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annotate test_mlp.py @ 286:2ee53bae9ee0

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renamed _nnet_ops.py to _test_nnet_opt.py to be used with autotest
author Frederic Bastien <bastienf@iro.umontreal.ca>
date Fri, 06 Jun 2008 13:55:59 -0400
parents ebbb0e749565
children
rev   line source
121
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1
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2 from mlp import *
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3 import dataset
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4 import nnet_ops
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6
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7 from functools import partial
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8 def separator(debugger, i, node, *ths):
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9 print "==================="
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10
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11 def what(debugger, i, node, *ths):
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12 print "#%i" % i, node
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13
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14 def parents(debugger, i, node, *ths):
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15 print [input.step for input in node.inputs]
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16
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17 def input_shapes(debugger, i, node, *ths):
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18 print "input shapes: ",
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19 for r in node.inputs:
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20 if hasattr(r.value, 'shape'):
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21 print r.value.shape,
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22 else:
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23 print "no_shape",
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24 print
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25
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26 def input_types(debugger, i, node, *ths):
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27 print "input types: ",
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28 for r in node.inputs:
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29 print r.type,
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30 print
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31
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32 def output_shapes(debugger, i, node, *ths):
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33 print "output shapes:",
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34 for r in node.outputs:
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35 if hasattr(r.value, 'shape'):
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36 print r.value.shape,
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37 else:
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38 print "no_shape",
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39 print
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40
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41 def output_types(debugger, i, node, *ths):
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42 print "output types:",
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43 for r in node.outputs:
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44 print r.type,
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45 print
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46
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47
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48 def test0():
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49 linker = 'c|py'
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50 #linker = partial(theano.gof.DebugLinker, linkers = [theano.gof.OpWiseCLinker],
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51 # debug_pre = [separator, what, parents, input_types, input_shapes],
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52 # debug_post = [output_shapes, output_types],
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53 # compare_fn = lambda x, y: numpy.all(x == y))
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54
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55 nnet = OneHiddenLayerNNetClassifier(10,2,.001,1000, linker = linker)
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56 training_set = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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57 [0, 1, 1],
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58 [1, 0, 1],
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59 [1, 1, 1]]),
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60 {'input':slice(2),'target':2})
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61 fprop=nnet(training_set)
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62
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63 output_ds = fprop(training_set)
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64
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65 for fieldname in output_ds.fieldNames():
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66 print fieldname+"=",output_ds[fieldname]
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67
186
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68 def test1():
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69 nnet = ManualNNet(2, 10,3,.1,1000)
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70 training_set = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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71 [0, 1, 1],
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72 [1, 0, 1],
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73 [1, 1, 1]]),
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74 {'input':slice(2),'target':2})
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75 fprop=nnet(training_set)
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76
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77 output_ds = fprop(training_set)
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78
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79 for fieldname in output_ds.fieldNames():
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80 print fieldname+"=",output_ds[fieldname]
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81
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82 def test2():
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83 training_set = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
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84 [0, 1, 1],
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85 [1, 0, 1],
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86 [1, 1, 1]]),
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87 {'input':slice(2),'target':2})
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88 nin, nhid=2, 10
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89 def sigm_layer(input):
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90 W1 = t.matrix('W1')
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91 b1 = t.vector('b1')
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92 return (nnet_ops.sigmoid(b1 + t.dot(input, W1)),
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93 [W1, b1],
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94 [(numpy.random.rand(nin, nhid) -0.5) * 0.001, numpy.zeros(nhid)])
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95 nnet = ManualNNet(nin, nhid, 3, .1, 1000, hidden_layer=sigm_layer)
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96 fprop=nnet(training_set)
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97
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98 output_ds = fprop(training_set)
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99
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100 for fieldname in output_ds.fieldNames():
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101 print fieldname+"=",output_ds[fieldname]
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102
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103 def test_interface_0():
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104 learner = ManualNNet(2, 10, 3, .1, 1000)
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105
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106 model = learner(training_set)
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107
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108 model2 = learner(training_set) # trains model a second time
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109
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110 learner.update(additional_data) # modifies nnet and model by side-effect
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111
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112
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113 def test_interface2_1():
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114 learn_algo = ManualNNet(2, 10, 3, .1, 1000)
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115
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116 prior = learn_algo()
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117
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118 model1 = learn_algo(training_set1)
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119
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120 model2 = learn_algo(training_set2)
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121
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122 model2.update(additional_data)
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123
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124 n_match = 0
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125 for o1, o2 in zip(model1.use(test_data), model2.use(test_data)):
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126 n_match += (o1 == o2)
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127
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128 print n_match
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129
186
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130 test1()
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131 test2()
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132