目前TF的RNN APIs主要集中在tensorflow.models.rnn中的rnn和rnn_cell两个模块。其中,后者定义了一些常用的RNN cells,包括RNN和优化的LSTM、GRU等等;前者则提供了一些helper方法。 创建一个基础的RNN很简单: 1 from tensorflow.models.rnn import rnn_cell 2 3 cell
num_layers= 2#层数hidden_size = [128,256]#每一层的隐节点个数(可以不一样)rnn_cells = []#包含所有层的列表foriinrange(num_layers):#构建一个基本rnn单元(一层)rnn_cell =tf.nn.rnn_cell.BasicRNNCell(lstm_size[i])#可以添加dropoutdrop_cell = tf.nn.rnn_cell.DropoutWrapper(rnn_cell , out...
classMultiRNNCell(RNNCell):def__init__(self,cells,state_is_tuple=True): cells : list of cell 返回一个多层的 cell 关于LSTM, LSTM(peephole), GRU 请见Understanding LSTM Networks
在之前的文章中,我们介绍了 RNN 的基本结构并将其按时间序列展开成 Cells 循环链,称为 RNN cells。下面,我们将揭示单个 RNN Cell 的内部结构和前向传播计算过程。 将其过程分解成多个步骤: 第一步:cell 接受两个输入:x⟨t⟩ 和 a⟨t-1⟩。 第二步:接下来,计算矩阵乘积 ⨂,W_xh 乘 x⟨t⟩...
_units def call(self, inputs, state): """Gated recurrent unit (GRU) with nunits cells.""" if self._gate_linear is None: bias_ones = self._bias_initializer if self._bias_initializer is None: bias_ones = init_ops.constant_initializer(1.0, dtype=inputs.dtype) with vs.variable_scope...
"""Gated recurrent unit (GRU) with nunits cells.""" if self._gate_linear is None: bias_ones = self._bias_initializer if self._bias_initializer is None: bias_ones = init_ops.constant_initializer(1.0, dtype=inputs.dtype) with vs.variable_scope("gates"): # Reset gate and update gate...
ReNet can be defined using any standard RNN cells, such as LSTM and GRU. One limitation is that standard RNN cells were designed for one dimensional sequential data and not for two dimensions like it is the case for image classification. We overcome this limitation by using DARTS to find ...
在之前的文章中,我们介绍了 RNN 的基本结构并将其按时间序列展开成 Cells 循环链,称为 RNN cells。下面,我们将揭示单个 RNN Cell 的内部结构和前向传播计算过程。 将其过程分解成多个步骤: 第一步:cell 接受两个输入:x⟨t⟩和a⟨t-1⟩。
import tensorflow as tf import numpy as np hidden_units=20 rnnLayerNum=1 rnnCells=[] for i in range(rnnLayerNum): rnnCells.append(tf.nn.rnn_cell.BasicRNNCell(num_units=hidden_units)) multiRnnCell=tf.nn.rnn_cell.MultiRNNCell(rnnCells) timesteps=5; batch_size=2 input=tf.placeholder(...
multi_cell=tf.nn.rnn_cell.MultiRNNCell(gru_cells) outputs, states= tf.nn.dynamic_rnn(multi_cell, X, dtype=tf.float32)returnstates[-1][1]elifcell =="GRU":#GRU和LSTM大致相同,但是states[-1]中只包含了短期状态。gru_cells = [tf.nn.rnn_cell.GRUCell(num_units=n_neurons)forlayerinrange...