def rearrange_dict_grad(fun):
"""
Decorator that allows us to save memory on the forward pass,
by precomputing the gradient
"""
@primitive
def wrapped_fun_helper(xdict, dummy):
## ag.value_and_grad() to avoid second forward pass
## ag.checkpoint() ensures hessian gets properly checkpointed
val, grad = ag.checkpoint(ag.value_and_grad(fun))(xdict)
assert len(val.shape) == 0
dummy.cache = grad
return val
def wrapped_fun_helper_grad(ans, xdict, dummy):
def grad(g):
#print("foo")
return {k: g * v for k, v in dummy.cache.items()}
return grad
defvjp(wrapped_fun_helper, wrapped_fun_helper_grad, None)
@functools.wraps(fun)
def wrapped_fun(xdict):
return wrapped_fun_helper(ag.dict(xdict), lambda: None)
return wrapped_fun
def make_stan_log_density(fitobj):
@primitive
def log_density(x):
return _vectorize_if_needed(fitobj.log_prob, x)
def log_density_vjp(ans, x):
return lambda g: _ensure_2d(g) * _vectorize_if_needed(fitobj.grad_log_prob, x)
defvjp(log_density, log_density_vjp)
return log_density
def test_check_vjp_1st_order_fail():
@primitive
def foo(x):
return x * 2.0
defvjp(foo, lambda ans, x : lambda g: g * 2.001)
assert_raises_regexp(AssertionError,
"\(VJP\) check of foo failed",
lambda: check_grads(foo, modes=['rev'])(1.0))
def test_check_vjp_1st_order_fail():
@primitive
def foo(x):
return x * 2.0
defvjp(foo, lambda ans, x: lambda g: g * 2.001)
with raises(AssertionError, match="\\(VJP\\) check of foo failed"):
check_grads(foo, modes=['rev'])(1.0)
def __new__(self, name, base, dic):
cls = type.__new__(container_mateclass, name, base, dic)
cls.register(_np.ndarray)
for type_ in [
float, _np.float64, _np.float32, _np.float16, complex,
_np.complex64, _np.complex128
]:
cls.register(type_)
for method_name in nondiff_methods + diff_methods:
setattr(cls, method_name, anp.__dict__[method_name])
setattr(cls, 'flatten', anp.__dict__['ravel'])
defvjp(func(cls.__getitem__),
lambda ans, A, idx: lambda g: untake(g, idx, vspace(A)))
defjvp(func(cls.__getitem__), 'same')
defjvp(untake, 'same')
setattr(cls, 'reshape', wrapped_reshape)
return cls
def get_model(self, *parameters, frame=None):
"""Get the model of the entire blend
Parameters
----------
parameters: tuple of optimization parameters
frame: `scarlet.Frame`
Alternative Frame to project the model into
Returns
-------
model: array
(Bands, Height, Width) data cube
"""
# boxed models of every source
models = self.get_models_of_children(*parameters, frame=None)
if frame is None:
frame = self.frame
# if this is the model frame then the slices are already cached
if frame == self.frame:
slices = tuple(
(src._model_frame_slices, src._model_slices) for src in self.sources
)
else:
slices = tuple(
overlapped_slices(frame.bbox, src.bbox) for src in self.sources
)
# We have to declare the function that inserts sources
# into the blend with autograd.
# This has to be done each time we fit a blend,
# since the number of components => the number of arguments,
# which must be linked to the autograd primitive function.
defvjp(
_add_models,
*([partial(_grad_add_models, index=k) for k in range(len(self.sources))])
)
full_model = np.zeros(frame.shape, dtype=frame.dtype)
full_model = _add_models(*models, full_model=full_model, slices=slices)
return full_model
def decorator(func):
"""Decorate a function to define its custome gradient(s).
Parameters
----------
func : callable
Function whose gradients will be assigned by grad_funcs.
Returns
-------
wrapped_function : callable
Function func with gradients specified by grad_funcs.
"""
wrapped_function = primitive(func)
def wrapped_grad_func(i, ans, *args, **kwargs):
grads = grad_funcs[i](*args, **kwargs)
if isinstance(grads, float):
return lambda g: g * grads
if grads.ndim == 2:
return lambda g: g[..., None] * grads
if grads.ndim == 3:
return lambda g: g[..., None, None] * grads
return lambda g: g * grads
if len(grad_funcs) == 1:
defvjp(
wrapped_function,
lambda ans, *args, **kwargs: wrapped_grad_func(0, ans, *args, **kwargs),
)
elif len(grad_funcs) == 2:
defvjp(
wrapped_function,
lambda ans, *args, **kwargs: wrapped_grad_func(0, ans, *args, **kwargs),
lambda ans, *args, **kwargs: wrapped_grad_func(1, ans, *args, **kwargs),
)
elif len(grad_funcs) == 3:
defvjp(
wrapped_function,
lambda ans, *args, **kwargs: wrapped_grad_func(0, ans, *args, **kwargs),
lambda ans, *args, **kwargs: wrapped_grad_func(1, ans, *args, **kwargs),
lambda ans, *args, **kwargs: wrapped_grad_func(2, ans, *args, **kwargs),
)
else:
raise NotImplementedError(
"custom_gradient is not yet implemented " "for more than 3 gradients."
)
return wrapped_function
def decorator(func):
wrapped_function = primitive(func)
def wrapped_grad_func(i, ans, *args, **kwargs):
grads = grad_funcs[i](*args, **kwargs)
if isinstance(grads, float):
return lambda g: g * grads
if grads.ndim == 2:
return lambda g: g[..., None] * grads
if grads.ndim == 3:
return lambda g: g[..., None, None] * grads
return lambda g: g * grads
if len(grad_funcs) == 1:
defvjp(
wrapped_function,
lambda ans, *args, **kwargs: wrapped_grad_func(0, ans, *args, **kwargs),
)
elif len(grad_funcs) == 2:
defvjp(
wrapped_function,
lambda ans, *args, **kwargs: wrapped_grad_func(0, ans, *args, **kwargs),
lambda ans, *args, **kwargs: wrapped_grad_func(1, ans, *args, **kwargs),
)
elif len(grad_funcs) == 3:
defvjp(
wrapped_function,
lambda ans, *args, **kwargs: wrapped_grad_func(0, ans, *args, **kwargs),
lambda ans, *args, **kwargs: wrapped_grad_func(1, ans, *args, **kwargs),
lambda ans, *args, **kwargs: wrapped_grad_func(2, ans, *args, **kwargs),
)
else:
raise NotImplementedError(
"custom_gradient is not yet implemented " "for more than 3 gradients."
