Class Tensor

tfjs-core/src/tensor.ts:257–505

 * @doc {heading: 'Tensors', subheading: 'Classes'}
 */
export class Tensor<R extends Rank = Rank> implements TensorInfo {
  /** Unique id of this tensor. */
  readonly id: number;
  /**
   * Id of the bucket holding the data for this tensor. Multiple arrays can
   * point to the same bucket (e.g. when calling array.reshape()).
   */
  dataId: DataId;
  /** The shape of the tensor. */
  readonly shape: ShapeMap[R];
  /** Number of elements in the tensor. */
  readonly size: number;
  /** The data type for the array. */
  readonly dtype: DataType;
  /** The rank type for the array (see `Rank` enum). */
  readonly rankType: R;

  /** Whether this tensor has been globally kept. */
  kept = false;
  /** The id of the scope this tensor is being tracked in. */
  scopeId: number;
  /** The keras mask that some keras layers attach to the tensor */
  kerasMask?: Tensor;

  /**
   * Number of elements to skip in each dimension when indexing. See
   * https://docs.scipy.org/doc/numpy/reference/generated/\
   * numpy.ndarray.strides.html
   */
  readonly strides: number[];

  constructor(shape: ShapeMap[R], dtype: DataType, dataId: DataId, id: number) {
    this.shape = shape.slice() as ShapeMap[R];
    this.dtype = dtype || 'float32';
    this.size = util.sizeFromShape(shape);
    this.strides = computeStrides(shape);
    this.dataId = dataId;
    this.id = id;
    this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher') as R;
  }

  get rank(): number {
    return this.shape.length;
  }

  /**
   * Returns a promise of `tf.TensorBuffer` that holds the underlying data.
   *
   * @doc {heading: 'Tensors', subheading: 'Classes'}
   */
  async buffer<D extends DataType = 'float32'>(): Promise<TensorBuffer<R, D>> {
    const vals = await this.data<D>();
    return opHandler.buffer(this.shape, this.dtype as D, vals);
  }

  /**
   * Returns a `tf.TensorBuffer` that holds the underlying data.
   * @doc {heading: 'Tensors', subheading: 'Classes'}