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提交 ca3fc8f6 authored 作者: Arnaud Bergeron's avatar Arnaud Bergeron
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Fix flake8.

上级 78657991
隐藏空白字符变更
内嵌 并排
正在显示 包含 27 行增加 和 59 行删除
theano/gpuarray/tests/test_type.py
浏览文件 @ ca3fc8f6
......@@ -13,7 +13,7 @@ from theano.tensor.tests.test_sharedvar import makeSharedTester
from .config import test_ctx_name
from .test_basic_ops import rand_gpuarray
from ..type import GpuArrayType, gpuarray_shared_constructor
from ..type import GpuArrayType, gpuarray_shared_constructor, get_context
import pygpu
......@@ -93,14 +93,14 @@ test_shared_options = makeSharedTester(
cls=pygpu._array.ndgpuarray),
test_internal_type_=lambda a: isinstance(a, pygpu.gpuarray.GpuArray),
theano_fct_=theano.tensor.exp,
ref_fct_=numpy.exp,
ref_fct_=np.exp,
cast_value_=lambda v: pygpu.asarray(v, context=get_context(test_ctx_name),
cls=pygpu._array.ndgpuarray),
name='test_shared_options')
test_shared_options2 = makeSharedTester(
shared_constructor_=gpuarray_shared_constructor
shared_constructor_=gpuarray_shared_constructor,
dtype_=theano.config.floatX,
get_value_borrow_true_alias_=False,
shared_borrow_true_alias_=False,
......@@ -112,7 +112,7 @@ test_shared_options2 = makeSharedTester(
cls=pygpu._array.ndgpuarray),
test_internal_type_=lambda a: isinstance(a, pygpu.gpuarray.GpuArray),
theano_fct_=theano.tensor.exp,
ref_fct_=numpy.exp,
ref_fct_=np.exp,
cast_value_=lambda v: pygpu.asarray(v, context=get_context(test_ctx_name),
cls=pygpu._array.ndgpuarray),
name='test_shared_options2')
theano/sandbox/multinomial.py
浏览文件 @ ca3fc8f6
......@@ -5,11 +5,10 @@ import warnings
import theano
from theano import Op, Apply
import theano.tensor as T
from theano.gof import local_optimizer
from theano.tensor import NotScalarConstantError, get_scalar_constant_value
from theano.scalar import as_scalar
import copy
class MultinomialFromUniform(Op):
# TODO : need description for parameter 'odtype'
"""
......
theano/sandbox/tests/test_multinomial.py
浏览文件 @ ca3fc8f6
......@@ -9,7 +9,6 @@ import numpy as np
import theano
from theano import config, function, tensor
from theano.sandbox import multinomial
from theano.compile.mode import get_default_mode
import theano.tests.unittest_tools as utt
from theano.compat import PY3
from theano.misc.pkl_utils import CompatUnpickler
......@@ -127,7 +126,7 @@ def test_multinomial_large():
p = tensor.fmatrix()
u = tensor.fvector()
m = multinomial.MultinomialFromUniform('auto')(p, u)
f = function([p, u], m * 2, allow_input_downcast=True, mode=mode)
f = function([p, u], m * 2, allow_input_downcast=True)
pval = np.arange(10000 * 4, dtype='float32').reshape((10000, 4)) + 0.1
pval = pval / pval.sum(axis=1)[:, None]
......
theano/sandbox/tests/test_rng_mrg.py
浏览文件 @ ca3fc8f6
from __future__ import absolute_import, print_function, division
import copy
import os
import sys
import time
import unittest
import functools
from nose.plugins.skip import SkipTest
from nose.tools import assert_raises
import numpy as np
from six.moves import xrange
......@@ -245,8 +242,8 @@ def test_uniform():
# TODO: test ndim!=size.ndim
# TODO: test bad seed
# TODO: test size=Var, with shape that change from call to call
if (mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
mode == 'Mode' and config.linker in ['py']):
if (config.mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
config.mode == 'Mode' and config.linker in ['py']):
sample_size = (10, 100)
steps = 50
else:
......@@ -266,8 +263,6 @@ def test_uniform():
# TEST CPU IMPLEMENTATION
# The python and C implementation are tested with DebugMode
# print ''
# print 'ON CPU with size=(%s):' % str(size)
x = tensor.matrix()
R = MRG_RandomStreams(234)
# Note: we specify `nstreams` to avoid a warning.
......@@ -276,15 +271,10 @@ def test_uniform():
# warning using the warning module.
u = R.uniform(size=size,
nstreams=rng_mrg.guess_n_streams(size, warn=False))
f = theano.function(var_input, u, mode=mode)
f = theano.function(var_input, u)
assert any([isinstance(node.op, theano.sandbox.rng_mrg.mrg_uniform)
for node in f.maker.fgraph.toposort()])
# theano.printing.debugprint(f)
cpu_out = f(*input)
# print 'CPU: random?[:10], random?[-10:]'
# print cpu_out[0, 0:10]
# print cpu_out[-1, -10:]
f(*input)
# Increase the number of steps if sizes implies only a few samples
if np.prod(const_size) < 10:
......@@ -293,12 +283,10 @@ def test_uniform():
steps_ = steps
basictest(f, steps_, const_size, prefix='mrg cpu', inputs=input)
# print ''
# print 'ON CPU w Numpy with size=(%s):' % str(size)
RR = theano.tensor.shared_randomstreams.RandomStreams(234)
uu = RR.uniform(size=size)
ff = theano.function(var_input, uu, mode=mode)
ff = theano.function(var_input, uu)
# It's not our problem if numpy generates 0 or 1
basictest(ff, steps_, const_size, prefix='numpy',
allow_01=True, inputs=input)
......@@ -345,8 +333,8 @@ def test_binomial():