)
return wrapped_function
if debug_log == 'true':
logger.info("sigsq_final = {}".format(sigsq_init + jitter))
return x_plus_constant
def AddJitterOp_vjp(ans: anp.ndarray,
inputs: anp.ndarray,
initial_jitter_factor=INITIAL_JITTER_FACTOR,
jitter_growth=JITTER_GROWTH,
debug_log='false'):
return lambda g: anp.append(anp.reshape(g, (-1, )), anp.sum(anp.diag(g)))
defvjp(AddJitterOp, AddJitterOp_vjp)
@primitive
def cholesky_factorization(a):
"""
Replacement for autograd.numpy.linalg.cholesky. Our backward (vjp) is
faster and simpler, while somewhat less general (only works if
a.ndim == 2).
See https://arxiv.org/abs/1710.08717 for derivation of backward (vjp)
expression.
:param a: Symmmetric positive definite matrix A
:return: Lower-triangular Cholesky factor L of A
"""
from __future__ import absolute_import
import scipy.misc
from autograd.extend import primitive, defvjp
import autograd.numpy as anp
from autograd.numpy.numpy_vjps import repeat_to_match_shape
logsumexp = primitive(scipy.misc.logsumexp)
def make_grad_logsumexp(ans, x, axis=None, b=1.0, keepdims=False):
shape, dtype = anp.shape(x), anp.result_type(x)
def vjp(g):
g_repeated, _ = repeat_to_match_shape(g, shape, dtype, axis, keepdims)
ans_repeated, _ = repeat_to_match_shape(ans, shape, dtype, axis, keepdims)
return g_repeated * b * anp.exp(x - ans_repeated)
return vjp
defvjp(logsumexp, make_grad_logsumexp)
from __future__ import division
import scipy.linalg
import autograd.numpy as anp
from autograd.numpy.numpy_wrapper import wrap_namespace
from autograd.extend import defvjp
wrap_namespace(scipy.linalg.__dict__, globals()) # populates module namespace
defvjp(sqrtm, lambda ans, A, **kwargs: lambda g: solve_lyapunov(ans, g))
def _flip(a, trans):
if anp.iscomplexobj(a):
return 'H' if trans in ('N', 0) else 'N'
else:
return 'T' if trans in ('N', 0) else 'N'
def grad_solve_triangular(ans, a, b, trans=0, lower=False, **kwargs):
tri = anp.tril if (lower ^ (_flip(a, trans) == 'N')) else anp.triu
transpose = lambda x: x if _flip(a, trans) != 'N' else x.T
al2d = lambda x: x if x.ndim > 1 else x[..., None]
def vjp(g):
v = al2d(solve_triangular(a, g, trans=_flip(a, trans), lower=lower))
return -transpose(tri(anp.dot(v, al2d(ans).T)))
return vjp
# Hotfix since _np.asarray doesn't have a gradient rule defined.
@primitive
def asarray(vals, *args, **kwargs):
"""Gradient supporting autograd asarray"""
if isinstance(vals, (onp.ndarray, _np.ndarray)):
return _np.asarray(vals, *args, **kwargs)
return _np.array(vals, *args, **kwargs)
def asarray_gradmaker(ans, *args, **kwargs):
"""Gradient maker for asarray"""
del ans, args, kwargs
return lambda g: g
defvjp(asarray, asarray_gradmaker, argnums=(0, ))
class tensor(_np.ndarray):
"""Constructs a PennyLane tensor for use with Autograd QNodes.
The ``tensor`` class is a subclass of ``numpy.ndarray``,
providing the same multidimensional, homogeneous data-structure
of fixed-size items, with an additional flag to indicate to PennyLane
whether the contained data is differentiable or not.
.. warning::
PennyLane ``tensor`` objects are only used as part of the Autograd QNode
interface. If using another machine learning library such as PyTorch or
TensorFlow, use their built-in ``tf.Variable`` and ``torch.tensor`` classes
def vjp_maker_spdot(b, A, x):
""" Gives vjp for b = spdot(A, x) w.r.t. x"""
def vjp(v):
return spdot(A.T, v)
return vjp
def jvp_spdot(g, b, A, x):
""" Gives jvp for b = spdot(A, x) w.r.t. x"""
return spdot(A, g)
defvjp(spdot, None, vjp_maker_spdot)
defjvp(spdot, None, jvp_spdot)
""" =================== PLOTTING AND MEASUREMENT =================== """
import matplotlib.pylab as plt
def aniplot(F, source, steps, component='Ez', num_panels=10):
""" Animate an FDTD (F) with `source` for `steps` time steps.
display the `component` field components at `num_panels` equally spaced.
"""
F.initialize_fields()
# initialize the plot
f, ax_list = plt.subplots(1, num_panels, figsize=(20 * num_panels, 20))
