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 testInvalidAxes(self):
sp_t = sparse_tensor.SparseTensor(self.ind, self.vals, self.dense_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()
with self.assertRaisesOpError("Invalid reduction dimension -3"):
sparse_ops.sparse_reduce_max(sp_t, -3).eval()
with self.assertRaisesOpError("Invalid reduction dimension 2"):
sparse_ops.sparse_reduce_max(sp_t, 2).eval()
def testInvalidAxes(self):
sp_t = sparse_tensor.SparseTensor(self.ind, self.vals, self.dense_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()
with self.assertRaisesOpError("Invalid reduction dimension -3"):
sparse_ops.sparse_reduce_max(sp_t, -3).eval()
with self.assertRaisesOpError("Invalid reduction dimension 2"):
sparse_ops.sparse_reduce_max(sp_t, 2).eval()
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 _compare(self, sp_t, reduction_axes, ndims, keep_dims, do_sum):
densified = self.evaluate(sparse_ops.sparse_tensor_to_dense(sp_t))
np_ans = densified
if reduction_axes is None:
if do_sum:
np_ans = np.sum(np_ans, keepdims=keep_dims)
else:
np_ans = np.max(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]:
if do_sum:
np_ans = np.sum(np_ans, axis=ra, keepdims=keep_dims)
else:
np_ans = np.max(np_ans, axis=ra, keepdims=keep_dims)
with self.cached_session():
if do_sum:
tf_dense_ans = sparse_ops.sparse_reduce_sum(sp_t, reduction_axes,
keep_dims)
else:
tf_dense_ans = sparse_ops.sparse_reduce_max(sp_t, reduction_axes,
keep_dims)
out_dense = self.evaluate(tf_dense_ans)
if do_sum:
tf_sparse_ans = sparse_ops.sparse_reduce_sum_sparse(sp_t,
reduction_axes,
keep_dims)
else:
tf_sparse_ans = sparse_ops.sparse_reduce_max_sparse(sp_t,
reduction_axes,
keep_dims)
# Convert to dense for comparison purposes.
out_sparse = sparse_ops.sparse_tensor_to_dense(tf_sparse_ans)
self.assertAllClose(np_ans, out_dense)
self.assertAllClose(np_ans, out_sparse)
def _compare(self, sp_t, reduction_axes, ndims, keep_dims, do_sum):
densified = sparse_ops.sparse_tensor_to_dense(sp_t).eval()
np_ans = densified
if reduction_axes is None:
if do_sum:
np_ans = np.sum(np_ans, keepdims=keep_dims)
else:
np_ans = np.max(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]:
if do_sum:
np_ans = np.sum(np_ans, axis=ra, keepdims=keep_dims)
else:
np_ans = np.max(np_ans, axis=ra, keepdims=keep_dims)
with self.test_session():
if do_sum:
tf_dense_ans = sparse_ops.sparse_reduce_sum(sp_t, reduction_axes,
keep_dims)
else:
tf_dense_ans = sparse_ops.sparse_reduce_max(sp_t, reduction_axes,
keep_dims)
out_dense = tf_dense_ans.eval()
if do_sum:
tf_sparse_ans = sparse_ops.sparse_reduce_sum_sparse(sp_t,
reduction_axes,
keep_dims)
else:
tf_sparse_ans = sparse_ops.sparse_reduce_max_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)
