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Merge pull request #6165 from lamblin/docstrings

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doc/library/gradient.txt
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......@@ -14,8 +14,8 @@
from theano.gradient import *
Symbolic gradient is usually computed from :func:`gradient.grad`, which offers a
more convenient syntax for the common case of wanting the gradient in some
expressions with respect to a scalar cost. The :func:`grad_sources_inputs`
more convenient syntax for the common case of wanting the gradient of some
scalar cost with respect to some input expressions. The :func:`grad_sources_inputs`
function does the underlying work, and is more flexible, but is also more
awkward to use when :func:`gradient.grad` can do the job.
......
theano/gradient.py
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......@@ -44,6 +44,7 @@ grad_time = 0
def format_as(use_list, use_tuple, outputs):
"""
Formats the outputs according to the flags `use_list` and `use_tuple`.
If `use_list` is True, `outputs` is returned as a list (if `outputs`
is not a list or a tuple then it is converted in a one element list).
If `use_tuple` is True, `outputs` is returned as a tuple (if `outputs`
......@@ -163,20 +164,23 @@ disconnected_type = DisconnectedType()
def Rop(f, wrt, eval_points, disconnected_outputs="raise",
return_disconnected="zero"):
"""
Computes the R operation on `f` wrt to `wrt` evaluated at points given
in `eval_points`. Mathematically this stands for the jacobian of `f` wrt
Computes the R operation on `f` wrt to `wrt` at `eval_points`.
Mathematically this stands for the jacobian of `f` wrt
to `wrt` right muliplied by the eval points.
:type f: Variable or list of Variables
`f` stands for the output of the computational graph to which you
want to apply the R operator
:type wrt: Variable or list of `Variables`s
variables for which you compute the R operator of the expression
described by `f`
:type eval_points: Variable or list of Variables
evalutation points for each of the variables in `wrt`
:type disconnected_outputs: str
Defines the behaviour if some of the variables in `f` are
Parameters
----------
f : :class:`~theano.gof.graph.Variable` or list of Variables
`f` stands for the output of the computational graph to which you
want to apply the R operator
wrt : :class:`~theano.gof.graph.Variable` or list of Variables
variables for which you compute the R operator of the expression
described by `f`
eval_points : :class:`~theano.gof.graph.Variable` or list of Variables
evalutation points for each of the variables in `wrt`
disconnected_outputs : str
Defines the behaviour if some of the variables in `f`
have no dependency on any of the variable in `wrt` (or if
all links are non-differentiable). The possible values are:
......@@ -184,16 +188,18 @@ def Rop(f, wrt, eval_points, disconnected_outputs="raise",
- 'warn': consider the gradient zero, and print a warning.
- 'raise': raise DisconnectedInputError.
:type return_disconnected : {'zero', 'None', 'Disconnected'}
return_disconnected : {'zero', 'None', 'Disconnected'}
- 'zero' : If wrt[i] is disconnected, return value i will be
wrt[i].zeros_like()
wrt[i].zeros_like()
- 'None' : If wrt[i] is disconnected, return value i will be
None
None
- 'Disconnected' : returns variables of type DisconnectedType
:rtype: :class:`~theano.gof.Variable` or list/tuple of Variables depending on type of f
:return: symbolic expression such that
R_op[i] = sum_j ( d f[i] / d wrt[j]) eval_point[j]
Returns
-------
:class:`~theano.gof.graph.Variable` or list/tuple of Variables depending on type of f
Symbolic expression such that
R_op[i] = sum_j (d f[i] / d wrt[j]) eval_point[j]
where the indices in that expression are magic multidimensional
indices that specify both the position within a list and all
coordinates of the tensor element in the last.
......@@ -349,22 +355,27 @@ def Rop(f, wrt, eval_points, disconnected_outputs="raise",
def Lop(f, wrt, eval_points, consider_constant=None,
disconnected_inputs='raise'):
"""
Computes the L operation on `f` wrt to `wrt` evaluated at points given
in `eval_points`. Mathematically this stands for the jacobian of `f` wrt
Computes the L operation on `f` wrt to `wrt` at `eval_points`.
Mathematically this stands for the jacobian of `f` wrt
to `wrt` left muliplied by the eval points.