Nx, Ny, _ = F.eps_r.shape
# -*- coding: utf-8 -*-
from __future__ import division
from scipy.stats import norm as _scipy_norm
import autograd.numpy as np
from autograd.scipy.stats import norm
from autograd.extend import primitive, defvjp
from autograd.numpy.numpy_vjps import unbroadcast_f
# TODO: next release of autograd will have this built in.
logsf = primitive(_scipy_norm.logsf)
defvjp(
logsf,
lambda ans, x, loc=0.0, scale=1.0: unbroadcast_f(
x, lambda g: -g * np.exp(
norm.logpdf(x, loc, scale) - logsf(x, loc, scale))),
lambda ans, x, loc=0.0, scale=1.0: unbroadcast_f(
loc, lambda g: g * np.exp(
norm.logpdf(x, loc, scale) - logsf(x, loc, scale))),
lambda ans, x, loc=0.0, scale=1.0: unbroadcast_f(
scale, lambda g: g * np.exp(
norm.logpdf(x, loc, scale) - logsf(x, loc, scale)) *
(x - loc) / scale),
)
for i, j in enumerate(range(offset, m - 1, 2)):
d[j, 0] = np.exp(1j * phis[i])
return d
def build_phi_layer_vjp(ans, phis, m, offset):
def _build_phi_layer_vjp(g):
out = np.zeros(phis.shape)
for i, j in enumerate(range(offset, m - 1, 2)):
out[i] += np.real(ans[j, 0] * 1j * g[j, 0])
return out
return _build_phi_layer_vjp
defvjp(build_phi_layer, build_phi_layer_vjp, None, None)
def clements_build(phis, m):
U = np.eye(m, dtype=complex)
ptr = 0
bss = [build_bs_layer(m, 0), build_bs_layer(m, 1)]
for i in range(m):
offset = i % 2
# Phis per layer
ppl = (m - offset) // 2
bs = bss[offset]
phi1 = build_phi_layer(phis[ptr:ptr + ppl], m, offset)
phi2 = build_phi_layer(phis[ptr + ppl:ptr + 2 * ppl], m, offset)
ptr += 2 * ppl
axes, shapes = parse_axes(A.shape, B.shape, axes, dot_axes, mode)
if argnum == 0:
X, Y = A, B
_X_, _Y_ = 'A', 'B'
ignore_Y = 'ignore_B'
elif argnum == 1:
X, Y = B, A
_X_, _Y_ = 'B', 'A'
ignore_Y = 'ignore_A'
else:
raise NotImplementedError("Can't take grad of convolve w.r.t. arg {0}".format(argnum))
if mode == 'full':
new_mode = 'valid'
else:
if any([x_size > y_size for x_size, y_size in zip(shapes[_X_]['conv'], shapes[_Y_]['conv'])]):
new_mode = 'full'
else:
new_mode = 'valid'
def vjp(g):
result = convolve(g, Y[flipped_idxs(Y.ndim, axes[_Y_]['conv'])],
axes = [axes['out']['conv'], axes[_Y_]['conv']],
dot_axes = [axes['out'][ignore_Y], axes[_Y_]['ignore']],
mode = new_mode)
new_order = npo.argsort(axes[_X_]['ignore'] + axes[_X_]['dot'] + axes[_X_]['conv'])
return np.transpose(result, new_order)
return vjp
defvjp(convolve, partial(grad_convolve, 0), partial(grad_convolve, 1))
class RKHSFun(object):
def __init__(self, kernel, alphas={}):
self.alphas = alphas
self.kernel = kernel
self.vs = RKHSFunVSpace(self)
@primitive
def __call__(self, x):
return sum([a * self.kernel(x, x_repr)
for x_repr, a in self.alphas.items()], 0.0)
def __add__(self, f): return self.vs.add(self, f)
def __mul__(self, a): return self.vs.scalar_mul(self, a)
# TODO: add vjp of __call__ wrt x (and show it in action)
defvjp(func(RKHSFun.__call__),
lambda ans, f, x: lambda g: RKHSFun(f.kernel, {x : 1}) * g)
class RKHSFunBox(Box, RKHSFun):
@property
def kernel(self): return self._value.kernel
RKHSFunBox.register(RKHSFun)
class RKHSFunVSpace(VSpace):
def __init__(self, value):
self.kernel = value.kernel
def zeros(self): return RKHSFun(self.kernel)
def randn(self):
# These arbitrary vectors are not analogous to randn in any meaningful way
N = npr.randint(1,3)
return RKHSFun(self.kernel, dict(zip(npr.randn(N), npr.randn(N))))
import scipy.stats
import autograd.numpy as anp
from autograd.extend import primitive, defvjp
from autograd.numpy.numpy_vjps import unbroadcast_f
pdf = primitive(scipy.stats.norm.pdf)
cdf = primitive(scipy.stats.norm.cdf)
sf = primitive(scipy.stats.norm.sf)
logpdf = primitive(scipy.stats.norm.logpdf)
logcdf = primitive(scipy.stats.norm.logcdf)
logsf = primitive(scipy.stats.norm.logsf)
defvjp(pdf,
lambda ans, x, loc=0.0, scale=1.0:
unbroadcast_f(x, lambda g: -g * ans * (x - loc) / scale**2),
lambda ans, x, loc=0.0, scale=1.0:
unbroadcast_f(loc, lambda g: g * ans * (x - loc) / scale**2),
lambda ans, x, loc=0.0, scale=1.0:
unbroadcast_f(scale, lambda g: g * ans * (((x - loc)/scale)**2 - 1.0)/scale))
defvjp(cdf,
lambda ans, x, loc=0.0, scale=1.0:
unbroadcast_f(x, lambda g: g * pdf(x, loc, scale)) ,
lambda ans, x, loc=0.0, scale=1.0:
unbroadcast_f(loc, lambda g: -g * pdf(x, loc, scale)),
lambda ans, x, loc=0.0, scale=1.0:
unbroadcast_f(scale, lambda g: -g * pdf(x, loc, scale)*(x-loc)/scale))
defvjp(logpdf,
lambda ans, x, loc=0.0, scale=1.0:
unbroadcast_f(x, lambda g: -g * (x - loc) / scale**2),
from __future__ import absolute_import
import autograd.numpy as np
import scipy.stats
from autograd.extend import primitive, defvjp
from autograd.numpy.numpy_vjps import unbroadcast_f
cdf = primitive(scipy.stats.poisson.cdf)
logpmf = primitive(scipy.stats.poisson.logpmf)
pmf = primitive(scipy.stats.poisson.pmf)
def grad_poisson_logpmf(k, mu):
return np.where(k % 1 == 0, k / mu - 1, 0)
defvjp(cdf, lambda ans, k, mu: unbroadcast_f(mu, lambda g: g * -pmf(np.floor(k), mu)), argnums=[1])
defvjp(logpmf, lambda ans, k, mu: unbroadcast_f(mu, lambda g: g * grad_poisson_logpmf(k, mu)), argnums=[1])
defvjp(pmf, lambda ans, k, mu: unbroadcast_f(mu, lambda g: g * ans * grad_poisson_logpmf(k, mu)), argnums=[1])
# Some formulas are from
# "An extended collection of matrix derivative results
# for forward and reverse mode algorithmic differentiation"
# by Mike Giles
# https://people.maths.ox.ac.uk/gilesm/files/NA-08-01.pdf
# transpose by swapping last two dimensions
def T(x): return anp.swapaxes(x, -1, -2)
_dot = partial(anp.einsum, '...ij,...jk->...ik')
# add two dimensions to the end of x
def add2d(x): return anp.reshape(x, anp.shape(x) + (1, 1))
defvjp(det, lambda ans, x: lambda g: add2d(g) * add2d(ans) * T(inv(x)))
defvjp(slogdet, lambda ans, x: lambda g: add2d(g[1]) * T(inv(x)))
def grad_inv(ans, x):
return lambda g: -_dot(_dot(T(ans), g), T(ans))
defvjp(inv, grad_inv)
