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Group: rnn-zero-sparsev2

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Timestamps visible

2023-05-11 14:16:41

[34227/65536] Loss: 3.446 - Accuracy: 0.162 | LearningRate: 0.00079 | StepTime: 0.657614s - Rate: 37,805.2 Tokens/s

2023-05-11 14:16:41

[34228/65536] Loss: 3.464 - Accuracy: 0.169 | LearningRate: 0.00079 | StepTime: 0.658381s - Rate: 37,805.5 Tokens/s

2023-05-11 14:16:43

[34229/65536] Loss: 3.465 - Accuracy: 0.157 | LearningRate: 0.00079 | StepTime: 0.657706s - Rate: 37,805.7 Tokens/s

2023-05-11 14:16:43

[34230/65536] Loss: 3.442 - Accuracy: 0.180 | LearningRate: 0.00079 | StepTime: 0.657514s - Rate: 37,806.0 Tokens/s

2023-05-11 14:16:43

[34231/65536] Loss: 3.436 - Accuracy: 0.164 | LearningRate: 0.00079 | StepTime: 0.658083s - Rate: 37,806.2 Tokens/s

2023-05-11 14:16:45

[34232/65536] Loss: 3.489 - Accuracy: 0.172 | LearningRate: 0.00079 | StepTime: 0.707858s - Rate: 37,806.4 Tokens/s

2023-05-11 14:16:45

[34233/65536] Loss: 3.428 - Accuracy: 0.170 | LearningRate: 0.00079 | StepTime: 0.658953s - Rate: 37,806.6 Tokens/s

2023-05-11 14:16:45

[34234/65536] Loss: 3.431 - Accuracy: 0.165 | LearningRate: 0.00079 | StepTime: 0.686798s - Rate: 37,806.8 Tokens/s

2023-05-11 14:16:47

[34235/65536] Loss: 3.477 - Accuracy: 0.157 | LearningRate: 0.00079 | StepTime: 0.657495s - Rate: 37,807.1 Tokens/s

2023-05-11 14:16:47

[34236/65536] Loss: 3.397 - Accuracy: 0.168 | LearningRate: 0.00079 | StepTime: 0.657584s - Rate: 37,807.3 Tokens/s

2023-05-11 14:16:47

[34237/65536] Loss: 3.453 - Accuracy: 0.178 | LearningRate: 0.00079 | StepTime: 0.658593s - Rate: 37,807.6 Tokens/s

2023-05-11 14:16:49

[34238/65536] Loss: 3.418 - Accuracy: 0.177 | LearningRate: 0.00079 | StepTime: 0.657555s - Rate: 37,807.8 Tokens/s

2023-05-11 14:16:49

[34239/65536] Loss: 3.410 - Accuracy: 0.180 | LearningRate: 0.00079 | StepTime: 0.657645s - Rate: 37,808.1 Tokens/s

2023-05-11 14:16:49

[34240/65536] Loss: 3.371 - Accuracy: 0.178 | LearningRate: 0.00079 | StepTime: 0.659216s - Rate: 37,808.3 Tokens/s

2023-05-11 14:16:51

[34241/65536] Loss: 3.375 - Accuracy: 0.170 | LearningRate: 0.00079 | StepTime: 0.658087s - Rate: 37,808.6 Tokens/s

2023-05-11 14:16:51

[34242/65536] Loss: 3.398 - Accuracy: 0.165 | LearningRate: 0.00079 | StepTime: 0.658590s - Rate: 37,808.8 Tokens/s

2023-05-11 14:16:51

[34243/65536] Loss: 3.482 - Accuracy: 0.164 | LearningRate: 0.00079 | StepTime: 0.657775s - Rate: 37,809.1 Tokens/s

2023-05-11 14:16:53

[34244/65536] Loss: 3.377 - Accuracy: 0.175 | LearningRate: 0.00079 | StepTime: 0.657671s - Rate: 37,809.3 Tokens/s

