Method predict

tfjs-node/src/saved_model.ts:292–343

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

(inputs: Tensor|Tensor[]|NamedTensorMap, config?: ModelPredictConfig)

   * @doc {heading: 'Models', subheading: 'SavedModel'}
   */
  predict(inputs: Tensor|Tensor[]|NamedTensorMap, config?: ModelPredictConfig):
      Tensor|Tensor[]|NamedTensorMap {
    if (this.disposed) {
      throw new Error('The TFSavedModel has already been deleted!');
    } else {
      let inputTensors: Tensor[] = [];
      if (inputs instanceof Tensor) {
        inputTensors.push(inputs);
        const result = this.backend.runSavedModel(
            this.sessionId, inputTensors, Object.values(this.signature.inputs),
            Object.values(this.outputNodeNames));
        return result.length > 1 ? result : result[0];
      } else if (Array.isArray(inputs)) {
        inputTensors = inputs;
        return this.backend.runSavedModel(
            this.sessionId, inputTensors, Object.values(this.signature.inputs),
            Object.values(this.outputNodeNames));
      } else {
        const inputTensorNames = Object.keys(this.signature.inputs);
        const providedInputNames = Object.keys(inputs);
        if (!stringArraysHaveSameElements(
                inputTensorNames, providedInputNames)) {
          throw new Error(`The model signatureDef input names are ${
              inputTensorNames.join()}, however the provided input names are ${
              providedInputNames.join()}.`);
        }
        const inputNodeNamesArray: ModelTensorInfo[] = [];
        for (let i = 0; i < inputTensorNames.length; i++) {
          inputTensors.push(inputs[inputTensorNames[i]]);
          inputNodeNamesArray.push(this.signature.inputs[inputTensorNames[i]]);
        }
        const outputTensorNames = Object.keys(this.outputNodeNames);
        const outputNodeNamesArray = [];
        for (let i = 0; i < outputTensorNames.length; i++) {
          outputNodeNamesArray.push(this.outputNodeNames[outputTensorNames[i]]);
        }
        const outputTensors = this.backend.runSavedModel(
            this.sessionId, inputTensors, inputNodeNamesArray,
            outputNodeNamesArray);
        util.assert(
            outputTensors.length === outputNodeNamesArray.length,
            () => 'Output tensors do not match output node names, ' +
                `receive ${outputTensors.length}) output tensors but ` +
                `there are ${this.outputNodeNames.length} output nodes.`);
        const outputMap: NamedTensorMap = {};
        for (let i = 0; i < outputTensorNames.length; i++) {
          outputMap[outputTensorNames[i]] = outputTensors[i];
        }
        return outputMap;
      }
    }
  }

  /**
   * Execute the inference for the input tensors and return activation