def testRandom(self):
np.random.seed(1618)
shapes = [(13, ), (6, 8), (1, 7, 1)]
for shape in shapes:
for dtype in [
np.int32, np.int64, np.float16, np.float32, np.float64
]:
a_np = np.random.randn(*shape).astype(dtype)
b_np = np.random.randn(*shape).astype(dtype)
sp_a, unused_a_nnz = _sparsify(a_np, thresh=-.5)
sp_b, unused_b_nnz = _sparsify(b_np, thresh=-.5)
with self.test_session(use_gpu=False):
maximum_tf = tf.sparse_maximum(sp_a, sp_b)
maximum_tf_densified = tf.sparse_tensor_to_dense(
maximum_tf).eval()
minimum_tf = tf.sparse_minimum(sp_a, sp_b)
minimum_tf_densified = tf.sparse_tensor_to_dense(
minimum_tf).eval()
a_densified = tf.sparse_tensor_to_dense(sp_a).eval()
b_densified = tf.sparse_tensor_to_dense(sp_b).eval()
self.assertAllEqual(np.maximum(a_densified, b_densified),
maximum_tf_densified)
self.assertAllEqual(np.minimum(a_densified, b_densified),
minimum_tf_densified)
def testBasic(self):
with self.test_session(use_gpu=False):
# 1-D, values at index 0.
sp_zero = ops.SparseTensor([[0]], [0], [7])
sp_one = ops.SparseTensor([[0]], [1], [7])
max_tf = tf.sparse_maximum(sp_zero, sp_one).eval()
min_tf = tf.sparse_minimum(sp_zero, sp_one).eval()
self._assertSparseTensorValueEqual(sp_one.eval(), max_tf)
self._assertSparseTensorValueEqual(sp_zero.eval(), min_tf)
# Values at different indices.
sp_zero = ops.SparseTensor([[0]], [0], [7])
sp_zero_2 = ops.SparseTensor([[1]], [0], [7])
expected = ops.SparseTensor([[0], [1]], [0, 0], [7])
max_tf = tf.sparse_maximum(sp_zero, sp_zero_2).eval()
min_tf = tf.sparse_minimum(sp_zero, sp_zero_2).eval()
self._assertSparseTensorValueEqual(expected.eval(), max_tf)
self._assertSparseTensorValueEqual(expected.eval(), min_tf)
def testBasic(self):
with self.test_session(use_gpu=False):
# 1-D, values at index 0.
sp_zero = ops.SparseTensor([[0]], [0], [7])
sp_one = ops.SparseTensor([[0]], [1], [7])
max_tf = tf.sparse_maximum(sp_zero, sp_one).eval()
min_tf = tf.sparse_minimum(sp_zero, sp_one).eval()
self._assertSparseTensorValueEqual(sp_one.eval(), max_tf)
self._assertSparseTensorValueEqual(sp_zero.eval(), min_tf)
# Values at different indices.
sp_zero = ops.SparseTensor([[0]], [0], [7])
sp_zero_2 = ops.SparseTensor([[1]], [0], [7])
expected = ops.SparseTensor([[0], [1]], [0, 0], [7])
max_tf = tf.sparse_maximum(sp_zero, sp_zero_2).eval()
min_tf = tf.sparse_minimum(sp_zero, sp_zero_2).eval()
self._assertSparseTensorValueEqual(expected.eval(), max_tf)
self._assertSparseTensorValueEqual(expected.eval(), min_tf)
def testRandom(self):
np.random.seed(1618)
shapes = [(13,), (6, 8), (1, 7, 1)]
for shape in shapes:
for dtype in [np.int32, np.int64, np.float16, np.float32, np.float64]:
a_np = np.random.randn(*shape).astype(dtype)
b_np = np.random.randn(*shape).astype(dtype)
sp_a, unused_a_nnz = _sparsify(a_np, thresh=-0.5)
sp_b, unused_b_nnz = _sparsify(b_np, thresh=-0.5)
with self.test_session(use_gpu=False):
maximum_tf = tf.sparse_maximum(sp_a, sp_b)
maximum_tf_densified = tf.sparse_tensor_to_dense(maximum_tf).eval()
minimum_tf = tf.sparse_minimum(sp_a, sp_b)
minimum_tf_densified = tf.sparse_tensor_to_dense(minimum_tf).eval()
a_densified = tf.sparse_tensor_to_dense(sp_a).eval()
b_densified = tf.sparse_tensor_to_dense(sp_b).eval()
self.assertAllEqual(np.maximum(a_densified, b_densified), maximum_tf_densified)
self.assertAllEqual(np.minimum(a_densified, b_densified), minimum_tf_densified)
tf.trainable_variables() tf.transpose() tf.truncated_normal() tf.truediv() tf.sparse_transpose() tf.sparse_tensor_dense_matmul() tf.sparse_accumulator_apply_gradient() tf.sparse_accumulator_take_gradient() tf.sparse_add() tf.sparse_concat() tf.sparse_conditional_accumulator() tf.sparse_mask() tf.sparse_matmul() tf.sparse_maximum() tf.sparse_merge() tf.sparse_minimum() tf.sparse_reduce_max() tf.sparse_reduce_max_sparse() tf.reduce_all() tf.reduce_any() tf.reduce_join() tf.reduce_logsumexp() tf.reduce_max() tf.reduce_mean() tf.reduce_min() tf.reduce_prod() tf.reduce_sum() tf.reduced_shape()
