refactor(Modify.py): 重构代码并改进模型训练流程
- 重构了代码结构,优化了导入顺序和格式 - 改进了模型训练流程,添加了早停机制和学习率调度器- 增加了模型测试和可视化部分的代码 -优化了量子卷积层和模型的实现 - 调整了训练参数和数据预处理方法
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Modify.py
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Modify.py
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# Modify.py
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#%% 导入所有需要的包
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#%%
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# 首先我们导入所有需要的包:
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import os
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import random
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import numpy as np
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import pandas as pd
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import deepquantum as dq
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import matplotlib.pyplot as plt
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import torchvision.transforms as transforms
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from torchvision.datasets import FashionMNIST
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from tqdm import tqdm
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from sklearn.metrics import roc_auc_score
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from torch.utils.data import DataLoader
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from multiprocessing import freeze_support
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from torchvision.datasets import FashionMNIST
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import deepquantum as dq
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import matplotlib.pyplot as plt
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#%% 设置随机种子以保证可复现
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def seed_torch(seed=1024):
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"""
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Set random seeds for reproducibility.
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Args:
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seed (int): Random seed number to use. Default is 1024.
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"""
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random.seed(seed)
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os.environ['PYTHONHASHSEED'] = str(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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# Seed all GPUs with the same seed if using multi-GPU
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torch.cuda.manual_seed_all(seed)
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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#%% 准确率计算函数
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seed_torch(42) # 使用更常见的随机种子值
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#%%
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def calculate_score(y_true, y_preds):
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# 将模型预测结果转为概率分布
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preds_prob = torch.softmax(y_preds, dim=1)
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# 获得预测的类别(概率最高的一类)
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preds_class = torch.argmax(preds_prob, dim=1)
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# 计算准确率
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correct = (preds_class == y_true).float()
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return (correct.sum() / len(correct)).cpu().numpy()
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accuracy = correct.sum() / len(correct)
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return accuracy.cpu().numpy()
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#%% 训练与验证函数
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def train_model(model, criterion, optimizer, scheduler, train_loader, valid_loader, num_epochs, device, save_path):
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model.to(device)
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best_acc = 0.0
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metrics = {'epoch': [], 'train_acc': [], 'valid_acc': [], 'train_loss': [], 'valid_loss': []}
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for epoch in range(1, num_epochs + 1):
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# --- 训练阶段 ---
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def train_model(model, criterion, optimizer, train_loader, valid_loader, num_epochs, device):
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"""
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训练和验证模型。
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Args:
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model (torch.nn.Module): 要训练的模型。
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criterion (torch.nn.Module): 损失函数。
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optimizer (torch.optim.Optimizer): 优化器。
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train_loader (torch.utils.data.DataLoader): 训练数据加载器。
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valid_loader (torch.utils.data.DataLoader): 验证数据加载器。
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num_epochs (int): 训练的epoch数。
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Returns:
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model (torch.nn.Module): 训练后的模型。
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"""
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model.train()
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running_loss, running_acc = 0.0, 0.0
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for imgs, labels in train_loader:
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imgs, labels = imgs.to(device), labels.to(device)
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train_loss_list = []
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valid_loss_list = []
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train_acc_list = []
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valid_acc_list = []
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best_valid_acc = 0.0
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patience = 10 # 早停耐心值
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counter = 0 # 计数器
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scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=10)
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with tqdm(total=num_epochs) as pbar:
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for epoch in range(num_epochs):
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# 训练阶段
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train_loss = 0.0
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train_acc = 0.0
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for images, labels in train_loader:
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images = images.to(device)
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labels = labels.to(device)
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optimizer.zero_grad()
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outputs = model(imgs)
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outputs = model(images)
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loss = criterion(outputs, labels)
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loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
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optimizer.step()
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running_loss += loss.item()
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running_acc += calculate_score(labels, outputs)
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train_loss += loss.item()
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train_acc += calculate_score(labels, outputs)
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train_loss = running_loss / len(train_loader)
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train_acc = running_acc / len(train_loader)
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scheduler.step()
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train_loss /= len(train_loader)
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train_acc /= len(train_loader)
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# --- 验证阶段 ---
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# 验证阶段
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model.eval()
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val_loss, val_acc = 0.0, 0.0
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valid_loss = 0.0
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valid_acc = 0.0
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with torch.no_grad():
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for imgs, labels in valid_loader:
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imgs, labels = imgs.to(device), labels.to(device)
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outputs = model(imgs)
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for images, labels in valid_loader:
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images = images.to(device)
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labels = labels.to(device)
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outputs = model(images)
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loss = criterion(outputs, labels)
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val_loss += loss.item()
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val_acc += calculate_score(labels, outputs)
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valid_loss += loss.item()
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valid_acc += calculate_score(labels, outputs)
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valid_loss = val_loss / len(valid_loader)
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valid_acc = val_acc / len(valid_loader)
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valid_loss /= len(valid_loader)
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valid_acc /= len(valid_loader)
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metrics['epoch'].append(epoch)
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metrics['train_loss'].append(train_loss)
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metrics['valid_loss'].append(valid_loss)
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metrics['train_acc'].append(train_acc)
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metrics['valid_acc'].append(valid_acc)
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# 学习率调度器更新
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scheduler.step(valid_acc)
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tqdm.write(f"[{save_path}] Epoch {epoch}/{num_epochs} "
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f"Train Acc: {train_acc:.4f} Valid Acc: {valid_acc:.4f}")
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# 早停机制
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if valid_acc > best_valid_acc:
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best_valid_acc = valid_acc
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torch.save(model.state_dict(), './data/notebook2/best_model.pt')
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counter = 0
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else:
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counter += 1
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if counter >= patience:
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print(f'Early stopping at epoch {epoch+1} due to no improvement in validation accuracy.')
