尽管Transformer与RNN在概念上被认为有着显著的不同——Transformer能够直接访问序列中的每个标记,而RNN则通过维护先前输入的递归状态来处理信息——我们在本工作中展示了,仅包含解码器的Transformer实际上可以被概念化为无限多状态的RNN(MSRNN),这是一种具有无限隐藏状态大小的RNN变体。随着每个解码步骤中先前标记数量的...
In other words, the RNNs may have finite modes, and the modes may switch (or jump) from one to another at different times. Recently, it has been shown in [14] that, the switching (or ...Bolle D,Dupont P,Vinck B.On the overlap dynamics of multi-state neural networks with a ...
一般来说,它们通过规则积累回合信念或通过各种递归神经网络建立转向模型信息(RNN)。 虽然这些基于 RNN 的方法依次建模对话,但它们通常将整个回合的话语直接传递给包含大部分噪声的 RNN,并导致不令人满意的性能。 主要贡献 本文提出在 后端数据 的帮助下,通过对对话回合的推理,逐步跟踪对话状态。实证结果表明...
cell= rnn.LSTMCell(num_hidden, reuse=tf.get_variable_scope().reuse)returnrnn.DropoutWrapper(cell, output_keep_prob=keep_prob)#第五步:使用tf.contrib.rnn.MultiRNNCellmlstm_cell = tf.contrib.rnn.MultiRNNCell([create_rnn_layer()for_inrange(lstm_num)], state_is_tuple=True)#第六步:使用mls...
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The proposed method was compared with ARIMA, ETS, ANN, k-nearest neighbors (KNN), recurrent neural network (RNN), support vector machine (SVM), and single-layer LSTM using demand data of a furniture company. The experimental results indicated that the proposed method is superior among the ...
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lstm_cell = rnn_cell.BasicLSTMCell(hidden_size, forget_bias=0.0) cell = rnn_cell.MultiRNNCell([lstm_cell] * 2) 什么是cell.state_size? 我得到的尺寸是 30 x 800,但我不明白它是怎么来的? PS:引用https://github.com/tensorflow/tensorflow/blob/97f585d506cccc57dc98f234f4d5fcd824dd3c03/ten...
Next, it’s fed into an “encoder” which can be any neural network. Of course, you want to use anRNN(recurrent neural network) or a Transformer for the best performance in natural language processing. *ERNIE 2.0 uses a transformer with the same settings as BERT and XLNET. ...
The LARNN Cell Note that the positional encoding is concatenated rather than added. Also, the ELU activation is used in the cell. There is also batch normalization at many places (not drawn). The Multi-Head Attention Mechanism uses an ELU activation rather than unactivated Linears, for the ...