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annotate pylearn/algorithms/logistic_regression.py @ 599:bd777e960c7c

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misc small changes
author James Bergstra <bergstrj@iro.umontreal.ca>
date Tue, 13 Jan 2009 20:08:06 -0500
parents 3f9ec536f2c1
children e242c12eb30d
rev   line source
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1 import sys, copy
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2 import theano
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3 from theano import tensor as T
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4 from theano.tensor import nnet
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5 from theano.compile import module
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6 from theano import printing, pprint
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7 from theano import compile
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8
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9 import numpy as N
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10
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11 class LogRegN(module.FancyModule):
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12 """
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13 A symbolic module for performing N-class logistic regression.
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14
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15 Notable variables
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16 -----------------
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17
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18 self.input
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19 self.target
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20 self.softmax
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21 self.argmax
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22 self.regularized_cost
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23 self.unregularized_cost
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24 """
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25
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26 def __init__(self,
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27 n_in=None, n_out=None,
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28 input=None, target=None,
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29 w=None, b=None,
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30 l2=None, l1=None):
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31 super(LogRegN, self).__init__() #boilerplate
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32
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33 self.n_in = n_in
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34 self.n_out = n_out
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35
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36 self.input = input if input is not None else T.matrix()
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37 self.target = target if target is not None else T.lvector()
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38
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39 self.w = w if w is not None else module.Member(T.dmatrix())
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40 self.b = b if b is not None else module.Member(T.dvector())
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41
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42 #the params of the model are the ones we fit to the data
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43 self.params = [p for p in [self.w, self.b] if p.owner is None]
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44
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45 #the hyper-parameters of the model are not fit to the data
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46 self.l2 = l2 if l2 is not None else module.Member(T.dscalar())
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47 self.l1 = l1 if l1 is not None else module.Member(T.dscalar())
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48
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49 #here we actually build the model
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50 self.linear_output = T.dot(self.input, self.w) + self.b
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51 if 0:
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52 # TODO: pending support for target being a sparse matrix
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53 self.softmax = nnet.softmax(self.linear_output)
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54
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55 self._max_pr, self.argmax = T.max_and_argmax(self.linear_output)
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56 self._xent = self.target * T.log(self.softmax)
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57 else:
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58 # TODO: when above is fixed, remove this hack (need an argmax
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59 # which is independent of targets)
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60 self.argmax_standalone = T.argmax(self.linear_output)
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61 (self._xent, self.softmax, self._max_pr, self.argmax) =\
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62 nnet.crossentropy_softmax_max_and_argmax_1hot(
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63 self.linear_output, self.target)
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64
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65 self.unregularized_cost = T.sum(self._xent)
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66 self.l1_cost = self.l1 * T.sum(abs(self.w))
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67 self.l2_cost = self.l2 * T.sum(self.w**2)
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68 self.regularized_cost = self.unregularized_cost + self.l1_cost + self.l2_cost
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69 self._loss_zero_one = T.mean(T.neq(self.argmax, self.target))
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70
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71 # METHODS
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72 if 0: #TODO: PENDING THE BETTER IMPLEMENTATION ABOVE
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73 self.predict = module.Method([self.input], self.argmax)
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74 self.label_probs = module.Method([self.input], self.softmax)
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75 self.validate = module.Method([self.input, self.target],
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76 [self._loss_zero_one, self.regularized_cost, self.unregularized_cost])
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77
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78 def _instance_initialize(self, obj):
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79 obj.w = N.zeros((self.n_in, self.n_out))
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80 obj.b = N.zeros(self.n_out)
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81 obj.__pp_hide__ = ['params']
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82
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83 def logistic_regression(n_in, n_out, l1, l2, minimizer=None):
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84 if n_out == 2:
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85 raise NotImplementedError()
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86 else:
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87 rval = LogRegN(n_in=n_in, n_out=n_out, l1=l1, l2=l2)
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88 print 'RVAL input target', rval.input, rval.target
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89 rval.minimizer = minimizer([rval.input, rval.target], rval.regularized_cost,
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90 rval.params)
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91 return rval.make(mode='FAST_RUN')
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92
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93 #TODO: grouping parameters by prefix does not play well with providing defaults. Think...