def grad_pinv(ans, x):
# https://mathoverflow.net/questions/25778/analytical-formula-for-numerical-derivative-of-the-matrix-pseudo-inverse
return lambda g: T(
-_dot(_dot(ans, T(g)), ans)
+ _dot(_dot(_dot(ans, T(ans)), g), anp.eye(x.shape[-2]) - _dot(x,ans))
+ _dot(_dot(_dot(anp.eye(ans.shape[-2]) - _dot(ans,x), g), T(ans)), ans)
)
defvjp(pinv, grad_pinv)
return -diff * (1.0 + df) / (diff**2 + df)
def grad_tlogpdf_x(x, df, loc, scale):
return grad_tlogpdf_diff((x - loc) / scale, df) / scale
def grad_tlogpdf_loc(x, df, loc, scale):
return -grad_tlogpdf_diff((x - loc) / scale, df) / scale
def grad_tlogpdf_scale(x, df, loc, scale):
diff = x - loc
return -(df * (scale**2 - diff**2))/(scale * (df * scale**2 + diff**2))
def grad_tlogpdf_df(x, df, loc, scale):
y = (x - loc)/scale
return 0.5 * ((y**2 * (df+1))/(df * (y**2 + df)) - np.log(y**2 / df + 1) - 1.0/df -psi(df/2.0) + psi((df + 1)/2.0))
defvjp(pdf, lambda ans, x, df, loc=0.0, scale=1.0:
unbroadcast_f(x, lambda g: g * ans * grad_tlogpdf_x( x, df, loc, scale)),
lambda ans, x, df, loc=0.0, scale=1.0:
unbroadcast_f(df, lambda g: g * ans * grad_tlogpdf_df( x, df, loc, scale)),
lambda ans, x, df, loc=0.0, scale=1.0:
unbroadcast_f(loc, lambda g: g * ans * grad_tlogpdf_loc( x, df, loc, scale)),
lambda ans, x, df, loc=0.0, scale=1.0:
unbroadcast_f(scale, lambda g: g * ans * grad_tlogpdf_scale(x, df, loc, scale)))
defvjp(cdf,
lambda ans, x, df, loc=0.0, scale=1.0:
unbroadcast_f(x, lambda g: g * pdf(x, df, loc, scale)),
lambda ans, x, df, loc=0.0, scale=1.0:
unbroadcast_f(loc, lambda g: -g * pdf(x, df, loc, scale)), argnums=(0,2))
defvjp(logpdf,
lambda ans, x, df, loc=0.0, scale=1.0:
unbroadcast_f(x, lambda g: g * grad_tlogpdf_x( x, df, loc, scale)),
lambda ans, x, df, loc=0.0, scale=1.0:
unbroadcast_f(df, lambda g: g * grad_tlogpdf_df( x, df, loc, scale)),
nograd_functions = [
anp.floor, anp.ceil, anp.round, anp.rint, anp.around, anp.fix, anp.trunc, anp.all,
anp.any, anp.argmax, anp.argmin, anp.argpartition, anp.argsort, anp.argwhere, anp.nonzero,
anp.flatnonzero, anp.count_nonzero, anp.searchsorted, anp.sign, anp.ndim, anp.shape,
anp.floor_divide, anp.logical_and, anp.logical_or, anp.logical_not, anp.logical_xor,
anp.isfinite, anp.isinf, anp.isnan, anp.isneginf, anp.isposinf, anp.allclose, anp.isclose,
anp.array_equal, anp.array_equiv, anp.greater, anp.greater_equal, anp.less, anp.less_equal,
anp.equal, anp.not_equal, anp.iscomplexobj, anp.iscomplex, anp.size, anp.isscalar,
anp.isreal, anp.zeros_like, anp.ones_like, anp.result_type]
for fun in nograd_functions:
register_notrace(VJPNode, fun)
# ----- Functions that are constant w.r.t. continuous inputs -----
defvjp(anp.nan_to_num, lambda ans, x: lambda g: anp.where(anp.isfinite(x), g, 0.))
# ----- Binary ufuncs -----
defvjp(anp.add, lambda ans, x, y : unbroadcast_f(x, lambda g: g),
lambda ans, x, y : unbroadcast_f(y, lambda g: g))
defvjp(anp.multiply, lambda ans, x, y : unbroadcast_f(x, lambda g: y * g),
lambda ans, x, y : unbroadcast_f(y, lambda g: x * g))
defvjp(anp.subtract, lambda ans, x, y : unbroadcast_f(x, lambda g: g),
lambda ans, x, y : unbroadcast_f(y, lambda g: -g))
defvjp(anp.divide, lambda ans, x, y : unbroadcast_f(x, lambda g: g / y),
lambda ans, x, y : unbroadcast_f(y, lambda g: - g * x / y**2))
defvjp(anp.maximum, lambda ans, x, y : unbroadcast_f(x, lambda g: g * balanced_eq(x, ans, y)),
lambda ans, x, y : unbroadcast_f(y, lambda g: g * balanced_eq(y, ans, x)))
defvjp(anp.minimum, lambda ans, x, y : unbroadcast_f(x, lambda g: g * balanced_eq(x, ans, y)),
lambda ans, x, y : unbroadcast_f(y, lambda g: g * balanced_eq(y, ans, x)))
from __future__ import absolute_import
import autograd.numpy as np
import scipy.stats
from autograd.extend import primitive, defvjp
from autograd.numpy.numpy_vjps import unbroadcast_f
from autograd.scipy.special import gamma, psi
cdf = primitive(scipy.stats.gamma.cdf)
logpdf = primitive(scipy.stats.gamma.logpdf)
pdf = primitive(scipy.stats.gamma.pdf)
def grad_gamma_logpdf_arg0(x, a):
return (a - x - 1) / x
def grad_gamma_logpdf_arg1(x, a):
return np.log(x) - psi(a)
defvjp(cdf, lambda ans, x, a: unbroadcast_f(x, lambda g: g * np.exp(-x) * np.power(x, a-1) / gamma(a)), argnums=[0])
defvjp(logpdf,
lambda ans, x, a: unbroadcast_f(x, lambda g: g * grad_gamma_logpdf_arg0(x, a)),
lambda ans, x, a: unbroadcast_f(a, lambda g: g * grad_gamma_logpdf_arg1(x, a)))
defvjp(pdf,
lambda ans, x, a: unbroadcast_f(x, lambda g: g * ans * grad_gamma_logpdf_arg0(x, a)),
lambda ans, x, a: unbroadcast_f(a, lambda g: g * ans * grad_gamma_logpdf_arg1(x, a)))
from .numpy_wrapper import wrap_namespace
from .numpy_vjps import match_complex
from . import numpy_wrapper as anp
from autograd.extend import primitive, defvjp, vspace
wrap_namespace(ffto.__dict__, globals())
# TODO: make fft gradient work for a repeated axis,
# e.g. by replacing fftn with repeated calls to 1d fft along each axis
def fft_grad(get_args, fft_fun, ans, x, *args, **kwargs):
axes, s, norm = get_args(x, *args, **kwargs)
check_no_repeated_axes(axes)
vs = vspace(x)
return lambda g: match_complex(x, truncate_pad(fft_fun(g, *args, **kwargs), vs.shape))
defvjp(fft, lambda *args, **kwargs:
fft_grad(get_fft_args, fft, *args, **kwargs))
defvjp(ifft, lambda *args, **kwargs:
fft_grad(get_fft_args, ifft, *args, **kwargs))
defvjp(fft2, lambda *args, **kwargs:
fft_grad(get_fft_args, fft2, *args, **kwargs))
defvjp(ifft2, lambda *args, **kwargs:
fft_grad(get_fft_args, ifft2, *args, **kwargs))
defvjp(fftn, lambda *args, **kwargs:
fft_grad(get_fft_args, fftn, *args, **kwargs))
defvjp(ifftn, lambda *args, **kwargs:
fft_grad(get_fft_args, ifftn, *args, **kwargs))
def rfft_grad(get_args, irfft_fun, ans, x, *args, **kwargs):
axes, s, norm = get_args(x, *args, **kwargs)
# on both the input to the original function (x), and the output of the
# original function (ans).
def logsumexp_vjp(ans, x):
