NNDL 作业8：RNN - 简单循环网络

1. 使用Numpy实现SRN

代码如下：

import numpy as np

inputs = np.array([[1., 1.],
                   [1., 1.],
                   [2., 2.]])  # 初始化输入序列
print('inputs is ', inputs)

state_t = np.zeros(2, )  # 初始化存储器
print('state_t is ', state_t)

w1, w2, w3, w4, w5, w6, w7, w8 = 1., 1., 1., 1., 1., 1., 1., 1.
U1, U2, U3, U4 = 1., 1., 1., 1.
print('--------------------------------------')
for input_t in inputs:
    print('inputs is ', input_t)
    print('state_t is ', state_t)
    in_h1 = np.dot([w1, w3], input_t) + np.dot([U2, U4], state_t)
    in_h2 = np.dot([w2, w4], input_t) + np.dot([U1, U3], state_t)
    state_t = in_h1, in_h2
    print('a', state_t, in_h1, in_h2)
    output_y1 = np.dot([w5, w7], [in_h1, in_h2])
    output_y2 = np.dot([w6, w8], [in_h1, in_h2])
    print('output_y is ', output_y1, output_y2)
    print('---------------')

运行结果：

2. 在1的基础上，增加激活函数tanh

代码如下：

import numpy as np

inputs = np.array([[1., 1.],
                   [1., 1.],
                   [2., 2.]])  # 初始化输入序列
print('inputs is ', inputs)

state_t = np.zeros(2, )  # 初始化存储器
print('state_t is ', state_t)

w1, w2, w3, w4, w5, w6, w7, w8 = 1., 1., 1., 1., 1., 1., 1., 1.
U1, U2, U3, U4 = 1., 1., 1., 1.
print('--------------------------------------')
for input_t in inputs:
    print('inputs is ', input_t)
    print('state_t is ', state_t)
    in_h1 = np.tanh(np.dot([w1, w3], input_t) + np.dot([U2, U4], state_t))
    in_h2 = np.tanh(np.dot([w2, w4], input_t) + np.dot([U1, U3], state_t))
    state_t = in_h1, in_h2
    output_y1 = np.dot([w5, w7], [in_h1, in_h2])
    output_y2 = np.dot([w6, w8], [in_h1, in_h2])
    print('output_y is ', output_y1, output_y2)
    print('---------------')

运行结果：

3. 分别使用nn.RNNCell、nn.RNN实现SRN

使用torch.nn.RNNCell()实现SRN

代码如下：

import torch

batch_size = 1
seq_len = 3  # 序列长度
input_size = 2  # 输入序列维度
hidden_size = 2  # 隐藏层维度
output_size = 2  # 输出层维度

# RNNCell
cell = torch.nn.RNNCell(input_size=input_size, hidden_size=hidden_size)
# 初始化参数 https://zhuanlan.zhihu.com/p/342012463
for name, param in cell.named_parameters():
    if name.startswith("weight"):
        torch.nn.init.ones_(param)
    else:
        torch.nn.init.zeros_(param)
# 线性层
liner = torch.nn.Linear(hidden_size, output_size)
liner.weight.data = torch.Tensor([[1, 1], [1, 1]])
liner.bias.data = torch.Tensor([0.0])

seq = torch.Tensor([[[1, 1]],
                    [[1, 1]],
                    [[2, 2]]])
hidden = torch.zeros(batch_size, hidden_size)
output = torch.zeros(batch_size, output_size)

for idx, input in enumerate(seq):
    print('=' * 20, idx, '=' * 20)

    print('Input :', input)
    print('hidden :', hidden)

    hidden = cell(input, hidden)
    output = liner(hidden)
    print('output :', output)

运行结果：

使用torch.nn.RNN实现SRN

代码如下：

import torch

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

cell = torch.nn.RNN(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, nonlinearity='relu')
for name, param in cell.named_parameters():  # 初始化参数
    if name.startswith("weight"):
        torch.nn.init.ones_(param)
    else:
        torch.nn.init.zeros_(param)

