CNN fist done

Qtorch/Models/Qcnn.py

@@ -1,48 +1,60 @@
+import torch
 import torch.nn as nn
-from Qtorch.Models.Qnn import Qnn
 from sklearn.preprocessing import LabelEncoder
+from Qtorch.Models.Qnn import Qnn
 
 class QCNN(Qnn):
-  def __init__(self, X_train, y_train, X_test, y_test, 
-               labels=None,
-               dropout_rate=0.3
-               ):
-    super(QCNN, self).__init__()
+    def __init__(self, X_train, y_train, X_test, y_test, labels=None, dropout_rate=0.3):
+        super(QCNN, self).__init__()
 
-    self.LABEL_ENCODER = LabelEncoder()
-    
-    self.X_train, self.y_train, self.X_test, self.y_test = X_train, y_train, X_test, y_test
-    
-    self.labels = labels
+        self.LABEL_ENCODER = LabelEncoder()
+
+        self.X_train, self.y_train, self.X_test, self.y_test = X_train, y_train, X_test, y_test
+        self.labels = labels
+
+        input_size = X_train.shape[1]  # 输入的长度
+        num_classes = len(set(y_train))  # 分类数
+
+        # 网络层：卷积层 + 池化层 + 全连接层
+        self.layers = nn.ModuleList()
+        self.layers.append(nn.Conv1d(in_channels=1, out_channels=16, kernel_size=3))  # 卷积层
+        self.layers.append(nn.MaxPool1d(kernel_size=2))  # 池化层
+        self.layers.append(nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3))  # 卷积层
+        self.layers.append(nn.MaxPool1d(kernel_size=2))  # 池化层
+
+        # 计算展平后的大小
+        conv_output_size = self._get_conv_output_size(input_size)  # 卷积后的输出大小
+        print(f"Conv output size: {conv_output_size}")  # 打印卷积后的输出大小
+        self.layers.append(nn.Linear(conv_output_size, 128))  # 全连接层
+        self.layers.append(nn.Linear(128, num_classes))  # 输出层
+
+        self.__init_weights()
+
+    def _get_conv_output_size(self, input_size):
+        # 计算卷积后的输出尺寸
+        x = torch.randn(1, 1, input_size)  # 创建一个假的输入张量
+        for layer in self.layers:
+            x = layer(x)  # 通过每一层
+        return int(x.numel())  # 返回展平后的输出大小
+
+
+    def forward(self, x):
+        # 通过卷积和池化层
+        for layer in self.layers[:-2]:  # 除去最后两个 Linear 层
+            x = layer(x)
         
-    input_size = X_train.shape[1]
-    num_classes = len(set(y_train))
-    
-    self.layers = nn.ModuleList()
+        # 展平卷积后的输出
+        x = x.view(x.size(0), -1)  # 这样 x 会变成 (batch_size, conv_output_size)
+        
+        # 通过全连接层
+        x = self.layers[-2](x)
+        x = self.layers[-1](x)
+        
+        return x
 
-    # Input layer to first Convolutional layer
-    self.layers.append(nn.Conv1d(in_channels=1, out_channels=32, kernel_size=3, stride=1, padding=1))
-    self.layers.append(nn.ReLU())
-    self.layers.append(nn.MaxPool1d(kernel_size=2, stride=2))
-    
-    # Calculate the size after convolutions and pooling
-    conv_output_size = input_size // 4  # Assuming two pooling layers with stride 2
-    self.layers.append(nn.Linear(32 * conv_output_size, 128))
-    self.layers.append(nn.ReLU())
-    self.layers.append(nn.Dropout(dropout_rate))
-    
-    # Output layer
-    self.layers.append(nn.Linear(128, num_classes))
-    self.__init_weights()
-
-  def forward(self, x):
-    for layer in self.layers:
-        x = layer(x)
-    return x
-
-  def __init_weights(self):
-    for m in self.modules():
-        if isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear):
-            nn.init.xavier_uniform_(m.weight)
-            if m.bias is not None:
-                m.bias.data.fill_(0.01)
+    def __init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear):
+                nn.init.xavier_uniform_(m.weight)
+                if m.bias is not None:
+                    m.bias.data.fill_(0.01)

Qtorch/Models/Qnn.py

@@ -36,11 +36,11 @@ class Qnn(nn.Module):
   def __prepare_data(self):
 
     # 将data转换为tensor形式
-    X_train_tensor = torch.tensor(self.X_train, dtype=torch.float32)
+    X_train_tensor = torch.tensor(self.X_train, dtype=torch.float32).unsqueeze(1)
     self.y_train = self.LABEL_ENCODER.fit_transform(self.y_train)
     y_train_tensor = torch.tensor(self.y_train, dtype=torch.long)
 
-    X_test_tensor = torch.tensor(self.X_test, dtype=torch.float32)
+    X_test_tensor = torch.tensor(self.X_test, dtype=torch.float32).unsqueeze(1)
     self.y_test = self.LABEL_ENCODER.transform(self.y_test)
     y_test_tensor = torch.tensor(self.y_test, dtype=torch.long)
 

main.py

@@ -3,11 +3,13 @@ from Qtorch.Models.Qcnn import QCNN
 from Qfunctions.divSet import divSet
 from Qfunctions.loaData import load_data
 from Qfunctions.saveToxlsx import save_to_xlsx as save_to_xlsx
+import string
 
 def main():
-  projet_name = '20241112Numbers'                                           # 输入元数据文件夹名称
-  label_names =['1', '2', '3', '4', '5', '6', '7' ,'8', '9']                # 请在[]内输入每一个分类的名称
-  data = load_data(projet_name, label_names, isDir=False, fileClass='xls')
+  projet_name = '20241130 EMG-write'                                           # 输入元数据文件夹名称
+  label_names = list(string.ascii_uppercase)                # 请在[]内输入每一个分类的名称
+  print(label_names)
+  data = load_data(projet_name, label_names, isDir=False, fileClass='xlsx')
   X_train, X_test, y_train, y_test, encoder = divSet(
     data=data, labels=label_names, test_size= 0.3
   )
@@ -17,6 +19,7 @@ def main():
   #   hidden_layers = [128],
   #   dropout_rate=0
   #   )
+
   model = QCNN(
      X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test,
      dropout_rate=0