Mercurial > pylearn
annotate pylearn/algorithms/logistic_regression.py @ 537:b054271b2504
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author | James Bergstra <bergstrj@iro.umontreal.ca> |
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date | Wed, 12 Nov 2008 21:57:54 -0500 |
parents | algorithms/logistic_regression.py@c7ce66b4e8f4 |
children | 85d3300c9a9c |
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 |
495 | 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 from ..datasets import make_dataset |
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12 from .minimizer import make_minimizer |
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13 from .stopper import make_stopper |
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14 |
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15 class LogRegN(module.FancyModule): |
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16 |
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17 def __init__(self, |
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18 n_in=None, n_out=None, |
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19 input=None, target=None, |
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20 w=None, b=None, |
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21 l2=None, l1=None): |
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22 super(LogRegN, self).__init__() #boilerplate |
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23 |
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24 self.n_in = n_in |
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25 self.n_out = n_out |
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26 |
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27 self.input = input if input is not None else T.matrix() |
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28 self.target = target if target is not None else T.lvector() |
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29 |
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30 self.w = w if w is not None else module.Member(T.dmatrix()) |
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31 self.b = b if b is not None else module.Member(T.dvector()) |
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32 |
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33 #the params of the model are the ones we fit to the data |
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34 self.params = [p for p in [self.w, self.b] if p.owner is None] |
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35 |
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36 #the hyper-parameters of the model are not fit to the data |
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37 self.l2 = l2 if l2 is not None else module.Member(T.dscalar()) |
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38 self.l1 = l1 if l1 is not None else module.Member(T.dscalar()) |
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39 |
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40 #here we actually build the model |
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41 self.linear_output = T.dot(self.input, self.w) + self.b |
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42 if 0: |
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43 self.softmax = nnet.softmax(self.linear_output) |
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44 |
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45 self._max_pr, self.argmax = T.max_and_argmax(self.linear_output) |
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46 self._xent = self.target * T.log(self.softmax) |
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47 else: |
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48 (self._xent, self.softmax, self._max_pr, self.argmax) =\ |
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49 nnet.crossentropy_softmax_max_and_argmax_1hot( |
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50 self.linear_output, self.target) |
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51 |
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52 self.unregularized_cost = T.sum(self._xent) |
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53 self.l1_cost = self.l1 * T.sum(abs(self.w)) |
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54 self.l2_cost = self.l2 * T.sum(self.w**2) |
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55 self.regularized_cost = self.unregularized_cost + self.l1_cost + self.l2_cost |
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56 self._loss_zero_one = T.mean(T.neq(self.argmax, self.target)) |
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57 |
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58 # METHODS |
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59 if 0: #TODO: PENDING THE BETTER IMPLEMENTATION ABOVE |
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60 self.predict = module.Method([self.input], self.argmax) |
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61 self.label_probs = module.Method([self.input], self.softmax) |
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62 self.validate = module.Method([self.input, self.target], |
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63 [self._loss_zero_one, self.regularized_cost, self.unregularized_cost]) |
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64 |
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65 def _instance_initialize(self, obj): |
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66 obj.w = N.zeros((self.n_in, self.n_out)) |
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67 obj.b = N.zeros(self.n_out) |
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68 obj.__pp_hide__ = ['params'] |
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69 |
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70 def logistic_regression(n_in, n_out, l1, l2, minimizer=None): |
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71 if n_out == 2: |
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72 raise NotImplementedError() |
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73 else: |
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74 rval = LogRegN(n_in=n_in, n_out=n_out, l1=l1, l2=l2) |
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75 rval.minimizer = minimizer([rval.input, rval.target], rval.regularized_cost, |
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76 rval.params) |
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77 return rval.make() |
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78 |
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79 #TODO: grouping parameters by prefix does not play well with providing defaults. Think... |
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80 class _fit_logreg_defaults(object): |
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81 minimizer_algo = 'dummy' |
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82 #minimizer_lr = 0.001 |
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83 dataset = 'MNIST_1k' |
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84 l1 = 0.0 |
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85 l2 = 0.0 |
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86 batchsize = 8 |
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87 verbose = 1 |
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88 |
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89 from ..datasets import MNIST |
