def argmax_crf1d(cost, xs):
"""Computes a state that maximizes a joint probability of the given CRF.
Args:
cost (Variable): A :math:`K \\times K` matrix which holds transition
cost between two labels, where :math:`K` is the number of labels.
xs (list of Variable): Input vector for each label.
``len(xs)`` denotes the length of the sequence,
and each :class:`~chainer.Variable` holds a :math:`B \\times K`
matrix, where :math:`B` is mini-batch size, :math:`K` is the number
of labels.
Note that :math:`B` s in all the variables are not necessary
the same, i.e., it accepts the input sequences with different
lengths.
Returns:
tuple: A tuple of :class:`~chainer.Variable` object ``s`` and a
:class:`list` ``ps``.
The shape of ``s`` is ``(B,)``, where ``B`` is the mini-batch size.
i-th element of ``s``, ``s[i]``, represents log-likelihood of i-th
data.
``ps`` is a list of :class:`numpy.ndarray` or
:class:`cupy.ndarray`, and denotes the state that maximizes the
point probability.
``len(ps)`` is equal to ``len(xs)``, and shape of each ``ps[i]`` is
the mini-batch size of the corresponding ``xs[i]``. That means,
``ps[i].shape == xs[i].shape[0:1]``.
"""
alpha = xs[0]
alphas = []
max_inds = []
for x in xs[1:]:
batch = x.shape[0]
if alpha.shape[0] > batch:
alpha, alpha_rest = split_axis.split_axis(alpha, [batch], axis=0)
alphas.append(alpha_rest)
else:
alphas.append(None)
b_alpha, b_cost = broadcast.broadcast(alpha[..., None], cost)
scores = b_alpha + b_cost
max_ind = minmax.argmax(scores, axis=1)
max_inds.append(max_ind)
alpha = minmax.max(scores, axis=1) + x
inds = minmax.argmax(alpha, axis=1)
path = [inds.data]
for m, a in zip(max_inds[::-1], alphas[::-1]):
inds = select_item.select_item(m, inds)
if a is not None:
inds = concat.concat([inds, minmax.argmax(a, axis=1)], axis=0)
path.append(inds.data)
path.reverse()
score = minmax.max(alpha, axis=1)
for a in alphas[::-1]:
if a is None:
continue
score = concat.concat([score, minmax.max(a, axis=1)], axis=0)
return score, path
def argmax_crf1d(cost, xs):
"""Computes a state that maximizes a joint probability of the given CRF.
Args:
cost (Variable): A :math:`K \\times K` matrix which holds transition
cost between two labels, where :math:`K` is the number of labels.
xs (list of Variable): Input vector for each label.
``len(xs)`` denotes the length of the sequence,
and each :class:`~chainer.Variable` holds a :math:`B \\times K`
matrix, where :math:`B` is mini-batch size, :math:`K` is the number
of labels.
Note that :math:`B`\\ s in all the variables are not necessary
the same, i.e., it accepts the input sequences with different
lengths.
Returns:
tuple: A tuple of :class:`~chainer.Variable` object ``s`` and a
:class:`list` ``ps``.
The shape of ``s`` is ``(B,)``, where ``B`` is the mini-batch size.
i-th element of ``s``, ``s[i]``, represents log-likelihood of i-th
data.
``ps`` is a list of :class:`numpy.ndarray` or
:class:`cupy.ndarray`, and denotes the state that maximizes the
point probability.
``len(ps)`` is equal to ``len(xs)``, and shape of each ``ps[i]`` is
the mini-batch size of the corresponding ``xs[i]``. That means,
``ps[i].shape == xs[i].shape[0:1]``.