# If you want to be able to take higher-order derivatives, then all the
# code inside this function must be itself differentiable by Autograd.
# This closure multiplies g with the Jacobian of logsumexp (d_ans/d_x).
# Because Autograd uses reverse-mode differentiation, g contains
# the gradient of the objective w.r.t. ans, the output of logsumexp.
# This returned VJP function doesn't close over `x`, so Python can
# garbage-collect `x` if there are no references to it elsewhere.
x_shape = x.shape
return lambda g: np.full(x_shape, g) * np.exp(x - np.full(x_shape, ans))
# Now we tell Autograd that logsumexmp has a gradient-making function.
defvjp(logsumexp, logsumexp_vjp)
if __name__ == '__main__':
# Now we can use logsumexp() inside a larger function that we want
# to differentiate.
def example_func(y):
z = y**2
lse = logsumexp(z)
return np.sum(lse)
grad_of_example = grad(example_func)
print("Gradient: \n", grad_of_example(npr.randn(10)))
# Check the gradients numerically, just to be safe.
check_grads(example_func, modes=['rev'])(npr.randn(10))
This function is required for integration with Autograd.
"""
# pylint: disable=unused-argument
def gradient_product(g):
"""Vector Jacobian product operator.
Args:
g (array): scalar or vector multiplying the Jacobian
from the left (output side).
Returns:
nested Sequence[float]: vector-Jacobian product, arranged
into the nested structure of the QNode input arguments.
"""
# Jacobian matrix of the circuit
jac = self.jacobian(args, **kwargs)
if not g.shape:
temp = g * jac # numpy treats 0d arrays as scalars, hence @ cannot be used
else:
temp = g @ jac
# restore the nested structure of the input args
temp = unflatten(temp.flat, args)
return temp
return gradient_product
# define the vector-Jacobian product function for QNode.__call__()
ae.defvjp(QNode.evaluate, QNode_vjp, argnums=[1])
""" Compute the expected error of A on W, under the following assumptions:
1. A is a sensitivity 1 strategy
2. A supports W
"""
AtA1 = np.linalg.pinv(np.dot(A.T, A))
return np.trace(np.dot(AtA1, WtW))
def grad_error(A, WtW):
AtA1 = np.linalg.pinv(np.dot(A.T, A))
X = -np.dot(AtA1, np.dot(WtW, AtA1))
return 2 * np.dot(A, X)
defvjp(mm_error,
lambda ans, A, WtW: lambda g: g * grad_error(A, WtW),
argnums=[0])
class CustomTemplate(templates.TemplateStrategy):
"""
The CustomTemplate strategy is specified by a function mapping parameters theta to
a strategy A(theta). Gradients + Optimization are handled automatically as long
as the passed function is compatible with autograd.
"""
def __init__(self, strategy, theta0, normalize=True, seed=None):
"""
:param strategy: a function mapping parameters theta to strategies A(theta)
:param theta0: the initial parameters
:param normalize: flag to determine if A(theta) should be normalized
Note: if normalize=False, A(theta) must always have bounded sensitivity for any theta
g_repeated = np.zeros(shape)
for I, (ist, ind) in enumerate(zip(Xstrides[:-1], Xstrides[1:])):
for J, (jst, jnd) in enumerate(zip(Ystrides[:-1], Ystrides[1:])):
if is_square is True:
if I < J:
g_repeated[ist:ind, jst:jnd] = g_repeated[jst:jnd,
ist:ind].T
continue
g_repeated[ist:ind,
jst:jnd] = g[I, J] / ((ind - ist) * (jnd - jst))
return g_repeated
return vjp
defvjp(average_kernel, grad_average_kernel, None, None)
def symmetrize(p):
Nsoap, Ncomp, _, nn = p.shape
p2 = np.empty((Nsoap, Ncomp * (Ncomp + 1) / 2, nn))
stride = [0] + list(range(Ncomp, 0, -1))
stride = np.cumsum(stride)
for i, st, nd in zip(range(Ncomp - 1), stride[:-1], stride[1:]):
p2[:, st] = p[:, i, i]
p2[:, st + 1:nd] = p[:, i, (i + 1):Ncomp] * np.sqrt(2.0)
p2[:, -1] = p[:, Ncomp - 1, Ncomp - 1]
return p2
def get_unlin_soap(rawsoap, params, global_species):
@primitive
def __call__(self, x):
return sum(
[a * self.kernel(x, x_repr) for x_repr, a in self.alphas.items()],
0.0)
def __add__(self, f):
return self.vs.add(self, f)
def __mul__(self, a):
return self.vs.scalar_mul(self, a)
# TODO: add vjp of __call__ wrt x (and show it in action)
defvjp(func(RKHSFun.__call__),
lambda ans, f, x: lambda g: RKHSFun(f.kernel, {x: 1}) * g)
class RKHSFunBox(Box, RKHSFun):
@property
def kernel(self):
return self._value.kernel
RKHSFunBox.register(RKHSFun)
class RKHSFunVSpace(VSpace):
def __init__(self, value):
self.kernel = value.kernel
from __future__ import absolute_import
import scipy.special
import autograd.numpy as np
from autograd.extend import primitive, defvjp
from autograd.numpy.numpy_vjps import unbroadcast_f
### Beta function ###
beta = primitive(scipy.special.beta)
betainc = primitive(scipy.special.betainc)
betaln = primitive(scipy.special.betaln)
defvjp(beta,
lambda ans, a, b: unbroadcast_f(a, lambda g: g * ans * (psi(a) - psi(a + b))),
lambda ans, a, b: unbroadcast_f(b, lambda g: g * ans * (psi(b) - psi(a + b))))
defvjp(betainc,
lambda ans, a, b, x: unbroadcast_f(x, lambda g: g * np.power(x, a - 1) * np.power(1 - x, b - 1) / beta(a, b)),
argnums=[2])
defvjp(betaln,
lambda ans, a, b: unbroadcast_f(a, lambda g: g * (psi(a) - psi(a + b))),
lambda ans, a, b: unbroadcast_f(b, lambda g: g * (psi(b) - psi(a + b))))
### Gamma functions ###
polygamma = primitive(scipy.special.polygamma)
psi = primitive(scipy.special.psi) # psi(x) is just polygamma(0, x)
digamma = primitive(scipy.special.digamma) # digamma is another name for psi.
