Returns `model_fn` closure for TPUEstimator.

1.use attention mechanism between query and hidden states, to get weighted sum of hidden state. 2.non-linearity of query and hidden state to

do online prediction. each time make prediction for one instance. you can change to a batch if you want. :param line: a list. element is:

given a list of labels, process it to fixed size('require_size') :param ys_list: a list :return: a list

:param ys_list: a list :return: a list

given a list of labels, process it to fixed size('require_size') :param ys_list: a list :return: a list

:param ys_list: a list :return: a list

:param ys_list: a list :return: a list

:param row: it is a list.length is number of labels. e.g. 2002 :param vocabulary_index2word_label :param result_list :return: a l

:param row: it is a list.length is number of labels. e.g. 2002 :param vocabulary_index2word_label :param result_list :return: a lable

process labels to get fixed size labels given a spense label :param trainY_batch: :return:

:param sentence: string. example:'w17314 w5521 w7729 w767 w10147 w111' :param n_gram: :return:string. example:'w17314 w17314w5521 w17314w

:param sentence: string. example:'w17314 w5521 w7729 w767 w10147 w111' :param n_gram: :return:string. example:'w17314 w17314w5521 w173

:param sentence: string. example:'w17314 w5521 w7729 w767 w10147 w111' :param n_gram: :return:string. example:'w17314 w17314w5521 w17314w

:param sentence: string. example:'w17314 w5521 w7729 w767 w10147 w111' :param n_gram: :return:string. example:'w17314 w17314w5521 w173

input:tokens of query:[batch_size,sequence_length] :return: representation of question:[batch_size,hidden_size]

RNN decoder for the sequence-to-sequence model. Args: decoder_inputs: A list of 2D Tensors [batch_size x input_size].it is decoder input.

run rnn for story to get last hidden state input is: story: [batch_size,story_length,embed_size] :return:

scaled dot product attention :param Q: query. shape:[batch,sequence_length,d_model] :param K_s: keys. shape:[batch,sequ

Serializes this instance to a Python dictionary.

based on the loss, use SGD to update parameter

based on the loss, use SGD to update parameter

based on the loss, use SGD to update parameter

based on the loss, use SGD to update parameter

based on the loss, use SGD to update parameter

based on the loss, use SGD to update parameter

based on the loss, use SGD to update parameter

based on the loss, use SGD to update parameter

based on the loss, use SGD to update parameter

based on the loss, use SGD to update parameter

based on the loss, use SGD to update parameter

based on the loss, use SGD to update parameter

based on the loss, use SGD to update parameter

based on the loss, use SGD to update parameter

based on the loss, use SGD to update parameter

based on the loss, use SGD to update parameter

based on the loss, use SGD to update parameter

convert to multi-hot style :param label_list: e.g.[0,1,4], here 4 means in the 4th position it is true value(as indicate by'1') :param la

Multi-headed, multi-layer Transformer from "Attention is All You Need". This is almost an exact implementation of the original Transformer encoder.

init all hyperparameter here

init all hyperparameter here

init all hyperparameter here

init all hyperparameter here

Constructs a BasicTokenizer. Args: do_lower_case: Whether to lower case the input.

Constructs a AdamWeightDecayOptimizer.

Constructs a InputExample. Args: guid: Unique id for the example. text_a: string. The untokenized text of the first sequence. For sing

Constructs BertConfig. Args: vocab_size: Vocabulary size of `inputs_ids` in `BertModel`. hidden_size: Size of the encoder layers and