:type f: Variable or list of Variables
Parameters
----------
f : :class:`~theano.gof.graph.Variable` or list of Variables
`f` stands for the output of the computational graph to which you
want to apply the L operator
:type wrt: Variable or list of `Variables`s
wrt : :class:`~theano.gof.graph.Variable` or list of Variables
variables for which you compute the L operator of the expression
described by `f`
:type eval_points: Variable or list of Variables
evalutation points for each of the variables in `f`
eval_points : :class:`~theano.gof.graph.Variable` or list of Variables
evalutation points for each of the variables in `f`
:rtype: :class:`~theano.gof.Variable` or list/tuple of Variables depending on type of f
:return: symbolic expression such that
L_op[i] = sum_i ( d f[i] / d wrt[j]) eval_point[i]
Returns
-------
:class:`~theano.gof.Variable` or list/tuple of Variables depending on type of f
Symbolic expression such that
L_op[i] = sum_i (d f[i] / d wrt[j]) eval_point[i]
where the indices in that expression are magic multidimensional
indices that specify both the position within a list and all
coordinates of the tensor element in the last
......@@ -405,8 +416,7 @@ def grad(cost, wrt, consider_constant=None,
known_grads=None, return_disconnected='zero',
null_gradients='raise'):
"""
Return symbolic gradients for one or more variables with respect to some
cost.
Return symbolic gradients of one cost with respect to one or more variables.
For more information about how automatic differentiation works in Theano,
see :mod:`gradient`. For information on how to implement the gradient of
......@@ -414,13 +424,13 @@ def grad(cost, wrt, consider_constant=None,
Parameters
----------
cost : :class:`~theano.gof.Variable` scalar (0-dimensional) tensor variable or None
Value with respect to which we are differentiating. May be
`None` if known_grads is provided.
wrt : :class:`~theano.gof.Variable` or list of Variables
term[s] for which we want gradients
cost : :class:`~theano.gof.graph.Variable` scalar (0-dimensional) tensor variable or ``None``
Value that we are differentiating (that we want the gradient of).
May be `None` if `known_grads` is provided.
wrt : :class:`~theano.gof.graph.Variable` or list of Variables
Term[s] with respect to which we want gradients
consider_constant : list of variables
expressions not to backpropagate through
Expressions not to backpropagate through
disconnected_inputs : {'ignore', 'warn', 'raise'}
Defines the behaviour if some of the variables in `wrt` are
not part of the computational graph computing `cost` (or if
......@@ -439,9 +449,9 @@ def grad(cost, wrt, consider_constant=None,
variables but do not know the original cost.
return_disconnected : {'zero', 'None', 'Disconnected'}
- 'zero' : If wrt[i] is disconnected, return value i will be
wrt[i].zeros_like()
wrt[i].zeros_like()
- 'None' : If wrt[i] is disconnected, return value i will be
None
None
- 'Disconnected' : returns variables of type DisconnectedType
null_gradients : {'raise', 'return'}
Defines the behaviour if some of the variables in `wrt` have a
......@@ -453,7 +463,7 @@ def grad(cost, wrt, consider_constant=None,
Returns
-------
variable or list/tuple of variables (matches `wrt`)
symbolic expression of gradient of `cost` with respect to each
Symbolic expression of gradient of `cost` with respect to each
of the `wrt` terms. If an element of `wrt` is not
differentiable with respect to the output, then a zero
variable is returned.
......@@ -670,50 +680,46 @@ def subgraph_grad(wrt, end, start=None, cost=None, details=False):
next_grad = dict(zip(grad_ends[i], next_grad))
param_grads.extend(param_grad)
:type wrt: list of variables
:param wrt:
Gradients are computed with respect to `wrt`.
Parameters
----------
:type end: list of variables
:param end:
Theano variables at which to end gradient descent (they are
considered constant in theano.grad). For convenience, the
gradients with respect to these variables are also returned.
wrt : list of variables
Gradients are computed with respect to `wrt`.
