Function checkInputData

tfjs-layers/src/engine/training.ts:297–349

* Check inputs provided by the user. * * Porting Note: This corresponds to _standardize_input_data() in Python * Keras. Because of the strong typing in TF.js, we do not need to convert * the data. Specifically: * 1) in PyKeras, `data` can be `DataFrame` instances from pandas, for *

(
    data: Tensor|Tensor[], names: string[], shapes?: Shape[],
    checkBatchAxis = true, exceptionPrefix = '')

 * @throws ValueError: on incorrect number of inputs or mismatches in shapes.
 */
function checkInputData(
    data: Tensor|Tensor[], names: string[], shapes?: Shape[],
    checkBatchAxis = true, exceptionPrefix = '') {
  let arrays: Tensor[];
  if (Array.isArray(data)) {
    if (data.length !== names.length) {
      throw new ValueError(
          `Error when checking model ${exceptionPrefix}: the Array of ` +
          `Tensors that you are passing to your model is not the size the ` +
          `the model expected. Expected to see ${names.length} Tensor(s),` +
          ` but instead got ${data.length} Tensors(s).`);
    }
    arrays = data;
  } else {
    if (names.length > 1) {
      throw new ValueError(
          `The model expects ${names.length} ${exceptionPrefix} Tensors, ` +
          `but only received one Tensor. Found: array with shape ` +
          `${JSON.stringify(data.shape)}.`);
    }
    arrays = [data];
  }

  if (shapes != null) {
    for (let i = 0; i < names.length; ++i) {
      if (shapes[i] == null) {
        continue;
      }
      const array = arrays[i];
      if (array.shape.length !== shapes[i].length) {
        throw new ValueError(
            `Error when checking ${exceptionPrefix}: expected ${names[i]} ` +
            `to have ${shapes[i].length} dimension(s), but got array with ` +
            `shape ${JSON.stringify(array.shape)}`);
      }
      for (let j = 0; j < shapes[i].length; ++j) {
        if (j === 0 && !checkBatchAxis) {
          continue;
        }
        const dim = array.shape[j];
        const refDim = shapes[i][j];
        if (refDim != null) {
          if (refDim !== dim) {
            throw new ValueError(
                `Error when checking ${exceptionPrefix}: expected ` +
                `${names[i]} to have shape ${JSON.stringify(shapes[i])} but ` +
                `got array with shape ${JSON.stringify(array.shape)}.`);
          }
        }
      }
    }
  }
}

/**
 * Maps metric functions to model outputs.