!mkdir '/content/gdrive/My Drive/conversation' ''' 将文本句子分解成单词,并构建词库 ''' path = '/content/gdrive/My Drive/conversation/' with open(path + 'question.txt', 'r') as fopen: text_question = fopen.read().lower().split('\n') with open(path + 'answer.txt', 'r…

import tensorflow as tf import numpy as np ''' 初始化运算图,它包含了上节提到的各个运算单元,它将为W,x,b,h构造运算部件,并将它们连接 起来 ''' graph = tf.Graph() #一次tensorflow代码的运行都要初始化一个session session = tf.InteractiveSession(graph=graph) ''' 我们定义三种变量,一种叫placeholder,它对应输入变量,也就是上节计算图所示的圆圈部分,…

!pip install gym import random import numpy as np import matplotlib.pyplot as plt from keras.layers import Dense, Dropout, Activation from keras.models import Sequential from keras.optimizers import Adam from keras import backend as K from collection…

from keras.layers import model = Sequential() model.add(embedding_layer) #使用一维卷积网络切割输入数据,参数5表示每各个单词作为切割小段 model.add(layers.Conv1D(32, 5, activation='relu')) #参数3表示,上层传下来的数据中,从每3个数值中抽取最大值 model.add(layers.MaxPooling1D(3)) #添加一个有记忆性的GRU层,其原理与LSTM相同,运行速…

from keras.layers import LSTM model = Sequential() model.add(embedding_layer) model.add(LSTM(32)) #当结果是输出多个分类的概率时,用softmax激活函数,它将为30个分类提供不同的可能性概率值 model.add(layers.Dense(len(int_category), activation='softmax')) #对于输出多个分类结果,最好的损失函数是categorical_crosse…

2. 神经网络的搭建以及迁移学习的测试 7.项目总结 通过本次水果图片卷积池化全连接试验分类项目的实践,我对卷积.池化.全连接等相关的理论的理解更加全面和清晰了.试验主要采用python高级编程语言的TensorFlow和Keras这两个库.在实验学习的过程中,开始时,对于TensorFlow和Keras并不是很了解,里面提供的许多方法也不熟悉,但经过老师课堂的讲解和演示一些关键的.和常用的方法或函数,以及对相关参数的传递.变化,如:权值的变化.图片尺寸的变化.图片通道的变化.偏置的设置.优化函…

数据集的概念 数据集通常是由数据构成的一个矩形数组,行表示观测,列表示变量.表2-1提供了一个假想的病例数据集. 不同的行业对于数据集的行和列叫法不同.统计学家称它们为观测(observation)和变量 (variable),数据库分析师则称其为记录(record)和字段(field),数据挖掘和机器学习学科的研 究者则把它们叫作示例(example)和属性(attribute). 我们在R中使用术语:观测和变量.可以清楚地看到此数据集的结构(本例中是一个矩形数组)以及其中包含的内容和数据类型…

2.3.6 导入 SPSS 数据 IBM SPSS数据集可以通过foreign包中的函数read.spss()导入到R中,也可以使用Hmisc 包中的spss.get()函数.函数spss.get()是对read.spss()的一个封装,它可以为你自动设 置后者的许多参数,让整个转换过程更加简单一致,最后得到数据分析人员所期望的结果. 首先,下载并安装Hmisc包(foreign包已被默认安装): install.packages("Hmisc") 然后使用以下代码导入数据: libr…

install.packages('模块包名称') 或者 install.packages('模块包名称',repos='http://cran.us.r-project.org')…

…

下载R语言和开发工具RStudio安装包 先安装R…

R可从键盘.文本文件.Microsoft Excel和Access.流行的统计软件.特殊格 式的文件.多种关系型数据库管理系统.专业数据库.网站和在线服务中导入数据. 使用键盘了.有两种常见的方式:用R内置的文本编辑器和 直接在代码中嵌入数据.我们首先考虑文本编辑器. R中的函数edit()会自动调用一个允许手动输入数据的文本编辑器.具体步骤如下: (1) 创建一个空数据框(或矩阵),其中变量名和变量的模式需与理想中的最终数据集一致: (2) 针对这个数据对象调用文本编辑器,输入你的数据,并将结…

