update divSet
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03fe15fcbb
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@ -1,6 +1,8 @@
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import torch
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import torch.nn as nn
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from sklearn.preprocessing import LabelEncoder
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from torch.utils.data import DataLoader, TensorDataset
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from Qtorch.Models.Qnn import Qnn
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class QCNN(Qnn):
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@ -58,3 +60,22 @@ class QCNN(Qnn):
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nn.init.xavier_uniform_(m.weight)
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if m.bias is not None:
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m.bias.data.fill_(0.01)
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def _prepare_data(self):
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# 将data转换为tensor形式, unsqueeze可以创建多一维度的，给
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X_train_tensor = torch.tensor(self.X_train, dtype=torch.float32).unsqueeze(1)
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self.y_train = self.LABEL_ENCODER.fit_transform(self.y_train)
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y_train_tensor = torch.tensor(self.y_train, dtype=torch.long)
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X_test_tensor = torch.tensor(self.X_test, dtype=torch.float32).unsqueeze(1)
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self.y_test = self.LABEL_ENCODER.transform(self.y_test)
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y_test_tensor = torch.tensor(self.y_test, dtype=torch.long)
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train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
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test_dataset = TensorDataset(X_test_tensor, y_test_tensor)
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train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
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test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
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return train_loader, test_loader
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@ -1,41 +1,37 @@
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import numpy as np
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import torch.nn as nn
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from Qtorch.Models.Qnn import Qnn
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from sklearn.preprocessing import LabelEncoder
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class Qmlp(Qnn):
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def __init__(self, X_train, y_train, X_test, y_test,
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def __init__(self, data,
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hidden_layers,
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labels=None,
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dropout_rate=0.3
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dropout_rate=0.3,
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test_size = 0.2,
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random_state=None
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):
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super(Qmlp, self).__init__()
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super(Qmlp, self).__init__(data=data, labels=labels, test_size=test_size, random_state=random_state)
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self.LABEL_ENCODER = LabelEncoder()
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self.X_train, self.y_train, self.X_test, self.y_test = X_train, y_train, X_test, y_test
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self.labels = labels
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input_size = X_train.shape[1]
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num_classes = len(set(y_train))
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input_size = self.X_train.shape[1]
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num_classes = len(np.unique(self.y_train))
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self.layers = nn.ModuleList()
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# Input layer to first hidden layer
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# 连接输入层和第一个隐藏层
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self.layers.append(nn.Linear(input_size, hidden_layers[0]))
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self.layers.append(nn.BatchNorm1d(hidden_layers[0]))
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self.layers.append(nn.ReLU())
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self.layers.append(nn.Dropout(dropout_rate))
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# Create hidden layers
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# 创建隐藏层
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for i in range(1, len(hidden_layers)):
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self.layers.append(nn.Linear(hidden_layers[i-1], hidden_layers[i]))
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self.layers.append(nn.BatchNorm1d(hidden_layers[i]))
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self.layers.append(nn.ReLU())
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self.layers.append(nn.Dropout(dropout_rate))
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# Output layer
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# 创建输出层
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self.layers.append(nn.Linear(hidden_layers[-1], num_classes))
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self.__init_weights()
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@ -5,24 +5,27 @@ from sklearn.decomposition import PCA
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from sklearn.metrics import confusion_matrix
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from torch.utils.data import DataLoader, TensorDataset
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# from Qfunctions.divSet import divSet as ds
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from Qtorch import divSet as DS
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# from Qfunctions.saveToxlsx import save_to_xlsx as stx
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class Qnn(nn.Module):
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def __init__(self):
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def __init__(self, data, labels, test_size = 0.2, random_state=None):
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super(Qnn, self).__init__()
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# 使用gpu进行加速, 没有gpu的话使用CPU
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self.DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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self.X_train, self.y_train, self.X_test, self.y_test = None, None, None, None
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# 划分测试集和训练集
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self.X_train, self.X_test, self.y_train, self.y_test, self.LABEL_ENCODER = DS(
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data=data, labels=labels, test_size=test_size, random_state=random_state
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)
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self.labels = None
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self.LABEL_ENCODER = None
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self.labels = labels
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# 存储过程数据的文件
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self.epoch_data = {
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'epoch': [],
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'train_loss': [],
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@ -30,17 +33,18 @@ class Qnn(nn.Module):
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'test_accuracy': []
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}
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# PCA 图片数据存储
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self.pca_2d, self.pca_3d = None, None
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def __prepare_data(self):
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def _prepare_data(self):
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# 将data转换为tensor形式
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X_train_tensor = torch.tensor(self.X_train, dtype=torch.float32).unsqueeze(1)
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X_train_tensor = torch.tensor(self.X_train, dtype=torch.float32)
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self.y_train = self.LABEL_ENCODER.fit_transform(self.y_train)
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y_train_tensor = torch.tensor(self.y_train, dtype=torch.long)
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X_test_tensor = torch.tensor(self.X_test, dtype=torch.float32).unsqueeze(1)
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X_test_tensor = torch.tensor(self.X_test, dtype=torch.float32)
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self.y_test = self.LABEL_ENCODER.transform(self.y_test)
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y_test_tensor = torch.tensor(self.y_test, dtype=torch.long)
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@ -52,7 +56,7 @@ class Qnn(nn.Module):
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return train_loader, test_loader