# we test size in a tuple of int and a tensor.shape.
# we test the param p with int.
if (mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
mode == 'Mode' and config.linker in ['py']):
if (config.mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
config.mode == 'Mode' and config.linker in ['py']):
sample_size = (10, 50)
steps = 50
rtol = 0.02
......@@ -373,8 +361,8 @@ def test_binomial():
def t_binomial(mean, size, const_size, var_input, input, steps, rtol):
R = MRG_RandomStreams(234)
u = R.binomial(size=size, p=mean)
f = theano.function(var_input, u, mode=mode)
out = f(*input)
f = theano.function(var_input, u)
f(*input)
# Increase the number of steps if sizes implies only a few samples
if np.prod(const_size) < 10:
......@@ -388,7 +376,7 @@ def t_binomial(mean, size, const_size, var_input, input, steps, rtol):
RR = theano.tensor.shared_randomstreams.RandomStreams(234)
uu = RR.binomial(size=size, p=mean)
ff = theano.function(var_input, uu, mode=mode)
ff = theano.function(var_input, uu)
# It's not our problem if numpy generates 0 or 1
basictest(ff, steps_, const_size, prefix='numpy', allow_01=True,
inputs=input, target_avg=mean, mean_rtol=rtol)
......@@ -399,8 +387,8 @@ def test_normal0():
steps = 50
std = 2.
if (mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
mode == 'Mode' and config.linker in ['py']):
if (config.mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
config.mode == 'Mode' and config.linker in ['py']):
sample_size = (25, 30)
default_rtol = .02
else:
......@@ -435,17 +423,13 @@ def test_normal0():
((2,), (2,), [], [], -5., default_rtol, 0.02),
((3,), (3,), [], [], -5., default_rtol, 0.02),
]:
# print ''
# print 'ON CPU:'
R = MRG_RandomStreams(234)
# Note: we specify `nstreams` to avoid a warning.
n = R.normal(size=size, avg=avg, std=std,
nstreams=rng_mrg.guess_n_streams(size, warn=False))
f = theano.function(var_input, n, mode=mode)
# theano.printing.debugprint(f)
out = f(*input)
# print 'random?[:10]\n', out[0, 0:10]
f = theano.function(var_input, n)
f(*input)
# Increase the number of steps if size implies only a few samples
if np.prod(const_size) < 10:
......@@ -458,8 +442,6 @@ def test_normal0():
sys.stdout.flush()
# print ''
# print 'ON CPU w NUMPY:'
RR = theano.tensor.shared_randomstreams.RandomStreams(234)
nn = RR.normal(size=size, avg=avg, std=std)
......@@ -497,42 +479,30 @@ def basic_multinomialtest(f, steps, sample_size, target_pvals, n_samples,
def test_multinomial():
steps = 100
mode_ = mode
if mode == 'FAST_COMPILE':
mode_ = 'FAST_RUN'
if (mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
mode == 'Mode' and config.linker in ['py']):
if (config.mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
config.mode == 'Mode' and config.linker in ['py']):
sample_size = (49, 5)
else:
sample_size = (450, 6)
mode_ = theano.compile.mode.get_mode(mode_)
# print ''
# print 'ON CPU:'
pvals = np.asarray(np.random.uniform(size=sample_size))
pvals = np.apply_along_axis(lambda row: row / np.sum(row), 1, pvals)
R = MRG_RandomStreams(234)
# Note: we specify `nstreams` to avoid a warning.
m = R.multinomial(pvals=pvals, dtype=config.floatX, nstreams=30 * 256)
f = theano.function([], m, mode=mode_)
# theano.printing.debugprint(f)
out = f()
f = theano.function([], m)
f()
basic_multinomialtest(f, steps, sample_size, pvals, n_samples=1,
prefix='mrg ')
def test_multinomial_n_samples():
mode_ = mode
if mode == 'FAST_COMPILE':
mode_ = 'FAST_RUN'
if (mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
mode == 'Mode' and config.linker in ['py']):
if (config.mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
config.mode == 'Mode' and config.linker in ['py']):
sample_size = (49, 5)
else:
sample_size = (450, 6)
mode_ = theano.compile.mode.get_mode(mode_)
pvals = np.asarray(np.random.uniform(size=sample_size))
pvals = np.apply_along_axis(lambda row: row / np.sum(row), 1, pvals)
......@@ -541,7 +511,7 @@ def test_multinomial_n_samples():
for n_samples, steps in zip([5, 10, 100, 1000], [20, 10, 1, 1]):
m = R.multinomial(pvals=pvals, n=n_samples,
dtype=config.floatX, nstreams=30 * 256)
f = theano.function([], m, mode=mode_)
f = theano.function([], m)
basic_multinomialtest(f, steps, sample_size, pvals,
n_samples, prefix='mrg ')
sys.stdout.flush()
......
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