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break
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pbar.set_description(f"Train loss: {train_loss:.3f} Valid Acc: {valid_acc:.3f}")
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pbar.update()
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train_loss_list.append(train_loss)
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valid_loss_list.append(valid_loss)
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train_acc_list.append(train_acc)
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valid_acc_list.append(valid_acc)
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# 加载最佳模型权重
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if os.path.exists('./data/notebook2/best_model.pt'):
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model.load_state_dict(torch.load('./data/notebook2/best_model.pt'))
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# 修改metrics构建方式,确保各数组长度一致
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metrics = {
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'epoch': list(range(1, len(train_loss_list) + 1)),
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'train_acc': train_acc_list,
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'valid_acc': valid_acc_list,
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'train_loss': train_loss_list,
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'valid_loss': valid_loss_list
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}
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if valid_acc > best_acc:
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best_acc = valid_acc
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torch.save(model.state_dict(), save_path)
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return model, metrics
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#%% 测试函数
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def test_model(model, test_loader, device):
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model.to(device).eval()
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acc = 0.0
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model.eval()
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test_acc = 0.0
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with torch.no_grad():
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for imgs, labels in test_loader:
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imgs, labels = imgs.to(device), labels.to(device)
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outputs = model(imgs)
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acc += calculate_score(labels, outputs)
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acc /= len(test_loader)
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print(f"Test Accuracy: {acc:.4f}")
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return acc
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for images, labels in test_loader:
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images = images.to(device)
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labels = labels.to(device)
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outputs = model(images)
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test_acc += calculate_score(labels, outputs)
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#%% 定义量子卷积层与模型
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singlegate_list = ['rx','ry','rz','s','t','p','u3']
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doublegate_list = ['rxx','ryy','rzz','swap','cnot','cp','ch','cu','ct','cz']
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test_acc /= len(test_loader)
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print(f'Test Acc: {test_acc:.3f}')
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return test_acc
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#%%
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# 定义图像变换
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trans1 = transforms.Compose([
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transforms.RandomHorizontalFlip(), # 随机水平翻转
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transforms.RandomRotation(10), # 随机旋转±10度
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transforms.ColorJitter(brightness=0.2, contrast=0.2), # 颜色调整
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transforms.Resize((18, 18)), # 调整大小为18x18
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transforms.ToTensor(), # 转换为张量
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transforms.Normalize((0.5,), (0.5,)) # 归一化到[-1, 1]
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])
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trans2 = transforms.Compose([
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transforms.RandomHorizontalFlip(), # 随机水平翻转
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transforms.RandomRotation(10), # 随机旋转±10度
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transforms.ColorJitter(brightness=0.2, contrast=0.2), # 颜色调整
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transforms.Resize((16, 16)), # 调整大小为16x16
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transforms.ToTensor(), # 转换为张量
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transforms.Normalize((0.5,), (0.5,)) # 归一化到[-1, 1]
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])
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train_dataset = FashionMNIST(root='./data/notebook2', train=False, transform=trans1,download=True)
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test_dataset = FashionMNIST(root='./data/notebook2', train=False, transform=trans1,download=True)
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# 定义训练集和测试集的比例
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train_ratio = 0.8 # 训练集比例为80%,验证集比例为20%
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valid_ratio = 0.2
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total_samples = len(train_dataset)
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train_size = int(train_ratio * total_samples)
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valid_size = int(valid_ratio * total_samples)
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# 分割训练集和测试集
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train_dataset, valid_dataset = torch.utils.data.random_split(train_dataset, [train_size, valid_size])
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# 加载随机抽取的训练数据集
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train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, drop_last=True)
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valid_loader = DataLoader(valid_dataset, batch_size=64, shuffle=False, drop_last=True)
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test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False, drop_last=True)
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#%%
