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提交 244857d2 authored 作者: Olivier Delalleau's avatar Olivier Delalleau
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Deleting the 'examples' sub-directory, which is deprecated

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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
import unittest
import theano
import numpy as N
from theano import tensor as T
from theano.tensor import nnet as NN
from theano.compile import module as M
class Blah(M.ModuleInstance):
# self.component #refer the Module
# def __init__(self, input = None, target = None, regularize = True):
# super(Blah, self)
def initialize(self,input_size = None, target_size = None, seed = 1827,
**init):
if input_size and target_size:
# initialize w and b in a special way using input_size and target_size
sz = (input_size, target_size)
rng = N.random.RandomState(seed)
self.w = rng.uniform(size = sz, low = -0.5, high = 0.5)
self.b = N.zeros(target_size)
self.stepsize = 0.01
#we call default_initialize after as we want the parameter to superseed the default value.
M.default_initialize(self,**init)#equivalent to previous line.
def __eq__(self, other):
if not isinstance(other.component, SoftmaxXERegression1) and not isinstance(other.component, SoftmaxXERegression2):
raise NotImplementedError
#we compare the member.
if (self.w==other.w).all() and (self.b==other.b).all() and self.stepsize == other.stepsize:
return True
return False
def __hash__(self):
raise NotImplementedError
def fit(self, train, test):
pass
class RegressionLayer1(M.Module):
InstanceType=Blah
def __init__(self, input = None, target = None, regularize = True):
super(RegressionLayer1, self).__init__() #boilerplate
# MODEL CONFIGURATION
self.regularize = regularize
# ACQUIRE/MAKE INPUT AND TARGET
if not input:
input = T.matrix('input')
if not target:
target = T.matrix('target')
# HYPER-PARAMETERS
self.stepsize = M.Member(T.scalar()) # a stepsize for gradient descent
# PARAMETERS
self.w = M.Member(T.matrix()) #the linear transform to apply to our input points
self.b = M.Member(T.vector()) #a vector of biases, which make our transform affine instead of linear
# REGRESSION MODEL
self.activation = T.dot(input, self.w) + self.b
self.prediction = self.build_prediction()
# CLASSIFICATION COST
self.classification_cost = self.build_classification_cost(target)
# REGULARIZATION COST
self.regularization = self.build_regularization()
# TOTAL COST
self.cost = self.classification_cost
if self.regularize:
self.cost = self.cost + self.regularization
# GET THE GRADIENTS NECESSARY TO FIT OUR PARAMETERS
self.grad_w, self.grad_b = T.grad(self.cost, [self.w, self.b])
# INTERFACE METHODS
self.update = M.Method([input, target],
self.cost,
w = self.w - self.stepsize * self.grad_w,
b = self.b - self.stepsize * self.grad_b)
self.apply = M.Method(input, self.prediction)
def params(self):
return self.w, self.b
def build_regularization(self):
return T.zero() # no regularization!
class RegressionLayer2(M.Module):
def __init__(self, input = None, target = None, regularize = True):
super(RegressionLayer2, self).__init__() #boilerplate
# MODEL CONFIGURATION
self.regularize = regularize
# ACQUIRE/MAKE INPUT AND TARGET
if not input:
input = T.matrix('input')
if not target:
target = T.matrix('target')
# HYPER-PARAMETERS
self.stepsize = M.Member(T.scalar()) # a stepsize for gradient descent
# PARAMETERS
self.w = M.Member(T.matrix()) #the linear transform to apply to our input points
self.b = M.Member(T.vector()) #a vector of biases, which make our transform affine instead of linear
# REGRESSION MODEL
self.activation = T.dot(input, self.w) + self.b
self.prediction = self.build_prediction()
# CLASSIFICATION COST
self.classification_cost = self.build_classification_cost(target)
# REGULARIZATION COST
self.regularization = self.build_regularization()
# TOTAL COST
self.cost = self.classification_cost
if self.regularize:
self.cost = self.cost + self.regularization
# GET THE GRADIENTS NECESSARY TO FIT OUR PARAMETERS
self.grad_w, self.grad_b = T.grad(self.cost, [self.w, self.b])
# INTERFACE METHODS
self.update = M.Method([input, target],
self.cost,
w = self.w - self.stepsize * self.grad_w,
b = self.b - self.stepsize * self.grad_b)
self.apply = M.Method(input, self.prediction)
def params(self):
return self.w, self.b
def _instance_initialize(self, obj, input_size = None, target_size = None,
seed = 1827, **init):
# obj is an "instance" of this module holding values for each member and
# functions for each method
if input_size and target_size:
# initialize w and b in a special way using input_size and target_size
sz = (input_size, target_size)
rng = N.random.RandomState(seed)
obj.w = rng.uniform(size = sz, low = -0.5, high = 0.5)
obj.b = N.zeros(target_size)
