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提交 f68f06ce authored 作者: Rami Al-Rfou's avatar Rami Al-Rfou
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Merge branch 'master' into grad_advinc_subtensor

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doc/library/scan.txt
浏览文件 @ f68f06ce
......@@ -133,8 +133,8 @@ This makes it safe to pass a very long arange, which we need to do for generalit
arange must have its length specified at creation time.
Simple accumulation into a scalar, ditching lamba
-------------------------------------------------
Simple accumulation into a scalar, ditching lambda
--------------------------------------------------
Although this example would seem almost self-explanatory, it stresses a
pitfall to be careful of: the initial output state that is supplied, that is
......
theano/__init__.py
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......@@ -88,7 +88,7 @@ from printing import \
import scan_module
from scan_module import scan, map, reduce, foldl, foldr, clone
from updates import Updates
from updates import Updates, OrderedUpdates
import tensor
import scalar
......
theano/compile/debugmode.py
浏览文件 @ f68f06ce
......@@ -172,7 +172,9 @@ class BadThunkOutput(DebugModeError):
print >> sio, "BadThunkOutput"
print >> sio, " variable :", self.r
print >> sio, " Outputs Type:", self.r.type
print >> sio, " Inputs Type :", [i.type for i in self.r.owner.inputs],
print >> sio, " Outputs Shape:", getattr(self.val1, 'shape', None)
print >> sio, " Outputs Strides:", getattr(self.val1, 'strides', None)
print >> sio, " Inputs Type :", [i.type for i in self.r.owner.inputs]
print >> sio, " Inputs Shape:", [getattr(val, 'shape', None)
for val in self.inputs_val]
print >> sio, " Inputs Strides:", [getattr(val, 'strides', None)
......@@ -1336,7 +1338,7 @@ def _check_preallocated_output(node, thunk, prealloc_modes, def_val,
for r in node.outputs:
if not r.type.values_eq_approx(r_vals[r], storage_map[r][0]):
# TODO: indicate it is not a C/Py problem
inputs_val = [storage_map[inp] for inp in r.owner.inputs]
inputs_val = [storage_map[inp][0] for inp in r.owner.inputs]
raise BadThunkOutput(r,
thunk1='Reference value', val1=r_vals[r],
thunk2=thunk_name, val2=storage_map[r][0],
......@@ -1918,7 +1920,7 @@ class _Linker(gof.link.LocalLinker):
if not r.type.values_eq_approx(r_vals[r], storage_map[r][0]):
#import pdb; pdb.set_trace()
#r.type.values_eq_approx(r_vals[r], storage_map[r][0])
inputs_val = [storage_map[inp] for inp in r.owner.inputs]
inputs_val = [storage_map[inp][0] for inp in r.owner.inputs]
raise BadThunkOutput(r,
thunk1='perform', val1=r_vals[r],
thunk2='c_code', val2=storage_map[r][0],
......
theano/compile/function.py
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......@@ -12,6 +12,8 @@ from function_module import orig_function
from profiling import ProfileStats
from pfunc import pfunc
from numpy import any # to work in python 2.4
import warnings
from theano import gof
def function(inputs, outputs=None, mode=None, updates=None, givens=None,
no_default_updates=False, accept_inplace=False, name=None,
......@@ -30,7 +32,7 @@ def function(inputs, outputs=None, mode=None, updates=None, givens=None,
:type mode: string or `Mode` instance.
:param mode: compilation mode
:type updates: iterable over pairs (shared_variable, new_expression). List, tuple or dict.
:type updates: iterable over pairs (shared_variable, new_expression). List, tuple or OrderedDict.
:param updates: update the values for SharedVariable inputs according to these expressions
:type givens: iterable over pairs (Var1, Var2) of Variables. List, tuple or dict. The Var1
......@@ -128,7 +130,7 @@ def function(inputs, outputs=None, mode=None, updates=None, givens=None,
def opt_log1p(node):
if not isinstance(node.op,Elemwise):
return
if not isinstance(node.op.scalar_op, log,):
if not isinstance(node.op.scalar_op, log):
return
inp = node.inputs[0]
if not inp.owner:
......@@ -159,10 +161,18 @@ def function(inputs, outputs=None, mode=None, updates=None, givens=None,
"""
#tuple are used in some tests, as we accepted them in the past
#I prefer to allow it as they act the same as list for what they are used.
if updates is None:
updates = []
if isinstance(updates, dict) and \
not isinstance(updates, gof.python25.OrderedDict):
warnings.warn("Expected OrderedDict, got "+str(type(updates))+ "Using "
"a standard dictionary here results in "
"non-deterministic behavior. You should use an OrderedDict"
" if you are using python2.7 or use a list of (shared, update)"
" pairs. Do not just convert your dictionary to this type before"
" the call as the conversion will still be non-deterministic.")
if givens is None:
givens = []
if not isinstance(inputs, (list, tuple)):
......
theano/compile/function_module.py
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......@@ -1337,6 +1337,7 @@ def orig_function(inputs, outputs, mode=None, accept_inplace=False,
profile.compile_time += t2 - t1
fn.name = name
fn.maker.fgraph.name = name
return fn
......
theano/compile/tests/test_pfunc.py
浏览文件 @ f68f06ce
......@@ -626,8 +626,15 @@ class Test_pfunc(unittest.TestCase):
# The order of the variables is not determined, so we try
# both shared variables.
f = theano.function([], [], updates={a: a, b: (2 * b)})
g = theano.function([], [], updates={a: (a * 2), b: b})
# TODO: explain the above comment. By "not determined" does
# this mean "not deterministic"?
# This test originally wrote the updates using dictionaries,
# and iterating over the dictionary was not deterministic.
# Is that all the comment above meant, or is the CVM intended
# to add extra non-determinism? Or is the CVM meant to
# deterministically but arbitrarily pick an order for the updates?
f = theano.function([], [], updates=[(a, a), (b, (2 * b))])
g = theano.function([], [], updates=[(a, (a * 2)), (b, b)])
f()
assert a.get_value(borrow=True).shape == (), a.get_value()
......@@ -642,10 +649,10 @@ class Test_pfunc(unittest.TestCase):
a = shared(1., 'a')
b = shared(numpy.ones((2, 3)), 'b')
# The order of the variables is not determined, so we try
# both shared variables.
f = theano.function([], [], updates={a: a, b: (2 * b - b)})
g = theano.function([], [], updates={a: (a * 2 - a), b: b})
# See comment in test_update_same about why we try both
# shared variables.
f = theano.function([], [], updates=[(a, a), (b, (2 * b - b))])
g = theano.function([], [], updates=[(a, (a * 2 - a)), (b, b)])
f()
assert a.get_value(borrow=True).shape == (), a.get_value()
......
theano/gof/null_type.py
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......@@ -35,3 +35,6 @@ class NullType(Type):
def __hash__(self, other):
return hash(type(self))
def __str__(self):
return 'NullType'
theano/gof/python25.py
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......@@ -162,7 +162,7 @@ else:
if sys.version_info[:2] < (2, 7):
# The following implementation of OrderedDict compatible with python 2.4
# was taked from http://pypi.python.org/pypi/ordereddict/1.1
# was taken from http://pypi.python.org/pypi/ordereddict/1.1
# It is under the MIT license.
# Copyright (c) 2009 Raymond Hettinger
......
theano/gradient.py
浏览文件 @ f68f06ce
......@@ -20,6 +20,7 @@ import theano
from itertools import izip
from theano import gof
from theano.gof import Variable
from theano.gof.python25 import OrderedDict
from theano.gof.python25 import all
import theano.gof.utils
from theano.gof.null_type import NullType
......@@ -144,6 +145,9 @@ class DisconnectedType(theano.gof.type.Type):
" a symbolic placeholder."
))
def __str__(self):
return 'DisconnectedType'
########################
# R Operator
......@@ -211,7 +215,7 @@ def Rop(f, wrt, eval_points):
# Tensor, Sparse and CudaNdArray have the ndim attribute
pass
seen_nodes = {}
seen_nodes = OrderedDict()
def _traverse(node):
""" TODO: writeme """
......@@ -432,14 +436,14 @@ def grad(cost, wrt, consider_constant=None,
if known_grads is not None:
outputs.extend(known_grads.keys())
var_to_node_to_idx = _populate_var_to_node_to_idx(
var_to_app_to_idx = _populate_var_to_app_to_idx(
outputs, wrt, consider_constant)
# build a dict mapping var to the gradient of cost with respect to var
grad_dict = {}
grad_dict = OrderedDict()
if known_grads is None:
known_grads = {}
known_grads = OrderedDict()
# The gradient of the cost is 1 unless specified otherwise by known_grads.
if cost is not None:
......@@ -501,10 +505,10 @@ def grad(cost, wrt, consider_constant=None,
# variables that do not influence the cost have zero gradient.
# if wrt is such a variable, populate the grad_dict with this info
# so that wrt not being in var_to_node_to_idx won't cause an error below
# so that wrt not being in var_to_app_to_idx won't cause an error below
# according to the flag, possibly raise an error if wrt is disconnected
for elem in wrt:
if elem not in var_to_node_to_idx and elem is not cost \
if elem not in var_to_app_to_idx and elem is not cost \
and elem not in grad_dict:
handle_disconnected(elem)
grad_dict[elem] = DisconnectedType()()
......@@ -521,7 +525,7 @@ def grad(cost, wrt, consider_constant=None,
if hasattr(g.type, 'dtype'):
assert g.type.dtype in tensor.float_dtypes
rval = _populate_grad_dict(var_to_node_to_idx,
rval = _populate_grad_dict(var_to_app_to_idx,
grad_dict, wrt, cost_name)
for i in xrange(len(rval)):
......@@ -579,7 +583,7 @@ def _node_to_pattern(node):
return connection_pattern
def _populate_var_to_node_to_idx(outputs, wrt, consider_constant):
def _populate_var_to_app_to_idx(outputs, wrt, consider_constant):
"""
Helper function for grad function.