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singlegate_list = ['rx', 'ry', 'rz', 's', 't', 'p', 'u3']
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doublegate_list = ['rxx', 'ryy', 'rzz', 'swap', 'cnot', 'cp', 'ch', 'cu', 'ct', 'cz']
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#%%
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# 随机量子卷积层
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class RandomQuantumConvolutionalLayer(nn.Module):
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def __init__(self, nqubit, num_circuits, seed=1024):
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super().__init__()
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def __init__(self, nqubit, num_circuits, seed:int=1024):
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super(RandomQuantumConvolutionalLayer, self).__init__()
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random.seed(seed)
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self.nqubit = nqubit
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self.cirs = nn.ModuleList([self.circuit(nqubit) for _ in range(num_circuits)])
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def circuit(self, nqubit):
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cir = dq.QubitCircuit(nqubit)
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cir.rxlayer(encode=True); cir.barrier()
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for _ in range(3):
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for i in range(nqubit):
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getattr(cir, random.choice(singlegate_list))(i)
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c,t = random.sample(range(nqubit),2)
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gate = random.choice(doublegate_list)
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if gate[0] in ['r','s']:
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getattr(cir, gate)([c,t])
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else:
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getattr(cir, gate)(c,t)
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cir.rxlayer(encode=True) # 对原论文的量子线路结构并无影响,只是做了一个数据编码的操作
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cir.barrier()
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for iter in range(3):
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for i in range(nqubit):
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singlegate = random.choice(singlegate_list)
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getattr(cir, singlegate)(i)
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control_bit, target_bit = random.sample(range(0, nqubit - 1), 2)
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doublegate = random.choice(doublegate_list)
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if doublegate[0] in ['r', 's']:
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getattr(cir, doublegate)([control_bit, target_bit])
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else:
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getattr(cir, doublegate)(control_bit, target_bit)
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cir.barrier()
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cir.observable(0)
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return cir
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def forward(self, x):
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k,s = 2,2
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x_unf = x.unfold(2,k,s).unfold(3,k,s)
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w = (x.shape[-1]-k)//s + 1
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x_r = x_unf.reshape(-1, self.nqubit)
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exps = []
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for cir in self.cirs:
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cir(x_r)
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exps.append(cir.expectation())
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exps = torch.stack(exps,1).reshape(x.size(0), len(self.cirs), w, w)
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return exps
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def forward(self, x):
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kernel_size, stride = 2, 2
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# [64, 1, 18, 18] -> [64, 1, 9, 18, 2] -> [64, 1, 9, 9, 2, 2]
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x_unflod = x.unfold(2, kernel_size, stride).unfold(3, kernel_size, stride)
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w = int((x.shape[-1] - kernel_size) / stride + 1)
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x_reshape = x_unflod.reshape(-1, self.nqubit)
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exps = []
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for cir in self.cirs: # out_channels
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cir(x_reshape)
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exp = cir.expectation()
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exps.append(exp)
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exps = torch.stack(exps, dim=1)
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exps = exps.reshape(x.shape[0], 3, w, w)
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return exps
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#%%
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net = RandomQuantumConvolutionalLayer(nqubit=4, num_circuits=3, seed=1024)
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net.cirs[0].draw()
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#%%
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# 基于随机量子卷积层的混合模型
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class RandomQCCNN(nn.Module):
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def __init__(self):
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super().__init__()
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super(RandomQCCNN, self).__init__()
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self.conv = nn.Sequential(
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RandomQuantumConvolutionalLayer(4,3,seed=1024),
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nn.ReLU(), nn.MaxPool2d(2,1),
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nn.Conv2d(3,6,2,1), nn.ReLU(), nn.MaxPool2d(2,1)
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RandomQuantumConvolutionalLayer(nqubit=4, num_circuits=3, seed=1024), # num_circuits=3代表我们在quanv1层只用了3个量子卷积核
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nn.BatchNorm2d(3), # 添加批量归一化
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2, stride=1),
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nn.Conv2d(3, 6, kernel_size=2, stride=1),
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nn.BatchNorm2d(6), # 添加批量归一化
