train_res_recon_error_smooth = savgol_filter(train_res_recon_error, 201, 7)

这是一个使用Savitzky-Golay滤波器对train_res_recon_error数据进行平滑处理的代码。具体来说，它使用了一个窗口大小为201个数据点和一个多项式阶数为7的滤波器来平滑数据，并将结果存储在train_res_recon_error_smooth中。这种平滑处理通常用于减少噪声和提高信号的可读性。

相关问题

def train(images, labels, epoch, training_mode): with tf.GradientTape() as tape: if training_mode == 'full': predictions = bc_model(images) elif training_mode == 'latent': z, _, _ = cmvae_model.encode(images) predictions = bc_model(z) elif training_mode == 'reg': z = reg_model(images) predictions = bc_model(z) recon_loss = tf.reduce_mean(compute_loss(labels, predictions)) gradients = tape.gradient(recon_loss, bc_model.trainable_variables) optimizer.apply_gradients(zip(gradients, bc_model.trainable_variables)) train_loss_rec_v(recon_loss)

这段代码实现了一个模型的训练过程，训练的是一个基于条件变分自编码器（CMVAE）的卷积神经网络（CNN），可以在完全训练模式、潜变量训练模式和正则化训练模式下进行。这里的`bc_model`是一个卷积神经网络模型，`cmvae_model`是一个条件变分自编码器模型，`reg_model`是一个正则化模型。`compute_loss`是一个计算损失函数的函数。

在训练过程中，首先根据训练模式（`training_mode`）选择不同的模型进行预测（`predictions = bc_model(images)`），然后计算预测结果与标签数据的损失（`recon_loss = tf.reduce_mean(compute_loss(labels, predictions))`）。接着，使用`tf.GradientTape`记录损失函数对模型参数的梯度，然后使用优化器（`optimizer`）对模型参数进行更新，使得模型能够更好地拟合数据。最后，将损失函数值记录在一个变量中（`train_loss_rec_v(recon_loss)`），以便进行可视化和监控训练过程中的损失变化。

现已用pytorch代码写好了VAE的两个编码器：1个3层的MLP对形状为（16968，12，307，2）的v_w数据集进行速度和星期特征的相关性计算，另一个是2层的GAT对形状为（16968，12，307，2）的v_d数据集进行速度和度特征的相关性计算。分别编码后送入进行拼接计算隐变量均值和方差，再将隐变量计算送入解码器得到recon_x。最后将这个recon_x和真实的x送入2维卷积构成的GRU模块生成最终的预测值。上述模块均已构建好，请你按照这个思路，帮我用pytorch定义一个def epoch_train_with_batch(train_flow_input, train_target, batch_size):，最终返回return sum(train_loss)/len(train_loss),sum(train_loss_vae)/len(train_loss_vae)

好的，根据你的描述，我尝试编写了一个`epoch_train_with_batch`函数，如下所示：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
from torch.nn.utils.rnn import pack_padded_sequence
import numpy as np

# 定义VAE编码器
class MLP(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(MLP, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

class GAT(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(GAT, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

class VAE(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(VAE, self).__init__()
        self.encoder1 = MLP(input_size[2]*input_size[3], hidden_size, output_size)
        self.encoder2 = GAT(input_size[2]*input_size[3], hidden_size, output_size)
        self.fc1 = nn.Linear(2*output_size, output_size)
        self.fc21 = nn.Linear(output_size, output_size)
        self.fc22 = nn.Linear(output_size, output_size)
        self.fc3 = nn.Linear(output_size, 2*output_size)
        self.decoder = nn.Linear(output_size, input_size[2]*input_size[3])

    def encode(self, x1, x2):
        h1 = self.encoder1(x1.view(-1, x1.shape[2]*x1.shape[3]))
        h2 = self.encoder2(x2.view(-1, x2.shape[2]*x2.shape[3]))
        h = torch.cat([h1, h2], dim=1)
        h = torch.relu(self.fc1(h))
        return self.fc21(h), self.fc22(h)

    def reparameterize(self, mu, logvar):
        std = torch.exp(0.5*logvar)
        eps = torch.randn_like(std)
        return eps.mul(std).add_(mu)

    def decode(self, z):
        h = torch.relu(self.fc3(z))
        h = self.decoder(h)
        return h.view(-1, input_size[2], input_size[3])

    def forward(self, x1, x2):
        mu, logvar = self.encode(x1, x2)
        z = self.reparameterize(mu, logvar)
        return self.decode(z), mu, logvar

# 定义GRU模块
class GRU(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers):
        super(GRU, self).__init__()
        self.gru = nn.GRU(input_size, hidden_size, num_layers, batch_first=True)
        self.fc1 = nn.Linear(hidden_size, 2)
        self.conv = nn.Conv2d(1, 1, (2,2))

    def forward(self, x):
        h, _ = self.gru(x) # h shape: (batch_size, seq_len, hidden_size)
        h = self.fc1(h[:, -1, :]) # 取最后一个时间步的输出
        h = h.unsqueeze(1) # h shape: (batch_size, 1, 2)
        h = self.conv(h) # h shape: (batch_size, 1, 1, 1)
        return h.view(-1)

def epoch_train_with_batch(train_flow_input, train_target, batch_size):
    # 超参数
    hidden_size = 128
    latent_dim = 32
    num_epochs = 10
    learning_rate = 0.001

    # 数据处理
    train_dataset = TensorDataset(torch.Tensor(train_flow_input), torch.Tensor(train_target))
    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

    # 模型定义
    model = VAE(train_flow_input.shape, hidden_size, latent_dim)
    gru_model = GRU(latent_dim, 64, 2)

    # 损失函数和优化器
    criterion_vae = nn.MSELoss()
    criterion_gru = nn.MSELoss()
    optimizer = optim.Adam(list(model.parameters()) + list(gru_model.parameters()), lr=learning_rate)

    # 训练循环
    train_loss = []
    train_loss_vae = []
    for epoch in range(num_epochs):
        for i, (x, y) in enumerate(train_loader):
            optimizer.zero_grad()
            x1 = x[:, :, :, 0] # 取速度特征
            x2 = x[:, :, :, 1] # 取星期特征
            recon_x, mu, logvar = model(x1, x2)
            loss_vae = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
            loss_vae /= batch_size * train_flow_input.shape[1]
            loss = criterion_vae(recon_x, x1) + loss_vae
            loss.backward()
            optimizer.step()
            train_loss.append(loss.item())
            train_loss_vae.append(loss_vae.item())

            # 计算GRU模型的损失
            z = gru_model(mu.unsqueeze(0))
            loss_gru = criterion_gru(z, y)
            optimizer.zero_grad()
            loss_gru.backward()
            optimizer.step()

    return sum(train_loss)/len(train_loss), sum(train_loss_vae)/len(train_loss_vae)

这段代码定义了一个`VAE`模型和一个`GRU`模型，分别用于特征编码和序列预测。训练循环中，首先对于每个batch，计算VAE模型的损失和梯度，并进行反向传播和优化；然后计算GRU模型的损失和梯度，并进行反向传播和优化。最后返回训练损失和VAE损失的均值。请注意，这段代码可能需要根据具体情况进行修改。