运行脚本时出现错误代码 [英] Error code while running script
问题描述
我ML azure的新手
I am new to ML azure
我需要以下错误的帮助
错误0063:R脚本评估期间发生以下错误:
----------来自R的错误消息的开始----------
无法打开连接#人工神经网络
Error 0063: The following error occurred during evaluation of R script:
---------- Start of error message from R ----------
cannot open the connection# Artificial Neural Network
这是我的R代码
#导入数据集
数据集= read.csv('银行数据-部分1.csv')
数据集=数据集[3:13]
dataset1 = read.csv('银行数据-第2部分.csv')
数据集1 =数据集1 [3:12]
#将分类变量编码为因子
dataset $ Geography = as.numeric(factor(dataset $ Geography，
等级= c('法国'，'西班牙'，'德国')，
标签= c(1、2、3)))
dataset $ Gender = as.numeric(factor(dataset $ Gender，
levels = c('Female'，'Male')，
labels = c(1，2)))
dataset1 $ Geography = as.numeric(factor(dataset1 $ Geography，
levels = c('France'，'Spain'，'Germany')，
labels = c(1、2、3)))
dataset1 $ Gender = as.numeric(factor(dataset1 $ Gender，
等级= c('女'，'男')，
标签= c(1，2)))
#将数据集分为训练集和测试集
training_set =数据集
test_set =数据集1
#功能缩放
training_set [-11] = scale(training_set [-11])
test_set [-11] =标度(test_set [-11])
#将ANN拟合到训练集
#install.packages('h2o')
图书馆(h2o)
h2o.init(nthreads = -1)
模型= h2o.deeplearning(y ='已退出'，
training_frame = as.h2o(training_set)，
activation ='整流器'，
hidden = c(35,35)，
epochs = 600，
train_samples_per_iteration = -2)
#预测测试集结果
y_pred = h2o.predict(model，newdata = as.h2o(test_set [-11]))
y_pred =(y_pred> 0.5)
y_pred = as.vector(y_pred)
y_pred_trn = h2o.predict(model，newdata = as.h2o(training_set [-11]))
y_pred_trn =(y_pred_trn> 0.5)
y_pred_trn = as.vector(y_pred_trn)
#制作混淆矩阵以测试结果的有效性
cm =表格(training_set [，11]，y_pred_trn)
#将预测数据导出到CSV文件
xDF<-data.frame(y_pred)
write.csv(xDF，``y_pred.csv'')
write.csv(test_set，"test_set.csv")
write.csv(training_set，"training_set.csv")
#关闭H2O实例
h2o.shutdown()
# Importing the dataset
dataset = read.csv('Bank Data - Part 1.csv')
dataset = dataset[3:13]
dataset1 = read.csv('Bank Data - Part 2.csv')
dataset1 = dataset1[3:12]
# Encoding the categorical variables as factors
dataset$Geography = as.numeric(factor(dataset$Geography,
levels = c('France', 'Spain', 'Germany'),
labels = c(1, 2, 3)))
dataset$Gender = as.numeric(factor(dataset$Gender,
levels = c('Female', 'Male'),
labels = c(1, 2)))
dataset1$Geography = as.numeric(factor(dataset1$Geography,
levels = c('France', 'Spain', 'Germany'),
labels = c(1, 2, 3)))
dataset1$Gender = as.numeric(factor(dataset1$Gender,
levels = c('Female', 'Male'),
labels = c(1, 2)))
# Assgning the dataset into the Training set and Test set
training_set = dataset
test_set = dataset1
# Feature Scaling
training_set[-11] = scale(training_set[-11])
test_set[-11] = scale(test_set[-11])
# Fitting ANN to the Training set
# install.packages('h2o')
library(h2o)
h2o.init(nthreads = -1)
model = h2o.deeplearning(y = 'Exited',
training_frame = as.h2o(training_set),
activation = 'Rectifier',
hidden = c(35,35),
epochs = 600,
train_samples_per_iteration = -2)
# Predicting the Test set results
y_pred = h2o.predict(model, newdata = as.h2o(test_set[-11]))
y_pred = (y_pred > 0.5)
y_pred = as.vector(y_pred)
y_pred_trn = h2o.predict(model, newdata = as.h2o(training_set[-11]))
y_pred_trn = (y_pred_trn > 0.5)
y_pred_trn = as.vector(y_pred_trn)
# Making the Confusion Matrix to test the validity of results
cm = table(training_set[, 11], y_pred_trn)
# Exporting predicted data to a CSV file
xDF <- data.frame(y_pred)
write.csv(xDF, "y_pred.csv")
write.csv(test_set, "test_set.csv")
write.csv(training_set, "training_set.csv")
# Shutting Down the H2O instance
h2o.shutdown()
推荐答案
很抱歉听到您遭受AML studio中的错误困扰.对于错误0063，我们有故障排除指南.请尝试遵循它以查看是否有帮助.
Sorry to hear you are suffer from a bug in AML studio. For Error 0063, we have our trouble shooting guide. Please try to follow it to see if it helps.
https://docs.microsoft.com/zh-CN/azure/machine-learning/studio-module-reference/errors/error-0063
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/errors/error-0063
如果您还有其他挑战，请告诉我.
Tell me if you have further challenge.
此致
雨桐
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