Basic RNN

文章目录

回顾
RNN
- RNN Cell
- RNNCell的使用
- RNN的使用
RNN例子
- 使用RNN Cell实现
- 使用RNN实现
嵌入层 Embedding
- 独热向量的缺点
- Embedding
LSTM
GRU(门控循环单元)
练习

回顾

DNN（全连接）：和CNN相比，拥有巨大的参数量，CNN权重共享因此参数量小很多。
在这里插入图片描述

RNN

RNN Cell

RNN主要是处理带有时间序列特征的数据（前后文拥有逻辑关系）

自然语言：依赖于词的顺序

以上的RNN cell为同一个线形层（处理一个序列），其实以上是一个循环：

RNN Cell具体计算过程如下：

RNNCell的使用

在这里插入图片描述

假设有以下这些条件：在这里插入图片描述
RNNCell的输入、输出的维度就应该是：

数据集的形状应该是：

seqLen应该放在最前面，方便循环。

#练习1
import torch

batch_size=1
seq_len=3
input_size=4
hidden_size=2

#构建RNNcell，RNNcell本质是一个Linear层
cell=torch.nn.RNNCell(input_size=input_size,hidden_size=hidden_size)

#(seq,batch,feartures)
#产生形状为(seq_len,batch_size,input_size)的序列
dataset= torch.randn(seq_len,batch_size,input_size)

#初始化hidden为0
hidden =torch.zeros(batch_size,hidden_size)

for idx, input in enumerate (dataset):#遍历datset中的序列
    print('='*20,'='*20)
    print('Input size:',input.shape)#[1, 4]

    hidden=cell(input,hidden)#上一个的output作为下一个的hidden

    print('output size:',hidden.shape) #[1, 2]，output size=hidden size,上一个的output作为下一个的hidden
    print(hidden)

结果：

==================== ====================
Input size: torch.Size([1, 4])
output size: torch.Size([1, 2])
tensor([[-0.4549,  0.6699]], grad_fn=<TanhBackward0>)
==================== ====================
Input size: torch.Size([1, 4])
output size: torch.Size([1, 2])
tensor([[-0.7693,  0.1919]], grad_fn=<TanhBackward0>)
==================== ====================
Input size: torch.Size([1, 4])
output size: torch.Size([1, 2])
tensor([[0.2945, 0.8171]], grad_fn=<TanhBackward0>)

RNN的使用

在这里插入图片描述

inputs:全部的输入序列；shape=(𝑠𝑒𝑞𝑆𝑖𝑧𝑒,𝑏𝑎𝑡𝑐ℎ,𝑖𝑛𝑝𝑢𝑡_𝑠𝑖𝑧𝑒)
out:全部的隐层输出;shape=(𝑠𝑒𝑞𝐿𝑒𝑛,𝑏𝑎𝑡𝑐ℎ,ℎ𝑖𝑑𝑑𝑒𝑛_𝑠𝑖𝑧𝑒)
hidden：最后一层的隐层输出;shape=(𝑛𝑢𝑚𝐿𝑎𝑦𝑒𝑟𝑠,𝑏𝑎𝑡𝑐ℎ,ℎ𝑖𝑑𝑑𝑒𝑛_𝑠𝑖𝑧𝑒)

需要的参数：
• 𝑏𝑎𝑡𝑐ℎ𝑆𝑖𝑧𝑒
• 𝑠𝑒𝑞𝐿𝑒𝑛
• 𝑖𝑛𝑝𝑢𝑡𝑆𝑖𝑧𝑒,ℎ𝑖𝑑𝑑𝑒𝑛𝑆𝑖𝑧𝑒,
• 𝑛𝑢𝑚𝐿𝑎𝑦𝑒𝑟𝑠

在这里插入图片描述

同一层的RNN Cell是同一个，以上其实只有3层。

在这里插入图片描述

# 练习2
import torch

batch_size=1
seq_len=3
input_size=4
hidden_size=2
num_layers=1

#Construction of RNN
cell=torch.nn.RNN(input_size,hidden_size,num_layers)
cell1=torch.nn.RNN(input_size,hidden_size,num_layers,batch_first=True)

#(seq,batch,inputSize)
inputs= torch.randn(seq_len,batch_size,input_size)
inputs1=torch.randn(batch_size,seq_len,input_size)

#初始化hidden为0
hidden =torch.zeros(num_layers,batch_size,hidden_size)


out,hidden=cell(inputs,hidden)
# out,hidden=cell1(inputs1,hidden)

print('Output size:',out.shape)#The shape of output is:[𝒔𝒆𝒒𝑺𝒊𝒛𝒆, 𝒃𝒂𝒕𝒄𝒉𝑺𝒊𝒛𝒆, 𝒉𝒊𝒅𝒅𝒆𝒏𝑺𝒊𝒛]
print('Output:',out)
print('Hidden size:',hidden.shape)#The shape of hidden is:[𝒏𝒖𝒎𝑳𝒂𝒚𝒆𝒓𝒔, 𝒃𝒂𝒕𝒄𝒉𝑺𝒊𝒛𝒆, 𝒉𝒊𝒅𝒅𝒆𝒏𝑺𝒊𝒛]
print("Hidden",hidden)

