原因是使用Adam优化函数时,Adam函数会创建一个Adam变量,目的是保存你使用tensorflow创建的graph中的每个可训练参数的动量,

words/_word_embeddings:0

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bi-lstm_secondLayer/bidirectional_rnn/bw/lstm_cell/bias:0

proj_secondLayer/W:0

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train_step_secondLayer/beta1_power:0

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train_step_secondLayer/words/_word_embeddings/Adam:0

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train_step_secondLayer/bi-lstm/bidirectional_rnn/fw/lstm_cell/kernel/Adam:0

train_step_secondLayer/bi-lstm/bidirectional_rnn/fw/lstm_cell/kernel/Adam_1:0

train_step_secondLayer/bi-lstm/bidirectional_rnn/fw/lstm_cell/bias/Adam:0

train_step_secondLayer/bi-lstm/bidirectional_rnn/fw/lstm_cell/bias/Adam_1:0

train_step_secondLayer/bi-lstm/bidirectional_rnn/bw/lstm_cell/kernel/Adam:0

train_step_secondLayer/bi-lstm/bidirectional_rnn/bw/lstm_cell/kernel/Adam_1:0

train_step_secondLayer/bi-lstm/bidirectional_rnn/bw/lstm_cell/bias/Adam:0

train_step_secondLayer/bi-lstm/bidirectional_rnn/bw/lstm_cell/bias/Adam_1:0

train_step_secondLayer/proj/W/Adam:0

train_step_secondLayer/proj/W/Adam_1:0

train_step_secondLayer/proj/b/Adam:0

train_step_secondLayer/proj/b/Adam_1:0

train_step_secondLayer/bi-lstm_secondLayer/bidirectional_rnn/fw/lstm_cell/kernel/Adam:0

train_step_secondLayer/bi-lstm_secondLayer/bidirectional_rnn/fw/lstm_cell/kernel/Adam_1:0

train_step_secondLayer/bi-lstm_secondLayer/bidirectional_rnn/fw/lstm_cell/bias/Adam:0

train_step_secondLayer/bi-lstm_secondLayer/bidirectional_rnn/fw/lstm_cell/bias/Adam_1:0

train_step_secondLayer/bi-lstm_secondLayer/bidirectional_rnn/bw/lstm_cell/kernel/Adam:0

train_step_secondLayer/bi-lstm_secondLayer/bidirectional_rnn/bw/lstm_cell/kernel/Adam_1:0

train_step_secondLayer/bi-lstm_secondLayer/bidirectional_rnn/bw/lstm_cell/bias/Adam:0

train_step_secondLayer/bi-lstm_secondLayer/bidirectional_rnn/bw/lstm_cell/bias/Adam_1:0

train_step_secondLayer/proj_secondLayer/W/Adam:0

train_step_secondLayer/proj_secondLayer/W/Adam_1:0

train_step_secondLayer/proj_secondLayer/b_secondLayer/Adam:0

train_step_secondLayer/proj_secondLayer/b_secondLayer/Adam_1:0

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