Method compile

tfjs-layers/src/engine/training.ts:583–785

* Configures and prepares the model for training and evaluation. Compiling * outfits the model with an optimizer, loss, and/or metrics. Calling `fit` * or `evaluate` on an un-compiled model will throw an error. * * @param args a `ModelCompileArgs` specifying the loss, optimizer, and

(args: ModelCompileArgs)

   * @doc {heading: 'Models', subheading: 'Classes'}
   */
  compile(args: ModelCompileArgs): void {
    if (args.loss == null) {
      args.loss = [];
    }
    this.loss = args.loss;

    if (typeof args.optimizer === 'string') {
      this.optimizer_ = optimizers.getOptimizer(args.optimizer);
      this.isOptimizerOwned = true;
    } else {
      if (!(args.optimizer instanceof Optimizer)) {
        throw new ValueError(
            `User-defined optimizer must be an instance of tf.Optimizer.`);
      }
      this.optimizer_ = args.optimizer;
      this.isOptimizerOwned = false;
    }

    // TODO(cais): Add lossWeights.
    // TODO(cais): Add sampleWeightMode.

    // Prepare loss functions.
    let lossFunctions: LossOrMetricFn[] = [];
    if (!Array.isArray(args.loss) && typeof args.loss !== 'string' &&
        typeof args.loss !== 'function') {
      args.loss = args.loss as {[outputName: string]: string};
      for (const name in args.loss) {
        if (this.outputNames.indexOf(name) === -1) {
          throw new ValueError(
              `Unknown entry in loss dictionary: "${name}". ` +
              `Only expected the following keys: ${this.outputNames}`);
        }
      }
      for (const name of this.outputNames) {
        if (args.loss[name] == null) {
          console.warn(
              `Output "${name}" is missing from loss dictionary. We assume ` +
              `this was done on purpose, and we will not be expecting data ` +
              `to be passed to ${name} during training`);
        }
        lossFunctions.push(losses.get(args.loss[name]));
      }
    } else if (Array.isArray(args.loss)) {
      if (args.loss.length !== this.outputs.length) {
        throw new ValueError(
            `When passing an Array as loss, it should have one entry per ` +
            `model output. The model has ${this.outputs.length} output(s), ` +
            `but you passed loss=${args.loss}.`);
      }
      const theLosses = args.loss as Array<string|LossOrMetricFn>;
      lossFunctions = theLosses.map(l => losses.get(l));
    } else {
      const lossFunction = losses.get(args.loss);
      this.outputs.forEach(_ => {
        lossFunctions.push(lossFunction);
      });
    }