Python sparse_ops.sparse_fill_empty_rows函数代码示例

  def testFillFloat(self):
    with self.test_session(use_gpu=False) as sess:
      values = constant_op.constant(
          [0.0, 10.0, 13.0, 14.0, 32.0, 33.0], dtype=dtypes.float64)
      default_value = constant_op.constant(-1.0, dtype=dtypes.float64)
      sp_input = sparse_tensor.SparseTensorValue(
          indices=np.array([[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]]),
          values=values,
          dense_shape=np.array([5, 6]))
      sp_output, empty_row_indicator = (sparse_ops.sparse_fill_empty_rows(
          sp_input, default_value))
      output, empty_row_indicator_out = sess.run(
          [sp_output, empty_row_indicator])

      self.assertAllEqual(output.indices, [[0, 0], [1, 0], [1, 3], [1, 4],
                                           [2, 0], [3, 2], [3, 3], [4, 0]])
      self.assertAllClose(output.values, [0, 10, 13, 14, -1, 32, 33, -1])
      self.assertAllEqual(output.dense_shape, [5, 6])
      self.assertAllEqual(empty_row_indicator_out,
                          np.array([0, 0, 1, 0, 1]).astype(np.bool))

      values_grad_err = gradient_checker.compute_gradient_error(
          values, values.shape.as_list(), sp_output.values, [8], delta=1e-8)
      self.assertGreater(values_grad_err, 0)
      self.assertLess(values_grad_err, 1e-8)

      default_value_grad_err = gradient_checker.compute_gradient_error(
          default_value,
          default_value.shape.as_list(),
          sp_output.values, [8],
          delta=1e-8)
      self.assertGreater(default_value_grad_err, 0)
      self.assertLess(default_value_grad_err, 1e-8)

    def testNoEmptyRows(self):
        with self.test_session(use_gpu=False) as sess:
            sp_input = self._SparseTensor_2x6()
            sp_output, empty_row_indicator = sparse_ops.sparse_fill_empty_rows(sp_input, -1)

            output, empty_row_indicator_out = sess.run([sp_output, empty_row_indicator])

            self.assertAllEqual(output.indices, [[0, 0], [1, 0], [1, 3], [1, 4]])
            self.assertAllEqual(output.values, [0, 10, 13, 14])
            self.assertAllEqual(output.shape, [2, 6])
            self.assertAllEqual(empty_row_indicator_out, np.zeros(2).astype(np.bool))

    def testFillString(self):
        with self.test_session(use_gpu=False) as sess:
            sp_input = self._SparseTensor_String5x6()
            sp_output, empty_row_indicator = sparse_ops.sparse_fill_empty_rows(sp_input, "")

            output, empty_row_indicator_out = sess.run([sp_output, empty_row_indicator])

            self.assertAllEqual(output.indices, [[0, 0], [1, 0], [1, 3], [1, 4], [2, 0], [3, 2], [3, 3], [4, 0]])
            self.assertAllEqual(output.values, ["a", "b", "c", "d", "", "e", "f", ""])
            self.assertAllEqual(output.shape, [5, 6])
            self.assertAllEqual(empty_row_indicator_out, np.array([0, 0, 1, 0, 1]).astype(np.bool))

    def testFillNumber(self):
        with self.test_session(use_gpu=False) as sess:
            sp_input = self._SparseTensor_5x6()
            sp_output, empty_row_indicator = sparse_ops.sparse_fill_empty_rows(sp_input, -1)

            output, empty_row_indicator_out = sess.run([sp_output, empty_row_indicator])

            self.assertAllEqual(output.indices, [[0, 0], [1, 0], [1, 3], [1, 4], [2, 0], [3, 2], [3, 3], [4, 0]])
            self.assertAllEqual(output.values, [0, 10, 13, 14, -1, 32, 33, -1])
            self.assertAllEqual(output.shape, [5, 6])
            self.assertAllEqual(empty_row_indicator_out, np.array([0, 0, 1, 0, 1]).astype(np.bool))