假设我们正在研究生理发育问 题,并收集了10名婴儿在出生后一年内的月龄和体重数据(见表1-).我们感兴趣的是体重的分 布及体重和月龄的关系. 可以使用函数c()以向量的形式输入月龄和体重数据,此函 数可将其参数组合成一个向量或列表.然后用mean().sd()和cor()函数分别获得体重的均值 和标准差,以及月龄和体重的相关度.最后使用plot()函数,从而用图形展示月龄和体重的关 系,这样就可以用可视化的方式检查其中可能存在的趋势.函数q()将结束会话并允许你退出R. age <- c(1,3…

1.基础数据结构 1.1 向量 # 创建向量a a <- c(1,2,3) print(a) 1.2 矩阵 #创建矩阵 mymat <- matrix(c(1:10), nrow=2, ncol=5, byrow=TRUE) #取第二行 mymat[2,] #取第二列 mymat[,2] #第一行第五列的元素 mymat[1,5] 1.3 数组 #创建数组 myarr <- array(c(1:12),dim=c(2,3,2)) print(myarr) #取矩阵或数组的维度 dim(m…

# ----------------------------------------------------# # R in Action (2nd ed): Chapter 3 # # Getting started with graphs # # requires that the Hmisc and RColorBrewer packages # # have been installed # # install.packages(c("Hmisc", "RColorB…

# ----------------------------------------------------# # R in Action (2nd ed): Chapter 3 # # Getting started with graphs # # requires that the Hmisc and RColorBrewer packages # # have been installed # # install.packages(c("Hmisc", "RColorB…

# ----------------------------------------------------# # R in Action (2nd ed): Chapter 3 # # Getting started with graphs # # requires that the Hmisc and RColorBrewer packages # # have been installed # # install.packages(c("Hmisc", "RColorB…

#---------------------------------------------------------------# # R in Action (2nd ed): Chapter 6 # # Basic graphs # # requires packages vcd, plotrix, sm, vioplot to be installed # # install.packages(c("vcd", "plotrix", "sm"…

#---------------------------------------------------------------# # R in Action (2nd ed): Chapter 6 # # Basic graphs # # requires packages vcd, plotrix, sm, vioplot to be installed # # install.packages(c("vcd", "plotrix", "sm"…

#---------------------------------------------------------------# # R in Action (2nd ed): Chapter 6 # # Basic graphs # # requires packages vcd, plotrix, sm, vioplot to be installed # # install.packages(c("vcd", "plotrix", "sm"…

#-----------------------------------# # R in Action (2nd ed): Chapter 5 # # Advanced data management # # requires that the reshape2 # # package has been installed # # install.packages("reshape2") # #-----------------------------------# # Class R…

#-----------------------------------# # R in Action (2nd ed): Chapter 5 # # Advanced data management # # requires that the reshape2 # # package has been installed # # install.packages("reshape2") # #-----------------------------------# # Class R…

#---------------------------------------------------------# # R in Action (2nd ed): Chapter 4 # # Basic data management # # requires that the reshape2 and sqldf packages have # # been installed # # install.packages(c('reshape2', 'sqldf')) # #--------…

#---------------------------------------------------------# # R in Action (2nd ed): Chapter 4 # # Basic data management # # requires that the reshape2 and sqldf packages have # # been installed # # install.packages(c('reshape2', 'sqldf')) # #--------…

#-------------------------------------------------------# # R in Action (2nd ed): Chapter 16 # # Cluster analysis # # requires packaged NbClust, flexclust, rattle # # install.packages(c("NbClust", "flexclust", "rattle")) # #-…

#-------------------------------------------------------# # R in Action (2nd ed): Chapter 16 # # Cluster analysis # # requires packaged NbClust, flexclust, rattle # # install.packages(c("NbClust", "flexclust", "rattle")) # #-…

#-----------------------------------------# # R in Action (2nd ed): Chapter 15 # # Time series # # requires forecast, tseries packages # # install.packages("forecast", "tseries") # #-----------------------------------------# par(ask=TR…

#-----------------------------------------# # R in Action (2nd ed): Chapter 15 # # Time series # # requires forecast, tseries packages # # install.packages("forecast", "tseries") # #-----------------------------------------# par(ask=TR…

#-----------------------------------------# # R in Action (2nd ed): Chapter 15 # # Time series # # requires forecast, tseries packages # # install.packages("forecast", "tseries") # #-----------------------------------------# par(ask=TR…

#-----------------------------------------# # R in Action (2nd ed): Chapter 15 # # Time series # # requires forecast, tseries packages # # install.packages("forecast", "tseries") # #-----------------------------------------# par(ask=TR…