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90 import sgd #TODO: necessary to add it to factory list |
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91 # consider pre-importing each file in algorithms, datasets (possibly with try/catch around each |
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92 # import so that this import failure is ignored) |
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93 |
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94 def fit_logistic_regression_online(state, channel=lambda *args, **kwargs:None): |
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95 #use stochastic gradient descent |
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96 state.use_defaults(_fit_logreg_defaults) |
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97 |
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98 dataset = make_dataset(**state.subdict(prefix='dataset_')) |
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99 train = dataset.train |
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100 valid = dataset.valid |
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101 test = dataset.test |
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102 |
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103 logreg = logistic_regression( |
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104 n_in=train.x.shape[1], |
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105 n_out=dataset.n_classes, |
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106 l2=state.l2, |
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107 l1=state.l1, |
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108 minimizer=make_minimizer(**state.subdict(prefix='minimizer_'))) |
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109 |
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110 batchsize = state.batchsize |
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111 verbose = state.verbose |
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112 iter = [0] |
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113 |
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114 def step(): |
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115 # step by making a pass through the training set |
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116 for j in xrange(0,len(train.x)-batchsize+1,batchsize): |
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117 cost_j = logreg.minimizer.step_cost(train.x[j:j+batchsize], train.y[j:j+batchsize]) |
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118 if verbose > 1: |
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119 print 'estimated train cost', cost_j |
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120 #TODO: consult iter[0] for periodic saving to cwd (model, minimizer, and stopper) |
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121 |
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122 def check(): |
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123 validate = logreg.validate(valid.x, valid.y) |
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124 if verbose > 0: |
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125 print 'iter', iter[0], 'validate', validate |
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126 sys.stdout.flush() |
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127 iter[0] += 1 |
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128 return validate[0] |
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129 |
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130 def save(): |
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131 return copy.deepcopy(logreg) |
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132 |
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133 stopper = make_stopper(**state.subdict(prefix='stopper_')) |
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134 stopper.find_min(step, check, save) |
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135 |
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136 state.train_01, state.train_rcost, state.train_cost = logreg.validate(train.x, train.y) |
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137 state.valid_01, state.valid_rcost, state.valid_cost = logreg.validate(valid.x, valid.y) |
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138 state.test_01, state.test_rcost, state.test_cost = logreg.validate(test.x, test.y) |
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139 |
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140 state.n_train = len(train.y) |
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141 state.n_valid = len(valid.y) |
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142 state.n_test = len(test.y) |
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143 |
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144 class LogReg2(module.FancyModule): |
497 | 145 def __init__(self, input=None, targ=None, w=None, b=None, lr=None, regularize=False): |
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146 super(LogReg2, self).__init__() #boilerplate |
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147 |
497 | 148 self.input = input if input is not None else T.matrix('input') |
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149 self.targ = targ if targ is not None else T.lcol() |
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150 |
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151 self.w = w if w is not None else module.Member(T.dmatrix()) |
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152 self.b = b if b is not None else module.Member(T.dvector()) |
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153 self.lr = lr if lr is not None else module.Member(T.dscalar()) |
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154 |
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155 self.params = [p for p in [self.w, self.b] if p.owner is None] |
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156 |
502 | 157 output = nnet.sigmoid(T.dot(self.x, self.w) + self.b) |
497 | 158 xent = -self.targ * T.log(output) - (1.0 - self.targ) * T.log(1.0 - output) |
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159 sum_xent = T.sum(xent) |
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160 |
497 | 161 self.output = output |
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162 self.xent = xent |
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163 self.sum_xent = sum_xent |
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164 self.cost = sum_xent |
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165 |
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166 #define the apply method |
497 | 167 self.pred = (T.dot(self.input, self.w) + self.b) > 0.0 |
168 self.apply = module.Method([self.input], self.pred) | |
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169 |
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170 #if this module has any internal parameters, define an update function for them |
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171 if self.params: |
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172 gparams = T.grad(sum_xent, self.params) |
497 | 173 self.update = module.Method([self.input, self.targ], sum_xent, |
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174 updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gparams))) |
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175 |
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176 |