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【时间序列篇】基于LSTM的序列分类-Pytorch实现 part1 案例复现 【时间序列篇】基于LSTM的序列分类-Pytorch实现 part2 自有数据集构建 【时间序列篇】基于LSTM的序列分类-Pytorch实现 part3 化为己用 在一个人体姿态估计的任务中#xff0c;需要用深度学习模型…系列文章目录
【时间序列篇】基于LSTM的序列分类-Pytorch实现 part1 案例复现 【时间序列篇】基于LSTM的序列分类-Pytorch实现 part2 自有数据集构建 【时间序列篇】基于LSTM的序列分类-Pytorch实现 part3 化为己用 在一个人体姿态估计的任务中需要用深度学习模型来进行序列分类。 化为己用实现成功。 文章目录 系列文章目录前言一、模型训练1 导入库和自用函数2 导入数据集3 设备部署4 网络模型5 训练过程6 运行结果7 完整代码SequenceClassifier 二、模型预测1 完整代码2 运行结果 三、模型评估1 完整代码2 运行结果 总结 前言
结合了part1 和 part2的文章处理现有序列分类任务。
基于LSTM的序列分类-Pytorch实现 这个部分先告一段落。 part3 主要是优化后的代码实现包括训练预测模型评估。 一、模型训练
这一部分就是对part1文章中代码的优化和运行结果。每一节都是一整个代码的一部分最后放完整代码。
1 导入库和自用函数
import os
import copy
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from tqdm import tqdmdef calculate_accuracy(y_pred, y_true):_, predicted_labels torch.max(y_pred, 1)correct (predicted_labels y_true).float()accuracy correct.sum() / len(correct)return accuracy2 导入数据集 /****************************************************/导入数据集
/****************************************************/# ----------------------------------------------------#
# 路径指定
# ----------------------------------------------------#
ROOT_PATH DATA/RT_Position_datasetdataset_path os.path.join(ROOT_PATH, dataset)
target_path os.path.join(ROOT_PATH, groups/Movement4_target.csv)
groups_path os.path.join(ROOT_PATH, groups/Movement4_DatasetGroup.csv)checkpoint_pth best_model.pth # 模型参数
seq_len 16 # 序列长度
# ----------------------------------------------------#
# sequences列表读取所有的样本
# ----------------------------------------------------#
path os.path.join(dataset_path, Movement4_)
sequences list()
for i in range(1, 3731): # 3731为样本数file_path path str(i) .csv# print(file_path)df pd.read_csv(file_path, header0)values df.valuessequences.append(values)# print(len(sequences))
# len_sequences []
# for one_seq in sequences:
# len_sequences.append(len(one_seq))
# ----------------------------------------------------#
# 数据集标签
# ----------------------------------------------------#
targets pd.read_csv(target_path)
targets targets.values[:, 1]
# ----------------------------------------------------#
# 数据集划分
# ----------------------------------------------------#
groups pd.read_csv(groups_path, header0)
groups groups.values[:, 1]# ----------------------------------------------------#
# Padding the sequence with the values in last row to max length
# ----------------------------------------------------#
# 函数用于填充和截断序列
def pad_truncate_sequences(sequences, max_len, dim4, truncatingpost, paddingpost):# 初始化一个空的numpy数组用于存储填充后的序列padded_sequences np.zeros((len(sequences), max_len, dim))for i, one_seq in enumerate(sequences):if len(one_seq) max_len: # 截断if truncating pre:padded_sequences[i] one_seq[-max_len:]else:padded_sequences[i] one_seq[:max_len]else: # 填充padding_len max_len - len(one_seq)to_concat np.repeat(one_seq[-1], padding_len).reshape(dim, padding_len).transpose()if padding pre:padded_sequences[i] np.concatenate([to_concat, one_seq])else:padded_sequences[i] np.concatenate([one_seq, to_concat])return padded_sequences# ----------------------------------------------------#
# 设置序列长度
# ----------------------------------------------------#
# 使用自定义函数进行填充和截断
final_seq pad_truncate_sequences(sequences, max_lenseq_len, dim4, truncatingpost, paddingpost)# 设置标签从 1~6 换为 0~5
targets np.array(targets)
final_targets targets - 1# ----------------------------------------------------#
# 数据集划分
# ----------------------------------------------------## 将numpy数组转换为PyTorch张量
final_seq torch.tensor(final_seq, dtypetorch.float)# 划分样本为 训练集验证集
train [final_seq[i] for i in range(len(groups)) if groups[i] 1]
validation [final_seq[i] for i in range(len(groups)) if groups[i] 2]
# 标签同理
train_target [final_targets[i] for i in range(len(groups)) if groups[i] 1]
validation_target [final_targets[i] for i in range(len(groups)) if groups[i] 2]# 转换为PyTorch张量
train torch.stack(train)
train_target torch.tensor(train_target).long()validation torch.stack(validation)
validation_target torch.tensor(validation_target).long()3 设备部署 /****************************************************/device
/****************************************************/
def device_on():device torch.device(cuda if torch.cuda.is_available() else cpu)print(fUsing {device} device)return device# ----------------------------------------------------#
