repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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chainer | chainer-master/chainer/functions/loss/mean_squared_error.py | import numpy
from chainer import function_node
import chainer.functions
from chainer.utils import type_check
class MeanSquaredError(function_node.FunctionNode):
"""Mean squared error (a.k.a. Euclidean loss) function."""
def check_type_forward(self, in_types):
type_check._argname(in_types, ('x0', 'x... | 2,966 | 31.25 | 75 | py |
chainer | chainer-master/chainer/functions/loss/negative_sampling.py | import numpy
import six
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import argument
from chainer.utils import precision
from chainer.utils import type_check
def _sigmoid_grad(x, y, gy):
return chainer.... | 13,928 | 32.085511 | 79 | py |
chainer | chainer-master/chainer/functions/loss/vae.py | import math
from chainer.functions.activation import softplus
from chainer.functions.math import average
from chainer.functions.math import exponential
from chainer.functions.math import sum
def gaussian_kl_divergence(mean, ln_var, reduce='sum'):
"""Computes the KL-divergence of Gaussian variables from the stand... | 6,812 | 36.85 | 79 | py |
chainer | chainer-master/chainer/functions/loss/hinge.py | import numpy
from chainer import backend
from chainer.backends import cuda
from chainer import function
from chainer.utils import type_check
def _hinge_fwd_kernel():
return cuda.elementwise(
'S t', 'raw T bottom_diff',
'int ind[] = {i, t}; bottom_diff[ind] *= -1',
'hinge_fwd')
class Hin... | 6,291 | 31.43299 | 79 | py |
chainer | chainer-master/chainer/functions/loss/huber_loss.py | import chainer
from chainer import backend
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
class HuberLoss(function_node.FunctionNode):
def __init__(self, delta, reduce='sum_along_second_axis'):
self.delta = delta
if reduce not in ('sum_along_seco... | 4,951 | 34.120567 | 79 | py |
chainer | chainer-master/chainer/functions/loss/triplet.py | import chainer
from chainer import backend
from chainer import function_node
from chainer.utils import type_check
class Triplet(function_node.FunctionNode):
"""Triplet loss function."""
def __init__(self, margin, reduce='mean'):
if margin <= 0:
raise ValueError('margin should be positive... | 5,403 | 34.788079 | 77 | py |
chainer | chainer-master/chainer/functions/loss/decov.py | from chainer import backend
from chainer import function
from chainer import utils
from chainer.utils import type_check
class DeCov(function.Function):
"""DeCov loss (https://arxiv.org/abs/1511.06068)"""
def __init__(self, reduce='half_squared_sum'):
self.h_centered = None
self.covariance = ... | 3,092 | 32.989011 | 77 | py |
chainer | chainer-master/chainer/functions/loss/softmax_cross_entropy.py | import numpy
import six
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import function_node
from chainer.functions.activation import log_softmax
from chainer.utils import type_check
from chainer import variable
import chainerx
def _broadcast_to(array, shape):
if hasattr... | 21,806 | 37.802491 | 79 | py |
chainer | chainer-master/chainer/functions/loss/discriminative_loss.py | from chainer import backend
from chainer.functions.activation.relu import relu
from chainer.functions.array.broadcast import broadcast_to
from chainer.functions.math.basic_math import absolute
from chainer.functions.math.sqrt import sqrt
from chainer.functions.math.sum import sum as c_sum
class DiscriminativeMarginBa... | 7,389 | 38.731183 | 76 | py |
chainer | chainer-master/chainer/functions/loss/black_out.py | from chainer.functions.array import broadcast
from chainer.functions.array import concat
from chainer.functions.array import expand_dims
from chainer.functions.array import reshape
from chainer.functions.connection import embed_id
from chainer.functions.math import average
from chainer.functions.math import exponential... | 3,306 | 35.744444 | 77 | py |
chainer | chainer-master/chainer/functions/loss/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/chainer/functions/loss/cross_covariance.py | import chainer
from chainer import backend
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
class CrossCovariance(function_node.FunctionNode):
"""Cross-covariance loss."""
def __init__(self, reduce='half_squared_sum'):
self.y_centered = None
se... | 4,497 | 35.569106 | 80 | py |
chainer | chainer-master/chainer/functions/loss/contrastive.py | from chainer import backend
from chainer import function_node
import chainer.functions
from chainer.utils import type_check
class Contrastive(function_node.FunctionNode):
"""Contrastive loss function."""
