Skip to content

P
pytensor
提交 244857d2 authored 作者: Olivier Delalleau's avatar Olivier Delalleau
浏览文件
操作

Deleting the 'examples' sub-directory, which is deprecated

上级 8779c7eb
全部展开 隐藏空白字符变更
内嵌 并排
正在显示 包含 0 行增加 和 265 行删除
examples/logistic_regression.py deleted 100644 → 0
浏览文件 @ 8779c7eb
import sys
sys.path.insert(0, '..')
import theano
from theano import tensor as T
from theano.tensor import nnet
from theano.compile import module
from theano import printing, pprint
from theano import compile
import numpy as N
class LogisticRegressionN(module.FancyModule):
class InstanceType(module.FancyModuleInstance):
def initialize(self, n_in, n_out, seed = None):
#self.component is the LogisticRegressionTemplate instance that built this guy.
rng = N.random.RandomState(seed)
self.w = rng.randn(n_in, n_out)
self.b = rng.randn(n_out)
self.lr = 0.01
self.__hide__ = ['params']
def __eq__(self, other):
if not isinstance(other.component, LogisticRegressionN) and not isinstance(other.component, LogisticRegression2):
raise NotImplementedError
#we compare the member.
if (N.abs(self.w-other.w)<1e-8).all() and (N.abs(self.b-other.b)<1e-8).all() and self.lr == other.lr:
return True
return False
def __hash__(self):
raise NotImplementedError
def __init__(self, x = None, targ = None):
super(LogisticRegressionN, self).__init__() #boilerplate
self.x = x if x is not None else T.matrix()
self.targ = targ if targ is not None else T.lvector()
self.w = module.Member(T.matrix()) #automatically names
self.b = module.Member(T.vector()) #automatically names
self.lr = module.Member(T.dscalar()) #provides an external interface to change it
#and makes it an implicit input to any Method you build.
self.params = [self.w, self.b]
xent, y = nnet.crossentropy_softmax_1hot(
T.dot(self.x, self.w) + self.b, self.targ)
xent = T.sum(xent)
self.y = y
self.xent = xent
gparams = T.grad(xent, self.params)
self.update = module.Method([self.x, self.targ], xent,
updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gparams)))
self.apply = module.Method([self.x], T.argmax(T.dot(self.x, self.w) + self.b, axis=1))
class LogisticRegression2(module.FancyModule):
class InstanceType(module.FancyModuleInstance):
def initialize(self, n_in, seed = 1827):
#self.component is the LogisticRegressionTemplate instance that built this guy.
rng = N.random.RandomState(seed)
self.w = rng.randn(n_in, 1)
self.b = rng.randn(1)
self.lr = 0.01
self.__hide__ = ['params']
def __eq__(self, other):
if not isinstance(other.component, LogisticRegressionN) and not isinstance(other.component, LogisticRegression2):
raise NotImplementedError
#we compare the member.
if (N.abs(self.w-other.w)<1e-8).all() and (N.abs(self.b-other.b)<1e-8).all() and self.lr == other.lr:
return True
return False
def __hash__(self):
raise NotImplementedError
def __init__(self, x = None, targ = None):
super(LogisticRegression2, self).__init__() #boilerplate
self.x = x if x is not None else T.matrix()
self.targ = targ if targ is not None else T.lcol()
self.w = module.Member(T.dmatrix()) #automatically names
self.b = module.Member(T.dvector()) #automatically names
self.lr = module.Member(T.dscalar()) #provides an external interface to change it
#and makes it an implicit input to any Method you build.
self.params = [self.w, self.b]
y = nnet.sigmoid(T.dot(self.x, self.w))
xent_elem = -self.targ * T.log(y) - (1.0 - self.targ) * T.log(1.0 - y)
xent = T.sum(xent_elem)
self.y = y
self.xent_elem = xent_elem
self.xent = xent
gparams = T.grad(xent, self.params)
self.update = module.Method([self.x, self.targ], xent,
updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gparams)))
self.apply = module.Method([self.x], T.argmax(T.dot(self.x, self.w) + self.b, axis=1))
def main():
pprint.assign(nnet.crossentropy_softmax_1hot_with_bias_dx, printing.FunctionPrinter('xsoftmaxdx'))
pprint.assign(nnet.crossentropy_softmax_argmax_1hot_with_bias, printing.FunctionPrinter('nll', 'softmax', 'argmax'))