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2, stride=1)
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)
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self.fc = nn.Sequential(
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nn.Linear(6*6*6,1024), nn.Dropout(0.4),
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nn.Linear(1024,10)
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nn.Linear(6 * 6 * 6, 1024),
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nn.BatchNorm1d(1024), # 添加批量归一化
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nn.Dropout(0.5), # 增加dropout比例
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nn.ReLU(),
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nn.Linear(1024, 10)
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)
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def forward(self,x):
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x = self.conv(x)
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x = x.view(x.size(0),-1)
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return self.fc(x)
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def forward(self, x):
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x = self.conv(x)
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x = x.reshape(x.size(0), -1)
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x = self.fc(x)
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return x
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#%%
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# 修改RandomQCCNN模型的训练参数
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num_epochs = 300
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(device)
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seed_torch(42) # 使用相同的随机种子值
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model = RandomQCCNN()
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model.to(device)
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.AdamW(model.parameters(), lr=3e-4, weight_decay=1e-5) # 使用AdamW优化器和适当的权重衰减
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optim_model, metrics = train_model(model, criterion, optimizer, train_loader, valid_loader, num_epochs, device)
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torch.save(optim_model.state_dict(), './data/notebook2/random_qccnn_weights.pt') # 保存训练好的模型参数,用于后续的推理或测试
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pd.DataFrame(metrics).to_csv('./data/notebook2/random_qccnn_metrics.csv', index='None') # 保存模型训练过程,用于后续图标展示
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#%%
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state_dict = torch.load('./data/notebook2/random_qccnn_weights.pt', map_location=device)
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random_qccnn_model = RandomQCCNN()
|
||||
random_qccnn_model.load_state_dict(state_dict)
|
||||
random_qccnn_model.to(device)
|
||||
|
||||
test_acc = test_model(random_qccnn_model, test_loader, device)
|
||||
#%%
|
||||
data = pd.read_csv('./data/notebook2/random_qccnn_metrics.csv')
|
||||
epoch = data['epoch']
|
||||
train_loss = data['train_loss']
|
||||
valid_loss = data['valid_loss']
|
||||
train_acc = data['train_acc']
|
||||
valid_acc = data['valid_acc']
|
||||
|
||||
# 创建图和Axes对象
|
||||
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
|
||||
|
||||
# 绘制训练损失曲线
|
||||
ax1.plot(epoch, train_loss, label='Train Loss')
|
||||
ax1.plot(epoch, valid_loss, label='Valid Loss')
|
||||
ax1.set_title('Training Loss Curve')
|
||||
ax1.set_xlabel('Epoch')
|
||||
ax1.set_ylabel('Loss')
|
||||
ax1.legend()
|
||||
|
||||
# 绘制训练准确率曲线
|
||||
ax2.plot(epoch, train_acc, label='Train Accuracy')
|
||||
ax2.plot(epoch, valid_acc, label='Valid Accuracy')
|
||||
ax2.set_title('Training Accuracy Curve')
|
||||
ax2.set_xlabel('Epoch')
|
||||
ax2.set_ylabel('Accuracy')
|
||||
ax2.legend()
|
||||
|
||||
plt.show()
|
||||
#%%
|
||||
class ParameterizedQuantumConvolutionalLayer(nn.Module):
|
||||
def __init__(self,nqubit,num_circuits):
|
||||
def __init__(self, nqubit, num_circuits):
|
||||
super().__init__()
|
||||
self.nqubit = nqubit
|
||||
self.cirs = nn.ModuleList([self.circuit(nqubit) for _ in range(num_circuits)])
|
||||
def circuit(self,nqubit):
|
||||
|
||||
def circuit(self, nqubit):
|
||||
cir = dq.QubitCircuit(nqubit)
|
||||
cir.rxlayer(encode=True); cir.barrier()
|
||||
for _ in range(4):
|
||||
cir.rylayer(); cir.cnot_ring(); cir.barrier()
|
||||
cir.rxlayer(encode=True) #对原论文的量子线路结构并无影响,只是做了一个数据编码的操作
|
||||
cir.barrier()
|
||||
for iter in range(4): #对应原论文中一个量子卷积线路上的深度为4,可控参数一共16个
|
||||
cir.rylayer()
|
||||
cir.cnot_ring()
|
||||
cir.barrier()
|
||||
|
||||
cir.observable(0)
|
||||
return cir
|
||||
def forward(self,x):
|
||||
k,s = 2,2
|
||||
x_unf = x.unfold(2,k,s).unfold(3,k,s)
|
||||
w = (x.shape[-1]-k)//s +1
|
||||
x_r = x_unf.reshape(-1,self.nqubit)
|
||||
exps = []
|
||||
for cir in self.cirs:
|
||||
cir(x_r); exps.append(cir.expectation())
|
||||
exps = torch.stack(exps,1).reshape(x.size(0),len(self.cirs),w,w)
|
||||
return exps
|
||||
|
||||
def forward(self, x):
|
||||
kernel_size, stride = 2, 2
|
||||
# [64, 1, 18, 18] -> [64, 1, 9, 18, 2] -> [64, 1, 9, 9, 2, 2]
|
||||
x_unflod = x.unfold(2, kernel_size, stride).unfold(3, kernel_size, stride)
|
||||
w = int((x.shape[-1] - kernel_size) / stride + 1)
|
||||
x_reshape = x_unflod.reshape(-1, self.nqubit)
|
||||
|
||||
exps = []
|
||||
for cir in self.cirs: # out_channels
|
||||
cir(x_reshape)
|
||||
exp = cir.expectation()
|
||||
exps.append(exp)
|
||||
|
||||
exps = torch.stack(exps, dim=1)
|
||||
exps = exps.reshape(x.shape[0], 3, w, w)
|
||||
return exps
|
||||
#%%
|
||||
# 此处我们可视化其中一个量子卷积核的线路结构:
|
||||
net = ParameterizedQuantumConvolutionalLayer(nqubit=4, num_circuits=3)
|
||||
net.cirs[0].draw()
|
||||
#%%
|
||||
# QCCNN整体网络架构:
|
||||
class QCCNN(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
super(QCCNN, self).__init__()
|
||||
self.conv = nn.Sequential(
|
||||
ParameterizedQuantumConvolutionalLayer(4,3),
|
||||
nn.ReLU(), nn.MaxPool2d(2,1)
|
||||
ParameterizedQuantumConvolutionalLayer(nqubit=4, num_circuits=3),
|
||||
nn.BatchNorm2d(3), # 添加批量归一化
|
||||
nn.ReLU(),
|
||||
nn.MaxPool2d(kernel_size=2, stride=1)
|
||||
)
|
||||
self.fc = nn.Sequential(
|
||||
nn.Linear(8*8*3,128), nn.Dropout(0.4), nn.ReLU(),
|
||||
nn.Linear(128,10)
|
||||
)
|
||||
def forward(self,x):
|
||||
x = self.conv(x); x = x.view(x.size(0),-1)
|
||||
return self.fc(x)
|
||||
|
||||
def vgg_block(in_c,out_c,n_convs):
|
||||
layers = [nn.Conv2d(in_c,out_c,3,padding=1), nn.ReLU()]
|
||||
for _ in range(n_convs-1):
|
||||
layers += [nn.Conv2d(out_c,out_c,3,padding=1), nn.ReLU()]
|
||||
layers.append(nn.MaxPool2d(2,2))
|
||||
self.fc = nn.Sequential(
|
||||
nn.Linear(8 * 8 * 3, 128),
|
||||
nn.BatchNorm1d(128), # 添加批量归一化
|
||||
nn.Dropout(0.5), # 增加dropout比例
|
||||
nn.ReLU(),
|
||||
nn.Linear(128, 10)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv(x)
|
||||
x = x.reshape(x.size(0), -1)
|
||||
x = self.fc(x)
|
||||