:type start: dictionary of variables
:param start:
If not None, a dictionary mapping variables to their
gradients. This is useful when the gradient on some variables
are known. These are used to compute the gradients backwards up
to the variables in `end` (they are used as known_grad in
theano.grad).
end : list of variables
Theano variables at which to end gradient descent (they are
considered constant in theano.grad). For convenience, the
gradients with respect to these variables are also returned.
:type cost: :class:`~theano.gof.Variable` scalar (0-dimensional) variable
:param cost:
Additional costs for which to compute the gradients. For
example, these could be weight decay, an l1 constraint, MSE,
NLL, etc. May optionally be None if start is provided. Warning
: If the gradients of `cost` with respect to any of the `start`
variables is already part of the `start` dictionary, then it may
be counted twice with respect to `wrt` and `end`.
start : dictionary of variables
If not None, a dictionary mapping variables to their
gradients. This is useful when the gradient on some variables
are known. These are used to compute the gradients backwards up
to the variables in `end` (they are used as known_grad in
theano.grad).
.. warning::
cost : :class:`~theano.gof.Variable` scalar (0-dimensional) variable
Additional costs for which to compute the gradients. For
example, these could be weight decay, an l1 constraint, MSE,
NLL, etc. May optionally be None if start is provided.
If the gradients of `cost` with respect to any of the `start`
variables is already part of the `start` dictionary, then it
may be counted twice with respect to `wrt` and `end`.
.. warning::
If the gradients of `cost` with respect to any of the `start`
variables is already part of the `start` dictionary, then it
may be counted twice with respect to `wrt` and `end`.
:type details: bool
:param details:
When True, additionally returns the list of gradients from
`start` and of `cost`, respectively, with respect to `wrt` (not
`end`).
details : bool
When True, additionally returns the list of gradients from
`start` and of `cost`, respectively, with respect to `wrt` (not
`end`).
:rtype: Tuple of 2 or 4 Lists of Variables
Returns
-------
Tuple of 2 or 4 Lists of Variables
Returns lists of gradients with respect to `wrt` and `end`,
respectively.
:return: Returns lists of gradients with respect to `wrt` and `end`,
respectively.
.. versionadded:: 0.7
'''
......@@ -808,37 +814,40 @@ def _populate_var_to_app_to_idx(outputs, wrt, consider_constant):
"""
Helper function for grad function.
outputs: a list of variables we want to take gradients of
Parameters
----------
outputs
a list of variables we want to take gradients of
wrt: a list of variables we want to take the gradient with
wrt
a list of variables we want to take the gradient with
respect to.
consider_constant: a list of variables not to backpropagate
through.
returns:
var_to_app_to_idx:
consider_constant
a list of variables not to backpropagate through.
A dictionary mapping a variable to a second dictionary.
The second dictionary maps apply nodes acting on this
variable to the variable's index in the apply node's
input list.
Returns
-------
var_to_app_to_idx:
A dictionary mapping a variable to a second dictionary.
The second dictionary maps apply nodes acting on this
variable to the variable's index in the apply node's
input list.
This dictionary will only contain variables that
meet two criteria:
This dictionary will only contain variables that
meet two criteria:
1) The elements of at least one output are a
function of the elements of the variable
1) The elements of at least one output are a
function of the elements of the variable
2) The elements of the variable are a function of the
elements of at least one member of wrt.
2) The elements of the variable are a function of the
elements of at least one member of wrt.
This set is exactly the set of variables that connect
the variables in wrt to the cost being differentiated.
This set is exactly the set of variables that connect
the variables in wrt to the cost being differentiated.
(A variable in consider_constant is not a function of
anything)
(A variable in consider_constant is not a function of
anything)
"""
......@@ -967,30 +976,35 @@ class DisconnectedInputError(ValueError):
def _populate_grad_dict(var_to_app_to_idx,
grad_dict, wrt, cost_name=None):
"""
Helper function for grad function.
var_to_app_to_idx: a dictionary mapping a variable to
a second dictionary.
the second dictionary maps apply nodes acting on
this variable to the variable's index in the apply
node's input list
grad_dict: A dictionary mapping variables to their gradients.