  def testNoEmptyRows(self):
    with test_util.force_cpu():
      sp_input = self._SparseTensor_2x6()
      sp_output, empty_row_indicator = (
          sparse_ops.sparse_fill_empty_rows(sp_input, -1))

      output, empty_row_indicator_out = self.evaluate(
          [sp_output, empty_row_indicator])

      self.assertAllEqual(output.indices, [[0, 0], [1, 0], [1, 3], [1, 4]])
      self.assertAllEqual(output.values, [0, 10, 13, 14])
      self.assertAllEqual(output.dense_shape, [2, 6])
      self.assertAllEqual(empty_row_indicator_out, np.zeros(2).astype(np.bool))

  def testFillNumber(self):
    with test_util.force_cpu():
      for sp_input in (self._SparseTensorValue_5x6(), self._SparseTensor_5x6()):
        sp_output, empty_row_indicator = (
            sparse_ops.sparse_fill_empty_rows(sp_input, -1))

        output, empty_row_indicator_out = self.evaluate(
            [sp_output, empty_row_indicator])

        self.assertAllEqual(
            output.indices,
            [[0, 0], [1, 0], [1, 3], [1, 4], [2, 0], [3, 2], [3, 3], [4, 0]])
        self.assertAllEqual(output.values, [0, 10, 13, 14, -1, 32, 33, -1])
        self.assertAllEqual(output.dense_shape, [5, 6])
        self.assertAllEqual(empty_row_indicator_out,
                            np.array([0, 0, 1, 0, 1]).astype(np.bool))

  def testFillString(self):
    with test_util.force_cpu():
      sp_input = self._SparseTensor_String5x6()
      sp_output, empty_row_indicator = (
          sparse_ops.sparse_fill_empty_rows(sp_input, ""))

      output, empty_row_indicator_out = self.evaluate(
          [sp_output, empty_row_indicator])

      self.assertAllEqual(
          output.indices,
          [[0, 0], [1, 0], [1, 3], [1, 4], [2, 0], [3, 2], [3, 3], [4, 0]])
      self.assertAllEqual(output.values,
                          [b"a", b"b", b"c", b"d", b"", b"e", b"f", b""])
      self.assertAllEqual(output.dense_shape, [5, 6])
      self.assertAllEqual(empty_row_indicator_out,
                          np.array([0, 0, 1, 0, 1]).astype(np.bool))

def safe_embedding_lookup_sparse(embedding_weights,
                                 sparse_ids,
                                 sparse_weights=None,
                                 combiner=None,
                                 default_id=None,
                                 name=None,
                                 partition_strategy="div",
                                 max_norm=None):
  """Lookup embedding results, accounting for invalid IDs and empty features.

  The partitioned embedding in `embedding_weights` must all be the same shape
  except for the first dimension. The first dimension is allowed to vary as the
  vocabulary size is not necessarily a multiple of `P`.  `embedding_weights`
  may be a `PartitionedVariable` as returned by using `tf.get_variable()` with a
  partitioner.

  Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs
  with non-positive weight. For an entry with no features, the embedding vector
  for `default_id` is returned, or the 0-vector if `default_id` is not supplied.

  The ids and weights may be multi-dimensional. Embeddings are always aggregated
  along the last dimension.

  Args:
    embedding_weights:  A list of `P` float tensors or values representing
        partitioned embedding tensors.  Alternatively, a `PartitionedVariable`,
        created by partitioning along dimension 0.  The total unpartitioned
        shape should be `[e_0, e_1, ..., e_m]`, where `e_0` represents the
        vocab size and `e_1, ..., e_m` are the embedding dimensions.
    sparse_ids: `SparseTensor` of shape `[d_0, d_1, ..., d_n]` containing the
        ids. `d_0` is typically batch size.
    sparse_weights: `SparseTensor` of same shape as `sparse_ids`, containing
        float weights corresponding to `sparse_ids`, or `None` if all weights
        are be assumed to be 1.0.
    combiner: A string specifying how to combine embedding results for each
        entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean"
        the default.
    default_id: The id to use for an entry with no features.
    name: A name for this operation (optional).
    partition_strategy: A string specifying the partitioning strategy.
        Currently `"div"` and `"mod"` are supported. Default is `"div"`.
    max_norm: If not None, all embeddings are l2-normalized to max_norm before
        combining.


