Method loadTrainingConfig

tfjs-layers/src/engine/training.ts:1991–2029

(trainingConfig: TrainingConfig)

  }

  loadTrainingConfig(trainingConfig: TrainingConfig) {
    if (trainingConfig.weighted_metrics != null) {
      throw new Error('Loading weight_metrics is not supported yet.');
    }
    if (trainingConfig.loss_weights != null) {
      throw new Error('Loading loss_weights is not supported yet.');
    }
    if (trainingConfig.sample_weight_mode != null) {
      throw new Error('Loading sample_weight_mode is not supported yet.');
    }

    const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config) as
        serialization.ConfigDict;
    const optimizer = deserialize(tsConfig) as Optimizer;

    let loss;
    if (typeof trainingConfig.loss === 'string') {
      loss = toCamelCase(trainingConfig.loss);
    } else if (Array.isArray(trainingConfig.loss)) {
      loss = trainingConfig.loss.map(lossEntry => toCamelCase(lossEntry));
    } else if (trainingConfig.loss != null) {
      loss = {} as {[outputName: string]: LossIdentifier};
      for (const key in trainingConfig.loss) {
        loss[key] = toCamelCase(trainingConfig.loss[key]) as LossIdentifier;
      }
    }

    let metrics;
    if (Array.isArray(trainingConfig.metrics)) {
      metrics = trainingConfig.metrics.map(metric => toCamelCase(metric));
    } else if (trainingConfig.metrics != null) {
      metrics = {} as {[outputName: string]: MetricsIdentifier};
      for (const key in trainingConfig.metrics) {
        metrics[key] = toCamelCase(trainingConfig.metrics[key]);
      }
    }

    this.compile({loss, metrics, optimizer});
  }

  /**
   * Save the configuration and/or weights of the LayersModel.