Should be populated by grad function, which should:
-Set the gradient with respect to the cost to 1
-Load all gradients from known_grads, possibly
overriding the cost
-Set the gradient for disconnected
inputs to a variable with type DisconnectedType()
wrt: the minimal set of variables that must be included in grad_dict
"""Helper function for grad function.
cost_name: The name of the cost being differentiated, optional.
used to name the grad with respect to x as
(d<cost_name>/dx)
Parameters
----------
var_to_app_to_idx : dict
a dictionary mapping a variable to a second dictionary.
the second dictionary maps apply nodes acting on
this variable to the variable's index in the apply
node's input list
grad_dict : dict
A dictionary mapping variables to their gradients.
Should be populated by grad function, which should:
- Set the gradient with respect to the cost to 1
- Load all gradients from known_grads, possibly
overriding the cost
- Set the gradient for disconnected
inputs to a variable with type DisconnectedType()
wrt : list of Variables
the minimal set of variables that must be included in `grad_dict`
cost_name: string
The name of the cost being differentiated, optional.
Used to name the grad with respect to x as (d<cost_name>/dx)
returns: a list of gradients corresponding to wrt
Returns
-------
list of Variables
A list of gradients corresponding to `wrt`
"""
# build a dict mapping node to the terms node contributes to each of
......@@ -1421,18 +1435,22 @@ class numeric_grad(object):
def __init__(self, f, pt, eps=None, out_type=None):
"""Return the gradient of f at pt.
:param f: a differentiable function such that f(*pt) is a scalar
:param pt: an ndarray, a list of ndarrays or tuple of ndarrays
:param out_type: dtype of output, if complex (i.e. 'complex32' or
'complex64')
This function computes the gradient by a one-sided finite
differences of a fixed step size (eps).
It is assumed that f(...) will return a scalar.
It is assumed that all f's inputs are numpy.ndarray objects.
:param eps: the stepsize for the finite differencing. None means
input dtype-dependent. See `type_eps`.
Parameters
----------
f : a differentiable function such that f(*pt) is a scalar
The function to compute the gradient of.
It is assumed that f(...) will return a scalar.
It is assumed that all f's inputs are numpy.ndarray objects.
pt : an ndarray, a list of ndarrays or tuple of ndarrays
The point where to evaluate the gradient
out_type: float
dtype of output, if complex (i.e. 'complex32' or 'complex64')
eps : float, optional
The stepsize for the finite differencing. None means
input dtype-dependent. See `type_eps`.
"""
def prod(inputs):
......@@ -1515,6 +1533,7 @@ class numeric_grad(object):
Formulas used:
abs_err = abs(a - b)
rel_err = abs_err / max(abs(a) + abs(b), 1e-8)
The denominator is clipped at 1e-8 to avoid dividing by 0 when a and b
......@@ -1609,46 +1628,56 @@ def verify_grad(fun, pt, n_tests=2, rng=None, eps=None,
no_debug_ref=True):
"""Test a gradient by Finite Difference Method. Raise error on failure.
Example:
>>> verify_grad(theano.tensor.tanh,
... (np.asarray([[2,3,4], [-1, 3.3, 9.9]]),),
... rng=np.random)
Raises an Exception if the difference between the analytic gradient and
numerical gradient (computed through the Finite Difference Method) of a
random projection of the fun's output to a scalar exceeds the given
tolerance.
:param fun: a Python function that takes Theano variables as inputs,
and returns a Theano variable. For instance, an Op instance with
a single output.
:param pt: the list of numpy.ndarrays to use as input values.
Examples
--------
>>> verify_grad(theano.tensor.tanh,
... (np.asarray([[2, 3, 4], [-1, 3.3, 9.9]]),),
... rng=np.random)
Parameters
----------
fun : a Python function
`fun` takes Theano variables as inputs, and returns a Theano variable.
For instance, an Op instance with a single output.
pt : list of numpy.ndarrays
Input values, points where the gradient is estimated.
These arrays must be either float16, float32, or float64 arrays.
:param n_tests: number of times to run the test
:param rng: random number generator used to sample u, we test gradient
of sum(u * fun) at pt
:param eps: stepsize used in the Finite Difference Method (Default
None is type-dependent)
Raising the value of eps can raise or lower the absolute and
relative errors of the verification depending on the
Op. Raising eps does not lower the verification quality
for linear operations. It
is better to raise eps than raising abs_tol or rel_tol.