# 线性层
liner = torch.nn.Linear(hidden_size, output_size)
liner.weight.data = torch.Tensor([[1, 1], [1, 1]])
liner.bias.data = torch.Tensor([0.0])

inputs = torch.Tensor([[[1, 1]],
                       [[1, 1]],
                       [[2, 2]]])
hidden = torch.zeros(num_layers, batch_size, hidden_size)
out, hidden = cell(inputs, hidden)
print('out', out, hidden)

print('Input :', inputs[0])
print('hidden:', 0, 0)
print('Output:', liner(out[0]))
print('--------------------------------------')
print('Input :', inputs[1])
print('hidden:', out[0])
print('Output:', liner(out[1]))
print('--------------------------------------')
print('Input :', inputs[2])
print('hidden:', out[1])
print('Output:', liner(out[2]))

运行结果：

4. 分析“二进制加法” 源代码（选做）

Anyone Can Learn To Code an LSTM-RNN in Python (Part 1: RNN) - i am trask
代码如下：

import copy, numpy as np

np.random.seed(0)


# compute sigmoid nonlinearity
def sigmoid(x):
    output = 1 / (1 + np.exp(-x))
    return output


# convert output of sigmoid function to its derivative
def sigmoid_output_to_derivative(output):
    return output * (1 - output)


# training dataset generation
int2binary = {}
binary_dim = 8

largest_number = pow(2, binary_dim)
binary = np.unpackbits(
    np.array([range(largest_number)], dtype=np.uint8).T, axis=1)
for i in range(largest_number):
    int2binary[i] = binary[i]

# input variables
alpha = 0.1
input_dim = 2
hidden_dim = 16
output_dim = 1

# initialize neural network weights
synapse_0 = 2 * np.random.random((input_dim, hidden_dim)) - 1
synapse_1 = 2 * np.random.random((hidden_dim, output_dim)) - 1
synapse_h = 2 * np.random.random((hidden_dim, hidden_dim)) - 1

synapse_0_update = np.zeros_like(synapse_0)
synapse_1_update = np.zeros_like(synapse_1)
synapse_h_update = np.zeros_like(synapse_h)

# training logic
for j in range(10000):

    # generate a simple addition problem (a + b = c)
    a_int = np.random.randint(largest_number / 2)  # int version
    a = int2binary[a_int]  # binary encoding

    b_int = np.random.randint(largest_number / 2)  # int version
    b = int2binary[b_int]  # binary encoding

    # true answer
    c_int = a_int + b_int
    c = int2binary[c_int]

    # where we'll store our best guess (binary encoded)
    d = np.zeros_like(c)

    overallError = 0

    layer_2_deltas = list()
    layer_1_values = list()
    layer_1_values.append(np.zeros(hidden_dim))

    # moving along the positions in the binary encoding
    for position in range(binary_dim):
        # generate input and output
        X = np.array([[a[binary_dim - position - 1], b[binary_dim - position - 1]]])
        y = np.array([[c[binary_dim - position - 1]]]).T

        # hidden layer (input ~+ prev_hidden)
        layer_1 = sigmoid(np.dot(X, synapse_0) + np.dot(layer_1_values[-1], synapse_h))

        # output layer (new binary representation)
        layer_2 = sigmoid(np.dot(layer_1, synapse_1))

        # did we miss?... if so, by how much?
        layer_2_error = y - layer_2
        layer_2_deltas.append((layer_2_error) * sigmoid_output_to_derivative(layer_2))
        overallError += np.abs(layer_2_error)

        # decode estimate so we can print it out
        d[binary_dim - position - 1] = np.round(layer_2[0][0])

        # store hidden layer so we can use it in the next timestep
        layer_1_values.append(copy.deepcopy(layer_1))

    future_layer_1_delta = np.zeros(hidden_dim)

    for position in range(binary_dim):
        X = np.array([[a[position], b[position]]])
        layer_1 = layer_1_values[-position - 1]
        prev_layer_1 = layer_1_values[-position - 2]

        # error at output layer
        layer_2_delta = layer_2_deltas[-position - 1]
        # error at hidden layer
        layer_1_delta = (future_layer_1_delta.dot(synapse_h.T) + layer_2_delta.dot(
            synapse_1.T)) * sigmoid_output_to_derivative(layer_1)