......@@ -638,7 +642,7 @@ def _populate_var_to_node_to_idx(outputs, wrt, consider_constant):
# var_to_app_to_idx[var][node] = [i,j] means node has
# var as input at positions i and j
var_to_app_to_idx = {}
var_to_app_to_idx = OrderedDict()
# Set of variables that have been added to their true parents
# ('true' here means that the elements of the variable are a function
......@@ -676,7 +680,13 @@ def _populate_var_to_node_to_idx(outputs, wrt, consider_constant):
continue
if ipt not in var_to_app_to_idx:
var_to_app_to_idx[ipt] = {}
# This object here *must* be an OrderedDict, because
# we iterate over its keys when adding up the terms of
# the gradient on ipt. If it is a regular dict, the grad
# method will return something that is analytically correct,
# but whose order of doing additions depends on the memory
# location of the apply nodes.
var_to_app_to_idx[ipt] = OrderedDict()
app_to_idx = var_to_app_to_idx[ipt]
if app not in app_to_idx:
app_to_idx[app] = []
......@@ -731,12 +741,12 @@ class DisconnectedInputError(ValueError):
disconnected_inputs='raise'.
"""
def _populate_grad_dict(var_to_node_to_idx,
def _populate_grad_dict(var_to_app_to_idx,
grad_dict, wrt, cost_name=None):
"""
Helper function for grad function.
var_to_node_to_idx: a dictionary mapping a variable to
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
......@@ -761,7 +771,7 @@ def _populate_grad_dict(var_to_node_to_idx,
"""
# build a dict mapping node to the terms node contributes to each of
# its inputs' gradients
term_dict = {}
term_dict = OrderedDict()
def access_term_cache(node):
""" Populates term_dict[node] and returns it """
......@@ -1001,15 +1011,17 @@ def _populate_grad_dict(var_to_node_to_idx,
#cache the result
term_dict[node] = input_grads
return term_dict[node]
# populate grad_dict[var] and return it
def access_grad_cache(var):
if var not in grad_dict:
# If var is not in grad_dict already, we must compute it
if var in var_to_node_to_idx:
if var in var_to_app_to_idx:
terms = []
node_to_idx = var_to_node_to_idx[var]
node_to_idx = var_to_app_to_idx[var]
for node in node_to_idx:
for idx in node_to_idx[node]:
......
theano/printing.py
浏览文件 @ f68f06ce
......@@ -8,6 +8,8 @@ import logging
import os
import StringIO
import sys
# Not available on all platforms
hashlib = None
import numpy
......@@ -1069,3 +1071,78 @@ def min_informative_str(obj, indent_level=0,
rval = indent + prefix + name
return rval
def var_descriptor(obj, _prev_obs=None, _tag_generator=None):
"""
Returns a string, with no endlines, fully specifying
how a variable is computed. Does not include any memory
location dependent information such as the id of a node.
"""
if hashlib is None:
try:
import hashlib
except ImportError:
raise RuntimeError("Can't run var_descriptor because hashlib is not available.")
if _prev_obs is None:
_prev_obs = {}
if id(obj) in _prev_obs:
tag = _prev_obs[id(obj)]
return '<' + tag + '>'
if _tag_generator is None:
_tag_generator = _TagGenerator()
cur_tag = _tag_generator.get_tag()
_prev_obs[id(obj)] = cur_tag
if hasattr(obj, '__array__'):
# hashlib hashes only the contents of the buffer, but
# it can have different semantics depending on the strides
# of the ndarray
name = '<ndarray:'
name += 'strides=['+','.join(str(stride) for stride in obj.strides)+']'
name += ',digest='+hashlib.md5(obj).hexdigest()+'>'
elif hasattr(obj, 'name') and obj.name is not None:
name = obj.name
elif hasattr(obj, 'owner') and obj.owner is not None:
name = str(obj.owner.op) + '('
name += ','.join(var_descriptor(ipt,
_prev_obs=_prev_obs, _tag_generator=_tag_generator) for ipt
in obj.owner.inputs)
name += ')'
else:
name = str(obj)
if ' at 0x' in name:
# The __str__ method is encoding the object's id in its str
name = position_independent_str(obj)
if ' at 0x' in name:
print name
assert False
prefix = cur_tag + '='
rval = prefix + name
return rval
def position_independent_str(obj):
if isinstance(obj, theano.gof.graph.Variable):
rval = 'theano_var'
rval += '{type='+str(obj.type)+'}'
else:
raise NotImplementedError()
return rval
theano/sandbox/cuda/cuda_ndarray.cu
浏览文件 @ f68f06ce
......@@ -4666,6 +4666,33 @@ int fprint_CudaNdarray(FILE * fd, const CudaNdarray *self)
return 0;
}
int CudaNdarray_prep_output(CudaNdarray ** arr, int nd,
const int * dims)
{
bool allocated = false;
if (*arr == NULL)
{
// This allocates the metadata but not the data
*arr = (CudaNdarray *) CudaNdarray_new_nd(nd);
if (*arr == NULL)
return -1;
allocated = true;
}
if (CudaNdarray_alloc_contiguous(*arr, nd, dims))
{
if (allocated)
{
Py_DECREF(*arr);
*arr = NULL;
}
return -1;
}
return 0;
}
/*
Local Variables:
mode:c++
......
theano/sandbox/cuda/cuda_ndarray.cuh
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......@@ -149,11 +149,11 @@ DllExport int
CudaNdarray_Equal(CudaNdarray *cnda1, CudaNdarray *cnda2);
/****
* Set the idx'th dimension to value d.
* Set the dimension[idx] to value d.
*
* Updates the log2dim shadow array.
*
* Does not sync structure to host.
* Does not sync structure to device.
*/
DllExport inline void __attribute__((always_inline))
CudaNdarray_set_dim(CudaNdarray * self, int idx, int d)
......@@ -229,7 +229,8 @@ DllExport PyObject * CudaNdarray_new_nd(const int nd);
/**
* [Re]allocate a CudaNdarray with access to 'nd' dimensions.
*
* Note: This does not allocate storage for data.
* Note: This does not allocate storage for data, or free
* pre-existing storage.
*/
DllExport inline int __attribute__((always_inline))
CudaNdarray_set_nd(CudaNdarray * self, const int nd)
......@@ -276,6 +277,7 @@ CudaNdarray_set_nd(CudaNdarray * self, const int nd)
* CudaNdarray_alloc_contiguous
*
* Allocate storage space for a tensor of rank 'nd' and given dimensions.
* (No-op if self already has a contiguous tensor of the right dimensions)
*
* Note: CudaNdarray_alloc_contiguous is templated to work for both int dimensions and npy_intp dimensions
*/
......@@ -286,13 +288,13 @@ static int CudaNdarray_alloc_contiguous(CudaNdarray *self, const int nd, const i
// return 0 on success
int size = 1; //set up the strides for contiguous tensor
assert (nd >= 0);
// Here we modify the host structure to have the desired shape and
// strides. This does not cause the storage to be freed or reallocated.
if (CudaNdarray_set_nd(self, nd))
{
return -1;
}
//TODO: check if by any chance our current dims are correct,
// and strides already contiguous
// in that case we can return right here.
for (int i = nd-1; i >= 0; --i)
{
CudaNdarray_set_stride(self, i, (dim[i] == 1) ? 0 : size);
......@@ -300,7 +302,11 @@ static int CudaNdarray_alloc_contiguous(CudaNdarray *self, const int nd, const i
size = size * dim[i];
}
if ((self->data_allocated == size) && CudaNdarray_is_c_contiguous(self))
// If the allocated buffer is already of the right size, we don't need to
// do anything else.
// Note: self->data_allocated is 0 for a view, so views will fail this
// check and be turned into independent arrays below.
if (self->data_allocated == size)
{
return 0;
}
......@@ -468,6 +474,15 @@ PyObject * CudaNdarray_View(const CudaNdarray * self);
PyObject * CudaNdarray_inplace_add(PyObject* py_self, PyObject * py_other);
// Ensures that *arr is a pointer to a contiguous ndarray of the specified
// dimensions.
