[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間：2025-09-04 (世界標準時間)。"],[],[],null,["# View model architecture\n\nThis page provides information about how to use Cloud Logging to\nview details about a Vertex AI model. Using\nLogging, you see:\n\n- The hyperparameters of the final model as key-value pairs.\n- The hyperparameters and object values used during model training and tuning, as well as an objective value.\n\nBy default, logs are deleted after 30 days.\n\nThe following topics are covered:\n\n1. [Viewing training logs](#training-logs).\n2. [Log fields](#log-fields).\n\n| **Note:** Model architecture logs are provided as part of the Cloud Logging service. For general information about Cloud Logging, see the [Cloud Logging](/logging/docs) documentation.\n\nBefore you begin\n----------------\n\nBefore you can view the hyperparameter logs for your model, you must\n[train it](/vertex-ai/docs/tabular-data/classification-regression/train-model).\n\nTo perform this task, you must have the following\n[permissions](/iam/docs/overview#permissions):\n\n- `logging.logServiceIndexes.list` on the project\n- `logging.logServices.list` on the project\n\nViewing training logs\n---------------------\n\nYou can use the Google Cloud console to access the hyperparameter logs of the\nfinal model and the hyperparameter logs of the tuning trials.\n\n1. In the Google Cloud console, go to the Vertex AI **Models** page.\n\n [Go to Models page](https://console.cloud.google.com/vertex-ai/models)\n2. In the **Region** drop-down, select the region where your model is located.\n\n3. From the list of models, select your model.\n\n4. Select your model's version number.\n\n5. Open the **Version Details** tab.\n\n6. To see the hyperparameter log of the final model, go to the **Model hyperparameters** row and click **Model**.\n\n 1. There is just one log entry. Expand the payload as shown below.\n For details, see [Log fields](#reading-logs).\n\n7. To see the hyperparameter log of the tuning trials, go to the **Model hyperparameters** row and click **Trials**.\n\n 1. There is one entry for each of the tuning trials. Expand the payload as\n shown below. For details, see [Log fields](#reading-logs).\n\nLog fields\n----------\n\nActivity logs are structured as described in the\n[LogEntry](/logging/docs/exported_logs#the_logentry_type) type\ndocumentation.\n\nVertex AI model logs have, among other fields:\n\n- `labels`: The `log_type` field is set to `automl_tables`.\n- `jsonPayload`: The specific details of the log entry, provided in JSON object format. For details, see [Payload contents for the hyperparameter log of the final model](#final-payload) or [Payload contents for the hyperparameter log of a tuning trial](#trial-payload).\n- `timestamp`: The date and time when the model was created or the trial was run.\n\n### Payload contents for the hyperparameter log of the final model\n\nThe `jsonPayload` field for the hyperparameter log of the final model contains a\n`modelParameters` field. This field contains one entry for each model that\ncontributes to the final ensemble model. Each entry has a `hyperparameters`\nfield, whose contents depend on the model type. For details, see [List of hyperparameters](#hps).\n\n### Payload contents for the hyperparameter log of a tuning trial\n\nThe `jsonPayload` field for the hyperparameter log of a tuning trial contains the following fields:\n\n### List of hyperparameters\n\nThe hyperparameter data provided in the logs differ for each type of\nmodel. The following sections describe the hyperparameters for each\nmodel type.\n\n#### Gradient boosted decision tree models\n\n- Tree L1 regularization\n- Tree L2 regularization\n- Max tree depth\n- Model type: `GBDT`\n- Number of trees\n- Tree complexity\n\n#### Feedforward neural network models\n\n- Dropout rate\n- Enable batchNorm (`True` or `False`)\n- Enable embedding L1 (`True` or `False`)\n- Enable embedding L2 (`True` or `False`)\n- Enable L1 (`True` or `False`)\n- Enable L2 (`True` or `False`)\n- Enable layerNorm (`True` or `False`)\n- Enable numerical embedding (`True` or `False`)\n- Hidden layer size\n- Model type: `nn`\n- Normalize numerical column (`True` or `False`)\n- Number of cross layers\n- Number of hidden layers\n- Skip connections type (`dense`, `disable`, `concat`, or `slice_or_padding`)\n\nWhat's next\n-----------\n\nOnce you're ready to make predictions with your classification or regression\nmodel, you have two options:\n\n- [Make online (real-time) predictions using your model](/vertex-ai/docs/tabular-data/classification-regression/get-online-predictions).\n- [Get batch predictions directly from your model](/vertex-ai/docs/tabular-data/classification-regression/get-batch-predictions).\n\nAdditionally, you can:\n\n- [Evaluate your model](/vertex-ai/docs/tabular-data/classification-regression/evaluate-model).\n- [Review general information about Cloud Logging](/logging/docs).\n- You can export your logs to BigQuery, Cloud Storage, or Pub/Sub. Read [Route logs to supported destinations](/logging/docs/export/configure_export_v2) in the Logging documentation to learn how to export activity logs."]]
