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提交 675e6d78 authored 作者: Ian Goodfellow's avatar Ian Goodfellow
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theano/tensor/basic.py
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......@@ -110,7 +110,7 @@ if 0:
transfert the value on the gpu
"""
if hasattr(x, '_as_CudaNdarrayVariable'):
#TODO: pass name and ndim arguments
# TODO: pass name and ndim arguments
return x._as_CudaNdarrayVariable()
return as_tensor_variable(x, name, ndim)
......@@ -144,7 +144,7 @@ def as_tensor_variable(x, name=None, ndim=None):
return x._as_TensorVariable() # TODO: pass name and ndim arguments
if isinstance(x, gof.Apply):
#TODO: use Apply's default output mechanism
# TODO: use Apply's default output mechanism
if len(x.outputs) != 1:
raise ValueError(
"It is ambiguous which output of a multi-output Op has"
......@@ -163,7 +163,7 @@ def as_tensor_variable(x, name=None, ndim=None):
return x
else:
if (x.type.ndim > ndim):
#TODO: strip off leading broadcastable dimensions
# TODO: strip off leading broadcastable dimensions
raise ValueError(
'TensorType could not be cast to have %i dimensions' %
ndim, x.type)
......@@ -371,7 +371,7 @@ def constant_or_value(x, rtype, name=None, ndim=None, dtype=None):
if len(bcastable) < ndim:
bcastable = [True] * (ndim - len(bcastable)) + bcastable
elif len(bcastable) > ndim:
#TODO: strip off dimensions of size 1
# TODO: strip off dimensions of size 1
raise ValueError(
'ndarray could not be cast to constant with %i dimensions' %
ndim)
......@@ -430,15 +430,15 @@ elif int(config.tensor.cmp_sloppy):
float64_rtol = 1e-4
float64_atol = 1e-3
else:
#If you change those value in test don't forget to put them back
#when the test end. Don't forget the case when the test fail.
# If you change those value in test don't forget to put them back
# when the test end. Don't forget the case when the test fail.
float32_atol = 1e-5
float32_rtol = 1e-5
# defaults in numpy.allclose
float64_rtol = 1.0000000000000001e-05
float64_atol = 1e-8
#more strict. Atleast float32 precision.
# more strict. Atleast float32 precision.
float64_rtol = 1.0000000000000001e-06
......@@ -497,9 +497,9 @@ def get_constant_value(v):
shape, val = v.owner.inputs
# fill(a,b) fills the shape of 'a' filled with 'b'
return get_constant_value(val)
#Don't act as the constant_folding optimization here as this
#fct is used too early in the optimization phase. This would
#mess with the stabilization optimization.
# Don't act as the constant_folding optimization here as this
# fct is used too early in the optimization phase. This would
# mess with the stabilization optimization.
if isinstance(v.owner.op, Elemwise) and isinstance(
v.owner.op.scalar_op, scal.Cast):
const = get_constant_value(v.owner.inputs[0])
......@@ -532,7 +532,7 @@ def get_constant_value(v):
ret = v.owner.inputs[0].owner.inputs[
v.owner.op.idx_list[0] + 1]
ret = get_constant_value(ret)
#join can cast implicitly its input in some case.
# join can cast implicitly its input in some case.
return theano._asarray(ret, dtype=v.type.dtype)
if (v.owner.inputs[0].owner and
isinstance(v.owner.inputs[0].owner.op,
......@@ -545,7 +545,7 @@ def get_constant_value(v):
ret = v.owner.inputs[0].owner.inputs[v.owner.op.idx_list[0]]
ret = get_constant_value(ret)
#MakeVector can cast implicitly its input in some case.
# MakeVector can cast implicitly its input in some case.
return theano._asarray(ret, dtype=v.type.dtype)
# This is needed when we take the grad as the Shape op
......@@ -750,8 +750,8 @@ class TensorType(Type):
This function is used internally as part of C code generation.