// *arr may initially be NULL, a pointer to an ndarray of the wrong size,
// or a pointer to an ndarray of the right size. In the last case it will
// not change.
int CudaNdarray_prep_output(CudaNdarray ** arr, int nd,
const int * dims);
#endif
/*
Local Variables:
......
theano/sandbox/cuda/tests/test_conv_cuda_ndarray.py
浏览文件 @ f68f06ce
......@@ -869,5 +869,5 @@ def test_stack_rows_segfault_070312():
out = theano.shared(numpy.random.rand(1, 2, 2, 3).astype('float32'))
op = theano.tensor.nnet.conv.ConvOp(imshp=(80, 96, 96), kshp=(9, 9),
nkern=1, bsize=1)
f = theano.function([], [], updates={out: op(img, kern)})
f = theano.function([], [], updates=[(out, op(img, kern))])
f()
theano/sandbox/cuda/tests/test_opt.py
浏览文件 @ f68f06ce
......@@ -106,7 +106,7 @@ def test_alloc_memset_0():
def test_gpuspecifyshape():
x = cuda.shared_constructor(numpy.ones(3,dtype='float32'), 'x')
m = theano.tensor.specify_shape(x + numpy.float32(1), (3,))
f = theano.function([], updates={x:m * numpy.float32(2)},
f = theano.function([], updates=[(x, m * numpy.float32(2))],
mode=mode_with_gpu)
l = f.maker.fgraph.toposort()
assert not numpy.any([isinstance(x.op, cuda.HostFromGpu) for x in l])
......
theano/sandbox/cuda/tests/test_var.py
浏览文件 @ f68f06ce
......@@ -60,11 +60,11 @@ class T_updates(unittest.TestCase):
data = numpy.float32([1, 2, 3, 4])
x = f32sc(data)
y = x ** 2
f = theano.function([], y, updates={x: x + 1})
f = theano.function([], y, updates=[(x, x + 1)])
f()
# Test that we can update with a CudaVariable
f = theano.function([], y, updates={x: cuda.gpu_from_host(x + 1)})
f = theano.function([], y, updates=[(x, cuda.gpu_from_host(x + 1))])
f()
def test_2(self):
......@@ -74,7 +74,7 @@ class T_updates(unittest.TestCase):
value=numpy.zeros((10, 10), 'float32'))
x = tensor.fmatrix('x')
output_updates = {output_var: x ** 2}
output_updates = [(output_var, x ** 2)]
output_givens = {x: data}
output_func = theano.function(inputs=[], outputs=[],
updates=output_updates, givens=output_givens)
......@@ -89,8 +89,8 @@ class T_updates(unittest.TestCase):
# the update_var has type matrix, and the update expression
# is a broadcasted scalar, and that should not be allowed.
self.assertRaises(TypeError, theano.function, inputs=[], outputs=[],
updates={output_var:
output_var.sum()})
updates=[(output_var,
output_var.sum())])
def test_err_broadcast(self):
# Test that we raise a good error message when we don't
......@@ -101,8 +101,8 @@ class T_updates(unittest.TestCase):
# the update_var has type matrix, and the update expression
# is a broadcasted scalar, and that should not be allowed.
self.assertRaises(TypeError, theano.function, inputs=[], outputs=[],
updates={output_var:
output_var.sum().dimshuffle('x', 'x')})
updates=[(output_var,
output_var.sum().dimshuffle('x', 'x'))])
def test_broadcast(self):
# Test that we can rebroadcast
......@@ -111,11 +111,11 @@ class T_updates(unittest.TestCase):
up = tensor.unbroadcast(output_var.sum().dimshuffle('x', 'x'), 0, 1)
output_func = theano.function(inputs=[], outputs=[],
updates={output_var: up})
updates=[(output_var, up)])
output_func()
up = tensor.patternbroadcast(output_var.sum().dimshuffle('x', 'x'),
output_var.type.broadcastable)
output_func = theano.function(inputs=[], outputs=[],
updates={output_var: up})
updates=[(output_var, up)])
output_func()
theano/sandbox/linalg/__init__.py
浏览文件 @ f68f06ce
from ops import (cholesky, matrix_inverse, solve,
diag, extract_diag, alloc_diag,
det, psd, eig,
det, psd, eig, eigh,
trace, spectral_radius_bound)
theano/sandbox/linalg/ops.py
浏览文件 @ f68f06ce
......@@ -12,6 +12,7 @@ from theano.tensor.opt import (register_stabilize,
register_specialize, register_canonicalize)
from theano.gof import local_optimizer
from theano.gof.opt import Optimizer
from theano.gradient import grad_not_implemented, DisconnectedType
try:
import scipy.linalg
......@@ -395,6 +396,8 @@ cholesky = Cholesky()
class CholeskyGrad(Op):
"""
"""
def __init__(self, lower=True):
self.lower = lower
self.destructive = False
......@@ -487,7 +490,7 @@ class MatrixPinv(Op):
This method is not faster then `matrix_inverse`. Its strength comes from
that it works for non-square matrices.
If you have a square matrix though, `matrix_inverse` can be both more
exact and faster to compute. Aslo this op does not get optimized into a
exact and faster to compute. Also this op does not get optimized into a
solve op.
"""
def __init__(self):
......@@ -880,9 +883,7 @@ class Eig(Op):
"""Compute the eigenvalues and right eigenvectors of a square array.
"""
def __init__(self):
pass
_numop = staticmethod(numpy.linalg.eig)
def props(self):
"""Function exposing different properties of each instance of the
......@@ -900,15 +901,17 @@ class Eig(Op):
def make_node(self, x):
x = as_tensor_variable(x)
assert x.ndim == 2
w = theano.tensor.vector(dtype=x.dtype)
v = theano.tensor.matrix(dtype=x.dtype)
return Apply(self, [x], [w, v])
def perform(self, node, (x,), (w, v)):
try:
w[0], v[0] = [z.astype(x.dtype) for z in numpy.linalg.eig(x)]
w[0], v[0] = [z.astype(x.dtype) for z in self._numop(x)]
except numpy.linalg.LinAlgError:
logger.debug('Failed to find eig of %s' % str(node.inputs[0]))
logger.debug('Failed to find %s of %s' % (self._numop.__name__,
node.inputs[0]))
raise
def infer_shape(self, node, shapes):
......@@ -916,6 +919,138 @@ class Eig(Op):
return [(n,), (n,n)]
def __str__(self):
return "Eig"
return self._numop.__name__.capitalize()
eig = Eig()
def _zero_disconnected(outputs, grads):
return [o.zeros_like()
if isinstance(g.type, DisconnectedType) else g
for o, g in zip(outputs, grads)]
class Eigh(Eig):
"""
Return the eigenvalues and eigenvectors of a Hermitian or symmetric matrix.
"""
_numop = staticmethod(numpy.linalg.eigh)
def __init__(self, UPLO='L'):
self.UPLO = UPLO
def __str__(self):
return 'Eigh{%s}' % self.UPLO
def props(self):
return self.UPLO,
def make_node(self, x):
x = as_tensor_variable(x)
assert x.ndim == 2
# Numpy's linalg.eigh may return either double or single
# presision eigenvalues depending on installed version of
# LAPACK. Rather than trying to reproduce the (rather
# involved) logic, we just probe linalg.eigh with a trivial
# input.
w_dtype = self._numop([[numpy.dtype(x.dtype).type()]])[0].dtype.name
w = theano.tensor.vector(dtype=w_dtype)
v = theano.tensor.matrix(dtype=x.dtype)
return Apply(self, [x], [w, v])
def perform(self, node, (x,), (w, v)):
try:
w[0], v[0] = self._numop(x, self.UPLO)
except numpy.linalg.LinAlgError:
logger.debug('Failed to find %s of %s' % (self._numop.__name__,
node.inputs[0]))
raise
def grad(self, inputs, g_outputs):
r"""The gradient function should return
.. math:: \sum_n\left(W_n\frac{\partial\,w_n}
{\partial a_{ij}} +
\sum_k V_{nk}\frac{\partial\,v_{nk}}
{\partial a_{ij}}\right),
where [:math:`W`, :math:`V`] corresponds to ``g_outputs``,
:math:`a` to ``inputs``, and :math:`(w, v)=\mbox{eig}(a)`.
Analytic formulae for eigensystem gradients are well-known in
perturbation theory:
.. math:: \frac{\partial\,w_n}
{\partial a_{ij}} = v_{in}\,v_{jn}
.. math:: \frac{\partial\,v_{kn}}
{\partial a_{ij}} =
\sum_{m\ne n}\frac{v_{km}v_{jn}}{w_n-w_m}
"""
x, = inputs
w, v = self(x)
# Replace gradients wrt disconnected variables with
# zeros. This is a work-around for issue #1063.
gw, gv = _zero_disconnected([w, v], g_outputs)
return [EighGrad(self.UPLO)(x, w, v, gw, gv)]
def eigh(a, UPLO='L'):
return Eigh(UPLO)(a)
class EighGrad(Op):
"""Gradient of an eigensystem of a Hermitian matrix.
"""
def __init__(self, UPLO='L'):
self.UPLO = UPLO
if UPLO == 'L':
self.tri0 = numpy.tril
self.tri1 = lambda a: numpy.triu(a, 1)
else:
self.tri0 = numpy.triu
self.tri1 = lambda a: numpy.tril(a, -1)
def props(self):
return ()
def __hash__(self):
return hash((type(self), self.props()))
def __eq__(self, other):
return (type(self) == type(other) and self.props() == other.props())
def __str__(self):
return 'EighGrad{%s}' % self.UPLO
def make_node(self, x, w, v, gw, gv):
x, w, v, gw, gv = map(as_tensor_variable, (x, w, v, gw, gv))
return Apply(self, [x, w, v, gw, gv], [x.type()])
def perform(self, node, inputs, outputs):
r"""
Implements the "reverse-mode" gradient for the eigensystem of
a square matrix.