def __init__(self, margin, reduce='mean'):
if margin <= 0:
raise ValueError('margin ... | 6,113 | 36.740741 | 79 | py |
chainer | chainer-master/chainer/functions/loss/squared_error.py | from chainer import function_node
from chainer import utils
from chainer.utils import type_check
class SquaredError(function_node.FunctionNode):
"""Squared error function."""
def check_type_forward(self, in_types):
type_check._argname(in_types, ('x0', 'x1'))
type_check.expect(
in... | 2,541 | 26.934066 | 78 | py |
chainer | chainer-master/chainer/functions/loss/crf1d.py | from chainer.functions.array import broadcast
from chainer.functions.array import concat
from chainer.functions.array import reshape
from chainer.functions.array import select_item
from chainer.functions.array import split_axis
from chainer.functions.connection import embed_id
from chainer.functions.math import logsume... | 8,264 | 37.985849 | 79 | py |
chainer | chainer-master/chainer/functions/math/fmod.py | from chainer import backend
from chainer import function_node
import chainer.functions
from chainer import utils
from chainer.utils import type_check
class Fmod(function_node.FunctionNode):
@property
def label(self):
return 'fmod'
def check_type_forward(self, in_types):
type_check._argna... | 1,315 | 26.416667 | 78 | py |
chainer | chainer-master/chainer/functions/math/einsum.py | import warnings
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import argument
from chainer.utils import type_check
def _enumerate_axes(subscripts):
if '@' in subscripts:
left_sub, right_sub = sub... | 11,517 | 32.289017 | 77 | py |
chainer | chainer-master/chainer/functions/math/cumprod.py | import six
import chainer
from chainer import backend
from chainer import function_node
from chainer.functions.array import flip
from chainer.utils import type_check
class Cumprod(function_node.FunctionNode):
"""Cumulative prod of array elements over a given axis."""
def __init__(self, axis):
if isi... | 3,552 | 27.198413 | 79 | py |
chainer | chainer-master/chainer/functions/math/maximum.py | import numpy
import chainer
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
class Maximum(function_node.FunctionNode):
"""Element-wise maximum of input variables."""
def check_type_forward(self, in_types):
type_check.... | 3,106 | 28.875 | 77 | py |
chainer | chainer-master/chainer/functions/math/minmax.py | import numpy
import six
from chainer import backend
from chainer import function_node
import chainer.functions
import chainer.utils
from chainer.utils import type_check
import chainerx
class SelectorBase(function_node.FunctionNode):
"""Select an array element from a given axis or set of axes."""
def __init_... | 6,399 | 29.331754 | 78 | py |
chainer | chainer-master/chainer/functions/math/identity.py | from chainer import function_node
class Identity(function_node.FunctionNode):
"""Identity function."""
def forward(self, xs):
return xs
def backward(self, indexes, gys):
return gys
def identity(*inputs):
"""Just returns input variables."""
ret = Identity().apply(inputs)
re... | 358 | 17.894737 | 43 | py |
chainer | chainer-master/chainer/functions/math/erfcinv.py | try:
from scipy import special
available_cpu = True
except ImportError as e:
available_cpu = False
_import_error = e
import math
import chainer
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
BACKWORDC = math.pi ** 0.5... | 1,699 | 25.5625 | 75 | py |
chainer | chainer-master/chainer/functions/math/lgamma.py | import chainer
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
_lgamma_cpu = None
class LGamma(function_node.FunctionNode):
@property
def label(self):
return 'lgamma'
def check_type_forward(self, in_types):
... | 1,562 | 26.910714 | 79 | py |
chainer | chainer-master/chainer/functions/math/floor.py | import chainer
from chainer import backend
from chainer import utils
def floor(x):
"""Elementwise floor function.
.. math::
y_i = \\lfloor x_i \\rfloor
Args:
x (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.
Returns:
~chainer.Variable: Output variable.