# device
# ----------------------------------------------------#
device device_on()4 网络模型 /****************************************************/网络模型
/****************************************************/
# ----------------------------------------------------#
# 创建模型
# ----------------------------------------------------#
class TimeSeriesClassifier(nn.Module):def __init__(self, n_features, hidden_dim256, output_size1):super().__init__()self.lstm nn.LSTM(input_sizen_features, hidden_sizehidden_dim, batch_firstTrue)self.fc nn.Linear(hidden_dim, output_size) # output_size classesdef forward(self, x):x, _ self.lstm(x) # LSTM层x x[:, -1, :] # 只取LSTM输出中的最后一个时间步x self.fc(x) # 通过一个全连接层return x# ----------------------------------------------------#
# 模型实例化
# ----------------------------------------------------#
seq_len 16 # 根据你的序列长度进行调整
n_features 4 # 根据你的特征数量进行调整
output_size 6
model TimeSeriesClassifier(n_featuresn_features, output_sizeoutput_size)# # 打印模型结构
print(model)
# ----------------------------------------------------#
# 模型部署
# ----------------------------------------------------#
model.to(device)5 训练过程 /****************************************************/训练过程
/****************************************************/
# 设置训练参数
epochs 100 # 训练轮数根据需要进行调整
batch_size 4 # 批大小根据你的硬件调整# DataLoader 加载数据集
train_dataset torch.utils.data.TensorDataset(train, train_target)
train_loader torch.utils.data.DataLoader(datasettrain_dataset, batch_sizebatch_size, shuffleTrue)validation_dataset torch.utils.data.TensorDataset(validation, validation_target)
validation_loader torch.utils.data.DataLoader(datasetvalidation_dataset, batch_sizebatch_size, shuffleTrue)# 定义损失函数和优化器
criterion nn.CrossEntropyLoss()
criterion criterion.to(device)# 学习率和优化策略
learning_rate 1e-3
optimizer optim.Adam(paramsmodel.parameters(), lrlearning_rate, weight_decay5e-4)
lr_scheduler optim.lr_scheduler.StepLR(optimizer, step_size2, gamma0.5) # 设置学习率下降策略# ----------------------------------------------------#
# 训练
# ----------------------------------------------------#
best_acc 0.0
for epoch in range(epochs):model.train() # 将模型设置为训练模式train_epoch_loss []train_epoch_accuracy []pbar tqdm(train_loader, totallen(train_loader))for index, (inputs, labels) in enumerate(pbar, start1):# 获取输入数据和目标并将它们转移到GPU如果可用inputs inputs.to(device)labels labels.to(device)# 清零梯度optimizer.zero_grad()# 前向传播outputs model(inputs)loss criterion(outputs, labels)loss.backward()optimizer.step()train_epoch_loss.append(loss.item())accuracy calculate_accuracy(outputs, labels)train_epoch_accuracy.append(accuracy.item())pbar.set_description(fEpoch [{epoch 1}/{epochs}])pbar.set_postfix(**{loss: loss.item(),accuracy: accuracy.item(),})# Validation accuracymodel.eval()valid_epoch_loss []valid_epoch_accuracy []pbar tqdm(validation_loader, totallen(validation_loader))for index, (inputs, labels) in enumerate(pbar, start1):inputs inputs.to(device)labels labels.to(device)outputs model(inputs)loss criterion(outputs, labels)valid_epoch_loss.append(loss.item())accuracy calculate_accuracy(outputs, labels)valid_epoch_accuracy.append(accuracy.item())pbar.set_description(valid)pbar.set_postfix(**{total_loss: loss.item(),accuracy: accuracy.item(),})# 计算平均精度print(--------------------------------------------)train_epoch_loss np.average(train_epoch_loss)train_epoch_accuracy np.average(train_epoch_accuracy)print(fEpoch {epoch 1}, train Accuracy: {train_epoch_accuracy:.4f})valid_epoch_loss np.average(valid_epoch_loss)valid_epoch_accuracy np.average(valid_epoch_accuracy)print(fEpoch {epoch 1}, Validation Accuracy: {valid_epoch_accuracy:.4f})print(--------------------------------------------)if valid_epoch_accuracy best_acc:best_acc valid_epoch_accuracybest_model_wts copy.deepcopy(model.state_dict())state {state_dict: model.state_dict(),best_acc: best_acc,optimizer: optimizer.state_dict(),}torch.save(state, checkpoint_pth)print(Finished Training)
print(Best val Acc: {:4f}.format(best_acc))6 运行结果 可以看到相比起part1的实验分类准确率更高。毕竟part1总共314个样本自有数据集样本有3730个。
7 完整代码
SequenceClassifier
二、模型预测
输入样本运行模型输出预测
1 完整代码
这里直接贴完整代码。 file name:predict.py
desc: 用于序列分类预测
import os
import pandas as pd
import torch
import torch.nn as nn
/****************************************************/输入一个数据样本
/****************************************************/# 读取CSV样本文件
csv_file DATA/RT_Position_dataset/dataset/Movement4_7.csv