return x
|
||||
#%%
|
||||
# 修改QCCNN模型的训练参数
|
||||
num_epochs = 300
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
model = QCCNN()
|
||||
model.to(device)
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
optimizer = optim.AdamW(model.parameters(), lr=3e-4, weight_decay=1e-5) # 使用AdamW优化器和适当的权重衰减
|
||||
optim_model, metrics = train_model(model, criterion, optimizer, train_loader, valid_loader, num_epochs, device)
|
||||
torch.save(optim_model.state_dict(), './data/notebook2/qccnn_weights.pt') # 保存训练好的模型参数,用于后续的推理或测试
|
||||
pd.DataFrame(metrics).to_csv('./data/notebook2/qccnn_metrics.csv', index='None') # 保存模型训练过程,用于后续图标展示
|
||||
#%%
|
||||
state_dict = torch.load('./data/notebook2/qccnn_weights.pt', map_location=device)
|
||||
qccnn_model = QCCNN()
|
||||
qccnn_model.load_state_dict(state_dict)
|
||||
qccnn_model.to(device)
|
||||
|
||||
test_acc = test_model(qccnn_model, test_loader, device)
|
||||
#%%
|
||||
def vgg_block(in_channel,out_channel,num_convs):
|
||||
layers = nn.ModuleList()
|
||||
assert num_convs >= 1
|
||||
layers.append(nn.Conv2d(in_channel,out_channel,kernel_size=3,padding=1))
|
||||
layers.append(nn.ReLU())
|
||||
for _ in range(num_convs-1):
|
||||
layers.append(nn.Conv2d(out_channel,out_channel,kernel_size=3,padding=1))
|
||||
layers.append(nn.ReLU())
|
||||
layers.append(nn.MaxPool2d(kernel_size=2,stride=2))
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
VGG = nn.Sequential(
|
||||
vgg_block(1,10,3),
|
||||
vgg_block(10,16,3),
|
||||
vgg_block(1, 32, 2), # 增加通道数和调整卷积层数量
|
||||
vgg_block(32, 64, 2),
|
||||
nn.Flatten(),
|
||||
nn.Linear(16*4*4,120), nn.Sigmoid(),
|
||||
nn.Linear(120,84), nn.Sigmoid(),
|
||||
nn.Linear(84,10), nn.Softmax(dim=-1)
|
||||
nn.Linear(64 * 4 * 4, 256), # 调整全连接层大小
|
||||
nn.BatchNorm1d(256), # 添加批量归一化
|
||||
nn.ReLU(),
|
||||
nn.Dropout(0.5), # 增加dropout比例
|
||||
nn.Linear(256, 128),
|
||||
nn.BatchNorm1d(128), # 添加批量归一化
|
||||
nn.ReLU(),
|
||||
nn.Dropout(0.5),
|
||||
nn.Linear(128, 10),
|
||||
nn.Softmax(dim=-1)
|
||||
)
|
||||
#%%
|
||||
# 修改VGG模型的训练参数
|
||||
num_epochs = 300
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
#%% 主入口
|
||||
if __name__ == '__main__':
|
||||
freeze_support()
|
||||
vgg_model = VGG
|
||||
vgg_model.to(device)
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
optimizer = optim.AdamW(vgg_model.parameters(), lr=3e-4, weight_decay=1e-5) # 使用AdamW优化器和适当的权重衰减
|
||||
vgg_model, metrics = train_model(vgg_model, criterion, optimizer, train_loader, valid_loader, num_epochs, device)
|
||||
torch.save(vgg_model.state_dict(), './data/notebook2/vgg_weights.pt') # 保存训练好的模型参数,用于后续的推理或测试
|
||||
pd.DataFrame(metrics).to_csv('./data/notebook2/vgg_metrics.csv', index='None') # 保存模型训练过程,用于后续图标展示
|
||||
#%%
|
||||
state_dict = torch.load('./data/notebook2/vgg_weights.pt', map_location=device)
|
||||
vgg_model = VGG
|
||||
vgg_model.load_state_dict(state_dict)
|
||||
vgg_model.to(device)
|
||||
|
||||
# 数据增广与加载
|
||||
train_transform = transforms.Compose([
|
||||
transforms.Resize((18, 18)),
|
||||
transforms.RandomRotation(15),
|
||||
transforms.RandomHorizontalFlip(),
|
||||
transforms.RandomVerticalFlip(0.3),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize((0.5,), (0.5,))
|
||||
])
|
||||
eval_transform = transforms.Compose([
|
||||
transforms.Resize((18, 18)),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize((0.5,), (0.5,))
|
||||
])
|
||||
vgg_test_acc = test_model(vgg_model, test_loader, device)
|
||||
#%%
|
||||
vgg_data = pd.read_csv('./data/notebook2/vgg_metrics.csv')
|
||||
qccnn_data = pd.read_csv('./data/notebook2/qccnn_metrics.csv')
|
||||
vgg_epoch = vgg_data['epoch']
|
||||
vgg_train_loss = vgg_data['train_loss']
|
||||
vgg_valid_loss = vgg_data['valid_loss']
|
||||
vgg_train_acc = vgg_data['train_acc']
|
||||
vgg_valid_acc = vgg_data['valid_acc']
|
||||
|
||||
full_train = FashionMNIST(root='./data/notebook2', train=True, transform=train_transform, download=True)
|
||||
test_dataset = FashionMNIST(root='./data/notebook2', train=False, transform=eval_transform, download=True)
|
||||
train_size = int(0.8 * len(full_train))
|
||||
valid_size = len(full_train) - train_size
|
||||
train_ds, valid_ds = torch.utils.data.random_split(full_train, [train_size, valid_size])
|
||||
valid_ds.dataset.transform = eval_transform
|
||||
qccnn_epoch = qccnn_data['epoch']
|
||||
qccnn_train_loss = qccnn_data['train_loss']
|
||||
qccnn_valid_loss = qccnn_data['valid_loss']
|
||||
qccnn_train_acc = qccnn_data['train_acc']
|
||||
qccnn_valid_acc = qccnn_data['valid_acc']
|
||||
|
||||
batch_size = 128
|
||||
train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4)
|
||||
valid_loader = DataLoader(valid_ds, batch_size=batch_size, shuffle=False, drop_last=True, num_workers=4)
|
||||
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, drop_last=False, num_workers=4)
|
||||
# 创建图和Axes对象
|
||||
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
|
||||
|
||||
# 三种模型配置
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
models = {
|
||||
'random_qccnn': (RandomQCCNN(), 1e-3, './data/notebook2/random_qccnn_best.pt'),
|
||||
'qccnn': (QCCNN(), 1e-4, './data/notebook2/qccnn_best.pt'),
|
||||
'vgg': (VGG, 1e-4, './data/notebook2/vgg_best.pt')
|
||||
}
|
||||
# 绘制训练损失曲线
|
||||
ax1.plot(vgg_epoch, vgg_train_loss, label='VGG Train Loss')
|
||||
ax1.plot(vgg_epoch, vgg_valid_loss, label='VGG Valid Loss')
|
||||
ax1.plot(qccnn_epoch, qccnn_train_loss, label='QCCNN Valid Loss')
|
||||
ax1.plot(qccnn_epoch, qccnn_valid_loss, label='QCCNN Valid Loss')
|
||||
ax1.set_title('Training Loss Curve')
|
||||
ax1.set_xlabel('Epoch')
|
||||
ax1.set_ylabel('Loss')
|
||||
ax1.legend()
|
||||
|
||||
all_metrics = {}
|
||||
for name, (model, lr, save_path) in models.items():
|
||||
seed_torch(1024)
|
||||
model = model.to(device)
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)
|
||||
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50)
|
||||
# 绘制训练准确率曲线
|
||||
ax2.plot(vgg_epoch, vgg_train_acc, label='VGG Train Accuracy')
|
||||
ax2.plot(vgg_epoch, vgg_valid_acc, label='VGG Valid Accuracy')
|
||||
ax2.plot(qccnn_epoch, qccnn_train_acc, label='QCCNN Train Accuracy')
|
||||
ax2.plot(qccnn_epoch, qccnn_valid_acc, label='QCCNN Valid Accuracy')
|
||||
ax2.set_title('Training Accuracy Curve')
|
||||
ax2.set_xlabel('Epoch')
|
||||
ax2.set_ylabel('Accuracy')
|
||||
ax2.legend()
|
||||
|
||||
print(f"\n=== Training {name} ===")
|
||||
_, metrics = train_model(
|
||||
model, criterion, optimizer, scheduler,
|
||||
train_loader, valid_loader,
|
||||
num_epochs=50, device=device, save_path=save_path
|
||||
)
|
||||
all_metrics[name] = metrics
|
||||
pd.DataFrame(metrics).to_csv(f'./data/notebook2/{name}_metrics.csv', index=False)
|
||||
plt.show()
|
||||
#%%
|
||||
# 这里我们对比不同模型之间可训练参数量的区别
|
||||
|
||||
# 测试与可视化
|
||||
plt.figure(figsize=(12,5))
|
||||
for i,(name,metrics) in enumerate(all_metrics.items(),1):
|
||||
model, _, save_path = models[name]
|
||||
best_model = model.to(device)
|
||||
best_model.load_state_dict(torch.load(save_path))
