如何对分组数据使用nn.Embeddings？

如何解决如何对分组数据使用nn.Embeddings？

我不熟悉Torch，也不熟悉LSTM，我正在为具有以下数据结构的数据集进行时间序列销售预测： Data Structure

我想对位置进行nn.embeddings（因为我读过它就像是一种热编码），因此，不必为每个位置运行一个单独的模型，而是为所有数据运行一个模型。因此，最终结构应该是这样的

After Embeddings

我应该将哪些参数传递给nn.embeddings？ 鉴于我的数据加载功能如下：

def sliding_windows(data,seq_length):
    x = []
    y = []

    for i in range(len(data)-seq_length-1):
        _x = data[i:(i+seq_length)]
        _y = data[i+seq_length]
        x.append(_x)
        y.append(_y)

    return np.array(x),np.array(y)

sc = MinMaxScaler()
training_data = sc.fit_transform(training_set)

seq_length = 4
x,y = sliding_windows(training_data,seq_length)

y = (y[:,0]).reshape(y[:,0].shape[0],1) 

train_size = int(len(y) * 0.9)
test_size = len(y) - train_size

dataX = Variable(torch.Tensor(np.array(x)))
dataY = Variable(torch.Tensor(np.array(y)))

trainX = Variable(torch.Tensor(np.array(x[0:train_size])))
trainY = Variable(torch.Tensor(np.array(y[0:train_size])))

testX = Variable(torch.Tensor(np.array(x[train_size:len(x)])))
testY = Variable(torch.Tensor(np.array(y[train_size:len(y)])))

，模型如下：

class LSTM(nn.Module):
    def __init__(self,num_classes,input_size,hidden_size,num_layers):
        super(LSTM,self).__init__()
        
        self.num_classes = num_classes
        self.num_layers = num_layers
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.seq_length = seq_length
        
        self.lstm = nn.LSTM(input_size=input_size,hidden_size=hidden_size,num_layers=num_layers,batch_first=True)
        
        self.fc = nn.Linear(hidden_size,num_classes)

    def forward(self,x):
        h_0 = Variable(torch.zeros(
            self.num_layers,x.size(0),self.hidden_size))
        
        c_0 = Variable(torch.zeros(
            self.num_layers,self.hidden_size))
        
        # Propagate input through LSTM
        ula,(h_out,_) = self.lstm(x,(h_0,c_0))
        
        h_out = h_out.view(-1,self.hidden_size)
        
        out = self.fc(h_out)
        
        return out

最后，训练循环如下：

num_epochs = 1000
learning_rate = 0.008

input_size = 4
hidden_size = 4
num_layers = 1

num_classes = 1

lstm = LSTM(num_classes,num_layers)

criterion = torch.nn.MSELoss()    # mean-squared error for regression
optimizer = torch.optim.Adam(lstm.parameters(),lr=learning_rate,weight_decay=0.001)
# Weight_decay to decay the errors == regularization technique
# optimizer = torch.optim.SGD(lstm.parameters(),lr=learning_rate)

# Train the model
for epoch in range(num_epochs):
    outputs = lstm(trainX)
    optimizer.zero_grad()
    
    # obtain the loss function
    loss = criterion(outputs,trainY)
    
    loss.backward()
    
    optimizer.step()
    if epoch % 100 == 0:
      print("Epoch: %d,loss: %1.5f" % (epoch,loss.item()))

对于预测部分我该怎么办？

预先感谢