def testSddmm(self, m, k, n, sparsity, use_gpu):
# Helpers to set up the matrices.
connector = connectors.Uniform(sparsity)
initializer = initializers.Uniform()
# Numpy matrices for verification.
lhs_np = initializer([m, k])
rhs_np = initializer([n, k])
output_np = connector(np.ones([m, n]))
# TensorFlow graph.
output_topology = sparse_matrix.SparseMatrix("output",
matrix=output_np)
lhs = tf.Variable(lhs_np, dtype=tf.float32)
rhs = tf.Variable(rhs_np, dtype=tf.float32)
output = ops.sddmm(lhs, rhs, output_topology, transpose_rhs=True)
# Execute the op and compare the results.
with self.test_session(use_gpu=use_gpu) as sess:
sess.run(tf.global_variables_initializer())
expected_output = self.dense_to_scipy(
output_np * np.dot(lhs_np, np.transpose(rhs_np)))
actual_output = self.sparse_to_scipy(*sess.run(
[output.values, output.row_offsets, output.column_indices]),
shape=expected_output.shape)
self.assert_sparse_matrix_equal(actual_output,
expected_output,
atol=1e-03,
rtol=1e-05)
def testSddmmGradient(self, m, k, n, sparsity, use_gpu):
# Helpers to set up the matrices.
connector = connectors.Uniform(sparsity)
initializer = initializers.Uniform()
# Numpy matrices for verification.
lhs_np = initializer([m, k])
rhs_np = initializer([n, k])
output_np = connector(np.ones([m, n]))
# TensorFlow graph.
output_topology = sparse_matrix.SparseMatrix("output",
matrix=output_np)
lhs = tf.Variable(lhs_np, dtype=tf.float32)
rhs = tf.Variable(rhs_np, dtype=tf.float32)
output = ops.sddmm(lhs, rhs, output_topology, transpose_rhs=True)
# Execute the op and compare the results.
with self.test_session(use_gpu=use_gpu) as sess:
sess.run(tf.global_variables_initializer())
error = tf.test.compute_gradient_error(
[lhs, rhs], [[m, k], [n, k]], output.values,
output.values.shape.as_list())
self.assertLess(error, 1e-3)