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提交 842da1f2 authored 作者: Taesup (TS) Kim's avatar Taesup (TS) Kim
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fix PEP8 in sandbox/*.py

上级 c51b2833
隐藏空白字符变更
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正在显示 包含 149 行增加 和 82 行删除
theano/sandbox/conv.py
浏览文件 @ 842da1f2
from __future__ import print_function
import sys
print("DEPRECATION: theano.sandbox.conv no longer provides conv. They have been moved to theano.tensor.nnet.conv", file=sys.stderr)
print("DEPRECATION: theano.sandbox.conv no longer provides conv. "
"They have been moved to theano.tensor.nnet.conv", file=sys.stderr)
from theano.tensor.nnet.conv import *
theano/sandbox/debug.py
浏览文件 @ 842da1f2
......@@ -71,10 +71,15 @@ class DebugLinker(gof.WrapLinker):
r.type.filter(r.value, strict=True)
except TypeError as e:
exc_type, exc_value, exc_trace = sys.exc_info()
exc = DebugException(e, "The output %s was filled with data with the wrong type using linker " \
("%s. This happened at step %i of the program." % (r, linker, i)) + \
"For more info, inspect this exception's 'original_exception', 'debugger', " \
"'output_at_fault', 'step', 'node', 'thunk' and 'linker' fields.")
exc = DebugException(
e,
"The output %s was filled with data with the wrong "
"type using linker " +
("%s. This happened at step %i of the program."
% (r, linker, i)) +
"For more info, inspect this exception's "
"'original_exception', 'debugger', 'output_at_fault', "
"'step', 'node', 'thunk' and 'linker' fields.")
exc.debugger = self
exc.original_exception = e
exc.output_at_fault = r
......@@ -88,11 +93,18 @@ class DebugLinker(gof.WrapLinker):
thunk0 = thunks[0]
linker0 = self.linkers[0]
for thunk, linker in zip(thunks[1:], self.linkers[1:]):
for o, output0, output in zip(node.outputs, thunk0.outputs, thunk.outputs):
for o, output0, output in zip(node.outputs,
thunk0.outputs,
thunk.outputs):
if not self.compare_fn(output0[0], output[0]):
exc = DebugException(("The variables from %s and %s for output %s are not the same. This happened at step %i." % (linker0, linker, o, step)) + \
"For more info, inspect this exception's 'debugger', 'output', 'output_value1', 'output_value2', " \
"'step', 'node', 'thunk1', 'thunk2', 'linker1' and 'linker2' fields.")
exc = DebugException(
("The variables from %s and %s for output %s are not "
"the same. This happened at step %i."
% (linker0, linker, o, step)) +
"For more info, inspect this exception's 'debugger', "
"'output', 'output_value1', 'output_value2', 'step', "
"'node', 'thunk1', 'thunk2', 'linker1' "
"and 'linker2' fields.")
exc.debugger = self
exc.output = o
exc.output_value1 = output0
......@@ -140,8 +152,12 @@ class DebugLinker(gof.WrapLinker):
exc_type, exc_value, exc_trace = sys.exc_info()
if isinstance(e, DebugException):
raise
exc = DebugException(e, ("An exception occurred while processing node %s at step %i of the program." % (node, i)) + \
"For more info, inspect this exception's 'original_exception', 'debugger', 'step', 'node' and 'thunks' fields.")
exc = DebugException(
e,
("An exception occurred while processing node %s at step %i "
"of the program." % (node, i)) +
"For more info, inspect this exception's 'original_exception',"
"'debugger', 'step', 'node' and 'thunks' fields.")
exc.debugger = self
exc.original_exception = e
exc.step = i
......@@ -169,7 +185,8 @@ def print_input_shapes(i, node, *thunks):
def print_input_types(i, node, *thunks):
print("input types:", ", ".join(str(type(input.value)) for input in node.inputs))
print("input types:", ", ".join(str(type(input.value))
for input in node.inputs))
def print_sep(i, node, *thunks):
......@@ -192,5 +209,3 @@ def numpy_debug_linker(pre, post=None):
pre,
post,
compare_fn=numpy_compare)
theano/sandbox/fourier.py
浏览文件 @ 842da1f2
......@@ -12,7 +12,7 @@ from theano.gof import Op, Apply, generic
class GradTodo(Op):
# TODO : need description for class
__props__ = ()
def make_node(self, x):
......@@ -24,6 +24,7 @@ grad_todo = GradTodo()
class FFT(Op):
# TODO : need description for parameters
"""
Fast Fourier Transform.