gamma = primitive(scipy.special.gamma)
gammaln = primitive(scipy.special.gammaln)
gammainc = primitive(scipy.special.gammainc)
gammaincc = primitive(scipy.special.gammaincc)
gammasgn = primitive(scipy.special.gammasgn)
rgamma = primitive(scipy.special.rgamma)
Returns
-------
np.ndarray
shape=(2,2)
"""
# print('mmbbvv, pu2', pu2r(*tlist) +1j* pu2r(*tlist))
return pu2r(*tlist) + 1j * pu2i(*tlist)
defvjp(
pu2r,
# defines vector-jacobian-product of pu2r
# g.shape == pu2r.shape
lambda ans, *tlist: lambda g: np.sum(g * np.real(d_u2(0, *tlist))),
lambda ans, *tlist: lambda g: np.sum(g * np.real(d_u2(1, *tlist))),
lambda ans, *tlist: lambda g: np.sum(g * np.real(d_u2(2, *tlist))),
lambda ans, *tlist: lambda g: np.sum(g * np.real(d_u2(3, *tlist))),
argnums=range(4))
defvjp(
pu2i,
# defines vector-jacobian-product of pu2i
# g.shape == pu2i.shape
lambda ans, *tlist: lambda g: np.sum(g * np.imag(d_u2(0, *tlist))),
lambda ans, *tlist: lambda g: np.sum(g * np.imag(d_u2(1, *tlist))),
lambda ans, *tlist: lambda g: np.sum(g * np.imag(d_u2(2, *tlist))),
lambda ans, *tlist: lambda g: np.sum(g * np.imag(d_u2(3, *tlist))),
argnums=range(4))
from __future__ import absolute_import, division
import autograd.numpy as np
import scipy.stats
from autograd.extend import primitive, defvjp
from autograd.numpy.numpy_vjps import unbroadcast_f
from autograd.scipy.special import gamma
cdf = primitive(scipy.stats.chi2.cdf)
logpdf = primitive(scipy.stats.chi2.logpdf)
pdf = primitive(scipy.stats.chi2.pdf)
def grad_chi2_logpdf(x, df):
return np.where(df % 1 == 0, (df - x - 2) / (2 * x), 0)
defvjp(cdf, lambda ans, x, df: unbroadcast_f(x, lambda g: g * np.power(2., -df/2) * np.exp(-x/2) * np.power(x, df/2 - 1) / gamma(df/2)), argnums=[0])
defvjp(logpdf, lambda ans, x, df: unbroadcast_f(x, lambda g: g * grad_chi2_logpdf(x, df)), argnums=[0])
defvjp(pdf, lambda ans, x, df: unbroadcast_f(x, lambda g: g * ans * grad_chi2_logpdf(x, df)), argnums=[0])
from __future__ import absolute_import
import scipy.special
import autograd.numpy as np
from autograd.extend import primitive, defvjp, defjvp
from autograd.numpy.numpy_vjps import unbroadcast_f, repeat_to_match_shape
### Beta function ###
beta = primitive(scipy.special.beta)
betainc = primitive(scipy.special.betainc)
betaln = primitive(scipy.special.betaln)
defvjp(beta,
lambda ans, a, b: unbroadcast_f(a, lambda g: g * ans * (psi(a) - psi(a + b))),
lambda ans, a, b: unbroadcast_f(b, lambda g: g * ans * (psi(b) - psi(a + b))))
defvjp(betainc,
lambda ans, a, b, x: unbroadcast_f(x, lambda g: g * np.power(x, a - 1) * np.power(1 - x, b - 1) / beta(a, b)),
argnums=[2])
defvjp(betaln,
lambda ans, a, b: unbroadcast_f(a, lambda g: g * (psi(a) - psi(a + b))),
lambda ans, a, b: unbroadcast_f(b, lambda g: g * (psi(b) - psi(a + b))))
### Gamma functions ###
polygamma = primitive(scipy.special.polygamma)
psi = primitive(scipy.special.psi) # psi(x) is just polygamma(0, x)
digamma = primitive(scipy.special.digamma) # digamma is another name for psi.
gamma = primitive(scipy.special.gamma)
gammaln = primitive(scipy.special.gammaln)
gammainc = primitive(scipy.special.gammainc)
gammaincc = primitive(scipy.special.gammaincc)
gammasgn = primitive(scipy.special.gammasgn)
rgamma = primitive(scipy.special.rgamma)
raise NotImplementedError("The multivariate normal pdf is not "
"differentiable w.r.t. a singular covariance matix")
J = np.linalg.inv(cov)
solved = np.matmul(J, np.expand_dims(x - mean, -1))
return 1./2 * (generalized_outer_product(solved) - J)
def solve(allow_singular):
if allow_singular:
return lambda A, x: np.dot(np.linalg.pinv(A), x)
else:
return np.linalg.solve
defvjp(logpdf,
lambda ans, x, mean, cov, allow_singular=False:
unbroadcast_f(x, lambda g: -np.expand_dims(g, 1) * solve(allow_singular)(cov, (x - mean).T).T),
lambda ans, x, mean, cov, allow_singular=False:
unbroadcast_f(mean, lambda g: np.expand_dims(g, 1) * solve(allow_singular)(cov, (x - mean).T).T),
lambda ans, x, mean, cov, allow_singular=False:
unbroadcast_f(cov, lambda g: -np.reshape(g, np.shape(g) + (1, 1)) * covgrad(x, mean, cov, allow_singular)))
