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Path: blob/master/Natural Language Processing with Attention Models/Week 4 - Chatbot/model/train/events.out.tfevents.1608284936.b0ea9a627a44
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�K" ���A brain.Event:2��>a. ��W� ���A*! metrics/CrossEntropyLosseU'A{�eQJ ��� vo���A*� � gin_configB�B�#### Parameters for Adam: Adam.b1 = 0.9 Adam.b2 = 0.999 Adam.clip_grad_norm = None Adam.eps = 1e-05 Adam.weight_decay_rate = 1e-05 #### Parameters for AddLossWeights: # None. #### Parameters for backend: backend.name = 'jax' #### Parameters for BucketByLength: BucketByLength.length_axis = 0 BucketByLength.length_keys = None BucketByLength.strict_pad_on_len = False #### Parameters for FastGelu: # None. #### Parameters for FilterByLength: FilterByLength.length_axis = 0 FilterByLength.length_keys = None #### Parameters for LogSoftmax: LogSoftmax.axis = -1 #### Parameters for random_spans_helper: # None. #### Parameters for layers.SelfAttention: layers.SelfAttention.attention_dropout = 0.0 layers.SelfAttention.bias = False layers.SelfAttention.chunk_len = None layers.SelfAttention.masked = False layers.SelfAttention.n_chunks_after = 0 layers.SelfAttention.n_chunks_before = 0 layers.SelfAttention.n_parallel_heads = None layers.SelfAttention.predict_drop_len = None layers.SelfAttention.predict_mem_len = None layers.SelfAttention.share_qk = False layers.SelfAttention.use_python_loop = False layers.SelfAttention.use_reference_code = False #### Parameters for SentencePieceVocabulary: # None. #### Parameters for Serial: # None. #### Parameters for Shuffle: Shuffle.queue_size = 1024 #### Parameters for data.Tokenize: # None. #### Parameters for tf_inputs.Tokenize: tf_inputs.Tokenize.keys = None tf_inputs.Tokenize.n_reserved_ids = 0 tf_inputs.Tokenize.vocab_type = 'subword' #### Parameters for Vocabulary: # None. #### Parameters for warmup_and_rsqrt_decay: # None.J text�F?#, ���E +|���A* training/learning_rateĚ'7�S$�/ m]P �|���A*" training/steps per second O�<YUl+ ��K �}���A* training/gradients_l2R�(@�:�Q# ��wC �~���A* training/losseU'A���) 7�_ ���A* training/weights_l2 �E�Hp. ��W� �[����A *! metrics/CrossEntropyLoss��$A� �, ���E ?i����A * training/learning_rateu��8t���/ m]P �i����A *" training/steps per second�]=���~+ ��K �j����A * training/gradients_l2K ,@�Q\�# ��wC �k����A * training/loss��$Ao�T�) 7�_ @l����A * training/weights_l2#�E�ed