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94 #FIX : Guillaume suggested a convention: plugin handlers (dataset_factory, minimizer_factory,
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95 # etc.) should never provide default arguments for parameters, and accept **kwargs to catch
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96 # irrelevant parameters.
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97 class _fit_logreg_defaults(object):
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98 minimizer_algo = 'dummy'
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99 #minimizer_lr = 0.001
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100 dataset = 'MNIST_1k'
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101 l1 = 0.0
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102 l2 = 0.0
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103 batchsize = 8
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104 verbose = 1
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105
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106 # consider pre-importing each file in algorithms, datasets (possibly with try/catch around each
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107 # import so that this import failure is ignored)
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108
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109 def fit_logistic_regression_online(state, channel=lambda *args, **kwargs:None):
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110 #use stochastic gradient descent
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111 state.use_defaults(_fit_logreg_defaults)
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112
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113 dataset = make(state.dataset)
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114 train = dataset.train
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115 valid = dataset.valid
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116 test = dataset.test
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117
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118 logreg = logistic_regression(
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119 n_in=train.x.shape[1],
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120 n_out=dataset.n_classes,
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121 l2=state.l2,
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122 l1=state.l1,
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123 minimizer=make_minimizer(**state.subdict(prefix='minimizer_')))
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124
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125 batchsize = state.batchsize
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126 verbose = state.verbose
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127 iter = [0]
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128
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129 def step():
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130 # step by making a pass through the training set
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131 for j in xrange(0,len(train.x)-batchsize+1,batchsize):
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132 cost_j = logreg.minimizer.step_cost(train.x[j:j+batchsize], train.y[j:j+batchsize])
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133 if verbose > 1:
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134 print 'estimated train cost', cost_j
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135 #TODO: consult iter[0] for periodic saving to cwd (model, minimizer, and stopper)
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136
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137 def check():
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138 validate = logreg.validate(valid.x, valid.y)
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139 if verbose > 0:
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140 print 'iter', iter[0], 'validate', validate
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141 sys.stdout.flush()
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142 iter[0] += 1
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143 return validate[0]
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144
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145 def save():
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146 return copy.deepcopy(logreg)
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147
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148 stopper = make_stopper(**state.subdict(prefix='stopper_'))
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149 stopper.find_min(step, check, save)
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150
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151 state.train_01, state.train_rcost, state.train_cost = logreg.validate(train.x, train.y)
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152 state.valid_01, state.valid_rcost, state.valid_cost = logreg.validate(valid.x, valid.y)
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153 state.test_01, state.test_rcost, state.test_cost = logreg.validate(test.x, test.y)
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154
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155 state.n_train = len(train.y)
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156 state.n_valid = len(valid.y)
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157 state.n_test = len(test.y)
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158
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159 class LogReg2(module.FancyModule):
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160 def __init__(self, input=None, targ=None, w=None, b=None, lr=None, regularize=False):
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161 super(LogReg2, self).__init__() #boilerplate
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162
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163 self.input = module.Member(input) if input is not None else T.matrix('input')
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164 self.targ = module.Member(targ) if targ is not None else T.lcol()
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165
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166 self.w = module.Member(w) if w is not None else module.Member(T.dmatrix())
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167 self.b = module.Member(b) if b is not None else module.Member(T.dvector())
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168 self.lr = module.Member(lr) if lr is not None else module.Member(T.dscalar())
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169
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170 self.params = [p for p in [self.w, self.b] if p.owner is None]
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171
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172 output = nnet.sigmoid(T.dot(self.x, self.w) + self.b)
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173 xent = -self.targ * T.log(output) - (1.0 - self.targ) * T.log(1.0 - output)
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174 sum_xent = T.sum(xent)
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175
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176 self.output = output
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177 self.xent = xent
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178 self.sum_xent = sum_xent
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179 self.cost = sum_xent
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180
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181 #define the apply method
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182 self.pred = (T.dot(self.input, self.w) + self.b) > 0.0
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183 self.apply = module.Method([self.input], self.pred)
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184
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185 #if this module has any internal parameters, define an update function for them
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186 if self.params:
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187 gparams = T.grad(sum_xent, self.params)
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188 self.update = module.Method([self.input, self.targ], sum_xent,
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189 updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gparams)))
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190
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191