"""
alpha = xs[0]
alphas = []
max_inds = []
for x in xs[1:]:
batch = x.shape[0]
if alpha.shape[0] > batch:
alpha, alpha_rest = split_axis.split_axis(alpha, [batch], axis=0)
alphas.append(alpha_rest)
else:
alphas.append(None)
b_alpha, b_cost = broadcast.broadcast(alpha[..., None], cost)
scores = b_alpha + b_cost
max_ind = minmax.argmax(scores, axis=1)
max_inds.append(max_ind)
alpha = minmax.max(scores, axis=1) + x
inds = minmax.argmax(alpha, axis=1)
path = [inds.data]
for m, a in zip(max_inds[::-1], alphas[::-1]):
inds = select_item.select_item(m, inds)
if a is not None:
inds = concat.concat([inds, minmax.argmax(a, axis=1)], axis=0)
path.append(inds.data)
path.reverse()
score = minmax.max(alpha, axis=1)
for a in alphas[::-1]:
if a is None:
continue
score = concat.concat([score, minmax.max(a, axis=1)], axis=0)
return score, path
def argmax_crf1d(cost, xs):
alpha = xs[0]
alphas = []
max_inds = []
for x in xs[1:]:
batch = x.shape[0]
if alpha.shape[0] > batch:
alpha, alpha_rest = split_axis.split_axis(alpha, [batch], axis=0)
alphas.append(alpha_rest)
else:
alphas.append(None)
b_alpha, b_cost = broadcast.broadcast(alpha[..., None], cost)
scores = b_alpha + b_cost
max_ind = minmax.argmax(scores, axis=1)
max_inds.append(max_ind)
alpha = minmax.max(scores, axis=1) + x
inds = minmax.argmax(alpha, axis=1)
path = [inds.data]
for m, a in zip(max_inds[::-1], alphas[::-1]):
inds = select_item.select_item(m, inds)
if a is not None:
inds = concat.concat([inds, minmax.argmax(a, axis=1)], axis=0)
path.append(inds.data)
path.reverse()
score = minmax.max(alpha, axis=1)
for a in alphas[::-1]:
if a is None:
continue
score = concat.concat([score, minmax.max(a, axis=1)], axis=0)
return score, path
def argmax_crf1d(cost, xs):
alpha = xs[0]
alphas = []
max_inds = []
for x in xs[1:]:
batch = x.shape[0]
if alpha.shape[0] > batch:
alpha, alpha_rest = split_axis.split_axis(alpha, [batch], axis=0)
alphas.append(alpha_rest)
else:
alphas.append(None)
b_alpha, b_cost = broadcast.broadcast(alpha[..., None], cost)
scores = b_alpha + b_cost
max_ind = minmax.argmax(scores, axis=1)
max_inds.append(max_ind)
alpha = minmax.max(scores, axis=1) + x
inds = minmax.argmax(alpha, axis=1)
path = [inds.data]
for m, a in zip(max_inds[::-1], alphas[::-1]):
inds = select_item.select_item(m, inds)
if a is not None:
inds = concat.concat([inds, minmax.argmax(a, axis=1)], axis=0)
path.append(inds.data)
path.reverse()
score = minmax.max(alpha, axis=1)
for a in alphas[::-1]:
if a is None:
continue
score = concat.concat([score, minmax.max(a, axis=1)], axis=0)
return score, path
def argmax_crf1d(cost, xs):
alpha = xs[0]
max_inds = []
for x in xs[1:]:
b_alpha, b_cost = broadcast.broadcast(alpha[..., None], cost)
scores = b_alpha + b_cost
max_ind = minmax.argmax(scores, axis=1)
max_inds.append(max_ind)
alpha = minmax.max(scores, axis=1) + x
inds = minmax.argmax(alpha, axis=1)
path = [inds.data]
for m in reversed(max_inds):
inds = select_item.select_item(m, inds)
path.append(inds.data)
path.reverse()
return minmax.max(alpha, axis=1), path
def maxout(x, pool_size, axis=1):
"""Maxout activation function.
It accepts an input tensor ``x``, reshapes the ``axis`` dimension
(say the size being ``M * pool_size``) into two dimensions
``(M, pool_size)``, and takes maximum along the ``axis`` dimension.
The output of this function is same as ``x`` except that ``axis`` dimension
is transformed from ``M * pool_size`` to ``M``.
Typically, ``x`` is the output of a linear layer or a convolution layer.
The following is the example where we use :func:`maxout` in combination
with a Linear link.
>>> import numpy, chainer, chainer.links as L
>>> in_size, out_size, pool_size = 100, 100, 100
>>> l = L.Linear(in_size, out_size * pool_size)
>>> x = chainer.Variable(numpy.zeros((1, in_size), 'f')) # prepare data
>>> x = l(x)
>>> y = maxout(x, pool_size)
Args:
x (~chainer.Variable): Input variable. Its first dimension is assumed
to be the *minibatch dimension*. The other dimensions are treated
as one concatenated dimension.
Returns:
~chainer.Variable: Output variable.
