@tensorflow/tfjs-converter

constructor

constructor(modelUrl: {}, loadOptions?: io.LoadOptions, tfio?: any);

• Parameter modelUrl

  url for the model, or an io.IOHandler.

  Parameter weightManifestUrl

  url for the weight file generated by scripts/convert.py script.

  Parameter requestOption

  options for Request, which allows to send credentials and custom headers.

  Parameter onProgress

  Optional, progress callback function, fired periodically before the load is completed.

property inputNodes

readonly inputNodes: string[];

property inputs

readonly inputs: TensorInfo[];

property metadata

readonly metadata: {};

property modelSignature

readonly modelSignature: {};

property modelStructuredOutputKeys

readonly modelStructuredOutputKeys: {};

property modelVersion

readonly modelVersion: string;

property outputNodes

readonly outputNodes: string[];

property outputs

readonly outputs: TensorInfo[];

property weights

readonly weights: NamedTensorsMap;

method dispose

dispose: () => void;

• Releases the memory used by the weight tensors and resourceManager.

  {heading: 'Models', subheading: 'Classes'}

method disposeIntermediateTensors

disposeIntermediateTensors: () => void;

• Dispose intermediate tensors for model debugging mode (flag KEEP_INTERMEDIATE_TENSORS is true).

  {heading: 'Models', subheading: 'Classes'}

method execute

execute: (
    inputs: Tensor | Tensor[] | NamedTensorMap,
    outputs?: string | string[]
) => Tensor | Tensor[];

• Executes inference for the model for given input tensors.

  Parameter inputs

  tensor, tensor array or tensor map of the inputs for the model, keyed by the input node names.

  Parameter outputs

  output node name from the TensorFlow model, if no outputs are specified, the default outputs of the model would be used. You can inspect intermediate nodes of the model by adding them to the outputs array.

  Returns

  A single tensor if provided with a single output or no outputs are provided and there is only one default output, otherwise return a tensor array. The order of the tensor array is the same as the outputs if provided, otherwise the order of outputNodes attribute of the model.

  {heading: 'Models', subheading: 'Classes'}

method executeAsync

executeAsync: (
    inputs: Tensor | Tensor[] | NamedTensorMap,
    outputs?: string | string[]
) => Promise<Tensor | Tensor[]>;

• Executes inference for the model for given input tensors in async fashion, use this method when your model contains control flow ops.

  Parameter inputs

  tensor, tensor array or tensor map of the inputs for the model, keyed by the input node names.

  Parameter outputs

  output node name from the TensorFlow model, if no outputs are specified, the default outputs of the model would be used. You can inspect intermediate nodes of the model by adding them to the outputs array.

  Returns

  A Promise of single tensor if provided with a single output or no outputs are provided and there is only one default output, otherwise return a tensor map.

  {heading: 'Models', subheading: 'Classes'}

method getIntermediateTensors

getIntermediateTensors: () => NamedTensorsMap;

• Get intermediate tensors for model debugging mode (flag KEEP_INTERMEDIATE_TENSORS is true).

  {heading: 'Models', subheading: 'Classes'}

method load

load: () => UrlIOHandler<ModelURL> extends io.IOHandlerSync
    ? boolean
    : Promise<boolean>;

• Loads the model and weight files, construct the in memory weight map and compile the inference graph.

method loadSync

loadSync: (artifacts: io.ModelArtifacts) => boolean;

• Synchronously construct the in memory weight map and compile the inference graph.

  {heading: 'Models', subheading: 'Classes', ignoreCI: true}

method predict

predict: (
    inputs: Tensor | Tensor[] | NamedTensorMap,
    config?: ModelPredictConfig
) => Tensor | Tensor[] | NamedTensorMap;

• Execute the inference for the input tensors.

  Parameter input

  The input tensors, when there is single input for the model, inputs param should be a tf.Tensor. For models with multiple inputs, inputs params should be in either tf.Tensor[] if the input order is fixed, or otherwise NamedTensorMap format.

  For model with multiple inputs, we recommend you use NamedTensorMap as the input type, if you use tf.Tensor[], the order of the array needs to follow the order of inputNodes array.

  Parameter config

  Prediction configuration for specifying the batch size. Currently the batch size option is ignored for graph model.

  Returns

  Inference result tensors. If the model is converted and it originally had structured_outputs in tensorflow, then a NamedTensorMap will be returned matching the structured_outputs. If no structured_outputs are present, the output will be single tf.Tensor if the model has single output node, otherwise Tensor[].

  {heading: 'Models', subheading: 'Classes'}

  See Also

  • GraphModel.inputNodes

    You can also feed any intermediate nodes using the NamedTensorMap as the input type. For example, given the graph InputNode => Intermediate => OutputNode, you can execute the subgraph Intermediate => OutputNode by calling model.execute('IntermediateNode' : tf.tensor(...));

    This is useful for models that uses tf.dynamic_rnn, where the intermediate state needs to be fed manually.

    For batch inference execution, the tensors for each input need to be concatenated together. For example with mobilenet, the required input shape is [1, 244, 244, 3], which represents the [batch, height, width, channel]. If we are provide a batched data of 100 images, the input tensor should be in the shape of [100, 244, 244, 3].

method predictAsync

predictAsync: (
    inputs: Tensor | Tensor[] | NamedTensorMap,
    config?: ModelPredictConfig
) => Promise<Tensor | Tensor[] | NamedTensorMap>;

• Execute the inference for the input tensors in async fashion, use this method when your model contains control flow ops.

