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

This commit is contained in:
newbie 2025-06-24 23:31:19 +08:00
parent 61e4af020d
commit 0667dd90d1
3 changed files with 5 additions and 112 deletions
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5
Qtorch/Models/Qnn.py
View File

@ -35,6 +35,8 @@ class Qnn(nn.Module):
self.pca_2d, self.pca_3d = None, None
self.cm, self.cmn = None, None
def __prepare_data(self):
# 将data转换为tensor形式
@ -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)

110
Scripts/test.py
View File

@ -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')

2
main.py
View File

@ -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
# )