Python sparse_tensor.SparseTensor方法代码示例

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensor [as 别名]
def dense_to_sparse(tensor, eos_token=0, outputs_collections=None, scope=None):
  """Converts a dense tensor into a sparse tensor.

  An example use would be to convert dense labels to sparse ones
  so that they can be fed to the ctc_loss.

  Args:
     tensor: An `int` `Tensor` to be converted to a `Sparse`.
     eos_token: An integer. It is part of the target label that signifies the
       end of a sentence.
     outputs_collections: Collection to add the outputs.
     scope: Optional scope for name_scope.
  """
  with variable_scope.variable_scope(scope, 'dense_to_sparse', [tensor]) as sc:
    tensor = ops.convert_to_tensor(tensor)
    indices = array_ops.where(
        math_ops.not_equal(tensor, constant_op.constant(eos_token,
                                                        tensor.dtype)))
    values = array_ops.gather_nd(tensor, indices)
    shape = array_ops.shape(tensor, out_type=dtypes.int64)
    outputs = sparse_tensor.SparseTensor(indices, values, shape)
    return utils.collect_named_outputs(outputs_collections, sc.name, outputs) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensor [as 别名]
def _maybe_select_class_id(labels, predictions_idx, selected_id=None):
  """If class ID is specified, filter all other classes.

  Args:
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels]. [D1, ... DN] must match
      `predictions_idx`.
    predictions_idx: `int64` `Tensor` of class IDs, with shape [D1, ... DN, k]
      where N >= 1. Commonly, N=1 and `predictions_idx` has shape
      [batch size, k].
    selected_id: Int id to select.

  Returns:
    Tuple of `labels` and `predictions_idx`, possibly with classes removed.
  """
  if selected_id is None:
    return labels, predictions_idx
  return (_select_class_id(labels, selected_id),
          _select_class_id(predictions_idx, selected_id)) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensor [as 别名]
def set_size(a, validate_indices=True):
  """Compute number of unique elements along last dimension of `a`.

  Args:
    a: `SparseTensor`, with indices sorted in row-major order.
    validate_indices: Whether to validate the order and range of sparse indices
       in `a`.

  Returns:
    `int32` `Tensor` of set sizes. For `a` ranked `n`, this is a `Tensor` with
    rank `n-1`, and the same 1st `n-1` dimensions as `a`. Each value is the
    number of unique elements in the corresponding `[0...n-1]` dimension of `a`.

  Raises:
    TypeError: If `a` is an invalid types.
  """
  a = sparse_tensor.convert_to_tensor_or_sparse_tensor(a, name="a")
  if not isinstance(a, sparse_tensor.SparseTensor):
    raise TypeError("Expected `SparseTensor`, got %s." % a)
  if a.values.dtype.base_dtype not in _VALID_DTYPES:
    raise TypeError("Invalid dtype %s." % a.values.dtype)
  # pylint: disable=protected-access
  return gen_set_ops.set_size(
      a.indices, a.values, a.dense_shape, validate_indices) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensor [as 别名]
def _convert_to_tensors_or_sparse_tensors(a, b):
  """Convert to tensor types, and flip order if necessary.

  Args:
    a: `Tensor` or `SparseTensor` of the same type as `b`.
    b: `Tensor` or `SparseTensor` of the same type as `a`.

  Returns:
    Tuple of `(a, b, flipped)`, where `a` and `b` have been converted to
    `Tensor` or `SparseTensor`, and `flipped` indicates whether the order has
    been flipped to make it dense,sparse instead of sparse,dense (since the set
    ops do not support the latter).
  """
  a = sparse_tensor.convert_to_tensor_or_sparse_tensor(a, name="a")
  if a.dtype.base_dtype not in _VALID_DTYPES:
    raise TypeError("'a' invalid dtype %s." % a.dtype)
  b = sparse_tensor.convert_to_tensor_or_sparse_tensor(b, name="b")
  if b.dtype.base_dtype != a.dtype.base_dtype:
    raise TypeError("Types don't match, %s vs %s." % (a.dtype, b.dtype))
  if (isinstance(a, sparse_tensor.SparseTensor) and
      not isinstance(b, sparse_tensor.SparseTensor)):
    return b, a, True
  return a, b, False 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensor [as 别名]
def shape(input, name=None, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin
  """Returns the shape of a tensor.

  This operation returns a 1-D integer tensor representing the shape of `input`.

  For example:

  ```python
  # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
  shape(t) ==> [2, 2, 3]
  ```

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to `tf.int32`.

  Returns:
    A `Tensor` of type `out_type`.
  """
  return shape_internal(input, name, optimize=True, out_type=out_type) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensor [as 别名]
def shape_internal(input, name=None, optimize=True, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin
  """Returns the shape of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the shape as a constant when possible.
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to tf.int32.

  Returns:
    A `Tensor` of type `out_type`.

  """
  with ops.name_scope(name, "Shape", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_math_ops.cast(input.dense_shape, out_type)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.is_fully_defined():
        return constant(input_shape.as_list(), out_type, name=name)
      return gen_array_ops.shape(input, name=name, out_type=out_type) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensor [as 别名]
def size(input, name=None, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin
  """Returns the size of a tensor.

  This operation returns an integer representing the number of elements in
  `input`.

  For example:

  ```python
  # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
  size(t) ==> 12
  ```

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to tf.int32.