"""
x, w, v, W, V = inputs
N = x.shape[0]
outer = numpy.outer
G = lambda n: sum(v[:,m]*V.T[n].dot(v[:,m])/(w[n]-w[m])
for m in xrange(N) if m != n)
g = sum(outer(v[:,n], v[:,n]*W[n] + G(n))
for n in xrange(N))
# Numpy's eigh(a, 'L') (eigh(a, 'U')) is a function of tril(a)
# (triu(a)) only. This means that partial derivative of
# eigh(a, 'L') (eigh(a, 'U')) with respect to a[i,j] is zero
# for i < j (i > j). At the same time, non-zero components of
# the gradient must account for the fact that variation of the
# opposite triangle contributes to variation of two elements
# of Hermitian (symmetric) matrix. The following line
# implements the necessary logic.
outputs[0][0] = self.tri0(g) + self.tri1(g).T
def infer_shape(self, node, shapes):
return [shapes[0]]
theano/sandbox/linalg/tests/test_linalg.py
浏览文件 @ f68f06ce
......@@ -29,7 +29,7 @@ from theano.sandbox.linalg.ops import (cholesky,
imported_scipy,
Eig,
)
from theano.sandbox.linalg import eig, eigh
from nose.plugins.skip import SkipTest
......@@ -471,29 +471,51 @@ class test_Solve(utt.InferShapeTester):
self.op_class)
class test_Eig(utt.InferShapeTester):
op_class = Eig
op = eig
dtype = 'float64'
def setUp(self):
super(test_Eig, self).setUp()
self.op_class = Eig
self.op = Eig()
self.rng = numpy.random.RandomState(utt.fetch_seed())
self.A = theano.tensor.matrix(dtype=self.dtype)
X = numpy.asarray(self.rng.rand(5, 5),
dtype=self.dtype)
self.S = X.dot(X.T)
def test_infer_shape(self):
rng = numpy.random.RandomState(utt.fetch_seed())
A = theano.tensor.matrix()
X = numpy.asarray(rng.rand(5, 5),
dtype=config.floatX)
A = self.A
S = self.S
self._compile_and_check([A], # theano.function inputs
self.op(A), # theano.function outputs
# A must be square
[X.dot(X.T)],
# S must be square
[S],
self.op_class)
def test_eval(self):
import math
A = theano.tensor.matrix()
A = theano.tensor.matrix(dtype=self.dtype)
self.assertEquals([e.eval({A: [[1]]}) for e in self.op(A)],
[[1.0], [[1.0]]])
w, v = [e.eval({A: [[0, 1], [1, 0]]})
for e in self.op(A)]
assert_array_almost_equal(w, [1, -1])
x = math.sqrt(2)/2
assert_array_almost_equal(v, [[x, -x], [x, x]])
x = [[0, 1], [1, 0]]
w, v = [e.eval({A: x}) for e in self.op(A)]
assert_array_almost_equal(numpy.dot(x,v), w * v)
class test_Eigh(test_Eig):
op = staticmethod(eigh)
def test_uplo(self):
S = self.S
a = theano.tensor.matrix()
wu, vu = [out.eval({a: S}) for out in self.op(a, 'U')]
wl, vl = [out.eval({a: S}) for out in self.op(a, 'L')]
assert_array_almost_equal(wu, wl)
assert_array_almost_equal(vu*numpy.sign(vu[0,:]),
vl*numpy.sign(vl[0,:]))
def test_grad(self):
S = self.S
utt.verify_grad(lambda x: self.op(x)[0], [S], rng=self.rng)
utt.verify_grad(lambda x: self.op(x)[1], [S], rng=self.rng)
utt.verify_grad(lambda x: self.op(x, 'U')[0], [S], rng=self.rng)
utt.verify_grad(lambda x: self.op(x, 'U')[1], [S], rng=self.rng)
class test_Eigh_float32(test_Eigh):
dtype = 'float32'
theano/sandbox/scan.py
浏览文件 @ f68f06ce
......@@ -13,14 +13,16 @@ __contact__ = "Razvan Pascanu <r.pascanu@gmail>"
import itertools
import logging
import numpy
import warnings
from theano.compile import SharedVariable, function
from theano import compile
from theano import gof
from theano.gof.python25 import OrderedDict
from theano.tensor import opt
from theano import tensor
from theano import config
from theano.updates import Updates
from theano.updates import OrderedUpdates
from theano.scan_module import scan_op
......@@ -147,7 +149,7 @@ def scan(fn,
n_seqs = len(seqs)
n_outs = len(outs_info)
return_steps = {}
return_steps = OrderedDict()
# wrap outputs info in a dictionary if they are not already in one
for i in xrange(n_outs):
if outs_info[i] is not None:
......@@ -242,7 +244,7 @@ def scan(fn,
mit_sot_inner_inputs = []
mit_sot_inner_slices = []
mit_sot_inner_outputs = []
mit_sot_return_steps = {}
mit_sot_return_steps = OrderedDict()
mit_sot_tap_array = []
mit_sot_rightOrder = []
......@@ -251,7 +253,7 @@ def scan(fn,
sit_sot_inner_inputs = []
sit_sot_inner_slices = []
sit_sot_inner_outputs = []
sit_sot_return_steps = {}
sit_sot_return_steps = OrderedDict()
sit_sot_rightOrder = []
nit_sot_steps = []
# go through outputs picking up time slices as needed
......@@ -398,7 +400,8 @@ def scan(fn,
not isinstance(arg, tensor.Constant))]
# when we apply the lambda expression we get a mixture of update rules
# and outputs that needs to be separated
condition, outputs, updates = scan_utils.get_updates_and_outputs(fn(*args))
lambda_result = fn(*args)
condition, outputs, updates = scan_utils.get_updates_and_outputs(lambda_result)
if condition is not None:
as_while = True
else:
......@@ -464,6 +467,13 @@ def scan(fn,
dummy_outs = outputs
if condition is not None:
dummy_outs.append(condition)
# If we use a regular dict here, the results are non-deterministic
if not isinstance(updates, (list, tuple)):
if isinstance(updates, dict) and \
not isinstance(updates, gof.python25.OrderedDict):
warnings.warn("Using non-deterministic dictionary.")
dummy_f = function(dummy_args,
dummy_outs,
updates=updates,
......@@ -508,7 +518,7 @@ def scan(fn,
sit_sot_inner_outputs.append(outputs[i])
## Step 5.3 Outputs that correspond to update rules of shared variables
givens = {}
givens = OrderedDict()
n_shared_outs = 0
shared_scan_inputs = []
shared_inner_inputs = []
......@@ -527,7 +537,7 @@ def scan(fn,
## Step 5.4 Outputs with no taps used in the input
n_nit_sot = 0
nit_sot_inner_outputs = []
nit_sot_return_steps = {}
nit_sot_return_steps = OrderedDict()
nit_sot_rightOrder = []
for i, out in enumerate(outs_info):
if not 'taps' in out:
......@@ -582,7 +592,7 @@ def scan(fn,
shared_inner_outputs)
if condition is not None:
inner_outs.append(condition)
new_givens = {}
new_givens = OrderedDict()
for w, w_copy in givens.iteritems():
new_givens[w] = w.type.filter_variable(w_copy)
......@@ -593,7 +603,7 @@ def scan(fn,
##
tap_array = mit_sot_tap_array + [[-1] for x in xrange(n_sit_sot)]
info = {}
info = OrderedDict()
info['tap_array'] = tap_array
info['n_seqs'] = n_seqs
......@@ -607,7 +617,7 @@ def scan(fn,
info['truncate_gradient'] = -1
info['name'] = name
info['mode'] = mode
info['destroy_map'] = {}
info['destroy_map'] = OrderedDict()
info['inplace'] = False
info['gpu'] = False
info['as_while'] = as_while
......@@ -641,7 +651,7 @@ def scan(fn,
### and so on ...
##
update_map = Updates()
update_map = OrderedUpdates()
offset = n_mit_mot
offsets = [abs(numpy.min(x)) for x in mit_sot_tap_array]
......@@ -675,4 +685,5 @@ def scan(fn,
elif len(scan_out_list) == 0:
scan_out_list = None
assert isinstance(update_map, dict) and 'Ordered' in str(type(update_map))
return (scan_out_list, update_map)
theano/sandbox/scan_module/scan.py
浏览文件 @ f68f06ce
......@@ -46,17 +46,12 @@ from itertools import izip
import logging
import numpy
from theano.compile import SharedVariable, function
from theano import compile
from theano import gof
from theano.tensor import opt, TensorVariable
from theano.tensor.sharedvar import TensorSharedVariable
from theano import tensor
from theano import config
from theano.updates import Updates
from theano.scalar.sharedvar import shared as scalar_shared
from theano.compile.pfunc import rebuild_collect_shared
import theano
import scan_op
import scan_utils
......