注意：在这里插入图片描述

batch_first=True:输入数据的batch_size需要放在最前面。很多时候batch需要放在最前面。

结果：

Output size: torch.Size([3, 1, 2])
Output: tensor([[[ 0.7220, -0.1743]],
        [[-0.2194, -0.1024]],
        [[ 0.5668, -0.0651]]], grad_fn=<StackBackward0>)
Hidden size: torch.Size([1, 1, 2])
Hidden tensor([[[ 0.5668, -0.0651]]], grad_fn=<StackBackward0>)

RNN例子

训练一个模型将：“hello” ->“ohlol”（seq to seq）

使用RNN Cell实现

在这里插入图片描述

RNNcell的输入应该是一组向量，我们需要将序列进行转换，转换为独热向量（One-Hot Vectors）：

RNNCell结果通过softmax转化为多分类问题，然后计算交叉熵损失。

#练习3 use RNNCell
import torch
# parameters
hidden_size = 4
input_size = 4
batch_size = 1
idx2char = ['e', 'h', 'l', 'o']#字典
x_data = [1, 0, 2, 2, 3]
y_data = [3, 1, 2, 3, 2]
one_hot_lookup=[[1,0,0,0],
                [0,1,0,0],
                [0,0,1,0],
                [0,0,0,1]]
x_one_hot=[one_hot_lookup[x]for x in x_data]#将x的索引转换为独热向量
inputs=torch.Tensor(x_one_hot).view(-1,batch_size,input_size)#(𝒔𝒆𝒒𝑳𝒆𝒏,𝒃𝒂𝒕𝒄𝒉𝑺𝒊𝒛𝒆,𝒊𝒏𝒑𝒖𝒕𝑺𝒊𝒛𝒆)
labels=torch.LongTensor(y_data).view(-1,1)#(𝒔𝒆𝒒𝑳𝒆𝒏,𝟏)
class Model(torch.nn.Module):
    def __init__(self,input_size,hidden_size,batch_size):
        super(Model, self).__init__()
        #初始化参数
        self.batch_size=batch_size
        self.input_size=input_size
        self.hidden_size=hidden_size
        self.rnncell=torch.nn.RNNCell(input_size=input_size,#(𝒃𝒂𝒕𝒄𝒉𝑺𝒊𝒛𝒆,𝒊𝒏𝒑𝒖𝒕𝑺𝒊𝒛𝒆)
                                      hidden_size=hidden_size)#(𝒃𝒂𝒕𝒄𝒉𝑺𝒊𝒛𝒆,𝒉𝒊𝒅𝒅𝒆𝒏𝑺𝒊𝒛𝒆)

    def forward(self, input,hidden):
        hidden=self.rnncell(input,hidden)
        return hidden
    def init_hidden(self):
        return torch.zeros(self.batch_size,self.hidden_size)#初始化隐藏层

net=Model(input_size,hidden_size,batch_size)

criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=0.1)

#训练
for epoch in range(15):
    loss=0
    optimizer.zero_grad()#梯度清零
    hidden=net.init_hidden()
    print("Predicted string:",end='')
    # input:(𝒔𝒆𝒒𝑳𝒆𝒏,𝒃𝒂𝒕𝒄𝒉𝑺𝒊𝒛𝒆,𝒊𝒏𝒑𝒖𝒕𝑺𝒊𝒛𝒆)->input:(𝒃𝒂𝒕𝒄𝒉𝑺𝒊𝒛𝒆,𝒊𝒏𝒑𝒖𝒕𝑺𝒊𝒛𝒆)
    for input,label in zip(inputs,labels):
        hidden=net(input,hidden)#RNNcell
        loss+=criterion(hidden,label)
        _,idx=hidden.max(dim=1)
        print(idx2char[idx.item()],end='')

    loss.backward()#backward
    optimizer.step()#更新

    print(', Epoch [%d/15] loss = %.3f' % (epoch + 1, loss.item()))

