optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.01, 'decay_steps': 100, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
training_precision: float32

Train Loss	Train End Logits Accuracy	Train Start Logits Accuracy	Epoch
5.9801	0.0	0.0	0
5.9338	0.0	0.0	1
5.9744	0.0	0.0	2
5.9011	0.0	0.0	3
5.9892	0.0	0.0	4
6.0409	0.0	0.0	5
5.8902	0.0	0.0	6
5.9480	0.0	0.0	7
6.0100	0.0	0.0	8
6.0898	0.0	0.0	9
5.9093	0.0	0.0	10
5.8435	0.0	0.0	11
5.9528	0.0	0.0	12
5.9702	0.0	0.0	13
6.2079	0.0	0.0	14
6.0707	0.0	0.0	15
6.0218	0.0	0.0	16
5.9175	0.0	0.0	17
5.8957	0.0	0.0	18
5.9021	0.0	0.0	19
6.1419	0.0	0.0	20
6.0310	0.0	0.0	21
5.8559	0.0	0.0	22
5.9768	0.0	0.0	23
6.0752	0.0	0.0	24
6.3935	0.0	0.0	25
6.2257	0.0	0.0	26
6.2152	0.0	0.0	27
6.1603	0.0	0.0	28
6.2708	0.0	0.0	29
5.9893	0.0	0.0	30
5.6298	0.0	0.2857	31
5.9713	0.0	0.0	32
6.1259	0.0	0.0	33
6.0831	0.0	0.0	34
6.1936	0.0	0.0	35
6.1549	0.0	0.0	36
6.1610	0.0	0.0	37
6.1028	0.0	0.0	38
6.3336	0.0	0.0	39
6.1848	0.0	0.0	40
6.1255	0.0	0.0	41
6.0896	0.0	0.0	42
6.2798	0.0	0.0	43
6.2555	0.0	0.0	44
6.3498	0.0	0.0	45
6.1329	0.0	0.0	46
6.1033	0.0	0.0	47
6.1298	0.1429	0.0	48
6.1285	0.0	0.0	49
6.3465	0.0	0.0	50
6.1177	0.0	0.0	51
6.1626	0.0	0.0	52
6.0304	0.0	0.0	53
6.0605	0.1429	0.0	54
5.9403	0.0	0.0	55
5.7870	0.0	0.0	56
6.1490	0.0	0.0	57
5.9711	0.0	0.1429	58
6.0982	0.0	0.0	59
5.7100	0.1429	0.0	60
5.9671	0.0	0.0	61
6.0133	0.0	0.0	62
6.1473	0.0	0.0	63
5.8185	0.0	0.0	64
5.8461	0.0	0.0	65
5.8286	0.1429	0.0	66
6.1176	0.0	0.0	67
6.0289	0.0	0.0	68
6.0143	0.0	0.0	69
6.1875	0.0	0.0	70
6.1716	0.0	0.0	71
5.8779	0.0	0.0	72
6.1317	0.0	0.0	73
6.0170	0.0	0.0	74
6.0243	0.0	0.0	75
5.9871	0.0	0.0	76
6.0451	0.0	0.0	77
6.0820	0.0	0.0	78
6.1378	0.0	0.0	79
6.0649	0.0	0.0	80
5.8903	0.0	0.1429	81