def testHigherRanks(self):
# For the first shape:
# First batch:
# [? e.]
# [1. ? ]
# Second batch:
# [e ? ]
# [e e ]
#
# The softmax results should be:
# [? 1.] [1 ?]
# [1. ? ] and [.5 .5]
# where ? means implicitly zero.
#
# The second shape: same input data, but with a higher-rank shape.
shapes = [[2, 2, 2], [2, 1, 2, 2]]
for shape in shapes:
values = np.asarray([0., np.e, 1., 0., np.e, 0., np.e,
np.e]).reshape(shape)
sp_t, unused_nnz = _sparsify(values, thresh=1e-2)
expected_values = [1., 1., 1., .5, .5]
with test_util.force_cpu():
result = sparse_ops.sparse_softmax(sp_t)
self.assertAllEqual(expected_values, result.values)
self.assertAllEqual(sp_t.indices, result.indices)
self.assertAllEqual(shape, result.dense_shape)
def testHigherRanks(self):
# For the first shape:
# First batch:
# [? e.]
# [1. ? ]
# Second batch:
# [e ? ]
# [e e ]
#
# The softmax results should be:
# [? 1.] [1 ?]
# [1. ? ] and [.5 .5]
# where ? means implicitly zero.
#
# The second shape: same input data, but with a higher-rank shape.
shapes = [[2, 2, 2], [2, 1, 2, 2]]
for shape in shapes:
values = np.asarray(
[0., np.e, 1., 0., np.e, 0., np.e, np.e]).reshape(shape)
sp_t, unused_nnz = _sparsify(values, thresh=1e-2)
expected_values = [1., 1., 1., .5, .5]
with self.test_session(use_gpu=False):
result = sparse_ops.sparse_softmax(sp_t).eval()
self.assertAllEqual(expected_values, result.values)
self.assertAllEqual(sp_t.indices.eval(), result.indices)
self.assertAllEqual(shape, result.shape)
def testGradient(self):
x_shape = [2, 5, 10]
with self.cached_session(use_gpu=False):
for dtype in [np.float32, np.float64]:
x_np = np.random.randn(*x_shape).astype(dtype)
x_tf, nnz = _sparsify(x_np)
y_tf = sparse_ops.sparse_softmax(x_tf)
err = gradient_checker.compute_gradient_error(
x_tf.values, (nnz, ), y_tf.values, (nnz, ))
self.assertLess(err, 1e-4)
def testGradient(self):
x_shape = [2, 5, 10]
with self.test_session(use_gpu=False):
for dtype in [np.float32, np.float64]:
x_np = np.random.randn(*x_shape).astype(dtype)
x_tf, nnz = _sparsify(x_np)
y_tf = sparse_ops.sparse_softmax(x_tf)
err = gradient_checker.compute_gradient_error(x_tf.values, (nnz,),
y_tf.values, (nnz,))
self.assertLess(err, 1e-4)
def testEquivalentToDensified(self):
np.random.seed(1618)
n, m = np.random.choice(20, size=2)
for dtype in [np.float32, np.float64]:
sp_vals_np = np.random.rand(n, m).astype(dtype)
batched_sp_t, unused_nnz1 = _sparsify(sp_vals_np.reshape((1, n, m)), thresh=0.0) # No masking.
with self.test_session(use_gpu=False):
densified = tf.constant(sp_vals_np)
sp_result = sparse_ops.sparse_softmax(batched_sp_t).eval().values.reshape((n, m))
dense_result = tf.nn.softmax(densified)
self.assertAllClose(dense_result.eval(), sp_result)
def testEquivalentToDensified(self):
np.random.seed(1618)
n, m = np.random.choice(20, size=2)
for dtype in [np.float32, np.float64]:
sp_vals_np = np.random.rand(n, m).astype(dtype)
batched_sp_t, unused_nnz1 = _sparsify(
sp_vals_np.reshape((1, n, m)), thresh=0.) # No masking.
with self.test_session(use_gpu=False):
densified = tf.constant(sp_vals_np)
sp_result = sparse_ops.sparse_softmax(
batched_sp_t).eval().values.reshape((n, m))
dense_result = tf.nn.softmax(densified)
self.assertAllClose(dense_result.eval(), sp_result)
def testEquivalentToDensified(self):
np.random.seed(1618)
n, m = np.random.choice(20, size=2)
for dtype in [np.float32, np.float64]:
sp_vals_np = np.random.rand(n, m).astype(dtype)
batched_sp_t, unused_nnz1 = _sparsify(
sp_vals_np.reshape((1, n, m)), thresh=0.) # No masking.
with test_util.force_cpu():
densified = constant_op.constant(sp_vals_np)
sp_result = self.evaluate(
sparse_ops.sparse_softmax(batched_sp_t)).values.reshape((n, m))
dense_result = nn_ops.softmax(densified)
self.assertAllClose(dense_result, sp_result)
def testEquivalentToDensified(self):
np.random.seed(1618)
n, m = np.random.choice(20, size=2)
for dtype in [np.float32, np.float64]:
sp_vals_np = np.random.rand(n, m).astype(dtype)
batched_sp_t, unused_nnz1 = _sparsify(
sp_vals_np.reshape((1, n, m)), thresh=0.) # No masking.
with test_util.force_cpu():
densified = constant_op.constant(sp_vals_np)
sp_result = self.evaluate(
sparse_ops.sparse_softmax(batched_sp_t)).values.reshape((n, m))
dense_result = nn_ops.softmax(densified)
self.assertAllClose(dense_result, sp_result)
def call(self, inputs): return sparse_ops.sparse_softmax(inputs)
