Python sparse_ops.sparse_concat方法代码示例

# 需要导入模块: from tensorflow.python.ops import sparse_ops [as 别名]
# 或者: from tensorflow.python.ops.sparse_ops import sparse_concat [as 别名]
def concatenate(tensors, axis=-1):
  """Concatenates a list of tensors alongside the specified axis.

  Arguments:
      tensors: list of tensors to concatenate.
      axis: concatenation axis.

  Returns:
      A tensor.
  """
  if axis < 0:
    rank = ndim(tensors[0])
    if rank:
      axis %= rank
    else:
      axis = 0

  if py_all([is_sparse(x) for x in tensors]):
    return sparse_ops.sparse_concat(axis, tensors)
  else:
    return array_ops.concat([to_dense(x) for x in tensors], axis) 

# 需要导入模块: from tensorflow.python.ops import sparse_ops [as 别名]
# 或者: from tensorflow.python.ops.sparse_ops import sparse_concat [as 别名]
def tensors_to_item(self, keys_to_tensors):
    """Maps the given dictionary of tensors to a concatenated list of bboxes.

    Args:
      keys_to_tensors: a mapping of TF-Example keys to parsed tensors.

    Returns:
      [time, num_boxes, 4] tensor of bounding box coordinates, in order
          [y_min, x_min, y_max, x_max]. Whether the tensor is a SparseTensor
          or a dense Tensor is determined by the return_dense parameter. Empty
          positions in the sparse tensor are filled with -1.0 values.
    """
    sides = []
    for key in self._full_keys:
      value = keys_to_tensors[key]
      expanded_dims = array_ops.concat(
          [math_ops.to_int64(array_ops.shape(value)),
           constant_op.constant([1], dtype=dtypes.int64)], 0)
      side = sparse_ops.sparse_reshape(value, expanded_dims)
      sides.append(side)
    bounding_boxes = sparse_ops.sparse_concat(2, sides)
    if self._return_dense:
      bounding_boxes = sparse_ops.sparse_tensor_to_dense(
          bounding_boxes, default_value=self._default_value)
    return bounding_boxes 

# 需要导入模块: from tensorflow.python.ops import sparse_ops [as 别名]
# 或者: from tensorflow.python.ops.sparse_ops import sparse_concat [as 别名]
def _ParseSparse(data):
  """Concat sparse tensors together.

  Args:
    data: A dict of name -> Tensor.

  Returns:
    A single sparse tensor and a 1-D input spec Tensor.

  Raises:
    NotImplementedError:  Combining dense and sparse tensors is not
      supported.
    ValueError: If data contains non-string Tensors.
  """
  for k in sorted(data.keys()):
    if not isinstance(data[k], sparse_tensor.SparseTensor):
      raise NotImplementedError(
          'Features should be either all sparse or all dense.  Use a '
          'feature engineering function to convert some of them.')

  data_spec = [
      constants.DATA_CATEGORICAL if data[data.keys()[0]].dtype == dtypes.string
      else constants.DATA_FLOAT
  ]
  return sparse_ops.sparse_concat(1, data.values()), data_spec 

# 需要导入模块: from tensorflow.python.ops import sparse_ops [as 别名]
# 或者: from tensorflow.python.ops.sparse_ops import sparse_concat [as 别名]
def _expand_and_tile(tensor, multiple, dim=0, name=None):
  """Slice `tensor` shape in 2, then tile along the sliced dimension.

  A new dimension is inserted in shape of `tensor` before `dim`, then values are
  tiled `multiple` times along the new dimension.

  Args:
    tensor: Input `Tensor` or `SparseTensor`.
    multiple: Integer, number of times to tile.
    dim: Integer, dimension along which to tile.
    name: Name of operation.

  Returns:
    `Tensor` result of expanding and tiling `tensor`.

