Execution Time0.32s

Test: TMVA-DNN-RNN-Backpropagation-Cpu (Passed)
Build: PR-4624-x86_64-ubuntu16-gcc54-opt (sft-ubuntu-1604-4) on 2019-11-14 19:02:07
Repository revision: ee743e1638624a8a6fed6adde874e58bb5acc139

Test Timing: Passed
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Test output
Testing RNN backward pass
Testing Weight Backprop using RNN with batchsize = 2 input = 2 state = 1 time = 1	using a random input
Testing weight input gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m0.176054[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in weight input gradients
Testing weight state gradients:      maximum error (absolute): [NON-XML-CHAR-0x1B][32m0[NON-XML-CHAR-0x1B][39m
Testing bias gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m0.00528216[NON-XML-CHAR-0x1B][39m
Testing Weight Backprop using RNN with batchsize = 2 input = 2 state = 3 time = 1	using a random input
Testing weight input gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m0.204278[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in weight input gradients
Testing weight state gradients:      maximum error (absolute): [NON-XML-CHAR-0x1B][32m0[NON-XML-CHAR-0x1B][39m
Testing bias gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m0.0083642[NON-XML-CHAR-0x1B][39m
Testing Weight Backprop using RNN with batchsize = 3 input = 5 state = 4 time = 2	using a random input
Testing weight input gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m0.178299[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in weight input gradients
Testing weight state gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m0.100156[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in weight state gradients
Testing bias gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m0.00665997[NON-XML-CHAR-0x1B][39m
Testing Weight Backprop using RNN with batchsize = 2 input = 5 state = 10 time = 4	using a random input
Testing weight input gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m1.60634[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in weight input gradients
Testing weight state gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m2.25532[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in weight state gradients
Testing bias gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m0.283403[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in bias state gradients
Testing Weight Backprop using RNN with batchsize = 64 input = 5 state = 10 time = 5	using a random input
Testing weight input gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m1.8506[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in weight input gradients
Testing weight state gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m2.76079[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in weight state gradients
Testing bias gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m0.0181839[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in bias state gradients
Testing Weight Backprop using RNN with batchsize = 1 input = 5 state = 10 time = 3	with a fixed input
Testing weight input gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m0.60837[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in weight input gradients
Testing weight state gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m38.2068[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in weight state gradients
Testing bias gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m0.084744[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in bias state gradients
Testing Weight Backprop using RNN with batchsize = 32 input = 20 state = 10 time = 4	using a random input and a dense layer
Testing weight input gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m34.0091[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in weight input gradients
Testing weight state gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m28.4757[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in weight state gradients
Testing bias gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m0.0217309[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in bias state gradients
Testing Weight Backprop using RNN with batchsize = 32 input = 5 state = 10 time = 4	using a random input and a dense layer and an extra RNN
Testing weight input gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m11.5563[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in weight input gradients
Testing weight state gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m26.1452[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in weight state gradients
Testing bias gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m0.262618[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in bias state gradients