def call(self, x): batch_size, _, _ = x.shape x = self.embeddings(x) # similar to embedding, expand feature dimensions to embedding_size m x = tf.transpose(x, perm=[0, 2, 1]) h_matrix = tf.zeros([batch_size, self.embedding_size, self.obs_len]) for i in tf.range(self.embedding_size): m = tf.reshape(x[:, i, :], shape=[batch_size, 1, -1]) h_m = self.lstm(m)[:, -1, :] for j in tf.range(batch_size): # update h_matrix h_matrix = tf.tensor_scatter_nd_update(h_matrix, [[j, i]], tf.reshape(h_m[j], shape=[1, -1])

这段代码是一个神经网络模型的前向传播过程的一部分。在这个模型中，输入x经过嵌入层(embeddings)进行特征转换，并且通过转置操作将特征维度与时间步维度进行交换。然后，一个全零的矩阵h_matrix被初始化，用于存储每个时间步中的隐藏状态。

接下来，通过循环遍历嵌入维度的每个元素，将x中对应位置的特征提取出来，形成一个新的矩阵m。然后，将m输入到LSTM层中进行计算，并且取LSTM输出序列的最后一个时间步的隐藏状态作为h_m。

在内层循环中，遍历批次中的每个样本，并且通过tf.tensor_scatter_nd_update函数将h_m的值更新到h_matrix中对应位置的元素上。

整个过程的目的是利用嵌入层和LSTM层将输入数据转换为隐藏状态序列，并将每个时间步的隐藏状态存储在h_matrix中。最终，h_matrix可以被用于模型的后续处理或输出。

相关问题

class TimeWindowSequence(Sequence): def __init__(self, x, y, batch_size, window_size): self.x = x self.y = y self.batch_size = batch_size self.window_size = window_size self.window_count = int(np.ceil(x.shape[0] / window_size)) def __len__(self): return int(np.ceil(self.x.shape[0] / self.batch_size)) def __getitem__(self, idx): batch_x = np.zeros((self.batch_size, self.window_size, self.x.shape[1])) batch_y = np.zeros((self.batch_size, self.y.shape[1])) for i in range(self.batch_size): j = idx * self.batch_size + i if j >= self.window_count: break window_x = self.x[j*self.window_size:(j+1)*self.window_size, :] window_y = self.y[j*self.window_size:(j+1)*self.window_size, :] batch_x[i, :window_x.shape[0], :] = window_x batch_y[i, :] = window_y[-1, :] return batch_x, batch_y出现

这段代码中似乎缺少了一个缩进，导致 `return` 语句在 `for` 循环内部。可以尝试将 `return` 语句缩进到 `for` 循环外部，如下所示：

class TimeWindowSequence(Sequence):
    def __init__(self, x, y, batch_size, window_size):
        self.x = x
        self.y = y
        self.batch_size = batch_size
        self.window_size = window_size
        self.window_count = int(np.ceil(x.shape[0] / window_size))

    def __len__(self):
        return int(np.ceil(self.x.shape[0] / self.batch_size))

    def __getitem__(self, idx):
        batch_x = np.zeros((self.batch_size, self.window_size, self.x.shape[1]))
        batch_y = np.zeros((self.batch_size, self.y.shape[1]))
        for i in range(self.batch_size):
            j = idx * self.batch_size + i
            if j >= self.window_count:
                break
            window_x = self.x[j*self.window_size:(j+1)*self.window_size, :]
            window_y = self.y[j*self.window_size:(j+1)*self.window_size, :]
            batch_x[i, :window_x.shape[0], :] = window_x
            batch_y[i, :] = window_y[-1, :]
        return batch_x, batch_y

这样应该就可以解决这个问题了。

class Net(nn.Module): def __init__(self,input_size,hidden_size,num_layers,output_size,batch_size,seq_length) -> None: super(Net,self).__init__() self.input_size=input_size self.hidden_size=hidden_size self.num_layers=num_layers self.output_size=output_size self.batch_size=batch_size self.seq_length=seq_length self.num_directions=1 # 单向LSTM self.lstm=nn.LSTM(input_size=input_size,hidden_size=hidden_size,num_layers=num_layers,batch_first=True) # LSTM层 self.fc=nn.Linear(hidden_size,output_size) # 全连接层 def forward(self,x): # e.g. x(10,3,100) 三个句子，十个单词，一百维的向量,nn.LSTM(input_size=100,hidden_size=20,num_layers=4) # out.shape=(10,3,20) h/c.shape=(4,b,20) batch_size, seq_len = x.size()[0], x.size()[1] # x.shape=(604,3,3) h_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size) c_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size) # output(batch_size, seq_len, num_directions * hidden_size) output, _ = self.lstm(x, (h_0, c_0)) # output(5, 30, 64) pred = self.fc(output) # (5, 30, 1) pred = pred[:, -1, :] # (5, 1) return pred改成python代码

这段代码是一个简单的使用 PyTorch 实现的 LSTM 模型。下面是将其转化为 Python 代码的版本：

import torch
import torch.nn as nn

class Net(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, seq_length):
        super(Net, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.output_size = output_size
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.num_directions = 1  # 单向LSTM

        # LSTM层
        self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True)

        # 全连接层
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        # x.shape=(10,3,100)，三个句子，十个单词，一百维的向量
        # out.shape=(10,3,20)，h/c.shape=(4,b,20)
        batch_size, seq_len = x.size()[0], x.size()[1]
        h_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size)
        c_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size)
        output, _ = self.lstm(x, (h_0, c_0))  # output(batch_size, seq_len, num_directions * hidden_size)
        pred = self.fc(output)  # (5, 30, 1)
        pred = pred[:, -1, :]  # (5, 1)

        return pred