RNN Pytorch实现——up主：刘二大人《PyTorch深度学习实践》

b站up主：刘二大人《PyTorch深度学习实践》
教程： https://www.bilibili.com/video/BV1Y7411d7Ys?p=6&vd_source=715b347a0d6cb8aa3822e5a102f366fe
$$\begin{gathered} 单层RNN:torch.nn.RNN+Embedding+FC \\ 交叉熵损失函数：nn.CrossEntropyLoss \\ 优化器：optim.Adam \\ 数据集：hello\to 期望输出ohlol \end{gathered}$$

网络结构：

训练过程：

源码：

import torch

input_size = 4
hidden_size = 4
batch_size = 1
embedding_size = 10
num_class = 4
num_layers = 1

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, input_size, hidden_size, batch_size, num_layers = 1):
    super(Model, self).__init__()
    self.batch_size=batch_size #批量大小
    self.input_size=input_size #freature in X， x_t的维度
    self.hidden_size=hidden_size #hidden层向量的维度 h_t的维度
    self.num_layers=num_layers #层数（上下）
    self.emb = torch.nn.Embedding(input_size, embedding_size)

    self.rnn = torch.nn.RNN(input_size=embedding_size,
              hidden_size=self.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(self.num_layers,
              self.batch_size,
              self.hidden_size)
    x = self.emb(x)
    x,_ = self.rnn(x, hidden)
    x = self.fc(x)
    return x.view(-1, num_class)

net=Model(input_size=input_size, 
    hidden_size=hidden_size,
    batch_size=batch_size,
    num_layers=num_layers)

criterion=torch.nn.CrossEntropyLoss()
optimizer=torch.optim.Adam(net.parameters(), lr=0.05)

for epoch in range(0, 30):
  optimizer.zero_grad()
  outputs = net(inputs)
  loss = criterion(outputs,labels)
  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[%d/30]loss=%.3f'%(epoch,loss.item()))