File "F:\pythonproject\GRU\GRU2.py", line 29 data_path = 'C:\Users\ASUS\Desktop\江苏扬州道旅\宝应县汽车运输总公司(扬321023000100)F4.xlsx' ^ SyntaxError: (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \UXXXXXXXX escape

GRU.zip_GRU python_GRU 时间序列_gru 时间_keras_python gru

GRU.py_gru_时间序列预测_时间序列_GRU时间序列_序列预测_

在时间序列预测领域，GRU（Gated Recurrent Unit，门控循环单元）是一种常见的神经网络结构，尤其适用于处理序列数据，如客流、车流等时间变化的数据。GRU是LSTM（Long Short-Term Memory）的一个变种，两者都属于...

GRU模型预测.py,gru模型全称,Python

GRU模型，全称为Gated Recurrent Unit，是循环神经网络（Recurrent Neural Network, RNN）的一个变体，主要用于处理序列数据。在本场景中，我们使用GRU模型进行短时客流预测，这是一种典型的时间序列预测问题，旨在...

Traceback (most recent call last): File "F:\pythonproject\GRU\GRU2.py", line 238, in <module> pred = model(input_data) File "E:\anaconda\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "F:\pythonproject\GRU\GRU2.py", line 160, in forward h_0 = x.data.new(self.num_layers, batch_size, self.hidden_size).fill_(0).float() File "E:\anaconda\envs\pytorch\lib\site-packages\pandas\core\generic.py", line 5989, in getattr return object.getattribute(self, name) AttributeError: 'DataFrame' object has no attribute 'data'

input_data = torch.tensor(input_data.values).float() 这里假设你的 input_data 是一个 pandas 的 DataFrame 对象，使用 values 将其转换为 numpy 数组，再使用 torch.tensor() 将其转换为 Tensor 类型...

这段代码哪里错了 class my_BiGRU(torch.nn.Module): def init(self, input_size, hidden_size, num_layers, output_size, gru_dropout): super(my_BiGRU, self).init() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.gru_dropout = gru_dropout self.gru = torch.nn.GRU(input_size, hidden_size, num_layers=num_layers, batch_first=True, bidirectional=True) self.fc = torch.nn.Linear(hidden_size * 2, output_size) def _setup_BiGRU_layers(self): self.BiGRU_layers = my_BiGRU(input_size=self.args.capsule_out_dim, output_size=self.args.apsule_out_features, hidden_size=self.args.BiGRU_hiddensize, num_layers=self.args.BiGRU_layers, gru_dropout=self.args.BiGRU_dropout, )

self.gru = torch.nn.GRU(input_size, hidden_size, num_layers=num_layers, batch_first=True, bidirectional=True) self.fc = torch.nn.Linear(hidden_size * 2, output_size) self.BiGRU_layers = my_BiGRU...

# 8.模型训练 for epoch in range(config.epochs_gru): gru.train() running_loss = 0 train_bar = tqdm(train_loader) # 形成进度条，了解当前模型的训练进度 for data in train_bar: x_train, y_train = data # 解包迭代器中的X和Y optimizer.zero_grad() y_train_pred = gru(x_train) loss = loss_function(y_train_pred, y_train.reshape(-1, 1)) loss.backward() optimizer.step() running_loss += loss.item() train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(epoch + 1, config.epochs_gru, loss) # 模型验证 gru.eval() test_loss = 0 with torch.no_grad(): test_bar = tqdm(test_loader) for data in test_bar: x_test, y_test = data y_test_pred = gru(x_test) test_loss = loss_function(y_test_pred, y_test.reshape(-1, 1)) if test_loss < config.best_loss: config.best_loss = test_loss torch.save(model.state_dict(), save_path) print('Finished Training')按句解释这一段代码的意思，每句话有什么作用，实现了什么功能？

这段代码是使用GRU模型进行训练和验证，并保存最好的模型。具体解释如下： 1. for epoch in range(config.epochs_gru):：对于指定的训练轮数，进行循环训练。 2. gru.train(): 将模型设置为训练状态。 3. ...