:param out_type: dtype of output, if complex (i.e. 'complex32' or
'complex64')
:param abs_tol: absolute tolerance used as threshold for gradient
comparison
:param rel_tol: relative tolerance used as threshold for gradient
comparison
:param cast_to_output_type: if the output is float32 and
cast_to_output_type is True, cast the random projection to
float32. Otherwise it is float64. float16 is not handled here.
:param no_debug_ref: Don't use DebugMode for the numerical
gradient function.
:note: This function does not support multiple outputs. In
tests/test_scan.py there is an experimental verify_grad that
covers that case as well by using random projections.
n_tests : int
number of times to run the test
rng : numpy.random.RandomState, optional
random number generator used to sample the output random projection `u`,
we test gradient of sum(u * fun) at `pt`
eps : float, optional
stepsize used in the Finite Difference Method (Default
None is type-dependent).
Raising the value of eps can raise or lower the absolute
and relative errors of the verification depending on the
Op. Raising eps does not lower the verification quality for
linear operations. It is better to raise `eps` than raising
`abs_tol` or `rel_tol`.
out_type : string
dtype of output, if complex (i.e., 'complex32' or 'complex64')
abs_tol : float
absolute tolerance used as threshold for gradient comparison
rel_tol : float
relative tolerance used as threshold for gradient comparison
cast_to_output_type : bool
if the output is float32 and cast_to_output_type is True, cast
the random projection to float32. Otherwise it is float64.
float16 is not handled here.
no_debug_ref : bool
Don't use DebugMode for the numerical gradient function.
Note
----
This function does not support multiple outputs. In
tests/test_scan.py there is an experimental verify_grad that
covers that case as well by using random projections.
"""
# The import is here to prevent circular import.
......@@ -1813,26 +1842,33 @@ verify_grad.E_grad = GradientError
def jacobian(expression, wrt, consider_constant=None,
disconnected_inputs='raise'):
"""
:type expression: Vector (1-dimensional) Variable
:type wrt: Variable or list of Variables
Compute the full Jacobian, row by row.
:param consider_constant: a list of expressions not to backpropagate
through
Parameters
----------
expression : Vector (1-dimensional) :class:`~theano.gof.graph.Variable`
Values that we are differentiating (that we want the Jacobian of)
wrt : :class:`~theano.gof.graph.Variable` or list of Variables
Term[s] with respect to which we compute the Jacobian
consider_constant : list of variables
Expressions not to backpropagate through
:type disconnected_inputs: string
:param disconnected_inputs: Defines the behaviour if some of the variables
in ``wrt`` are not part of the computational graph computing ``cost``
disconnected_inputs: string
Defines the behaviour if some of the variables
in `wrt` are not part of the computational graph computing `cost`
(or if all links are non-differentiable). The possible values are:
- 'ignore': considers that the gradient on these parameters is zero.
- 'warn': consider the gradient zero, and print a warning.
- 'raise': raise an exception.
:return: either a instance of Variable or list/tuple of Variables
(depending upon `wrt`) repesenting the jacobian of `expression`
with respect to (elements of) `wrt`. If an element of `wrt` is not
differentiable with respect to the output, then a zero
variable is returned. The return value is of same type
as `wrt`: a list/tuple or TensorVariable in all cases.
Returns
-------
:class:`~theano.gof.graph.Variable` or list/tuple of Variables (depending upon `wrt`)
The Jacobian of `expression` with respect to (elements of) `wrt`.
If an element of `wrt` is not differentiable with respect to the
output, then a zero variable is returned. The return value is
of same type as `wrt`: a list/tuple or TensorVariable in all cases.
"""
from theano.tensor import arange
# Check inputs have the right format
......@@ -1886,27 +1922,29 @@ def jacobian(expression, wrt, consider_constant=None,
def hessian(cost, wrt, consider_constant=None,
disconnected_inputs='raise'):
"""
:type cost: Scalar (0-dimensional) Variable.
