jupyter操作LSTM模型，词向量模型理解

1.jupyter没有torch模块，参考下面链接的解决办法

【jupyter notebook安装配置教程，导入pytorch解决No module named torch-哔哩哔哩】 https://b23.tv/jYGvyVR

2.jupyter中没有某一模块怎么办，可以用pycharm打开一个项目，在该项目中下载所需要的模块，然后jupyter notebook打开这个项目，在同路径下打开ipython文件。

3.LSTM模型的输入，输出与与注意事项。

模型构建，最基本的是五个参数 input_dim 输入维度，即输入的特征的个数 hidden_dim 隐藏层特征的维度 num_layers lstm的连结个数 output_dim 输出层的维度，预测即为1，分类则为分类的个数 num_epochs 迭代的次数，每次计算损失函数，反向回归，优化参数，得出新的预测值，再计算损失函数

python 复制代码

input_dim = 1
hidden_dim =72
num_layers = 3
output_dim = 1
num_epochs = 100
# LSTM 模型定义
class LSTM(nn.Module):
    def __init__(self, input_dim, hidden_dim,num_layers,output_dim):
        super(LSTM, self).__init__()
        self.hidden_dim = hidden_dim
        self.num_layers =num_layers
        self.lstm = nn.LSTM(input_dim, hidden_dim,num_layers,batch_first=True)
        # 全连接层
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        h0=torch.zeros(self.num_layers,x.size(0),self.hidden_dim).requires_grad_()
        c0=torch.zeros(self.num_layers,x.size(0),self.hidden_dim).requires_grad_()
        out,(hn,cn)=self.lstm(x,(h0.detach(),c0.detach()))
        out = self.fc(out[:,-1,:])
        return out

python 复制代码

model = LSTM(input_dim=input_dim,hidden_dim=hidden_dim,output_dim=output_dim,num_layers=num_layers)
criterion = torch.nn.MSELoss()
optimiser = torch.optim.Adam(model.parameters(),lr=0.01)
hist = np.zeros(num_epochs)

python 复制代码

import time
hist = np.zeros(num_epochs)
start_time = time.time()
lstm=[]
for t in range(num_epochs):
    y_train_pred = model(x_train)
    loss = criterion(y_train_pred,y_train_lstm)
    print('EPOCH',t,'MSE',loss.item())
    hist[t]=loss.item()
    optimiser.zero_grad()
    loss.backward()
    optimiser.step()
    
training_time = time.time()-start_time
print(training_time)

4.词向量模型理解