import torch import torch.nn as nn import torch.optim as optim import numpy as np 定义基本循环神经网络模型 class RNNModel(nn.Module): def init(self, rnn_type, input_size, hidden_size, output_size, num_layers=1): super(RNNModel, self).init() self.rnn_type = rnn_type self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layers = num_layers self.encoder = nn.Embedding(input_size, hidden_size) if rnn_type == 'RNN': self.rnn = nn.RNN(hidden_size, hidden_size, num_layers) elif rnn_type == 'GRU': self.rnn = nn.GRU(hidden_size, hidden_size, num_layers) self.decoder = nn.Linear(hidden_size, output_size) def forward(self, input, hidden): input = self.encoder(input) output, hidden = self.rnn(input, hidden) output = output.view(-1, self.hidden_size) output = self.decoder(output) return output, hidden def init_hidden(self, batch_size): if self.rnn_type == 'RNN': return torch.zeros(self.num_layers, batch_size, self.hidden_size) elif self.rnn_type == 'GRU': return torch.zeros(self.num_layers, batch_size, self.hidden_size) 定义数据集 with open('汉语音节表.txt', encoding='utf-8') as f: chars = f.readline() chars = list(chars) idx_to_char = list(set(chars)) char_to_idx = dict([(char, i) for i, char in enumerate(idx_to_char)]) corpus_indices = [char_to_idx[char] for char in chars] 定义超参数 input_size = len(idx_to_char) hidden_size = 256 output_size = len(idx_to_char) num_layers = 1 batch_size = 32 num_steps = 5 learning_rate = 0.01 num_epochs = 100 定义模型、损失函数和优化器 model = RNNModel('RNN', input_size, hidden_size, output_size, num_layers) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=learning_rate) 训练模型 for epoch in range(num_epochs): model.train() hidden = model.init_hidden(batch_size) loss = 0 for X, Y in data_iter_consecutive(corpus_indices, batch_size, num_steps): optimizer.zero_grad() hidden = hidden.detach() output, hidden = model(X, hidden) loss = criterion(output, Y.view(-1)) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) optimizer.step() if epoch % 10 == 0: print(f"Epoch {epoch}, Loss: {loss.item()}")请正确缩进代码

python import torch import torch.nn as nn import torch.optim as optim import numpy as np # 定义基本循环神经网络模型 class RNNModel(nn.Module): def __init__(self, rnn_type, input_size, ...

Traceback (most recent call last): File "F:\pythonproject\GRU\GRU2.py", line 233, in <module> input_data = input_data.permute(1, 0, 2) # 调整维度顺序 RuntimeError: permute(sparse_coo): number of dimensions in the tensor input does not match the length of the desired ordering of dimensions i.e. input.dim() = 2 is not equal to len(dims) = 3

这个错误可能是因为你的输入数据的维度不对，...你可以使用input_data.size()检查输入数据的维度，使用permute()方法来调整维度顺序。如果你无法解决这个问题，可以将你的代码和数据发送给我，我可以帮你排查错误。

Traceback (most recent call last): File "F:\pythonproject\GRU\GRU2.py", line 86, in <module> df = pd.read_csv(config.data_path, index_col=0) File "E:\anaconda\envs\pytorch\lib\site-packages\pandas\io\parsers\readers.py", line 912, in read_csv return _read(filepath_or_buffer, kwds) File "E:\anaconda\envs\pytorch\lib\site-packages\pandas\io\parsers\readers.py", line 577, in _read parser = TextFileReader(filepath_or_buffer, **kwds) File "E:\anaconda\envs\pytorch\lib\site-packages\pandas\io\parsers\readers.py", line 1407, in init self._engine = self._make_engine(f, self.engine) File "E:\anaconda\envs\pytorch\lib\site-packages\pandas\io\parsers\readers.py", line 1661, in _make_engine self.handles = get_handle( File "E:\anaconda\envs\pytorch\lib\site-packages\pandas\io\common.py", line 859, in get_handle handle = open( FileNotFoundError: [Errno 2] No such file or directory: 'C:/Users/ASUS/Desktop/F4负向标准化/常州道旅差/常州道顺商旅汽车服务有限公司(常320401000415)_result'

这是一个文件读取错误的 traceback，看起来你在尝试读取一个名为 'C:/Users/ASUS/Desktop/F4负向标准化/常州道旅差/常州道顺商旅汽车服务有限公司(常320401000415)_result' 的文件，但是系统找不到这个文件。...