.. seealso:: :class:`~chainer.links.Maxout`
"""
if pool_size <= 0:
raise ValueError('pool_size must be a positive integer.')
x_shape = x.data.shape
if x_shape[axis] % pool_size != 0:
expect = 'x.data.shape[axis] % pool_size == 0'
actual = 'x.data.shape[axis]={}, pool_size={}'.format(
x_shape[axis], pool_size)
msg = 'axis dimension must be divided by pool_size'
raise type_check.InvalidType(expect, actual, msg)
shape = (x_shape[:axis] +
(x_shape[axis] // pool_size, pool_size) +
x_shape[axis + 1:])
x = reshape.reshape(x, shape)
return minmax.max(x, axis=axis + 1)
def maxout(x, pool_size, axis=1):
"""Maxout activation function.
It accepts an input tensor ``x``, reshapes the ``axis`` dimension
(say the size being ``M * pool_size``) into two dimensions
``(M, pool_size)``, and takes maximum along the ``axis`` dimension.
The output of this function is same as ``x`` except that ``axis`` dimension
is transformed from ``M * pool_size`` to ``M``.
Typically, ``x`` is the output of a linear layer or a convolution layer.
The following is the example where we use :func:`maxout` in combination
with a Linear link.
>>> import numpy, chainer, chainer.links as L
>>> in_size, out_size, pool_size = 100, 100, 100
>>> l = L.Linear(in_size, out_size * pool_size)
>>> x = chainer.Variable(numpy.zeros((1, in_size), 'f')) # prepare data
>>> x = l(x)
>>> y = maxout(x, pool_size)
Args:
x (~chainer.Variable): Input variable. Its first dimension is assumed
to be the *minibatch dimension*. The other dimensions are treated
as one concatenated dimension.
Returns:
~chainer.Variable: Output variable.
.. seealso:: :class:`~chainer.links.Maxout`
"""
if pool_size <= 0:
raise ValueError('pool_size must be a positive integer.')
x_shape = x.data.shape
if x_shape[axis] % pool_size != 0:
expect = 'x.data.shape[axis] % pool_size == 0'
actual = 'x.data.shape[axis]={}, pool_size={}'.format(
x_shape[axis], pool_size)
msg = 'axis dimension must be divided by pool_size'
raise type_check.InvalidType(expect, actual, msg)
shape = (x_shape[:axis] + (x_shape[axis] // pool_size, pool_size) +
x_shape[axis + 1:])
x = reshape.reshape(x, shape)
return minmax.max(x, axis=axis + 1)
def maxout(x, pool_size, axis=1):
"""Maxout activation function.
It accepts an input tensor ``x``, reshapes the ``axis`` dimension
(say the size being ``M * pool_size``) into two dimensions
``(M, pool_size)``, and takes maximum along the ``axis`` dimension.
Args:
x (:class:`~chainer.Variable` or :class:`numpy.ndarray` or \
:class:`cupy.ndarray`):
Input variable. A :math:`n`-dimensional (:math:`n \\ge` ``axis``)
float array. In general, its first dimension is assumed to be the
*minibatch dimension*. The other dimensions are treated as one
concatenated dimension.
pool_size (int):
The size used for downsampling of pooling layer.
axis (int):
The ``axis`` dimension to be reshaped. The size of ``axis``
dimension should be ``M * pool_size``.
Returns:
~chainer.Variable:
Output variable. The shape of the output is same as ``x`` except
that ``axis`` dimension is transformed from ``M * pool_size`` to
``M``.
.. seealso:: :class:`~chainer.links.Maxout`
.. admonition:: Example
Typically, ``x`` is the output of a linear layer or a convolution
layer. The following is the example where we use :func:`maxout` in
combination with a Linear link.
>>> in_size, out_size, pool_size = 10, 10, 10
>>> bias = np.arange(out_size * pool_size).astype('f')
>>> l = L.Linear(in_size, out_size * pool_size, initial_bias=bias)
>>> x = np.zeros((1, in_size), 'f') # prepare data
>>> x = l(x)
>>> y = F.maxout(x, pool_size)
>>> x.shape
(1, 100)
>>> y.shape
(1, 10)
>>> x.reshape((out_size, pool_size)).data
array([[ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],
[ 10., 11., 12., 13., 14., 15., 16., 17., 18., 19.],
[ 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.],
[ 30., 31., 32., 33., 34., 35., 36., 37., 38., 39.],
[ 40., 41., 42., 43., 44., 45., 46., 47., 48., 49.],
[ 50., 51., 52., 53., 54., 55., 56., 57., 58., 59.],
[ 60., 61., 62., 63., 64., 65., 66., 67., 68., 69.],
[ 70., 71., 72., 73., 74., 75., 76., 77., 78., 79.],
[ 80., 81., 82., 83., 84., 85., 86., 87., 88., 89.],
[ 90., 91., 92., 93., 94., 95., 96., 97., 98., 99.]], \
dtype=float32)
>>> y.data
array([[ 9., 19., 29., 39., 49., 59., 69., 79., 89., 99.]], \
dtype=float32)
"""
if pool_size <= 0:
raise ValueError('pool_size must be a positive integer.')
