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Merge pull request #3334 from abergeron/delete_old_crap

Delete old stuff
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theano/sandbox/scan.py deleted 100644 → 0
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theano/sandbox/scan_module/__init__.py deleted 100644 → 0
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"""
This module provides the Scan Op.
Scanning is a general form of recurrence, which can be used for looping.
The idea is that you *scan* a function along some input sequence, producing
an output at each time-step that can be seen (but not modified) by the
function at the next time-step. Technically, the function can see the
previous K time-steps of your outputs and L time steps (from the past and
future) of your inputs.
So for example, ``sum()`` could be computed by scanning the ``z+x_i``
function over a list, given an initial state of ``z=0``.
Special cases:
* A *reduce* operation can be performed by returning only the last
output of a ``scan``.
* A *map* operation can be performed by applying a function that
ignores previous steps of the outputs.
Often a for-loop can be expressed as a ``scan()`` operation, and ``scan`` is
the closest that theano comes to looping. The advantage of using ``scan``
over for loops is that it allows the number of iterations to be a part of
the symbolic graph.
The Scan Op should typically be used by calling any of the following
functions: ``scan()``, ``map()``, ``reduce()``, ``foldl()``,
``foldr()``.
"""
__docformat__ = 'restructedtext en'
__authors__ = ("Razvan Pascanu "
"Frederic Bastien "
"James Bergstra "
"Pascal Lamblin "
"Arnaud Bergeron ")
__copyright__ = "(c) 2010, Universite de Montreal"
__contact__ = "Razvan Pascanu <r.pascanu@gmail>"
from .scan import scan
theano/sandbox/scan_module/scan.py deleted 100644 → 0
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theano/sandbox/scan_module/scan_op.py deleted 100644 → 0
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theano/sandbox/symbolic_module.py deleted 100644 → 0
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theano/sandbox/tests/test_scan.py deleted 100644 → 0
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import theano
import numpy
from theano.sandbox import scan
def test_001():
x0 = theano.tensor.fvector('x0')
state = theano.tensor.unbroadcast(
theano.tensor.shape_padleft(x0), 0)
out, _ = scan.scan(lambda x: x+numpy.float32(1),
states=state,
n_steps=5)
fn = theano.function([x0], out[0])
val_x0 = numpy.float32([1, 2, 3])
assert numpy.all(fn(val_x0) == val_x0 + 5)
def test_002():
x0 = theano.tensor.fvector('x0')
state = theano.tensor.alloc(
theano.tensor.constant(numpy.float32(0)),
6,
x0.shape[0])
state = theano.tensor.set_subtensor(state[0], x0)
out, _ = scan.scan(lambda x: x+numpy.float32(1),
states=state,
n_steps=5)
fn = theano.function([x0], out)
val_x0 = numpy.float32([1, 2, 3])
assert numpy.all(fn(val_x0)[-1] == val_x0 + 5)
assert numpy.all(fn(val_x0)[0] == val_x0)
def test_003():
x0 = theano.tensor.fvector('x0')
sq = theano.tensor.fvector('sq')
state = theano.tensor.alloc(
theano.tensor.constant(numpy.float32(0)),
6,
x0.shape[0])
state = theano.tensor.set_subtensor(state[0], x0)
out, _ = scan.scan(lambda s, x: x+s,
sequences=sq,
states=state,
n_steps=5)
fn = theano.function([sq, x0], out)
val_x0 = numpy.float32([1, 2, 3])
val_sq = numpy.float32([1, 2, 3, 4, 5])
assert numpy.all(fn(val_sq, val_x0)[-1] == val_x0 + 15)
assert numpy.all(fn(val_sq, val_x0)[0] == val_x0)
def test_004():
sq = theano.tensor.fvector('sq')
nst = theano.tensor.iscalar('nst')
out, _ = scan.scan(lambda s: s+numpy.float32(1),
sequences=sq,
states=[],
n_steps=nst)
fn = theano.function([sq, nst], out)
val_sq = numpy.float32([1, 2, 3, 4, 5])
assert numpy.all(fn(val_sq, 5) == val_sq + 1)
def test_005():
sq = theano.tensor.fvector('sq')
nst = theano.tensor.iscalar('nst')
out, _ = scan.scan(lambda s: s+numpy.float32(1),
sequences=sq,
states=[None],
n_steps=nst)
fn = theano.function([sq, nst], out)
val_sq = numpy.float32([1, 2, 3, 4, 5])
assert numpy.all(fn(val_sq, 5) == val_sq + 1)
if __name__ == '__main__':
test_001()
test_002()
test_003()
test_004()
test_005()
theano/sandbox/tests/test_theano_object.py deleted 100644 → 0
