def test_update_layer(self):
cOutput = random.randint(5, 10)
cInput = random.randint(5, 10)
listDblOutputs = randlist(-1.0, 1.0, cOutput)
listDblError = []
for dblOut in listDblOutputs:
listDblError.append(1.0 / (1.0 - dblOut))
listDblInput = randlist(-1.0, 1.0, cInput)
listPcpt = []
listListDblWOrig = []
for ixPcpt in xrange(cOutput):
listDblW = randlist(-1.0, 1.0, cInput)
listListDblWOrig.append(listDblW)
listPcpt.append(nn.Perceptron(list(listDblW), 0.0, ixPcpt))
listDblDelta = nn.layer_deltas(listDblOutputs, listDblError)
layer = nn.NeuralNetLayer(cInput, listPcpt)
dblLearningRate = random.random()
nn.update_layer(layer, listDblInput, listDblDelta, dblLearningRate)
listZip = zip(layer.listPcpt, listListDblWOrig, listDblOutputs)
for pcpt, listDblWOrig, dblOut in listZip:
listZipInner = zip(pcpt.listDblW, listDblWOrig, listDblInput)
for dblW, dblWOrig, dblIn in listZipInner:
dblExpectedDiff = dblLearningRate * dblIn * dblOut
self.assertAlmostEqual(dblExpectedDiff, dblW - dblWOrig, 4)
def test_update_layer(self):
cOutput = random.randint(5,10)
cInput = random.randint(5,10)
listDblOutputs = randlist(-1.0,1.0,cOutput)
listDblError = []
for dblOut in listDblOutputs:
listDblError.append(1.0/(1.0 - dblOut))
listDblInput = randlist(-1.0,1.0,cInput)
listPcpt = []
listListDblWOrig = []
for ixPcpt in xrange(cOutput):
listDblW = randlist(-1.0,1.0,cInput)
listListDblWOrig.append(listDblW)
listPcpt.append(nn.Perceptron(list(listDblW),0.0,ixPcpt))
listDblDelta = nn.layer_deltas(listDblOutputs,listDblError)
layer = nn.NeuralNetLayer(cInput,listPcpt)
dblLearningRate = random.random()
nn.update_layer(layer, listDblInput, listDblDelta, dblLearningRate)
listZip = zip(layer.listPcpt,listListDblWOrig,listDblOutputs)
for pcpt,listDblWOrig,dblOut in listZip:
listZipInner = zip(pcpt.listDblW,listDblWOrig,listDblInput)
for dblW,dblWOrig,dblIn in listZipInner:
dblExpectedDiff = dblLearningRate*dblIn*dblOut
self.assertAlmostEqual(dblExpectedDiff, dblW - dblWOrig, 4)
def test_layer_deltas(self):
cInput = random.randint(10, 20)
listDblTarget = randlist(-3.0, 3.0, cInput)
listDblActivation, listDblError = pairwise_factors(listDblTarget)
listDblDelta = nn.layer_deltas(listDblActivation, listDblError)
listZip = zip(listDblDelta, listDblTarget, listDblActivation)
for dblDelta, dblTarget, dblAct in listZip:
self.assertAlmostEqual(dblTarget * (1.0 - dblAct), dblDelta, 5)
def test_layer_deltas(self):
cInput = random.randint(10,20)
listDblTarget = randlist(-3.0,3.0,cInput)
listDblActivation,listDblError = pairwise_factors(listDblTarget)
listDblDelta = nn.layer_deltas(listDblActivation,listDblError)
listZip = zip(listDblDelta,listDblTarget,listDblActivation)
for dblDelta,dblTarget,dblAct in listZip:
self.assertAlmostEqual(dblTarget*(1.0 - dblAct),dblDelta, 5)
