Execution Time1.33s

Test: TMVA-DNN-RNN-Backpropagation-Cpu (Passed)
Build: master-x86_64-mac1013-clang100 (macphsft16.dyndns.cern.ch) on 2019-11-12 00:51:32

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][31m0.02342[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][31m0.118792[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 = 2 input = 2 state = 3 time = 1	using a random input
Testing weight input gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m0.0130793[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][31m0.00514169[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][31m1.51475[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][31m0.214114[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][31m0.175307[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 = 2 input = 5 state = 10 time = 4	using a random input
Testing weight input gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m14.908[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][31m5.96559[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][31m0.144754[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][31m3.0901[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][31m2.12153[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][31m0.0775209[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 and a dense layer and an extra RNN
Testing weight input gradients:      maximum error (relative): [NON-XML-CHAR-0x1B][31m15.127[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][31m13.863[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][31m12.9372[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][31m7.38742[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][31m72.1356[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][31m0.0244692[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][31m2.7068[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][31m61.5027[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][31m0.0589262[NON-XML-CHAR-0x1B][39m
[NON-XML-CHAR-0x1B][31m Error [NON-XML-CHAR-0x1B][39m in bias state gradients