神经网络Python练习

最新推荐文章于 2024-09-05 19:31:46 发布

原创

最新推荐文章于 2024-09-05 19:31:46 发布 · 786 阅读

2 ·

CC 4.0 BY-SA版权

该博客介绍了如何使用TensorFlow构建全连接神经网络（FNN），应用Adagrad优化器和dropout技术来防止过拟合。作者在实践中遇到过拟合问题，并分享了尝试解决的方法，但效果不理想，需要进一步优化。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

用TensorFlow创建FNN神经网络模型，梯度下降采用Adagrad，使用dropout防止过拟合，尝试保存模型再调用的操作。

import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import metrics

#定义读取数据的函数
def load_data(train_path=""):
    train=pd.read_csv(train_path,names=["feature1",	"feature2"	,"feature3"	,"feature4"	,"feature5",
                                        "feature6"	,"feature7"	,"feature8",	"feature9"	,"feature10",
                                        "feature11"	,"feature12",	"feature13"	,"feature14",	"feature15"	,
                                        "feature16"	,"feature17"	,"feature18",	"feature19",	"feature20",
                                        "feature21"	,"feature22",	"feature23"	,"feature24"	,"feature25",
                                        "feature26",	"feature27",	"feature28"	,"feature29",	"feature30",
                                        "feature31"	,"feature32",	"feature33",	"feature34"	,"feature35",