x_shape = x.shape
if x_shape[axis] % pool_size != 0:
expect = 'x.shape[axis] % pool_size == 0'
actual = 'x.shape[axis]={}, pool_size={}'.format(
x_shape[axis], pool_size)
msg = 'axis dimension must be divided by pool_size'
raise type_check.InvalidType(expect, actual, msg)
shape = (x_shape[:axis] +
(x_shape[axis] // pool_size, pool_size) +
x_shape[axis + 1:])
x = reshape.reshape(x, shape)
return minmax.max(x, axis=axis + 1)
def maxout(x, pool_size, axis=1):
"""Maxout activation function.
It accepts an input tensor ``x``, reshapes the ``axis`` dimension
(say the size being ``M * pool_size``) into two dimensions
``(M, pool_size)``, and takes maximum along the ``axis`` dimension.
Args:
x (:class:`~chainer.Variable` or :ref:`ndarray`):
Input variable. A :math:`n`-dimensional (:math:`n \\ge` ``axis``)
float array. In general, its first dimension is assumed to be the
*minibatch dimension*. The other dimensions are treated as one
concatenated dimension.
pool_size (int):
The size used for downsampling of pooling layer.
axis (int):
The ``axis`` dimension to be reshaped. The size of ``axis``
dimension should be ``M * pool_size``.
Returns:
~chainer.Variable:
Output variable. The shape of the output is same as ``x`` except
that ``axis`` dimension is transformed from ``M * pool_size`` to
``M``.
.. seealso:: :class:`~chainer.links.Maxout`
.. admonition:: Example
Typically, ``x`` is the output of a linear layer or a convolution
layer. The following is the example where we use :func:`maxout` in
combination with a Linear link.
>>> in_size, out_size, pool_size = 10, 10, 10
>>> bias = np.arange(out_size * pool_size).astype(np.float32)
>>> l = L.Linear(in_size, out_size * pool_size, initial_bias=bias)
>>> x = np.zeros((1, in_size), np.float32) # prepare data
>>> x = l(x)
>>> y = F.maxout(x, pool_size)
>>> x.shape
(1, 100)
>>> y.shape
(1, 10)
>>> x.reshape((out_size, pool_size)).data
array([[ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],
[10., 11., 12., 13., 14., 15., 16., 17., 18., 19.],
[20., 21., 22., 23., 24., 25., 26., 27., 28., 29.],
[30., 31., 32., 33., 34., 35., 36., 37., 38., 39.],
[40., 41., 42., 43., 44., 45., 46., 47., 48., 49.],
[50., 51., 52., 53., 54., 55., 56., 57., 58., 59.],
[60., 61., 62., 63., 64., 65., 66., 67., 68., 69.],
[70., 71., 72., 73., 74., 75., 76., 77., 78., 79.],
[80., 81., 82., 83., 84., 85., 86., 87., 88., 89.],
[90., 91., 92., 93., 94., 95., 96., 97., 98., 99.]], \
dtype=float32)
>>> y.data
array([[ 9., 19., 29., 39., 49., 59., 69., 79., 89., 99.]], \
dtype=float32)
"""
if pool_size <= 0:
raise ValueError('pool_size must be a positive integer.')
x_shape = x.shape
if x_shape[axis] % pool_size != 0:
expect = 'x.shape[axis] % pool_size == 0'
actual = 'x.shape[axis]={}, pool_size={}'.format(
x_shape[axis], pool_size)
msg = 'axis dimension must be divided by pool_size'
raise type_check.InvalidType(expect, actual, msg)
shape = (x_shape[:axis] + (x_shape[axis] // pool_size, pool_size) +
x_shape[axis + 1:])
x = reshape.reshape(x, shape)
return minmax.max(x, axis=axis + 1)