        # let's update all our weights so we can try again
        synapse_1_update += np.atleast_2d(layer_1).T.dot(layer_2_delta)
        synapse_h_update += np.atleast_2d(prev_layer_1).T.dot(layer_1_delta)
        synapse_0_update += X.T.dot(layer_1_delta)

        future_layer_1_delta = layer_1_delta

    synapse_0 += synapse_0_update * alpha
    synapse_1 += synapse_1_update * alpha
    synapse_h += synapse_h_update * alpha

    synapse_0_update *= 0
    synapse_1_update *= 0
    synapse_h_update *= 0

    # print out progress
    if (j % 1000 == 0):
        print("Error:" + str(overallError))
        print("Pred:" + str(d))
        print("True:" + str(c))
        out = 0
        for index, x in enumerate(reversed(d)):
            out += x * pow(2, index)
        print(str(a_int) + " + " + str(b_int) + " = " + str(out))
        print("------------")

运行结果：

Error:[[3.45638663]]
Pred:[0 0 0 0 0 0 0 1]
True:[0 1 0 0 0 1 0 1]
9 + 60 = 1
------------
Error:[[3.63389116]]
Pred:[1 1 1 1 1 1 1 1]
True:[0 0 1 1 1 1 1 1]
28 + 35 = 255
------------
Error:[[3.91366595]]
Pred:[0 1 0 0 1 0 0 0]
True:[1 0 1 0 0 0 0 0]
116 + 44 = 72
------------
Error:[[3.72191702]]
Pred:[1 1 0 1 1 1 1 1]
True:[0 1 0 0 1 1 0 1]
4 + 73 = 223
------------
Error:[[3.5852713]]
Pred:[0 0 0 0 1 0 0 0]
True:[0 1 0 1 0 0 1 0]
71 + 11 = 8
------------
Error:[[2.53352328]]
Pred:[1 0 1 0 0 0 1 0]
True:[1 1 0 0 0 0 1 0]
81 + 113 = 162
------------
Error:[[0.57691441]]
Pred:[0 1 0 1 0 0 0 1]
True:[0 1 0 1 0 0 0 1]
81 + 0 = 81
------------
Error:[[1.42589952]]
Pred:[1 0 0 0 0 0 0 1]
True:[1 0 0 0 0 0 0 1]
4 + 125 = 129
------------
Error:[[0.47477457]]
Pred:[0 0 1 1 1 0 0 0]
True:[0 0 1 1 1 0 0 0]
39 + 17 = 56
------------
Error:[[0.21595037]]
Pred:[0 0 0 0 1 1 1 0]
True:[0 0 0 0 1 1 1 0]
11 + 3 = 14
------------

5. 实现“Character-Level Language Models”源代码（必做）

翻译Character-Level Language Models 相关内容

The Unreasonable Effectiveness of Recurrent Neural Networks

好的，所以我们对 RNN 是什么、为什么它们非常令人兴奋以及它们是如何工作的有了一个概念。我们现在将把它放在一个有趣的应用程序中：我们将训练 RNN 字符级语言模型。也就是说，我们将给 RNN 一大块文本，并要求它对给定一系列先前字符的序列中下一个字符的概率分布进行建模。这将允许我们一次生成一个字符的新文本。

作为一个工作示例，假设我们只有四个可能的字母“helo”的词汇表，并且想要在训练序列“hello”上训练一个 RNN。该训练序列实际上是 4 个单独训练示例的来源：1. “e”的概率应该可能在“h”的上下文中，2.“l”应该可能在“he”的上下文中，3 . “l” 也应该可能出现在“hel” 的上下文中，最后是 4. “o” 应该很可能出现在“hell” 的上下文中。

step具体来说，我们将使用 1-of-k 编码将每个字符编码为一个向量（即除词汇表中字符索引处的单个 1 外，全为零），并使用函数一次将它们输入 RNN . 然后，我们将观察一系列 4 维输出向量（每个字符一维），我们将其解释为 RNN 当前分配给序列中下一个字符的置信度。这是一个图表：

