Mercurial > pylearn

comparison test_mlp.py @ 186:562f308873f0

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added ManualNNet
author James Bergstra <bergstrj@iro.umontreal.ca>
date Tue, 13 May 2008 20:10:03 -0400
parents 25d0a0c713da
children ebbb0e749565
comparison
equal deleted inserted replaced
185:3d953844abd3 186:562f308873f0
1 1
2 from mlp import * 2 from mlp import *
3 import dataset 3 import dataset
4 import nnet_ops
4 5
5 6
6 from functools import partial 7 from functools import partial
7 def separator(debugger, i, node, *ths): 8 def separator(debugger, i, node, *ths):
8 print "===================" 9 print "==================="
62 output_ds = fprop(training_set) 63 output_ds = fprop(training_set)
63 64
64 for fieldname in output_ds.fieldNames(): 65 for fieldname in output_ds.fieldNames():
65 print fieldname+"=",output_ds[fieldname] 66 print fieldname+"=",output_ds[fieldname]
66 67
67 test0() 68 def test1():
69 nnet = ManualNNet(2, 10,3,.1,1000)
70 training_set = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
71 [0, 1, 1],
72 [1, 0, 1],
73 [1, 1, 1]]),
74 {'input':slice(2),'target':2})
75 fprop=nnet(training_set)
68 76
77 output_ds = fprop(training_set)
78
79 for fieldname in output_ds.fieldNames():
80 print fieldname+"=",output_ds[fieldname]
81
82 def test2():
83 training_set = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
84 [0, 1, 1],
85 [1, 0, 1],
86 [1, 1, 1]]),
87 {'input':slice(2),'target':2})
88 nin, nhid=2, 10
89 def sigm_layer(input):
90 W1 = t.matrix('W1')
91 b1 = t.vector('b1')
92 return (nnet_ops.sigmoid(b1 + t.dot(input, W1)),
93 [W1, b1],
94 [(numpy.random.rand(nin, nhid) -0.5) * 0.001, numpy.zeros(nhid)])
95 nnet = ManualNNet(nin, nhid, 3, .1, 1000, hidden_layer=sigm_layer)
96 fprop=nnet(training_set)
97
98 output_ds = fprop(training_set)
99
100 for fieldname in output_ds.fieldNames():
101 print fieldname+"=",output_ds[fieldname]
102 test1()
103 test2()
104