......@@ -44,7 +45,8 @@ class FFT(Op):
# don't return the plan object in the 'buf' output
half = False
"""Only return the first half (positive-valued) of the frequency components."""
"""Only return the first half (positive-valued) of the frequency
components."""
__props__ = ("half", "inverse")
def __init__(self, half=False, inverse=False):
......@@ -82,11 +84,13 @@ class FFT(Op):
M, N = fft.shape
if axis == 0:
if (M % 2):
raise ValueError('halfFFT on odd-length vectors is undefined')
raise ValueError(
'halfFFT on odd-length vectors is undefined')
spectrogram[0] = fft[0:M / 2, :]
elif axis == 1:
if (N % 2):
raise ValueError('halfFFT on odd-length vectors is undefined')
raise ValueError(
'halfFFT on odd-length vectors is undefined')
spectrogram[0] = fft[:, 0:N / 2]
else:
raise NotImplementedError()
......@@ -105,6 +109,7 @@ half_ifft = FFT(half=True, inverse=True)
def dct_matrix(rows, cols, unitary=True):
# TODO : need description for parameters
"""
Return a (rows x cols) matrix implementing a discrete cosine transform.
......@@ -115,7 +120,8 @@ def dct_matrix(rows, cols, unitary=True):
col_range = numpy.arange(cols)
scale = numpy.sqrt(2.0 / cols)
for i in xrange(rows):
rval[i] = numpy.cos(i * (col_range * 2 + 1) / (2.0 * cols) * numpy.pi) * scale
rval[i] = numpy.cos(
i * (col_range * 2 + 1) / (2.0 * cols) * numpy.pi) * scale
if unitary:
rval[0] *= numpy.sqrt(0.5)
......
theano/sandbox/minimal.py
浏览文件 @ 842da1f2
......@@ -9,17 +9,19 @@ from theano.tests import unittest_tools as utt
class Minimal(gof.Op):
# TODO : need description for class
# if the Op has any attributes,
# consider using them in the eq function. If two Apply nodes have the same inputs and the
# ops compare equal... then they will be MERGED so they had better have computed the same
# thing!
# if the Op has any attributes, consider using them in the eq function.
# If two Apply nodes have the same inputs and the ops compare equal...
# then they will be MERGED so they had better have computed the same thing!
def __init__(self):
# If you put things here, think about whether they change the outputs computed by
# self.perform()
# - If they do, then you should take them into consideration in __eq__ and __hash__
# - If they do not, then you should not use them in __eq__ and __hash__
# If you put things here, think about whether they change the outputs
# computed by # self.perform()
# - If they do, then you should take them into consideration in
# __eq__ and __hash__
# - If they do not, then you should not use them in
# __eq__ and __hash__
super(Minimal, self).__init__()
......
theano/sandbox/multinomial.py
浏览文件 @ 842da1f2
......@@ -16,6 +16,7 @@ if cuda_available:
class MultinomialFromUniform(Op):
# TODO : need description for parameter 'odtype'
"""
Converts samples from a uniform into sample from a multinomial.
......@@ -197,7 +198,8 @@ class MultinomialFromUniform(Op):
class MultinomialWOReplacementFromUniform(MultinomialFromUniform):
"""
Converts samples from a uniform into sample (without replacement) from a multinomial.
Converts samples from a uniform into sample (without replacement) from a
multinomial.