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from __future__ import print_function
from theano.sandbox.theano_object import *
RUN_TESTS = False
def run(TF):
def deco(f):
if TF and RUN_TESTS:
print('running test', f.__name__)
f()
if RUN_TESTS:
return f
else: return None
return deco
class MyModule(TheanoObject):
def __init__(self, a=3, b=9):
super(MyModule, self).__init__()
self.a = self.symbolic_member(2)
self.b = self.symbolic_member(3)
self.c = 100 # a constant
self.d = [self.symbolic_member(5), self.symbolic_member(6)]
self.e = ['a', self.symbolic_member(6)]
@symbolic_fn
def add(self, x):
return RVal(self.a + self.b + x)
@symbolic_fn_opts(mode='FAST_COMPILE')
def sub(self, x):
outputs = (self.a - x, self.b - x)
updates = {self.b: self.b-x}
return RVal(outputs, updates)
def normal_function(self, x):
return self.add(x) + self.sub(x) #use numpy addition
@symbolic_fn
def use_submodule(self, x):
return RVal(self.a + x + self.submodule.b)
@run(True)
def test_outputs():
MM = MyModule(3, 4)
assert MM.add(5) == 12
assert MM.b.get() == 4
MM.sub(3)
assert MM.b.get() == 1 # test get()
assert MM.add(5) == 9 # test that b's container is shared between add and sub
MM.b.set(2) # test set
assert MM.b.get() == 2 # test get()
assert MM.add(5) == 10 # test that b's container is shared between add and sub
@run(True)
def test_submodule():
MM = MyModule(1, 2)
MM.submodule = MyModule(3, 4)
assert MM.add(5) == 8
MM.submodule.sub(7)
assert MM.submodule.b.get() == -3
assert MM.use_submodule(0) == -2 # self.a is 1 + self.submodule.b is -3
@run(False)
def test_misc_prints():
MM = MyModule()
print(MM)
print('add', MM.add(4))
print('b', MM.value(MM.b))
print('sub', MM.sub(45))
print('b', MM.value(MM.b))
print(MM.sub(23))
print(MM.add(9))
print(MM.add(19))
print('b', MM.value(MM.b))
print('a', MM.value(MM.a))
MM.value_set(MM.a, 6)
MM.value_set(MM.b, 6)
print(MM.add(6))
try:
MM.b = 5
except Exception as e:
print(e)
MM.del_member(MM.b)
try:
print('b', MM.value(MM.b))
except Exception as e:
print(e)
MM.b = 'asdffd'
try:
print('b', MM.value(MM.b))
except Exception as e:
print(e)
try:
print('b', MM.value(MM.b))
except Exception as e:
print('E', e)
print(MM.b)
print('a', MM.value(MM.a))
theano/sandbox/theano_object.py deleted 100644 → 0
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"""
DRAFT: TheanoObject
N.B. the gotcha with this design is listed in the documentation of
`TheanoObject`.
"""
from __future__ import print_function
import theano
from theano import tensor
import numpy
def theano_type(x):
"""
Return a theano Type instance suitable for containing value `x`.
"""
if type(x) is int:
return tensor.lscalar
else:
raise NotImplementedError()
class symbolic_fn_callable(object):
"""
This is the class whose instance you get when you access a symbolic function
in a `TheanoObject`.
When you call a symbolic function (`symbolic_fn`) of a TheanoObject,
the `__call__` of this class handles your request.
You can also access the symbolic outputs and updates of a symbolic function
through this class.
Examples
--------
class T(TheanoObject):
@symbolic_fn
def add(self, x):
...
add_outputs = ...
add_updates = ...
return RVal(add_outputs, add_updates)
t = T()
t.add.outputs(5) # returns `add_outputs` from when `x=theano_type(5)`
t.add.updates(5) # returns `add_updates` from when `x=theano_type(5)`
t.add.theano_function(5) # returns the `Function` compiled when
# `x=theano_type(5)`
t.add(5) # runs the `Function` compiled when `x=theano_type(5)`
# with arguments `(5,)`
"""
def __init__(self, fn, mode):
self.fn = fn
self.mode = mode
def on(self, o_self):
"""
Silly method to work with symbolic_fn.__get__.
"""
self.o_self = o_self
return self
def run_symbolic(self, *args, **kwargs):
return self.o_self._get_method_impl(self.fn, self.o_self, args, kwargs, mode=self.mode)
def __call__(self, *args, **kwargs):
return self.run_symbolic(*args, **kwargs)['theano_function'](*args, **kwargs)
def theano_function(self, *args, **kwargs):
return self.run_symbolic(*args, **kwargs)['theano_function']
def outputs(self, *args, **kwargs):
return self.run_symbolic(*args, **kwargs)['outputs']
def updates(self, *args, **kwargs):
return self.run_symbolic(*args, **kwargs)['updates']
class symbolic_fn(object):
"""
A property-like class for decorating symbolic functions in `TheanoObject`.
