Python array_ops.invert_permutation函数代码示例

 def testInvertPermutation(self):
   for dtype in [dtypes.int32, dtypes.int64]:
     with self.test_session(use_gpu=True):
       x = constant_op.constant([3, 4, 0, 2, 1], dtype=dtype)
       y = array_ops.invert_permutation(x)
       self.assertAllEqual(y.get_shape(), [5])
       self.assertAllEqual(y.eval(), [2, 4, 3, 0, 1])

def _SparseReorderGrad(op, unused_output_indices_grad, output_values_grad):
  """Gradients for the SparseReorder op.

  Args:
    op: the SparseReorder op
    unused_output_indices_grad: the incoming gradients of the output indices
    output_values_grad: the incoming gradients of the output values

  Returns:
    Gradient for each of the 3 input tensors:
      (input_indices, input_values, input_shape)
    The gradients for input_indices and input_shape is None.
  """
  input_indices = op.inputs[0]
  input_shape = op.inputs[2]

  num_entries = array_ops.shape(input_indices)[0]
  entry_indices = math_ops.range(num_entries)
  sp_unordered = sparse_tensor.SparseTensor(
      input_indices, entry_indices, input_shape)
  sp_ordered = sparse_ops.sparse_reorder(sp_unordered)
  inverted_permutation = array_ops.invert_permutation(sp_ordered.values)

  return (None,
          array_ops.gather(output_values_grad, inverted_permutation),
          None)

def _ConjugateTransposeGrad(op, grad):
  """Returns conj(unshuffle(grad))."""
  p = op.inputs[1]
  return [
      array_ops.transpose(
          grad, array_ops.invert_permutation(p), conjugate=True), None
  ]

 def reshape_inv(y):
   # Expand the extra dims hanging off the end, "b_extra_sh".
   # Note we use y_sh[:-1] + [b_main_sh[-1]] rather than b_main_sh, because y
   # Could have different batch dims than a and b, because of broadcasting.
   y_extra_shape = array_ops.concat(
       (array_ops.shape(y)[:-1], [b_main_sh[-1]], b_extra_sh), 0)
   y_extra_on_end = array_ops.reshape(y, y_extra_shape)
   return array_ops.transpose(
       y_extra_on_end, perm=array_ops.invert_permutation(perm))

def _ProdGrad(op, grad):
  """Gradient for Prod."""
  # The gradient can be expressed by dividing the product by each entry of the
  # input tensor, but this approach can't deal with zeros in the input.
  # Here, we avoid this problem by composing the output as a product of two
  # cumprod operations.

  input_shape = array_ops.shape(op.inputs[0])
  # Reshape reduction indices for the case where the parameter is a scalar
  reduction_indices = array_ops.reshape(op.inputs[1], [-1])

  # Expand grad to full input shape
  output_shape_kept_dims = math_ops.reduced_shape(input_shape, op.inputs[1])
  tile_scaling = _safe_shape_div(input_shape, output_shape_kept_dims)
  grad = array_ops.reshape(grad, output_shape_kept_dims)
  grad = array_ops.tile(grad, tile_scaling)

  # Pack all reduced dimensions into a single one, so we can perform the
  # cumprod ops. If the reduction dims list is empty, it defaults to float32,
  # so we need to cast here.  We put all the shape-related ops on CPU to avoid
  # copying back and forth, and since listdiff is CPU only.
  with ops.device("/cpu:0"):
    rank = array_ops.rank(op.inputs[0])
    reduction_indices = (reduction_indices + rank) % rank
    reduced = math_ops.cast(reduction_indices, dtypes.int32)
    idx = math_ops.range(0, rank)
    other, _ = array_ops.setdiff1d(idx, reduced)
    perm = array_ops.concat([reduced, other], 0)
    reduced_num = math_ops.reduce_prod(array_ops.gather(input_shape, reduced))
    other_num = math_ops.reduce_prod(array_ops.gather(input_shape, other))
  permuted = array_ops.transpose(op.inputs[0], perm)
  permuted_shape = array_ops.shape(permuted)
  reshaped = array_ops.reshape(permuted, (reduced_num, other_num))

  # Calculate product, leaving out the current entry
  left = math_ops.cumprod(reshaped, axis=0, exclusive=True)
  right = math_ops.cumprod(reshaped, axis=0, exclusive=True, reverse=True)
  # For complex inputs, the gradient is in the conjugate direction.
  y = array_ops.reshape(math_ops.conj(left) * math_ops.conj(right),
                        permuted_shape)

  # Invert the transpose and reshape operations.
  # Make sure to set the statically known shape information through a reshape.
  out = grad * array_ops.transpose(y, array_ops.invert_permutation(perm))
  return array_ops.reshape(out, input_shape), None

def _ProdGrad(op, grad):
  """Gradient for Prod."""
  # The gradient can be expressed by dividing the product by each entry of the
  # input tensor, but this approach can't deal with zeros in the input.
  # Here, we avoid this problem by composing the output as a product of two
  # cumprod operations.

  input_shape = array_ops.shape(op.inputs[0])

  # Expand grad to full input shape
  output_shape_kept_dims = math_ops.reduced_shape(input_shape, op.inputs[1])
  tile_scaling = _safe_shape_div(input_shape, output_shape_kept_dims)
  grad = array_ops.reshape(grad, output_shape_kept_dims)
  grad = array_ops.tile(grad, tile_scaling)

  # Pack all reduced dimensions into a single one, so we can perform the
  # cumprod ops. If the reduction dims list is empty, it defaults to float32,
  # so we need to cast here.
  reduced = math_ops.cast(op.inputs[1], dtypes.int32)
  idx = math_ops.range(0, array_ops.rank(op.inputs[0]))
  other, _ = array_ops.listdiff(idx, reduced)
  perm = array_ops.concat(0, [reduced, other])
  reduced_num = math_ops.reduce_prod(array_ops.gather(input_shape, reduced))
  other_num = math_ops.reduce_prod(array_ops.gather(input_shape, other))
  permuted = array_ops.transpose(op.inputs[0], perm)
  permuted_shape = array_ops.shape(permuted)
  reshaped = array_ops.reshape(permuted, (reduced_num, other_num))

  # Calculate product, leaving out the current entry
  left = math_ops.cumprod(reshaped, axis=0, exclusive=True)
  right = math_ops.cumprod(reshaped, axis=0, exclusive=True, reverse=True)
  y = array_ops.reshape(left * right, permuted_shape)

  # Invert the transpose and reshape operations.
  # Make sure to set the statically known shape information through a reshape.
  out = grad * array_ops.transpose(y, array_ops.invert_permutation(perm))
  return array_ops.reshape(out, input_shape), None

def _TransposeGrad(op, grad):
  """Returns unshuffle(grad)."""
  p = op.inputs[1]
  return [array_ops.transpose(grad, array_ops.invert_permutation(p)), None]

 def testInvertPermutationTwiceIsNoop(self):
   self._assertOpOutputMatchesExpected(
       lambda x: array_ops.invert_permutation(array_ops.invert_permutation(x)),
       np.array([1, 2, 0], np.int32),
       expected=np.array([1, 2, 0], dtype=np.int32))

 def _inverse(self, y):
   return array_ops.gather(
       y,
       array_ops.invert_permutation(self.permutation),
       axis=-1)