if 1:
lrc = LogisticRegressionN()
print '================'
print lrc.update.pretty()
print '================'
print lrc.update.pretty(mode = theano.Mode('py', 'fast_run'))
print '================'
# print lrc.update.pretty(mode = compile.FAST_RUN.excluding('inplace'))
# print '================'
# sys.exit(0)
lr = lrc.make(10, 2, mode=theano.Mode('c|py', 'fast_run'))
#lr = lrc.make(10, 2, mode=compile.FAST_RUN.excluding('fast_run'))
#lr = lrc.make(10, 2, mode=theano.Mode('py', 'merge')) #'FAST_RUN')
data_x = N.random.randn(5, 10)
data_y = (N.random.randn(5) > 0)
for i in xrange(10000):
lr.lr = 0.02
xe = lr.update(data_x, data_y)
if i % 100 == 0:
print i, xe
print
print 'TRAINED MODEL:'
print lr
if 0:
lrc = LogisticRegression2()
lr = lrc.make(10, mode=theano.Mode('c|py', 'merge')) #'FAST_RUN')
data_x = N.random.randn(5, 10)
data_y = (N.random.randn(5, 1) > 0)
for i in xrange(10000):
xe = lr.update(data_x, data_y)
if i % 100 == 0:
print i, xe
print
print 'TRAINED MODEL:'
print lr
if __name__ == '__main__':
main()
examples/tests/test_logistic_regression.py deleted 100644 → 0
浏览文件 @ 8779c7eb
#!/usr/bin/env python
#
# UNIT TEST
#
import unittest
import numpy
from theano import gof
from theano.gradient import *
from theano import gradient
import theano
import sys
from theano import tensor as T
from theano.tensor import nnet
from theano.compile import module
from theano import printing, pprint
from theano import compile
import numpy as N
class test_logistic_regression_example(unittest.TestCase):
def test_example_main(self):
"""Test that the file execute without trouble"""
import os
sys.path.append(os.path.realpath(".."))
import logistic_regression
logistic_regression.main()
def test_example_moduleN(self):
"""Test that the LogisticRegressionN module execute the same with different mode"""
import os
sys.path.append(os.path.realpath(".."))
import logistic_regression
pprint.assign(nnet.crossentropy_softmax_1hot_with_bias_dx, printing.FunctionPrinter('xsoftmaxdx'))
pprint.assign(nnet.crossentropy_softmax_argmax_1hot_with_bias, printing.FunctionPrinter('nll', 'softmax', 'argmax'))
lrc = logistic_regression.LogisticRegressionN()
lr0 = lrc.make(10, 2, seed=1827)
lr1 = lrc.make(10, 2, mode=theano.Mode('c|py', 'fast_run'), seed=1827)
lr2 = lrc.make(10, 2, mode=theano.Mode('py', 'fast_run'), seed=1827)
lr3 = lrc.make(10, 2, mode=theano.Mode('py', 'merge'), seed=1827) #'FAST_RUN')
lr4 = lrc.make(10, 2, mode=compile.FAST_RUN.excluding('fast_run'), seed=1827)
#FAST_RUN, FAST_COMPILE,
data_x = N.random.randn(5, 10)
data_y = (N.random.randn(5) > 0)
def train(lr):
for i in xrange(1000):
lr.lr = 0.02
xe = lr.update(data_x, data_y)
train(lr0)
train(lr1)
train(lr2)
train(lr3)
train(lr4)
assert lr0==lr1
assert lr0==lr2
assert lr0==lr3
assert lr0==lr4
def test_example_module2(self):
"""Test that the LogisticRegression2 module execute the same with different mode"""
import os
sys.path.append(os.path.realpath(".."))
import logistic_regression
lrc = logistic_regression.LogisticRegression2() #TODO: test 2==N
lr0 = lrc.make(10,1827)
lr1 = lrc.make(10, mode=theano.Mode('c|py', 'fast_run'), seed=1827)
lr2 = lrc.make(10, mode=theano.Mode('py', 'fast_run'), seed=1827)
lr3 = lrc.make(10, mode=theano.Mode('py', 'merge'), seed=1827) #'FAST_RU
lr4 = lrc.make(10, mode=compile.FAST_RUN.excluding('fast_run'), seed=1827)
#FAST_RUN, FAST_COMPILE,
data_x = N.random.randn(5, 10)
data_y = (N.random.randn(5) > 0)
data_y = data_y.reshape((data_y.shape[0],1))#need to be a column
def train(lr):
for i in xrange(1000):
lr.lr = 0.02
xe = lr.update(data_x, data_y)
train(lr0)
train(lr1)
train(lr2)
train(lr3)
train(lr4)
assert lr0==lr1
assert lr0==lr2
assert lr0==lr3
assert lr0==lr4
# self.fail("NotImplementedError")
if __name__ == '__main__':
from theano.tests import main
main(__file__)
examples/tests/test_wiki.py deleted 100644 → 0
浏览文件 @ 8779c7eb
差异被折叠。
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论