"""... | 499 | 21.727273 | 73 | py |
chainer | chainer-master/chainer/functions/math/fft.py | from chainer import backend
from chainer import function_node
from chainer.utils import type_check
class FFT(function_node.FunctionNode):
"""Fast Fourier transform."""
def __init__(self, method):
self._method = method
def check_type_forward(self, in_types):
type_check._argname(in_types,... | 2,544 | 28.941176 | 76 | py |
chainer | chainer-master/chainer/functions/math/average.py | import six
import chainer
from chainer import function_node
from chainer.functions.array import broadcast
from chainer.functions.array import reshape
from chainer.functions.math import sum as sum_mod
from chainer import utils
from chainer.utils import type_check
class Mean(function_node.FunctionNode):
"""Mean of... | 4,665 | 33.820896 | 79 | py |
chainer | chainer-master/chainer/functions/math/hyperbolic.py | from chainer import backend
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
class Cosh(function_node.FunctionNode):
@property
def label(self):
return 'cosh'
def check_type_forward(self, in_types):
type_check._argname(in_types, ('x',))
... | 1,767 | 21.379747 | 73 | py |
chainer | chainer-master/chainer/functions/math/batch_l2_norm_squared.py | import six
import chainer
from chainer.backends import cuda
from chainer import function_node
from chainer.functions.math import sum as _sum
from chainer.utils import type_check
class BatchL2NormSquared(function_node.FunctionNode):
def check_type_forward(self, in_types):
type_check._argname(in_types, ('... | 2,585 | 29.423529 | 77 | py |
chainer | chainer-master/chainer/functions/math/cholesky.py | import chainer
from chainer import function_node
from chainer.utils import type_check
import chainerx
class Cholesky(function_node.FunctionNode):
@property
def label(self):
return 'cholesky'
def check_type_forward(self, in_types):
type_check._argname(in_types, ('a', ))
a_type, = ... | 1,435 | 24.192982 | 73 | py |
chainer | chainer-master/chainer/functions/math/clip.py | import numpy
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
class Clip(function_node.FunctionNode):
"""Clips (limits) elements of input variable."""
def __init__(self, x_min, x_max):
if x_min is None and x_max is Non... | 2,762 | 28.393617 | 76 | py |
chainer | chainer-master/chainer/functions/math/erfc.py | import math
import warnings
import numpy
import chainer
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
_erfc_cpu = None
class Erfc(function_node.FunctionNode):
@property
def label(self):
return 'erfc'
def che... | 1,779 | 25.176471 | 77 | py |
chainer | chainer-master/chainer/functions/math/digamma.py | import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
_digamma_cpu = None
class DiGamma(function_node.FunctionNode):
@property
def label(self):
return 'digamma'
def check_typ... | 1,648 | 26.949153 | 79 | py |
chainer | chainer-master/chainer/functions/math/scale.py | import chainer
from chainer.functions.array import broadcast
from chainer.functions.array import reshape
def scale(x, y, axis=1):
"""Elementwise product with broadcasting.
Computes a elementwise product of two input variables, with the shape of
the latter variable broadcasted to match the shape of the fo... | 1,607 | 31.816327 | 78 | py |
chainer | chainer-master/chainer/functions/math/arctanh.py | from chainer import backend
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
class Arctanh(function_node.FunctionNode):
"""Elementwise inverse hyperbolic tangent function."""
def check_type_forward(self, in_types):
type_check._argname(in_types, ('x',))... | 1,037 | 23.714286 | 73 | py |
chainer | chainer-master/chainer/functions/math/sum.py | import numpy
import six
import chainer
from chainer import backend
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
import chainerx
class Sum(function_node.FunctionNode):
"""Sum of array elements over a given axis."""
keepdims = False
def __init__(self, a... | 4,692 | 28.149068 | 79 | py |
chainer | chainer-master/chainer/functions/math/trigonometric.py | import numpy
from chainer import backend
from chainer.backends import cuda
from chainer import function_node
import chainer.functions
from chainer import utils
from chainer.utils import type_check
class Sin(function_node.FunctionNode):
@property
def label(self):
return 'sin'
def check_type_forw... | 11,758 | 25.306488 | 76 | py |
chainer | chainer-master/chainer/functions/math/cumsum.py | import six
from chainer import backend
from chainer import function_node
from chainer.functions.array import flip
from chainer.utils import type_check
class Cumsum(function_node.FunctionNode):
"""Cumulative sum of array elements over a given axis."""