"""
......@@ -222,8 +224,8 @@ class MultinomialWOReplacementFromUniform(MultinomialFromUniform):
(z,) = outs
if n_samples > pvals.shape[1]:
raise ValueError("Cannot sample without replacement n samples bigger "
"than the size of the distribution.")
raise ValueError("Cannot sample without replacement n samples "
"bigger than the size of the distribution.")
if unis.shape[0] != pvals.shape[0] * n_samples:
raise ValueError("unis.shape[0] != pvals.shape[0] * n_samples",
......@@ -233,7 +235,8 @@ class MultinomialWOReplacementFromUniform(MultinomialFromUniform):
odtype = 'int64'
else:
odtype = self.odtype
if z[0] is None or not numpy.all(z[0].shape == [pvals.shape[0], n_samples]):
if (z[0] is None or
not numpy.all(z[0].shape == [pvals.shape[0], n_samples])):
z[0] = -1 * numpy.ones((pvals.shape[0], n_samples), dtype=odtype)
nb_multi = pvals.shape[0]
......@@ -249,7 +252,8 @@ class MultinomialWOReplacementFromUniform(MultinomialFromUniform):
cummul += pvals[n, m]
if (cummul > unis_n):
z[0][n, c] = m
# set to zero and re-normalize so that it's not selected again
# set to zero and re-normalize so that it's not
# selected again
pvals[n, m] = 0.
pvals[n] /= pvals[n].sum()
break
......@@ -443,6 +447,7 @@ class GpuMultinomialFromUniform(MultinomialFromUniform, GpuOp):
@local_optimizer([MultinomialFromUniform])
def local_gpu_multinomial(node):
# TODO : need description for function
if type(node.op) is MultinomialFromUniform:
if len(node.inputs) == 2:
p, u = node.inputs
......
theano/sandbox/neighbourhoods.py
浏览文件 @ 842da1f2
......@@ -116,7 +116,8 @@ class NeighbourhoodsFromImages(Op):
return dims, num_strides
# for inverse mode
# "output" here actually referes to the Op's input shape (but it's inverse mode)
# "output" here actually referes to the Op's input shape (but it's inverse
# mode)
def in_shape(self, output_shape):
out_dims = list(output_shape[:self.n_dims_before])
num_strides = []
......@@ -168,9 +169,10 @@ class NeighbourhoodsFromImages(Op):
for dim in self.dims_neighbourhoods:
prod *= dim
if x.shape[-1] != prod:
raise ValueError("Last dimension of neighbourhoods (%s) is not"
" the product of the neighbourhoods dimensions"
" (%s)" % (str(x.shape[-1]), str(prod)))
raise ValueError(
"Last dimension of neighbourhoods (%s) is not"
" the product of the neighbourhoods dimensions"
" (%s)" % (str(x.shape[-1]), str(prod)))
else:
if len(x.shape) != (self.n_dims_before +
len(self.dims_neighbourhoods)):
......@@ -195,6 +197,7 @@ class NeighbourhoodsFromImages(Op):
exec(self.code)
def make_py_code(self):
# TODO : need description for method and return
code = self._py_outerloops()
for i in xrange(len(self.strides)):
code += self._py_innerloop(i)
......@@ -202,6 +205,7 @@ class NeighbourhoodsFromImages(Op):
return code, builtins.compile(code, '<string>', 'exec')
def _py_outerloops(self):
# TODO : need description for method, parameter and return
code_before = ""
for dim_idx in xrange(self.n_dims_before):
code_before += ('\t' * (dim_idx)) + \
......@@ -210,6 +214,7 @@ class NeighbourhoodsFromImages(Op):
return code_before
def _py_innerloop(self, inner_dim_no):
# TODO : need description for method, parameter and return
base_indent = ('\t' * (self.n_dims_before + inner_dim_no * 2))
code_before = base_indent + \
"for stride_idx_%d in xrange(num_strides[%d]):\n" % \
......@@ -229,10 +234,12 @@ class NeighbourhoodsFromImages(Op):
return code_before
def _py_flattened_idx(self):
# TODO : need description for method and return
return "+".join(["neigh_strides[%d]*neigh_idx_%d" % (i, i)
for i in xrange(len(self.strides))])
def _py_assignment(self):
# TODO : need description for method and return
input_idx = "".join(["outer_idx_%d," % (i,)
for i in xrange(self.n_dims_before)])
input_idx += "".join(["dim_%d_offset+neigh_idx_%d," %
......@@ -259,6 +266,7 @@ class NeighbourhoodsFromImages(Op):
class ImagesFromNeighbourhoods(NeighbourhoodsFromImages):
# TODO : need description for class, parameters
def __init__(self, n_dims_before, dims_neighbourhoods,
strides=None, ignore_border=False):
NeighbourhoodsFromImages.__init__(self, n_dims_before,
......
