fluxo tensor:: ops:: TakeManySparseFromTensorsMap

#include <sparse_ops.h>

Converte uma representação esparsa em um tensor denso.

Resumo

Constrói um array dense com formato output_shape tal que

Se sparse_indices for escalar

denso[i] = (i == índices_esparsos? valores_esparsos: valor_padrão)

Se sparse_indices for um vetor, então para cada i

denso[índices_esparsos[i]] = valores_esparsos[i]

Se sparse_indices for uma matriz n por d, então para cada i em [0, n)

denso[índices_esparsos[i][0], ..., índices_esparsos[i][d-1]] = valores_esparsos[i]

  

    
All other values in `dense` are set to `default_value`.  If `sparse_values` is a
    scalar, all sparse indices are set to this single value.
    Indices should be sorted in lexicographic order, and indices must not
    contain any repeats. If `validate_indices` is true, these properties
    are checked during execution.
    Arguments:
    * scope: A Scope object
    * sparse_indices: 0-D, 1-D, or 2-D.  `sparse_indices[i]` contains the complete
    index where `sparse_values[i]` will be placed.
    * output_shape: 1-D.  Shape of the dense output tensor.
    * sparse_values: 1-D.  Values corresponding to each row of `sparse_indices`,
    or a scalar value to be used for all sparse indices.
    * default_value: Scalar value to set for indices not specified in
    `sparse_indices`.
    Optional attributes (see `Attrs`):
    * validate_indices: If true, indices are checked to make sure they are sorted in
    lexicographic order and that there are no repeats.
    Returns:
    * `Output`: Dense output tensor of shape `output_shape`. */
class SparseToDense {
 public:
  /// Optional attribute setters for SparseToDense
  struct Attrs {
    /** If true, indices are checked to make sure they are sorted in
        lexicographic order and that there are no repeats.
        Defaults to true */
    TF_MUST_USE_RESULT Attrs ValidateIndices(bool x) {
      Attrs ret = *this;
      ret.validate_indices_ = x;
      return ret;
    }
    bool validate_indices_ = true;
  };
  SparseToDense(const tensorflow::Scope& scope, tensorflow::Input
              sparse_indices, tensorflow::Input output_shape,
              tensorflow::Input sparse_values, tensorflow::Input
              default_value);
  SparseToDense(const tensorflow::Scope& scope, tensorflow::Input
              sparse_indices, tensorflow::Input output_shape,
              tensorflow::Input sparse_values, tensorflow::Input
              default_value, const SparseToDense::Attrs& attrs);
  operator ::tensorflow::Output() const { return dense; }
  operator ::tensorflow::Input() const { return dense; }
  ::tensorflow::Node* node() const { return dense.node(); }
  static Attrs ValidateIndices(bool x) {
    return Attrs().ValidateIndices(x);
  }
Operation operation;
  tensorflow::Output dense;
};
/** Read `SparseTensors` from a `SparseTensorsMap` and concatenate them.
    The input `sparse_handles` must be an `int64` matrix of shape `[N, 1]` where
    `N` is the minibatch size and the rows correspond to the output handles of
    `AddSparseToTensorsMap` or `AddManySparseToTensorsMap`.  The ranks of the
    original `SparseTensor` objects that went into the given input ops must all
    match.  When the final `SparseTensor` is created, it has rank one
    higher than the ranks of the incoming `SparseTensor` objects
    (they have been concatenated along a new row dimension on the left).
    The output `SparseTensor` object's shape values for all dimensions but the
    first are the max across the input `SparseTensor` objects' shape values
    for the corresponding dimensions.  Its first shape value is `N`, the minibatch
    size.
    The input `SparseTensor` objects' indices are assumed ordered in
    standard lexicographic order.  If this is not the case, after this
    step run `SparseReorder` to restore index ordering.
    For example, if the handles represent an input, which is a `[2, 3]` matrix
    representing two original `SparseTensor` objects:
 index = [ 0] [10] [20] values = [1, 2, 3] shape = [50]