2023-05-11 14:16:53

[34245/65536] Loss: 3.395 - Accuracy: 0.171 | LearningRate: 0.00079 | StepTime: 0.657587s - Rate: 37,809.6 Tokens/s

2023-05-11 14:16:53

[34246/65536] Loss: 3.485 - Accuracy: 0.168 | LearningRate: 0.00079 | StepTime: 0.658913s - Rate: 37,809.8 Tokens/s

2023-05-11 14:16:55

[34247/65536] Loss: 3.452 - Accuracy: 0.160 | LearningRate: 0.00079 | StepTime: 0.657522s - Rate: 37,810.0 Tokens/s

2023-05-11 14:16:55

[34248/65536] Loss: 3.467 - Accuracy: 0.164 | LearningRate: 0.00079 | StepTime: 0.656933s - Rate: 37,810.3 Tokens/s

2023-05-11 14:16:55

[34249/65536] Loss: 3.485 - Accuracy: 0.165 | LearningRate: 0.00079 | StepTime: 0.657976s - Rate: 37,810.5 Tokens/s

2023-05-11 14:16:57

[34250/65536] Loss: 3.409 - Accuracy: 0.168 | LearningRate: 0.00079 | StepTime: 0.657531s - Rate: 37,810.8 Tokens/s

2023-05-11 14:16:57

[34251/65536] Loss: 3.491 - Accuracy: 0.159 | LearningRate: 0.00079 | StepTime: 0.657617s - Rate: 37,811.0 Tokens/s

2023-05-11 14:16:57

[34252/65536] Loss: 3.466 - Accuracy: 0.161 | LearningRate: 0.00079 | StepTime: 0.656983s - Rate: 37,811.3 Tokens/s

2023-05-11 14:16:59

[34253/65536] Loss: 3.494 - Accuracy: 0.161 | LearningRate: 0.00079 | StepTime: 0.656605s - Rate: 37,811.5 Tokens/s

2023-05-11 14:16:59

[34254/65536] Loss: 3.412 - Accuracy: 0.167 | LearningRate: 0.00079 | StepTime: 0.658605s - Rate: 37,811.8 Tokens/s

2023-05-11 14:16:59

[34255/65536] Loss: 3.435 - Accuracy: 0.178 | LearningRate: 0.00079 | StepTime: 0.657649s - Rate: 37,812.0 Tokens/s

2023-05-11 14:17:01

[34256/65536] Loss: 3.446 - Accuracy: 0.163 | LearningRate: 0.00079 | StepTime: 0.658358s - Rate: 37,812.3 Tokens/s

2023-05-11 14:17:01

[34257/65536] Loss: 3.461 - Accuracy: 0.161 | LearningRate: 0.00079 | StepTime: 0.657295s - Rate: 37,812.5 Tokens/s

2023-05-11 14:17:01

[34258/65536] Loss: 3.503 - Accuracy: 0.163 | LearningRate: 0.00079 | StepTime: 0.658092s - Rate: 37,812.8 Tokens/s

2023-05-11 14:17:03

[34259/65536] Loss: 3.483 - Accuracy: 0.167 | LearningRate: 0.00079 | StepTime: 0.659001s - Rate: 37,813.0 Tokens/s

2023-05-11 14:17:03

[34260/65536] Loss: 3.420 - Accuracy: 0.167 | LearningRate: 0.00079 | StepTime: 0.657551s - Rate: 37,813.2 Tokens/s

2023-05-11 14:17:03

[34261/65536] Loss: 3.534 - Accuracy: 0.171 | LearningRate: 0.00079 | StepTime: 0.657733s - Rate: 37,813.5 Tokens/s

2023-05-11 14:17:05

[34262/65536] Loss: 3.428 - Accuracy: 0.160 | LearningRate: 0.00079 | StepTime: 0.658305s - Rate: 37,813.7 Tokens/s

2023-05-11 14:17:05

[34263/65536] Loss: 3.428 - Accuracy: 0.166 | LearningRate: 0.00079 | StepTime: 0.657417s - Rate: 37,814.0 Tokens/s