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def __train_model(self, train_loader, test_loader, epochs_times=100):
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def _train_model(self, train_loader, test_loader, epochs_times=100):
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model = self.to(self.DEVICE)
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@ -62,7 +66,9 @@ class Qnn(nn.Module):
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best_test_accuracy = 0
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patience = 100
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counter = 0
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accuracy_threshold = 0.99 # 99% 的准确率阈值
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# 99% 的准确率阈值
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accuracy_threshold = 0.99
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for epoch in range(epochs_times):
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@ -130,10 +136,11 @@ class Qnn(nn.Module):
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return
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def fit(self, epoch_times = 100):
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train_loader, test_loader = self.__prepare_data()
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self.__train_model(train_loader, test_loader, epochs_times=epoch_times)
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train_loader, test_loader = self._prepare_data()
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self._train_model(train_loader, test_loader, epochs_times=epoch_times)
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return
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# 外部获取PCA图像数据的接口
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def get_PCA(self):
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# PCA 2D 图像
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@ -143,16 +150,18 @@ class Qnn(nn.Module):
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df_pca2d['labels'] = self.y_train
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# PCA 3D 图像
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pca_3d = PCA(n_components=3)
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pca_3d = PCA(n_components=3) # 保留三个主成分
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principalComponents = pca_3d.fit_transform(self.X_train)
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df_pca3d = pd.DataFrame(data=principalComponents, columns=['PC1', 'PC2', 'PC3'])
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df_pca3d['labels'] = self.y_train
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return df_pca2d, df_pca3d
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# 外部获取混淆矩阵的接口
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def get_cm(self):
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return pd.DataFrame(self.cm, columns=self.labels, index=self.labels)
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# 外部获取迭代数据的接口
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def get_epoch_data(self):
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return pd.DataFrame(self.epoch_data)
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@ -0,0 +1,3 @@
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# Qtorch/__init__.py
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from .Functions.divSet import divSet
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from .Models import Qnn, Qmlp, Qcnn
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33
README.md
33
README.md
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#README
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## Preliminary
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1. 对于每一个类，将数据如下处理, 保存成xlsx或者xls文件
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| | | | |
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|-------|-------|-------|-------|
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| arbitrary value | value | arbitrary value | vlaue |
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| arbitrary value | value | arbitrary value | vlaue |
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即偶数列为一次循环的数据，奇数列为任意值即可
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2. 配置conda环境
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> pass
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## Quickly Start
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1. 将项目文件夹编辑成**日期+项目名**
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2. 编辑好label名称，label名称命名变成英文或者数字
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label_names = ['Acrlic', 'Ecoflex', 'PDMS', 'PLA', 'Wood']
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# 使用库 divSet 划分训练集和数据集
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data = load_data(projet_name, label_names, isDir=False, fileClass='xlsx')
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X_train, X_test, y_train, y_test, encoder = divSet(
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data=data, labels=label_names, test_size= 0.3
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)
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```
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5. 创建神经网络类
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save_to_xlsx(project_name=projet_name, file_name="cm", data=cm )
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save_to_xlsx(project_name=projet_name, file_name="acc_and_loss", data=epoch_data)
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```
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## Advanced
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### loadData 处理数据工具的使用
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||参数类型|默认值|参数作用|
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|folder|str|必填项|指定数据存放在Static下的哪个文件夹|
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|lableNames| list| 必填项| 指定每一个类的label名称, 既可以用来读取相应的文件，也可以用来给label排序|
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|isDir| bool| True| 若是上文Quickly Strat章节2.1情况需要改成False，2.2情况则是True|
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|fileClass| str| 'xlsx'| 数据文件的后缀|
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> tips: 数据读取是按照一下情况读取的(2.1和2.2是Quickly Start章节的2.1和2.2简称)：
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> 2.1情况的第一类数据读取的地址是 ./Static/folder/labelsNames[0].xlsx, 其他类同理
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> 2.2情况的第二类数据读取的地址是 ./Static/folder/labelsNames[0]/*.xlsx, 其他同理
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### Qmlp 模型使用
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> pass
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26
main.py
26
main.py
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from Qtorch.Models.Qmlp import Qmlp
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from Qtorch.Models.Qcnn import QCNN
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from Qfunctions.divSet import divSet
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from Qfunctions.loaData import load_data
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from Qfunctions.saveToxlsx import save_to_xlsx as save_to_xlsx
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import string
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def main():
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projet_name = '20241130 EMG-write' # 输入元数据文件夹名称
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label_names = list(string.ascii_uppercase) # 请在[]内输入每一个分类的名称
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projet_name = '20240821Sound' # 输入元数据文件夹名称
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label_names = ['flim', 'nano', 'pressure', 'sensor', 'water'] # 请在[]内输入每一个分类的名称
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print(label_names)
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data = load_data(projet_name, label_names, isDir=False, fileClass='xlsx')
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X_train, X_test, y_train, y_test, encoder = divSet(
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data=data, labels=label_names, test_size= 0.3
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)
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# model = Qmlp(
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# X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test,
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# hidden_layers = [128],
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# dropout_rate=0
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# )
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model = QCNN(
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X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test,
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model = Qmlp(
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data=data,
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labels=label_names,
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hidden_layers = [128],
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dropout_rate=0
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)
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# model = QCNN(
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# X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test,
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# dropout_rate=0
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# )
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pca_2d, pca_3d = model.get_PCA()
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model.fit(300)
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