130,3,1,93,25,136,0,255,254,193,254,0,254,0,62,0,30,0,30,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,0,0,0,0,0,148,210,91,0,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,95,23,145,0,254,254,252,30,240,2,224,0,224,0,224,0,192,0,192,0,192,0,192,6,192,6,192,6,192,6,192,6,192,6,224,6,224,14,0,0,0,0,0,0,0,0,0,0,0,0,0,30,$false
239,1,255,255,255,255,2,0,130,3,1,85,27,138,0,255,254,240,126,224,30,192,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,14,0,30,0,30,0,30,192,62,224,62,0,0,0,0,0,0,85,17,74,101,0,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,105,19,144,0,192,254,128,126,0,62,0,30,128,30,128,30,128,14,192,14,192,14,192,14,224,14,224,14,240,14,240,14,248,14,254,30,255,30,0,0,0,0,0,0,0,0,0,0,0,0,0,254,$false
239,1,255,255,255,255,2,0,130,3,1,86,37,116,0,255,254,254,14,252,6,248,2,240,0,240,0,224,0,192,0,192,0,128,0,0,0,0,2,0,2,0,2,0,2,0,6,0,6,0,0,0,0,0,0,94,157,90,28,219,0,0,0,$239,1,255,255,255,255,2,0,130,3,1,99,26,130,0,255,254,248,14,240,2,224,0,192,0,192,0,192,0,128,0,192,0,192,0,192,0,192,0,224,0,240,2,248,6,255,254,255,254,0,0,0,0,0,0,0,0,0,0,0,0,0,213,$true

我用这段代码尝试解析数据：

import tensorflow as tf
import os
import array as arr
import numpy as np
import json

os.environ["TF_CPP_MIN_LOG_LEVEL"]="2"

f= open("thumb_and_index.txt","r")

dataset = [] 
if f.mode == 'r':
    contents =f.read()
    #list of lines
    lines = contents.splitlines()
    print("No. of lines : "+str(len(lines)))

    for line in lines:
        words = line.split(',')
        mainlist = []
        list = []
        flag = 0
        for word in words:
            print("word : " + word)
            if '$' in word:
                if flag == 1:
                    mainlist.append(list)
                    mainlist.append(word[1:])
                    dataset.append(mainlist)
                else:
                    mainlist.append(list)
                    del list[0:len(list)]
                    list.append(int(word[1:]))

                flag = flag + 1
            else:
                list.append(int(word))

    print(json.dumps(dataset, indent = 4))

我想在 tensorflow(python) 中使用 keras 将解析后的数据输入 model.fit()。

我还想问一下神经网络。我应该在我的神经网络中保留多少层和节点？建议一个起点。

【问题讨论】：

标签： python tensorflow keras

【解决方案1】：

有很多方法可以做到这一点（格式化数据），您可以为数据创建 2D 矩阵，该矩阵有 62 列数据和另一个处理该数据结果的数组（X_data，Y_data）。

您也可以使用 pandas 为数据创建数据框（与数组相同，但最好显示和可视化数据）。

将文本文件读入熊猫数据框的示例

import pandas

df = pandas.read_table('./input/dists.txt', delim_whitespace=True, names=('A', 'B', 'C'))

将数据拆分为 x&y，然后将其放入您的模型中

对于神经网络中隐藏层的大小，众所周知，添加的层数越多，得到的结果就越准确（不考虑过度拟合），因此这取决于您的数据。

我建议你从如下顺序层开始（62->2048->1024->512->128->64->sigmoid）

【讨论】：

我有 2 个指纹要匹配，一个指纹有 63 个特征。所以如果我制作一个有 63 列的二维矩阵。我只能存储一个指纹。我在哪里存储另一个指纹？
我不明白你在比较两个不同的指纹？？
它们是同一根手指的指纹

【解决方案2】：

最好的方法，特别是假设数据集很大，是使用tf.data 数据集。内置了一个 CSV 阅读器。dataset api 提供了预处理数据集所需的所有功能，它提供了内置的多核处理，等等。

一旦你构建了数据集，Keras 将直接接受它作为输入，所以fit(my_dataset, inputs=... outputs=...)。

dataset api 的结构需要一点学习，但非常值得。这是包含大量示例的主要指南：

https://www.tensorflow.org/guide/datasets

向下滚动到“导入 CSV 数据”部分，查看精彩示例。

这是一个使用 keras 的数据集 API 的好例子：How to Properly Combine TensorFlow's Dataset API and Keras?

【讨论】：