data pd.read_csv(csv_file)# 将Pandas DataFrame转换为NumPy数组
data_array data.values# 将NumPy数组转换为PyTorch张量
input torch.tensor(data_array, dtypetorch.float).unsqueeze(0)
/****************************************************/device
/****************************************************/
def device_on():device torch.device(cuda if torch.cuda.is_available() else cpu)print(fUsing {device} device)return device# ----------------------------------------------------#
# device
# ----------------------------------------------------#
device device_on()
/****************************************************/网络模型
/****************************************************/
# ----------------------------------------------------#
# 创建模型
# ----------------------------------------------------#
class TimeSeriesClassifier(nn.Module):def __init__(self, n_features, hidden_dim256, output_size1):super().__init__()self.lstm nn.LSTM(input_sizen_features, hidden_sizehidden_dim, batch_firstTrue)self.fc nn.Linear(hidden_dim, output_size) # output_size classesdef forward(self, x):x, _ self.lstm(x) # LSTM层x x[:, -1, :] # 只取LSTM输出中的最后一个时间步x self.fc(x) # 通过一个全连接层return x# ----------------------------------------------------#
# 模型实例化
# ----------------------------------------------------#
seq_len 16 # 根据你的序列长度进行调整
n_features 4 # 根据你的特征数量进行调整
output_size 6
model TimeSeriesClassifier(n_featuresn_features, output_sizeoutput_size)# ----------------------------------------------------#
# 模型部署
# ----------------------------------------------------#
model.to(device)# ----------------------------------------------------#
# 导入模型参数
# ----------------------------------------------------#
save_pth best_model.pth
checkpoint torch.load(save_pth)
best_acc checkpoint[best_acc]
model.load_state_dict(checkpoint[state_dict])/****************************************************/预测输出
/****************************************************/model.eval() # 设置为评估模式# 假设你有一个预处理好的序列数据shape为(batch_size, seq_len, n_features)
# 例如一个序列长度为16每个时间步有4个特征
# input torch.randn(1, 16, 4) # 替换为你的数据with torch.no_grad():output model(input)# 将输出转换为概率分布prediction torch.softmax(output, dim1)# 取得最高概率的类别作为预测结果predicted_class torch.argmax(prediction, dim1)
print(fPredicted class: {predicted_class})
2 运行结果 三、模型评估
1 完整代码 file name:evaluate.py
desc: 用于模型评估
import os
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, f1_scoredef calculate_accuracy(y_pred, y_true):_, predicted_labels torch.max(y_pred, 1)correct (predicted_labels y_true).float()accuracy correct.sum() / len(correct)return accuracy
/****************************************************/导入数据集
/****************************************************/# ----------------------------------------------------#
# 路径指定
# ----------------------------------------------------#
ROOT_PATH DATA/RT_Position_datasetdataset_path os.path.join(ROOT_PATH, dataset)
target_path os.path.join(ROOT_PATH, groups/Movement4_target.csv)
groups_path os.path.join(ROOT_PATH, groups/Movement4_DatasetGroup.csv)checkpoint_pth best_model.pth # 模型参数
seq_len 16 # 序列长度
# ----------------------------------------------------#
# sequences列表读取所有的样本
# ----------------------------------------------------#
path os.path.join(dataset_path, Movement4_)
sequences list()
for i in range(1, 3731): # 3731为样本数file_path path str(i) .csv# print(file_path)df pd.read_csv(file_path, header0)values df.valuessequences.append(values)# print(len(sequences))
# len_sequences []
# for one_seq in sequences:
# len_sequences.append(len(one_seq))
# ----------------------------------------------------#
# 数据集标签
# ----------------------------------------------------#
targets pd.read_csv(target_path)
targets targets.values[:, 1]
# ----------------------------------------------------#
# 数据集划分
# ----------------------------------------------------#
groups pd.read_csv(groups_path, header0)
groups groups.values[:, 1]# ----------------------------------------------------#
# Padding the sequence with the values in last row to max length
# ----------------------------------------------------#
# 函数用于填充和截断序列
def pad_truncate_sequences(sequences, max_len, dim4, truncatingpost, paddingpost):# 初始化一个空的numpy数组用于存储填充后的序列padded_sequences np.zeros((len(sequences), max_len, dim))for i, one_seq in enumerate(sequences):if len(one_seq) max_len: # 截断if truncating pre:padded_sequences[i] one_seq[-max_len:]else:padded_sequences[i] one_seq[:max_len]else: # 填充padding_len max_len - len(one_seq)to_concat np.repeat(one_seq[-1], padding_len).reshape(dim, padding_len).transpose()if padding pre:padded_sequences[i] np.concatenate([to_concat, one_seq])else:padded_sequences[i] np.concatenate([one_seq, to_concat])return padded_sequences# ----------------------------------------------------#