  Returns:
    Dense tensor of shape `[d_0, d_1, ..., d_{n-1}, e_1, ..., e_m]`.

  Raises:
    ValueError: if `embedding_weights` is empty.
  """
  if combiner is None:
    logging.warn("The default value of combiner will change from \"mean\" "
                 "to \"sqrtn\" after 2016/11/01.")
    combiner = "mean"
  if embedding_weights is None:
    raise ValueError("Missing embedding_weights %s." % embedding_weights)
  if isinstance(embedding_weights, variables.PartitionedVariable):
    embedding_weights = list(embedding_weights)  # get underlying Variables.
  if not isinstance(embedding_weights, list):
    embedding_weights = [embedding_weights]
  if len(embedding_weights) < 1:
    raise ValueError("Missing embedding_weights %s." % embedding_weights)

  dtype = sparse_weights.dtype if sparse_weights is not None else None
  if isinstance(embedding_weights, variables.PartitionedVariable):
    embedding_weights = list(embedding_weights)
  embedding_weights = [
      ops.convert_to_tensor(w, dtype=dtype) for w in embedding_weights
  ]

  contrib_tensor_util.assert_same_float_dtype(embedding_weights +
                                              [sparse_weights])

  with ops.name_scope(name, "embedding_lookup",
                      embedding_weights + [sparse_ids,
                                           sparse_weights]) as scope:
    # Reshape higher-rank sparse ids and weights to linear segment ids.
    original_shape = sparse_ids.dense_shape
    original_rank_dim = sparse_ids.dense_shape.get_shape()[0]
    original_rank = (
        array_ops.size(original_shape)
        if original_rank_dim.value is None
        else original_rank_dim.value)
    sparse_ids = sparse_ops.sparse_reshape(sparse_ids, [
        math_ops.reduce_prod(
            array_ops.slice(original_shape, [0], [original_rank - 1])),
        array_ops.gather(original_shape, original_rank - 1)])
    if sparse_weights is not None:
      sparse_weights = sparse_tensor.SparseTensor(
          sparse_ids.indices,
          sparse_weights.values, sparse_ids.dense_shape)

    # Prune invalid ids and weights.
    sparse_ids, sparse_weights = _prune_invalid_ids(sparse_ids, sparse_weights)

    # Fill in dummy values for empty features, if necessary.
    sparse_ids, is_row_empty = sparse_ops.sparse_fill_empty_rows(sparse_ids,
                                                                 default_id or
                                                                 0)
#.........这里部分代码省略.........

def scattered_embedding_lookup_sparse(params,
                                      sparse_values,
                                      dimension,
                                      combiner=None,
                                      default_value=None,
                                      name=None,
                                      hash_key=None):
  """Looks up embeddings of a sparse feature using parameter hashing.

  See `tf.contrib.layers.scattered_embedding_lookup` for embedding with hashing.

  Args:
    params: A `Tensor`, `list` of `Tensors`, or `PartitionedVariable`.
      Each tensor must be of rank 1 with fully-defined shape.
    sparse_values: A 2-D `SparseTensor` containing the values to be embedded.
      Some rows may be empty.
    dimension: Embedding dimension
    combiner: A string specifying how to combine embedding results for each
        entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean"
        the default.
    default_value: The value to use for an entry with no features.
    name: An optional name for this op.
    hash_key: Specify the hash_key that will be used by the `FingerprintCat64`
      function to combine the crosses fingerprints on SparseFeatureCrossOp
      (optional).