theano/sandbox/rng_mrg.py
浏览文件 @ 842da1f2
......@@ -33,11 +33,13 @@ from theano.sandbox.gpuarray.fp16_help import write_w
def matVecModM(A, s, m):
# TODO : need description for method, parameter and return
assert A.dtype == 'int64'
return numpy.int32(numpy.sum((A*s) % m, 1) % m)
def multMatVect(v, A, m1, B, m2):
# TODO : need description for parameter and return
"""
Multiply the first half of v by A with a modulo of m1 and the second half
by B with a modulo of m2.
......@@ -193,13 +195,13 @@ class DotModulo(Op):
# MRG31k3p
# generator constants :
M1 = numpy.asarray(numpy.int32(2147483647)) #2^31 - 1
M2 = numpy.asarray(numpy.int32(2147462579)) #2^31 - 21069
MASK12 = numpy.int32(511) #2^9 - 1
MASK13 = numpy.int32(16777215) #2^24 - 1
MASK2 = numpy.int32(65535) #2^16 - 1
M1 = numpy.asarray(numpy.int32(2147483647)) # 2^31 - 1
M2 = numpy.asarray(numpy.int32(2147462579)) # 2^31 - 21069
MASK12 = numpy.int32(511) # 2^9 - 1
MASK13 = numpy.int32(16777215) # 2^24 - 1
MASK2 = numpy.int32(65535) # 2^16 - 1
MULT2 = numpy.int32(21069)
NORM = 4.656612873077392578125e-10 #1./2^31
NORM = 4.656612873077392578125e-10 # 1./2^31
# A1p0 = numpy.asarray([[0, 4194304, 129], [1, 0, 0], [0, 1, 0]],
# dtype='int64')
......@@ -229,14 +231,17 @@ np_int32_vals = [numpy.int32(i) for i in (0, 7, 9, 15, 16, 22, 24)]
def ff_2p134(rstate):
# TODO : need description for method, parameter and return
return multMatVect(rstate, A1p134, M1, A2p134, M2)
def ff_2p72(rstate):
# TODO : need description for method, parameter and return
return multMatVect(rstate, A1p72, M1, A2p72, M2)
def mrg_next_value(rstate, new_rstate):
# TODO : need description for method, parameter and return
x11, x12, x13, x21, x22, x23 = rstate
assert type(x11) == numpy.int32
......@@ -286,7 +291,7 @@ def mrg_next_value(rstate, new_rstate):
class mrg_uniform_base(Op):
# TODO : need description for class, parameter
__props__ = ("output_type", "inplace")
def __init__(self, output_type, inplace=False):
......@@ -314,9 +319,9 @@ class mrg_uniform_base(Op):
[rstate.type(), self.output_type()])
def grad(self, inputs, ograd):
return [gradient.grad_undefined(
self, k, inp,
'No gradient defined through random sampling op')
return [gradient.grad_undefined(self, k, inp,
'No gradient defined through '
'random sampling op')
for k, inp in enumerate(inputs)]
def R_op(self, inputs, eval_points):
......@@ -371,9 +376,12 @@ class mrg_uniform(mrg_uniform_base):
assert isinstance(node.inputs[0].type, TensorType)
o_rstate, o_sample = out
if self.inplace:
o_rstate_requirement = 'NPY_ARRAY_C_CONTIGUOUS|NPY_ARRAY_ALIGNED'
o_rstate_requirement = (
'NPY_ARRAY_C_CONTIGUOUS|NPY_ARRAY_ALIGNED')
else:
o_rstate_requirement = 'NPY_ARRAY_ENSURECOPY|NPY_ARRAY_C_CONTIGUOUS|NPY_ARRAY_ALIGNED'
o_rstate_requirement = (
'NPY_ARRAY_ENSURECOPY|NPY_ARRAY_C_CONTIGUOUS|'
'NPY_ARRAY_ALIGNED')
ndim = self.output_type.ndim
o_type_num = numpy.asarray(0, dtype=self.output_type.dtype).dtype.num
fail = sub['fail']
......@@ -1021,6 +1029,7 @@ class GPUA_mrg_uniform(GpuKernelBase, mrg_uniform_base):
def guess_n_streams(size, warn=False):
# TODO : need description for parameter 'size'
"""
Return a guess at a good number of streams.
