def __init__(
self,
input=None,
target=None,
n_input=1,
n_hidden=1,
n_output=1,
lr=1e-3,
**kw,
):
super().__init__(**kw)
if input is None:
input = dvector("input")
if target is None:
target = dvector("target")
self.input = input
self.target = target
self.lr = shared(lr, "learning_rate")
self.w1 = shared(np.zeros((n_hidden, n_input)), "w1")
self.w2 = shared(np.zeros((n_output, n_hidden)), "w2")
# print self.lr.type
self.hidden = sigmoid(dot(self.w1, self.input))
self.output = dot(self.w2, self.hidden)
self.cost = aet_sum((self.output - self.target)**2)
self.sgd_updates = {
self.w1: self.w1 - self.lr * grad(self.cost, self.w1),
self.w2: self.w2 - self.lr * grad(self.cost, self.w2),
}
self.sgd_step = pfunc(
params=[self.input, self.target],
outputs=[self.output, self.cost],
updates=self.sgd_updates,
)
self.compute_output = pfunc([self.input], self.output)
self.output_from_hidden = pfunc([self.hidden], self.output)
def function(
inputs,
outputs=None,
mode=None,
updates=None,
givens=None,
no_default_updates=False,
accept_inplace=False,
name=None,
rebuild_strict=True,
allow_input_downcast=None,
profile=None,
on_unused_input=None,
):
"""
Return a :class:`callable object <aesara.compile.function.types.Function>`
that will calculate `outputs` from `inputs`.
Parameters
----------
inputs : list of either Variable or In instances.
Function parameters, these are not allowed to be shared variables.
outputs : list or dict of Variables or Out instances.
If it is a dict, the keys must be strings. Expressions to compute.
mode : string or `Mode` instance.
Compilation mode.
updates : iterable over pairs (shared_variable, new_expression). List, tuple
or dict.
Updates the values for SharedVariable inputs according to these
expressions.
givens : iterable over pairs (Var1, Var2) of Variables. List, tuple or dict.
The Var1 and Var2 in each pair must have the same Type.
Specific substitutions to make in the computation graph (Var2 replaces
Var1).
no_default_updates: either bool or list of Variables
If True, do not perform any automatic update on Variables. If False
(default), perform them all. Else, perform automatic updates on all
Variables that are neither in "updates" nor in "no_default_updates".
accept_inplace : bool
True iff the graph can contain inplace operations prior to the
optimization phase (default is False). *Note* this parameter is unsupported,
and its use is not recommended.
name : str
An optional name for this function. The profile mode will print the time
spent in this function.
rebuild_strict : bool
True (Default) is the safer and better tested setting, in which case
`givens` must substitute new variables with the same Type as the
variables they replace.
False is a you-better-know-what-you-are-doing setting, that permits
`givens` to replace variables with new variables of any Type.
The consequence of changing a Type is that all results depending on that
variable may have a different Type too (the graph is rebuilt from inputs
to outputs). If one of the new types does not make sense for one of the
Ops in the graph, an Exception will be raised.
allow_input_downcast: bool or None
True means that the values passed as inputs when calling the function
can be silently down-casted to fit the dtype of the corresponding
Variable, which may lose precision. False means that it will only be
cast to a more general, or precise, type. None (default) is almost like
False, but allows down-casting of Python float scalars to floatX.
profile: None, True, or ProfileStats instance
Accumulate profiling information into a given ProfileStats instance.
If argument is `True` then a new ProfileStats instance will be used.
If argument is a string, a new ProfileStats instance will be created
with that string as its ``message`` attribute.
This profiling object will be available via self.profile.
on_unused_input
What to do if a variable in the 'inputs' list is not used in the graph.
Possible values are 'raise', 'warn', 'ignore' and None.
Returns
-------
:class:`aesara.compile.function.types.Function` instance
A callable object that will compute the outputs (given the inputs) and
update the implicit function arguments according to the `updates`.
Notes
-----
Regarding givens: Be careful to make sure that these
substitutions are independent--behaviour when Var1 of one pair
appears in the graph leading to Var2 in another expression is
undefined. Replacements specified with givens are different
from optimizations in that Var2 is not expected to be
equivalent to Var1.
Internal documentation:
What happens when you call aesara.function?
1. RemoveShared: shared variables are just an abstraction to make
things more convenient for the user. The shared variables are
transformed into implicit inputs and implicit outputs. The
optimizations don't see which variables are shared or not.