  Returns:
    A `Tensor` of type `out_type`. Defaults to tf.int32.
  """
  return size_internal(input, name, optimize=True, out_type=out_type) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensor [as 别名]
def size_internal(input, name=None, optimize=True, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin,protected-access
  """Returns the size of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the size as a constant when possible.
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to tf.int32.

  Returns:
    A `Tensor` of type `out_type`.
  """
  with ops.name_scope(name, "Size", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_math_ops._prod(
          gen_math_ops.cast(input.dense_shape, out_type), 0, name=name)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.is_fully_defined():
        return constant(input_shape.num_elements(), out_type, name=name)
      return gen_array_ops.size(input, name=name, out_type=out_type) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensor [as 别名]
def rank_internal(input, name=None, optimize=True):
  # pylint: disable=redefined-builtin
  """Returns the rank of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the rank as a constant when possible.

  Returns:
    A `Tensor` of type `int32`.
  """
  with ops.name_scope(name, "Rank", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_array_ops.size(input.dense_shape, name=name)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.ndims is not None:
        return constant(input_shape.ndims, dtypes.int32, name=name)
      return gen_array_ops.rank(input, name=name) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensor [as 别名]
def _convert_to_sparse_tensor(sp_input):
  """Convert `sp_input` to `SparseTensor` and return it.

  Args:
    sp_input: `SparseTensor` or `SparseTensorValue`.

  Returns:
    `sp_input` converted to `SparseTensor`.

  Raises:
    ValueError: if `sp_input` is neither `SparseTensor` nor `SparseTensorValue`.
  """
  if isinstance(sp_input, sparse_tensor.SparseTensorValue):
    return sparse_tensor.SparseTensor.from_value(sp_input)
  if not isinstance(sp_input, sparse_tensor.SparseTensor):
    raise TypeError("Input must be a SparseTensor.")
  return sp_input 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensor [as 别名]
def _convert_to_sparse_tensors(sp_inputs):
  """Convert `sp_inputs` to `SparseTensor` objects and return them.

  Args:
    sp_inputs: `list` or `tuple` of `SparseTensor` or `SparseTensorValue`
      objects.

  Returns:
    `sp_inputs` converted to `SparseTensor` objects.

  Raises:
    ValueError: if any item in `sp_inputs` is neither `SparseTensor` nor
      `SparseTensorValue`.
  """
  if isinstance(sp_inputs, list):
    return [_convert_to_sparse_tensor(sp_input) for sp_input in sp_inputs]
  if isinstance(sp_inputs, tuple):
    return (_convert_to_sparse_tensor(sp_input) for sp_input in sp_inputs)
  raise TypeError("Inputs must be a list or tuple.")


# pylint: disable=protected-access 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensor [as 别名]
def sparse_dense_cwise_add(sp_t, dense_t):
  """Adds up a SparseTensor and a dense Tensor, using these special rules:

  (1) Broadcasts the dense side to have the same shape as the sparse side, if
      eligible;
  (2) Then, only the dense values pointed to by the indices of the SparseTensor
      participate in the cwise addition.

  By the rules, the result is a logical SparseTensor with exactly the same
  indices and shape, but possibly with different non-zero values.  The output of
  this Op is the resultant non-zero values.

  Args:
    sp_t: the SparseTensor operand.
    dense_t: the dense Tensor operand; must have the same dtype and a
      broadcast-compatible shape as `sp_t`.

  Returns:
    output: the SparseTensor output.
  """
  result = gen_sparse_ops.sparse_dense_cwise_add(sp_t.indices, sp_t.values,
                                                 sp_t.dense_shape, dense_t)
  return sparse_tensor.SparseTensor(sp_t.indices, result, sp_t.dense_shape) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensor [as 别名]
def serialize_sparse(sp_input, name=None):
  """Serialize a `SparseTensor` into a string 3-vector (1-D `Tensor`) object.

  Args:
    sp_input: The input `SparseTensor`.
    name: A name prefix for the returned tensors (optional).

  Returns:
    A string 3-vector (1D `Tensor`), with each column representing the
    serialized `SparseTensor`'s indices, values, and shape (respectively).

  Raises:
    TypeError: If `sp_input` is not a `SparseTensor`.
  """
  sp_input = _convert_to_sparse_tensor(sp_input)

  return gen_sparse_ops._serialize_sparse(
      sp_input.indices, sp_input.values, sp_input.dense_shape, name=name) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensor [as 别名]
def _add_sparse_to_tensors_map(sp_input, container=None,
                               shared_name=None, name=None):
  """Add a `SparseTensor` to a `SparseTensorsMap` and return its handle.

  Args:
    sp_input: The input `SparseTensor`.
    container: The container for the underlying `SparseTensorsMap` (optional).
    shared_name: The shared name for the underlying `SparseTensorsMap`
      (optional, defaults to the name of the newly created op).
    name: A name prefix for the returned tensors (optional).

  Returns:
    A string 1-vector (1D `Tensor`), with the single element representing the
    a unique handle to a `SparseTensor` stored by the `SparseTensorMap`
    underlying this op.

  Raises:
    TypeError: If `sp_input` is not a `SparseTensor`.
  """
  sp_input = _convert_to_sparse_tensor(sp_input)

  return gen_sparse_ops._add_sparse_to_tensors_map(
      sp_input.indices, sp_input.values, sp_input.dense_shape,
      container=container, shared_name=shared_name, name=name) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensor [as 别名]
def _neg(x, name=None):
  """Computes numerical negative value element-wise.

  I.e., \\(y = -x\\).

  Args:
    x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
      `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`.
  """
  return negative(x, name)


# pylint: enable=g-docstring-has-escape