def __init__(self, axis=None):
if isinstance(axis... | 1,792 | 27.919355 | 68 | py |
chainer | chainer-master/chainer/functions/math/inv.py | import numpy.linalg
import chainer
from chainer.backends import cuda
from chainer import function_node
import chainer.functions
from chainer.functions.math import matmul
from chainer import utils
from chainer.utils import precision
from chainer.utils import type_check
def _inv_gpu(b):
# We do a batched LU decomp... | 5,386 | 33.312102 | 79 | py |
chainer | chainer-master/chainer/functions/math/basic_math.py | import math
import numpy
import six
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer.backends import intel64
from chainer import function_node
import chainer.functions
from chainer.functions.math import floor as _floor
from chainer import utils
from chainer.utils import type_c... | 25,827 | 26.712446 | 77 | py |
chainer | chainer-master/chainer/functions/math/minimum.py | from chainer import backend
from chainer.backends import cuda
from chainer import function_node
import chainer.functions
from chainer import utils
from chainer.utils import type_check
class Minimum(function_node.FunctionNode):
"""Element-wise minimum of input variables."""
def check_type_forward(self, in_typ... | 2,462 | 30.177215 | 77 | py |
chainer | chainer-master/chainer/functions/math/linear_interpolate.py | from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
class LinearInterpolate(function_node.FunctionNode):
def check_type_forward(self, in_types):
type_check._argname(in_types, ('p', 'x', 'y'))
p_type, x_type, y_type = i... | 2,707 | 27.505263 | 73 | py |
chainer | chainer-master/chainer/functions/math/zeta.py | from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
_zeta_cpu = None
class Zeta(function_node.FunctionNode):
def __init__(self, x):
self._x = x
@property
def label(self):
return 'zeta'
def check_type_fo... | 1,776 | 25.522388 | 79 | py |
chainer | chainer-master/chainer/functions/math/square.py | from chainer import backend
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
class Square(function_node.FunctionNode):
@property
def label(self):
return 'square'
def check_type_forward(self, in_types):
type_check._argname(in_types, ('x',))
... | 1,419 | 23.912281 | 73 | py |
chainer | chainer-master/chainer/functions/math/sparse_matmul.py | import numpy
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
try:
from scipy import sparse
_scipy_available = True
except ImportError:
_scipy_available = False
def _coo_matmul(sp... | 16,155 | 32.588358 | 79 | py |
chainer | chainer-master/chainer/functions/math/matmul.py | import warnings
import numpy
from chainer import backend
from chainer.backends import cuda
from chainer import function_node
import chainer.functions
from chainer import utils
from chainer.utils import type_check
import chainerx
def _mat_ptrs(a):
"""Creates an array of pointers to matrices
Args:
a:... | 11,485 | 33.389222 | 79 | py |
chainer | chainer-master/chainer/functions/math/prod.py | import numpy
import six
from chainer import backend
from chainer import function_node
import chainer.functions
from chainer.utils import type_check
class Prod(function_node.FunctionNode):
"""Product of array elements over a given axis."""
keepdims = False
def __init__(self, axis=None, keepdims=False):
... | 3,528 | 30.792793 | 78 | py |
chainer | chainer-master/chainer/functions/math/det.py | import chainer
from chainer import function_node
import chainer.functions
from chainer.utils import precision
from chainer.utils import type_check
class BatchDet(function_node.FunctionNode):
@property
def label(self):
return 'det'
def check_type_forward(self, in_types):
type_check._argna... | 2,352 | 29.558442 | 79 | py |
chainer | chainer-master/chainer/functions/math/erfcx.py | import numpy
try:
from scipy import special
available_cpu = True
except ImportError as e:
available_cpu = False
_import_error = e
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
class Erfcx(function_node.FunctionNode)... | 1,722 | 25.921875 | 75 | py |
chainer | chainer-master/chainer/functions/math/tensordot.py | import numpy
import six
from chainer import backend
from chainer import function_node
from chainer import utils
from chainer.utils import collections_abc
from chainer.utils import type_check
def _tensordot(a, b, a_axes, b_axes, c_axes=None):
a_col_ndim = len(a_axes[1])
b_row_ndim = len(b_axes[0])
if a_co... | 6,126 | 34.622093 | 79 | py |
chainer | chainer-master/chainer/functions/math/ndtri.py | try:
from scipy import special
available_cpu = True
except ImportError as e:
available_cpu = False
_import_error = e
import math
import chainer
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
class Ndtri(function_node.... | 1,676 | 26.048387 | 75 | py |
chainer | chainer-master/chainer/functions/math/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/chainer/functions/math/ndtr.py | import math
import warnings
import numpy
import chainer
from chainer.backends import cuda
from chainer import function_node
from chainer.functions.math import exponential
from chainer import utils
from chainer.utils import type_check
_ndtr_cpu = None
def _slow_ndtr_cpu(x):
return 0.5 * math.erfc(-x / 2 ** 0.5... | 1,932 | 25.479452 | 79 | py |
chainer | chainer-master/chainer/functions/math/sqrt.py | import numpy
from chainer import backend
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
import chainerx
class Sqrt(function_node.FunctionNode):
@property
def label(self):
return 'sqrt'
def check_type_forward(sel... | 2,385 | 24.115789 | 79 | py |
chainer | chainer-master/chainer/functions/math/log_ndtr.py | import numpy
from chainer.backends import cuda
from chainer import function_node
from chainer.functions.math import erfcx
from chainer import utils
from chainer.utils import type_check
_log_ndtr_cpu = None
class LogNdtr(function_node.FunctionNode):
@property
def label(self):
return 'log_ndtr'
... | 1,898 | 26.521739 | 79 | py |
chainer | chainer-master/chainer/functions/math/polygamma.py | from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
_polygamma_cpu = None
class PolyGamma(function_node.FunctionNode):
@property
def label(self):
return 'polygamma'
def check_type_forward(self, in_types):
ty... | 1,796 | 27.078125 | 79 | py |
chainer | chainer-master/chainer/functions/math/sign.py | import chainer
from chainer import backend
from chainer import utils
def sign(x):
"""Elementwise sign function.