  Raises:
    ValueError: if `multiple` is less than 1, or `dim` is not in
    `[-rank(tensor), rank(tensor)]`.
  """
  if multiple < 1:
    raise ValueError('Invalid multiple %s, must be > 0.' % multiple)
  with ops.name_scope(
      name, 'expand_and_tile', (tensor, multiple, dim)) as scope:
    # Sparse.
    tensor = sparse_tensor.convert_to_tensor_or_sparse_tensor(tensor)
    if isinstance(tensor, sparse_tensor.SparseTensor):
      if dim < 0:
        expand_dims = array_ops.reshape(
            array_ops.size(tensor.dense_shape) + dim, [1])
      else:
        expand_dims = [dim]
      expanded_shape = array_ops.concat(
          (array_ops.slice(tensor.dense_shape, [0], expand_dims), [1],
           array_ops.slice(tensor.dense_shape, expand_dims, [-1])),
          0,
          name='expanded_shape')
      expanded = sparse_ops.sparse_reshape(
          tensor, shape=expanded_shape, name='expand')
      if multiple == 1:
        return expanded
      return sparse_ops.sparse_concat(
          dim - 1 if dim < 0 else dim, [expanded] * multiple, name=scope)

    # Dense.
    expanded = array_ops.expand_dims(
        tensor, dim if (dim >= 0) else (dim - 1), name='expand')
    if multiple == 1:
      return expanded
    ones = array_ops.ones_like(array_ops.shape(tensor))
    tile_multiples = array_ops.concat(
        (ones[:dim], (multiple,), ones[dim:]), 0, name='multiples')
    return array_ops.tile(expanded, tile_multiples, name=scope) 

# 需要导入模块: from tensorflow.python.ops import sparse_ops [as 别名]
# 或者: from tensorflow.python.ops.sparse_ops import sparse_concat [as 别名]
def _create_joint_embedding_lookup(columns_to_tensors,
                                   embedding_lookup_arguments,
                                   num_outputs,
                                   trainable,
                                   weight_collections):
  """Creates an embedding lookup for all columns sharing a single weight."""
  for arg in embedding_lookup_arguments:
    assert arg.weight_tensor is None, (
        'Joint sums for weighted sparse columns are not supported. '
        'Please use weighted_sum_from_feature_columns instead.')
    assert arg.combiner == 'sum', (
        'Combiners other than sum are not supported for joint sums. '
        'Please use weighted_sum_from_feature_columns instead.')
  assert len(embedding_lookup_arguments) >= 1, (
      'At least one column must be in the model.')
  prev_size = 0
  sparse_tensors = []
  for a in embedding_lookup_arguments:
    t = a.input_tensor
    values = t.values + prev_size
    prev_size += a.vocab_size
    sparse_tensors.append(
        sparse_tensor_py.SparseTensor(t.indices,
                                      values,
                                      t.dense_shape))
  sparse_tensor = sparse_ops.sparse_concat(1, sparse_tensors)
  with variable_scope.variable_scope(
      None, default_name='linear_weights', values=columns_to_tensors.values()):
    variable = contrib_variables.model_variable(
        name='weights',
        shape=[prev_size, num_outputs],
        dtype=dtypes.float32,
        initializer=init_ops.zeros_initializer(),
        trainable=trainable,
        collections=weight_collections)
    if fc._is_variable(variable):  # pylint: disable=protected-access
      variable = [variable]
    else:
      variable = variable._get_variable_list()  # pylint: disable=protected-access
    predictions = embedding_ops.safe_embedding_lookup_sparse(
        variable,
        sparse_tensor,
        sparse_weights=None,
        combiner='sum',
        name='_weights')
    return variable, predictions 

# 需要导入模块: from tensorflow.python.ops import sparse_ops [as 别名]
# 或者: from tensorflow.python.ops.sparse_ops import sparse_concat [as 别名]
def expand_and_tile(tensor, multiple, dim=0, name=None):
  """Slice `tensor` shape in 2, then tile along the sliced dimension.

  A new dimension is inserted in shape of `tensor` before `dim`, then values are
  tiled `multiple` times along the new dimension.

  Args:
    tensor: Input `Tensor` or `SparseTensor`.
    multiple: Integer, number of times to tile.
    dim: Integer, dimension along which to tile.
    name: Name of operation.

  Returns:
    `Tensor` result of expanding and tiling `tensor`.