|
||||
print(f"\n--- Testing {name} ---")
|
||||
test_model(best_model, test_loader, device)
|
||||
def count_parameters(model):
|
||||
"""
|
||||
计算模型的参数数量
|
||||
"""
|
||||
return sum(p.numel() for p in model.parameters() if p.requires_grad)
|
||||
|
||||
plt.subplot(1,3,i)
|
||||
plt.plot(metrics['epoch'], metrics['valid_acc'], label=f'{name} Val Acc')
|
||||
plt.xlabel('Epoch'); plt.ylabel('Valid Acc')
|
||||
plt.title(name); plt.legend()
|
||||
|
||||
plt.tight_layout(); plt.show()
|
||||
|
||||
# 参数量统计
|
||||
def count_parameters(m):
|
||||
return sum(p.numel() for p in m.parameters() if p.requires_grad)
|
||||
|
||||
print("\nParameter Counts:")
|
||||
for name,(model,_,_) in models.items():
|
||||
print(f"{name}: {count_parameters(model)}")
|
||||
number_params_VGG = count_parameters(VGG)
|
||||
number_params_QCCNN = count_parameters(QCCNN())
|
||||
print(f'VGG 模型可训练参数量:{number_params_VGG}\t QCCNN模型可训练参数量:{number_params_QCCNN}')
|
@ -1,301 +1,33 @@
|
||||
,epoch,train_acc,valid_acc,train_loss,valid_loss
|
||||
0,1,0.12575,0.21421370967741934,2.2937231807708742,2.2676862593620055
|
||||
1,2,0.248,0.3240927419354839,2.2377592124938963,2.1976155081102924
|
||||
2,3,0.354375,0.3815524193548387,2.129876153945923,2.044375258107339
|
||||
3,4,0.42625,0.4324596774193548,1.9269471073150635,1.815262294584705
|
||||
4,5,0.482125,0.5282258064516129,1.6988583679199218,1.6081839684517152
|
||||
5,6,0.528375,0.5151209677419355,1.5167635107040405,1.4654438649454424
|
||||
6,7,0.5465,0.5574596774193549,1.3927514476776124,1.3655932180343135
|
||||
7,8,0.5705,0.5403225806451613,1.3035813112258912,1.2985444838000881
|
||||
8,9,0.586875,0.5902217741935484,1.2359741706848144,1.2315824993195073
|
||||
9,10,0.5985,0.5967741935483871,1.1828507437705993,1.1789349471369097
|
||||
10,11,0.61075,0.6013104838709677,1.1400936079025268,1.146019495302631
|
||||
11,12,0.61425,0.6098790322580645,1.105873393535614,1.1111935800121677
|
||||
12,13,0.623,0.6234879032258065,1.076672074317932,1.089227545645929
|
||||
13,14,0.63475,0.6159274193548387,1.0523606872558593,1.0597389667264876
|
||||
14,15,0.63575,0.5866935483870968,1.0302134299278258,1.0517296521894393
|
||||
15,16,0.638625,0.625,1.0127733755111694,1.030857661078053
|
||||
16,17,0.644125,0.623991935483871,0.9972539176940918,1.0212982143125227
|
||||
17,18,0.647125,0.6491935483870968,0.9847306752204895,1.0031032081573241
|
||||
18,19,0.651625,0.6295362903225806,0.9718224520683288,0.9922776433729357
|
||||
19,20,0.653375,0.655241935483871,0.9614444556236267,1.004007081831655
|
||||
20,21,0.65475,0.639616935483871,0.9525526990890503,0.9718767077692093
|
||||
21,22,0.657125,0.6491935483870968,0.944016770362854,0.9612755986952013
|
||||
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BIN
data/notebook2/best_model.pt
Normal file
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data/notebook2/best_model.pt
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@ -1,51 +1,49 @@
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
50,0.7677083333333333,0.7825940860215054,0.6438165396849315,0.5961719805835396
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
28,29,0.780625,0.7520161290322581,0.5824392430782318,0.6569667564284417
|
||||
29,30,0.78475,0.7686491935483871,0.5810435285568237,0.6306867743692091
|
||||
30,31,0.789,0.7706653225806451,0.5672282783985138,0.6261125274242894
|
||||
31,32,0.78525,0.7560483870967742,0.5757509377002716,0.6505500414679127
|
||||
32,33,0.792,0.7681451612903226,0.5613697295188904,0.629849144527989
|
||||
33,34,0.793875,0.7620967741935484,0.5625830183029175,0.6189906856706066
|
||||
34,35,0.791875,0.7681451612903226,0.5602230775356293,0.6212261732547514
|
||||
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|
||||
36,37,0.794625,0.7701612903225806,0.5573954427242279,0.6205428954093687
|
||||
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|
||||
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|
||||
39,40,0.796375,0.7711693548387096,0.5491654114723206,0.6137347759739045
|
||||
40,41,0.799,0.7641129032258065,0.5372960684299469,0.6470560056547965
|
||||
41,42,0.800375,0.7671370967741935,0.5395989503860473,0.6211921880322118
|
||||
42,43,0.806,0.7671370967741935,0.5370515692234039,0.6075864828401997
|
||||
43,44,0.801875,0.7605846774193549,0.5388010408878326,0.5891174308715328
|
||||
44,45,0.800875,0.766633064516129,0.539761929512024,0.610026998865989
|
||||
45,46,0.802375,0.780241935483871,0.5270701496601105,0.6000283591208919
|
||||
46,47,0.799875,0.7772177419354839,0.5320828959941865,0.5864833074231302
|
||||
47,48,0.807,0.7837701612903226,0.5309389193058014,0.5761975809451072
|
||||
|
|
BIN
data/notebook2/qccnn_weights.pt
Normal file
BIN
data/notebook2/qccnn_weights.pt
Normal file
Binary file not shown.
@ -1,51 +1,34 @@
|
||||
epoch,train_acc,valid_acc,train_loss,valid_loss
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
50,0.8408125,0.836861559139785,0.43524290529886883,0.4449239748139535
|
||||
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|
||||
0,1,0.561125,0.6476814516129032,1.271985122203827,0.9911825291572078
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
13,14,0.767375,0.7379032258064516,0.6460576868057251,0.6988139988914612
|
||||
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|
||||
15,16,0.763125,0.7368951612903226,0.6262373595237732,0.714297366719092
|
||||
16,17,0.76925,0.7227822580645161,0.6279029569625855,0.7181522269402781
|
||||
17,18,0.77025,0.7449596774193549,0.6159816448688507,0.6757595616002237
|
||||
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|
||||
19,20,0.775375,0.7469758064516129,0.6000997524261474,0.6749713065162781
|
||||
20,21,0.7805,0.748991935483871,0.5928600332736969,0.6656959902855658
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||||
21,22,0.77525,0.7444556451612904,0.599046837568283,0.6857193170055267
|
||||
22,23,0.785,0.7384072580645161,0.5875316572189331,0.6785462142959717
|
||||
23,24,0.778375,0.765625,0.588378502368927,0.627939272311426
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||||
24,25,0.78575,0.7459677419354839,0.5700427904129028,0.6502643679418871
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||||
25,26,0.7805,0.75,0.5785817315578461,0.6712307555060233
|
||||
26,27,0.78675,0.7474798387096774,0.5676946561336518,0.6555941143343526
|
||||
27,28,0.787875,0.7505040322580645,0.575938116312027,0.6507430134281036
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||||
28,29,0.789,0.7605846774193549,0.5655779435634612,0.6520830373610219
|
||||
29,30,0.790625,0.7434475806451613,0.5578647639751434,0.6789213486256138
|
||||
30,31,0.787375,0.7565524193548387,0.5687701859474182,0.649224087115257
|
||||
31,32,0.79575,0.7560483870967742,0.5432633152008056,0.6388471222692921
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||||
32,33,0.7895,0.7595766129032258,0.5557173223495483,0.6391933618053314
|
||||
|
|
BIN
data/notebook2/random_qccnn_weights.pt
Normal file
BIN
data/notebook2/random_qccnn_weights.pt
Normal file
Binary file not shown.
