Python sparse_ops.sparse_reduce_sum函数代码示例

 def testInvalidAxes(self):
   sp_t = ops.SparseTensor(self.ind, self.vals, self.shape)
   with self.test_session(use_gpu=False):
     with self.assertRaisesOpError("Invalid reduction dimension -3"):
       sparse_ops.sparse_reduce_sum(sp_t, -3).eval()
     with self.assertRaisesOpError("Invalid reduction dimension 2"):
       sparse_ops.sparse_reduce_sum(sp_t, 2).eval()

  def testGradient(self):
    if np.__version__ == "1.13.0":
      self.skipTest("numpy 1.13.0 bug")

    np.random.seed(8161)
    test_dims = [(11, 1, 5, 7, 1), (2, 2)]
    with self.test_session(use_gpu=False):
      for dims in test_dims:
        sp_t, nnz = _sparsify(np.random.randn(*dims))
        # reduce random axes from 1D to N-D
        for d in range(1, len(dims) + 1):
          axes = np.random.choice(len(dims), size=d, replace=False).tolist()
          reduced = sparse_ops.sparse_reduce_sum(sp_t, axes)

          err = gradient_checker.compute_gradient_error(sp_t.values, (nnz,),
                                                        reduced,
                                                        reduced.eval().shape)
          self.assertLess(err, 1e-3)

        # Tests for negative axes.
        reduced = sparse_ops.sparse_reduce_sum(sp_t, -1)
        err = gradient_checker.compute_gradient_error(sp_t.values, (nnz,),
                                                      reduced,
                                                      reduced.eval().shape)
        self.assertLess(err, 1e-3)

 def testInvalidAxes(self):
   sp_t = sparse_tensor.SparseTensor(self.ind, self.vals, self.dense_shape)
   with test_util.force_cpu():
     with self.assertRaisesOpError("Invalid reduction dimension -3"):
       self.evaluate(sparse_ops.sparse_reduce_sum(sp_t, -3))
     with self.assertRaisesOpError("Invalid reduction dimension 2"):
       self.evaluate(sparse_ops.sparse_reduce_sum(sp_t, 2))
     with self.assertRaisesOpError("Invalid reduction dimension -3"):
       self.evaluate(sparse_ops.sparse_reduce_max(sp_t, -3))
     with self.assertRaisesOpError("Invalid reduction dimension 2"):
       self.evaluate(sparse_ops.sparse_reduce_max(sp_t, 2))

def calculate_loss(input_mat, row_factors, col_factors, regularization=None,
                   w0=1., row_weights=None, col_weights=None):
  """Calculates the loss of a given factorization.

  Using a non distributed method, different than the one implemented in the
  WALS model. The weight of an observed entry (i, j) (i.e. such that
  input_mat[i, j] is non zero) is (w0 + row_weights[i]col_weights[j]).

  Args:
    input_mat: The input matrix, a SparseTensor of rank 2.
    row_factors: The row factors, a dense Tensor of rank 2.
    col_factors: The col factors, a dense Tensor of rank 2.
    regularization: the regularization coefficient, a scalar.
    w0: the weight of unobserved entries. A scalar.
    row_weights: A dense tensor of rank 1.
    col_weights: A dense tensor of rank 1.

  Returns:
    The total loss.
  """
  wr = (array_ops.expand_dims(row_weights, 1) if row_weights is not None
        else constant_op.constant(1.))
  wc = (array_ops.expand_dims(col_weights, 0) if col_weights is not None
        else constant_op.constant(1.))
  reg = (regularization if regularization is not None
         else constant_op.constant(0.))

  row_indices, col_indices = array_ops.split(input_mat.indices,
                                             axis=1,
                                             num_or_size_splits=2)
  gathered_row_factors = array_ops.gather(row_factors, row_indices)
  gathered_col_factors = array_ops.gather(col_factors, col_indices)
  sp_approx_vals = array_ops.squeeze(math_ops.matmul(
      gathered_row_factors, gathered_col_factors, adjoint_b=True))
  sp_approx = sparse_tensor.SparseTensor(
      indices=input_mat.indices,
      values=sp_approx_vals,
      dense_shape=input_mat.dense_shape)

  sp_approx_sq = math_ops.square(sp_approx)
  row_norm = math_ops.reduce_sum(math_ops.square(row_factors))
  col_norm = math_ops.reduce_sum(math_ops.square(col_factors))
  row_col_norm = math_ops.reduce_sum(math_ops.square(math_ops.matmul(
      row_factors, col_factors, transpose_b=True)))

  resid = sparse_ops.sparse_add(input_mat, sp_approx * (-1))
  resid_sq = math_ops.square(resid)
  loss = w0 * (
      sparse_ops.sparse_reduce_sum(resid_sq) -
      sparse_ops.sparse_reduce_sum(sp_approx_sq)
      )
  loss += (sparse_ops.sparse_reduce_sum(wr * (resid_sq * wc)) +
           w0 * row_col_norm + reg * (row_norm + col_norm))
  return loss.eval()