"""
#TODO: add more type correspondances for e.g. int32, int64, float32,
#complex64, etc.
# TODO: add more type correspondances for e.g. int32, int64, float32,
# complex64, etc.
try:
return {
'float32': (float, 'npy_float32', 'NPY_FLOAT32'),
......@@ -789,7 +789,7 @@ class TensorType(Type):
@staticmethod
def values_eq(a, b, force_same_dtype=True):
#TODO: check to see if the shapes must match
# TODO: check to see if the shapes must match
# for now, we err on safe side...
if a.shape != b.shape:
return False
......@@ -866,14 +866,14 @@ class TensorType(Type):
# Find places where both a and b have inf of the same sign.
both_inf = a_inf * numpy.isinf(b)
#cmp_elemwise is weird when we have inf and -inf.
#set it to False
# cmp_elemwise is weird when we have inf and -inf.
# set it to False
cmp_elemwise = numpy.where(
both_inf & cmp_elemwise,
a == b,
cmp_elemwise)
#check the sign of the inf
# check the sign of the inf
both_inf = numpy.where(both_inf, (a == b), both_inf)
if allow_remove_inf:
......@@ -1247,21 +1247,21 @@ tensor4s, ftensor4s, dtensor4s, itensor4s, ltensor4s = _multi(
class _tensor_py_operators:
#UNARY
# UNARY
def __abs__(self):
return abs_(self)
def __neg__(self):
return neg(self)
#CASTS
# CASTS
#### REMOVED THESE BECAUSE PYTHON appears to require __int__ to return
#### an int. -JB 20081112
#def __int__(self): return convert_to_int32(self)
#def __float__(self): return convert_to_float64(self)
#def __complex__(self): return convert_to_complex128(self)
#COMPARISONS
# COMPARISONS
_is_nonzero = True
def __lt__(self, other):
......@@ -1297,7 +1297,7 @@ class _tensor_py_operators:
else:
raise TypeError("Variable does not support boolean operations.")
#BITWISE
# BITWISE
def __invert__(self):
return invert(self)
......@@ -1319,16 +1319,16 @@ class _tensor_py_operators:
def __rxor__(self, other):
return xor(other, self)
#def __iand__(self, other):
# def __iand__(self, other):
# return _and_inplace(self, other)
#
#def __ior__(self, other):
# def __ior__(self, other):
# return _or_inplace(self, other)
#
#def __ixor__(self, other):
# return _xor_inplace(self, other)
#ARITHMETIC - NORMAL
# ARITHMETIC - NORMAL
def __add__(self, other):
try:
return add(self, other)
......@@ -1442,7 +1442,7 @@ class _tensor_py_operators:
def __rpow__(self, other):
return pow(other, self)
#TRANSPOSE
# TRANSPOSE
T = property(lambda self: transpose(self))
def transpose(self, *axes):
......@@ -1544,7 +1544,7 @@ class _tensor_py_operators:
def astype(self, dtype):
return cast(self, dtype)
#SLICING
# SLICING
# Do not define __getslice__ here:
# When calling t[1:], for instance, the arguments passed to __getslice__
# are (1, sys.maxsize), which is a pain to deal with, and can even not be
......@@ -1604,7 +1604,7 @@ class _tensor_py_operators:
return Subtensor(args)(self, *Subtensor.collapse(args,
lambda entry: isinstance(entry, Variable)))
#COPYING
# COPYING
def copy(self):
return tensor_copy(self)
......@@ -1631,7 +1631,7 @@ class _tensor_py_operators:
dtype = property(lambda self: self.type.dtype)
""" The dtype of this tensor. """
#extra pseudo-operator symbols
# extra pseudo-operator symbols
def __dot__(left, right):
return dot(left, right)
