google-cloud-automl

问题 I imported a data set with more than 50 columns, I wanted to test adding and removing columns and then run "train" a few times. How can I remove the columns after importing a dataset? 回答1: I found that we can exclude columns in before the "train" step. 回答2: As May - 2020 when you go to the next step "train", then in the "train" section you can select the columns 来源： https://stackoverflow.com/questions/57859376/exclude-columns-from-dataset-in-gcp-automl-tables

问题 Google Cloud AutoML has python example code for detection, but I have error when importing these modules from google.cloud import automl_v1beta1 from google.cloud.automl_v1beta1.proto import service_pb2 It says cannot import name automl_v1beta1 . I know it's a common problem and there are many solutions on internet but nothing has worked so far. I'm using Windows 10 and run python 2.7 on Anaconda environment. I tried these, but nothing worked : conda install -c conda-forge google-cloud-sdk

问题 Google Cloud AutoML has python example code for detection, but I have error when importing these modules from google.cloud import automl_v1beta1 from google.cloud.automl_v1beta1.proto import service_pb2 It says cannot import name automl_v1beta1 . I know it's a common problem and there are many solutions on internet but nothing has worked so far. I'm using Windows 10 and run python 2.7 on Anaconda environment. I tried these, but nothing worked : conda install -c conda-forge google-cloud-sdk

问题 Google Cloud AutoML has python example code for detection, but I have error when importing these modules from google.cloud import automl_v1beta1 from google.cloud.automl_v1beta1.proto import service_pb2 It says cannot import name automl_v1beta1 . I know it's a common problem and there are many solutions on internet but nothing has worked so far. I'm using Windows 10 and run python 2.7 on Anaconda environment. I tried these, but nothing worked : conda install -c conda-forge google-cloud-sdk

问题 I am trying to use the Google AutoML natural language for training the custom text multi-label classifier. Manually, I could do the dataset creation, import data from Google Storage and train the model. However, I want to automate this entire process. My current approach is as follows: I have created google storage bucket to store the annotated data I spin up the Cloud Function to create the dataset, import data into the dataset and train the model. However, importing data into dataset, takes

问题 I have trying to import dataset into AutoML NL Text Classification. However, the Ui gave me an error of Invalid row in CSV file , Error details: Error detected: "FILE_TYPE_NOT_SUPPORTED" I am uploading the csv file, what should I do? 回答1: Please make sure there is no hidden quotes in your dataset. Complete requirements can be found on “Preparing your training data” page. Common .csv errors: Using Unicode characters in labels. For example, Japanese characters are not supported. Using spaces

问题 I am new to the google AutoML, once I trained a model, I want to see the model details, i.e. feature factors and the related coefficients. Any suggestions? 回答1: Assuming you are talking about an AutoML Vision model (both classification or object detection work similar): You can choose to train an edge model when starting the training. This enables you to download the model afterwards as TensorFlow saved_model.pb . With this, you could then e.g. use Netron to visualize the network. Or you can

问题 I trained a model using AutoML Natural Language on Googles Cloud Platform. Now i need to call the AI via JavaScript to do a sentiment analysis, using my own model. My goal is to embed the whole thing into a website. There i'll have a input form to analyze text. That's all for now actually. I wasn't able to find anything relating that use case when i did my research. Google provides a REST API but there is literally no tutorial on how to handle it. Sorry, I know this might not meet the

问题 I am running the following in a browser: INDEX.HTML (BODY) <script src="https://unpkg.com/@tensorflow/tfjs"></script> <script src="https://unpkg.com/@tensorflow/tfjs-automl"></script> <img id="daisy" crossorigin="anonymous" src="https://storage.googleapis.com/tfjs-testing/tfjs-automl/img_classification/daisy.jpg" /> <script> async function run() { const model = await tf.automl.loadImageClassification("model.json"); const image = document.getElementById("daisy"); const predictions = await

问题 I did lots of search, but I cannot understand what the difference between google ai platform and ml engine . It seems that both of them can be used for training and deploying models. Other words like google-cloud-automl, google ai hub are also very confusing. What are the differences between them? Thanks 回答1: The short answer is: there isn't. In 2019 "ML Engine" was renamed to "AI Platform" and in time some services changed and expanded. To see what has changed, check the release notes,