MATLAB实现LSTM模型：毕业设计项目深度解析

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毕业设计
matlab
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资源摘要信息: "本资源是关于使用MATLAB实现LSTM（长短期记忆网络）的毕业设计项目压缩包。LSTM是一种特殊类型的循环神经网络（RNN），非常适合于处理和预测时间序列数据中的重要事件，具有解决长序列学习问题的能力。项目包含多个MATLAB脚本文件，涉及网络初始化、数据加载、模型测试以及主程序执行等关键步骤。通过这个项目，可以了解到如何利用MATLAB工具箱实现复杂的神经网络模型，为处理序列数据提供了一个很好的实例。" 知识点详细说明： 1. LSTM网络基础：LSTM是一种特殊的循环神经网络（RNN），它能够学习长期依赖信息。LSTM的关键之处在于它引入了“门”的概念，包括输入门、遗忘门和输出门，这些门控制着信息的流入、保留和流出。LSTM通过这种方式解决了传统RNN中梯度消失或梯度爆炸的问题，特别适合于处理和预测时间序列数据中的重要事件。 2. MATLAB在深度学习中的应用：MATLAB是一个高性能的数值计算环境和第四代编程语言，提供了丰富的工具箱支持深度学习、机器学习、信号处理、图像处理等众多领域。在深度学习方面，MATLAB内置了深度网络设计器、自动微分、优化算法等功能，使得开发深度学习模型变得更加方便快捷。 3. 实现LSTM的MATLAB代码结构：通过所提供的文件列表，我们可以看到实现LSTM的MATLAB代码主要结构。其中，batch_equal_nomask_lstm.m和batch_cell_lstm.m可能涉及到批量处理LSTM网络输入的函数；testmodel.m负责测试训练好的LSTM模型；Main.m文件作为主程序文件，可能是整个LSTM网络实现的入口；netInit.m则可能负责神经网络的初始化；clientLoadDataMinibatchNomask_ref.m和server_batch_cell_lstm.m可能涉及到客户端和服务器端的数据加载和处理；aStart.m可能是一个脚本用于启动项目；gputype.m可能涉及GPU类型的配置；runClient.m则可能是用于运行客户端的脚本。 4. MATLAB的GPU计算：在深度学习模型训练和预测过程中，利用GPU的并行计算能力可以显著提高计算速度。MATLAB支持GPU计算，可以利用内置函数自动检测和使用GPU进行加速。在资源中提及的gputype.m可能就与配置和使用GPU类型有关，以优化性能。 5. 神经网络训练与测试：在深度学习项目中，网络训练和测试是核心部分。通过编写相应的MATLAB脚本，可以实现对LSTM网络的训练过程进行控制，包括设定学习率、迭代次数、损失函数等参数，以及对模型性能进行评估和测试。 通过上述文件和描述，可以了解到项目涉及的具体内容和目的，以及使用MATLAB进行深度学习开发的相关技术点。这对于学习深度学习、神经网络结构设计、MATLAB编程以及GPU加速等领域的学生或研究者具有一定的参考价值。

LSTM_LSTM的matlab实现_lstm代码_LSTM_lstmmatlab_matlablstm.zip

2021-10-10 上传
标题中的“LSTM_LSTM的matlab实现_lstm代码_LSTM_lstmmatlab_matlablstm.zip”表明这是一个关于使用MATLAB实现长短期记忆网络（LSTM）的代码库。LSTM是一种递归神经网络（RNN）的变体，特别适合处理序列数据，如时间...

LSTM_LSTM_lstmmatlab_lstm预测_lstm预测matlab_cell_源码.zip

2021-09-30 上传
标题中的“LSTM_LSTM_lstmmatlab_lstm预测_lstm预测matlab_cell_源码.zip”表明这是一个关于使用MATLAB实现长短期记忆网络（LSTM）进行时间序列预测的源代码压缩包。LSTM是一种特殊的递归神经网络，常用于处理序列...

LSTM_LSTM_lstmmatlab_lstm预测_lstm预测matlab_cell.zip

2021-10-11 上传
标题中的“LSTM_LSTM_lstmmatlab_lstm预测_lstm预测matlab_cell”表明这是一个关于使用MATLAB实现LSTM（Long Short-Term Memory）神经网络进行序列预测的项目。LSTM是一种特殊的循环神经网络（RNN），它能有效地处理...