结果：

Predicted string:ooool, Epoch [1/15] loss = 5.873
Predicted string:ooool, Epoch [2/15] loss = 5.184
Predicted string:oooll, Epoch [3/15] loss = 5.083
Predicted string:oolll, Epoch [4/15] loss = 4.925
Predicted string:ollll, Epoch [5/15] loss = 4.669
Predicted string:ollll, Epoch [6/15] loss = 4.335
Predicted string:oooll, Epoch [7/15] loss = 4.070
Predicted string:oholl, Epoch [8/15] loss = 3.936
Predicted string:oholl, Epoch [9/15] loss = 3.841
Predicted string:oholl, Epoch [10/15] loss = 3.739
Predicted string:ohlll, Epoch [11/15] loss = 3.635
Predicted string:ohlll, Epoch [12/15] loss = 3.541
Predicted string:ohlll, Epoch [13/15] loss = 3.459
Predicted string:ohlll, Epoch [14/15] loss = 3.380
Predicted string:ohlll, Epoch [15/15] loss = 3.298

使用RNN实现

#练习4 use RNN
import torch
# parameters
input_size = 4
hidden_size = 4
num_layers = 1
batch_size = 1
seq_len = 5
idx2char = ['e', 'h', 'l', 'o']#字典
x_data = [1, 0, 2, 2, 3]
y_data = [3, 1, 2, 3, 2]
one_hot_lookup=[[1,0,0,0],
                [0,1,0,0],
                [0,0,1,0],
                [0,0,0,1]]
x_one_hot=[one_hot_lookup[x]for x in x_data]#将x的索引转换为独热向量
inputs=torch.Tensor(x_one_hot).view(seq_len,batch_size,input_size)#(𝒔𝒆𝒒𝑳𝒆𝒏,𝒃𝒂𝒕𝒄𝒉𝑺𝒊𝒛𝒆,𝒊𝒏𝒑𝒖𝒕𝑺𝒊𝒛𝒆)
labels=torch.LongTensor(y_data)#(𝒔𝒆𝒒𝑳𝒆𝒏×𝒃𝒂𝒕𝒄𝒉𝑺𝒊𝒛𝒆,𝟏)变成二维矩阵，方便使用交叉熵损失计算
class Model(torch.nn.Module):
    def __init__(self,input_size, hidden_size,batch_size,num_layers=1):
        super(Model, self).__init__()
        self.input_size=input_size
        self.hidden_size=hidden_size
        self.batch_size=batch_size
        self.num_layers=num_layers
        self.rnn = torch.nn.RNN(input_size=input_size,
                                hidden_size=hidden_size,
                                num_layers=num_layers)



    def forward(self, input):
        #hidden:(𝒏𝒖𝒎𝑳𝒂𝒚𝒆𝒓𝒔,𝒃𝒂𝒕𝒄𝒉𝑺𝒊𝒛𝒆,𝒉𝒊𝒅𝒅𝒆𝒏𝑺𝒊𝒛𝒆) 初始化隐层
        hidden=torch.zeros(self.num_layers,
                           self.batch_size,
                           self.hidden_size)
        out,_=self.rnn(input,hidden)
        return out.view(-1,self.hidden_size)
        #reshape:(𝒔𝒆𝒒𝑳𝒆𝒏×𝒃𝒂𝒕𝒄𝒉𝑺𝒊𝒛𝒆,𝒉𝒊𝒅𝒅𝒆𝒏𝑺𝒊𝒛𝒆)：变成二维矩阵，方便使用交叉熵损失计算

net = Model(input_size,hidden_size,batch_size,num_layers)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=0.05)
for epoch in range(15):
    #Training step
    optimizer.zero_grad()
    outputs = net(inputs)
    loss = criterion(outputs, labels)
    loss.backward()
    optimizer.step()
    # _,idx = outputs.max(dim=1)
    # idx = idx.data.numpy()
    idx = outputs.argmax(dim=1)
    idx=idx.data.numpy()
    print('Predicted: ', ''.join([idx2char[x] for x in idx]), end='')
    print(', Epoch [%d/15] loss = %.3f' % (epoch + 1, loss.item()))

结果：

Predicted:  eeeee, Epoch [1/15] loss = 1.440
Predicted:  oelll, Epoch [2/15] loss = 1.304
Predicted:  oelll, Epoch [3/15] loss = 1.183
Predicted:  ohlll, Epoch [4/15] loss = 1.084
Predicted:  ohlll, Epoch [5/15] loss = 1.002
Predicted:  ohlll, Epoch [6/15] loss = 0.932
Predicted:  ohlll, Epoch [7/15] loss = 0.865
Predicted:  ohlol, Epoch [8/15] loss = 0.800
Predicted:  ohlol, Epoch [9/15] loss = 0.740
Predicted:  ohlol, Epoch [10/15] loss = 0.693
Predicted:  ohlol, Epoch [11/15] loss = 0.662
Predicted:  ohlol, Epoch [12/15] loss = 0.641
Predicted:  ohlol, Epoch [13/15] loss = 0.625
Predicted:  ohlol, Epoch [14/15] loss = 0.611
Predicted:  ohlol, Epoch [15/15] loss = 0.599