For a given input :math:`x`, this function returns :math:`sgn(x)`
defined as
.. math::
sgn(x) = \\left \\{ \\begin{array}{cc}
-1 & {\\rm if~x < 0} \\\\
0 & {\\rm i... | 893 | 23.162162 | 74 | py |
chainer | chainer-master/chainer/functions/math/bias.py | import chainer
from chainer.functions.array import broadcast
from chainer.functions.array import reshape
def bias(x, y, axis=1):
"""Elementwise summation with broadcasting.
Computes a elementwise summation of two input variables, with the shape of
the latter variable broadcasted to match the shape of the... | 1,607 | 31.816327 | 78 | py |
chainer | chainer-master/chainer/functions/math/exponential.py | import math
import numpy
from chainer import backend
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
import chainerx
class Exp(function_node.FunctionNode):
@property
def label(self):
return 'exp'
def check_type_... | 3,676 | 22.125786 | 73 | py |
chainer | chainer-master/chainer/functions/math/logsumexp.py | import six
import chainer
from chainer import backend
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
import chainerx
class LogSumExp(function_node.FunctionNode):
def __init__(self, axis=None):
if axis is None:
self.axis = None
elif is... | 3,116 | 30.484848 | 79 | py |
chainer | chainer-master/chainer/functions/math/ceil.py | import chainer
from chainer import backend
from chainer import utils
def ceil(x):
"""Elementwise ceil function.
.. math::
y_i = \\lceil x_i \\rceil
Args:
x (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.
Returns:
~chainer.Variable: Output variable.
"""
... | 493 | 21.454545 | 73 | py |
chainer | chainer-master/chainer/functions/math/logarithm_1p.py | import numpy
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
class Log1p(function_node.FunctionNode):
@property
def label(self):
return 'log1p'
def check_type_forward(self, in_types):
type_check._argname(... | 985 | 22.47619 | 73 | py |
chainer | chainer-master/chainer/functions/math/exponential_m1.py | import numpy
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
class Expm1(function_node.FunctionNode):
@property
def label(self):
return 'expm1'
def check_type_forward(self, in_types):
type_check._argname(... | 983 | 22.428571 | 73 | py |
chainer | chainer-master/chainer/functions/math/erfinv.py | try:
from scipy import special
available_cpu = True
except ImportError as e:
available_cpu = False
_import_error = e
import math
import chainer
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
BACKWORDC = math.pi ** 0.5... | 1,674 | 25.171875 | 75 | py |
chainer | chainer-master/chainer/functions/math/fix.py | import chainer
from chainer import backend
from chainer import utils
def fix(x):
"""Elementwise fix function.
.. math::
y_i = \\lfix x_i \\rfix
Args:
x (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.
Returns:
~chainer.Variable: Output variable.