:type wrt: Vector (1-dimensional tensor) 'Variable' or list of
vectors (1-dimensional tensors) Variables
:param consider_constant: a list of expressions not to backpropagate
through
:type disconnected_inputs: string
:param disconnected_inputs: Defines the behaviour if some of the variables
Parameters
----------
cost: Scalar (0-dimensional) variable.
wrt: Vector (1-dimensional tensor) 'Variable' or list of
vectors (1-dimensional tensors) Variables
consider_constant:
a list of expressions not to backpropagate through
disconnected_inputs: string
Defines the behaviour if some of the variables
in ``wrt`` are not part of the computational graph computing ``cost``
(or if all links are non-differentiable). The possible values are:
- 'ignore': considers that the gradient on these parameters is zero.
- 'warn': consider the gradient zero, and print a warning.
- 'raise': raise an exception.
:return: either a instance of Variable or list/tuple of Variables
(depending upon `wrt`) repressenting the Hessian of the `cost`
with respect to (elements of) `wrt`. If an element of `wrt` is not
differentiable with respect to the output, then a zero
variable is returned. The return value is of same type
as `wrt`: a list/tuple or TensorVariable in all cases.
Returns
-------
:class:`~theano.gof.graph.Variable` or list/tuple of Variables
The Hessian of the `cost` with respect to (elements of) `wrt`.
If an element of `wrt` is not differentiable with respect to the
output, then a zero variable is returned. The return value is
of same type as `wrt`: a list/tuple or TensorVariable in all cases.
"""
from theano.tensor import arange
# Check inputs have the right format
......@@ -2034,10 +2072,16 @@ def zero_grad(x):
through with a value of zero. In other words, the gradient of
the expression is truncated to 0.
:param x: A Theano expression whose gradient should be truncated.
Parameters
----------
x: :class:`~theano.gof.graph.Variable`
A Theano expression whose gradient should be truncated.
:return: The expression is returned unmodified, but its gradient
is now truncated to 0.
Returns
-------
:class:`~theano.gof.graph.Variable`
An expression equivalent to ``x``, with its gradient
truncated to 0.
"""
return zero_grad_(x)
......@@ -2058,18 +2102,24 @@ undefined_grad_ = UndefinedGrad()
def undefined_grad(x):
"""
Consider the gradient of this variable undefined and
generate an error message if its gradient is taken.
Consider the gradient of this variable undefined.
This will generate an error message if its gradient is taken.
The expression itself is unaffected, but when its gradient is
computed, or the gradient of another expression that this
expression is a subexpression of, an error message will be generated
specifying such gradient is not defined.
:param x: A Theano expression whose gradient should be undefined.
Parameters
----------
x: :class:`~theano.gof.graph.Variable`
A Theano expression whose gradient should be undefined.
:return: The expression is returned unmodified, but its gradient
is now undefined.
Returns
-------
:class:`~theano.gof.graph.Variable`
An expression equivalent to ``x``, with its gradient undefined.
"""
return undefined_grad_(x)
......@@ -2090,8 +2140,9 @@ disconnected_grad_ = DisconnectedGrad()
def disconnected_grad(x):
"""
Consider an expression constant when computing gradients,
while effectively not backpropagating through it.
Consider an expression constant when computing gradients.
It will effectively not backpropagating through it.
The expression itself is unaffected, but when its gradient is
computed, or the gradient of another expression that this
......@@ -2101,11 +2152,17 @@ def disconnected_grad(x):
has to go through the underlying computational graph related to the
expression.
:param x: A Theano expression whose gradient should not be
backpropagated through.
Parameters
----------
x: :class:`~theano.gof.graph.Variable`
A Theano expression whose gradient should not be
backpropagated through.
:return: The expression is returned unmodified, but its gradient
is now effectively truncated to 0.
Returns
-------
:class:`~theano.gof.graph.Variable`
An expression equivalent to ``x``, with its gradient
now effectively truncated to 0.
"""
return disconnected_grad_(x)
......@@ -2133,23 +2190,28 @@ def grad_clip(x, lower_bound, upper_bound):
This is an elemwise operation.
:param x: the variable we want its gradient inputs clipped
:param lower_bound: The lower bound of the gradient value
:param upper_bound: The upper bound of the gradient value.
