mnist

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/QFire/article/details/90437796 import tensorflow as tf import numpy as np import matplotlib.pyplot as plt # 导入 MINST 数据集 from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/data/", one_hot=True) # 网络模型参数 learning_rate = 0.01 n_hidden_1 = 256 # 第一层256 n_hidden_2 = 128 # 第二层128 n_input = 784 x = tf.placeholder("float", [None, n_input]) y = x # 参数 weights = { "encoder_h1": tf.Variable(tf.random_normal([n_input, n_hidden_1])), "encoder_h2": tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), "decoder

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler -y sudo apt-get install --no-install-recommends libboost-all-dev -y sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev -y sudo apt-get install python-dev -y 下载caffe源码 git clone https://github.com/BVLC/caffe.git 修改编译配置文件： cd caffe 复制示例文件为编译配置文件： nano Makefile.config GPU版：将#USE_CUDNN := 1 修改成： USE_CUDNN := 1 将# Whatever else you find you need goes here.下面的 INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib 修改为：

一、简介 　 　首先介绍MNIST 数据集。如图1-1 所示， MNIST 数据集主要由一些手写数字的图片和相应的标签组成，图片一共有10 类，分别对应从0～9 ，共10 个阿拉伯数字。 　　 　　原始的MNIST 数据库一共包含下面4 个文件， 见表1-1 。 　　 　　 在表1 - 1 中，图像数据是指很多张手写字符的图像，图像的标签是指每一张图像实际对应的数字是几，也就是说，在MNIST 数据集中的每一张图像都事先标明了对应的数字。 在MNIST 数据集中有两类图像：一类是训练图像（对应文件train-images-idx3-ubyte.gz 和train - labels-idx1-ubyte.gz ）， 另一类是测试图像（对应文件t10k-images-idx3-ubyte.gz 和t10k-labels-idx1-ubyte.gz ） 。训练图像一共有60000 张，供研究人员训练出合适的模型。测试图像一共有10000 张，供研究人员测试训练的模型的性能。在TensorFlow 中， 可以使用下面的Python 代码下载MNIST 数据（在随书附赠的代码中，该代码对应的文件是donwload.py ）。 # 从tensorflow.examples.tutorials.mnist引入模块。这是TensorFlow为了教学MNIST而提前编制的程序 from

来源：https://www.numpy.org.cn/deep/basics/fit_a_line.html 数字识别 本教程源代码目录在 book/recognize_digits ,初次使用请您参考 Book文档使用说明 。 # 说明: 硬件环境要求： 本文可支持在CPU、GPU下运行 Docker镜像支持的CUDA/cuDNN版本： 如果使用了Docker运行Book，请注意：这里所提供的默认镜像的GPU环境为 CUDA 8/cuDNN 5，对于NVIDIA Tesla V100等要求CUDA 9的 GPU，使用该镜像可能会运行失败。 文档和脚本中代码的一致性问题： 请注意：为使本文更加易读易用，我们拆分、调整了train.py的代码并放入本文。本文中代码与train.py的运行结果一致，可直接运行 train.py 进行验证。 # 背景介绍 当我们学习编程的时候，编写的第一个程序一般是实现打印"Hello World"。而机器学习（或深度学习）的入门教程，一般都是 MNIST 数据库上的手写识别问题。原因是手写识别属于典型的图像分类问题，比较简单，同时MNIST数据集也很完备。MNIST数据集作为一个简单的计算机视觉数据集，包含一系列如图1所示的手写数字图片和对应的标签。图片是28x28的像素矩阵，标签则对应着0~9的10个数字。每张图片都经过了大小归一化和居中处理。

1 import sys , os 2 sys . path . append ( os . pardir ) 3 import numpy as np 4 from dataset . mnist import load_mnist 5 from PIL import Image 6 7 def img_show ( img ): 8 pil_img = Image . fromarray ( np . uint8 ( img )) 9 pil_img . show () 10 11 ( x_train , t_train ), ( x_test , t_test ) = load_mnist ( flatten = True , 12 normalize = False ) 13 img = x_train [ 0 ] 14 label = t_train [ 0 ] 15 print ( label ) # 5 16 17 print ( img . shape ) # (784,) 18 img = img . reshape ( 28 , 28 ) # 把图像的形状变成原来的尺寸 19 print ( img . shape ) # (28, 28) 20 21 img_show ( img ) 显示mnist图像，执行上述代码后，训练图像的第一张会显示出来。 sys

I'm trying to use the Theano library in python to do some experiments with Deep Belief Networks. I use the code in this address: DBN full code . This code use the MNIST Handwritten database . This file is already in pickle format. It is unpicked in: train_set valid_set test_set Which is further unpickled in: train_set_x, train_set_y = train_set valid_set_x, valid_set_y = valid_set test_set_x, test_set_y = test_set Please can someone give me the code that constructs this dataset in order to create my own? The DBN example I use needs the data in this format and I don't know how to do it. if

I have created a simple function for facerecognition by using the facerecognizer from OpenCV. It works all fine with images from people. Now I would like to make a test by using handwritten characters instead of people. I came across MNIST dataset, but they store images in a weird file which I have never seen before. I simply need to extract a few images from: train-images.idx3-ubyte and save them in a folder as .gif Or am I missunderstand this MNIST thing. If yes where could I get such a dataset? EDIT I also have the gzip file: train-images-idx3-ubyte.gz I am trying to read the content, but

I have a pkl file from MNIST dataset, which consists of handwritten digit images. I'd like to take a look at each of those digit images, so I need to unpack the pkl file, except I can't find out how. Is there a way to unpack/unzip pkl file? Generally Your pkl file is, in fact, a serialized pickle file, which means it has been dumped using Python's pickle module. To un-pickle the data you can: import pickle with open('serialized.pkl', 'rb') as f: data = pickle.load(f) For the MNIST data set Note gzip is only needed if the file is compressed: import gzip import pickle with gzip.open('mnist.pkl

解决贝叶斯网络的ipynb文件运行问题 问题描述 使用fetch_mldata函数一直下载不下来数据集 from sklearn.datasets import fetch_mldata mnist = fetch_mldata('MNIST original') 使用时报错问题 D:\CodeSoftware\anaconda\lib\site-packages\sklearn\utils\deprecation.py:85: DeprecationWarning: Function fetch_mldata is deprecated; fetch_mldata was deprecated in version 0.20 and will be removed in version 0.22. Please use fetch_openml. warnings.warn(msg, category=DeprecationWarning) D:\CodeSoftware\anaconda\lib\site-packages\sklearn\utils\deprecation.py:85: DeprecationWarning: Function mldata_filename is deprecated; mldata_filename was deprecated in