theano/scan_module/scan.py
浏览文件 @ f68f06ce
......@@ -52,8 +52,9 @@ from theano import gof
from theano.tensor import opt
from theano import tensor
from theano import config
from theano.updates import Updates
from theano.updates import OrderedUpdates
from theano.compile import ops
from theano.gof.python25 import OrderedDict
import scan_op
......@@ -112,7 +113,7 @@ def scan(fn,
, outputs_info = [ dict(initial = Output1, taps = [-3,-5])
, dict(initial = Output2, taps = None)
, Output3 ]
, non_sequences = [ Argument1, Argument 2])
, non_sequences = [ Argument1, Argument2])
``fn`` should expect the following arguments in this given order:
......@@ -376,11 +377,11 @@ def scan(fn,
n_seqs = len(seqs)
n_outs = len(outs_info)
return_steps = {}
return_steps = OrderedDict()
# wrap sequences in a dictionary if they are not already dictionaries
for i in xrange(n_seqs):
if not isinstance(seqs[i], dict):
seqs[i] = dict(input=seqs[i], taps=[0])
seqs[i] = OrderedDict([('input', seqs[i]), ('taps', [0])])
elif seqs[i].get('taps', None):
seqs[i]['taps'] = wrap_into_list(seqs[i]['taps'])
elif seqs[i].get('taps', True) is None:
......@@ -402,7 +403,7 @@ def scan(fn,
if not isinstance(outs_info[i], dict):
# by default any output has a tap value of -1
outs_info[i] = dict(initial=outs_info[i], taps=[-1])
outs_info[i] = OrderedDict([('initial', outs_info[i]), ('taps', [-1])])
elif (not outs_info[i].get('initial', None) and
outs_info[i].get('taps', None)):
# ^ no initial state but taps provided
......@@ -421,8 +422,8 @@ def scan(fn,
outs_info[i]['taps'] = [-1]
else:
# if a None is provided as the output info we replace it
# with an empty dict() to simplify handling
outs_info[i] = dict()
# with an empty OrdereDict() to simplify handling
outs_info[i] = OrderedDict()
##
### Step 2. Generate inputs and outputs of the inner functions
......@@ -565,7 +566,7 @@ def scan(fn,
mit_sot_inner_inputs = []
mit_sot_inner_slices = []
mit_sot_inner_outputs = []
mit_sot_return_steps = {}
mit_sot_return_steps = OrderedDict()
mit_sot_tap_array = []
mit_sot_rightOrder = []
......@@ -574,7 +575,7 @@ def scan(fn,
sit_sot_inner_inputs = []
sit_sot_inner_slices = []
sit_sot_inner_outputs = []
sit_sot_return_steps = {}
sit_sot_return_steps = OrderedDict()
sit_sot_rightOrder = []
# go through outputs picking up time slices as needed
......@@ -777,7 +778,7 @@ def scan(fn,
# as non sequences at the end of our args
fake_nonseqs = [x.type() for x in non_seqs]
fake_outputs = scan_utils.clone(outputs,
replace=dict(zip(non_seqs,
replace=OrderedDict(zip(non_seqs,
fake_nonseqs)))
all_inputs = itertools.ifilter(
lambda x: (isinstance(x, gof.Variable) and
......@@ -825,7 +826,7 @@ def scan(fn,
n_outs = len(dummy_f.maker.outputs)
if as_while:
n_outs = n_outs - 1
outs_info = [dict() for x in xrange(n_outs)]
outs_info = [OrderedDict() for x in xrange(n_outs)]
## Step 5.1 Outputs with taps different then -1
......@@ -839,7 +840,7 @@ def scan(fn,
sit_sot_inner_outputs.append(outputs[i])
## Step 5.3 Outputs that correspond to update rules of shared variables
givens = {}
givens = OrderedDict()
n_shared_outs = 0
shared_scan_inputs = []
shared_inner_inputs = []
......@@ -879,7 +880,7 @@ def scan(fn,
## Step 5.4 Outputs with no taps used in the input
n_nit_sot = 0
nit_sot_inner_outputs = []
nit_sot_return_steps = {}
nit_sot_return_steps = OrderedDict()
nit_sot_rightOrder = []
for i, out in enumerate(outs_info):
if not 'taps' in out:
......@@ -902,7 +903,7 @@ def scan(fn,
if (not isinstance(arg, SharedVariable) and
not isinstance(arg, tensor.Constant))]
givens.update(dict(zip(other_scan_args, other_inner_args)))
givens.update(OrderedDict(zip(other_scan_args, other_inner_args)))
other_shared_scan_args = [arg.variable for arg
in dummy_f.maker.expanded_inputs
if (isinstance(arg.variable, SharedVariable) and
......@@ -911,7 +912,7 @@ def scan(fn,
in dummy_f.maker.expanded_inputs
if (isinstance(arg.variable, SharedVariable) and
not arg.update)]
givens.update(dict(zip(other_shared_scan_args,
givens.update(OrderedDict(zip(other_shared_scan_args,
other_shared_inner_args)))
##
......@@ -943,7 +944,7 @@ def scan(fn,
# replace w with w_copy, where w is CudaNdarray
# and w_copy is TensorType. This is caused because shared
# variables are put on GPU right aways >:| ,
new_givens = {}
new_givens = OrderedDict()
for w, w_copy in givens.iteritems():
if (isinstance(w.type, cuda.CudaNdarrayType)
......@@ -962,7 +963,7 @@ def scan(fn,
##
tap_array = mit_sot_tap_array + [[-1] for x in xrange(n_sit_sot)]
info = {}
info = OrderedDict()
info['tap_array'] = tap_array
info['n_seqs'] = n_seqs
......@@ -976,7 +977,7 @@ def scan(fn,
info['truncate_gradient'] = truncate_gradient
info['name'] = name
info['mode'] = mode
info['destroy_map'] = {}
info['destroy_map'] = OrderedDict()
info['gpu'] = False
info['as_while'] = as_while
info['profile'] = profile
......@@ -1012,7 +1013,7 @@ def scan(fn,
### and so on ...
##
update_map = Updates()
update_map = OrderedUpdates()
def remove_dimensions(outs, steps_return, offsets=None):
out_ls = []
......
theano/scan_module/scan_utils.py
浏览文件 @ f68f06ce
......@@ -18,12 +18,13 @@ import logging
from itertools import izip
import numpy
import warnings
import theano
from theano.compile.pfunc import rebuild_collect_shared
from theano import gof
from theano import tensor, scalar
from theano.gof.python25 import all
from theano.gof.python25 import all, OrderedDict
from theano.tensor.basic import get_constant_value
......@@ -181,12 +182,17 @@ def clone(output,
def get_updates_and_outputs(ls):
"""
This function tries to recognize the updates dictionary, the
This function tries to recognize the updates OrderedDict, the
list of outputs and the stopping condition returned by the
lambda expression and arrange them in a predefined order
WRITEME: what is the type of ls? how is it formatted?
if it's not in the predefined order already, how does
this function know how to put it in that order?
"""
def is_outputs(elem):
if (isinstance(elem, (list, tuple)) and
all([isinstance(x, theano.Variable) for x in elem])):
......@@ -197,6 +203,11 @@ def get_updates_and_outputs(ls):
def is_updates(elem):
if isinstance(elem, dict):
# Make sure the updates will be applied in a deterministic order
if not isinstance(elem, gof.python25.OrderedDict):
warnings.warn("Expected OrderedDict or OrderedUpdates, got "\
+str(type(elem))+". This can make your script non-"
"deterministic.")
return True
# Dictionaries can be given as lists of tuples
if (isinstance(elem, (list, tuple)) and
......@@ -242,10 +253,11 @@ def get_updates_and_outputs(ls):
'values, you can use `tensor.constant` to turn them into '
'Theano variables.')
if is_outputs(ls):
return None, _list(ls), {}
return None, _list(ls), OrderedDict()
if is_updates(ls):
return None, [], dict(ls)
return None, [], OrderedDict(ls)
error_msg = ('Scan cannot parse the return value of your lambda '
'expression, which is: %s' % (ls,))
if not isinstance(ls, (list, tuple)):
......@@ -258,16 +270,16 @@ def get_updates_and_outputs(ls):
if len(ls) == 2:
if is_outputs(ls[0]):
if is_updates(ls[1]):
return (None, _list(ls[0]), dict(ls[1]))
return (None, _list(ls[0]), OrderedDict(ls[1]))
elif is_condition(ls[1]):
return (ls[1].condition, _list(ls[0]), {})
return (ls[1].condition, _list(ls[0]), OrderedDict())
else:
raise ValueError(error_msg)
elif is_updates(ls[0]):
if is_outputs(ls[1]):
raise ValueError(deprecation_msg)
elif is_condition(ls[1]):
return (ls[1].condition, [], dict(ls[0]))
return (ls[1].condition, [], OrderedDict(ls[0]))
else:
raise ValueError(error_msg)
else:
......@@ -276,7 +288,7 @@ def get_updates_and_outputs(ls):
if is_outputs(ls[0]):
if is_updates(ls[1]):
if is_condition(ls[2]):
return (ls[2].condition, _list(ls[0]), dict(ls[1]))
return (ls[2].condition, _list(ls[0]), OrderedDict(ls[1]))
else:
raise ValueError(error_msg)
else:
......
theano/scan_module/tests/test_scan.py
浏览文件 @ f68f06ce
......@@ -16,6 +16,7 @@ from theano.compile.pfunc import rebuild_collect_shared
from theano.gof.python25 import any
from theano.tests import unittest_tools as utt
import theano.scalar.sharedvar
from theano.gof.python25 import OrderedDict
from numpy.testing.noseclasses import KnownFailureTest
......@@ -1009,7 +1010,7 @@ class T_Scan(unittest.TestCase):
x0 = theano.tensor.constant(x0)
to_replace = outputs[0].owner.inputs[0].owner.inputs[1]
outputs = theano.clone(outputs,
replace={to_replace: x0})
replace=[(to_replace, x0)])
mode = theano.compile.mode.get_mode(None).including('inplace')
f9 = theano.function([],
outputs,
......@@ -1299,7 +1300,7 @@ class T_Scan(unittest.TestCase):
state = theano.shared(v_state, 'vstate')
def f_2():
return {state: 2 * state}
return OrderedDict([(state, 2 * state)])
n_steps = theano.tensor.iscalar('nstep')
output, updates = theano.scan(f_2,
[],
......@@ -1829,7 +1830,7 @@ class T_Scan(unittest.TestCase):
X = theano.shared(numpy.array(1))
out, updates = theano.scan(
lambda: {X: X + 1},
lambda: OrderedDict([(X, (X + 1))]),
outputs_info=[],
non_sequences=[],
sequences=[],
......@@ -1844,7 +1845,7 @@ class T_Scan(unittest.TestCase):
y = theano.shared(numpy.array(1))
out, updates = theano.scan(
lambda: {x: x + 1, y: x},
lambda: OrderedDict([(x, x + 1), (y, x)]),
outputs_info=[],
non_sequences=[],
sequences=[],
......@@ -1880,11 +1881,11 @@ class T_Scan(unittest.TestCase):
b = theano.shared(numpy.random.rand(5, 4))
def inner_func(a):
return a + 1, {b: 2 * b}
return a + 1, OrderedDict([(b, 2 * b)])
out, updates = theano.scan(
inner_func,
outputs_info=[{'initial': init_a}],
outputs_info=[OrderedDict([('initial', init_a)])],
n_steps=1)
out = out[-1]
assert out.type.ndim == a.type.ndim
......@@ -1967,7 +1968,7 @@ class T_Scan(unittest.TestCase):
f1 = z * (x + y) ** 2 + 5
f2 = theano.clone(f1,
replace={y: y2},
replace=OrderedDict([(y, y2)]),
strict=True,
copy_inputs=True)
f2_inp = theano.gof.graph.inputs([f2])
......@@ -1986,7 +1987,7 @@ class T_Scan(unittest.TestCase):
f1 = z * (x + y) ** 2 + 5
f2 = theano.clone(f1,
replace={y: y2},
replace=OrderedDict([(y, y2)]),
strict=False,
copy_inputs=True)
f2_inp = theano.gof.graph.inputs([f2])
......@@ -2005,7 +2006,7 @@ class T_Scan(unittest.TestCase):
f1 = z * (x + y) ** 2 + 5
f2 = theano.clone(f1,
replace={y: y2},
replace=[(y, y2)],
strict=True,
copy_inputs=False)
f2_inp = theano.gof.graph.inputs([f2])
......@@ -2024,7 +2025,7 @@ class T_Scan(unittest.TestCase):
f1 = z * (x + y) ** 2 + 5
f2 = theano.clone(f1,
replace={y: y2},
replace=[(y, y2)],
strict=False,
copy_inputs=False)
f2_inp = theano.gof.graph.inputs([f2])
......@@ -2204,15 +2205,15 @@ class T_Scan(unittest.TestCase):
v2 = theano.shared(numpy.ones((5, 5), dtype=theano.config.floatX))
shapef = theano.function([W],
expr,
givens={initial: v1,
inpt: v2})
givens=OrderedDict([(initial, v1),
(inpt, v2)]))
# First execution to cache n_steps
shapef(numpy.ones((5, 5), dtype=theano.config.floatX))
cost = expr.sum()
d_cost_wrt_W = tensor.grad(cost, [W])
f = theano.function([W, inpt], d_cost_wrt_W,
givens={initial: theano.shared(numpy.zeros(5))})
givens=OrderedDict([(initial, theano.shared(numpy.zeros(5)))]))
rval = numpy.asarray([[5187989] * 5] * 5, dtype=theano.config.floatX)
arg1 = numpy.ones((5, 5), dtype=theano.config.floatX)
......@@ -3166,7 +3167,7 @@ class T_Scan(unittest.TestCase):
shared_var = theano.shared(numpy.float32(1.))
