Add confusion matrix attributes to Qnn model; remove unused test.py file; comment out QCNN model instantiation in main.py

Qtorch/Models/Qnn.py

@@ -34,6 +34,8 @@ class Qnn(nn.Module):
     }
     
     self.pca_2d, self.pca_3d = None, None
+
+    self.cm, self.cmn = None, None
     
   def __prepare_data(self):
 
@@ -139,6 +141,7 @@ class Qnn(nn.Module):
           print(f"Early stopping at epoch {epoch+1}")
           break
 
+    # cmn为归一化矩阵
     self.cm  = confusion_matrix(all_labels, all_predicted)
     self.cmn = confusion_matrix(all_labels, all_predicted, normalize='true')
 
@@ -170,7 +173,7 @@ class Qnn(nn.Module):
     return pd.DataFrame(self.cm, columns=self.labels, index=self.labels)
   
   def get_cmn(self):
-    return pd.DataFrame(self.cm, columns=self.labels, index=self.labels)
+    return pd.DataFrame(self.cmn, columns=self.labels, index=self.labels)
 
   def get_epoch_data(self):
     return pd.DataFrame(self.epoch_data)

Scripts/test.py

@@ -1,110 +0,0 @@
-from graphviz import Digraph
-import os
-
-
-class layer:
-    def __init__(self, graph, name, size, color):
-        self.name = name
-        self.size = size
-        self.color = color
-        self.graph = graph
-    
-    def draw():
-        pass
-
-class input_layer(layer):
-    def __init__(self, graph, size):
-        super().__init__(graph, f"Input Layer({size})", size)
-        self.graph.node(self.name, shape='circle', style='filled', fillcolor=self.color, label=" ")
-        self.graph.attr(label=f'{self.name} Layer({self.size})', fontname='Times New Roman', fontweight='bold', fontsize='36')
-
-
-def draw_neural_net(input_size, hidden_sizes, num_classes, show_hidden=3):
-    g = Digraph('G', filename='neural_network', format='png')
-    g.attr(rankdir='LR', size='10,8', nodesep='1', ranksep='2', bgcolor='transparent', dpi='300')
-
-    # Input layer
-    with g.subgraph(name='cluster_input') as c:
-        c.attr(color='white')
-        for i in range(input_size):
-            c.node(f'input_{i}', shape='circle', style='filled', fillcolor='darkorange:orange', label=" ")
-        c.attr(label=f'Input Layer({input_size})', fontname='Times New Roman', fontweight='bold', fontsize='36')
-
-    # Hidden layers
-    previous_layer = 'input'
-    previous_layer_size = input_size
-    for layer_idx, hidden_size in enumerate(hidden_sizes):
-        with g.subgraph(name=f'cluster_hidden_{layer_idx}') as c:
-            c.attr(color='white')
-            for i in range(show_hidden):
-                c.node(f'hidden_{layer_idx}_{i}', shape='circle', style='filled', fillcolor='darkgreen:lightgreen', label=" ")
-            if hidden_size > show_hidden * 2:
-                c.node(f'ellipsis_{layer_idx}', shape='plaintext', label='...')
-            for i in range(hidden_size - show_hidden, hidden_size):
-                c.node(f'hidden_{layer_idx}_{i}', shape='circle', style='filled', fillcolor='darkgreen:lightgreen', label=" ")
-            c.attr(label=f'Hidden Layer {layer_idx + 1}({hidden_size})', fontname='Times New Roman', fontweight='bold', fontsize='36')
-
-          # Add edges from previous layer to current hidden layer
-        if layer_idx == 0:  # Only connect input layer to first hidden layer
-            for i in range(previous_layer_size):
-                for j in range(show_hidden):
-                    g.edge(f'{previous_layer}_{i}', f'hidden_{layer_idx}_{j}')
-                for j in range(hidden_size - show_hidden, hidden_size):
-                    g.edge(f'{previous_layer}_{i}', f'hidden_{layer_idx}_{j}')
-        else:
-            for i in range(show_hidden):
-                for j in range(show_hidden):
-                    g.edge(f'hidden_{layer_idx - 1}_{i}', f'hidden_{layer_idx}_{j}')
-                for j in range(hidden_size - show_hidden, hidden_size):
-                    g.edge(f'hidden_{layer_idx - 1}_{i}', f'hidden_{layer_idx}_{j}')
-            for i in range(hidden_size - show_hidden, hidden_size):
-                for j in range(show_hidden):
-                    g.edge(f'hidden_{layer_idx - 1}_{i}', f'hidden_{layer_idx}_{j}')
-                for j in range(hidden_size - show_hidden, hidden_size):
-                    g.edge(f'hidden_{layer_idx - 1}_{i}', f'hidden_{layer_idx}_{j}')
-
-        previous_layer = f'hidden_{layer_idx}'
-        previous_layer_size = hidden_size
-
-    # Output layer
-    with g.subgraph(name='cluster_output') as c:
-        c.attr(color='white')
-        for i in range(num_classes):
-            c.node(f'output_{i}', shape='circle', style='filled', fillcolor='darkorange:orange', label=" ")
-        c.attr(label=f'Output Layer({num_classes})', fontname='Times New Roman', fontweight='bold', fontsize='36')
-
-    # Add edges from last hidden layer to output layer
-    # for i in range(previous_layer_size):
-    #     for j in range(num_classes):
-    #         g.edge(f'{previous_layer}_{i}', f'output_{j}')
-    # # Add edges
-    # # Add edges from input to visible hidden nodes
-    # for i in range(input_size):
-    #     for j in range(show_hidden):
-    #         g.edge(f'input_{i}', f'hidden_{j}')
-    # for i in range(input_size):
-    #     for j in range(hidden_size - show_hidden, hidden_size):
-    #         g.edge(f'input_{i}', f'hidden_{j}')
-
-    # # Add edges from visible hidden nodes to output layer
-    # for i in range(show_hidden):
-    #     for j in range(num_classes):
-    #         g.edge(f'hidden_{i}', f'output_{j}')
-    # for i in range(hidden_size - show_hidden, hidden_size):
-    #     for j in range(num_classes):
-    #         g.edge(f'hidden_{i}', f'output_{j}')
-    # Add edges from last hidden layer to output layer
-    for i in range(show_hidden):
-        for j in range(num_classes):
-            g.edge(f'{previous_layer}_{i}', f'output_{j}')
-    for i in range(previous_layer_size - show_hidden, previous_layer_size):
-        for j in range(num_classes):
-            g.edge(f'{previous_layer}_{i}', f'output_{j}')
-
-    return g
-
-if __name__ == '__main__':
-    g = draw_neural_net(7, [60, 60], 7)
-    output_path = g.render(view=False)
-    print(output_path)
-    os.system(f'explorer.exe neural_network.png')

main.py

@@ -20,7 +20,7 @@ def main():
     dropout_rate=0      
     )
 
-  # model = QCNN(
+  # model = QCNN
   #    X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test,
   #    dropout_rate=0
   # )