Python tensorflow.sparse_mask方法代码示例

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import sparse_mask [as 别名]
def testBasic(self):
    values = np.random.rand(4, 4).astype(np.single)
    indices = np.array([0, 2, 3, 4], dtype=np.int32)
    mask_indices = np.array([0], dtype=np.int32)

    out_values = values[1:, :]
    out_indices = np.array([2, 3, 4], dtype=np.int32)

    with self.test_session() as sess:
      values_tensor = tf.convert_to_tensor(values)
      indices_tensor = tf.convert_to_tensor(indices)
      mask_indices_tensor = tf.convert_to_tensor(mask_indices)

      t = tf.IndexedSlices(values_tensor, indices_tensor)
      masked_t = tf.sparse_mask(t, mask_indices_tensor)

      tf_out_values, tf_out_indices = sess.run([masked_t.values,
                                                masked_t.indices])

      self.assertAllEqual(tf_out_values, out_values)
      self.assertAllEqual(tf_out_indices, out_indices) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import sparse_mask [as 别名]
def sparse_mask(a, mask_indices, name=None):
  """Masks elements of `IndexedSlices`.

  Given an `IndexedSlices` instance `a`, returns another `IndexedSlices` that
  contains a subset of the slices of `a`. Only the slices at indices not
  specified in `mask_indices` are returned.

  This is useful when you need to extract a subset of slices in an
  `IndexedSlices` object.

  For example:

  ```python
  # `a` contains slices at indices [12, 26, 37, 45] from a large tensor
  # with shape [1000, 10]
  a.indices => [12, 26, 37, 45]
  tf.shape(a.values) => [4, 10]

  # `b` will be the subset of `a` slices at its second and third indices, so
  # we want to mask its first and last indices (which are at absolute
  # indices 12, 45)
  b = tf.sparse_mask(a, [12, 45])

  b.indices => [26, 37]
  tf.shape(b.values) => [2, 10]

  ```

  Args:
    a: An `IndexedSlices` instance.
    mask_indices: Indices of elements to mask.
    name: A name for the operation (optional).

  Returns:
    The masked `IndexedSlices` instance.
  """
  with ops.name_scope(name, "sparse_mask", [a, mask_indices]) as name:
    indices = a.indices
    out_indices, to_gather = setdiff1d(indices, mask_indices)
    out_values = gather(a.values, to_gather, name=name)
    return ops.IndexedSlices(out_values, out_indices, a.dense_shape) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import sparse_mask [as 别名]
def sparse_mask(a, mask_indices, name=None):
  """Masks elements of `IndexedSlices`.

  Given an `IndexedSlices` instance `a`, returns another `IndexedSlices` that
  contains a subset of the slices of `a`. Only the slices at indices not
  specified in `mask_indices` are returned.

  This is useful when you need to extract a subset of slices in an
  `IndexedSlices` object.

  For example:

  ```python
  # `a` contains slices at indices [12, 26, 37, 45] from a large tensor
  # with shape [1000, 10]
  a.indices => [12, 26, 37, 45]
  tf.shape(a.values) => [4, 10]

  # `b` will be the subset of `a` slices at its second and third indices, so
  # we want to mask its first and last indices (which are at absolute
  # indices 12, 45)
  b = tf.sparse_mask(a, [12, 45])

  b.indices => [26, 37]
  tf.shape(b.values) => [2, 10]

  ```

  Args:
    * `a`: An `IndexedSlices` instance.
    * `mask_indices`: Indices of elements to mask.
    * `name`: A name for the operation (optional).

  Returns:
    The masked `IndexedSlices` instance.
  """
  with ops.name_scope(name, "sparse_mask", [a, mask_indices]) as name:
    indices = a.indices
    out_indices, to_gather = setdiff1d(indices, mask_indices)
    out_values = gather(a.values, to_gather, name=name)
    return ops.IndexedSlices(out_values, out_indices, a.dense_shape) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import sparse_mask [as 别名]
def sparse_mask(a, mask_indices, name=None):
  """Masks elements of `IndexedSlices`.

  Given an `IndexedSlices` instance `a`, returns another `IndexedSlices` that
  contains a subset of the slices of `a`. Only the slices at indices not
  specified in `mask_indices` are returned.

  This is useful when you need to extract a subset of slices in an
  `IndexedSlices` object.

  For example:

  ```python
  # `a` contains slices at indices [12, 26, 37, 45] from a large tensor
  # with shape [1000, 10]
  a.indices  # [12, 26, 37, 45]
  tf.shape(a.values)  # [4, 10]

  # `b` will be the subset of `a` slices at its second and third indices, so
  # we want to mask its first and last indices (which are at absolute
  # indices 12, 45)
  b = tf.sparse_mask(a, [12, 45])

  b.indices  # [26, 37]
  tf.shape(b.values)  # [2, 10]
  ```

  Args:
    a: An `IndexedSlices` instance.
    mask_indices: Indices of elements to mask.
    name: A name for the operation (optional).

  Returns:
    The masked `IndexedSlices` instance.
  """
  with ops.name_scope(name, "sparse_mask", [a, mask_indices]) as name:
    indices = a.indices
    out_indices, to_gather = setdiff1d(indices, mask_indices)
    out_values = gather(a.values, to_gather, name=name)
    return ops.IndexedSlices(out_values, out_indices, a.dense_shape)