from chainer import function
from chainer.utils import type_check
def _as_mat(x):
if x.ndim == 2:
return x
return x.reshape(len(x), -1)
class LinearFunction(function.Function):
def check_type_forward(self, in_types):
n_in = in_types.size()
type_check.expect(2 <= n_in, n_in <= 3)
x_type, w_type = in_types[:2]
type_check.expect(
x_type.dtype.kind == 'f',
w_type.dtype.kind == 'f',
x_type.ndim >= 2,
w_type.ndim == 2,
type_check.prod(x_type.shape[1:]) == w_type.shape[1],
)
if n_in.eval() == 3:
b_type = in_types[2]
type_check.expect(
b_type.dtype == x_type.dtype,
b_type.ndim == 1,
b_type.shape[0] == w_type.shape[0],
)
def forward(self, inputs):
x = _as_mat(inputs[0])
W = inputs[1]
if not type_check.same_types(*inputs):
raise ValueError('numpy and cupy must not be used together\n'
'type(W): {0}, type(x): {1}'
.format(type(W), type(x)))
y = x.dot(W.T).astype(x.dtype, copy=False)
if len(inputs) == 3:
b = inputs[2]
y += b
return y,
def backward(self, inputs, grad_outputs):
x = _as_mat(inputs[0])
W = inputs[1]
gy = grad_outputs[0]
if not type_check.same_types(*inputs):
raise ValueError('numpy and cupy must not be used together\n'
'type(W): {0}, type(x): {1}'
.format(type(W), type(x)))
gx = gy.dot(W).astype(x.dtype, copy=False).reshape(inputs[0].shape)
gW = gy.T.dot(x).astype(W.dtype, copy=False)
if len(inputs) == 3:
gb = gy.sum(0)
return gx, gW, gb
else:
return gx, gW
[docs]def linear(x, W, b=None):
"""Linear function, or affine transformation.
It accepts two or three arguments: an input minibatch ``x``, a weight
matrix ``W``, and optionally a bias vector ``b``. It computes
.. math:: Y = xW^\\top + b.
Args:
x (:class:`~chainer.Variable` or :class:`numpy.ndarray` or \
:class:`cupy.ndarray`): Input variable, which is a :math:`(s_B, s_1, \
s_2, ..., s_n)`-shaped float array. Its first dimension
:math:`(s_B)` is assumed to be the *minibatch dimension*. The
other dimensions are treated as concatenated one dimension whose
size must be :math:`(s_1 * ... * s_n = N)`.
W (:class:`~chainer.Variable` or :class:`numpy.ndarray` or \
:class:`cupy.ndarray`): Weight variable of shape :math:`(M, N)`,
where :math:`(N = s_1 * ... * s_n)`.
b (:class:`~chainer.Variable` or :class:`numpy.ndarray` or \
:class:`cupy.ndarray`): Bias variable (optional) of shape
:math:`(M,)`.
Returns:
~chainer.Variable: Output variable. A float array with shape
of :math:`(s_B, M)`.
.. seealso:: :class:`~chainer.links.Linear`
.. admonition:: Example
>>> x = np.random.uniform(0, 1, (3, 4)).astype('f')
>>> W = np.random.uniform(0, 1, (5, 4)).astype('f')
>>> b = np.random.uniform(0, 1, (5,)).astype('f')
>>> y = F.linear(x, W, b)
>>> y.shape
(3, 5)
"""
if b is None:
return LinearFunction()(x, W)
else:
return LinearFunction()(x, W, b)