  Raises:
    ValueError: if `multiple` is less than 1, or `dim` is not in
    `[-rank(tensor), rank(tensor)]`.
  """
  if multiple < 1:
    raise ValueError('Invalid multiple %s, must be > 0.' % multiple)
  with ops.name_scope(
      name, 'expand_and_tile', (tensor, multiple, dim)) as scope:
    # Sparse.
    if isinstance(tensor, sparse_tensor.SparseTensorValue):
      tensor = sparse_tensor.SparseTensor.from_value(tensor)
    if isinstance(tensor, sparse_tensor.SparseTensor):
      if dim < 0:
        expand_dims = array_ops.reshape(
            array_ops.size(tensor.dense_shape) + dim, [1])
      else:
        expand_dims = [dim]
      expanded_shape = array_ops.concat(
          (array_ops.strided_slice(tensor.dense_shape, [0], expand_dims), [1],
           array_ops.strided_slice(
               tensor.dense_shape, expand_dims, [-1], end_mask=1 << 0)),
          0,
          name='expanded_shape')
      expanded = sparse_ops.sparse_reshape(
          tensor, shape=expanded_shape, name='expand')
      if multiple == 1:
        return expanded
      return sparse_ops.sparse_concat(
          dim - 1 if dim < 0 else dim, [expanded] * multiple, name=scope)

    # Dense.
    expanded = array_ops.expand_dims(
        tensor, dim if (dim >= 0) else (dim - 1), name='expand')
    if multiple == 1:
      return expanded
    ones = array_ops.ones_like(array_ops.shape(tensor))
    tile_multiples = array_ops.concat(
        (ones[:dim], (multiple,), ones[dim:]), 0, name='multiples')
    return array_ops.tile(expanded, tile_multiples, name=scope) 

# 需要导入模块: from tensorflow.python.ops import sparse_ops [as 别名]
# 或者: from tensorflow.python.ops.sparse_ops import sparse_concat [as 别名]
def _create_joint_embedding_lookup(columns_to_tensors,
                                   embedding_lookup_arguments,
                                   num_outputs,
                                   trainable,
                                   weight_collections):
  """Creates an embedding lookup for all columns sharing a single weight."""
  for arg in embedding_lookup_arguments:
    assert arg.weight_tensor is None, (
        'Joint sums for weighted sparse columns are not supported. '
        'Please use weighted_sum_from_feature_columns instead.')
    assert arg.combiner == 'sum', (
        'Combiners other than sum are not supported for joint sums. '
        'Please use weighted_sum_from_feature_columns instead.')
  assert len(embedding_lookup_arguments) >= 1, (
      'At least one column must be in the model.')
  prev_size = 0
  sparse_tensors = []
  for a in embedding_lookup_arguments:
    t = a.input_tensor
    values = t.values + prev_size
    prev_size += a.vocab_size
    sparse_tensors.append(
        sparse_tensor_py.SparseTensor(t.indices,
                                      values,
                                      t.dense_shape))
  sparse_tensor = sparse_ops.sparse_concat(1, sparse_tensors)
  with variable_scope.variable_scope(
      None, default_name='linear_weights', values=columns_to_tensors.values()):
    variable = contrib_variables.model_variable(
        name='weights',
        shape=[prev_size, num_outputs],
        dtype=dtypes.float32,
        initializer=init_ops.zeros_initializer(),
        trainable=trainable,
        collections=weight_collections)
    if isinstance(variable, variables.Variable):
      variable = [variable]
    else:
      variable = variable._get_variable_list()  # pylint: disable=protected-access
    predictions = embedding_ops.safe_embedding_lookup_sparse(
        variable,
        sparse_tensor,
        sparse_weights=None,
        combiner='sum',
        name='_weights')
    return variable, predictions 

# 需要导入模块: from tensorflow.python.ops import sparse_ops [as 别名]
# 或者: from tensorflow.python.ops.sparse_ops import sparse_concat [as 别名]
def expand_and_tile(tensor, multiple, dim=0, name=None):
  """Slice `tensor` shape in 2, then tile along the sliced dimension.