2023-05-11 14:17:05

[34264/65536] Loss: 3.503 - Accuracy: 0.157 | LearningRate: 0.00079 | StepTime: 0.659209s - Rate: 37,814.2 Tokens/s

2023-05-11 14:17:07

[34265/65536] Loss: 3.442 - Accuracy: 0.158 | LearningRate: 0.00079 | StepTime: 0.658968s - Rate: 37,814.5 Tokens/s

2023-05-11 14:17:07

[34266/65536] Loss: 3.427 - Accuracy: 0.174 | LearningRate: 0.00079 | StepTime: 0.657379s - Rate: 37,814.7 Tokens/s

2023-05-11 14:17:07

[34267/65536] Loss: 3.457 - Accuracy: 0.165 | LearningRate: 0.00079 | StepTime: 0.657803s - Rate: 37,815.0 Tokens/s

2023-05-11 14:17:09

[34268/65536] Loss: 3.477 - Accuracy: 0.168 | LearningRate: 0.00079 | StepTime: 0.658208s - Rate: 37,815.2 Tokens/s

2023-05-11 14:17:09

[34269/65536] Loss: 3.485 - Accuracy: 0.151 | LearningRate: 0.00079 | StepTime: 0.658791s - Rate: 37,815.4 Tokens/s

2023-05-11 14:17:09

[34270/65536] Loss: 3.529 - Accuracy: 0.167 | LearningRate: 0.00079 | StepTime: 0.658844s - Rate: 37,815.7 Tokens/s

2023-05-11 14:17:11

[34271/65536] Loss: 3.461 - Accuracy: 0.167 | LearningRate: 0.00079 | StepTime: 0.657902s - Rate: 37,815.9 Tokens/s

2023-05-11 14:17:11

[34272/65536] Loss: 3.461 - Accuracy: 0.156 | LearningRate: 0.00079 | StepTime: 0.658062s - Rate: 37,816.2 Tokens/s

2023-05-11 14:17:11

[34273/65536] Loss: 3.413 - Accuracy: 0.165 | LearningRate: 0.00079 | StepTime: 0.658085s - Rate: 37,816.4 Tokens/s

2023-05-11 14:17:13

[34274/65536] Loss: 3.464 - Accuracy: 0.170 | LearningRate: 0.00079 | StepTime: 0.658227s - Rate: 37,816.7 Tokens/s

2023-05-11 14:17:13

[34275/65536] Loss: 3.462 - Accuracy: 0.168 | LearningRate: 0.00079 | StepTime: 0.658460s - Rate: 37,816.9 Tokens/s

2023-05-11 14:17:13

[34276/65536] Loss: 3.424 - Accuracy: 0.166 | LearningRate: 0.00079 | StepTime: 0.658872s - Rate: 37,817.2 Tokens/s

2023-05-11 14:17:15

[34277/65536] Loss: 3.446 - Accuracy: 0.165 | LearningRate: 0.00079 | StepTime: 0.658001s - Rate: 37,817.4 Tokens/s

2023-05-11 14:17:15

[34278/65536] Loss: 3.454 - Accuracy: 0.164 | LearningRate: 0.00079 | StepTime: 0.658758s - Rate: 37,817.6 Tokens/s

2023-05-11 14:17:15

[34279/65536] Loss: 3.450 - Accuracy: 0.178 | LearningRate: 0.00079 | StepTime: 0.658004s - Rate: 37,817.9 Tokens/s

2023-05-11 14:17:17

[34280/65536] Loss: 3.508 - Accuracy: 0.158 | LearningRate: 0.00079 | StepTime: 0.658106s - Rate: 37,818.1 Tokens/s

2023-05-11 14:17:17

[34281/65536] Loss: 3.447 - Accuracy: 0.167 | LearningRate: 0.00079 | StepTime: 0.657972s - Rate: 37,818.4 Tokens/s

2023-05-11 14:17:17

[34282/65536] Loss: 3.446 - Accuracy: 0.171 | LearningRate: 0.00079 | StepTime: 0.656911s - Rate: 37,818.6 Tokens/s