Traceback (most recent call last): File "F:\pythonproject\GRU\GRU2.py", line 182, in <module> optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate) # 定义优化器 AttributeError: 'Config' object has no attribute 'learning_rate'

这个错误提示显示你的 Config 对象没有 learning_rate 属性。可能是因为你的 Config 类定义中没有定义 learning_rate 属性。你可以检查一下你的 Config 类定义中是否有 learning_rate 属性，并且确保...

def init(self, adj, num_gru_units: int, output_dim: int, bias: float = 0.0): super(TGCNGraphConvolution, self).init() self._num_gru_units = num_gru_units self._output_dim = output_dim self._bias_init_value = bias self.register_buffer( "laplacian", calculate_laplacian_with_self_loop(torch.FloatTensor(adj)) ) self.weights = nn.Parameter( torch.FloatTensor(self._num_gru_units + 1, self._output_dim) ) self.biases = nn.Parameter(torch.FloatTensor(self._output_dim)) self.reset_parameters()

ChitGPT无法回答您的问题，因为这是一段Python代码，它是用于创建TGCNGraphConvolution类的构造函数。该类用于执行图卷积并生成输出。如果您需要更多关于这段代码的解释，建议您查看相关文献或咨询开发人员。

请详解class GRU(nn.Module): definit(self,feature_size,hidden_size,num_layers,output_size): super(GRU,self).init

这是一个定义了GRU模型的PyTorch类。GRU是一种循环神经网络，用于处理序列数据，其可以记住过去的信息并输出相应的预测结果。下面是对于这个类的具体解释： - class GRU(nn.Module):：定义了一个名为GRU的PyTorch...

class Config(): data_path = 'C:/Users/ASUS/Desktop/F4/常州道旅好/常州公路运输集团有限公司(常320401000352).csv' timestep = 1 # 时间步长，就是利用多少时间窗口 batch_size = 30 # 批次大小 feature_size = 1 # 每个步长对应的特征数量，这里只使用1维，每天的风速 hidden_size = 256 # 隐层大小 output_size = 1 # 由于是单输出任务，最终输出层大小为1，预测未来1天风速 num_layers = 2 # gru的层数 epochs = 10 # 迭代轮数 best_loss = 0 # 记录损失 learning_rate = 0.01 # 学习率 model_name = 'gru' # 模型名称 save_path = './{}.pth'.format(model_name) # 最优模型保存路径如何在这段代码中将Excel文件改成遍历一个文件中的所有文件中的Excel

可以使用Python的os模块和pandas库来实现。具体操作如下： 1. 导入os和pandas库 import os import pandas as pd 2. 定义要遍历的文件夹路径 folder_path = 'your_folder_path' 3. 遍历文件夹中...

class Config(): data_path = 'C:\\Users\\ASUS\\Desktop\\F4负向标准化\\镇江危险品差\\镇江新区平安运输服务有限公司(镇321113301016)_result.csv' timestep = 1 # 时间步长，就是利用多少时间窗口 batch_size = 30 # 批次大小 feature_size = 1 # 每个步长对应的特征数量，这里只使用1维，每天的风速 hidden_size = 256 # 隐层大小 output_size = 1 # 由于是单输出任务，最终输出层大小为1，预测未来1天风速 num_layers = 2 # gru的层数 epochs = 10 # 迭代轮数 best_loss = 0 # 记录损失 learning_rate = 0.001 # 学习率 model_name = 'gru' # 模型名称 save_path = './{}.pth'.format(model_name) # 最优模型保存路径将这段代码中的数据地址改成依次读取一个文件夹中的所有Excel

file_path = os.path.join(folder_path, filename) # 拼接文件路径 df = pd.read_excel(file_path) # 读取Excel文件内容到DataFrame data = pd.concat([data, df], axis=0, ignore_index=True) # 将读取到的数据...