model_LSTM = keras.models.Sequential() model_LSTM.add(keras.layers.LSTM(units=64, input_shape=(time_steps, input_dim), return_sequences=True)) model_LSTM.add(keras.layers.Dropout(rate=0.2)) model_LSTM.add(keras.layers.LSTM(units=32, return_sequences=True)) model_LSTM.add(keras.layers.Dropout(rate=0.2)) model_LSTM.add(keras.layers.LSTM(units=16)) model_LSTM.add(keras.layers.Dropout(rate=0.2)) model_LSTM.add(keras.layers.Dense(units=output_dim, activation='softmax'))

2023-06-03 上传
model_LSTM.add(keras.layers.LSTM(units=64, input_shape=(time_steps, input_dim), return_sequences=True)) # 添加一个LSTM层，包含64个神经元，输入数据的形状为(time_steps, input_dim)，且输出数据也是一个...

class my_LSTM(torch.nn.Module): def __init__(self, lstm_inputsize, lstm_hiddensize, lstm_layers, lstm_dropout): super(my_LSTM, self).__init__() self.lstm_inputsize = lstm_inputsize self.lstm_hiddensize = lstm_hiddensize self.lstm_layers = lstm_layers self.lstm_dropout = lstm_dropout self.setup_layers() def setup_layers(self): self.lstm = torch.nn.LSTM( input_size = self.lstm_inputsize, hidden_size = self.lstm_hiddensize, num_layers = self.lstm_layers, batch_first=True, dropout=(0 if self.lstm_layers == 1 else self.lstm_dropout), bidirectional=False ) def forward(self, input): out, (h_n, c_n) = self.lstm(input) return out[:, -1, :]

2023-06-08 上传
该模型的输入大小为lstm_inputsize，隐藏状态大小为lstm_hiddensize，LSTM的层数为lstm_layers。该模型还使用了dropout来减少过拟合。在setup_layers函数中，该模型设置了一个LSTM层。在forward函数中，它将输入...

observation_shape = (DimConfig.observation_shape,) legal_action_shape = (DimConfig.legal_action_shape,) sub_action_mask_shape = (DimConfig.sub_action_mask_shape,) lstm_hidden_shape = (DimConfig.lstm_hidden_shape,) lstm_cell_shape = (DimConfig.lstm_cell_shape,) return { 'observation': ArraySpec(observation_shape, np.float64), 'legal_action': ArraySpec(legal_action_shape, np.float64), 'sub_action_mask': ArraySpec(sub_action_mask_shape, np.float64), 'lstm_hidden': ArraySpec(lstm_hidden_shape, np.float64), 'lstm_cell': ArraySpec(lstm_cell_shape, np.float64) }

2023-07-14 上传
- 'lstm_hidden': 使用DimConfig.lstm_hidden_shape定义的形状，np.float64数据类型的ArraySpec对象。 - 'lstm_cell': 使用DimConfig.lstm_cell_shape定义的形状，np.float64数据类型的ArraySpec对象。 这个函数的...

class LSTM(nn.Module): def __init__(self, input_dim, hidden_dim, output_dim, num_layers): super(LSTM, self).__init__() self.hidden_dim = hidden_dim self.num_layers = num_layers self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True) self.fc = nn.Linear(hidden_dim, output_dim) def forward(self, x): h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).to(device) c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).to(device) out, _ = self.lstm(x, (h0, c0)) out = self.fc(out) return out[:, -1, :]为我的这段代码添加一个dropout层

2023-03-07 上传
super(LSTM, self).__init__() self.hidden_dim = hidden_dim self.num_layers = num_layers self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True) self.dropout = nn.Dropout(p=.5) # ...

这段代码中加一个test loss功能 class LSTM(nn.Module): def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, device): super().__init__() self.device = device self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.num_directions = 1 # 单向LSTM self.batch_size = batch_size self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True) self.linear = nn.Linear(65536, self.output_size) def forward(self, input_seq): h_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device) c_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device) output, _ = self.lstm(input_seq, (h_0, c_0)) pred = self.linear(output.contiguous().view(self.batch_size, -1)) return pred if __name__ == '__main__': # 加载已保存的模型参数 saved_model_path = '/content/drive/MyDrive/危急值/model/dangerous.pth' device = 'cuda:0' lstm_model = LSTM(input_size=1, hidden_size=64, num_layers=1, output_size=3, batch_size=256, device='cuda:0').to(device) state_dict = torch.load(saved_model_path) lstm_model.load_state_dict(state_dict) dataset = ECGDataset(X_train_df.to_numpy()) dataloader = DataLoader(dataset, batch_size=256, shuffle=True, num_workers=0, drop_last=True) loss_fn = nn.CrossEntropyLoss() optimizer = optim.SGD(lstm_model.parameters(), lr=1e-4) for epoch in range(200000): print(f'epoch:{epoch}') lstm_model.train() epoch_bar = tqdm(dataloader) for x, y in epoch_bar: optimizer.zero_grad() x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor)) loss = loss_fn(x_out, y.long().to(device)) loss.backward() epoch_bar.set_description(f'loss:{loss.item():.4f}') optimizer.step() if epoch % 100 == 0 or epoch == epoch - 1: torch.save(lstm_model.state_dict(), "/content/drive/MyDrive/危急值/model/dangerous.pth") print("权重成功保存一次")

