Method execute

tfjs-layers/src/engine/training.ts:936–977

* Execute internal tensors of the model with input data feed. * @param inputs Input data feed. Must match the inputs of the model. * @param outputs Names of the output tensors to be fetched. Must match * names of the SymbolicTensors that belong to the graph. * @returns Fetched values f

(inputs: Tensor|Tensor[]|NamedTensorMap, outputs: string|string[])

   * @returns Fetched values for `outputs`.
   */
  execute(inputs: Tensor|Tensor[]|NamedTensorMap, outputs: string|string[]):
      Tensor|Tensor[] {
    if (Array.isArray(outputs) && outputs.length === 0) {
      throw new ValueError(
          '`outputs` is an empty Array, which is not allowed.');
    }

    const outputsIsArray = Array.isArray(outputs);
    const outputNames =
        (outputsIsArray ? outputs : [outputs]);
    const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);

    // Format the input into a FeedDict.
    const feedDict = new FeedDict();
    if (inputs instanceof Tensor) {
      inputs = [inputs];
    }
    if (Array.isArray(inputs)) {
      if (inputs.length !== this.inputs.length) {
        throw new ValueError(
            `The number of inputs provided (${inputs.length}) ` +
            `does not match the number of inputs of this model ` +
            `(${this.inputs.length}).`);
      }
      for (let i = 0; i < this.inputs.length; ++i) {
        feedDict.add(this.inputs[i], inputs[i]);
      }
    } else {
      for (const input of this.inputs) {
        const tensorValue = inputs[input.name];
        if (tensorValue == null) {
          throw new ValueError(
              `No value is provided for the model's input ${input.name}`);
        }
        feedDict.add(input, tensorValue);
      }
    }

    // Run execution.
    const executeOutputs = execute(outputSymbolicTensors, feedDict) as Tensor[];
    return outputsIsArray ? executeOutputs : executeOutputs[0];
  }

  /**
   * Retrieve the model's internal symbolic tensors from symbolic-tensor names.