问题描述
我正在尝试使用 deepenet 包的 dbn.dnn.train 函数训练MNIST数据集.任务是一种分类.我正在使用以下命令
I am trying to train the MNIST dataset using deepenet package's dbn.dnn.train function. The task is a classification one.I am using the following command
dbn.deepnet <- dbn.dnn.train(train.image.data,train.image.labels,hidden=c(5,5))
我面临的问题是:
1)标签应为因子类型向量.但是，当我输入标签作为因子时，该函数给出了一个错误，即"y应该是矩阵或向量".因此，我将标签用作数字.如何进行分类任务
1) The labels should be factor type vector. But when i input the labels as factor the function gives an error that "y should be a matrix or vector". So, I am using labels as numeric. How to proceed for a classification task
2)对dbn.dnn.train进行预测的功能是什么.我正在使用nn.predict，但是文档中提到输入应该是由函数nn.train训练的神经网络(未提及dbn.dnn.train).所有记录的输出为0.9986
2) What it the function to make the predictions for dbn.dnn.train. I am using nn.predict but the documentation mentions that the input should be neural network trained by function nn.train (dbn.dnn.train is not mentioned).The output is 0.9986 for all records
nn.predict(dbn.deepnet,train.image.data)
推荐答案
不知道您是否还在研究它，或者是否找到了解决方案，但: 1/尝试一下:train.image.labels<-data.matrix(train.image.labels)
Don't know if you are still working on it, or if you've found the solution but : 1/ try this : train.image.labels <- data.matrix(train.image.labels)
2/即使我的神经网络是由dbn.dnn.train训练的，我也使用nn.predict.
2/ i use nn.predict, even if the neural network is trained by dbn.dnn.train.
这篇关于R包Deepnet:培训和测试MNIST数据集的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！