......@@ -1651,7 +1651,7 @@ class _tensor_py_operators:
raise NotImplementedError()
if numpy.isinf(L):
raise NotImplementedError()
#optimizations will/should catch cases like L=1, L=2
# optimizations will/should catch cases like L=1, L=2
return pow(pow(abs_(self), L).sum(axis=axis), 1.0 / L)
def mean(self, axis=None, dtype=None, keepdims=False):
......@@ -1670,7 +1670,7 @@ class _tensor_py_operators:
"""See `theano.tensor.max`"""
return max(self, axis, keepdims=keepdims)
#TO TRUMP NUMPY OPERATORS
# TO TRUMP NUMPY OPERATORS
__array_priority__ = 1000
def get_constant_value(self):
......@@ -1699,7 +1699,7 @@ class TensorConstantSignature(tuple):
except Exception:
return False
#N.B. compare shape to ensure no broadcasting in ==
# N.B. compare shape to ensure no broadcasting in ==
if t0 != t1 or d0.shape != d1.shape:
return False
......@@ -1874,7 +1874,7 @@ def _scal_elemwise_with_nfunc(nfunc, nin, nout):
if getattr(symbol, '__doc__', False):
rval.__doc__ = symbol.__doc__ + '\n' + rval.__doc__
#for the meaning of this see the ./epydoc script
# for the meaning of this see the ./epydoc script
# it makes epydoc display rval as if it were a function, not an object
rval.__epydoc_asRoutine = symbol
rval.__module__ = 'tensor'
......@@ -2080,13 +2080,13 @@ class Shape(Op):
return self.__class__.__name__
def make_node(self, x):
#Must work for all type that have a shape attribute.
#This will fail at execution time.
# Must work for all type that have a shape attribute.
# This will fail at execution time.
x = as_tensor_variable(x)
#Each type variable should implement their .shape attribute
#and have the fct infer_shape() implemented in the op that convert
#the type to TensorVariable to have the optimization working
#correctly.
# Each type variable should implement their .shape attribute
# and have the fct infer_shape() implemented in the op that convert
# the type to TensorVariable to have the optimization working
# correctly.
return Apply(self, [x], [lvector()])
def perform(self, node, inp, out_):
......@@ -2098,19 +2098,19 @@ class Shape(Op):
return [[len(in_shapes[0])]]
def connection_pattern(self):
#the grad returns the gradient with respect to the
#elements of a tensor variable
#the elements of the tensor variable do not participate
#in the computation of the shape, so they are not really
#part of the graph
# the grad returns the gradient with respect to the
# elements of a tensor variable
# the elements of the tensor variable do not participate
# in the computation of the shape, so they are not really
# part of the graph
return [False]
def grad(self, inp, grads):
#the grad returns the gradient with respect to the
#elements of a tensor variable
#the elements of the tensor variable do not participate
#in the computation of the shape, so they are not really
#part of the graph
# the grad returns the gradient with respect to the
# elements of a tensor variable
# the elements of the tensor variable do not participate
# in the computation of the shape, so they are not really
# part of the graph
return [None]
def R_op(self, inputs, eval_points):
......@@ -2129,7 +2129,7 @@ def old_shape(a):
shape at graph-execution time.