"""
if... | 490 | 20.347826 | 73 | py |
chainer | chainer-master/chainer/functions/math/erf.py | import math
import warnings
import numpy
import chainer
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
_erf_cpu = None
class Erf(function_node.FunctionNode):
@property
def label(self):
return 'erf'
def check_... | 1,749 | 24.735294 | 79 | py |
chainer | chainer-master/chainer/functions/noise/simplified_dropconnect.py | import numpy
from chainer import backend
from chainer import function_node
import chainer.functions
from chainer.utils import type_check
from chainer import variable
def _as_mat(x):
if x.ndim == 2:
return x
return x.reshape(len(x), -1)
def _matmul(a, b, xp):
if xp is numpy:
# numpy 1.9 ... | 7,164 | 34.295567 | 78 | py |
chainer | chainer-master/chainer/functions/noise/gumbel_softmax.py | from chainer import backend
import chainer.functions
from chainer import variable
def gumbel_softmax(log_pi, tau=0.1, axis=1):
"""Gumbel-Softmax sampling function.
This function draws samples :math:`y_i` from Gumbel-Softmax distribution,
.. math::
y_i = {\\exp((g_i + \\log\\pi_i)/\\tau)
... | 1,281 | 31.05 | 78 | py |
chainer | chainer-master/chainer/functions/noise/gaussian.py | import numpy
import chainer
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import argument
from chainer.utils import type_check
class Gaussian(function_node.FunctionNode):
"""Gaussian sampling function.
.. note::
In forward calculat... | 4,588 | 32.253623 | 79 | py |
chainer | chainer-master/chainer/functions/noise/dropout.py | import numpy
import chainer
from chainer.backends import cuda
from chainer.backends import intel64
from chainer import configuration
from chainer import function_node
from chainer.utils import argument
from chainer.utils import type_check
if cuda.cudnn_enabled:
cudnn = cuda.cudnn
class Dropout(function_node.Fun... | 7,599 | 34.514019 | 79 | py |
chainer | chainer-master/chainer/functions/noise/zoneout.py | import numpy
from chainer import backend
from chainer import configuration
from chainer import function_node
from chainer.utils import argument
from chainer.utils import type_check
class Zoneout(function_node.FunctionNode):
"""Zoneout regularization."""
def __init__(self, zoneout_ratio):
self.zoneo... | 2,199 | 28.72973 | 79 | py |
chainer | chainer-master/chainer/functions/noise/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/chainer/testing/doctest_helper.py | import os
import pkg_resources
_gpu_limit = int(os.getenv('CHAINER_TEST_GPU_LIMIT', '-1'))
def skipif(condition):
# In the readthedocs build, doctest should never be skipped, because
# otherwise the code would disappear from the documentation.
if os.environ.get('READTHEDOCS') == 'True':
return ... | 663 | 22.714286 | 72 | py |
chainer | chainer-master/chainer/testing/attr.py | import os
import unittest
try:
import pytest
_error = None
except ImportError as e:
_error = e
def is_available():
return _error is None
def check_available():
if _error is not None:
raise RuntimeError('''\
{} is not available.
Reason: {}: {}'''.format(__name__, type(_error).__name__,... | 1,878 | 21.638554 | 78 | py |
chainer | chainer-master/chainer/testing/_bundle.py | import collections
import inspect
import sys
# A tuple that represents a test case.
# For bare (non-generated) test cases, [1] and [2] are None.
# [0] Test case class
# [1] Module name in whicn the class is defined
# [2] Class name
_TestCaseTuple = collections.namedtuple(
'_TestCaseTuple', ('klass', 'module_name'... | 3,437 | 33.727273 | 79 | py |
chainer | chainer-master/chainer/testing/condition.py | import functools
import unittest
import six
try:
import _pytest.outcomes
_error = None
except ImportError as e:
_error = e
class QuietTestRunner(object):
def run(self, suite):
result = unittest.TestResult()
suite(result)
return result
def repeat_with_success_at_least(times... | 4,230 | 31.05303 | 77 | py |
chainer | chainer-master/chainer/testing/array.py | import numpy
import six
import chainer
from chainer import backend
from chainer import utils
import chainerx
def assert_allclose(x, y, atol=1e-5, rtol=1e-4, verbose=True):
"""Asserts if some corresponding element of x and y differs too much.
This function can handle both CPU and GPU arrays simultaneously.
... | 3,556 | 32.87619 | 78 | py |
chainer | chainer-master/chainer/testing/unary_math_function_test.py | import unittest
import warnings
import numpy
from chainer.backends import cuda
from chainer import function
from chainer import functions
from chainer import variable
try:
from chainer.testing import attr
_error = attr.get_error()
except ImportError as e:
_error = e
def is_available():
return _erro... | 11,829 | 38.302326 | 79 | py |
chainer | chainer-master/chainer/testing/training.py | from __future__ import division
from chainer import training
try:
import mock
_error = None
except ImportError as e:
_error = e
def is_available():
return _error is None
def check_available():
if _error is not None:
raise RuntimeError('''\
{} is not available.
