def test_structured_add_s_v_grad(self): sp_types = {'csc': sp.csc_matrix, 'csr': sp.csr_matrix} for format in ['csr', 'csc']: for dtype in ['float32', 'float64']: spmat = sp_types[format](random_lil((4, 3), dtype, 3)) mat = numpy.asarray(numpy.random.rand(3), dtype=dtype) S.verify_grad_sparse(S2.mul_s_v, [spmat, mat], structured=True)
def test_structured_add_s_v_grad(self): sp_types = {'csc': sp.csc_matrix, 'csr': sp.csr_matrix} for format in ['csr', 'csc']: for dtype in ['float32', 'float64']: spmat = sp_types[format](random_lil((4, 3), dtype, 3)) mat = numpy.ones(3, dtype=dtype) S.verify_grad_sparse(S2.structured_add_s_v, [spmat, mat], structured=True)
def test_col_scale(): x = theano.sparse.csc_dmatrix() s = theano.tensor.dvector() rng = numpy.random.RandomState(8723) R = 5 C = 8 x_val_dense = numpy.zeros((R, C), dtype="d") for idx in [(0, 0), (4, 1), (2, 1), (3, 3), (4, 4), (3, 7), (2, 7)]: x_val_dense.__setitem__(idx, rng.randn()) x_val = scipy.sparse.csc_matrix(x_val_dense) s_val = rng.randn(C) f = theano.function([x, s], sp.col_scale(x, s)) # print 'A', f(x_val, s_val).toarray() # print 'B', (x_val_dense * s_val) assert numpy.all(f(x_val, s_val).toarray() == (x_val_dense * s_val)) verify_grad_sparse(sp.col_scale, [x_val, s_val], structured=False)
def test_col_scale(): x = theano.sparse.csc_dmatrix() s = theano.tensor.dvector() rng = numpy.random.RandomState(8723) R = 5 C = 8 x_val_dense = numpy.zeros((R, C), dtype='d') for idx in [(0, 0), (4, 1), (2, 1), (3, 3), (4, 4), (3, 7), (2, 7)]: x_val_dense.__setitem__(idx, rng.randn()) x_val = scipy.sparse.csc_matrix(x_val_dense) s_val = rng.randn(C) f = theano.function([x, s], sp.col_scale(x, s)) # print 'A', f(x_val, s_val).toarray() # print 'B', (x_val_dense * s_val) assert numpy.all(f(x_val, s_val).toarray() == (x_val_dense * s_val)) verify_grad_sparse(sp.col_scale, [x_val, s_val], structured=False)