"""
def __init__(self, fn, mode=None):
self.fn = fn
self.callable = symbolic_fn_callable(fn, mode)
def __get__(self, o_self, o_cls):
return self.callable.on(o_self)
def __set__(self, o_self, new_val):
pass
# return NotImplemented
def symbolic_fn_opts(**kwargs):
"""
Return a decorator for symbolic_functions in a `TheanoObject`.
`kwargs` passed here are passed to `theano.function` via `symbolic_fn`.
"""
def deco(f):
return symbolic_fn(f, **kwargs)
return deco
class RVal(object):
"""
A Return-Value object for a `symbolic_fn`.
"""
outputs = []
"""
The method will compute values for the variables in this list.
"""
updates = {}
"""The method will update module variables in this dictionary.
For items ``(k,v)`` in this dictionary, ``k`` must be a `symbolic_member`
of some module.
On each call to this compiled function, the value of ``k`` will be replaced
with the computed value of the Variable ``v``.
"""
def __init__(self, outputs, updates=None):
if updates is None:
updates = {}
self.outputs = outputs
assert type(updates) is dict
self.updates = updates
class TheanoObject(object):
"""
Base for Theano-supported classes.
This class provides support for symbolic_fn class attributes.
These will be compiled on demand so that they can be used just like normal
(non-symbolic) methods.
The symbolic functions in a TheanoObject can share member variables that
have been created using the `symbolic_member` method.
Notes
-----
Other variables (ones not created using ``self.symbolic_member``) referred
to in the body of a symbolic function will *not* be shared between symbolic
functions, or between symbolic functions and this class. These other
variables will be locked away in the closure of a symbolic function when
that function is compiled.
.. warning:: It is not recommended for code to interleave
(a) changes to non-symbolic instance variables with
(b) calls to symbolic functions that use those instance variables.
A symbolic function may be compiled multiple times because it must be
compiled for each set of argument types.
Each time the function is compiled, the values of non-symbolic variables
will be locked into the compiled function. Subsequent changes to those
non-symbolic instance variables will not have any effect on the behaviour
of the already-compiled symbolic function.
:todo: Is there an efficient way of recognizing when a compiled symbolic
function is stale, wrt the current values of the class's instance variables?
- One option is to re-evaluate symbolic functions symbolically and see if
the graph can be completely merged with the original graph. This is not
fast enough to do all the time by default though.
"""
def __init__(self):
self.module_method_cache = {}
def _get_method_impl(self, fn, o_self, args, kwargs, mode):
"""
Retrieve information about the symbolic function (`fn`) in TheanoObject
instance `o_self`, being evaluated on arguments `args` and `kwargs`.
Returns
-------
dict with entries 'theano_function', 'outputs', 'updates'
The theano function compiled for these arguments, the symbolic
outputs of that function, and the symbolic updates performed by
that function.
Notes
-----
This function caches return values in self.`module_method_cache`.
:todo: This may at some point become a class-level cache rather than an
instance-level cache.
"""
if kwargs:
raise NotImplementedError()
cache = self.module_method_cache
args_types = tuple(theano_type(arg) for arg in args)
key = (fn, args_types)
if key not in cache:
inputs = [a() for a in args_types]
print('compiling', fn, 'for inputs', inputs)
rval = fn(o_self, *inputs)
print('compiling to compute outputs', rval.outputs)
if isinstance(rval.outputs, (tuple, list)):
all_required_inputs = theano.gof.graph.inputs(rval.outputs)
else:
all_required_inputs = theano.gof.graph.inputs([rval.outputs])
# construct In instances for the symbolic_member instances that can automatically be
# included here.
module_inputs = [theano.compile.io.In(
variable=v,
value=v._theanoclass_container,
mutable=(v in rval.updates),
update=rval.updates.get(v, None))
for v in all_required_inputs \
if hasattr(v, '_theanoclass_container') and not (v in inputs)]
cache[key] = dict(theano_function=theano.function(inputs+module_inputs, rval.outputs),
updates=rval.updates,
outputs=rval.outputs,
mode=mode)
return cache[key]
def symbolic_member(self, ival, name=None):
"""
Create a Variable instance to hold value `ival`.
This function also immediately creates a Container object for ival.
When the returned Variable is used as input to a `TheanoObject`
`symbolic_fn`, (but does not appear as an argument to that symbolic_fn),
then this Container will be used to retrieve (and store) values for the
Variable.
This Variable's Container's contents can be retrieved by its `get()`
method.
This Variable's Container's contents can be written using its
`set(newval)` method.
"""
if type(ival) is not int:
raise NotImplementedError()
v = tensor.lscalar(name)
v._theanoclass_container = \
theano.gof.Container(v,
storage=[theano._asarray(ival, dtype='int64')],
readonly=False)
assert not hasattr(v, 'set')
assert not hasattr(v, 'get')
v.get = lambda : v._theanoclass_container.data
def setval_in_v(newval):
v._theanoclass_container.data = newval
v.set = setval_in_v
return v
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