def inner_fn():
return [], {shared_var: shared_var + numpy.float32(1.)}
return [], OrderedDict([(shared_var, shared_var + numpy.float32(1.))])
_, updates = theano.scan(inner_fn,
n_steps=10,
truncate_gradient=-1,
......@@ -3239,7 +3240,7 @@ class T_Scan(unittest.TestCase):
seq = tensor.matrix()
initial_value = theano.shared(numpy.zeros((4, 1),
dtype=theano.config.floatX))
outputs_info = [{'initial': initial_value, 'taps': [-4]}, None]
outputs_info = [OrderedDict([('initial', initial_value), ('taps', [-4])]), None]
results, updates = theano.scan(fn=onestep,
sequences=seq,
outputs_info=outputs_info)
......@@ -3259,13 +3260,13 @@ class T_Scan(unittest.TestCase):
seq = tensor.matrix()
initial_value = theano.shared(numpy.zeros((4, 1),
dtype=theano.config.floatX))
outputs_info = [{'initial': initial_value, 'taps': [-4]}, None]
outputs_info = [OrderedDict([('initial', initial_value), ('taps', [-4])]), None]
results, _ = theano.scan(fn=onestep,
sequences=seq,
outputs_info=outputs_info)
sharedvar = theano.shared(numpy.zeros((1, 1),
dtype=theano.config.floatX))
updates = {sharedvar: results[0][-1:]}
updates = OrderedDict([(sharedvar, results[0][-1:])])
f = theano.function([seq], results[1], updates=updates)
assert numpy.all(exp_out == f(inp))
......@@ -3354,9 +3355,9 @@ def test_speed():
theano.printing.debugprint(s_rinc)
f = theano.function([],
[],
updates={
s_i: s_i + 1,
shared_r: s_rinc},
updates=OrderedDict([
(s_i, s_i + 1),
(shared_r, s_rinc)]),
mode=theano.Mode(linker='cvm'))
f._check_for_aliased_inputs = False
t2 = time.time()
......@@ -3430,9 +3431,9 @@ def test_speed_rnn():
w)),
tolerate_inplace_aliasing=True)
f = theano.function([], [],
updates={
s_i: s_i + 1,
shared_r: s_rinc},
updates=OrderedDict([
(s_i, s_i + 1),
(shared_r, s_rinc)]),
mode=theano.Mode(linker='cvm'))
#theano.printing.debugprint(f)
f_fn = f.fn
......@@ -3495,9 +3496,9 @@ def test_speed_batchrnn():
tolerate_inplace_aliasing=True)
f = theano.function([],
[],
updates={
s_i: s_i + 1,
shared_r: s_rinc},
updates=[
(s_i, s_i + 1),
(shared_r, s_rinc)],
mode=theano.Mode(linker='cvm'))
#theano.printing.debugprint(f)
f_fn = f.fn
......
theano/sparse/tests/test_basic.py
浏览文件 @ f68f06ce
......@@ -1219,7 +1219,7 @@ class UsmmTests(unittest.TestCase):
mode = theano.compile.mode.get_default_mode().excluding('fusion')
if inplace:
updates = {z: z - a * theano.sparse.dot(x, y)}
updates = [(z, z - a * theano.sparse.dot(x, y))]
f_a = theano.function([a, x, y], [],
updates=updates,
mode=mode)
......
theano/tensor/basic.py
浏览文件 @ f68f06ce
......@@ -27,6 +27,7 @@ from theano.tensor.utils import hash_from_ndarray
from theano.scalar import ComplexError, IntegerDivisionError
import theano.scalar.sharedvar
from theano.gradient import grad_undefined
from theano.gradient import grad_not_implemented
from theano.gradient import DisconnectedType
### set up the external interface
......@@ -1639,6 +1640,9 @@ class _tensor_py_operators:
def ravel(self):
return flatten(self)
def diagonal(self, offset=0, axis1=0, axis2=1):
return diagonal(self, offset, axis1, axis2)
# CASTING
def astype(self, dtype):
return cast(self, dtype)
......@@ -1796,6 +1800,8 @@ class _tensor_py_operators:
"""See `theano.tensor.conj`"""
return conj(self)
conjugate = conj
def repeat(self, repeats, axis=None):
"""See `theano.tensor.repeat`"""
from theano.tensor.extra_ops import repeat
......@@ -7310,3 +7316,96 @@ def all(x, axis=None, keepdims=False):
if keepdims:
out = makeKeepDims(x, out, axis)
return out
class Diagonal(Op):
"""Return specified diagonals.
:param x: A tensor variable with x.ndim >= 2.
:return: A vector representing the diagonal elements.
"""
def __init__(self, offset=0, axis1=0, axis2=1):
self.offset = offset
self.axis1 = axis1
self.axis2 = axis2
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
def make_node(self, x):
x = as_tensor_variable(x)
assert x.ndim >= 2
return Apply(self, [x], [tensor(dtype=x.dtype,
broadcastable=[False] * (x.ndim -1))])
def perform(self, node, (x,), (z,)):
z[0] = x.diagonal(self.offset, self.axis1, self.axis2)
def grad(self, (x,), (gz,)):
return [grad_not_implemented(self, 0, x)]
def infer_shape(self, node, shapes):
in_shape, = shapes
dim1 = in_shape[self.axis1]
dim2 = in_shape[self.axis2]
out_shape = [d for i,d in enumerate(in_shape)
if i not in (self.axis1, self.axis2)]
# The following logic is inspired by C code of PyArray_Diagonal().
offset = self.offset
if offset > 0:
diag_size = clip(dim2 - offset, 0, dim1)
elif offset < 0:
diag_size = clip(dim1 + offset, 0, dim2)
else:
diag_size = minimum(dim1, dim2)
out_shape.append(diag_size)
return [tuple(out_shape)]
def __str__(self):
return self.__class__.__name__
def diagonal(a, offset=0, axis1=0, axis2=1):
if (offset, axis1, axis2) == (0, 0, 1):
from theano.sandbox.linalg import extract_diag
return extract_diag(a)
return Diagonal(offset, axis1, axis2)(a)
class Diag(Op):
def __eq__(self, other):
return type(self) == type(other)
def __hash__(self):
return hash(type(self))
def make_node(self, diag):
diag = as_tensor_variable(diag)
if diag.type.ndim != 1:
raise TypeError('data argument must be a vector', diag.type)
return Apply(self, [diag], [matrix(dtype=diag.dtype)])
def perform(self, node, inputs, (z,)):
z[0] = numpy.diag(inputs[0])
def grad(self, inputs, (gz,)):
return [diagonal(gz)]
def infer_shape(self, nodes, shapes):
return [(shapes[0][0],) * 2]
def __str__(self):
return self.__class__.__name__
def diag(v, k=0):
if v.ndim == 1:
assert k == 0, "diagonals other than main are not implemented"
return Diag()(v)
elif v.ndim == 2:
return diagonal(v, k)
else:
raise ValueError("Input must be 1- or 2-d.")
theano/tensor/tests/mlp_test.py
浏览文件 @ f68f06ce
......@@ -9,7 +9,7 @@ import numpy
import theano
import theano.tensor as T
from theano.gof.python25 import any
from theano.gof.python25 import any, OrderedDict
def gen_data():
......@@ -293,7 +293,7 @@ def test_mlp():
# TODO: refine that and include only those
mode = theano.compile.get_default_mode().including('fast_run')
updates2 = {}
updates2 = OrderedDict()
updates2[classifier.hiddenLayer.params[0]]=T.grad(cost,classifier.hiddenLayer.params[0])
train_model =theano.function( inputs = [index],
......