  Parameter input

  The input tensors, when there is single input for the model, inputs param should be a tf.Tensor. For models with mutliple inputs, inputs params should be in either tf.Tensor[] if the input order is fixed, or otherwise NamedTensorMap format.

  For model with multiple inputs, we recommend you use NamedTensorMap as the input type, if you use tf.Tensor[], the order of the array needs to follow the order of inputNodes array.

  Parameter config

  Prediction configuration for specifying the batch size. Currently the batch size option is ignored for graph model.

  Returns

  A Promise of inference result tensors. If the model is converted and it originally had structured_outputs in tensorflow, then a NamedTensorMap will be returned matching the structured_outputs. If no structured_outputs are present, the output will be single tf.Tensor if the model has single output node, otherwise Tensor[].

  {heading: 'Models', subheading: 'Classes'}

  See Also

  • GraphModel.inputNodes

    You can also feed any intermediate nodes using the NamedTensorMap as the input type. For example, given the graph InputNode => Intermediate => OutputNode, you can execute the subgraph Intermediate => OutputNode by calling model.execute('IntermediateNode' : tf.tensor(...));

    This is useful for models that uses tf.dynamic_rnn, where the intermediate state needs to be fed manually.

    For batch inference execution, the tensors for each input need to be concatenated together. For example with mobilenet, the required input shape is [1, 244, 244, 3], which represents the [batch, height, width, channel]. If we are provide a batched data of 100 images, the input tensor should be in the shape of [100, 244, 244, 3].

method save

save: (
    handlerOrURL: io.IOHandler | string,
    config?: io.SaveConfig
) => Promise<io.SaveResult>;

• Save the configuration and/or weights of the GraphModel.

  An IOHandler is an object that has a save method of the proper signature defined. The save method manages the storing or transmission of serialized data ("artifacts") that represent the model's topology and weights onto or via a specific medium, such as file downloads, local storage, IndexedDB in the web browser and HTTP requests to a server. TensorFlow.js provides IOHandler implementations for a number of frequently used saving mediums, such as tf.io.browserDownloads and tf.io.browserLocalStorage. See tf.io for more details.

  This method also allows you to refer to certain types of IOHandlers as URL-like string shortcuts, such as 'localstorage://' and 'indexeddb://'.

  Example 1: Save model's topology and weights to browser [local storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage); then load it back.

  const modelUrl =
     'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
  const model = await tf.loadGraphModel(modelUrl);
  const zeros = tf.zeros([1, 224, 224, 3]);
  model.predict(zeros).print();
  
  const saveResults = await model.save('localstorage://my-model-1');
  
  const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');
  console.log('Prediction from loaded model:');
  model.predict(zeros).print();

  Parameter handlerOrURL

  An instance of IOHandler or a URL-like, scheme-based string shortcut for IOHandler.

  Parameter config

  Options for saving the model.

  Returns

  A Promise of SaveResult, which summarizes the result of the saving, such as byte sizes of the saved artifacts for the model's topology and weight values.

  {heading: 'Models', subheading: 'Classes', ignoreCI: true}

property attrs

attrs: {
    [key: string]: ValueType;
};

property inputs

inputs: Tensor[];

property b

b?: boolean | null;

• AttrValue b

property f

f?: number | null;

• AttrValue f

property func

func?: INameAttrList | null;

• AttrValue func

property i

i?: number | string | null;

• AttrValue i

property list

list?: AttrValue.IListValue | null;

• AttrValue list

property placeholder

placeholder?: string | null;

• AttrValue placeholder

property s

s?: string | null;

• AttrValue s

property shape

shape?: ITensorShape | null;

• AttrValue shape

property tensor

tensor?: ITensor | null;

• AttrValue tensor

property type

type?: DataType | null;

• AttrValue type

property attr

attr?: {
    [k: string]: IAttrValue;
} | null;

• NameAttrList attr

property name

name?: string | null;

• NameAttrList name

property attr

attr?: {
    [k: string]: IAttrValue;
} | null;

• NodeDef attr

property device

device?: string | null;

• NodeDef device

property input

input?: string[] | null;

• NodeDef input

property name

name?: string | null;

• NodeDef name

property op

op?: string | null;

• NodeDef op

property boolVal

boolVal?: boolean[] | null;

• Tensor boolVal

property doubleVal

doubleVal?: number[] | null;

• Tensor doubleVal

property dtype

dtype?: DataType | null;

• Tensor dtype

property floatVal

floatVal?: number[] | null;

• Tensor floatVal

property int64Val

int64Val?: (number | string)[] | null;

• Tensor int64Val

property intVal

intVal?: number[] | null;

• Tensor intVal

property scomplexVal

scomplexVal?: number[] | null;

• Tensor scomplexVal

property stringVal

stringVal?: Uint8Array[] | null;

• Tensor stringVal

property tensorContent

tensorContent?: Uint8Array | null;

• Tensor tensorContent

property tensorShape

tensorShape?: ITensorShape | null;

• Tensor tensorShape

property uint32Val

uint32Val?: number[] | null;

• Tensor uint32Val

property uint64Val

uint64Val?: (number | string)[] | null;

• Tensor uint64Val

property versionNumber

versionNumber?: number | null;

• Tensor versionNumber

property dim

dim?: TensorShape.IDim[] | null;

• TensorShape dim

property unknownRank

unknownRank?: boolean | null;

• TensorShape unknownRank

call signature

(node: GraphNode): Tensor | Tensor[] | Promise<Tensor | Tensor[]>;