:examples:
x = theano.tensor.scalar()
z = theano.tensor.grad(grad_clip(x, -1, 1)**2, x)
z2 = theano.tensor.grad(x**2, x)
f = theano.function([x], outputs = [z, z2])
print(f(2.0)) # output (1.0, 4.0)
:note: We register an opt in tensor/opt.py that remove the GradClip.
So it have 0 cost in the forward and only do work in the grad.
Parameters
----------
x:
The variable we want its gradient inputs clipped
lower_bound:
The lower bound of the gradient value
upper_bound:
The upper bound of the gradient value.
Examples
--------
>>> x = theano.tensor.scalar()
>>> z = theano.tensor.grad(grad_clip(x, -1, 1)**2, x)
>>> z2 = theano.tensor.grad(x**2, x)
>>> f = theano.function([x], outputs = [z, z2])
>>> print(f(2.0))
[array(1.0), array(4.0)]
Note
----
We register an opt in tensor/opt.py that remove the GradClip.
So it have 0 cost in the forward and only do work in the grad.
"""
return GradClip(lower_bound, upper_bound)(x)
......@@ -2167,21 +2229,25 @@ def grad_scale(x, multiplier):
"""
This op scale or inverse the gradient in the backpropagation.
:param x: the variable we want its gradient inputs scale
:param multiplier: scale of the gradient
:examples:
x = theano.tensor.fscalar()
fx = theano.tensor.sin(x)
fp = theano.tensor.grad(fx, wrt=x)
fprime = theano.function([x], fp)
print(fprime(2))#-0.416
f_inverse=grad_scale(fx,-1.)
fpp = theano.tensor.grad(f_inverse, wrt=x)
fpprime = theano.function([x], fpp)
print(fpprime(2))#0.416
Parameters
----------
x:
The variable we want its gradient inputs scale
multiplier:
Scale of the gradient
Examples
--------
>>> x = theano.tensor.fscalar()
>>> fx = theano.tensor.sin(x)
>>> fp = theano.tensor.grad(fx, wrt=x)
>>> fprime = theano.function([x], fp)
>>> print(fprime(2)) # doctest: +ELLIPSIS
-0.416...
>>> f_inverse=grad_scale(fx, -1.)
>>> fpp = theano.tensor.grad(f_inverse, wrt=x)
>>> fpprime = theano.function([x], fpp)
>>> print(fpprime(2)) # doctest: +ELLIPSIS
0.416...
"""
return GradScale(multiplier)(x)
theano/tensor/slinalg.py
浏览文件 @ c58166ca
......@@ -84,11 +84,11 @@ class Cholesky(Op):
"""
Cholesky decomposition reverse-mode gradient update.
Symbolic expression for reverse-mode Cholesky gradient taken from [0]_
Symbolic expression for reverse-mode Cholesky gradient taken from [#]_
References
----------
.. [0] I. Murray, "Differentiation of the Cholesky decomposition",
.. [#] I. Murray, "Differentiation of the Cholesky decomposition",
http://arxiv.org/abs/1602.07527
"""
......@@ -158,12 +158,12 @@ class CholeskyGrad(Op):
def perform(self, node, inputs, outputs):
"""
Implements the "reverse-mode" gradient [1]_ for the
Implements the "reverse-mode" gradient [#]_ for the
Cholesky factorization of a positive-definite matrix.
References
----------
.. [1] S. P. Smith. "Differentiation of the Cholesky Algorithm".
.. [#] S. P. Smith. "Differentiation of the Cholesky Algorithm".
Journal of Computational and Graphical Statistics,
Vol. 4, No. 2 (Jun.,1995), pp. 134-147
http://www.jstor.org/stable/1390762
......@@ -268,13 +268,13 @@ class Solve(Op):
def grad(self, inputs, output_gradients):
"""
Reverse-mode gradient updates for matrix solve operation c = A \ b.
Reverse-mode gradient updates for matrix solve operation c = A \\\ b.
Symbolic expression for updates taken from [1]_.
Symbolic expression for updates taken from [#]_.
References
----------
..[1] M. B. Giles, "An extended collection of matrix derivative results
.. [#] M. B. Giles, "An extended collection of matrix derivative results
for forward and reverse mode automatic differentiation",
http://eprints.maths.ox.ac.uk/1079/
......
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