def testDegenerate(self):
for use_gpu in False, True:
with self.test_session(use_gpu=use_gpu):
for dtype in (tf.bool, ):
# A large number is needed to get Eigen to die
x = tf.zeros((0, 9938), dtype=dtype)
y = tf.reduce_nnz(x, [0])
self.assertAllEqual(y.eval(), np.zeros(9938))
def testDegenerate(self):
for use_gpu in False, True:
with self.test_session(use_gpu=use_gpu):
for dtype in (tf.bool,):
# A large number is needed to get Eigen to die
x = tf.zeros((0, 9938), dtype=dtype)
y = tf.reduce_nnz(x, [0])
self.assertAllEqual(y.eval(), np.zeros(9938))
def _compare(self,
x,
reduction_axes,
keep_dims,
use_gpu=False,
feed_dict=None):
np_ans = (x != 0).astype(np.int32)
if reduction_axes is None:
np_ans = np.sum(np_ans, keepdims=keep_dims)
else:
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(use_gpu=use_gpu) as sess:
tf_ans = tf.reduce_nnz(x, reduction_axes, keep_dims)
out = sess.run(tf_ans, feed_dict)
self.assertAllClose(np_ans, out)
self.assertShapeEqual(np_ans, tf_ans)
def _compare(self,
x,
reduction_axes,
keep_dims,
use_gpu=False,
feed_dict=None):
np_ans = (x != 0).astype(np.int32)
if reduction_axes is None:
np_ans = np.sum(np_ans, keepdims=keep_dims)
else:
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(use_gpu=use_gpu) as sess:
tf_ans = tf.reduce_nnz(x, reduction_axes, keep_dims)
out = sess.run(tf_ans, feed_dict)
self.assertAllClose(np_ans, out)
self.assertShapeEqual(np_ans, tf_ans)