2023-06-03 上传
super().__init__() self.device = device self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.num_directions = 1 # 单向...

class LSTM(nn.Module): def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size): super().__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.num_directions = 1 # 单向LSTM self.batch_size = batch_size self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True) self.linear = nn.Linear(self.hidden_size, self.output_size) def forward(self, input_seq): batch_size, seq_len = input_seq[0], input_seq[1] h_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(device) c_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(device) # output(batch_size, seq_len, num_directions * hidden_size) output, _ = self.lstm(input_seq, (h_0, c_0)) pred = self.linear(output) pred = pred[:, -1, :] return pred这些代码分别是什么意思

2023-06-02 上传
这些代码是一个 PyTorch 模型的实现，该模型是一个基于 LSTM 的序列预测模型。具体解释如下： - `class LSTM(nn.Module):` 定义了一个 LSTM 模型类，继承自 PyTorch 的 nn.Module 类。 - `def __init__(self, input...

下面的这段python代码，哪里有错误，修改一下：import numpy as np import matplotlib.pyplot as plt import pandas as pd import torch import torch.nn as nn from torch.autograd import Variable from sklearn.preprocessing import MinMaxScaler training_set = pd.read_csv('CX2-36_1971.csv') training_set = training_set.iloc[:, 1:2].values def sliding_windows(data, seq_length): x = [] y = [] for i in range(len(data) - seq_length): _x = data[i:(i + seq_length)] _y = data[i + seq_length] x.append(_x) y.append(_y) return np.array(x), np.array(y) sc = MinMaxScaler() training_data = sc.fit_transform(training_set) seq_length = 1 x, y = sliding_windows(training_data, seq_length) train_size = int(len(y) * 0.8) test_size = len(y) - train_size dataX = Variable(torch.Tensor(np.array(x))) dataY = Variable(torch.Tensor(np.array(y))) trainX = Variable(torch.Tensor(np.array(x[1:train_size]))) trainY = Variable(torch.Tensor(np.array(y[1:train_size]))) testX = Variable(torch.Tensor(np.array(x[train_size:len(x)]))) testY = Variable(torch.Tensor(np.array(y[train_size:len(y)]))) class LSTM(nn.Module): def __init__(self, num_classes, input_size, hidden_size, num_layers): super(LSTM, self).__init__() self.num_classes = num_classes self.num_layers = num_layers self.input_size = input_size self.hidden_size = hidden_size self.seq_length = seq_length self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, num_classes) def forward(self, x): h_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) c_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) # Propagate input through LSTM ula, (h_out, _) = self.lstm(x, (h_0, c_0)) h_out = h_out.view(-1, self.hidden_size) out = self.fc(h_out) return out num_epochs = 2000 learning_rate = 0.001 input_size = 1 hidden_size = 2 num_layers = 1 num_classes = 1 lstm = LSTM(num_classes, input_size, hidden_size, num_layers) criterion = torch.nn.MSELoss() # mean-squared error for regression optimizer = torch.optim.Adam(lstm.parameters(), lr=learning_rate) # optimizer = torch.optim.SGD(lstm.parameters(), lr=learning_rate) runn = 10 Y_predict = np.zeros((runn, len(dataY))) # Train the model for i in range(runn): print('Run: ' + str(i + 1)) for epoch in range(num_epochs): outputs = lstm(trainX) optimizer.zero_grad() # obtain the loss function loss = criterion(outputs, trainY) loss.backward() optimizer.step() if epoch % 100 == 0: print("Epoch: %d, loss: %1.5f" % (epoch, loss.item())) lstm.eval() train_predict = lstm(dataX) data_predict = train_predict.data.numpy() dataY_plot = dataY.data.numpy() data_predict = sc.inverse_transform(data_predict) dataY_plot = sc.inverse_transform(dataY_plot) Y_predict[i,:] = np.transpose(np.array(data_predict)) Y_Predict = np.mean(np.array(Y_predict)) Y_Predict_T = np.transpose(np.array(Y_Predict))

2023-05-27 上传
optimizer = torch.optim.Adam(lstm.parameters(), lr=learning_rate) # 训练模型 runn = 10 Y_predict = np.zeros((runn, len(dataY))) for i in range(runn): print('Run: ' + str(i + 1)) for epoch in range...
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