azure-machine-learning-studio

问题 I have an experiment in AzureML which has a R module at its core. Additionally, I have some .RData files stored in Azure blob storage. The blob container is set as private (no anonymous access). Now, I am trying to make a https call from inside the R script to the azure blob storage container in order to download some files. I am using the httr package's GET() function and properly set up the url, authentication etc...The code works in R on my local machine but the same code gives me the

问题 I have a minimal example of a neural network with a back-propagation trainer, testing it on the IRIS data set. I started of with 7 hidden nodes and it worked well. I lowered the number of nodes in the hidden layer to 1 (expecting it to fail), but was surprised to see that the accuracy went up. I set up the experiment in azure ml, just to validate that it wasn't my code. Same thing there, 98.3333% accuracy with a single hidden node. Can anyone explain to me what is happening here? 回答1: First,

问题 I have a minimal example of a neural network with a back-propagation trainer, testing it on the IRIS data set. I started of with 7 hidden nodes and it worked well. I lowered the number of nodes in the hidden layer to 1 (expecting it to fail), but was surprised to see that the accuracy went up. I set up the experiment in azure ml, just to validate that it wasn't my code. Same thing there, 98.3333% accuracy with a single hidden node. Can anyone explain to me what is happening here? 回答1: First,

问题 I am very new to Azure Machine Learning things, one of our client use to sell some fresh products to business people. They have a 'suggested buy' system, a feature will suggest some quantities to buy based on customer's sales history. After client came to know about Microsoft's Azure ML, they want to use that prediction system to suggest quantities to customers. We have sales data with these columns, CustomerName ItemName OrderDate QuantityPurchased QuantitySold We would like customers have

问题 I just wanted to test how good can neural network approximate multiplication function (regression task). I am using Azure Machine Learning Studio. I have 6500 samples, 1 hidden layer (I have tested 5 /30 /100 neurons per hidden layer), no normalization. And default parameters Learning rate - 0.005, Number of learning iterations - 200, The initial learning weigh - 0.1, The momentum - 0 [description]. I got extremely bad accuracy, close to 0. At the same time boosted Decision forest regression

问题 On my Windows 10, I installed Microsoft Azure Bot Framework Emulator from the here (I had used botframework-emulator-setup-4.0.15-alpha.exe from the GitHub link that Microsoft Article provided). But when I run the Emulator, configure a bot and send a text message it says couldn't send (as highlighted in snapshot 3 below: Step 1: Install Emulator Step2: Create a new bot Configuration Step3: Send message 回答1: Install ngrok tunneling. https://github.com/microsoft/botframework-emulator/wiki

问题 Assume I have an Execute R Script that calculates multiple variables, say X and Y. Is it possible to save X as a dataset ds_X and Y as a dataset ds_Y? The problem is that there is only 1 output port available that needs to be mapped to a data.frame. Am I missing an option to add more output ports? Same problem for input ports. I may connect 2 of the "Enter Data Manually" modules to it, but what if I need 3? The current workaround is to put CSV files in a ZIP file and connect that. Are there

问题 Is it possible to import images from your Azure storage account from within a Python script module as opposed to using the Import Images module that Azure ML Studio provides. Ideally I would like to use cv2.imread() . I only want to read in grayscale data but the Import Images module reads in RGB. Can I use the BlockBlobService library as if I were calling it from an external Python script? 回答1: yes, you should be able to do that using Python. At the very least, straight REST calls should

问题 I have an experiment (exp) which is published as a web service (exp [Predictive Exp.]) in the azure machine learning studio, the data used by this experiment was pushed by R using AzureML package library(AzureML) ws <- workspace( id = 'xxxxxxxxx', auth = 'xxxxxxxxxxx' ) upload.dataset(data_for_azure, ws, "data_for_azure") The above thing worked, but lets say I want to update the dataset(same schema just added more rows) I tired this but this does not work: delete.datasets(ws, "data_for_azure"

问题 I'm trying to use pyodbc to import a dataframe in Azure ML Workbench. This works in local runs, but not for docker. It fails when trying to establish a connection to the SQL Server, because the driver is not present. cnxn = pyodbc.connect('DRIVER='{ODBC Driver 13 for SQL Server}';PORT=1433;SERVER='+server+';PORT=1443;DATABASE='+database+';UID='+username+';PWD='+ password) Error Message: pyodbc.Error: ('01000', "[01000] [unixODBC][Driver Manager]Can't open lib 'ODBC Driver 13 for SQL Server' :