  def _compare(self, sp_t, reduction_axes, ndims, keep_dims):
    densified = sparse_ops.sparse_tensor_to_dense(sp_t).eval()

    np_ans = densified
    if reduction_axes is None:
      np_ans = np.sum(np_ans, keepdims=keep_dims)
    else:
      if not isinstance(reduction_axes, list):  # Single scalar.
        reduction_axes = [reduction_axes]
      reduction_axes = np.array(reduction_axes).astype(np.int32)
      # Handles negative axes.
      reduction_axes = (reduction_axes + ndims) % ndims
      # Loop below depends on sorted.
      reduction_axes.sort()
      for ra in reduction_axes.ravel()[::-1]:
        np_ans = np.sum(np_ans, axis=ra, keepdims=keep_dims)

    with self.test_session():
      tf_dense_ans = sparse_ops.sparse_reduce_sum(sp_t, reduction_axes,
                                                  keep_dims)
      out_dense = tf_dense_ans.eval()

      tf_sparse_ans = sparse_ops.sparse_reduce_sum_sparse(sp_t, reduction_axes,
                                                          keep_dims)
      # Convert to dense for comparison purposes.
      out_sparse = sparse_ops.sparse_tensor_to_dense(tf_sparse_ans).eval()

    self.assertAllClose(np_ans, out_dense)
    self.assertAllClose(np_ans, out_sparse)

def _SparseSoftmaxGrad(op, grad):
  """Gradients for SparseSoftmax.

  The calculation is the same as SoftmaxGrad:

    grad_x = grad_softmax * softmax - sum(grad_softmax * softmax) * softmax

  where we now only operate on the non-zero values present in the SparseTensors.

  Args:
    op: the SparseSoftmax op.
    grad: the upstream gradient w.r.t. the non-zero SparseSoftmax output values.

  Returns:
    Gradients w.r.t. the input (sp_indices, sp_values, sp_shape).
  """
  indices, shape = op.inputs[0], op.inputs[2]
  out_vals = op.outputs[0]
  sp_output = sparse_tensor.SparseTensor(indices, out_vals, shape)
  sp_grad = sparse_tensor.SparseTensor(indices, grad, shape)
  sp_product = sparse_tensor.SparseTensor(
      indices, sp_output.values * sp_grad.values, shape)

  # [..., B, 1], dense.
  sum_reduced = -sparse_ops.sparse_reduce_sum(sp_product, [-1], keep_dims=True)
  # sparse [..., B, C] + dense [..., B, 1] with broadcast; outputs sparse.
  sp_sum = sparse_ops.sparse_dense_cwise_add(sp_grad, sum_reduced)

  grad_x = sp_sum.values * sp_output.values
  return [None, grad_x, None]

def _build_multilabel_adjacency(sparse_labels):
  """Builds multilabel adjacency matrix.

  As of March 14th, 2017, there's no op for the dot product between
  two sparse tensors in TF. However, there is `sparse_minimum` op which is
  equivalent to an AND op between two sparse boolean tensors.
  This computes the dot product between two sparse boolean inputs.

  Args:
    sparse_labels: List of 1-D boolean sparse tensors.

  Returns:
    adjacency_matrix: 2-D dense `Tensor`.
  """
  num_pairs = len(sparse_labels)
  adjacency_matrix = array_ops.zeros([num_pairs, num_pairs])
  for i in range(num_pairs):
    for j in range(num_pairs):
      sparse_dot_product = math_ops.to_float(
          sparse_ops.sparse_reduce_sum(sparse_ops.sparse_minimum(
              sparse_labels[i], sparse_labels[j])))
      sparse_dot_product = array_ops.expand_dims(sparse_dot_product, 0)
      sparse_dot_product = array_ops.expand_dims(sparse_dot_product, 1)
      one_hot_matrix = array_ops.pad(sparse_dot_product,
                                     [[i, num_pairs-i-1],
                                      [j, num_pairs-j-1]], 'CONSTANT')
      adjacency_matrix += one_hot_matrix

  return adjacency_matrix

  def testGradient(self):
    np.random.seed(8161)
    test_dims = [(11, 1, 5, 7, 1), (2, 2)]
    with self.test_session(use_gpu=False):
      for dims in test_dims:
        sp_t, nnz = _sparsify(np.random.randn(*dims))
        # reduce random axes from 1D to N-D
        for d in range(1, len(dims) + 1):
          axes = np.random.choice(len(dims), size=d, replace=False).tolist()
          reduced = sparse_ops.sparse_reduce_sum(sp_t, axes)

          err = tf.test.compute_gradient_error(sp_t.values, (nnz,), reduced,
                                               reduced.eval().shape)
          self.assertLess(err, 1e-3)

  def _compare(self, sp_t, reduction_axes, keep_dims):
    densified = sparse_ops.sparse_tensor_to_dense(sp_t).eval()

    np_ans = densified
    if reduction_axes is None:
      np_ans = np.sum(np_ans, keepdims=keep_dims)
    else:
      if isinstance(reduction_axes, list):
        reduction_axes = sorted(reduction_axes)  # loop below depends on sorted
      reduction_axes = np.array(reduction_axes).astype(np.int32)
      for ra in reduction_axes.ravel()[::-1]:
        np_ans = np.sum(np_ans, axis=ra, keepdims=keep_dims)

    with self.test_session():
      tf_ans = sparse_ops.sparse_reduce_sum(sp_t, reduction_axes, keep_dims)
      out = tf_ans.eval()

    self.assertAllClose(np_ans, out)

