save

Qtorch/Models/Qcnn.py

@@ -1,32 +1,48 @@
-import torch
 import torch.nn as nn
-import torch.optim as optim
+from Qtorch.Models.Qnn import Qnn
+from sklearn.preprocessing import LabelEncoder
 
-class Simple1DCNN(nn.Module):
-    def __init__(self, input_size, num_classes):
-        super(Simple1DCNN, self).__init__()
-        self.conv1 = nn.Conv1d(in_channels=1, out_channels=32, kernel_size=3, stride=1, padding=1)
-        self.relu = nn.ReLU()
-        self.pool = nn.MaxPool1d(kernel_size=2, stride=2)
-        self.conv2 = nn.Conv1d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
-        self.fc1 = nn.Linear(64 * (input_size // 4), 128)  # 假设经过两次池化后，长度减半两次
-        self.fc2 = nn.Linear(128, num_classes)
+class QCNN(Qnn):
+  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
+        
+    input_size = X_train.shape[1]
+    num_classes = len(set(y_train))
+    
+    self.layers = nn.ModuleList()
+
+    # 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):
-        x = self.pool(self.relu(self.conv1(x)))
-        x = self.pool(self.relu(self.conv2(x)))
-        x = x.view(-1, 64 * (self.input_size // 4))  # 展平特征图
-        x = self.relu(self.fc1(x))
-        x = self.fc2(x)
+    for layer in self.layers:
+        x = layer(x)
     return x
 
-# 实例化模型
-input_size = 100  # 假设n=100
-num_classes = 10  # 假设有10个类别
-model = Simple1DCNN(input_size, num_classes)
-
-# 定义损失函数和优化器
-criterion = nn.CrossEntropyLoss()
-optimizer = optim.Adam(model.parameters(), lr=0.001)
-
-# 训练和评估模型的代码与之前类似，这里不再赘述。
+  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/Qmlp.py

@@ -1,9 +1,6 @@
-import torch
 import torch.nn as nn
 from Qtorch.Models.Qnn import Qnn
 from sklearn.preprocessing import LabelEncoder
-from sklearn.metrics import confusion_matrix
-import pandas as pd
 
 
 class Qmlp(Qnn):

main.py

@@ -1,4 +1,5 @@
 from Qtorch.Models.Qmlp import Qmlp
+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
@@ -11,15 +12,18 @@ def main():
     data=data, labels=label_names, test_size= 0.3
   )
   
-  model = Qmlp(
+  # model = Qmlp(
+  #   X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test,
+  #   hidden_layers = [128],
+  #   dropout_rate=0
+  #   )
+  model = QCNN(
      X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test,
-    hidden_layers=[128, 128],
      dropout_rate=0
   )
   
   pca_2d, pca_3d = model.get_PCA()
 
-
   model.fit(300)
   
   cm = model.get_cm()