@ -1,51 +1,86 @@
|
||||
epoch,train_acc,valid_acc,train_loss,valid_loss
|
||||
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||||
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||||
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||||
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||||
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|
||||
22,0.7876666666666666,0.7848622311827957,1.6801685171127319,1.6815461574062225
|
||||
23,0.7879791666666667,0.7848622311827957,1.6788572374979656,1.6818229216401295
|
||||
24,0.7890833333333334,0.7859543010752689,1.6776897859573365,1.6799169009731663
|
||||
25,0.7902708333333334,0.7883904569892473,1.6763870159784953,1.6777271570697907
|
||||
26,0.791,0.789986559139785,1.6751194190979004,1.6777111407249206
|
||||
27,0.7918541666666666,0.7886424731182796,1.674490571975708,1.6776156912567795
|
||||
28,0.7936666666666666,0.7905745967741935,1.6728278172810873,1.6744540147883917
|
||||
29,0.793375,0.7876344086021505,1.671903525352478,1.6776354030896259
|
||||
30,0.7945833333333333,0.7901545698924731,1.6710031661987306,1.6751063805754467
|
||||
31,0.7959166666666667,0.7932627688172043,1.6700964994430543,1.6719800926023913
|
||||
32,0.7955833333333333,0.7933467741935484,1.6697869466145834,1.6723043623790945
|
||||
33,0.7971458333333333,0.792002688172043,1.668815224647522,1.6722341519530102
|
||||
34,0.7974375,0.7931787634408602,1.6682115157445272,1.6723163063808153
|
||||
35,0.7979791666666667,0.7947748655913979,1.6676976483662924,1.670254216399244
|
||||
36,0.7984583333333334,0.7950268817204301,1.6669070380528768,1.6704239499184392
|
||||
37,0.799,0.795866935483871,1.6664897476832072,1.6691849411174815
|
||||
38,0.7991875,0.7948588709677419,1.6660230029424032,1.6700230785595473
|
||||
39,0.800125,0.795950940860215,1.6655609397888183,1.6688298884258475
|
||||
40,0.8006458333333333,0.7956989247311828,1.6652250595092772,1.6690674840763051
|
||||
41,0.8006875,0.7962869623655914,1.664971160888672,1.6688041135828982
|
||||
42,0.8015416666666667,0.795866935483871,1.6647087678909303,1.668453808753721
|
||||
43,0.8013958333333333,0.7962029569892473,1.6643381754557292,1.6682484457569737
|
||||
44,0.8016458333333333,0.7965389784946236,1.6641663211186728,1.668392535178892
|
||||
45,0.8017708333333333,0.7957829301075269,1.6640429185231527,1.6681856429705055
|
||||
46,0.8021041666666666,0.7965389784946236,1.663849822362264,1.6680005634984663
|
||||
47,0.8024583333333334,0.7966229838709677,1.663770879427592,1.6679569816076627
|
||||
48,0.8025833333333333,0.7962029569892473,1.6636734215418498,1.6680130330465173
|
||||
49,0.8024583333333334,0.7970430107526881,1.6635978577931723,1.6679544077124646
|
||||
50,0.8025,0.7970430107526881,1.6635685895284016,1.6679527682642783
|
||||
,epoch,train_acc,valid_acc,train_loss,valid_loss
|
||||
0,1,0.575125,0.7011088709677419,2.031563010215759,1.8535375249001287
|
||||
1,2,0.723875,0.7515120967741935,1.7430778923034669,1.713461822079074
|
||||
2,3,0.76425,0.7525201612903226,1.6991203842163085,1.7051894049490652
|
||||
3,4,0.779875,0.7716733870967742,1.6807003259658813,1.690080665772961
|
||||
4,5,0.79525,0.7696572580645161,1.6654855661392212,1.6926762519344207
|
||||
5,6,0.80175,0.7888104838709677,1.6598792200088501,1.6711357831954956
|
||||
6,7,0.80875,0.7721774193548387,1.6529147624969482,1.6847506146277151
|
||||
7,8,0.812375,0.8004032258064516,1.649049828529358,1.6597587523921844
|
||||
8,9,0.815375,0.7893145161290323,1.6458229179382324,1.6707303101016628
|
||||
9,10,0.825,0.811491935483871,1.6370085287094116,1.6518691432091497
|
||||
10,11,0.832375,0.795866935483871,1.630222158432007,1.6616583139665666
|
||||
11,12,0.83225,0.8145161290322581,1.6293829317092896,1.6454557026586225
|
||||
12,13,0.834875,0.8125,1.6268642024993896,1.6500283018235238
|
||||
13,14,0.840875,0.8104838709677419,1.6199358901977539,1.652194688397069
|
||||
14,15,0.83975,0.8069556451612904,1.6210605192184449,1.6520756982987927
|
||||
15,16,0.84175,0.8004032258064516,1.6195698108673096,1.6582853217278757
|
||||
16,17,0.838625,0.8049395161290323,1.6228710346221924,1.656198097813514
|
||||
17,18,0.83775,0.8140120967741935,1.6226149969100951,1.6462942131104008
|
||||
18,19,0.845375,0.8251008064516129,1.6150365982055663,1.6358921681680987
|
||||
19,20,0.85125,0.8296370967741935,1.6097205333709717,1.6333312757553593
|
||||
20,21,0.851875,0.8210685483870968,1.609424132347107,1.6388044895664338
|
||||
21,22,0.850375,0.8210685483870968,1.6101643466949462,1.6369191561975787
|
||||
22,23,0.858125,0.8185483870967742,1.6031983375549317,1.6421872877305554
|
||||
23,24,0.857125,0.8361895161290323,1.6038059520721435,1.62519553015309
|
||||
24,25,0.854,0.8301411290322581,1.606280044555664,1.6290803609355804
|
||||
25,26,0.858375,0.8119959677419355,1.6011180095672608,1.648349738890125