Reason: {}: {}'''.forma... | 1,956 | 26.56338 | 77 | py |
chainer | chainer-master/chainer/testing/helper.py | import contextlib
import sys
import unittest
import warnings
import pkg_resources
try:
import mock
_mock_error = None
except ImportError as e:
_mock_error = e
def _check_mock_available():
if _mock_error is not None:
raise RuntimeError(
'mock is not available: Reason: {}'.format(_m... | 3,610 | 26.356061 | 76 | py |
chainer | chainer-master/chainer/testing/serializer.py | import os
from chainer import serializers
from chainer import utils
def save_and_load(src, dst, filename, saver, loader):
"""Saves ``src`` and loads it to ``dst`` using a de/serializer.
This function simply runs a serialization and deserialization to check if
the serialization code is correctly implemen... | 1,562 | 27.418182 | 77 | py |
chainer | chainer-master/chainer/testing/distribution_test.py | import unittest
import chainer.types
try:
import pytest # NOQA
_error = None
except ImportError as e:
_error = e
if _error is None:
from chainer.testing._distribution_test import distribution_unittest
elif not chainer.types.TYPE_CHECKING:
class distribution_unittest(unittest.TestCase):
... | 468 | 20.318182 | 72 | py |
chainer | chainer-master/chainer/testing/random.py | from __future__ import absolute_import
import atexit
import functools
import os
import random
import types
import numpy
from chainer.backends import cuda
from chainer.testing import _bundle
_old_python_random_state = None
_old_numpy_random_state = None
def _numpy_do_setup(deterministic=True):
global _old_pyth... | 4,706 | 27.527273 | 79 | py |
chainer | chainer-master/chainer/testing/backend.py | import functools
import chainer
from chainer import backend
from chainer.testing import _bundle
from chainer.testing import attr
import chainerx
# TODO(hvy): BackendConfig.__enter__ does not have to modify the current
# device. Change it so that it does not.
class BackendConfig(object):
_props = [
# Cha... | 6,385 | 30.93 | 79 | py |
chainer | chainer-master/chainer/testing/__init__.py | from chainer.testing.array import assert_allclose # NOQA
from chainer.testing.backend import BackendConfig # NOQA
from chainer.testing.backend import inject_backend_tests # NOQA
from chainer.testing.distribution_test import distribution_unittest # NOQA
from chainer.testing.function_link import FunctionTestCase # N... | 2,027 | 48.463415 | 89 | py |
chainer | chainer-master/chainer/testing/_distribution_test.py | import functools
import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer.testing import array
from chainer.testing import attr
from chainer import utils
def skip_not_in_test_target(test_target):
def decorator(f):
@functools.wraps(f)
def new_f(self, *args, **kwa... | 12,806 | 32.264935 | 78 | py |
chainer | chainer-master/chainer/testing/parameterized.py | import functools
import itertools
import types
import typing as tp # NOQA
import unittest
import numpy
import six
from chainer.testing import _bundle
from chainer import utils
def _param_to_str(obj):
if isinstance(obj, type):
return obj.__name__
elif hasattr(obj, '__name__') and isinstance(obj.__na... | 7,227 | 31.558559 | 115 | py |
chainer | chainer-master/chainer/testing/matrix.py | import numpy
from chainer.utils import argument
def generate_matrix(shape, dtype=float, **kwargs):
r"""generate_matrix(shape, dtype=float, *, singular_values)
Generates a random matrix with given singular values.
