def _fn():
num_rows = np.shape(np_matrix)[0]
num_cols = np.shape(np_matrix)[1]
row_ids = math_ops.range(num_rows, dtype=dtypes.int64)
col_ids = math_ops.range(num_cols, dtype=dtypes.int64)
sp_mat = self.np_array_to_sparse(np_matrix)
sp_mat_t = sparse_ops.sparse_transpose(sp_mat)
row_batch = input_lib.batch([row_ids, sp_mat],
batch_size=min(batch_size, num_rows),
capacity=10,
enqueue_many=True)
col_batch = input_lib.batch([col_ids, sp_mat_t],
batch_size=min(batch_size, num_cols),
capacity=10,
enqueue_many=True)
features = extract_features(row_batch, col_batch,
sp_mat.dense_shape)
if projection_weights is not None:
weights_batch = input_lib.batch(projection_weights,
batch_size=batch_size,
capacity=10,
enqueue_many=True)
features[wals_lib.WALSMatrixFactorization.
PROJECTION_WEIGHTS] = (weights_batch)
if project_row is not None:
features[wals_lib.WALSMatrixFactorization.PROJECT_ROW] = (
constant_op.constant(project_row))
labels = None
return features, labels
def _fn():
num_rows = np.shape(np_matrix)[0]
num_cols = np.shape(np_matrix)[1]
row_ids = math_ops.range(num_rows, dtype=dtypes.int64)
col_ids = math_ops.range(num_cols, dtype=dtypes.int64)
sp_mat = self.np_array_to_sparse(np_matrix)
sp_mat_t = sparse_ops.sparse_transpose(sp_mat)
row_batch = input_lib.batch(
[row_ids, sp_mat],
batch_size=min(batch_size, num_rows),
capacity=10,
enqueue_many=True)
col_batch = input_lib.batch(
[col_ids, sp_mat_t],
batch_size=min(batch_size, num_cols),
capacity=10,
enqueue_many=True)
features = extract_features(row_batch, col_batch, sp_mat.dense_shape)
if projection_weights is not None:
weights_batch = input_lib.batch(
projection_weights,
batch_size=batch_size,
capacity=10,
enqueue_many=True)
features[wals_lib.WALSMatrixFactorization.PROJECTION_WEIGHTS] = (
weights_batch)
if project_row is not None:
features[wals_lib.WALSMatrixFactorization.PROJECT_ROW] = (
constant_op.constant(project_row))
labels = None
return features, labels
def testTransposePreservesShape(self):
with ops.Graph().as_default():
t = sparse_tensor.SparseTensor(indices=[[0, 0]],
values=[0.],
dense_shape=[3, 4])
self.assertTrue(t.shape.is_fully_defined)
transposed = sparse_ops.sparse_transpose(t)
self.assertAllEqual(transposed.shape, [4, 3])
def _sparse_dense_dense_grad(op, grad): a = op.inputs[0] b = op.inputs[1] indices = op.inputs[2] print(a.get_shape(), b.get_shape(), grad) result_shape = tf.cast(tf.stack([tf.shape(a)[0], tf.shape(b)[0]]), dtype=dtypes.int64) grad = tf.SparseTensor(indices=indices, values=grad, dense_shape=result_shape) grad_T = sparse_ops.sparse_transpose(grad) grad_a = sparse_ops.sparse_tensor_dense_matmul(grad, b) grad_b = sparse_ops.sparse_tensor_dense_matmul(grad_T, a) return [grad_a, grad_b, None]
def testTranspose(self):
with self.test_session(use_gpu=False):
np.random.seed(1618)
shapes = [np.random.randint(1, 10, size=rank) for rank in range(1, 6)]
for shape in shapes:
for dtype in [np.int32, np.int64, np.float32, np.float64]:
dn_input = np.random.randn(*shape).astype(dtype)
rank = array_ops.rank(dn_input).eval()
perm = np.random.choice(rank, rank, False)
sp_input, unused_a_nnz = _sparsify(dn_input)
sp_trans = sparse_ops.sparse_transpose(sp_input, perm=perm)
dn_trans = sparse_ops.sparse_tensor_to_dense(sp_trans).eval()
expected_trans = array_ops.transpose(dn_input, perm=perm).eval()
self.assertAllEqual(dn_trans, expected_trans)
def testTranspose(self):
with self.test_session(use_gpu=False):
np.random.seed(1618)
shapes = [np.random.randint(1, 10, size=rank) for rank in range(1, 6)]
for shape in shapes:
for dtype in [np.int32, np.int64, np.float32, np.float64]:
dn_input = np.random.randn(*shape).astype(dtype)
rank = array_ops.rank(dn_input).eval()
perm = np.random.choice(rank, rank, False)