  A new dimension is inserted in shape of `tensor` before `dim`, then values are
  tiled `multiple` times along the new dimension.

  Args:
    tensor: Input `Tensor` or `SparseTensor`.
    multiple: Integer, number of times to tile.
    dim: Integer, dimension along which to tile.
    name: Name of operation.

  Returns:
    `Tensor` result of expanding and tiling `tensor`.

  Raises:
    ValueError: if `multiple` is less than 1, or `dim` is not in
    `[-rank(tensor), rank(tensor)]`.
  """
  if multiple < 1:
    raise ValueError('Invalid multiple %s, must be > 0.' % multiple)
  with ops.name_scope(
      name, 'expand_and_tile', (tensor, multiple, dim)) as scope:
    # Sparse.
    if isinstance(tensor, sparse_tensor.SparseTensorValue):
      tensor = sparse_tensor.SparseTensor.from_value(tensor)
    if isinstance(tensor, sparse_tensor.SparseTensor):
      if dim < 0:
        expand_dims = array_ops.reshape(
            array_ops.size(tensor.shape) + dim, [1])
      else:
        expand_dims = [dim]
      expanded_shape = array_ops.concat(
          0, (array_ops.slice(tensor.shape, [0], expand_dims), [1],
              array_ops.slice(tensor.shape, expand_dims, [-1])),
          name='expanded_shape')
      expanded = sparse_ops.sparse_reshape(
          tensor, shape=expanded_shape, name='expand')
      if multiple == 1:
        return expanded
      return sparse_ops.sparse_concat(
          dim - 1 if dim < 0 else dim, [expanded] * multiple, name=scope)

    # Dense.
    expanded = array_ops.expand_dims(
        tensor, dim if (dim >= 0) else (dim - 1), name='expand')
    if multiple == 1:
      return expanded
    ones = array_ops.ones_like(array_ops.shape(tensor))
    tile_multiples = array_ops.concat(
        0, (ones[:dim], (multiple,), ones[dim:]), name='multiples')
    return array_ops.tile(expanded, tile_multiples, name=scope) 

# 需要导入模块: from tensorflow.python.ops import sparse_ops [as 别名]
# 或者: from tensorflow.python.ops.sparse_ops import sparse_concat [as 别名]
def _create_joint_embedding_lookup(columns_to_tensors,
                                   embedding_lookup_arguments,
                                   num_outputs,
                                   trainable,
                                   weight_collections):
  """Creates an embedding lookup for all columns sharing a single weight."""
  for arg in embedding_lookup_arguments:
    assert arg.weight_tensor is None, (
        'Joint sums for weighted sparse columns are not supported. '
        'Please use weighted_sum_from_feature_columns instead.')
    assert arg.combiner == 'sum', (
        'Combiners other than sum are not supported for joint sums. '
        'Please use weighted_sum_from_feature_columns instead.')
  assert len(embedding_lookup_arguments) >= 1, (
      'At least one column must be in the model.')
  prev_size = 0
  sparse_tensors = []
  for a in embedding_lookup_arguments:
    t = a.input_tensor
    values = t.values + prev_size
    prev_size += a.vocab_size
    sparse_tensors.append(
        sparse_tensor_py.SparseTensor(t.indices,
                                      values,
                                      t.shape))
  sparse_tensor = sparse_ops.sparse_concat(1, sparse_tensors)
  with variable_scope.variable_scope(
      None, default_name='linear_weights', values=columns_to_tensors.values()):
    variable = contrib_variables.model_variable(
        name='weights',
        shape=[prev_size, num_outputs],
        dtype=dtypes.float32,
        initializer=init_ops.zeros_initializer,
        trainable=trainable,
        collections=weight_collections)
    if isinstance(variable, variables.Variable):
      variable = [variable]
    else:
      variable = variable._get_variable_list()  # pylint: disable=protected-access
    predictions = embedding_ops.safe_embedding_lookup_sparse(
        variable,
        sparse_tensor,
        sparse_weights=None,
        combiner='sum',
        name='_weights')
    return variable, predictions