嵌入层 Embedding

独热向量的缺点

维度太高（维度爆炸）
稀疏
硬编码（每个词对应每个向量，不是学习出来的）

那么能不能找到一个变换，把词的编码变成：

低纬
稠密
从数据中学习

Embedding

将高维的、稀疏向量映射到低纬稠密的空间里。（也就是降维）
在这里插入图片描述
假设输入是4维的，嵌入层是5维，则需要构造如下的矩阵：

假设要查找的是2：从矩阵中输出对应那一行数据

torch.nn.Embedding：
- num_embeddings：embbeding size，嵌入层的维度（）
- embedding_dim：每一个输入数据的向量维度（比如说，x1~x5都是4维）

在这里插入图片描述

#练习5 Use Embedding
import torch
# parameters
num_class = 4
input_size = 4
hidden_size = 8
embedding_size = 10
num_layers = 2
batch_size = 1
seq_len = 5
idx2char = ['e', 'h', 'l', 'o']
x_data = [[1, 0, 2, 2, 3]]# (batch, seq_len)
y_data = [3, 1, 2, 3, 2] # (batch * seq_len)
inputs = torch.LongTensor(x_data)
labels = torch.LongTensor(y_data)
class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.emb = torch.nn.Embedding(input_size, embedding_size)
        self.rnn = torch.nn.RNN(input_size=embedding_size,
                                hidden_size=hidden_size,
                                num_layers=num_layers,
                                batch_first=True)

        self.fc = torch.nn.Linear(hidden_size, num_class)
    def forward(self, x):
       # hidden = (torch.zeros(num_layers, x.size(0), hidden_size),torch.zeros(num_layers, x.size(0), hidden_size))#The LSTM requires two hidden states
        hidden=torch.zeros(num_layers, x.size(0), hidden_size)
        x= self.emb(x) # (batch, seqLen, embeddingSize)
        x,states= self.rnn(x, hidden)#返回类型为tuble，切割tubel   by splitting up the tuple so that out is just your output tensor.
                                       #out then stores the hidden states, while states is another tuple that contains the last hidden and cell state.
        x = self.fc(x)
        return x.view(-1, num_class)

net = Model()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=0.05)
for epoch in range(15):
    optimizer.zero_grad()
    outputs = net(inputs)
    loss = criterion(outputs, labels)
    loss.backward()
    optimizer.step()
    # _,idx = outputs.max(dim=1)
    # idx = idx.data.numpy()
    idx = outputs.argmax(dim=1)
    idx=idx.data.numpy()
    print('Predicted: ', ''.join([idx2char[x] for x in idx]), end='')
    print(', Epoch [%d/15] loss = %.3f' % (epoch + 1, loss.item()))

结果：

Predicted: ooooo, Epoch [1/15] loss = 1.441
Predicted: ooooo, Epoch [2/15] loss = 1.148
Predicted: ooool, Epoch [3/15] loss = 1.007
Predicted: olool, Epoch [4/15] loss = 0.884
Predicted: olool, Epoch [5/15] loss = 0.760
Predicted: ohool, Epoch [6/15] loss = 0.609
Predicted: ohlol, Epoch [7/15] loss = 0.447
Predicted: ohlol, Epoch [8/15] loss = 0.313
Predicted: ohlol, Epoch [9/15] loss = 0.205
Predicted: ohlol, Epoch [10/15] loss = 0.135
Predicted: ohlol, Epoch [11/15] loss = 0.093
Predicted: ohlol, Epoch [12/15] loss = 0.066
Predicted: ohlol, Epoch [13/15] loss = 0.047
Predicted: ohlol, Epoch [14/15] loss = 0.033
Predicted: ohlol, Epoch [15/15] loss = 0.024

LSTM

现在常用的memory管理方式叫做长短期记忆(Long Short-term Memory)，简称LSTM
LSTM对信息进行选择性的保留，是通过门控机制进行实现的。（即可以选择保留觉得有用的信息，遗忘觉得没用的信息。）
冷知识：可以被理解为比较长的短期记忆，因此是short-term，而非是long-short term

在这里插入图片描述

官网文档

        self.rnn=torch.nn.LSTM(input_size=embedding_size,
                               hidden_size=hidden_size,
                               num_layers=num_layers,
                               batch_first=True)

LSMT学习能力比RNN强，但是时间复杂度高，训练时间长！

GRU(门控循环单元)

GRU 旨在解决标准 RNN 中出现的梯度消失问题。GRU 也可以被视为 LSTM 的变体，因为它们基础的理念都是相似的，且在某些情况能产生同样出色的结果。
GRU 背后的原理与 LSTM 非常相似，即用门控机制控制输入、记忆等信息而在当前时间步做出预测:

在这里插入图片描述

        self.rnn=torch.nn.GRAU(input_size=embedding_size,
                              hidden_size=hidden_size,
                              num_layers=num_layers,
                              batch_first=True)