sp_input, unused_a_nnz = _sparsify(dn_input)
sp_trans = sparse_ops.sparse_transpose(sp_input, perm=perm)
dn_trans = sparse_ops.sparse_tensor_to_dense(sp_trans).eval()
expected_trans = array_ops.transpose(dn_input, perm=perm).eval()
self.assertAllEqual(dn_trans, expected_trans)
def testTranspose(self):
if np.__version__ == "1.13.0":
self.skipTest("numpy 1.13.0 bug")
with test_util.force_cpu():
np.random.seed(1618)
shapes = [np.random.randint(1, 10, size=rank) for rank in range(1, 6)]
for shape in shapes:
for dtype in [np.int32, np.int64, np.float32, np.float64]:
dn_input = np.random.randn(*shape).astype(dtype)
rank = self.evaluate(array_ops.rank(dn_input))
perm = np.random.choice(rank, rank, False)
sp_input, unused_a_nnz = _sparsify(dn_input)
sp_trans = sparse_ops.sparse_transpose(sp_input, perm=perm)
dn_trans = sparse_ops.sparse_tensor_to_dense(sp_trans)
expected_trans = array_ops.transpose(dn_input, perm=perm)
self.assertAllEqual(expected_trans.shape, sp_trans.get_shape())
self.assertAllEqual(dn_trans, expected_trans)
def testTranspose(self):
if np.__version__ == "1.13.0":
self.skipTest("numpy 1.13.0 bug")
with test_util.force_cpu():
np.random.seed(1618)
shapes = [np.random.randint(1, 10, size=rank) for rank in range(1, 6)]
for shape in shapes:
for dtype in [np.int32, np.int64, np.float32, np.float64]:
dn_input = np.random.randn(*shape).astype(dtype)
rank = self.evaluate(array_ops.rank(dn_input))
perm = np.random.choice(rank, rank, False)
sp_input, unused_a_nnz = _sparsify(dn_input)
sp_trans = sparse_ops.sparse_transpose(sp_input, perm=perm)
dn_trans = sparse_ops.sparse_tensor_to_dense(sp_trans)
expected_trans = array_ops.transpose(dn_input, perm=perm)
self.assertAllEqual(expected_trans.shape, sp_trans.get_shape())
self.assertAllEqual(dn_trans, expected_trans)
def _fn():
num_rows = np.shape(np_matrix)[0]
num_cols = np.shape(np_matrix)[1]
row_ids = math_ops.range(num_rows, dtype=dtypes.int64)
col_ids = math_ops.range(num_cols, dtype=dtypes.int64)
sp_mat = self.np_array_to_sparse(np_matrix)
sp_mat_t = sparse_ops.sparse_transpose(sp_mat)
row_batch = input_lib.batch(
[row_ids, sp_mat],
batch_size=min(batch_size, num_rows),
capacity=10,
enqueue_many=True)
col_batch = input_lib.batch(
[col_ids, sp_mat_t],
batch_size=min(batch_size, num_cols),
capacity=10,
enqueue_many=True)
features = extract_features(row_batch, col_batch, num_rows, num_cols)
if mode == model_fn.ModeKeys.INFER or mode == model_fn.ModeKeys.EVAL:
self.assertTrue(
project_row is not None,
msg='project_row must be specified in INFER or EVAL mode.')
features[wals_lib.WALSMatrixFactorization.PROJECT_ROW] = (
constant_op.constant(project_row))
if mode == model_fn.ModeKeys.INFER and projection_weights is not None:
weights_batch = input_lib.batch(
projection_weights,
batch_size=batch_size,
capacity=10,
enqueue_many=True)
features[wals_lib.WALSMatrixFactorization.PROJECTION_WEIGHTS] = (
weights_batch)
labels = None
return features, labels
def _fn():
num_rows = np.shape(np_matrix)[0]
num_cols = np.shape(np_matrix)[1]
row_ids = math_ops.range(num_rows, dtype=dtypes.int64)
col_ids = math_ops.range(num_cols, dtype=dtypes.int64)
sp_mat = self.np_array_to_sparse(np_matrix)
sp_mat_t = sparse_ops.sparse_transpose(sp_mat)
row_batch = input_lib.batch(
[row_ids, sp_mat],
batch_size=min(batch_size, num_rows),
capacity=10,
enqueue_many=True)
col_batch = input_lib.batch(
[col_ids, sp_mat_t],
batch_size=min(batch_size, num_cols),
capacity=10,
enqueue_many=True)
features = extract_features(row_batch, col_batch, num_rows, num_cols)
if mode == model_fn.ModeKeys.INFER or mode == model_fn.ModeKeys.EVAL:
self.assertTrue(
project_row is not None,
msg='project_row must be specified in INFER or EVAL mode.')