obj.stepsize = 0.01
# here we call the default_initialize method, which takes all the name: value
# pairs in init and sets the property with that name to the provided value
# this covers setting stepsize, l2_coef; w and b can be set that way too
# we call it after as we want the parameter to superseed the default value.
M.default_initialize(obj,**init)
def build_regularization(self):
return T.zero() # no regularization!
class SoftmaxXERegression1(RegressionLayer1):
""" XE mean cross entropy"""
def build_prediction(self):
return NN.softmax(self.activation)
def build_classification_cost(self, target):
#self.classification_cost_matrix = target * T.log(self.prediction) + (1 - target) * T.log(1 - self.prediction)
self.classification_cost_matrix = (target - self.prediction)**2
self.classification_costs = -T.sum(self.classification_cost_matrix, axis=1)
return T.sum(self.classification_costs)
def build_regularization(self):
self.l2_coef = M.Member(T.scalar()) # we can add a hyper parameter if we need to
return self.l2_coef * T.sum(self.w * self.w)
class SoftmaxXERegression2(RegressionLayer2):
""" XE mean cross entropy"""
def build_prediction(self):
return NN.softmax(self.activation)
def build_classification_cost(self, target):
#self.classification_cost_matrix = target * T.log(self.prediction) + (1 - target) * T.log(1 - self.prediction)
self.classification_cost_matrix = (target - self.prediction)**2
self.classification_costs = -T.sum(self.classification_cost_matrix, axis=1)
return T.sum(self.classification_costs)
def build_regularization(self):
self.l2_coef = M.Member(T.scalar()) # we can add a hyper parameter if we need to
return self.l2_coef * T.sum(self.w * self.w)
class T_test_wiki_module(unittest.TestCase):
def test_Module_basic_example1(self):
n, c = T.scalars('nc')
inc = theano.function([n, ((c, c + n), 0)], [])
dec = theano.function([n, ((c, c - n), inc.container[c])], []) # we need to pass inc's container in order to share
plus10 = theano.function([(c, inc.container[c])], c + 10)
assert inc[c] == 0
inc(2)
assert inc[c] == 2 and dec[c] == inc[c]
dec(3)
assert inc[c] == -1 and dec[c] == inc[c]
assert plus10() == 9
def test_Module_basic_example2(self):
m = M.Module()
n = T.scalar('n')
m.c = M.Member(T.scalar()) # state variables must be wrapped with ModuleMember
m.inc = M.Method(n, [], c = m.c + n) # m.c <= m.c + n
m.dec = M.Method(n, [], c = m.c - n) # k.c <= k.c - n
m.dec = M.Method(n, [], updates = {m.c: m.c - n})
#m.dec = M.Method(n, [], updates = {c: m.c - n})#global c don't exist
#m.dec = M.Method(n, [], m.c = m.c - n) #python don't suppor this syntax
m.plus10 = M.Method([], m.c + 10) # m.c is always accessible since it is a member of this mlass
inst = m.make(c = 0) # here, we make an "instance" of the module with c initialized to 0
assert inst.c == 0
inst.inc(2)
assert inst.c == 2
inst.dec(3)
assert inst.c == -1
assert inst.plus10() == 9
inst = m.make(c = 5) # here, we make an "instance" of the module with c initialized to 0
assert inst.c == 5
inst.inc(2)
assert inst.c == 7
inst.dec(3)
assert inst.c == 4
assert inst.plus10() == 14
def test_Module_nesting_example1(self):
def make_incdec_function():
n, c = T.scalars('nc')
inc = theano.function([n, ((c, c + n), 0)], [])
dec = theano.function([n, ((c, c - n), inc.container[c])], [])
return inc,dec
inc1, dec1 = make_incdec_function()
inc2, dec2 = make_incdec_function()
a, b = T.scalars('ab')
sum = theano.function([(a, inc1.container['c']), (b, inc2.container['c'])], a + b)
inc1(2)
dec1(4)
inc2(6)
assert inc1['c'] == -2 and inc2['c'] == 6
assert sum() == 4 # -2 + 6