  Returns:
     Dense tensor with shape [N, dimension] with N the number of rows in
       sparse_values.

  Raises:
    TypeError: If sparse_values is not a SparseTensor.
    ValueError: If combiner is not one of {"mean", "sqrtn", "sum"}.
  """
  if combiner is None:
    logging.warn("The default value of combiner will change from \"mean\" "
                 "to \"sqrtn\" after 2016/11/01.")
    combiner = "mean"
  if isinstance(params, variables.PartitionedVariable):
    params = list(params)
  if not isinstance(params, list):
    params = [params]
  if not isinstance(sparse_values, sparse_tensor.SparseTensor):
    raise TypeError("sparse_values must be SparseTensor")

  with ops.name_scope(name, "scattered_embedding_lookup_sparse",
                      params + [sparse_values]) as scope:
    # Fill in the empty rows.
    if default_value is None:
      # Random default values to reduce the risk of collision.
      if sparse_values.dtype == dtypes.string:
        default_value = "6ZxWzWOHxZ"
      else:
        default_value = 1288896567
    sparse_values, _ = sparse_ops.sparse_fill_empty_rows(
        sparse_values, default_value)

    segment_ids = sparse_values.indices[:, 0]
    if segment_ids.dtype != dtypes.int32:
      segment_ids = math_ops.cast(segment_ids, dtypes.int32)

    values = sparse_values.values
    values, idx = array_ops.unique(values)

    embeddings = scattered_embedding_lookup(
        params, values, dimension, hash_key=hash_key)

    if combiner == "sum":
      embeddings = math_ops.sparse_segment_sum(embeddings, idx, segment_ids,
                                               name=scope)
    elif combiner == "mean":
      embeddings = math_ops.sparse_segment_mean(embeddings, idx, segment_ids,
                                                name=scope)
    elif combiner == "sqrtn":
      embeddings = math_ops.sparse_segment_sqrt_n(embeddings, idx, segment_ids,
                                                  name=scope)
    else:
      raise ValueError("Combiner must be one of 'mean', 'sqrtn' or 'sum'.")

    return embeddings

def hashed_embedding_lookup_sparse(params,
                                   sparse_values,
                                   dimension,
                                   combiner="mean",
                                   default_value=None,
                                   name=None):
  """Looks up embeddings of a sparse feature using parameter hashing.

  See `tf.contrib.layers.hashed_embedding_lookup` for embedding with hashing.

  Args:
    params: A `Tensor` or `list` of `Tensors`.
      Each tensor must be of rank 1 with fully-defined shape.
    sparse_values: A 2-D `SparseTensor` containing the values to be embedded.
      Some rows may be empty.
    dimension: Embedding dimension
    combiner: A string specifying how to combine embedding results for each
        entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean"
        the default.
    default_value: The value to use for an entry with no features.
    name: An optional name for this op.

  Returns:
     Dense tensor with shape [N, dimension] with N the number of rows in
       sparse_values.

  Raises:
    TypeError: If sparse_values is not a SparseTensor.
    ValueError: If combiner is not one of {"mean", "sqrtn", "sum"}.
  """

  if not isinstance(params, list):
    params = [params]
  if not isinstance(sparse_values, ops.SparseTensor):
    raise TypeError("sparse_values must be SparseTensor")

  with ops.name_scope(name, "hashed_sparse_embedding_lookup",
                      params + [sparse_values]) as scope:
    # Fill in the empty rows.
    if default_value is None:
      # Random default values to reduce the risk of collision.
      if sparse_values.dtype == dtypes.string:
        default_value = "6ZxWzWOHxZ"
      else:
        default_value = 1288896567
    sparse_values, _ = sparse_ops.sparse_fill_empty_rows(
        sparse_values, default_value)