......@@ -1035,7 +1044,7 @@ def guess_n_streams(size, warn=False):
# Note that this code was moved out of `MRG_RandomStreams` so that it can
# be easily accessed from tests, where we want to disable the warning.
if (isinstance(size, (tuple, list)) and
all([isinstance(i, int) for i in size])):
all([isinstance(i, int) for i in size])):
# We can make a guess.
r = 1
for s in size:
......@@ -1044,8 +1053,9 @@ def guess_n_streams(size, warn=False):
r = r // 6 # chosen as fastest for rbm_benchmark
# The purpose of sampling from many streams is to be able to use
# the GPU to its full capacity. It just wastes RAM and stream-initialization time to
# allocate more streams than necessary for the GPU.
# the GPU to its full capacity. It just wastes RAM and
# stream-initialization time to allocate more streams than necessary
# for the GPU.
# XXX: This number is chosen to be good for 280 and 480 architectures,
# Better would be to use pycuda to query the number of
# processors on the GPU device,
......@@ -1053,14 +1063,15 @@ def guess_n_streams(size, warn=False):
return min(r, 60 * 256)
else:
if warn:
warnings.warn((
"MRG_RandomStreams Can't determine #streams from "
"size (%s), guessing 60*256") % str(size),
stacklevel=3)
warnings.warn(
("MRG_RandomStreams Can't determine #streams "
"from size (%s), guessing 60*256") % str(size),
stacklevel=3)
return 60 * 256
class MRG_RandomStreams(object):
# TODO : need description for parameter 'use_cuda'
"""
Module component with similar interface to numpy.random
(numpy.random.RandomState).
......@@ -1077,11 +1088,13 @@ class MRG_RandomStreams(object):
"""
def updates(self):
# TODO : need description for method and return
return list(self.state_updates)
def __init__(self, seed=12345, use_cuda=None):
# A list of pairs of the form (input_r, output_r), representing the
# update rules of all the random states generated by this RandomStreams.
# update rules of all the random states generated
# by this RandomStreams.
self.state_updates = []
super(MRG_RandomStreams, self).__init__()
......@@ -1097,6 +1110,7 @@ class MRG_RandomStreams(object):
self.use_cuda = use_cuda
def set_rstate(self, seed):
# TODO : need description for method, parameter
if isinstance(seed, int):
if seed == 0:
raise ValueError('seed should not be 0', seed)
......@@ -1158,11 +1172,12 @@ class MRG_RandomStreams(object):
start.
"""
#self.rstate = ff_2p134(self.rstate)
# self.rstate = ff_2p134(self.rstate)
self.rstate = multMatVect(self.rstate, A1p134, M1, A2p134, M2)
assert self.rstate.dtype == numpy.int32
def get_substream_rstates(self, n_streams, dtype, inc_rstate=True):
# TODO : need description for parameter and return
"""
Initialize a matrix in which each row is a MRG stream state,
and they are spaced by 2**72 samples.
