使用 get_model_evaluation 方法获取用于图片分类的模型评估。
深入探索
如需查看包含此代码示例的详细文档，请参阅以下内容：
代码示例
Java
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
import com.google.cloud.aiplatform.v1.ModelEvaluation;
import com.google.cloud.aiplatform.v1.ModelEvaluationName;
import com.google.cloud.aiplatform.v1.ModelServiceClient;
import com.google.cloud.aiplatform.v1.ModelServiceSettings;
import java.io.IOException;
public class GetModelEvaluationImageClassificationSample {
public static void main(String[] args) throws IOException {
// TODO(developer): Replace these variables before running the sample.
// To obtain evaluationId run the code block below after setting modelServiceSettings.
//
// try (ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings))
// {
// String location = "us-central1";
// ModelName modelFullId = ModelName.of(project, location, modelId);
// ListModelEvaluationsRequest modelEvaluationsrequest =
// ListModelEvaluationsRequest.newBuilder().setParent(modelFullId.toString()).build();
// for (ModelEvaluation modelEvaluation :
// modelServiceClient.listModelEvaluations(modelEvaluationsrequest).iterateAll()) {
// System.out.format("Model Evaluation Name: %s%n", modelEvaluation.getName());
// }
// }
String project = "YOUR_PROJECT_ID";
String modelId = "YOUR_MODEL_ID";
String evaluationId = "YOUR_EVALUATION_ID";
getModelEvaluationImageClassificationSample(project, modelId, evaluationId);
}
static void getModelEvaluationImageClassificationSample(
String project, String modelId, String evaluationId) throws IOException {
ModelServiceSettings modelServiceSettings =
ModelServiceSettings.newBuilder()
.setEndpoint("us-central1-aiplatform.googleapis.com:443")
.build();
// Initialize client that will be used to send requests. This client only needs to be created
// once, and can be reused for multiple requests. After completing all of your requests, call
// the "close" method on the client to safely clean up any remaining background resources.
try (ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings)) {
String location = "us-central1";
ModelEvaluationName modelEvaluationName =
ModelEvaluationName.of(project, location, modelId, evaluationId);
ModelEvaluation modelEvaluation = modelServiceClient.getModelEvaluation(modelEvaluationName);
System.out.println("Get Model Evaluation Image Classification Response");
System.out.format("Model Name: %s\n", modelEvaluation.getName());
System.out.format("Metrics Schema Uri: %s\n", modelEvaluation.getMetricsSchemaUri());
System.out.format("Metrics: %s\n", modelEvaluation.getMetrics());
System.out.format("Create Time: %s\n", modelEvaluation.getCreateTime());
System.out.format("Slice Dimensions: %s\n", modelEvaluation.getSliceDimensionsList());
}
}
}Node.js
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Node.js 设置说明执行操作。如需了解详情，请参阅 Vertex AI Node.js API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
/**
* TODO(developer): Uncomment these variables before running the sample
* (not necessary if passing values as arguments). To obtain evaluationId,
* instantiate the client and run the following the commands.
*/
// const parentName = `projects/${project}/locations/${location}/models/${modelId}`;
// const evalRequest = {
// parent: parentName
// };
// const [evalResponse] = await modelServiceClient.listModelEvaluations(evalRequest);
// console.log(evalResponse);
// const modelId = 'YOUR_MODEL_ID';
// const evaluationId = 'YOUR_EVALUATION_ID';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
// Imports the Google Cloud Model Service Client library
const {ModelServiceClient} = require('@google-cloud/aiplatform');
// Specifies the location of the api endpoint
const clientOptions = {
apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};
// Instantiates a client
const modelServiceClient = new ModelServiceClient(clientOptions);
async function getModelEvaluationImageClassification() {
// Configure the name resources
const name = `projects/${project}/locations/${location}/models/${modelId}/evaluations/${evaluationId}`;
const request = {
name,
};
// Create get model evaluation request
const [response] = await modelServiceClient.getModelEvaluation(request);
console.log('Get model evaluation image classification response');
console.log(`\tName : ${response.name}`);
console.log(`\tMetrics schema uri : ${response.metricsSchemaUri}`);
console.log(`\tMetrics : ${JSON.stringify(response.metrics)}`);
console.log(`\tCreate time : ${JSON.stringify(response.createTime)}`);
console.log(`\tSlice dimensions : ${response.sliceDimensions}`);
const modelExplanation = response.modelExplanation;
if (modelExplanation === null) {
console.log(`\tModel explanation: ${JSON.stringify(modelExplanation)}`);
} else {
const meanAttributions = modelExplanation.meanAttributions;
for (const meanAttribution of meanAttributions) {
console.log('\t\tMean attribution');
console.log(
`\t\t\tBaseline output value : \
${meanAttribution.baselineOutputValue}`
);
console.log(
`\t\t\tInstance output value : \
${meanAttribution.instanceOutputValue}`
);
console.log(
`\t\t\tFeature attributions : \
${JSON.stringify(meanAttribution.featureAttributions)}`
);
console.log(`\t\t\tOutput index : ${meanAttribution.outputIndex}`);
console.log(
`\t\t\tOutput display name : \
${meanAttribution.outputDisplayName}`
);
console.log(
`\t\t\tApproximation error : \
${meanAttribution.approximationError}`
);
}
}
}
getModelEvaluationImageClassification();Python
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Python 设置说明执行操作。如需了解详情，请参阅 Vertex AI Python API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
from google.cloud import aiplatform
def get_model_evaluation_image_classification_sample(
project: str,
model_id: str,
evaluation_id: str,
location: str = "us-central1",
api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
"""
To obtain evaluation_id run the following commands where LOCATION
is the region where the model is stored, PROJECT is the project ID,
and MODEL_ID is the ID of your model.
model_client = aiplatform.gapic.ModelServiceClient(
client_options={
'api_endpoint':'LOCATION-aiplatform.googleapis.com'
}
)
evaluations = model_client.list_model_evaluations(parent='projects/PROJECT/locations/LOCATION/models/MODEL_ID')
print("evaluations:", evaluations)
"""
# The AI Platform services require regional API endpoints.
client_options = {"api_endpoint": api_endpoint}
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform.gapic.ModelServiceClient(client_options=client_options)
name = client.model_evaluation_path(
project=project, location=location, model=model_id, evaluation=evaluation_id
)
response = client.get_model_evaluation(name=name)
print("response:", response)
后续步骤
如需搜索和过滤其他 Google Cloud 产品的代码示例，请参阅 Google Cloud 示例浏览器。