# Same as log pdf, but multiplied by the pdf (ans).
defvjp(pdf,
lambda ans, x, mean, cov, allow_singular=False:
unbroadcast_f(x, lambda g: -np.expand_dims(ans * g, 1) * solve(allow_singular)(cov, (x - mean).T).T),
lambda ans, x, mean, cov, allow_singular=False:
unbroadcast_f(mean, lambda g: np.expand_dims(ans * g, 1) * solve(allow_singular)(cov, (x - mean).T).T),
lambda ans, x, mean, cov, allow_singular=False:
unbroadcast_f(cov, lambda g: -np.reshape(ans * g, np.shape(g) + (1, 1)) * covgrad(x, mean, cov, allow_singular)))
defvjp(entropy, None,
lambda ans, mean, cov:
T, K = ll.shape
# Forward pass to get alphas
alphas = np.zeros((T, K))
forward_pass(log_pi0, log_Ps, ll, alphas)
grad_hmm_normalizer(log_Ps, alphas, dlog_pi0, dlog_Ps, dll)
if argnum == 0:
return lambda g: g * dlog_pi0
if argnum == 1:
return lambda g: g * dlog_Ps
if argnum == 2:
return lambda g: g * dll
defvjp(hmm_normalizer,
partial(_make_grad_hmm_normalizer, 0),
partial(_make_grad_hmm_normalizer, 1),
partial(_make_grad_hmm_normalizer, 2))
def hmm_expected_states(log_pi0, log_Ps, ll):
T, K = ll.shape
# Make sure everything is C contiguous
log_pi0 = to_c(log_pi0)
log_Ps = to_c(log_Ps)
ll = to_c(ll)
alphas = np.zeros((T, K))
forward_pass(log_pi0, log_Ps, ll, alphas)
normalizer = logsumexp(alphas[-1])
from __future__ import division
import scipy.linalg
import autograd.numpy as anp
from autograd.numpy.numpy_wrapper import wrap_namespace
from autograd.extend import defvjp, defvjp_argnums, defjvp, defjvp_argnums
wrap_namespace(scipy.linalg.__dict__, globals()) # populates module namespace
def _vjp_sqrtm(ans, A, disp=True, blocksize=64):
assert disp, "sqrtm vjp not implemented for disp=False"
ans_transp = anp.transpose(ans)
def vjp(g):
return anp.real(solve_sylvester(ans_transp, ans_transp, g))
return vjp
defvjp(sqrtm, _vjp_sqrtm)
def _flip(a, trans):
if anp.iscomplexobj(a):
return 'H' if trans in ('N', 0) else 'N'
else:
return 'T' if trans in ('N', 0) else 'N'
def grad_solve_triangular(ans, a, b, trans=0, lower=False, **kwargs):
tri = anp.tril if (lower ^ (_flip(a, trans) == 'N')) else anp.triu
transpose = lambda x: x if _flip(a, trans) != 'N' else x.T
al2d = lambda x: x if x.ndim > 1 else x[...,None]
def vjp(g):
v = al2d(solve_triangular(a, g, trans=_flip(a, trans), lower=lower))
return -transpose(tri(anp.dot(v, al2d(ans).T)))
return vjp
raise NotImplementedError(
"Can't take grad of convolve w.r.t. arg {0}".format(argnum))
if mode == 'full':
new_mode = 'valid'
else:
if any([
x_size > y_size for x_size, y_size in zip(
shapes[_X_]['conv'], shapes[_Y_]['conv'])
]):
new_mode = 'full'
else:
new_mode = 'valid'
def vjp(g):
result = convolve(
g,
Y[tuple(_autograd_signal.flipped_idxs(Y.ndim, axes[_Y_]['conv']))],
axes=[axes['out']['conv'], axes[_Y_]['conv']],
dot_axes=[axes['out'][ignore_Y], axes[_Y_]['ignore']],
mode=new_mode)
new_order = npo.argsort(axes[_X_]['ignore'] + axes[_X_]['dot'] +
axes[_X_]['conv'])
return np.transpose(result, new_order)
return vjp
defvjp(_torch_convolve, partial(_torch_grad_convolve, 0),
partial(_torch_grad_convolve, 1))
def check_probs_matrix(x):
x = truncate0(x)
rowsums = np.sum(x, axis=1)
assert np.allclose(rowsums, 1.0)
return np.einsum('ij,i->ij', x, 1.0 / rowsums)
@primitive
def set0(x, indices):
y = np.array(x)
y[indices] = 0
return y
defvjp(set0, lambda ans, x, indices: lambda g: set0(g, indices))
#set0.defgrad(lambda ans, x, indices: lambda g: set0(g, indices))
#set0.defvjp(lambda g, ans, vs, gvs, x, indices: set0(g, indices))
def closeleq(x, y):
return np.logical_or(np.isclose(x, y), x <= y)
def closegeq(x, y):
return np.logical_or(np.isclose(x, y), x >= y)
@primitive
def make_constant(x):
return x
nograd_functions = [
anp.floor, anp.ceil, anp.round, anp.rint, anp.around, anp.fix, anp.trunc, anp.all,
anp.any, anp.argmax, anp.argmin, anp.argpartition, anp.argsort, anp.argwhere, anp.nonzero,
anp.flatnonzero, anp.count_nonzero, anp.searchsorted, anp.sign, anp.ndim, anp.shape,
anp.floor_divide, anp.logical_and, anp.logical_or, anp.logical_not, anp.logical_xor,
anp.isfinite, anp.isinf, anp.isnan, anp.isneginf, anp.isposinf, anp.allclose, anp.isclose,
anp.array_equal, anp.array_equiv, anp.greater, anp.greater_equal, anp.less, anp.less_equal,
anp.equal, anp.not_equal, anp.iscomplexobj, anp.iscomplex, anp.size, anp.isscalar,
anp.isreal, anp.zeros_like, anp.ones_like, anp.result_type]
for fun in nograd_functions:
register_notrace(VJPNode, fun)
# ----- Functions that are constant w.r.t. continuous inputs -----
defvjp(anp.nan_to_num, lambda ans, x: lambda g: anp.where(anp.isfinite(x), g, 0.))
# ----- Binary ufuncs -----
defvjp(anp.add, lambda ans, x, y : unbroadcast_f(x, lambda g: g),
lambda ans, x, y : unbroadcast_f(y, lambda g: g))
defvjp(anp.multiply, lambda ans, x, y : unbroadcast_f(x, lambda g: y * g),
lambda ans, x, y : unbroadcast_f(y, lambda g: x * g))
defvjp(anp.subtract, lambda ans, x, y : unbroadcast_f(x, lambda g: g),
lambda ans, x, y : unbroadcast_f(y, lambda g: -g))
defvjp(anp.divide, lambda ans, x, y : unbroadcast_f(x, lambda g: g / y),
lambda ans, x, y : unbroadcast_f(y, lambda g: - g * x / y**2))
defvjp(anp.maximum, lambda ans, x, y : unbroadcast_f(x, lambda g: g * balanced_eq(x, ans, y)),
lambda ans, x, y : unbroadcast_f(y, lambda g: g * balanced_eq(y, ans, x)))
defvjp(anp.minimum, lambda ans, x, y : unbroadcast_f(x, lambda g: g * balanced_eq(x, ans, y)),
lambda ans, x, y : unbroadcast_f(y, lambda g: g * balanced_eq(y, ans, x)))
from autograd.misc.optimizers import adam, sgd
from matplotlib import pyplot as plt
from scipy.optimize import basinhopping
@primitive
def relu(x):
return x * (x > 0)
def relu_vjp(ans, x):
return lambda x: (x > 0).astype(float)
defvjp(relu, relu_vjp)
def init_random_params(scale, layer_sizes, rs=npr.RandomState()):
"""Build a list of (weights, biases) tuples,
one for each layer in the net."""