theano/tensor/tests/test_basic.py
浏览文件 @ f68f06ce
......@@ -40,7 +40,7 @@ from theano.tensor import (_shared, wvector, bvector, autocast_float_as,
tile, patternbroadcast, Eye, Shape, Default, Dot, PermuteRowElements,
ScalarFromTensor, TensorFromScalar, dtensor4, Rebroadcast, Alloc,
dtensor3, SpecifyShape, Mean, IncSubtensor, AdvancedIncSubtensor1,
itensor3, Tile, AdvancedIncSubtensor, switch)
itensor3, Tile, AdvancedIncSubtensor, switch, Diagonal, Diag)
from theano.tests import unittest_tools as utt
from theano.printing import debugprint
......@@ -6590,6 +6590,34 @@ class TestInferShape(utt.InferShapeTester):
[Eye()(aiscal, biscal, ciscal)],
[3, 5, 0], Eye)
# Diagonal
atens3 = tensor3()
atens3_val = rand(4, 5, 3)
atens3_diag = Diagonal()(atens3)
self._compile_and_check([atens3], [atens3_diag],
[atens3_val], Diagonal)
atens3_diag = Diagonal(1)(atens3)
self._compile_and_check([atens3], [atens3_diag],
[atens3_val], Diagonal)
atens3_diag = Diagonal(-1)(atens3)
self._compile_and_check([atens3], [atens3_diag],
[atens3_val], Diagonal)
atens3_diag = Diagonal(1,0,2)(atens3)
self._compile_and_check([atens3], [atens3_diag],
[atens3_val], Diagonal)
atens3_diag = Diagonal(1,1,2)(atens3)
self._compile_and_check([atens3], [atens3_diag],
[atens3_val], Diagonal)
atens3_diag = Diagonal(1,2,0)(atens3)
self._compile_and_check([atens3], [atens3_diag],
[atens3_val], Diagonal)
# Diag
advec = dvector()
advec_val = rand(4)
self._compile_and_check([advec], [Diag()(advec)],
[advec_val], Diag)
# Shape
# 'opt.Makevector' precludes optimizer from disentangling
# elements of shape
......@@ -7070,7 +7098,7 @@ class TestTensorInstanceMethods(unittest.TestCase):
assert_array_equal(X.argsort().eval({X: x}), x.argsort())
assert_array_equal(X.argsort(1).eval({X: x}), x.argsort(1))
def test_dot(self):
def test_clip(self):
X, Y = self.vars
x, y = self.vals
Z = X.clip(0.5 - Y, 0.5 + Y)
......@@ -7099,6 +7127,7 @@ class TestTensorInstanceMethods(unittest.TestCase):
Z = X + Y * 1j
z = x + y * 1j
assert_array_equal(Z.conj().eval({Z: z}), z.conj())
assert_array_equal(Z.conjugate().eval({Z: z}), z.conj())
def test_round(self):
X, _ = self.vars
......@@ -7128,6 +7157,16 @@ class TestTensorInstanceMethods(unittest.TestCase):
x, _ = self.vals
assert_array_equal(X.ravel().eval({X: x}), x.ravel())
def test_diagonal(self):
X, _ = self.vars
x, _ = self.vals
assert_array_equal(X.diagonal().eval({X: x}), x.diagonal())
assert_array_equal(X.diagonal(1).eval({X: x}), x.diagonal(1))
assert_array_equal(X.diagonal(-1).eval({X: x}), x.diagonal(-1))
for offset, axis1, axis2 in [(1,0,1), (-1,0,1), (0,1,0), (-2,1,0)]:
assert_array_equal(X.diagonal(offset, axis1, axis2).eval({X: x}),
x.diagonal(offset, axis1, axis2))
if __name__ == '__main__':
......
theano/tensor/tests/test_blas.py
浏览文件 @ f68f06ce
......@@ -185,8 +185,8 @@ class t_gemm(TestCase):
l2_reg = T.constant(0.0001).astype(config.floatX)
#test constant merge with gemm
f = theano.function([a, b], updates={s: lr1 * T.dot(a, b) +
l2_reg * lr2 * s},
f = theano.function([a, b], updates=[(s, lr1 * T.dot(a, b) +
l2_reg * lr2 * s)],
mode=mode_not_fast_compile).maker.fgraph.toposort()
#[Gemm{inplace}(<TensorType(float64, matrix)>, 0.01,
# <TensorType(float64, matrix)>, <TensorType(float64, matrix)>,
......@@ -195,8 +195,8 @@ class t_gemm(TestCase):
assert f[0].op == gemm_inplace
#test factored scalar with merge
f = theano.function([a, b], updates={s: lr1 * (T.dot(a, b) -
l2_reg * s)},
f = theano.function([a, b], updates=[(s, lr1 * (T.dot(a, b) -
l2_reg * s))],
mode=mode_not_fast_compile).maker.fgraph.toposort()
#[Gemm{inplace}(<TensorType(float64, matrix)>, 0.01,
# <TensorType(float64, matrix)>, <TensorType(float64, matrix)>,
......@@ -206,7 +206,7 @@ class t_gemm(TestCase):
#test factored scalar with merge and neg
f = theano.function([a, b],
updates={s: s - lr1 * (s * .0002 + T.dot(a, b))},
updates=[(s, s - lr1 * (s * .0002 + T.dot(a, b)))],
mode=mode_not_fast_compile).maker.fgraph.toposort()
#[Gemm{inplace}(<TensorType(float64, matrix)>, -0.01,
# <TensorType(float64, matrix)>, <TensorType(float64, matrix)>,
......@@ -368,7 +368,7 @@ class t_gemm(TestCase):
tz_i = gemm_no_inplace(tz[:, :, i], ta, tx[
:, :, i], ty[:, :, i], tb)
g_i = theano.function([], tz_i,
updates={tz: T.set_subtensor(tz[:, :, i], tz_i)},
updates=[(tz, T.set_subtensor(tz[:, :, i], tz_i))],
mode=compile.Mode(optimizer=None, linker=l))
for j in xrange(3):
g_i()
......@@ -801,7 +801,7 @@ def test_gemm_unrolled():
cur_V = update_V(cur_H)
cur_H = update_H(cur_V)
unrolled_theano = theano.function([], updates={V: cur_V, H: cur_H},
unrolled_theano = theano.function([], updates=[(V, cur_V), (H, cur_H)],
name='unrolled_theano')
nb_dot = sum([1 for node in unrolled_theano.maker.fgraph.toposort()
if isinstance(node.op, (theano.tensor.Dot,
......@@ -1032,7 +1032,7 @@ def test_dot_w_self():
p = T.dot(A, A) * B
grad = T.grad(T.mean(p), A)
f = theano.function([B], p, updates={A: A - grad})
f = theano.function([B], p, updates=[(A, A - grad)])
# tests correctness in debugmode
f(numpy.asarray([[0, 1], [2, 3]], dtype=config.floatX))
......@@ -1119,7 +1119,7 @@ class TestGemv(TestCase, unittest_tools.TestOptimizationMixin):
assert topo[0].op.inplace == False
#test the inplace version
g = theano.function([], [], updates={v2: v2 + theano.dot(m, v1)},
g = theano.function([], [], updates=[(v2, v2 + theano.dot(m, v1))],
mode=mode_blas_opt)
# Assert they produce the same output
......@@ -1169,7 +1169,7 @@ class TestGemv(TestCase, unittest_tools.TestOptimizationMixin):
assert topo[-1].op.inplace == False
#test the inplace version
g = theano.function([], [], updates={v2: v2 + theano.dot(v1, m)},
g = theano.function([], [], updates=[(v2, v2 + theano.dot(v1, m))],
mode=mode_blas_opt)
# Assert they produce the same output
......@@ -1575,7 +1575,7 @@ class TestGer(TestCase, unittest_tools.TestOptimizationMixin):
def function(self, inputs, outputs, updates=None):
if updates is None:
updates = {}
updates = []
return theano.function(inputs, outputs, self.mode, updates=updates)
def b(self, bval):
......@@ -1691,8 +1691,8 @@ class TestGer(TestCase, unittest_tools.TestOptimizationMixin):
def test_inplace(self):
A = self.shared(numpy.random.rand(4, 5).astype(self.dtype))
f = self.function([self.x, self.y], [],
updates={A: A + T.constant(0.1, dtype=self.dtype) *
T.outer(self.x, self.y)})
updates=[(A, A + T.constant(0.1, dtype=self.dtype) *
T.outer(self.x, self.y))])
self.assertFunctionContains(f, self.ger_destructive)
f(numpy.random.rand(4).astype(self.dtype),
numpy.random.rand(5).astype(self.dtype))
......@@ -1731,15 +1731,15 @@ class TestBlasStrides(TestCase):
bt_dev = b_t.get_value(borrow=False, return_internal_type=True)
ct_dev = c_t.get_value(borrow=False, return_internal_type=True)
f_nn = theano.function([], [], updates={a: tensor.dot(b, c)},
f_nn = theano.function([], [], updates=[(a, tensor.dot(b, c))],
mode=self.mode)
#print 'class name:', self.__class__.__name__
#theano.printing.debugprint(f_nn)
f_nt = theano.function([], [], updates={a: tensor.dot(b, c_t.T)},
f_nt = theano.function([], [], updates=[(a, tensor.dot(b, c_t.T))],
mode=self.mode)
f_tn = theano.function([], [], updates={a: tensor.dot(b_t.T, c)},
f_tn = theano.function([], [], updates=[(a, tensor.dot(b_t.T, c))],
mode=self.mode)
f_tt = theano.function([], [], updates={a: tensor.dot(b_t.T, c_t.T)},
f_tt = theano.function([], [], updates=[(a, tensor.dot(b_t.T, c_t.T))],
mode=self.mode)
# Try with all stride patterns, and all transposed pattern
......@@ -1802,14 +1802,14 @@ class TestBlasStrides(TestCase):
bt_dev = b_t.get_value(borrow=False, return_internal_type=True)
ct_dev = c_t.get_value(borrow=False, return_internal_type=True)