具有 4 维输入和输出层以及 3 个单元（神经元）的隐藏层的示例 RNN。该图显示了当向 RNN 输入字符“hell”作为输入时，前向传递中的激活。输出层包含 RNN 为下一个字符分配的置信度（词汇是“h,e,l,o”）；我们希望绿色数字高，红色数字低。
例如，我们看到，在第一个时间步，当 RNN 看到字符“h”时，它为下一个字母“h”分配置信度 1.0，将 2.2 分配给字母“e”，-3.0 分配给“l”，以及 4.1也”。由于在我们的训练数据（字符串“hello”）中，下一个正确字符是“e”，我们希望增加它的置信度（绿色）并降低所有其他字母的置信度（红色）。同样，在我们希望网络赋予更大置信度的 4 个时间步长中的每一个时间步长上，我们都有一个期望的目标角色。由于 RNN 完全由可微分运算组成，我们可以运行反向传播算法（这只是微积分中链式法则的递归应用）来确定我们应该向哪个方向调整每个权重以增加正确目标的分数（绿色粗体数字）。然后我们可以执行一个参数更新，在这个梯度方向上微调每个权重。如果我们在参数更新后向 RNN 提供相同的输入，我们会发现正确字符的分数（例如第一个时间步中的“e”）会略高（例如 2.3 而不是 2.2），并且错误字符的分数会略低。然后我们一遍又一遍地重复这个过程，直到网络收敛并且它的预测最终与训练数据一致，因为正确的字符总是被预测到下一个。

更技术性的解释是，我们同时对每个输出向量使用标准 Softmax 分类器（通常也称为交叉熵损失）。RNN 使用小批量随机梯度下降进行训练，我喜欢使用RMSProp或 Adam（每参数自适应学习率方法）来稳定更新。

另请注意，第一次输入字符“l”时，目标是“l”，但第二次输入的目标是“o”。因此，RNN 不能单独依赖输入，必须使用其循环连接来跟踪上下文以完成此任务。

在测试时，我们将一个字符输入 RNN 并获得接下来可能出现的字符的分布。我们从这个分布中采样，并直接反馈给它以获得下一个字母。重复此过程，您正在对文本进行采样！现在让我们在不同的数据集上训练一个 RNN，看看会发生什么。

编码实现该模型
代码如下：

import torch

# 使用RNN 有嵌入层和线性层
num_class = 4  # 4个类别
input_size = 4  # 输入维度是4
hidden_size = 8  # 隐层是8个维度
embedding_size = 10  # 嵌入到10维空间
batch_size = 1
num_layers = 2  # 两层的RNN
seq_len = 5  # 序列长度是5

# 准备数据
idx2char = ['e', 'h', 'l', 'o']  # 字典
x_data = [[1, 0, 2, 2, 3]]  # hello  维度（batch,seqlen）
y_data = [3, 1, 2, 3, 2]  # ohlol    维度 (batch*seqlen)

inputs = torch.LongTensor(x_data)
labels = torch.LongTensor(y_data)


# 构造模型
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)
        x = self.emb(x)  # (batch,seqlen,embeddingsize)
        x, _ = self.rnn(x, hidden)
        x = self.fc(x)
        return x.view(-1, num_class)  # 转变维2维矩阵，seq*batchsize*numclass -》((seq*batchsize),numclass)


model = Model()

# 损失函数和优化器
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.05)  # lr = 0.01学习的太慢

# 训练
for epoch in range(15):
    optimizer.zero_grad()
    outputs = model(inputs)  # inputs是（seq,Batchsize,Inputsize） outputs是(seq,Batchsize,Hiddensize)
    loss = criterion(outputs, labels)  # labels是（seq，batchsize，1）
    loss.backward()
    optimizer.step()

    _, idx = outputs.max(dim=1)
    idx = idx.data.numpy()
    print("Predicted:", ''.join([idx2char[x] for x in idx]), end='')
    print(",Epoch {}/15 loss={:.3f}".format(epoch + 1, loss.item()))

运行结果：

6. 分析“序列到序列”源代码（选做）

h0相当于初始隐状态输入，h是正常的输入，1、2、3、4分别是不同的隐状态进入到下一个RNN Cell中去，由上一个的隐状态向量和当前输入确定当前输出和隐状态向量输出，从而将“hello”翻译成了"ohlol"。
按照这个字典对“hello进行编码”（这里是独热编码）得到的码片序列为：0100 1000 0010 0010 0001从而喂入数据进行运算，喂入之后，通过初始化隐状态向量和当前输入做隐状态向量的更新和当前的一个输出，再进行解码器按照字典对照表解码，从而输出第一个字母，依次类推，从而得到输出序列。