return [
[
scale * rs.randn(m, n), # weight matrix
scale * rs.randn(n)
] # bias vector
for m, n in zip(layer_sizes[:-1], layer_sizes[1:])
]
def init_ones_params(scale, layer_sizes, rs=npr.RandomState()):
T, K = ll.shape
# Forward pass to get alphas
alphas = np.zeros((T, K))
forward_pass(log_pi0, log_Ps, ll, alphas)
grad_hmm_normalizer(log_Ps, alphas, dlog_pi0, dlog_Ps, dll)
if argnum == 0:
return lambda g: g * dlog_pi0
if argnum == 1:
return lambda g: g * dlog_Ps
if argnum == 2:
return lambda g: g * dll
defvjp(hmm_normalizer,
partial(_make_grad_hmm_normalizer, 0),
partial(_make_grad_hmm_normalizer, 1),
partial(_make_grad_hmm_normalizer, 2))
def hmm_expected_states(log_pi0, log_Ps, ll):
T, K = ll.shape
# Make sure everything is C contiguous
to_c = lambda arr: np.copy(arr, 'C') if not arr.flags['C_CONTIGUOUS'] else arr
log_pi0 = to_c(getval(log_pi0))
log_Ps = to_c(getval(log_Ps))
ll = to_c(getval(ll))
alphas = np.zeros((T, K))
forward_pass(log_pi0, log_Ps, ll, alphas)
normalizer = logsumexp(alphas[-1])
def logsumexp_vjp(ans, x):
# If you want to be able to take higher-order derivatives, then all the
# code inside this function must be itself differentiable by Autograd.
# This closure multiplies g with the Jacobian of logsumexp (d_ans/d_x).
# Because Autograd uses reverse-mode differentiation, g contains
# the gradient of the objective w.r.t. ans, the output of logsumexp.
# This returned VJP function doesn't close over `x`, so Python can
# garbage-collect `x` if there are no references to it elsewhere.
x_shape = x.shape
return lambda g: np.full(x_shape, g) * np.exp(x - np.full(x_shape, ans))
# Now we tell Autograd that logsumexmp has a gradient-making function.
defvjp(logsumexp, logsumexp_vjp)
if __name__ == '__main__':
# Now we can use logsumexp() inside a larger function that we want
# to differentiate.
def example_func(y):
z = y**2
lse = logsumexp(z)
return np.sum(lse)
grad_of_example = grad(example_func)
print("Gradient: \n", grad_of_example(npr.randn(10)))
# Check the gradients numerically, just to be safe.
check_grads(example_func, modes=['rev'])(npr.randn(10))
# batched diagonal, similar to matrix_diag in tensorflow
def _matrix_diag(a):
reps = anp.array(a.shape)
reps[:-1] = 1
reps[-1] = a.shape[-1]
newshape = list(a.shape) + [a.shape[-1]]
return _diag(anp.tile(a, reps).reshape(newshape))
# add two dimensions to the end of x
def add2d(x):
return anp.reshape(x, anp.shape(x) + (1, 1))
defvjp(det, lambda ans, x: lambda g: add2d(g) * add2d(ans) * T(inv(x)))
defvjp(slogdet, lambda ans, x: lambda g: add2d(g[1]) * T(inv(x)))
def grad_inv(ans, x):
return lambda g: -_dot(_dot(T(ans), g), T(ans))
defvjp(inv, grad_inv)
def grad_pinv(ans, x):
# https://mathoverflow.net/questions/25778/analytical-formula-for-numerical-derivative-of-the-matrix-pseudo-inverse
return lambda g: T(-_dot(_dot(ans, T(g)), ans) + _dot(
_dot(_dot(ans, T(ans)), g),
anp.eye(x.shape[-2]) - _dot(x, ans)) + _dot(
from autograd.extend import primitive, defvjp, vspace
from autograd.builtins import tuple
from autograd import make_vjp
@primitive
def fixed_point(f, a, x0, distance, tol):
_f = f(a)
x, x_prev = _f(x0), x0
while distance(x, x_prev) > tol:
x, x_prev = _f(x), x
return x
def fixed_point_vjp(ans, f, a, x0, distance, tol):
def rev_iter(params):
a, x_star, x_star_bar = params
vjp_x, _ = make_vjp(f(a))(x_star)
vs = vspace(x_star)
return lambda g: vs.add(vjp_x(g), x_star_bar)
vjp_a, _ = make_vjp(lambda x, y: f(x)(y))(a, ans)
return lambda g: vjp_a(fixed_point(rev_iter, tuple((a, ans, g)),
vspace(x0).zeros(), distance, tol))
defvjp(fixed_point, None, fixed_point_vjp, None)
out = ag_np.zeros_like(x)
out[mask0] = ag_np.log1p(ag_np.exp(x[mask0]))
out[mask1] = x[mask1] + ag_np.log1p(ag_np.exp(-x[mask1]))
return out
if x > 0:
return x + ag_np.log1p(ag_np.exp(-x))
else:
return ag_np.log1p(ag_np.exp(x))
def make_grad__to_common_arr(ans, x):
x = ag_np.asarray(x)
def gradient_product(g):
return ag_np.full(x.shape, g) * ag_np.exp(x - ans)
return gradient_product
defvjp(to_common_arr, make_grad__to_common_arr)
@primitive
def to_unconstrained_arr(p):
""" Numerically stable transform from positive reals to real line
Implements ag_np.log(ag_np.exp(x) - 1.0)
Autograd friendly and fully vectorized
Args
----
p : array of values in (0, +\infty)
Returns
from __future__ import absolute_import
import scipy.stats
import autograd.numpy as np
from autograd.scipy.special import digamma
from autograd.extend import primitive, defvjp
rvs = primitive(scipy.stats.dirichlet.rvs)
pdf = primitive(scipy.stats.dirichlet.pdf)
logpdf = primitive(scipy.stats.dirichlet.logpdf)
defvjp(logpdf,lambda ans, x, alpha: lambda g:
g * (alpha - 1) / x,
lambda ans, x, alpha: lambda g:
g * (digamma(np.sum(alpha)) - digamma(alpha) + np.log(x)))
# Same as log pdf, but multiplied by the pdf (ans).
defvjp(pdf,lambda ans, x, alpha: lambda g:
g * ans * (alpha - 1) / x,
lambda ans, x, alpha: lambda g:
g * ans * (digamma(np.sum(alpha)) - digamma(alpha) + np.log(x)))
