LSTM Layer : 在定义LSTM层时,我们保持Batch First = True和隐藏单元的数量= 512。 1 # initializing the hidden state to 0 2 hidden=None 3 lstm = nn.LSTM(input_size=embedding_dim, hidden_size=512, num_layers=1, batch_first=True) 4 lstm_out, h = lstm(embeds_out, hidden) 5 print ('L...
https://github.com/karpathy/char-rnn/blob/master/model/LSTM.lua, and Brendan Shillingford. Usage: local rnn = LSTM(input_size, rnn_size, n, dropout, bn) ]]-- require 'nn' require 'nngraph' local function LSTM(input_size, rnn_size, n, dropout, bn) dropout = dropout or 0 -- the...
高频数据下股票波动率预测 ———基于Realized GARCH与LSTM的混合模型 股市是一把"双刃剑",既可加速我国社会经济的发展,提升我国的总体经济实力,又因具有强烈的波动效应而存在较高的金融风险.我国股市在2015年和2018年发生过两次重大的股... 张颖芝 - 浙江工商大学 被...
基于AQPSO-LSTM-BN的APU故障诊断模型 辅助动力装置(Auxiliary Power Unit,APU)作为飞机的重要装置,不仅可以保证飞机安全启动,在飞机停在地面时,还为飞机供气、供电,保证客舱舒适性。因此,对飞机APU进行故障诊断研究显得尤为重要。 APU故障发生时,排故人员会结合故障发生的现场和自身的相关经验、故障手册的规定等对故障情...
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我想定义一个多层LSTM_cell,需要对每层输出增加一个BN层和激活函数,代码如下: def get_lstm_cell(rnn_size,keep_prob): lstm_cell = tf.contrib.rnn.LSTMCell(rnn_size, initializer=tf.truncated_normal_initializer(stddev=0.1,seed=2)) lstm_cell = tf.layers.batch_normalization(lstm_cell,training=True)...
A recurrent attention module consisting of an LSTM cell which can query its own past cell states by the means of windowed multi-head attention. The formulas are derived from the BN-LSTM and the Transformer Network. The LARNN cell with attention can be easily used inside a loop on the cell...
The adaptive network mainly consists of two LSTM layers followed by a pair of batch normalization (BN) layers, a dropout layer and a binary classifier. In order to capture the important profit points, we propose to use an adaptive cross-entropy loss function that enhances the prediction ...
However, the traditional LSTM financial diagnosis model has the disadvantage of low accuracy; the specific reason is that the LSTM model has the problems of overfitting and gradient disappearance in risk diagnosis. Therefore, Dropout is adopted to solve the overfitting problem i...
LSTM 一个layer,用来获取 forget gate 的比例,激活函数是 sigmoid。用来计算前一个 cell 有多少部分被保留了。 两个layer 左边为 input gate 的比例(sigmoid),说明有多少比例可以被输入 右边为从xtxt输入数据的部分(tanh) 两者相乘,表示有多少xtxt被输入进去 ...