Function modelFromJSON

tfjs-layers/src/models.ts:62–103

(
    modelAndWeightsConfig: ModelAndWeightsConfig|PyJsonDict,
    customObjects?: serialization.ConfigDict)

 * @returns A TensorFlow.js Layers `tf.LayersModel` instance (uncompiled).
 */
export async function modelFromJSON(
    modelAndWeightsConfig: ModelAndWeightsConfig|PyJsonDict,
    customObjects?: serialization.ConfigDict): Promise<LayersModel> {
  if (!('modelTopology' in modelAndWeightsConfig)) {
    modelAndWeightsConfig = {modelTopology: modelAndWeightsConfig};
  }
  modelAndWeightsConfig = modelAndWeightsConfig as ModelAndWeightsConfig;

  let modelTopology = modelAndWeightsConfig.modelTopology;
  if (modelTopology['model_config'] != null) {
    // If the model-topology JSON contains a 'model_config' field, then it is
    // a full model JSON (e.g., from `keras.Model.save()`), which contains
    // not only the model's architecture in its 'model_config' field, but
    // additional information such as the model's optimizer. We use only the
    // 'model_config' field currently.
    modelTopology = modelTopology['model_config'] as PyJsonDict;
  }
  const tsConfig =
      convertPythonicToTs(modelTopology) as serialization.ConfigDict;
  const model = deserialize(tsConfig, customObjects) as LayersModel;

  if (modelAndWeightsConfig.weightsManifest != null) {
    // Load the weight values keyed by the original tensor names in the model
    // file that was loaded.  These should match the keys of the weight
    // manifest.
    const weightValues = await io.loadWeights(
        modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix,
        model.weights.map(weight => weight.originalName));

    // Map the weights to the unique tensor names generated during model loading
    const uniqueWeightValues: NamedTensorMap = {};
    for (const weight of model.weights) {
      uniqueWeightValues[weight.originalName] =
          weightValues[weight.originalName];
    }

    model.loadWeights(uniqueWeightValues);
    // Dispose temporary weight values.
    dispose(weightValues);
  }
  return model;
}

/**
 * Options for loading a saved mode in TensorFlow.js format.