    segment_ids = sparse_values.indices[:, 0]
    if segment_ids.dtype != dtypes.int32:
      segment_ids = math_ops.cast(segment_ids, dtypes.int32)

    values = sparse_values.values
    values, idx = array_ops.unique(values)

    embeddings = hashed_embedding_lookup(params, values, dimension)

    if combiner == "sum":
      embeddings = math_ops.sparse_segment_sum(embeddings, idx, segment_ids,
                                               name=scope)
    elif combiner == "mean":
      embeddings = math_ops.sparse_segment_mean(embeddings, idx, segment_ids,
                                                name=scope)
    elif combiner == "sqrtn":
      embeddings = math_ops.sparse_segment_sqrt_n(embeddings, idx, segment_ids,
                                                  name=scope)
    else:
      raise ValueError("Combiner must be one of 'mean', 'sqrtn' or 'sum'.")

    return embeddings

def safe_embedding_lookup_sparse(
    embedding_weights, sparse_ids, sparse_weights=None, combiner="mean",
    default_id=None, name=None, partition_strategy="div"):
  """Lookup embedding results, accounting for invalid IDs and empty features.

  The partitioned embedding in `embedding_weights` must all be the same shape
  except for the first dimension. The first dimension is allowed to vary as the
  vocabulary size is not necessarily a multiple of `P`.

  Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs
  with non-positive weight. For an entry with no features, the embedding vector
  for `default_id` is returned, or the 0-vector if `default_id` is not supplied.

  Args:
    embedding_weights:  A list of `P` float tensors or values representing
        partitioned embedding tensors.
    sparse_ids: `SparseTensor` of shape `[batch_size, ?]` containing the ids.
    sparse_weights: `SparseTensor` of same shape as `sparse_ids`, containing
        float weights corresponding to `sparse_ids`, or `None` if all weights
        are be assumed to be 1.0.
    combiner: A string specifying how to combine embedding results for each
        entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean"
        the default.
    default_id: The id to use for an entry with no features.
    name: A name for this operation (optional).
    partition_strategy: A string specifying the partitioning strategy.
        Currently `"div"` and `"mod"` are supported. Default is `"div"`.


  Returns:
    Dense tensor of shape `[batch_size, embed_dim]`.

  Raises:
    ValueError: if `embedding_weights` is empty.
  """
  if embedding_weights is None or len(embedding_weights) < 1:
    raise ValueError("Missing embedding_weights %s." % embedding_weights)

  dtype = sparse_weights.dtype if sparse_weights else None
  embedding_weights = [
      ops.convert_to_tensor(w, dtype=dtype) for w in embedding_weights]

  contrib_tensor_util.assert_same_float_dtype(
      embedding_weights + [sparse_weights])

  with ops.op_scope(
      embedding_weights + [sparse_ids, sparse_weights], name,
      "embedding_lookup") as scope:
    # Prune invalid ids and weights.
    sparse_ids, sparse_weights = _prune_invalid_ids(sparse_ids, sparse_weights)

    # Fill in dummy values for empty features, if necessary.
    sparse_ids, is_row_empty = sparse_ops.sparse_fill_empty_rows(
        sparse_ids, default_id or 0)
    if sparse_weights:
      sparse_weights, _ = sparse_ops.sparse_fill_empty_rows(
          sparse_weights, 1.0)

    result = tf_embedding_ops.embedding_lookup_sparse(
        embedding_weights, sparse_ids, sparse_weights, combiner=combiner,
        partition_strategy=partition_strategy,
        name=None if default_id is None else scope)

    if default_id is None:
      # Broadcast is_row_empty to the same shape as embedding_lookup_result,
      # for use in Select.
      is_row_empty = array_ops.tile(
          array_ops.reshape(is_row_empty, [-1, 1]),
          array_ops.pack([1, array_ops.shape(result)[1]]))

      result = math_ops.select(
          is_row_empty, array_ops.zeros_like(result), result, name=scope)

    return result