1})\\y_{t}=W_{O}h_{t}\end{aligned} $$ $$ \begin{array}{c}y\\ \xrightarrow{RNN}y\\ \xrightarrow{RNN}y\\ \xrightarrow{RNN}y\\ x_{0}\\ x_{1}\\ x_{2}\end{array} $$ $$ \frac{\partial E_{t}}{\partial \frac{\partial h_{k}}{\partial h_{1}}=\prod_{i}^{k}diag(f^{\prime}(W_{I}x_{i}+W_{R}h_{i-1}))W_{R} $$ ## 下一课时 RNN Layer使用 ## Thank You

## PyTorch ## RNN训练难题 主讲人：龙良曲  ## Simple Yet? Nothing is straightforward.   #### RNN V.S. LSTM Gradient Visualization ![Image](/uploads/documents/a/b/d/0/abd0d9394eecc78a1f9d9c6dec4ff1e4/p10_1

## PyTorch ## RNN Layer使用 主讲人：龙良曲 ## Folded model [batch, feature len]@[hidden len, feature len] $ ^{T} $ +[batch, hidden len]@[hidden len, hidden len] $ ^{T} $ [0,0,0 ... ] $$ x_{t}@w_{xh} + h_{t}@w_{hh} In [13]: rnn=nn.RNN(100, 10) In [12]: rnn._parameters.keys() Out[12]: odict_keys(['weight_ih_10', 'weight_hh_10', 'bias_ih_10', 'bias_hh_10']) In [15]: rnn.weight_hh_10 shape, rnn.weight_ih_10.shape Out[15]: (torch.Size([10, 10]), torch.Size([10, 10])) In [16]: rnn.bias_hh_10.shape, rnn.bias_ih_10.shape Out[16]: (torch.Size([10]), torch.Size([10])) ### nn.RNN ## ___init___

num_time_steps - 1, 1) ## ☀️ ☁️ ☁️ Network def forward(self, x, hidden_prev): out, hidden_prev = self.rnn(x, hidden_prev) # [1, seq, h] => [seq, h] out = out.view(-1, hidden_size) out = self _1.jpg)  ## 下一课时 RNN训练难题 ## Thank You

Ignore Ignore  RNN The RNN food ## 👍 🔧 Linear Classifier good ## Sentiment Classification 👍 🔧 Linear Classifier pos ## ☐ ☐ ☐ Network class RNN(nn.Module): def __init__(self, vocab_size, embedding_dim, hidden_dim): super(RNN, self).__init__() # [0-10001] => embedding = nn.Embedding(vocab_size, embedding_dim) # [100] => [256] self.rnn = nn.LSTM(embedding_dim, hidden_dim, num_layers=2, bidirectional=True, dropout=0.5)

序列模型概述 02 循环神经网络(RNN) 03 长短期记忆(LSTM) 04 双向循环神经网络 05 深层循环神经网络 ### 1. 序列模型概述 ## 01 序列模型概述 02 循环神经网络(RNN) 03 长短期记忆(LSTM) 04 双向循环神经网络 05 深层循环神经网络 ### 1. 序列模型概述 循环神经网络（RNN）之类的模型在语音识别、自然语言处理和其他领域中引起变革。 循环神经网络(RNN) 01 序列模型概述 02 循环神经网络(RNN) 03 长短期记忆(LSTM) 04 双向循环神经网络 05 深层循环神经网络 ### 2. 循环神经网络(RNN) ## RNN的前向传播 $$ a^{<0>}=0 $$ $$ a^{(0)}\xrightarrow{RNN-cell}a^{(1)}\xrightarrow{RNN-cel l}a^{(2)}\xrightarrow{RNN-cell}a^{(3)}\xrightarrow{RNN-cell}a^{(3)} $$ $$ \begin{aligned}a^{<1>}=&g_{1}(W_{aa}a^{<0>}+W_{ax}x^{<1>}+b_{a})\\&\hat{y}^{<1>}=g_{2}(W_{y

understand its predecessor, Recurrent Neural Network or RNN which, unlike attention, doesn't have the flexibility to look at the entire text sequence. A RNN contains a recurrent cell which operates on an input problem mentioned earlier, each news article can be represented as a sequence of words. Hence, an RNN with a softmax classifier stacked on top is a good choice to solve this problem. ![Image](/upload sequences respectively. This problem requires two RNN networks namely: an encoder network and a decoder network as shown in figure 4-15. The encoder RNN transforms the english sequence x to a latent representation

[Image](/uploads/documents/8/8/8/7/8887628e2d8fc15bf7c4dbcb3b6b5199/p15_1.jpg) Figure 7-5: The architecture of an RNN controller for NAS. Each time step t outputs a token p. The output token is fed as input to the next problem. Given a set of actions $ a_{1..T} $ which produce a child network with an accuracy R, an RNN controller maximizes the expected reward (accuracy) $ J(\theta_{c}) $ represented as follows: $$ which provide a good accuracy-latency tradeoff. Overall, it still followed the fundamental design of a RNN based controller similar to its predecessors. The idea to design block and cell structures by predicting

32 3.3.13 如何在每个 epoch 后记录训练集和验证集的误差和准确率? ..... 32 3.3.14 如何「冻结」网络层? ..... 33 3.3.15 如何使用有状态 RNN (stateful RNNs)? ..... 33 3.3.16 如何从 Sequential 模型中移除一个层? ..... 34 3.3.17 如何在 Keras 中使用预训练的模型? [source] ..... 86 5.5.2 LocallyConnected2D [source] ..... 87 5.6 循环层 Recurrent ..... 89 5.6.1 RNN [source] ..... 89 5.6.2 SimpleRNN [source] ..... 91 5.6.3 GRU [source] ..... 92 5.6.4 LSTM [source] 在验证集的误差不再下降时，如何中断训练？ · 验证集划分是如何计算的？ · 在训练过程中数据是否会混洗？ 如何在每个 epoch 后记录训练集和验证集的误差和准确率？ · 如何「冻结」网络层？ • 如何使用有状态 RNN (stateful RNNs)? - 如何从 Sequential 模型中移除一个层？ • 如何在 Keras 中使用预训练的模型？ • 如何在 Keras 中使用 HDF5 输入？ • Keras

第11章 循环神经网络 11.1 序列表示方法 11.2 循环神经网络 11.3 梯度传播 11.4 RNN 层使用方法 11.5 RNN 情感分类问题实战 11.6 梯度弥散和梯度爆炸 11.7 RNN 短时记忆 11.8 LSTM 原理 11.9 LSTM 层使用方法 11.10 GRU 简介 的任务。循环神经网络(Recurrent Neural Network，简称 RNN)在 Yoshua Bengio、Jürgen Schmidhuber 等人的持续研究下，被证明非常擅长处理序列信号。1997 年，Jürgen Schmidhuber 提出了 LSTM 网络，作为 RNN 的变种，它较好地克服了 RNN 缺乏长期记忆、不擅长处理长序列的问题，在自然语言处理中得到了广泛的应用。基于 Seq2Seq 模型，并成功商用于谷歌神经机器翻译系统(GNMT)。其他的 RNN 变种还有 GRU、双向 RNN 等。我们将在第 11 章详细介绍循环神经网络原理。 #### 6.7.3 注意力(机制)网络 RNN 并不是自然语言处理的最终解决方案，近年来随着注意力机制(Attention Mechanism)的提出，克服了 RNN 训练不稳定、难以并行化等缺陷，在自然语言处理和图片生成等领域中逐渐崭露头角，甚至基于自注意力