网络数据集的相关内容

文章 2021-12-22 来自：开发者社区

我用 PyTorch 复现了 LeNet-5 神经网络（MNIST 手写数据集篇）！

大家好，我是红色石头！在上一篇文章：这可能是神经网络 LeNet-5 最详细的解释了！详细介绍了卷积神经网络 LeNet-5 的理论部分。今天我们将使用 Pytorch 来实现 LeNet-5 模型，并用它来解决 MNIST数据集的识别。正文开始！一、使用 LeNet-5 网络结构创建 MNIST 手写数字识别分类器MNIST是一个非常有名的手写体数字识别数据集，训练样本：共60000个，其中5....

文章 2021-11-18 来自：开发者社区

Pytorch入门与实践——AI插画师：生成对抗网络数据集制作

目录摘要1、用爬虫爬取二次元妹子的图片2、获取图片中的头像摘要最近想搞一搞GAN，但是发现《Pytorch入门与实践——AI插画师：生成对抗网络》，但是发现数据集的链接失效了，所以自己制作一份。代码来自https://www.zhihu.com/people/he-zhi-yuan-16，我做了一些修改。1、用爬虫爬取二次元妹子的图片数据从https://konachan.net/网站中下载的，....

文章 2021-11-06 来自：开发者社区

DL之DCGAN：基于keras框架利用深度卷积对抗网络DCGAN算法对MNIST数据集实现图像生成

设计思路输出结果X像素取值范围是[-1.0, 1.0]_________________________________________________________________Layer (type) Output Shape &...

文章 2021-11-01 来自：开发者社区

DL之CNN：利用卷积神经网络算法(2→2,基于Keras的API-Sequential)利用MNIST(手写数字图片识别)数据集实现多分类预测

输出结果1.10.0Size of:- Training-set: 55000- Validation-set: 5000- Test-set: 10000Epoch 1/1 128/55000 [..............................] - ETA: 15:39 - loss: 2.3021 - acc: 0.0703 25....

文章 2021-10-31 来自：开发者社区

DL之DNN：自定义2层神经网络TwoLayerNet模型(封装为层级结构)利用MNIST数据集进行训练、预测

输出结果设计思路核心代码 class TwoLayerNet: def __init__(self, input_size, hidden_size, output_size, weight_init_std = 0.01): self.params = {} &...

文章 2021-10-31 来自：开发者社区

DL之DNN：自定义2层神经网络TwoLayerNet模型(封装为层级结构)利用MNIST数据集进行训练、GC对比

输出结果设计思路核心代码 class TwoLayerNet: def __init__(self, input_size, hidden_size, output_size, weight_init_std = 0.01): self.params = {} &...

文章 2021-10-28 来自：开发者社区

TF之LSTM：基于Tensorflow框架采用PTB数据集建立LSTM网络的自然语言建模

关于PTB数据集PTB （Penn Treebank Dataset）文本数据集是语言模型学习中目前最被广泛使用数据集。ptb.test.txt #测试集数据文件ptb.train.txt #训练集数据文件ptb.valid.txt #验证集数据文件这三个数据文件中的数据已经经过了预处理，包含了10000 个不同的词语和语句结束标记符（在文本....

文章 2021-10-27 来自：开发者社区

CV之YOLOv3：深度学习之计算机视觉神经网络Yolov3-5clessses训练自己的数据集全程记录(第二次)

训练记录

文章 2021-10-27 来自：开发者社区

CV之YOLO：深度学习之计算机视觉神经网络tiny-yolo-5clessses训练自己的数据集全程记录

训练全部流程记录1、采用tiny-yolo-5clessses训练自己的数据集全部流程记录

文章 2021-10-27 来自：开发者社区

MAT之GRNN/PNN：基于GRNN、PNN两神经网络实现并比较鸢尾花(iris数据集)种类识别正确率、各个模型运行时间对比

输出结果实现代码load iris_data.mat P_train = [];T_train = [];P_test = [];T_test = [];for i = 1:3 temp_input = features((i-1)*50+1:i*50,:); temp_output = classes((i-1)*50+1:i*50,:);.....

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