......@@ -1186,7 +1201,7 @@ class MRG_RandomStreams(object):
f.input_storage[5].storage[0] = M2
for i in xrange(1, n_streams):
# Inline the following call to bypass Python overhead
#rval[i] = ff_2p72(rval[i - 1])
# rval[i] = ff_2p72(rval[i - 1])
v = rval[i - 1]
f.input_storage[1].storage[0] = v[:3]
f.input_storage[4].storage[0] = v[3:]
......@@ -1208,9 +1223,11 @@ class MRG_RandomStreams(object):
return rval
def n_streams(self, size):
# TODO : need description for method, parameter and return
return guess_n_streams(size)
def pretty_return(self, node_rstate, new_rstate, sample, size, nstreams):
# TODO : need description for method, parameter and return
sample.rstate = node_rstate
sample.update = (node_rstate, new_rstate)
self.state_updates.append((node_rstate, new_rstate, size, nstreams))
......@@ -1219,6 +1236,7 @@ class MRG_RandomStreams(object):
def uniform(self, size, low=0.0, high=1.0, ndim=None, dtype=None,
nstreams=None):
# TODO : need description for parameter 'size', 'ndim', 'nstreams'
"""
Sample a tensor of given size whose element from a uniform
distribution between low and high.
......@@ -1306,6 +1324,7 @@ class MRG_RandomStreams(object):
def binomial(self, size=None, n=1, p=0.5, ndim=None, dtype='int64',
nstreams=None):
# TODO : need description for method, parameter and return
if n == 1:
if dtype == 'float32' and self.use_cuda:
x = self.uniform(size=size, dtype=dtype, nstreams=nstreams)
......@@ -1317,6 +1336,7 @@ class MRG_RandomStreams(object):
def multinomial(self, size=None, n=1, pvals=None, ndim=None, dtype='int64',
nstreams=None):
# TODO : need description for parameter and return
"""
Sample `n` (`n` needs to be >= 1, default 1) times from a multinomial
distribution defined by probabilities pvals.
......@@ -1347,15 +1367,15 @@ class MRG_RandomStreams(object):
size)
if size is not None:
raise ValueError("Provided a size argument to "
"MRG_RandomStreams.multinomial, which does not use "
"the size argument.")
raise ValueError(
"Provided a size argument to MRG_RandomStreams.multinomial, "
"which does not use the size argument.")
if ndim is not None:
raise ValueError("Provided an ndim argument to "
"MRG_RandomStreams.multinomial, which does not use "
"the ndim argument.")
raise ValueError(
"Provided an ndim argument to MRG_RandomStreams.multinomial, "
"which does not use the ndim argument.")
if pvals.ndim == 2:
size = pvals[:,0].shape * n
size = pvals[:, 0].shape * n
unis = self.uniform(size=size, ndim=1, nstreams=nstreams)
op = multinomial.MultinomialFromUniform(dtype)
n_samples = as_tensor_variable(n)
......@@ -1364,8 +1384,9 @@ class MRG_RandomStreams(object):
raise NotImplementedError(("MRG_RandomStreams.multinomial only"
" implemented for pvals.ndim = 2"))
def multinomial_wo_replacement(self, size=None, n=1, pvals=None, ndim=None, dtype='int64',
nstreams=None):
def multinomial_wo_replacement(self, size=None, n=1, pvals=None,
ndim=None, dtype='int64', nstreams=None):
# TODO : need description for parameter
"""
Sample `n` times *WITHOUT replacement* from a multinomial distribution
defined by probabilities pvals, and returns the indices of the sampled
......@@ -1395,25 +1416,27 @@ class MRG_RandomStreams(object):
if size is not None:
raise ValueError("Provided a size argument to "
"MRG_RandomStreams.multinomial_wo_replacement, which does not use "
"the size argument.")
"MRG_RandomStreams.multinomial_wo_replacement, "
"which does not use the size argument.")
if ndim is not None:
raise ValueError("Provided an ndim argument to "
"MRG_RandomStreams.multinomial_wo_replacement, which does not use "
"the ndim argument.")