"""
va = as_tensor_variable(a)
#print 'HERE', va, va.type
# print 'HERE', va, va.type
if None in va.type.shape:
# Some shape components are unknown at this time
return _shape(va)
......@@ -2333,7 +2333,7 @@ class MaxAndArgmax(Op):
g_max_disconnected = isinstance(g_max.type, DisconnectedType)
g_max_idx_disconnected = isinstance(g_max_idx.type, DisconnectedType)
#if the op is totally disconnected, so are its inputs
# if the op is totally disconnected, so are its inputs
if g_max_disconnected and g_max_idx_disconnected:
return [DisconnectedType()(), DisconnectedType()()]
......@@ -2341,8 +2341,8 @@ class MaxAndArgmax(Op):
"argmax is not defined for non-integer axes so"
" argmax(x, axis+eps) is undefined")
#if the max is disconnected but the argmax is not,
#the gradient on its inputs is zero
# if the max is disconnected but the argmax is not,
# the gradient on its inputs is zero
if g_max_disconnected:
return [x.zeros_like(), axis_grad]
xmax = max(x, axis)
......@@ -2486,7 +2486,7 @@ def min(x, axis=None, keepdims=False):
if str_x_type.startswith('float') or str_x_type in int_dtypes:
return -max(-x, axis=axis, keepdims=keepdims)
else:
#Be careful about unsigned integers, complex
# Be careful about unsigned integers, complex
raise NotImplementedError()
......@@ -2507,7 +2507,7 @@ def argmin(x, axis=None, keepdims=False):
if str_x_type.startswith('float') or str_x_type in int_dtypes:
return argmax(-x, axis=axis, keepdims=keepdims)
else:
#Be careful about unsigned integers, complex
# Be careful about unsigned integers, complex
raise NotImplementedError()
......@@ -3121,7 +3121,7 @@ class Alloc(gof.Op):
out[0] = numpy.empty(sh, dtype=v.dtype)
out[0][...] = v # broadcast v to fill us up
else:
#reuse the allocated memory.
# reuse the allocated memory.
out[0][...] = v # broadcast v to fill us up
def c_code(self, node, name, inp, out, sub):
......@@ -3309,7 +3309,7 @@ class Mean(elemwise.CAReduce):
if self.axis is not None:
return super(Op, self).c_code(node, name, inames, onames, sub)
ret = elemwise.CAReduce.c_code(self, node, name, inames, onames, sub)
#TODO: c_code perform support only axis is None
# TODO: c_code perform support only axis is None
return ret + """
*((double *)PyArray_DATA(%s)) /= PyArray_SIZE(%s);
""" % (onames[0], inames[0])
......@@ -3408,13 +3408,13 @@ def var(input, axis=None, keepdims=False):
if isinstance(axis, int):
axis = [axis]
#compute the axis-wise mean
# compute the axis-wise mean
mean_input = mean(input, axis, keepdims=True)
#center the input
# center the input
centered_input = input - mean_input
#return the mean sqr
# return the mean sqr
return mean((centered_input ** 2), axis, keepdims=keepdims)
......@@ -3457,8 +3457,8 @@ if 0:
type = TensorType(dtype=input.type.dtype,
broadcastable=broadcastable)
#backport
#type = TensorType(dtype=input.type.dtype,
# backport
# type = TensorType(dtype=input.type.dtype,
# broadcastable=[
# False if i==axis else x
# for i, x in enumerate(input.broadcastable)])
......@@ -3893,7 +3893,7 @@ class Subtensor(Op):
exception.subtensor_invalid = True
raise exception
#infer the broadcasting pattern
# infer the broadcasting pattern
padded = (idx_list
+ [slice(None, None, None)] * (x.type.ndim - len(idx_list)))
broadcastable = [bc for p, bc in zip(padded, x.type.broadcastable)
......@@ -3976,7 +3976,7 @@ class Subtensor(Op):
return type(self) == type(other) and self.idx_list == other.idx_list
def __hash__(self):
#TODO: optimize by cache this hash value
# TODO: optimize by cache this hash value
msg = []
for entry in self.idx_list:
if isinstance(entry, slice):
......@@ -3985,8 +3985,8 @@ class Subtensor(Op):
msg += [entry]
idx_list = tuple(msg)
#backport
#idx_list = tuple((entry.start, entry.stop, entry.step)
# backport
# idx_list = tuple((entry.start, entry.stop, entry.step)
# if isinstance(entry, slice)
# else entry
# for entry in self.idx_list)
......@@ -4023,7 +4023,7 @@ class Subtensor(Op):
fail = sub['fail']
init_cmds = [] # initialization for subtensor_spec
is_slice = []
#TODO: change that, it might lead to unexpected results,
# TODO: change that, it might lead to unexpected results,
# see assembla-#767
NONE_CODE = maxsize - 1
......@@ -4074,7 +4074,7 @@ class Subtensor(Op):
for entry in idx_list:
init_entry(entry)
#make sure we used all inputs
# make sure we used all inputs
assert input_pos() == len(inputs), input_pos()
assert len(is_slice) <= node.inputs[0].ndim, node.inputs[0].ndim
......@@ -4247,7 +4247,7 @@ class Subtensor(Op):
}
PyArray_UpdateFlags(xview, NPY_C_CONTIGUOUS|NPY_F_CONTIGUOUS);
""" % locals()
#print rval
# print rval
return rval
@staticmethod
......@@ -4432,7 +4432,7 @@ class IncSubtensor(Op):
msg += [entry]
idx_list = tuple(msg)
#backport
# backport
#idx_list = tuple((entry.start, entry.stop, entry.step)
# if isinstance(entry, slice)
# else entry
......@@ -4709,7 +4709,7 @@ class Split(Op):
def perform(self, node, inputs, outputs):
"""WRITEME"""
x, axis, splits = inputs
#in python 2.4, x.shape[numpy.asarray(1)] don't work.
