mnist

So i have a bunch of images (PNG) and their names can be used as labels. Is there a tool or an example on how i can take those images and create a dataset like the MNIST IDX one. There are a lot of examples on how to read them but no examples on how to create them. Any help would be greatly appreciated! sinhayash This can help. The -images- files were written using the function save-idx3-ubyte and the -labels- files with the function save-idx1-ubyte. The functions in questions are defined in lush/lsh/libidx/idx-io.lsh The file format is described in the section "IDX file format for compilable

This question already has an answer here: TensorFlow - Show image from MNIST DataSet 5 answers I am using tensorflow to import some MNIST input data. I followed this tutorial... https://www.tensorflow.org/get_started/mnist/beginners I am importing them as so... from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) I want to be able to display any of the images from the training set. I know the location of the images is mnist.train.images , so I try to access the first images and display it like so... with tf.Session() as sess:

关于Deformable Convolutional Networks的论文解读，共分为5个部分，本章是第五部分： [ ] Part1： 快速学习实现仿射变换 [ ] Part2： Spatial Transfomer Networks论文解读 [ ] Part3： TenosorFlow实现STN [ ] Part4： Deformable Convolutional Networks论文解读 [x] Part5： TensorFlow实现Deformable ConvNets 本章讲解使用TensorFlow实现Deformable ConvNets。 Deformable Convolution介绍 论文给出的代码是 MXNet 写的，blog里使用的是TensorFlow/Keras实现的简易版。 注意该TensorFlow版存在的问题： 前向速度太慢，下面案例模型带变形卷积层的前向传播约需要240ms，而正常的CNN需要10ms都不到。 只是简单的变形层实现，没有Deformable Align-Rol层。 使用的Keras，原MXNet是要快很多的 相关资源地址： 论文-Deformable Convolutional Networks 参考代码：Deformable Convolution in TensorFlow / Keras 在变形的MNIST上示意图：

获取数据集 读取小批量样本 小结 本节将使用torchvision包，它是服务于pytorch深度学习框架的，主要用来构建计算机视觉模型。 torchvision主要由以下几个部分构成： torchvision.datasets:一些加载数据的函数以及常用的数据集的接口 torchvision.models: 包含常用的模型结构(含预训练模型),例如AlexNet，VGG，ResNet； torchvision.transforms:常用的图片变换，例如裁剪，旋转等； torchvision.utils: 其他的一些有用的方法 获取数据集 导入本节需要的包或者模块 import torch import torchvision import torchvision.transforms as transforms import matplotlib.pyplot as plt import time import sys sys.path.append('..') # 为了导入上层目录的d2lzh_pytorch import d2lzh_pytorch as d2l 通过调用torchvision的torchvision.datasets来下载这个数据集 可以通过train参数获取指定的训练集或者测试集、 测试集只用了评估模型，并不用来训练模型 同时指定了参数transform

I'm trying to load the mnist character dataset (following the tutorial outlined here: http://neuralnetworksanddeeplearning.com/chap1.html ) when I run the load_data_wrapper function I get the error. UnicodeDecodeError: 'ascii' codec can't decode byte 0x90 in position 614: ordinal not in range(128) The code run is: import numpy as np import gzip def load_data(): f = gzip.open('../data/mnist.pkl.gz', 'rb') training_data, validation_data, test_data = pickle.load(f) f.close() return (training_data, validation_data, test_data) def load_data_wrapper(): tr_d, va_d, te_d = load_data() training_inputs

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am trying to run several sessions of TensorFlow concurrently on a CentOS 7 machine with 64 CPUs. My colleague reports that he can use the following two blocks of code to produce a parallel speedup on his machine using 4 cores: mnist.py import numpy as np import input_data from PIL import Image import tensorflow as tf import time def main(randint): print 'Set new seed:', randint np.random.seed(randint) tf.set_random_seed(randint) mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # Setting up the softmax architecture x = tf

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: I am trying a simple demo code of tensorflow from github link . I'm currently using python version 3.5.2 Z : \downloads\tensorflow_demo - master\tensorflow_demo - master > py Python 3.5 . 2 ( v3 . 5.2 : 4def2a2901a5 , Jun 25 2016 , 22 : 18 : 55 ) [ MSC v . 1900 64 bit ( AMD64 )] on win32 Type "help" , "copyright" , "credits" or "license" for more information . I ran into this error when I tried board.py in command-line. I have installed all the dependencies that are required for this to run. def _read32 ( bytestream ): dt = numpy .

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am trying a simple demo code of tensorflow from github link . I'm currently using python version 3.5.2 Z:\downloads\tensorflow_demo-master\tensorflow_demo-master>py Python 3.5.2 (v3.5.2:4def2a2901a5, Jun 25 2016, 22:18:55) [MSC v.1900 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. I ran into this error when I tried board.py in command-line. I have installed all the dependencies that are required for this to run. def _read32(bytestream): dt = numpy.dtype(numpy.uint32).newbyteorder('>') return

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I pip installed Tensor Flow so I don't have tensorflow.examples so I got the souce input_data from GitHub. How ever I am getting the following error. PermissionError: [Errno 13] Permission denied: 'C:\Users\Nikhil\AppData\Local\Temp\tmp5gr8f26y' This is my code. import input_data mnist = input_data.read_data_sets("/MNIST_data/", one_hot = True) How do I solve this problem? There is another thread dealing with the exact same issue but that solution did not solve my problem. What should I do now? The input_data.py file is in my projects's