"MRG_RandomStreams.multinomial_wo_replacement, "
"which does not use the ndim argument.")
if pvals.ndim == 2:
# size = [pvals.shape[0], as_tensor_variable(n)]
size = pvals[:,0].shape * n
size = pvals[:, 0].shape * n
unis = self.uniform(size=size, ndim=1, nstreams=nstreams)
op = multinomial.MultinomialWOReplacementFromUniform(dtype)
n_samples = as_tensor_variable(n)
return op(pvals, unis, n_samples)
else:
raise NotImplementedError(("MRG_RandomStreams.multinomial_wo_replacement only"
" implemented for pvals.ndim = 2"))
raise NotImplementedError(
"MRG_RandomStreams.multinomial_wo_replacement only implemented"
" for pvals.ndim = 2")
def normal(self, size, avg=0.0, std=1.0, ndim=None,
dtype=None, nstreams=None):
# TODO : need description for method
"""
Parameters
----------
......@@ -1443,7 +1466,8 @@ class MRG_RandomStreams(object):
evened = False
constant = False
if isinstance(size, tuple) and all([isinstance(i, (numpy.integer, int)) for i in size]):
if (isinstance(size, tuple) and
all([isinstance(i, (numpy.integer, int)) for i in size])):
constant = True
# Force dtype because it defaults to float when size is empty
n_samples = numpy.prod(size, dtype='int64')
......@@ -1464,16 +1488,18 @@ class MRG_RandomStreams(object):
U1 = flattened[:prod(flattened.shape) // 2]
U2 = flattened[prod(flattened.shape) // 2:]
#normal_samples = zeros_like(flattened)
# normal_samples = zeros_like(flattened)
sqrt_ln_U1 = sqrt(-2.0 * log(U1))
# TypeError: 'TensorVariable' object does not support item assignment
# so this doesn't work...
#normal_samples[:n_samples/2] = sqrt_ln_U1 * cos(2.0*numpy.pi*U2)
#normal_samples[n_samples/2:] = sqrt_ln_U1 * sin(2.0*numpy.pi*U2)
# normal_samples[:n_samples/2] = sqrt_ln_U1 * cos(2.0*numpy.pi*U2)
# normal_samples[n_samples/2:] = sqrt_ln_U1 * sin(2.0*numpy.pi*U2)
# so trying this instead
first_half = sqrt_ln_U1 * cos(numpy.array(2.0 * numpy.pi, dtype=dtype) * U2)
second_half = sqrt_ln_U1 * sin(numpy.array(2.0 * numpy.pi, dtype=dtype) * U2)
first_half = sqrt_ln_U1 * cos(
numpy.array(2.0 * numpy.pi, dtype=dtype) * U2)
second_half = sqrt_ln_U1 * sin(
numpy.array(2.0 * numpy.pi, dtype=dtype) * U2)
normal_samples = join(0, first_half, second_half)
final_samples = None
......@@ -1501,8 +1527,9 @@ from theano.sandbox.gpuarray.opt import (register_opt as register_gpua,
@register_gpua('fast_compile')
@local_optimizer([mrg_uniform])
def local_gpua_mrg(node):
# TODO : need description for function
if (type(node.op) == mrg_uniform and
isinstance(node.inputs[0].type, GpuArrayType)):
isinstance(node.inputs[0].type, GpuArrayType)):
outs = GPUA_mrg_uniform.new(node.inputs[0],
node.op.output_type.ndim,
node.op.output_type.dtype,
......@@ -1515,6 +1542,7 @@ MRG_RNGs = (mrg_uniform, GPU_mrg_uniform, GPUA_mrg_uniform)
@local_optimizer(MRG_RNGs)
def mrg_random_make_inplace(node):
op = node.op
if isinstance(op, MRG_RNGs) and not op.inplace:
# op might be gpu version
......
theano/sandbox/softsign.py
浏览文件 @ 842da1f2
......@@ -4,6 +4,7 @@ import theano.tensor
class ScalarSoftsign(theano.scalar.UnaryScalarOp):
# TODO : need description for class
@staticmethod
def static_impl(x):
return x / (1.0 + abs(x))
......
theano/sandbox/solve.py
浏览文件 @ 842da1f2
......@@ -24,7 +24,8 @@ class Solve(gof.Op):
# sym_pos, lower, overwrite_a, overwrite_b
# TODO: Add C code that calls the underlying LAPACK routines
# and keeps a memory workspace from call to call as a non-default Op output
# and keeps a memory workspace from call to call as a non-default Op
# output
def __eq__(self, other):
return type(self) == type(other)
......
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