This function generates a random NumPy matrix (or a stack of matrices) that
has specif... | 2,736 | 37.013889 | 79 | py |
chainer | chainer-master/chainer/testing/function_link.py | import contextlib
import typing as tp # NOQA
import unittest
import numpy
import six
import chainer
from chainer import backend
from chainer import initializers
from chainer.testing import array as array_module
from chainer import utils
class _TestError(AssertionError):
"""Parent class to Chainer test errors.... | 46,984 | 36.860596 | 79 | py |
chainer | chainer-master/chainer/distributions/gumbel.py | import numpy
import chainer
from chainer.backends import cuda
from chainer import distribution
from chainer.functions.math import exponential
from chainer.functions.math import lgamma
from chainer.utils import cache
EULER = 0.57721566490153286060651209008240243104215933593992
class Gumbel(distribution.Distribution... | 2,898 | 25.59633 | 73 | py |
chainer | chainer-master/chainer/distributions/chisquare.py | import numpy
import chainer
from chainer.backends import cuda
from chainer import distribution
from chainer.functions.math import digamma
from chainer.functions.math import exponential
from chainer.functions.math import lgamma
from chainer.utils import cache
class Chisquare(distribution.Distribution):
"""Chi-Sq... | 2,110 | 23.264368 | 72 | py |
chainer | chainer-master/chainer/distributions/cauchy.py | import warnings
import numpy
import chainer
from chainer.backends import cuda
from chainer import distribution
from chainer.functions.math import exponential
from chainer.functions.math import trigonometric
from chainer.utils import cache
def _cauchy_icdf(x):
x = chainer.as_variable(x)
h = (x - 0.5) * numpy... | 3,108 | 26.27193 | 78 | py |
chainer | chainer-master/chainer/distributions/pareto.py | import chainer
from chainer.backends import cuda
from chainer import distribution
from chainer.functions.array import where
from chainer.functions.math import exponential
from chainer import utils
from chainer.utils import cache
class Pareto(distribution.Distribution):
"""Pareto Distribution.
.. math::
... | 3,490 | 25.24812 | 79 | py |
chainer | chainer-master/chainer/distributions/dirichlet.py | import numpy
import chainer
from chainer import distribution
from chainer.functions.array import expand_dims
from chainer.functions.math import digamma
from chainer.functions.math import exponential
from chainer.functions.math import lgamma
from chainer.functions.math import sum as sum_mod
from chainer.utils import ca... | 3,449 | 27.04878 | 79 | py |
chainer | chainer-master/chainer/distributions/uniform.py | import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import distribution
from chainer.functions.array import broadcast
from chainer.functions.array import where
from chainer.functions.math import clip
from chainer.functions.math import exponential
from chainer.functions.math import ... | 4,642 | 28.01875 | 76 | py |
chainer | chainer-master/chainer/distributions/geometric.py | import chainer
from chainer.backends import cuda
from chainer import distribution
from chainer.functions.math import exponential
from chainer.utils import cache
class Geometric(distribution.Distribution):
"""Geometric Distribution.
The probability mass function of the distribution is expressed as
.. ma... | 2,020 | 23.646341 | 78 | py |
chainer | chainer-master/chainer/distributions/normal.py | import math
import numpy
import chainer
from chainer.backends import cuda
from chainer import distribution
from chainer.functions.math import exponential
from chainer.functions.math import log_ndtr
from chainer.functions.math import ndtr
from chainer.functions.math import ndtri
from chainer.utils import argument
from... | 5,011 | 28.656805 | 79 | py |
chainer | chainer-master/chainer/distributions/beta.py | import chainer
from chainer import backend
from chainer import distribution
from chainer.functions.array import where
from chainer.functions.math import digamma
from chainer.functions.math import exponential
from chainer.functions.math import lgamma
from chainer import utils
from chainer.utils import cache
def _lbeta... | 3,139 | 26.304348 | 78 | py |
chainer | chainer-master/chainer/distributions/one_hot_categorical.py | import chainer
from chainer.backends import cuda
from chainer import distribution
from chainer.functions.math import exponential
import chainer.functions.math.sum as sum_mod
from chainer.utils import cache
def _stack(xp, xs, axis):
try:
return xp.stack(xs, axis)
except AttributeError:
# in cas... | 2,684 | 26.680412 | 79 | py |
chainer | chainer-master/chainer/distributions/log_normal.py | import math
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import distribution
from chainer.functions.math import exponential
from chainer.utils import cache
LOGPROBC = - 0.5 * math.log(2 * math.pi)
class LogNormal(distribution.Distribution):
"""Logatithm Normal Dist... | 2,707 | 25.038462 | 74 | py |
chainer | chainer-master/chainer/distributions/utils.py | import warnings
import chainer
from chainer.functions.array import where
from chainer.functions.math import exponential
from chainer import utils
class ModifiedXLogX(chainer.function_node.FunctionNode):
def __init__(self, logx):
self._logx = logx
def forward(self, inputs):
x, = inputs
... | 1,029 | 25.410256 | 60 | py |
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