nnGRUCell是单个step，可以更灵活使用
bias_initializer:偏置的初始化矩阵,可以不配置,默认为None 这两个参数都是在实现GRU内部机制时需要用到的(call函数中使用的),同时也是需要被训练的参数。 以最简单的方式调用GRU: import tensorflow as tf import numpy as np gru_cell = tf.nn.rnn_cell.GRUCell(num_units=128) print(gru_cell.state_size)...
在python中使用pnnx转换含有nn.GRUCell模块的模型出现如下报错: v_9 = aten::unsafe_chunk(v_8, v_6, v_7) ^ SyntaxError: invalid syntaxSign up for free to join this conversation on GitHub. Already have an account? Sign in to comment ...
链1接1: 单个RNN单元可以使用torch.nn.RNNCell(), LSTMCell(), GRUCell() ——使用for循环来处理循环神经网络(时间维度) 可以直接调用torch.nn.RNN(), LSTM(), GRU() rnn = nn.LSTM(10, 20, 2) # 初始化LSTM, 输入x的特征数=10, 输出隐藏状态的特征数=20, LSTM的层数=2 input = torch.randn(...
GRUCell classtorch.nn.GRUCell(input_size,hidden_size,bias=True)[source] A gated recurrent unit (GRU) cell r=σ(Wirx+bir+Whrh+bhr)z=σ(Wizx+biz+Whzh+bhz)n=tanh(Winx+bin+r∗(Whnh+bhn))h′=(1−z)∗n+z∗h\begin{array}{ll} r = \sigma(W_{ir} x + b_{ir} + ...
e_cell = nn.GRUCell(D_p,D_e) if listener_state: self.l_cell = nn.GRUCell(D_m+D_p,D_p) self.dropout = nn.Dropout(dropout) if context_attention=='simple': self.attention = SimpleAttention(D_g) else: self.attention = MatchingAttention(D_g, D_m, D_a, context_attention) ...
context = Variable(torch.zeros(layers, batch_size, cell.hidden_size))ifconfig.CONFIG.cuda: hidden = hidden.cuda() context = context.cuda()returnhidden, contextifisinstance(cell, (nn.GRU, nn.GRUCell)): hidden = Variable(torch.zeros(layers, batch_size, cell.hidden_size))ifconfig.CONFIG.cud...
GRUCell.weight_ih: [3*hidden_size, input_size]GRUCell.weight_hh: [3*hidden_size, hidden_size...
cells = [tf.nn.rnn_cell.GRUCell(num_units=n) for n in num_units] stacked_rnn_cell = tf.nn.rnn_cell.MultiRNNCell(cells) rnn_outputs, states = tf.nn.dynamic_rnn(stacked_rnn_cell, X, dtype=tf.float32) 1. 2. 3. 4. 最后一个cell的num_units必须和下面的n_neurons相同 ...