# Model
class Seq2Seq(nn.Module):
    def __init__(self):
        super(Seq2Seq, self).__init__()
        self.encoder = nn.RNN(input_size=n_class, hidden_size=n_hidden, dropout=0.5) # encoder
        self.decoder = nn.RNN(input_size=n_class, hidden_size=n_hidden, dropout=0.5) # decoder
        self.fc = nn.Linear(n_hidden, n_class)
 
    def forward(self, enc_input, enc_hidden, dec_input):
        # enc_input(=input_batch): [batch_size, n_step+1, n_class]
        # dec_inpu(=output_batch): [batch_size, n_step+1, n_class]
        enc_input = enc_input.transpose(0, 1) # enc_input: [n_step+1, batch_size, n_class]
        dec_input = dec_input.transpose(0, 1) # dec_input: [n_step+1, batch_size, n_class]
 
        # h_t : [num_layers(=1) * num_directions(=1), batch_size, n_hidden]
        _, h_t = self.encoder(enc_input, enc_hidden)
        # outputs : [n_step+1, batch_size, num_directions(=1) * n_hidden(=128)]
        outputs, _ = self.decoder(dec_input, h_t)
 
        model = self.fc(outputs) # model : [n_step+1, batch_size, n_class]
        return model
 
model = Seq2Seq().to(device)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

7. “编码器-解码器”的简单实现（必做）

代码如下：

import torch
import numpy as np
import torch.nn as nn
import torch.utils.data as Data

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# S: Symbol that shows starting of decoding input
# E: Symbol that shows starting of decoding output
# ?: Symbol that will fill in blank sequence if current batch data size is short than n_step

letter = [c for c in 'SE?abcdefghijklmnopqrstuvwxyz']
letter2idx = {n: i for i, n in enumerate(letter)}

seq_data = [['man', 'women'], ['black', 'white'], ['king', 'queen'], ['girl', 'boy'], ['up', 'down'], ['high', 'low']]

# Seq2Seq Parameter
n_step = max([max(len(i), len(j)) for i, j in seq_data])  # max_len(=5)
n_hidden = 128
n_class = len(letter2idx)  # classfication problem
batch_size = 3


def make_data(seq_data):
    enc_input_all, dec_input_all, dec_output_all = [], [], []

    for seq in seq_data:
        for i in range(2):
            seq[i] = seq[i] + '?' * (n_step - len(seq[i]))  # 'man??', 'women'

        enc_input = [letter2idx[n] for n in (seq[0] + 'E')]  # ['m', 'a', 'n', '?', '?', 'E']
        dec_input = [letter2idx[n] for n in ('S' + seq[1])]  # ['S', 'w', 'o', 'm', 'e', 'n']
        dec_output = [letter2idx[n] for n in (seq[1] + 'E')]  # ['w', 'o', 'm', 'e', 'n', 'E']

        enc_input_all.append(np.eye(n_class)[enc_input])
        dec_input_all.append(np.eye(n_class)[dec_input])
        dec_output_all.append(dec_output)  # not one-hot

    # make tensor
    return torch.Tensor(enc_input_all), torch.Tensor(dec_input_all), torch.LongTensor(dec_output_all)


'''
enc_input_all: [6, n_step+1 (because of 'E'), n_class]
dec_input_all: [6, n_step+1 (because of 'S'), n_class]
dec_output_all: [6, n_step+1 (because of 'E')]
'''
enc_input_all, dec_input_all, dec_output_all = make_data(seq_data)


class TranslateDataSet(Data.Dataset):
    def __init__(self, enc_input_all, dec_input_all, dec_output_all):
        self.enc_input_all = enc_input_all
        self.dec_input_all = dec_input_all
        self.dec_output_all = dec_output_all

    def __len__(self):  # return dataset size
        return len(self.enc_input_all)

    def __getitem__(self, idx):
        return self.enc_input_all[idx], self.dec_input_all[idx], self.dec_output_all[idx]


loader = Data.DataLoader(TranslateDataSet(enc_input_all, dec_input_all, dec_output_all), batch_size, True)