features[wals_lib.WALSMatrixFactorization.PROJECT_ROW] = (
constant_op.constant(project_row))
if mode == model_fn.ModeKeys.INFER and projection_weights is not None:
weights_batch = input_lib.batch(
projection_weights,
batch_size=batch_size,
capacity=10,
enqueue_many=True)
features[wals_lib.WALSMatrixFactorization.PROJECTION_WEIGHTS] = (
weights_batch)
labels = None
return features, labels
def _process_input_helper(self,
update_row_factors,
sp_input=None,
transpose_input=False,
row_weights=None):
"""Creates the graph for processing a sparse slice of input.
Args:
update_row_factors: if True, update or project the row_factors, else
update or project the column factors.
sp_input: Please refer to comments for update_row_factors,
update_col_factors, project_row_factors, and project_col_factors for
restrictions.
transpose_input: If True, the input is logically transposed and then the
corresponding rows/columns of the transposed input are updated.
row_weights: If not None, this is the row/column weights to be used for
the update or projection. If None, use the corresponding weights from
the model. Note that the feature (column/row) weights will be
determined by the model. When not None, it can either be a scalar or
a rank-1 tensor with the same number of elements as the number of rows
of columns to be updated/projected.
Returns:
A tuple consisting of the following elements:
new_values: New values for the row/column factors.
update_op: An op that assigns the newly computed values to the row/column
factors.
unregularized_loss: A tensor (scalar) that contains the normalized
minibatch loss corresponding to sp_input, without the regularization
term. Add the regularization term below to yield the loss.
regularization: A tensor (scalar) that contains the normalized
regularization term for the minibatch loss corresponding to sp_input.
sum_weights: The sum of the weights corresponding to sp_input. This
can be used with unregularized loss to calculate the root weighted
squared error.
"""
assert isinstance(sp_input, sparse_tensor.SparseTensor)
if update_row_factors:
left = self._row_factors
right_factors = self._col_factors_cache
row_wt = self._row_wt_cache
col_wt = self._col_wt_cache
total_rows = self._input_rows
total_cols = self._input_cols
sharding_func = WALSModel._get_sharding_func(self._input_rows,
self._num_row_shards)
gramian = self._col_gramian_cache
else:
left = self._col_factors
right_factors = self._row_factors_cache
row_wt = self._col_wt_cache
col_wt = self._row_wt_cache
total_rows = self._input_cols
total_cols = self._input_rows
sharding_func = WALSModel._get_sharding_func(self._input_cols,
self._num_col_shards)
gramian = self._row_gramian_cache
transpose_input = not transpose_input
# Note that the row indices of sp_input are based on the original full input
# Here we reindex the rows and give them contiguous ids starting at 0.
# We use tf.unique to achieve this reindexing. Note that this is done so
# that the downstream kernel can assume that the input is "dense" along the
# row dimension.
row_ids, col_ids = array_ops.split(
value=sp_input.indices, num_or_size_splits=2, axis=1)
update_row_indices, all_row_ids = array_ops.unique(row_ids[:, 0])
update_col_indices, all_col_ids = array_ops.unique(col_ids[:, 0])
col_ids = array_ops.expand_dims(math_ops.cast(all_col_ids, dtypes.int64), 1)
row_ids = array_ops.expand_dims(math_ops.cast(all_row_ids, dtypes.int64), 1)
if transpose_input:
update_indices = update_col_indices
row_shape = [
math_ops.cast(array_ops.shape(update_row_indices)[0], dtypes.int64)
]
gather_indices = update_row_indices
else:
update_indices = update_row_indices
row_shape = [
math_ops.cast(array_ops.shape(update_col_indices)[0], dtypes.int64)
]
gather_indices = update_col_indices
num_rows = math_ops.cast(array_ops.shape(update_indices)[0], dtypes.int64)
col_shape = [num_rows]
right = embedding_ops.embedding_lookup(
right_factors, gather_indices, partition_strategy="div")
new_sp_indices = array_ops.concat([row_ids, col_ids], 1)
new_sp_shape = (array_ops.concat([row_shape, col_shape], 0)
if transpose_input else
array_ops.concat([col_shape, row_shape], 0))
new_sp_input = sparse_tensor.SparseTensor(
indices=new_sp_indices,
values=sp_input.values,
dense_shape=new_sp_shape)
# Compute lhs and rhs of the normal equations
total_lhs = (self._unobserved_weight * gramian)
if self._regularization_matrix is not None:
total_lhs += self._regularization_matrix
if self._row_weights is None:
# Special case of ALS. Use a much simpler update rule.
total_rhs = (
self._unobserved_weight * sparse_ops.sparse_tensor_dense_matmul(
new_sp_input, right, adjoint_a=transpose_input))
# TODO (rmlarsen): handle transposing in tf.matrix_solve instead of id:1138
# https://github.com/imdone/tensorflow/issues/1139
# transposing explicitly.
# TODO (rmlarsen): multi-thread tf.matrix_solve. id:1249
# https://github.com/imdone/tensorflow/issues/1250
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 id:845
# https://github.com/imdone/tensorflow/issues/846
# 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)
def _process_input_helper(self,
update_row_factors,
sp_input=None,
transpose_input=False,
row_weights=None):
"""Creates the graph for processing a sparse slice of input.
Args:
update_row_factors: if True, update or project the row_factors, else
update or project the column factors.
sp_input: Please refer to comments for update_row_factors,
update_col_factors, project_row_factors, and project_col_factors for
restrictions.
transpose_input: If True, the input is logically transposed and then the
corresponding rows/columns of the transposed input are updated.
row_weights: If not None, this is the row/column weights to be used for
the update or projection. If None, use the corresponding weights from
the model. Note that the feature (column/row) weights will be
determined by the model. When not None, it can either be a scalar or
a rank-1 tensor with the same number of elements as the number of rows
of columns to be updated/projected.
Returns:
A tuple consisting of the following elements:
new_values: New values for the row/column factors.
update_op: An op that assigns the newly computed values to the row/column
factors.
unregularized_loss: A tensor (scalar) that contains the normalized
minibatch loss corresponding to sp_input, without the regularization
term. Add the regularization term below to yield the loss.
regularization: A tensor (scalar) that contains the normalized
regularization term for the minibatch loss corresponding to sp_input.
sum_weights: The sum of the weights corresponding to sp_input. This
can be used with unregularized loss to caluclate the root weighted
squared error.
"""
assert isinstance(sp_input, sparse_tensor.SparseTensor)
if update_row_factors:
left = self._row_factors
right_factors = self._col_factors_cache
row_wt = self._row_wt_cache
col_wt = self._col_wt_cache
total_rows = self._input_rows
total_cols = self._input_cols
sharding_func = WALSModel._get_sharding_func(self._input_rows,
self._num_row_shards)
gramian = self._col_gramian_cache
else:
left = self._col_factors
right_factors = self._row_factors_cache
row_wt = self._col_wt_cache
col_wt = self._row_wt_cache
total_rows = self._input_cols
total_cols = self._input_rows
sharding_func = WALSModel._get_sharding_func(self._input_cols,
self._num_col_shards)
gramian = self._row_gramian_cache
transpose_input = not transpose_input
# Note that the row indices of sp_input are based on the original full input
# Here we reindex the rows and give them contiguous ids starting at 0.
# We use tf.unique to achieve this reindexing. Note that this is done so
# that the downstream kernel can assume that the input is "dense" along the
# row dimension.
row_ids, col_ids = array_ops.split(
value=sp_input.indices, num_or_size_splits=2, axis=1)
update_row_indices, all_row_ids = array_ops.unique(row_ids[:, 0])
update_col_indices, all_col_ids = array_ops.unique(col_ids[:, 0])
col_ids = array_ops.expand_dims(math_ops.cast(all_col_ids, dtypes.int64), 1)
row_ids = array_ops.expand_dims(math_ops.cast(all_row_ids, dtypes.int64), 1)
if transpose_input:
update_indices = update_col_indices
row_shape = [
math_ops.cast(array_ops.shape(update_row_indices)[0], dtypes.int64)
]
gather_indices = update_row_indices
else:
update_indices = update_row_indices
row_shape = [
math_ops.cast(array_ops.shape(update_col_indices)[0], dtypes.int64)
]
gather_indices = update_col_indices
num_rows = math_ops.cast(array_ops.shape(update_indices)[0], dtypes.int64)
col_shape = [num_rows]
right = embedding_ops.embedding_lookup(
right_factors, gather_indices, partition_strategy="div")
new_sp_indices = array_ops.concat([row_ids, col_ids], 1)
new_sp_shape = (array_ops.concat([row_shape, col_shape], 0)
if transpose_input else
array_ops.concat([col_shape, row_shape], 0))
new_sp_input = sparse_tensor.SparseTensor(
indices=new_sp_indices,
values=sp_input.values,
dense_shape=new_sp_shape)
# Compute lhs and rhs of the normal equations
total_lhs = (self._unobserved_weight * gramian)
if self._regularization_matrix is not None:
total_lhs += self._regularization_matrix
if self._row_weights is None:
# Special case of ALS. Use a much simpler update rule.
total_rhs = (
self._unobserved_weight * sparse_ops.sparse_tensor_dense_matmul(
new_sp_input, right, adjoint_a=transpose_input))
# TODO(rmlarsen): handle transposing in tf.matrix_solve instead of
# transposing explicitly.
# 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)
def test_fn(tensor):
tensor = sparse_ops.sparse_transpose(tensor)
self.assertEqual(tensor.shape.rank, 2)
return tensor
def __init__(self,
loss,
penalty,
At,
D,
b,
tau=None,
sigma=None,
sess=None,
dtype=dtypes.float32,
devices='',
aggregate=False,
init_var=None):
if not tau:
raise ValueError("Must set tau")
if not sigma:
raise ValueError("Must set sigma")
if sess is None:
sess = session.Session()
self.tau = tau
self.sigma = sigma
self.aggregate = aggregate
#print(type(tau), type(sigma))
#assert type(tau)==type(sigma)
if isinstance(self.tau, float):
self.parammode = 'static'
else:
self.parammode = 'variable'
self.sess = sess
self.loss = loss
self.penalty = penalty
self.dtype = dtype
self.devices = devices
self.init_var = True if init_var else False
if isinstance(self.devices, list):
self.master = self.devices[0]
else:
self.master = self.devices
if not isinstance(devices, list):
self.matmul = tf.matmul
self.spmatmul = tf.sparse_tensor_dense_matmul
else:
self.matmul = distops.matmul
self.spmatmul = distops.spmatmul
# check shape.
self.m, self.n = At.T.shape
self.l, _ = D.shape
print(At.T.shape)
print(D.shape)
print(self.n, D.shape[1])
assert (self.n == D.shape[1])
# setup variables.
# for A, we need to consider the case where A is larger than 2GB. (to be distributed)
if not isinstance(At, distmat.DistMat):
if not isinstance(devices, list):
with tf.device(self.devices):
Ap = tf.placeholder(dtype, shape=At.shape)
self.At = variables.Variable(Ap)
sess.run(self.At.initializer, feed_dict={Ap: At})
self.A = None
else:
self.At = distmat.DistMat.from_dataset(At,
devices=self.devices,
sess=sess,
dtype=self.dtype)
self.A = None
else:
assert all([d1 == d2 for d1, d2 in zip(At.devices, self.devices)])
self.At = At
if not isinstance(D, distmat.DistSpMat):
# D should be COO format.
# dtype induced from original matrix.
# avoid recomputation of transpose.
if isinstance(D, coo_matrix):
pass
elif isinstance(D, spmatrix):
D = D.tocoo()
else:
raise ValueError("must be a scipy sparse matrix")
if not isinstance(devices, list):
D_tensor = coo_to_sparsetensor(D)
D_sorted_op = sparse_ops.sparse_reorder(D_tensor)
Dt_sorted_op = sparse_ops.sparse_reorder(
sparse_ops.sparse_transpose(D_tensor))
D_sorted, Dt_sorted = sess.run([D_sorted_op, Dt_sorted_op])
with tf.device(self.devices):
self.D = sparse_tensor.SparseTensor.from_value(D_sorted)
self.Dt = sparse_tensor.SparseTensor.from_value(Dt_sorted)
# b is a constant. (to duplicate)
else:
self.D = distmat.DistSpMat.from_spmatrix(
D, devices_r=self.devices)
self.Dt = None
else:
assert all([d1 == d2 for d1, d2 in zip(D.devices_r, self.devices)])
self.D = D
self.Dt = None
with tf.device(self.master):
self.b = constant_op.constant(b, dtype=dtype)
self._setup_variables(init_var)
self._setup_evals()