# in python 2.4, x.shape[numpy.asarray(1)] don't work.
if sys.version_info[0:2] == (2, 4) and axis.size == 1:
axis = int(axis)
......@@ -5432,7 +5432,7 @@ class Reshape(Op):
# The following expression leads to cycles in feature_shape,
# because it tries to replace the Shape_i node by the switch
# statement, which depends on Shape_i.
#return [tuple([switch(eq(node.inputs[1][i], -1),
# return [tuple([switch(eq(node.inputs[1][i], -1),
# theano.tensor.opt.Shape_i(i)(node.outputs[0]),
# node.inputs[1][i])
# for i in xrange(self.ndim)]
......@@ -5591,7 +5591,7 @@ def flatten(x, outdim=1):
# """
# Calculates the gradient of the Tile Op.
# """
# #this is so weird, I can't think of how to make this a general thing.
# # this is so weird, I can't think of how to make this a general thing.
# def make_node(self, x, reps, g_out):
# return gof.Apply(self, [x, reps, g_out], [x.type()])
#
......@@ -5697,7 +5697,7 @@ def tile(x, reps, ndim=None):
ndim = len(reps)
# backport
# ndim = len(reps) if ndim is None else ndim #not sure if len(shp) is going
# ndim = len(reps) if ndim is None else ndim # not sure if len(shp) is going
# to work.
if ndim not in tile.op:
tile.op[ndim] = Tile(ndim)
......@@ -6180,7 +6180,7 @@ class AdvancedSubtensor(Op):
def make_node(self, x, *inputs):
x = as_tensor_variable(x)
#FIXME
# FIXME
# Note (9 Jul 2012): what does this 'FIXME' mean? Possibly that the
# current implementation must be generalized? Please specify.
if x.ndim == 2 and len(inputs) == 2:
......@@ -6243,7 +6243,7 @@ class AdvancedSubtensor(Op):
'are too big (>= 2^32 elements). It is possible that '
'out[0] (%s), with shape %s, is not correctly filled.'
% (out[0], out[0].shape))
#return
# return
#raise NotImplementedError()
def grad(self, inputs, grads):
......@@ -6266,8 +6266,8 @@ class AdvancedIncSubtensor(Op):
def __init__(self, inplace=False, set_instead_of_inc=False):
self.inplace = inplace
self.set_instead_of_inc = set_instead_of_inc
#The assert is needed as in the pass the first argument was
#something else that was not used.
# The assert is needed as in the pass the first argument was
# something else that was not used.
assert isinstance(inplace, bool)
if self.inplace:
raise NotImplementedError('In place computation is not'
......@@ -6386,7 +6386,7 @@ class Dot(Op):
numpy_semantics = 0
if numpy_semantics:
#numpy defines dot for tensor pairs with any rank
# numpy defines dot for tensor pairs with any rank
if len(inputs) != 2:
raise TypeError(
"Wrong number of inputs for %s (got %i, expected 2)" %
......
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