def testCorrectForwardWeightsUpdateNonVec(self):
testRandom = TestConstRandom(1)
testMLP = MultiLayerPerceptronNetwork(testRandom, [3, 3, 3],
learningRate=0.5,
momentum=0)
outputVectors = MagicMock(spec=list)
with patch(
'mlpBp.mlp.MultiLayerPerceptronNetwork._calcDerivErrorWeightNonVec',
lambda _, __: 1):
testMLP._forwardWeightsUpdateNonVec(outputVectors)
for layerId in range(len(testMLP.networkWeights)):
self.assertTrue(
np.alltrue(testMLP.networkWeights[layerId] == 0.5))
self.assertTrue(
np.alltrue(testMLP.tMinus1NetworkWeights[layerId] == 1))
def testForwardWeightUpdateNonVecEqualsVecVersion(self, out1, out2, out3,
delta1, delta2):
testRandom = TestSeqRandom()
testVecMLP = MultiLayerPerceptronNetwork(testRandom, [3, 4, 3], 0.5,
0.6)
testNonVecMLP = MultiLayerPerceptronNetwork(testRandom, [3, 4, 3], 0.5,
0.6)
prevWeights = copy.deepcopy(testVecMLP.networkWeights)
testVecMLP.networkDeltas = [delta1, delta2]
testNonVecMLP.networkDeltas = [delta1, delta2]
outputVectors = [out1, out2, out3]
testNonVecMLP._forwardWeightsUpdateNonVec(outputVectors)
testVecMLP._forwardWeightsUpdateVec(outputVectors)
for i in range(len(testVecMLP.networkWeights)):
assert_array_equal(testVecMLP.networkWeights[i],
testNonVecMLP.networkWeights[i])
assert_raises(AssertionError, assert_array_equal,
testVecMLP.networkWeights[i], prevWeights[i])