# 设置序列长度
# ----------------------------------------------------#
# 使用自定义函数进行填充和截断
final_seq pad_truncate_sequences(sequences, max_lenseq_len, dim4, truncatingpost, paddingpost)# 设置标签从 1~6 换为 0~5
targets np.array(targets)
final_targets targets - 1# ----------------------------------------------------#
# 数据集划分
# ----------------------------------------------------## 将numpy数组转换为PyTorch张量
final_seq torch.tensor(final_seq, dtypetorch.float)# 划分样本为 训练集验证集
train [final_seq[i] for i in range(len(groups)) if groups[i] 1]
validation [final_seq[i] for i in range(len(groups)) if groups[i] 2]
# 标签同理
train_target [final_targets[i] for i in range(len(groups)) if groups[i] 1]
validation_target [final_targets[i] for i in range(len(groups)) if groups[i] 2]# 转换为PyTorch张量
train torch.stack(train)
train_target torch.tensor(train_target).long()validation torch.stack(validation)
validation_target torch.tensor(validation_target).long()
/****************************************************/device
/****************************************************/
def device_on():device torch.device(cuda if torch.cuda.is_available() else cpu)print(fUsing {device} device)return device# ----------------------------------------------------#
# device
# ----------------------------------------------------#
device device_on()
/****************************************************/网络模型
/****************************************************/
# ----------------------------------------------------#
# 创建模型
# ----------------------------------------------------#
class TimeSeriesClassifier(nn.Module):def __init__(self, n_features, hidden_dim256, output_size1):super().__init__()self.lstm nn.LSTM(input_sizen_features, hidden_sizehidden_dim, batch_firstTrue)self.fc nn.Linear(hidden_dim, output_size) # output_size classesdef forward(self, x):x, _ self.lstm(x) # LSTM层x x[:, -1, :] # 只取LSTM输出中的最后一个时间步x self.fc(x) # 通过一个全连接层return x# ----------------------------------------------------#
# 模型实例化
# ----------------------------------------------------#
seq_len 16 # 根据你的序列长度进行调整
n_features 4 # 根据你的特征数量进行调整
output_size 6
model TimeSeriesClassifier(n_featuresn_features, output_sizeoutput_size)# # 打印模型结构
print(model)
# ----------------------------------------------------#
# 模型部署
# ----------------------------------------------------#
model.to(device)# ----------------------------------------------------#
# 导入模型参数
# ----------------------------------------------------#
checkpoint_pth best_model.pth
checkpoint torch.load(checkpoint_pth)
best_acc checkpoint[best_acc]
model.load_state_dict(checkpoint[state_dict])
batch_size 4# DataLoader 加载数据集
train_dataset torch.utils.data.TensorDataset(train, train_target)
train_loader torch.utils.data.DataLoader(datasettrain_dataset, batch_sizebatch_size, shuffleTrue)validation_dataset torch.utils.data.TensorDataset(validation, validation_target)
validation_loader torch.utils.data.DataLoader(datasetvalidation_dataset, batch_sizebatch_size, shuffleTrue)
/****************************************************/模型评估
/****************************************************/# ----------------------------------------------------#
# 评估
# ----------------------------------------------------## Validation
model.eval()
# 存储所有预测和真实标签
all_preds []
all_labels []
# 不计算梯度减少计算和内存消耗
with torch.no_grad():for data, labels in validation_loader:# data和labels的预处理如转移到GPU、标准化等...# 生成预测并获取最可能的类别outputs model(data)_, predicted torch.max(outputs, 1)# 收集预测和真实标签all_preds.extend(predicted.cpu().numpy())all_labels.extend(labels.cpu().numpy())# 计算性能指标
accuracy accuracy_score(all_labels, all_preds)
conf_matrix confusion_matrix(all_labels, all_preds)
precision precision_score(all_labels, all_preds, averagemacro)
recall recall_score(all_labels, all_preds, averagemacro)
f1 f1_score(all_labels, all_preds, averagemacro)print(fAccuracy: {accuracy})
print(fConfusion Matrix:\n{conf_matrix})
print(fPrecision: {precision})
print(fRecall: {recall})
print(fF1 Score: {f1})
2 运行结果 总结
到此基于LSTM的序列分类任务就结束啦。 完整项目 SequenceClassifier
