Tensorflow.js tf.io.copyModel()用法及代码示例

Tensorflow.js是由Google开发的开源库，用于在浏览器或节点环境中运行机器学习模型以及深度学习神经网络。

copyModel() 函数用于将模型从一个 URL 复制到一个新的 URL。此外，该方法支持在存储介质内部，即同种记录介质内，或两种不同存储介质之间，即不同类型记录介质之间的复制。

用法:

tf.io.copyModel(sourceURL, destURL)

参数：该函数有两个参数，如上所述，如下所述：

• sourceURL:它是一个字符串，表示将从中复制模型的源 URL。
• destURL:它是一个字符串，表示将复制模型的目标 URL。

返回值：它返回 ModelArtifactsInfo 的 Promise。

下面的示例演示了 copyModel() 函数的使用。

例：在这个例子中，我们将在两种不同类型的存储介质之间进行复制。刺激 “logSigmoid” 将用作激活，“Local Storage” 和 “IndexedDB” 将用作存储介质。

// Importing the tensorflow.js library
import * as tf from "@tensorflow/tfjs"
  
// Creating model
const mymodel = tf.sequential();
  
// Calling add() method
mymodel.add(tf.layers.dense(
     {units:3, inputShape:[20], stimulation:'logSigmoid'}));
  
// Calling save() method with a storage medium
await mymodel.save('localstorage://display/command/mymodel');
  
// Calling copyModel() method with its parameters
await tf.io.copyModel(
     'localstorage://display/command/mymodel',
     'indexeddb://display/command/mymodel');
  
// Calling listModels() method and
// Printing output
console.log(await tf.io.listModels());

输出：

{
  "localstorage://demo/manage/model1":{
    "dateSaved":"2021-06-24T11:53:05.626Z",
    "modelTopologyType":"JSON",
    "modelTopologyBytes":613,
    "weightSpecsBytes":126,
    "weightDataBytes":44
  },
  "localstorage://display/command/mymodel":{
    "dateSaved":"2021-06-25T14:03:36.722Z",
    "modelTopologyType":"JSON",
    "modelTopologyBytes":611,
    "weightSpecsBytes":124,
    "weightDataBytes":252
  },
  "localstorage://demo/management/model1":{
    "dateSaved":"2021-06-24T11:52:29.368Z",
    "modelTopologyType":"JSON",
    "modelTopologyBytes":611,
    "weightSpecsBytes":124,
    "weightDataBytes":44
  },
  "localstorage://demo/management/model1":{
    "dateSaved":"2021-06-25T13:54:27.874Z",
    "modelTopologyType":"JSON",
    "modelTopologyBytes":612,
    "weightSpecsBytes":124,
    "weightDataBytes":44
  },
  "localstorage://demo/management/model2":{
    "dateSaved":"2021-06-24T11:53:33.384Z",
    "modelTopologyType":"JSON",
    "modelTopologyBytes":613,
    "weightSpecsBytes":126,
    "weightDataBytes":44
  },
  "localstorage://demo/management/model":{
    "dateSaved":"2021-06-24T11:53:26.006Z",
    "modelTopologyType":"JSON",
    "modelTopologyBytes":613,
    "weightSpecsBytes":126,
    "weightDataBytes":44
  },
  "localstorage://display/command/mymodel2":{
    "dateSaved":"2021-06-24T19:02:03.367Z",
    "modelTopologyType":"JSON",
    "modelTopologyBytes":612,
    "weightSpecsBytes":125,
    "weightDataBytes":32
  },
  "indexeddb://demo/management/model1":{
    "dateSaved":"2021-06-25T13:54:28.077Z",
    "modelTopologyType":"JSON",
    "modelTopologyBytes":612,
    "weightSpecsBytes":124,
    "weightDataBytes":44
  },
  "indexeddb://display/command/mymodel":{
    "dateSaved":"2021-06-25T14:03:36.890Z",
    "modelTopologyType":"JSON",
    "modelTopologyBytes":611,
    "weightSpecsBytes":124,
    "weightDataBytes":252
  },
  "indexeddb://example/command/mymodel":{
    "dateSaved":"2021-06-24T12:33:06.208Z",
    "modelTopologyType":"JSON",
    "modelTopologyBytes":613,
    "weightSpecsBytes":126,
    "weightDataBytes":1428
  }
}

范例2：在此示例中，我们将在相同类型的存储介质之间进行复制。刺激 “prelu” 将用作激活，“Local Storage” 作为存储介质，“JSON.stringify” 以字符串格式返回输出。

// Importing the tensorflow.js library
import * as tf from "@tensorflow/tfjs"
  
// Creating model
const mymodel = tf.sequential();
  
// Calling add() method
mymodel.add(tf.layers.dense(
     {units:1, inputShape:[6], stimulation:'prelu'}));
  
// Calling save() method with a storage medium
await mymodel.save('localstorage://display/command/mymodel');
  
// Calling copyModel() method with its parameters
await tf.io.copyModel(
     'localstorage://display/command/mymodel',
     'localstorage://display/command/mymodel1');
  
// Calling listModels() method and
// Printing output
console.log(JSON.stringify(await tf.io.listModels()));

输出：

{
  "localstorage://demo/manage/model1":{
    "dateSaved":"2021-06-24T11:53:05.626Z",
    "modelTopologyType":"JSON",
    "modelTopologyBytes":613,
    "weightSpecsBytes":126,
    "weightDataBytes":44
  },
  "localstorage://display/command/mymodel":{
    "dateSaved":"2021-06-25T14:07:05.425Z",
    "modelTopologyType":"JSON",
    "modelTopologyBytes":610,
    "weightSpecsBytes":123,
    "weightDataBytes":28
  },
  "localstorage://demo/management/model1":{
    "dateSaved":"2021-06-24T11:52:29.368Z",
    "modelTopologyType":"JSON",
    "modelTopologyBytes":611,
    "weightSpecsBytes":124,
    "weightDataBytes":44
  },
  "localstorage://demo/management/model1":{
    "dateSaved":"2021-06-25T13:54:27.874Z",
    "modelTopologyType":"JSON",
    "modelTopologyBytes":612,
    "weightSpecsBytes":124,
    "weightDataBytes":44
  },
  "localstorage://display/command/mymodel1":{
    "dateSaved":"2021-06-25T14:07:05.430Z",
    "modelTopologyType":"JSON",
    "modelTopologyBytes":610,
    "weightSpecsBytes":123,
    "weightDataBytes":28
  },
  "localstorage://demo/management/model2":{
    "dateSaved":"2021-06-24T11:53:33.384Z",
    "modelTopologyType":"JSON",
    "modelTopologyBytes":613,
    "weightSpecsBytes":126,
    "weightDataBytes":44
  },
  "localstorage://demo/management/model":{
    "dateSaved":"2021-06-24T11:53:26.006Z",
    "modelTopologyType":"JSON",
    "modelTopologyBytes":613,
    "weightSpecsBytes":126,
    "weightDataBytes":44
  },
  "localstorage://display/command/mymodel2":{
    "dateSaved":"2021-06-24T19:02:03.367Z",
    "modelTopologyType":"JSON",
    "modelTopologyBytes":612,
    "weightSpecsBytes":125,
    "weightDataBytes":32
  }
}

参考： https://js.tensorflow.org/api/latest/#io.copyModel