问题

简短的介绍：

我想训练一个将输入x（形状为[n_sample, timestamp, feature]）映射到输出y（形状完全相同）的模型。这就像映射 2 个空间

更长的版本：

我有 2 个 floatndarray形状[n_sample, timestamp, feature]，代表音频文件MFCC的特征。n_sample这 2 个ndarray是 2 个说话者在同一个语料库中的语音，由 DTW 对齐。让我们将这 2 个数组命名x为y. 我想训练一个模型，它预测y[k]给定x[k]的 . 这就像从空间x到空间的映射y，输出必须与输入的形状完全相同

我试过的

这是时间序列问题，所以我决定使用RNN方法。这是我在 PyTorch 中的代码（我在代码中添加了注释。为简单起见，我删除了平均损失的计算）。请注意，我尝试了许多学习率选项，行为仍然相同

类定义

class Net(nn.Module):
    def __init__(self, in_size, hidden_size, out_size, nb_lstm_layers):
        super().__init__()
        self.in_size = in_size
        self.hidden_size = hidden_size
        self.out_size = out_size
        self.nb_lstm_layers = nb_lstm_layers

        # self.fc1 = nn.Linear()
        self.lstm = nn.LSTM(input_size=self.in_size, hidden_size=self.hidden_size, num_layers=self.nb_lstm_layers, batch_first=True, bias=True)
        # self.fc = nn.Linear(self.hidden_size, self.out_size)
        self.fc1 = nn.Linear(self.hidden_size, 128)
        self.fc2 = nn.Linear(128, 128)
        self.fc3 = nn.Linear(128, self.out_size)

    def forward(self, x, h_state):
        out, h_state = self.lstm(x, h_state)
        output_fc = []

        for frame in out:
            output_fc.append(self.fc3(torch.tanh(self.fc1(frame)))) # I added fully connected layer to each frame, to make an output with same shape as input

        return torch.stack(output_fc), h_state

    def hidden_init(self):
        if use_cuda:
            h_state = torch.stack([torch.zeros(nb_lstm_layers, batch_size, 20) for _ in range(2)]).cuda()
        else:
            h_state = torch.stack([torch.zeros(nb_lstm_layers, batch_size, 20) for _ in range(2)])

        return h_state

训练步骤：

net = Net(20, 20, 20, nb_lstm_layers)
optimizer = optim.Adam(net.parameters(), lr=0.0001, weight_decay=0.0001)
criterion = nn.MSELoss()

for epoch in range(nb_epoch):
    count = 0
    loss_sum = 0

    batch_x = None
    for i in (range(len(data))): 
    # data is my entire data, which contain A and B i specify above.
        temp_x = torch.tensor(data[i][0])
        temp_y = torch.tensor(data[i][1])

        for ii in range(0, data[i][0].shape[0] - nb_frame_in_batch*2 + 1): # Create batches 
            batch_x, batch_y = get_batches(temp_x, temp_y, ii, batch_size, nb_frame_in_batch)  
            # this will return 2 tensor of shape (batch_size, nb_frame_in_batch, 20), 
            # with `batch_size` is the number of sample each time I feed to the net, 
            # nb_frame_in_batch is the number of frame in each sample
            optimizer.zero_grad()

            h_state = net.hidden_init()

            prediction, h_state = net(batch_x.float(), h_state)
            loss = criterion(prediction.float(), batch_y.float())

            h_state = (h_state[0].detach(), h_state[1].detach())

            loss.backward()
            optimizer.step()

问题是，损失似乎没有减少而是波动很大，没有明确的行为

请帮我。任何建议将不胜感激。如果有人可以检查我的代码并提供一些评论，那就太好了。
提前致谢！

标签： pythondeep-learninglstmpytorch

python - 实现多对多回归任务

问题描述

问题

简短的介绍：

更长的版本：

我试过的

类定义

训练步骤：

解决方案

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