如何使用 pytorch 构建多维自动编码器

Posted 2023-03-12

【中文标题】如何使用 pytorch 构建多维自动编码器

【英文标题】：how to build a multidimensional autoencoder with pytorch

【发布时间】：2019-10-18 15:17:19

【问题描述】：

我遵循了序列自动编码器的这个很好的答案，

LSTM autoencoder always returns the average of the input sequence.

但我在尝试更改代码时遇到了一些问题：

问题一： 您的解释很专业，但问题与我的有点不同，我附上了一些我从您的示例中更改的代码。我的输入特征是二维的，我的输出与输入相同。 例如：

input_x = torch.Tensor([[0.0,0.0], [0.1,0.1], [0.2,0.2], [0.3,0.3], [0.4,0.4]])
output_y = torch.Tensor([[0.0,0.0], [0.1,0.1], [0.2,0.2], [0.3,0.3], [0.4,0.4]])
the input_x and output_y are same, 5-timesteps, 2-dimensional feature.

        import torch
        import torch.nn as nn
        import torch.optim as optim

        class LSTM(nn.Module):
            def __init__(self, input_dim, latent_dim, num_layers):
                super(LSTM, self).__init__()
               self.input_dim = input_dim
                self.latent_dim = latent_dim
                self.num_layers = num_layers
                self.encoder = nn.LSTM(self.input_dim, self.latent_dim, self.num_layers)

                # I changed here, to 40 dimesion, I think there is some problem 
                # self.decoder = nn.LSTM(self.latent_dim, self.input_dim, self.num_layers)
                self.decoder = nn.LSTM(40, self.input_dim, self.num_layers)

            def forward(self, input):
                # Encode
                _, (last_hidden, _) = self.encoder(input)
                # It is way more general that way
                encoded = last_hidden.repeat(input.shape)
                # Decode
                y, _ = self.decoder(encoded)
               return torch.squeeze(y)

        model = LSTM(input_dim=2, latent_dim=20, num_layers=1)
        loss_function = nn.MSELoss()
        optimizer = optim.Adam(model.parameters())
        y = torch.Tensor([[0.0,0.0], [0.1,0.1], [0.2,0.2], [0.3,0.3], [0.4,0.4]])
        x = y.view(len(y), -1, 2)   # I changed here 

        while True:
            y_pred = model(x)
            optimizer.zero_grad()
            loss = loss_function(y_pred, y)
            loss.backward()
            optimizer.step()
            print(y_pred)

上面的代码可以很好的学习，能不能帮忙复习一下代码并给出一些说明。

当我输入 2 个示例作为模型的输入时，模型无法工作：

例如，更改代码：

y = torch.Tensor([[0.0,0.0], [0.1,0.1], [0.2,0.2], [0.3,0.3], [0.4,0.4]])

到：

y = torch.Tensor([[[0.0,0.0],[0.5,0.5]], [[0.1,0.1], [0.6,0.6]], [[0.2,0.2],[0.7,0.7]], [[0.3,0.3],[0.8,0.8]], [[0.4,0.4],[0.9,0.9]]])

当我计算损失函数时，它抱怨一些错误？谁能帮忙看看

问题二： 我的训练样本长度不同： 例如：

x1 = [[0.0,0.0], [0.1,0.1], [0.2,0.2], [0.3,0.3], [0.4,0.4]]   #with 5 timesteps
x2 = [[0.5,0.5], [0.6,0.6], [0.7,0.7]] #with only 3 timesteps

如何将这两个训练样本同时输入到模型中进行批量训练。

【问题讨论】：

第 1 题有哪些错误？

如上所述，我有问题一，问题二，如何实现问题二？谢谢你的回复

【参考方案1】：

循环 N 维自动编码器

首先，LSTM 处理 1D 样本，你的是 2D，因为它通常用于用单个向量编码的单词。

不过不用担心，您可以将此2D 样本展平为1D，例如：

import torch

var = torch.randn(10, 32, 100, 100)
var.reshape((10, 32, -1))  # shape: [10, 32, 100 * 100]

请注意它真的不通用，如果你有3D 输入怎么办？下面的片段将此概念推广到样本的任何维度，前提是前面的维度是 batch_size 和 seq_len：

import torch

input_size = 2

var = torch.randn(10, 32, 100, 100, 35)
var.reshape(var.shape[:-input_size] + (-1,)) # shape: [10, 32, 100 * 100 * 35]

最后，您可以在神经网络中使用它，如下所示。尤其是forward 方法和构造函数参数：

import torch


class LSTM(nn.Module):
    # input_dim has to be size after flattening
    # For 20x20 single input it would be 400
    def __init__(
        self,
        input_dimensionality: int,
        input_dim: int,
        latent_dim: int,
        num_layers: int,
    ):
        super(LSTM, self).__init__()
        self.input_dimensionality: int = input_dimensionality
        self.input_dim: int = input_dim  # It is 1d, remember
        self.latent_dim: int = latent_dim
        self.num_layers: int = num_layers
        self.encoder = torch.nn.LSTM(self.input_dim, self.latent_dim, self.num_layers)
        # You can have any latent dim you want, just output has to be exact same size as input
        # In this case, only encoder and decoder, it has to be input_dim though
        self.decoder = torch.nn.LSTM(self.latent_dim, self.input_dim, self.num_layers)

    def forward(self, input):
        # Save original size first:
        original_shape = input.shape
        # Flatten 2d (or 3d or however many you specified in constructor)
        input = input.reshape(input.shape[: -self.input_dimensionality] + (-1,))

        # Rest goes as in my previous answer
        _, (last_hidden, _) = self.encoder(input)
        encoded = last_hidden.repeat(input.shape)
        y, _ = self.decoder(encoded)

        # You have to reshape output to what the original was
        reshaped_y = y.reshape(original_shape)
        return torch.squeeze(reshaped_y)

请记住，在这种情况下，您必须 reshape 您的输出。它应该适用于任何尺寸。

批处理

当涉及到批处理和不同长度的序列时，情况会稍微复杂一些。

在通过网络推送之前，您必须批量填充每个序列。通常，您填充的值是零，但您可以在 LSTM 中配置它。

您可以查看this link 的示例。您必须使用torch.nn.pack_padded_sequence 等函数才能使其工作，您可以查看this answer。

哦，从 PyTorch 1.1 开始，您无需按长度对序列进行排序即可打包它们。但是当涉及到这个话题时，抓住一些教程，应该会更清楚。

最后：请把你的问题分开。如果您使用单个示例执行自动编码，请继续进行批处理，如果您有问题，请在 *** 上发布一个新问题，谢谢。