def test_Module_nesting_example2(self):
def make_incdec_module():
m = M.Module()
n = T.scalar('n')
m.c = M.Member(T.scalar()) # state variables must be wrapped with ModuleMember
m.inc = M.Method(n, [], c = m.c + n) # m.c <= m.c + n
m.dec = M.Method(n, [], c = m.c - n) # k.c <= k.c - n
return m
m = M.Module()
m.incdec1 = make_incdec_module()
m.incdec2 = make_incdec_module()
m.sum = M.Method([], m.incdec1.c + m.incdec2.c)
inst = m.make(incdec1 = dict(c=0), incdec2 = dict(c=0))
assert inst.incdec1.c==0 and inst.incdec2.c==0
inst.incdec1.inc(2)
inst.incdec1.dec(4)
inst.incdec2.inc(6)
assert inst.incdec1.c == -2 and inst.incdec2.c == 6
assert inst.sum() == 4 # -2 + 6
def test_Module_Advanced_example(self):
data_x = N.random.randn(4, 10)
data_y = [ [int(x)] for x in N.random.randn(4) > 0]
def test(model):
model = model.make(input_size = 10,
target_size = 1,
stepsize = 0.1)
print model.stepsize
self.failUnless( model.w.shape == (10,1) and model.b.shape == (1,))
assert model.stepsize == 0.1
for i in xrange(1000):
xe = model.update(data_x, data_y)
if i % 100 == 0:
print i, xe
pass
#for inputs, targets in my_training_set():
#print "cost:", model.update(inputs, targets)
print "final weights:", model.w
print "final biases:", model.b
#Print "some prediction:", model.prediction(some_inputs)
return model
m1=test(SoftmaxXERegression1(regularize = False))
m2=test(SoftmaxXERegression2(regularize = False))
print "m1",m1
print "m2",m2
print m2==m1
print m1==m2
assert m2==m1 and m1==m2
def test_Module_extending_module_methods(self):
model_module = SoftmaxXERegression1(regularize = False)
model_module.sum = M.Member(T.scalar()) # we add a module member to hold the sum
model_module.update.updates.update(sum = model_module.sum + model_module.cost) # now update will also update sum!
model = model_module.make(input_size = 4,
target_size = 2,
stepsize = 0.1,
sum = 0) # we mustn't forget to initialize the sum
print model.stepsize
self.failUnless( model.w.shape == (4,2) and model.b.shape == (2,))
assert model.stepsize == 0.1
test = model.update([[0,0,1,0]], [[0,1]])
test += model.update([[0,1,0,0]], [[1,0]])
assert model.sum == test
def test_Module_basic_example2_more(self):
m = M.Module()
m2 = M.Module()
m2.name="m2" # for better error
#top level don't have name, but other have auto name.
n = T.scalar('n')
m.c = M.Member(T.scalar()) # state variables must be wrapped with ModuleMember
m2.c = M.Member(T.scalar()) # state variables must be wrapped with ModuleMember
m.dec = M.Method(n, [], c = m.c - n)
m.inc = M.Method(n, [], c = m.c + n) # m.c <= m.c + n
# m.inc = M.Method(n, [], c = c + n)#fail c not defined
#syntax error
# m.inc = M.Method(n, [], m.c = m.c + n)#fail
m.inc = M.Method(n, [], updates={m.c: m.c + n})
# m.inc = M.Method(n, [], updates={c: m.c + n})#fail with NameError
# m.inc = M.Method(n, [], updates={m.c: c + n})#fail with NameError
# m.inc = M.Method(n, [], updates={c: c + n})#fail with NameError
m.inc = M.Method(n, [], updates={m.c: m2.c + n})#work! should be allowed?
a = M.Module()
a.m1 = m
a.m2 = m2
a.make()#should work.
# self.assertRaises(m.make(c = 0), Error)
m.inc = M.Method(n, [], updates={m2.c: m.c + n})#work! should be allowed?
# self.assertRaises(m.make(c = 0), Error)
# m.inc = M.Method(n, [], updates={m.c: m2.c + m.c+ n})#work! should be allowed?
m2.inc = M.Method(n, [], updates={m2.c: m2.c + 2*m.c+ n})#work! should be allowed?
# self.assertRaises(m.make(c = 0), Error)
if __name__ == '__main__':
from theano.tests import main
main("test_wiki")
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