|
||||
26,27,0.861375,0.8371975806451613,1.6000273780822754,1.6237776010267195
|
||||
27,28,0.8705,0.8351814516129032,1.5907322645187378,1.6240986316434798
|
||||
28,29,0.87175,0.828125,1.5896439476013184,1.6316479982868317
|
||||
29,30,0.867375,0.8266129032258065,1.5936143741607667,1.632056209348863
|
||||
30,31,0.86575,0.8377016129032258,1.5949567575454713,1.6240303862479426
|
||||
31,32,0.86975,0.8336693548387096,1.5907692136764526,1.6251203167823054
|
||||
32,33,0.86975,0.8397177419354839,1.5919580411911012,1.6214879328204739
|
||||
33,34,0.872375,0.8422379032258065,1.5886561880111694,1.6194973222671016
|
||||
34,35,0.876,0.8331653225806451,1.585767653465271,1.6247131862948019
|
||||
35,36,0.871875,0.8392137096774194,1.5895709590911866,1.6215527211466143
|
||||
36,37,0.871125,0.8245967741935484,1.590462643623352,1.6370407227546937
|
||||
37,38,0.87325,0.8301411290322581,1.5876847248077393,1.6306157150576193
|
||||
38,39,0.8705,0.8240927419354839,1.590844289779663,1.634965923524672
|
||||
39,40,0.88125,0.8402217741935484,1.5803495874404907,1.6220719968118975
|
||||
40,41,0.881875,0.8462701612903226,1.5788077726364136,1.614263488400367
|
||||
41,42,0.87625,0.8487903225806451,1.5843929862976074,1.6131737078389814
|
||||
42,43,0.88175,0.842741935483871,1.5789586782455445,1.6184405088424683
|
||||
43,44,0.880375,0.8417338709677419,1.5802030544281005,1.6189708786626016
|
||||
44,45,0.880375,0.8492943548387096,1.5803771505355835,1.610948174230514
|
||||
45,46,0.877625,0.8523185483870968,1.5828620948791503,1.6089561793111986
|
||||
46,47,0.882125,0.8442540322580645,1.578727219581604,1.6160847948443504
|
||||
47,48,0.872875,0.8392137096774194,1.5874832077026366,1.6228746021947553
|
||||
48,49,0.884125,0.8371975806451613,1.5771635084152222,1.6237398655183855
|
||||
49,50,0.88975,0.8341733870967742,1.5712118272781372,1.6256768280459988
|
||||
50,51,0.876,0.8432459677419355,1.585065812110901,1.6188401445265739
|
||||
51,52,0.883,0.844758064516129,1.5785351629257203,1.6169025359615203
|
||||
52,53,0.889125,0.8548387096774194,1.5723771095275878,1.607426397262081
|
||||
53,54,0.88625,0.8518145161290323,1.5749757404327394,1.6076742141477522
|
||||
54,55,0.891125,0.8452620967741935,1.5695797872543336,1.6156867473356185
|
||||
55,56,0.891625,0.8497983870967742,1.5691107511520386,1.6118658050414054
|
||||
56,57,0.883375,0.8508064516129032,1.577266471862793,1.6087883749315817
|
||||
57,58,0.891625,0.8422379032258065,1.5702907581329346,1.6168161553721274
|
||||
58,59,0.89375,0.8553427419354839,1.5679762859344482,1.6071604336461713
|
||||
59,60,0.885875,0.8482862903225806,1.5749410953521727,1.6108381056016492
|
||||
60,61,0.891875,0.8417338709677419,1.5696327953338622,1.6187968907817718
|
||||
61,62,0.894,0.8392137096774194,1.5671770153045654,1.6208721822307957
|
||||
62,63,0.891125,0.8392137096774194,1.569726734161377,1.6224797733368412
|
||||
63,64,0.89325,0.8518145161290323,1.568245210647583,1.606977516605008
|
||||
64,65,0.895125,0.8462701612903226,1.5658181638717652,1.6140611633177726
|
||||
65,66,0.89175,0.8568548387096774,1.5686321334838866,1.6044510756769488
|
||||
66,67,0.896375,0.8412298387096774,1.5650024271011354,1.6208048520549652
|
||||
67,68,0.8895,0.8477822580645161,1.571241024017334,1.6136699338113107
|
||||
68,69,0.901625,0.8472782258064516,1.5600450325012207,1.612250724146443
|
||||
69,70,0.89025,0.8306451612903226,1.5705311574935914,1.630635638390818
|
||||
70,71,0.89675,0.8538306451612904,1.5637341051101685,1.6056317244806597
|
||||
71,72,0.897,0.8412298387096774,1.5633089447021484,1.6192954970944313
|
||||
72,73,0.900375,0.8503024193548387,1.560647201538086,1.6097132775091356
|
||||
73,74,0.895625,0.8533266129032258,1.565330421447754,1.6075265484471475
|
||||
74,75,0.895125,0.8543346774193549,1.565690894126892,1.6060891266792052
|
||||
75,76,0.900625,0.8608870967741935,1.560530616760254,1.6011101891917567
|
||||
76,77,0.897125,0.8563508064516129,1.563725378036499,1.6037284789546844
|
||||
77,78,0.89175,0.8482862903225806,1.5693257989883422,1.6122698245509979
|
||||
78,79,0.90225,0.8487903225806451,1.5589981718063355,1.6111104180735927
|
||||
79,80,0.893375,0.8442540322580645,1.5669814805984497,1.6161983090062295
|
||||
80,81,0.897125,0.8573588709677419,1.56400607585907,1.6040064134905416
|
||||
81,82,0.898,0.8568548387096774,1.5621892538070679,1.6044225615839804
|
||||
82,83,0.89075,0.8503024193548387,1.5697740039825439,1.6114465228972896
|
||||
83,84,0.900625,0.8598790322580645,1.5606124105453492,1.6002429416102748
|
||||
84,85,0.89825,0.8553427419354839,1.5628600664138794,1.603792409743032
|
||||
|
|
BIN
data/notebook2/vgg_weights.pt
Normal file
BIN
data/notebook2/vgg_weights.pt
Normal file
Binary file not shown.
Loading…
Reference in New Issue
Block a user