本次分类问题使用的数据集是MNIST,每个图像的大小为\(28*28\). 编写代码的步骤如下 载入数据集,分别为训练集和测试集 让数据集可以迭代 定义模型,定义损失函数,训练模型 代码 import torch import torch.nn as nn import torchvision.transforms as transforms import torchvision.datasets as dsets from torch.autograd import Variable '''下
本文译自2018CVPR DeepBack-Projection Networks For Super-Resolution 代码: github 特点:不同于feedback net,引入back projection net 结果:state of the art,尤其在大尺度上面,例如x8倍 摘要: 近来提出的前馈网络结构学习低分辨输入的表征和由SR(low-resoluton)至HR(high-resolution)的非线性映射.然而这种方法并没有完整处理SR和HR图像的相互依赖.我们提
系列前言 參考文献: RNNLM - Recurrent Neural Network Language Modeling Toolkit(点此阅读) Recurrent neural network based language model(点此阅读) EXTENSIONS OF RECURRENT NEURAL NETWORK LANGUAGE MODEL(点此阅读) Strategies for Training Large Scale Neural Network Language
转自:https://blog.csdn.net/qq_16912257/article/details/79099581 https://blog.csdn.net/thriving_fcl/article/details/51406780 1.简单使用 from gensim.models import word2vec sents = [ 'I am a good student'.split(), 'Good good study day day up'.split() ] model