#.........这里部分代码省略.........
      # TODO(rmlarsen): multi-thread tf.matrix_solve.
      new_left_values = array_ops.transpose(
          linalg_ops.matrix_solve(total_lhs, array_ops.transpose(total_rhs)))
    else:
      if row_weights is None:
        # TODO(yifanchen): Add special handling for single shard without using
        # embedding_lookup and perform benchmarks for those cases. Same for
        # col_weights lookup below.
        row_weights_slice = embedding_ops.embedding_lookup(
            row_wt, update_indices, partition_strategy="div")
      else:
        num_indices = array_ops.shape(update_indices)[0]
        with ops.control_dependencies(
            [check_ops.assert_less_equal(array_ops.rank(row_weights), 1)]):
          row_weights_slice = control_flow_ops.cond(
              math_ops.equal(array_ops.rank(row_weights), 0),
              lambda: (array_ops.ones([num_indices]) * row_weights),
              lambda: math_ops.cast(row_weights, dtypes.float32))

      col_weights = embedding_ops.embedding_lookup(
          col_wt, gather_indices, partition_strategy="div")
      partial_lhs, total_rhs = (
          gen_factorization_ops.wals_compute_partial_lhs_and_rhs(
              right,
              col_weights,
              self._unobserved_weight,
              row_weights_slice,
              new_sp_input.indices,
              new_sp_input.values,
              num_rows,
              transpose_input,
              name="wals_compute_partial_lhs_rhs"))
      total_lhs = array_ops.expand_dims(total_lhs, 0) + partial_lhs
      total_rhs = array_ops.expand_dims(total_rhs, -1)
      new_left_values = array_ops.squeeze(
          linalg_ops.matrix_solve(total_lhs, total_rhs), [2])

    update_op_name = "row_update" if update_row_factors else "col_update"
    update_op = self.scatter_update(
        left,
        update_indices,
        new_left_values,
        sharding_func,
        name=update_op_name)

    # Create the loss subgraph
    loss_sp_input = (sparse_ops.sparse_transpose(new_sp_input)
                     if transpose_input else new_sp_input)
    # sp_approx is the low rank estimate of the input matrix, formed by
    # computing the product <u_i, v_j> for (i, j) in loss_sp_input.indices.
    sp_approx_vals = gen_factorization_ops.masked_matmul(
        new_left_values,
        right,
        loss_sp_input.indices,
        transpose_a=False,
        transpose_b=True)
    sp_approx = sparse_tensor.SparseTensor(
        loss_sp_input.indices, sp_approx_vals, loss_sp_input.dense_shape)
    sp_approx_sq = math_ops.square(sp_approx)
    sp_residual = sparse_ops.sparse_add(loss_sp_input, sp_approx * (-1))
    sp_residual_sq = math_ops.square(sp_residual)
    row_wt_mat = (constant_op.constant(0.)
                  if self._row_weights is None else array_ops.expand_dims(
                      row_weights_slice, 1))
    col_wt_mat = (constant_op.constant(0.)
                  if self._col_weights is None else array_ops.expand_dims(
                      col_weights, 0))

    # We return the normalized loss
    partial_row_gramian = math_ops.matmul(
        new_left_values, new_left_values, transpose_a=True)
    normalization_factor = total_rows / math_ops.cast(num_rows, dtypes.float32)

    unregularized_loss = (
        self._unobserved_weight * (  # pyformat line break
            sparse_ops.sparse_reduce_sum(sp_residual_sq) -  # pyformat break
            sparse_ops.sparse_reduce_sum(sp_approx_sq) +  # pyformat break
            math_ops.trace(math_ops.matmul(partial_row_gramian, gramian))) +
        sparse_ops.sparse_reduce_sum(row_wt_mat * (sp_residual_sq * col_wt_mat))
    ) * normalization_factor

    if self._regularization is not None:
      regularization = self._regularization * (
          math_ops.trace(partial_row_gramian) * normalization_factor +
          math_ops.trace(gramian))
    else:
      regularization = constant_op.constant(0.)

    sum_weights = self._unobserved_weight * math_ops.cast(
        total_rows * total_cols, dtypes.float32)
    if self._row_weights is not None and self._col_weights is not None:
      ones = sparse_tensor.SparseTensor(
          indices=loss_sp_input.indices,
          values=array_ops.ones(array_ops.shape(loss_sp_input.values)),
          dense_shape=loss_sp_input.dense_shape)
      sum_weights += sparse_ops.sparse_reduce_sum(row_wt_mat * (
          ones * col_wt_mat)) * normalization_factor

    return (new_left_values, update_op, unregularized_loss, regularization,
            sum_weights)