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LSTM记录

Recurrent Neural Networks目录Recurrent Neural Networks一.The Problem of Long-Term Dependencies二. LSTM Networks三. The Core Idea Behind LSTMs四. Step-by-Step LSTM Walk Through五. Variants on Long Short Term Memory六. ConclusionRNN展开：RNN的实现很多任

原理（英文为例）：1.把每个单词都拆成单个字母，在单词结尾加上结束符，并标记上这个词出现的频率2.（以2-gram为例）把连续相邻的2个字母，进行组合，比如a b c-->ab bc ，同样标记上这个词出现的频率3.统计不同两个字母组合的最大频率，并选出最大字母组合。4.用选出的最大字母组合替换掉第一步中拆开的 两个字母 比如用ab 去替换掉 每个单词中出现的a b5....

import torchimport torch.nn as nnimport numpy as npfrom torch.nn.utils import clip_grad_norm_from data_utils import Dictionary, Corpus# data_utils代码在https://github.com/yunjey/pytorch-tutorial/bl...

import osimport torchimport torchvisionimport torch.nn as nnfrom torchvision import transformsfrom torchvision.utils import save_image# Device configurationdevice = torch.device('cuda' if to...

import torchimport torch.nn as nnimport torchvisionimport torchvision.transforms as transforms# Device configurationdevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')# Hy...

# ---------------------------------------------------------------------------- ## An implementation of https://arxiv.org/pdf/1512.03385.pdf ## See section 4.2 for the model archi...

import pandas as pdimport numpy as npimport torchimport timeimport randomimport osfrom torchtext import datafrom torchtext.vocab import Vectorsfrom torch.nn import initfrom tqdm import tqdm...

import torchimport torch.nn as nnimport torchvisionimport torchvision.transforms as transforms# Device configuration; torch.device代表将torch.Tensor分配到的设备的对象。device = torch.device('cuda'if torch.c...

import torchimport torch.nn as nnimport torchvisionimport torchvision.transforms as transforms# Device configurationdevice = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')# Hy...

import torchimport torch.nn as nnimport torchvision # 包含目前流行的数据集、模型结构和常用的图片import torchvision.transforms as transforms"""1. 设置超参数2. 下载数据，加载数据3. 加载模型4. 定义损失函数和优化器5. 开始训练6. 测试模型7. 保存模型"""...

import torchimport torch.nn as nnimport numpy as npimport matplotlib.pyplot as plt# Hyper-parameters 设置超参数input_size = 1output_size = 1num_epochs = 60learning_rate = 0.001# Toy dataset ...

import torch # 包 torch 包含了多维张量的数据结构以及基于其上的多种数学操作。另外，它也提供了多种工具，其中一些可以更有效地对张量和任意类型进行序列化import torchvision # torchvision包 包含了目前流行的数据集，模型结构和常用的图片转换工具import torch.nn as nnimport numpy as ...