2. FunctionGraph: determines whether a graph is valid. For example,
suppose
you merge the two apply nodes in our example above, ie, do the
addition and the tanh at the same time. If you propose a merge that
changes the resulting dtype or broadcastable pattern of V4, the fgraph
will detect this.
inplace optimizations: say we have an apply node that
does + on V1 and V2, with output V3. We can change the output to be
V1, to use less memory. aesara must be told that this optimization is
happening though, so that other parts of the graph are given the
correct (pre + or post + ) version of V1.
fgraph will raise an error if any of these types of
modifications causes an error
fgraph also adds a field called "clients" to all variables.
clients is a list of apply nodes that use the variable. this makes it
possible to traverse the graph in both directions. this is useful for
determining whether to do some optimizations. for example, a fusion
operation that removes V3 is not very helpful if V3 is also needed for
some other apply node. fusion operations result in a composite op that
takes a minigraph of aesara scalars and uses this to do elemwise
operations on aesara tensors
3. Optimization
How well do optimizations apply to new ops?
Usually there are no optimizations for new ops. In fact, new
ops can disrupt patterns and break currently working optimizations.
Since the Print op, for example, is not known by any optimization,
setting a Print op in the middle of a pattern that is usually
optimized out will block the optimization. for example, log(1+x)
optimizes to log1p(x) but log(1+Print(x)) is unaffected by
optimizations.
One exception is elemwise ops. If you implement your new op
as a scalar op then it will automatically work with all the elemwise
fusion machinery.
Local optimizations try to replace some node in the graph
with a different node. In the case of log(1+x), we want to replace the
log node.
def opt_log1p(node):
if not isinstance(node.op,Elemwise):
return
if not isinstance(node.op.scalar_op, log):
return
inp = node.inputs[0]
if not inp.owner:
return
if not isinstance(inp.owner.op, add):
return
inp2 = inp.owner.inputs
check that this has length 2, and that one of the inputs
is 1. assign the other input to x
return log1p(x)
4. Linker
The linker uses a Python loop to execute the code associated
with all the Apply nodes in the graph in the correct order.
The C Virtual Machine (CVM) is a linker that replaces this
Python loop with a C loop to avoid continuously changing
between Python and C. The CVM is faster for 2 reasons:
1) Its internal logic is in C, so no Python interpreter
overhead.
2) It makes native calls from the VM logic into thunks that
have been compiled using the CLinker.
The VM is a linker that was developed to prototype the CVM. it
was easier to develop the VM in Python then translate it to C instead
of just writing it in C from scratch.
"""
if isinstance(outputs, dict):
output_items = list(outputs.items())
for item_pair in output_items:
assert isinstance(item_pair[0], str)
output_items_sorted = sorted(output_items)
output_keys = []
outputs = []
for pair in output_items_sorted:
output_keys.append(pair[0])
outputs.append(pair[1])
else:
output_keys = None
if name is None:
# Determine possible file names
source_file = re.sub(r"\.pyc?", ".py", __file__)
compiled_file = source_file + "c"
stack = tb.extract_stack()
idx = len(stack) - 1
last_frame = stack[idx]
if last_frame[0] == source_file or last_frame[0] == compiled_file:
func_frame = stack[idx - 1]
while "aesara/graph" in func_frame[0] and idx > 0:
idx -= 1
# This can happen if we call var.eval()
func_frame = stack[idx - 1]
name = func_frame[0] + ":" + str(func_frame[1])
if updates is None:
updates = []
if givens is None:
givens = []
if not isinstance(inputs, (list, tuple)):
raise Exception(
"Input variables of an Aesara function should be "
"contained in a list, even when there is a single "
"input."
)
# compute some features of the arguments:
uses_tuple = any(isinstance(i, (list, tuple)) for i in inputs)
uses_updates = bool(updates)
uses_givens = bool(givens)
if uses_tuple:
# we must use old semantics in this case.
if profile:
raise NotImplementedError(
"profiling not supported in old-style " "function"
)
if uses_updates or uses_givens:
raise NotImplementedError(
"In() instances and tuple inputs trigger the old "
"semantics, which disallow using updates and givens"
)
fn = orig_function(
inputs, outputs, mode=mode, accept_inplace=accept_inplace, name=name
)
else:
# note: pfunc will also call orig_function -- orig_function is
# a choke point that all compilation must pass through
fn = pfunc(
params=inputs,
outputs=outputs,
mode=mode,
updates=updates,
givens=givens,
no_default_updates=no_default_updates,
accept_inplace=accept_inplace,
name=name,
rebuild_strict=rebuild_strict,
allow_input_downcast=allow_input_downcast,
on_unused_input=on_unused_input,
profile=profile,
output_keys=output_keys,
)
return fn