Mercurial > pylearn

comparison mlp.py @ 179:9911d2cc3c01

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merged
author James Bergstra <bergstrj@iro.umontreal.ca>
date Tue, 13 May 2008 15:14:04 -0400
parents 4090779e39a9 e9a95e19e6f8
children 2698c0feeb54
comparison
equal deleted inserted replaced
178:4090779e39a9 179:9911d2cc3c01
7 7
8 from learner import * 8 from learner import *
9 from theano import tensor as t 9 from theano import tensor as t
10 from nnet_ops import * 10 from nnet_ops import *
11 import math 11 import math
12 12 from misc import *
13 def sum_l2_cost(*params):
14 p = params[0]
15 rval = t.sum(p*p)
16 for p in params[1:]:
17 rval = rval + t.sum(p*p)
18 return rval
19
20 def activation(w, b, v, c, x):
21 return t.dot(t.tanh(t.dot(x, w) + b), v) + c
22 def nll(w, b, v, c, x, y):
23 return crossentropy_softmax_1hot(prediction(w, b, v, c, x), y)[0]
24 def output(w, b, v, c, x, y):
25 return crossentropy_softmax_1hot(prediction(w, b, v, c, x), y)[1]
26
27
28 13
29 class OneHiddenLayerNNetClassifier(OnlineGradientTLearner): 14 class OneHiddenLayerNNetClassifier(OnlineGradientTLearner):
30 """ 15 """
31 Implement a straightforward classicial feedforward 16 Implement a straightforward classicial feedforward
32 one-hidden-layer neural net, with L2 regularization. 17 one-hidden-layer neural net, with L2 regularization.
100 self._W2 = t.matrix('W2') 85 self._W2 = t.matrix('W2')
101 self._b1 = t.row('b1') 86 self._b1 = t.row('b1')
102 self._b2 = t.row('b2') 87 self._b2 = t.row('b2')
103 self._regularization_term = self._L2_regularizer * (t.sum(self._W1*self._W1) + t.sum(self._W2*self._W2)) 88 self._regularization_term = self._L2_regularizer * (t.sum(self._W1*self._W1) + t.sum(self._W2*self._W2))
104 self._output_activations =self._b2+t.dot(t.tanh(self._b1+t.dot(self._input,self._W1.T)),self._W2.T) 89 self._output_activations =self._b2+t.dot(t.tanh(self._b1+t.dot(self._input,self._W1.T)),self._W2.T)
105 self._nll,self._output = crossentropy_softmax_1hot(self._output_activations,self._target_vector) 90 self._nll,self._output = crossentropy_softmax_1hot(Print("output_activations")(self._output_activations),self._target_vector)
106 self._output_class = t.argmax(self._output,1) 91 self._output_class = t.argmax(self._output,1)
107 self._class_error = t.neq(self._output_class,self._target_vector) 92 self._class_error = t.neq(self._output_class,self._target_vector)
108 self._minibatch_criterion = self._nll + self._regularization_term / t.shape(self._input)[0] 93 self._minibatch_criterion = self._nll + self._regularization_term / t.shape(self._input)[0]
109 OnlineGradientTLearner.__init__(self) 94 OnlineGradientTLearner.__init__(self)
110 95