File "F:\gaze net\run_gazeNet.py", line 77, in <module> model = gazeNET(config, num_classes) File "F:\gaze net\model.py", line 201, in init _rnn = BatchRNN(input_size=rnn_input_size, hidden_size=hidden_size, File "F:\gaze net\model.py", line 136, in init self.rnn = nn.GRU(input_size=input_size, File "F:\gaze net\venv\lib\site-packages\torch\nn\modules\rnn.py", line 943, in init super().init('GRU', *args, kwargs) File "F:\gaze net\venv\lib\site-packages\torch\nn\modules\rnn.py", line 98, in init w_ih = Parameter(torch.empty((gate_size, layer_input_size), factory_kwargs))出现报错TypeError: empty(): argument 'size' must be tuple of ints, but found element of type float at pos 2怎么解决

这个报错可能是因为你传入了一个 float 类型的参数，而 torch.empty() 函数需要的参数必须是 int 类型的元组。你可以尝试将参数转换为 int 类型，或者使用 torch.zeros() 或 torch.ones() 来创建一个相同大小...

for epoch in range(config.epochs): model.train() running_loss = 0 train_bar = tqdm(train_loader) # 形成进度条 for data in train_bar: x_train, y_train = data # 解包迭代器中的X和Y optimizer.zero_grad() y_train_pred = model(x_train) loss = loss_function(y_train_pred, y_train.reshape(-1, 1)) loss.backward() optimizer.step() running_loss += loss.item() train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(epoch + 1, config.epochs, loss) # 模型验证 model.eval() test_loss = 0 with torch.no_grad(): test_bar = tqdm(test_loader) for data in test_bar: x_test, y_test = data y_test_pred = model(x_test) test_loss = loss_function(y_test_pred, y_test.reshape(-1, 1)) # r2 = r2_score(y_test, y_test_pred) # Adjust_RR = 1 - (1 - r2) * (1440 - 1) / (1440 - 1 - 1) if test_loss < config.best_loss: config.best_loss = test_loss torch.save(model.state_dict(), config.save_path在这段代码后加上一段根据现有拟合好的GRU模型预测未来一段时间的值

input_tensor = preprocess_input_data(input_data) # 根据需要自行实现 # 使用模型进行预测 with torch.no_grad(): output_tensor = model(input_tensor) # 对预测结果进行后处理 output_data = postprocess_...

class6_LSTM_pythonLSTM_python实现的LSTM长短时记忆网络_RNN_gru_

在本文中，我们将深入探讨标题和描述中提及的“class6_LSTM_pythonLSTM_python实现的LSTM长短时记忆网络_RNN_gru”，这显然与深度学习中的序列模型相关，特别是LSTM（长短时记忆网络）和GRU（门控循环单元）。...

File "F:\pythonproject\GRU\GRU2.py", line 29 data_path = 'C:\Users\ASUS\Desktop\江苏扬州道旅\宝应县汽车运输总公司(扬321023000100)F4.xlsx' ^ SyntaxError: (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \UXXXXXXXX escape

Traceback (most recent call last): File "F:\pythonproject\GRU\GRU2.py", line 93, in <module> df = pd.read_csv(config.data_path, index_col=0) AttributeError: 'Config' object has no attribute 'data_path'

Traceback (most recent call last): File "F:\pythonproject\GRU\GRU2.py", line 182, in <module> loss_function = nn.functional.ELU() # 定义损失函数 AttributeError: module 'torch.nn.functional' has no attribute 'ELU'

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File "F:\pythonproject\GRU\GRU2.py", line 29 data_path = 'C:\Users\ASUS\Desktop\江苏扬州道旅\宝应县汽车运输总公司(扬321023000100)F4.xlsx' ^ SyntaxError: (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \UXXXXXXXX escape

Traceback (most recent call last): File "F:\pythonproject\GRU\GRU2.py", line 93, in <module> df = pd.read_csv(config.data_path, index_col=0) AttributeError: 'Config' object has no attribute 'data_path'

Traceback (most recent call last): File "F:\pythonproject\GRU\GRU2.py", line 182, in <module> loss_function = nn.functional.ELU() # 定义损失函数 AttributeError: module 'torch.nn.functional' has no attribute 'ELU'

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GRU模型预测.py,gru模型全称,Python

Traceback (most recent call last): File "F:\pythonproject\GRU\GRU2.py", line 182, in <module> optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate) # 定义优化器 AttributeError: 'Config' object has no attribute 'learning_rate'

请详解class GRU(nn.Module): def__init__(self,feature_size,hidden_size,num_layers,output_size): super(GRU,self).__init__

class6_LSTM_pythonLSTM_python实现的LSTM长短时记忆网络_RNN_gru_

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请详解class GRU(nn.Module): definit(self,feature_size,hidden_size,num_layers,output_size): super(GRU,self).init