Method makeTestFunction

tfjs-layers/src/engine/training.ts:1389–1428

* Create a function which, when invoked with an array of `tf.Tensor`s as a * batch of inputs, returns the prespecified loss and metrics of the model * under the batch of input data.

()

   * under the batch of input data.
   */
  private makeTestFunction() {
    this.testFunction = (data: Tensor[]) => {
      return tfc.tidy(() => {
        const valOutputs: Scalar[] = [];
        let totalLoss: Scalar;
        const inputs = data.slice(0, this.inputs.length);
        const targets = data.slice(
            this.inputs.length, this.inputs.length + this.outputs.length);
        const feeds = [];
        for (let i = 0; i < this.inputs.length; ++i) {
          feeds.push({key: this.inputs[i], value: inputs[i]});
        }
        const feedDict = new FeedDict(feeds);
        const outputs = execute(this.outputs, feedDict) as Tensor[];
        // Compute total loss.
        for (let i = 0; i < this.lossFunctions.length; ++i) {
          const lossFunction = this.lossFunctions[i];
          // TODO(cais): Add sample weighting and replace the simple
          // averaging.
          const loss: Scalar = tfc.mean(lossFunction(targets[i], outputs[i]));
          if (i === 0) {
            totalLoss = loss;
          } else {
            totalLoss = tfc.add(totalLoss, loss);
          }
          valOutputs.push(totalLoss);
        }
        // Compute the metrics.
        for (let i = 0; i < this.metricsTensors.length; ++i) {
          const metric = this.metricsTensors[i][0];
          const outputIndex = this.metricsTensors[i][1];
          // TODO(cais): Replace K.mean() with a proper weighting function.
          const meanMetric =
              tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));
          valOutputs.push(meanMetric as Scalar);
        }
        return valOutputs;
      });
    };
  }

  /**
   * Trains the model for a fixed number of epochs (iterations on a