f_nn = theano.function([], [], updates={a: l * tensor.dot(b, c)},
f_nn = theano.function([], [], updates=[(a, l * tensor.dot(b, c))],
mode=self.mode)
f_nt = theano.function([], [], updates={a: l * tensor.dot(b, c_t.T)},
f_nt = theano.function([], [], updates=[(a, l * tensor.dot(b, c_t.T))],
mode=self.mode)
f_tn = theano.function([], [], updates={a: l * tensor.dot(b_t.T, c)},
f_tn = theano.function([], [], updates=[(a, l * tensor.dot(b_t.T, c))],
mode=self.mode)
f_tt = theano.function([], [],
updates={a: l * tensor.dot(b_t.T, c_t.T)},
updates=[(a, l * tensor.dot(b_t.T, c_t.T))],
mode=self.mode)
# Try with all stride patterns, and all transposed pattern
......@@ -1875,28 +1875,28 @@ class TestBlasStrides(TestCase):
ct_dev = c_t.get_value(borrow=False, return_internal_type=True)
f_nnn = theano.function([], [],
updates={a: (l * a + tensor.dot(b, c))},
updates=[(a, (l * a + tensor.dot(b, c)))],
mode=self.mode)
f_nnt = theano.function([], [],
updates={a: (l * a + tensor.dot(b, c_t.T))},
updates=[(a, (l * a + tensor.dot(b, c_t.T)))],
mode=self.mode)
f_ntn = theano.function([], [],
updates={a: (l * a + tensor.dot(b_t.T, c))},
updates=[(a, (l * a + tensor.dot(b_t.T, c)))],
mode=self.mode)
f_ntt = theano.function([], [],
updates={a: (l * a + tensor.dot(b_t.T, c_t.T))},
updates=[(a, (l * a + tensor.dot(b_t.T, c_t.T)))],
mode=self.mode)
f_tnn = theano.function([], [],
updates={a_t: (l * a_t + tensor.dot(b, c).T)},
updates=[(a_t, (l * a_t + tensor.dot(b, c).T))],
mode=self.mode)
f_tnt = theano.function([], [],
updates={a_t: (l * a_t + tensor.dot(b, c_t.T).T)},
updates=[(a_t, (l * a_t + tensor.dot(b, c_t.T).T))],
mode=self.mode)
f_ttn = theano.function([], [],
updates={a_t: (l * a_t + tensor.dot(b_t.T, c).T)},
updates=[(a_t, (l * a_t + tensor.dot(b_t.T, c).T))],
mode=self.mode)
f_ttt = theano.function([], [],
updates={a_t: (l * a_t + tensor.dot(b_t.T, c_t.T).T)},
updates=[(a_t, (l * a_t + tensor.dot(b_t.T, c_t.T).T))],
mode=self.mode)
# Try with all stride patterns, and all transposed pattern
......@@ -1985,11 +1985,11 @@ class TestBlasStrides(TestCase):
b_dev = b.get_value(borrow=False, return_internal_type=True)
c_dev = c.get_value(borrow=False, return_internal_type=True)
f_n = theano.function([], [], updates={a: (a + l * tensor.dot(b, c))},
f_n = theano.function([], [], updates=[(a, (a + l * tensor.dot(b, c)))],
mode=self.mode)
f_t = theano.function([], [],
updates={a: (a + l * tensor.dot(b_t.T, c))},
updates=[(a, (a + l * tensor.dot(b_t.T, c)))],
mode=self.mode)
# Try with all stride patterns, and all transposed pattern
......@@ -2041,11 +2041,11 @@ class TestBlasStrides(TestCase):
c_dev = c.get_value(borrow=False, return_internal_type=True)
f_n = theano.function([], [],
updates={a: (a + l * tensor.outer(b, c))},
updates=[(a, (a + l * tensor.outer(b, c)))],
mode=self.mode)
f_t = theano.function([], [],
updates={a_t: (a_t + l * tensor.outer(b, c).T)},
updates=[(a_t, (a_t + l * tensor.outer(b, c).T))],
mode=self.mode)
# Try with all stride patterns, and all transposed patterns
......
theano/tensor/tests/test_blas_c.py
浏览文件 @ f68f06ce
......@@ -185,7 +185,7 @@ class TestCGemv(TestCase, TestOptimizationMixin):
#test the inplace version
g = theano.function([], [],
updates={v2: v2 + theano.dot(m, v1)},
updates=[(v2, v2 + theano.dot(m, v1))],
mode=self.mode)
# Assert they produce the same output
......
theano/tensor/tests/test_sharedvar.py
浏览文件 @ f68f06ce
......@@ -526,8 +526,8 @@ def makeSharedTester(shared_constructor_,
s = self.cast_value(s)
s_shared = self.shared_constructor(s)
f = theano.function([],
updates={s_shared:theano.dot(a_shared,b_shared)
+s_shared})
updates=[(s_shared, theano.dot(a_shared,b_shared)
+s_shared)])
topo=f.maker.fgraph.toposort()
f()
#[Gemm{inplace}(<TensorType(float64, matrix)>, 0.01, <TensorType(float64, matrix)>, <TensorType(float64, matrix)>, 2e-06)]
......@@ -541,8 +541,8 @@ def makeSharedTester(shared_constructor_,
#now test with the specify shape op in the output
f = theano.function([], s_shared.shape,
updates={s_shared:theano.dot(a_shared,b_shared)
+s_shared_specify})
updates=[(s_shared, theano.dot(a_shared,b_shared)
+s_shared_specify)])
topo=f.maker.fgraph.toposort()
shp=f()
assert numpy.all(shp == (40,40))
......@@ -557,8 +557,8 @@ def makeSharedTester(shared_constructor_,
b_shared.get_value(borrow=True).shape)
f = theano.function([], s_shared.shape,
updates={s_shared:theano.dot(a_shared,b_shared)
+s_shared_specify})
updates=[(s_shared, theano.dot(a_shared,b_shared)
+s_shared_specify)])
topo=f.maker.fgraph.toposort()
shp=f()
assert numpy.all(shp == (40,40))
......
theano/tests/diverse_tests.py
浏览文件 @ f68f06ce
......@@ -55,7 +55,7 @@ class T_scipy(unittest.TestCase):
train = function(
inputs=[x,y],
outputs=[prediction, xent],
updates={w:w-0.1*gw, b:b-0.1*gb})
updates=[(w, w-0.1*gw), (b, b-0.1*gb)])
predict = function(inputs=[x], outputs=prediction)
N = 4
......
theano/tests/test_updates.py
浏览文件 @ f68f06ce
import unittest
import theano
from theano.updates import Updates
from theano.updates import OrderedUpdates
import theano.tensor as T
class test_ifelse(unittest.TestCase):
def test_updates_init(self):
self.assertRaises(TypeError, Updates, dict(d=3))
self.assertRaises(TypeError, OrderedUpdates, dict(d=3))
sv = theano.shared('asdf')
Updates({sv:3})
OrderedUpdates({sv:3})
def test_updates_setitem(self):
ok = True
up = Updates()
up = OrderedUpdates()
sv = theano.shared('asdf')
# keys have to be SharedVariables
......@@ -27,8 +27,8 @@ class test_ifelse(unittest.TestCase):
def test_updates_add(self):
up1 = Updates()
up2 = Updates()
up1 = OrderedUpdates()
up2 = OrderedUpdates()
a = theano.shared('a')
b = theano.shared('b')
......
theano/updates.py
浏览文件 @ f68f06ce
......@@ -8,23 +8,27 @@ __contact__ = "theano-dev <theano-dev@googlegroups.com>"
__docformat__ = "restructuredtext en"
from theano.gof.python25 import OrderedDict
from theano.compile.sharedvalue import SharedVariable
import logging
logger = logging.getLogger('theano.updates')
import warnings
class Updates(dict):
# Must be an OrderedDict or updates will be applied in a non-deterministic order
class OrderedUpdates(OrderedDict):
"""
Dict-like mapping from SharedVariable keys to their new values.
This mapping supports the use of the "+" operator for the union of updates.
"""
def __init__(self, *key, **kwargs):
ret = super(Updates, self).__init__(*key, **kwargs)
ret = super(OrderedUpdates, self).__init__(*key, **kwargs)
for key in self:
if not isinstance(key, SharedVariable):
raise TypeError(
'Updates keys must inherit from SharedVariable',
'OrderedUpdates keys must inherit from SharedVariable',
key)
return ret
......@@ -38,12 +42,14 @@ class Updates(dict):
# value. Should it be cast to a GPU value right away? Should
# literals be transformed into constants immediately?
return super(Updates, self).__setitem__(key, value)
return super(OrderedUpdates, self).__setitem__(key, value)
else:
raise TypeError('Updates keys must inherit from SharedVariable',
raise TypeError('OrderedUpdates keys must inherit from SharedVariable',
key)
def update(self, other):
def update(self, other=None):
if other is None:
return
for key, val in dict(other).iteritems():
if key in self:
if self[key] == val:
......@@ -52,13 +58,17 @@ class Updates(dict):
self[key] = val # __setitem__ does type-checking
def __add__(self, other):
rval = Updates()
rval = OrderedUpdates()
rval.update(self)
rval.update(other)
return rval
def __radd__(other, self):
rval = Updates()
rval = OrderedUpdates()
rval.update(other)
rval.update(self)
return rval
def Updates(*key, **kwargs):
warnings.warn("Updates is deprecated. Switch to OrderedUpdates.")
return OrderedUpdates(*key, **kwargs)
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