# Model
class Seq2Seq(nn.Module):
    def __init__(self):
        super(Seq2Seq, self).__init__()
        self.encoder = nn.RNN(input_size=n_class, hidden_size=n_hidden, dropout=0.5)  # encoder
        self.decoder = nn.RNN(input_size=n_class, hidden_size=n_hidden, dropout=0.5)  # decoder
        self.fc = nn.Linear(n_hidden, n_class)

    def forward(self, enc_input, enc_hidden, dec_input):
        # enc_input(=input_batch): [batch_size, n_step+1, n_class]
        # dec_inpu(=output_batch): [batch_size, n_step+1, n_class]
        enc_input = enc_input.transpose(0, 1)  # enc_input: [n_step+1, batch_size, n_class]
        dec_input = dec_input.transpose(0, 1)  # dec_input: [n_step+1, batch_size, n_class]

        # h_t : [num_layers(=1) * num_directions(=1), batch_size, n_hidden]
        _, h_t = self.encoder(enc_input, enc_hidden)
        # outputs : [n_step+1, batch_size, num_directions(=1) * n_hidden(=128)]
        outputs, _ = self.decoder(dec_input, h_t)

        model = self.fc(outputs)  # model : [n_step+1, batch_size, n_class]
        return model


model = Seq2Seq().to(device)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for epoch in range(5000):
    for enc_input_batch, dec_input_batch, dec_output_batch in loader:
        # make hidden shape [num_layers * num_directions, batch_size, n_hidden]
        h_0 = torch.zeros(1, batch_size, n_hidden).to(device)

        (enc_input_batch, dec_intput_batch, dec_output_batch) = (
        enc_input_batch.to(device), dec_input_batch.to(device), dec_output_batch.to(device))
        # enc_input_batch : [batch_size, n_step+1, n_class]
        # dec_intput_batch : [batch_size, n_step+1, n_class]
        # dec_output_batch : [batch_size, n_step+1], not one-hot
        pred = model(enc_input_batch, h_0, dec_intput_batch)
        # pred : [n_step+1, batch_size, n_class]
        pred = pred.transpose(0, 1)  # [batch_size, n_step+1(=6), n_class]
        loss = 0
        for i in range(len(dec_output_batch)):
            # pred[i] : [n_step+1, n_class]
            # dec_output_batch[i] : [n_step+1]
            loss += criterion(pred[i], dec_output_batch[i])
        if (epoch + 1) % 1000 == 0:
            print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()


# Test
def translate(word):
    enc_input, dec_input, _ = make_data([[word, '?' * n_step]])
    enc_input, dec_input = enc_input.to(device), dec_input.to(device)
    # make hidden shape [num_layers * num_directions, batch_size, n_hidden]
    hidden = torch.zeros(1, 1, n_hidden).to(device)
    output = model(enc_input, hidden, dec_input)
    # output : [n_step+1, batch_size, n_class]

    predict = output.data.max(2, keepdim=True)[1]  # select n_class dimension
    decoded = [letter[i] for i in predict]
    translated = ''.join(decoded[:decoded.index('E')])

    return translated.replace('?', '')


print('test')
print('man ->', translate('man'))
print('mans ->', translate('mans'))
print('king ->', translate('king'))
print('black ->', translate('black'))
print('up ->', translate('up'))

运行结果：

---

总结

FNN与CNN都只能单独的取处理一个个的输入，前一个输入和后一个输入是完全没有关系的。但是，某些任务需要能够更好的处理序列的信息，即前面的输入和后面的输入是有关系的。所以为了解决一些这样类似的问题，能够更好的处理序列的信息，RNN就诞生了。

RNN的总括图：

参考链接

完全图解RNN、RNN变体、Seq2Seq、Attention机制 - 知乎 (zhihu.com)
《PyTorch深度学习实践》完结合集_哔哩哔哩_bilibili
Seq2Seq的PyTorch实现 - mathor
一文搞懂RNN（循环神经网络）基础篇 - 知乎