{ "cells": [ { "metadata": { "ExecuteTime": { "end_time": "2025-06-26T01:24:15.197491Z", "start_time": "2025-06-26T01:24:15.193517Z" } }, "cell_type": "code", "source": [ "# 首先我们导入所有需要的包:\n", "import os\n", "import random\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "import torchvision.transforms as transforms\n", "from tqdm import tqdm\n", "from sklearn.metrics import roc_auc_score\n", "from torch.utils.data import DataLoader\n", "from torchvision.datasets import FashionMNIST\n", "import deepquantum as dq\n", "import matplotlib.pyplot as plt\n", "\n", "def seed_torch(seed=1024):\n", " \"\"\"\n", " Set random seeds for reproducibility.\n", "\n", " Args:\n", " seed (int): Random seed number to use. Default is 1024.\n", " \"\"\"\n", "\n", " random.seed(seed)\n", " os.environ['PYTHONHASHSEED'] = str(seed)\n", " np.random.seed(seed)\n", " torch.manual_seed(seed)\n", " torch.cuda.manual_seed(seed)\n", "\n", " # Seed all GPUs with the same seed if using multi-GPU\n", " torch.cuda.manual_seed_all(seed)\n", " torch.backends.cudnn.benchmark = False\n", " torch.backends.cudnn.deterministic = True\n", "\n", "seed_torch(42) # 使用更常见的随机种子值" ], "id": "ce18f89946d8c18b", "outputs": [], "execution_count": 16 }, { "metadata": { "ExecuteTime": { "end_time": "2025-06-26T01:24:15.226309Z", "start_time": "2025-06-26T01:24:15.218309Z" } }, "cell_type": "code", "source": [ "def calculate_score(y_true, y_preds):\n", " # 将模型预测结果转为概率分布\n", " preds_prob = torch.softmax(y_preds, dim=1)\n", " # 获得预测的类别(概率最高的一类)\n", " preds_class = torch.argmax(preds_prob, dim=1)\n", " # 计算准确率\n", " correct = (preds_class == y_true).float()\n", " accuracy = correct.sum() / len(correct)\n", " return accuracy.cpu().numpy()\n", "\n", "\n", "def train_model(model, criterion, optimizer, train_loader, valid_loader, num_epochs, device):\n", " \"\"\"\n", " 训练和验证模型。\n", "\n", " Args:\n", " model (torch.nn.Module): 要训练的模型。\n", " criterion (torch.nn.Module): 损失函数。\n", " optimizer (torch.optim.Optimizer): 优化器。\n", " train_loader (torch.utils.data.DataLoader): 训练数据加载器。\n", " valid_loader (torch.utils.data.DataLoader): 验证数据加载器。\n", " num_epochs (int): 训练的epoch数。\n", "\n", " Returns:\n", " model (torch.nn.Module): 训练后的模型。\n", " \"\"\"\n", "\n", " model.train()\n", " train_loss_list = []\n", " valid_loss_list = []\n", " train_acc_list = []\n", " valid_acc_list = []\n", "\n", " best_valid_acc = 0.0\n", " patience = 10 # 早停耐心值\n", " counter = 0 # 计数器\n", "\n", " scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=5)\n", "\n", " with tqdm(total=num_epochs) as pbar:\n", " for epoch in range(num_epochs):\n", " # 训练阶段\n", " train_loss = 0.0\n", " train_acc = 0.0\n", " for images, labels in train_loader:\n", " images = images.to(device)\n", " labels = labels.to(device)\n", " optimizer.zero_grad()\n", " outputs = model(images)\n", " loss = criterion(outputs, labels)\n", " loss.backward()\n", " optimizer.step()\n", " train_loss += loss.item()\n", " train_acc += calculate_score(labels, outputs)\n", "\n", " train_loss /= len(train_loader)\n", " train_acc /= len(train_loader)\n", "\n", " # 验证阶段\n", " model.eval()\n", " valid_loss = 0.0\n", " valid_acc = 0.0\n", " with torch.no_grad():\n", " for images, labels in valid_loader:\n", " images = images.to(device)\n", " labels = labels.to(device)\n", " outputs = model(images)\n", " loss = criterion(outputs, labels)\n", " valid_loss += loss.item()\n", " valid_acc += calculate_score(labels, outputs)\n", "\n", " valid_loss /= len(valid_loader)\n", " valid_acc /= len(valid_loader)\n", "\n", " # 学习率调度器更新\n", " scheduler.step(valid_acc)\n", "\n", " # 早停机制\n", " if valid_acc > best_valid_acc:\n", " best_valid_acc = valid_acc\n", " torch.save(model.state_dict(), './data/notebook2/best_model.pt')\n", " counter = 0\n", " else:\n", " counter += 1\n", "\n", " if counter >= patience:\n", " print(f'Early stopping at epoch {epoch+1} due to no improvement in validation accuracy.')\n", " break\n", "\n", " pbar.set_description(f\"Train loss: {train_loss:.3f} Valid Acc: {valid_acc:.3f}\")\n", " pbar.update()\n", "\n", "\n", " train_loss_list.append(train_loss)\n", " valid_loss_list.append(valid_loss)\n", " train_acc_list.append(train_acc)\n", " valid_acc_list.append(valid_acc)\n", "\n", " # 加载最佳模型权重\n", " if os.path.exists('./data/notebook2/best_model.pt'):\n", " model.load_state_dict(torch.load('./data/notebook2/best_model.pt'))\n", "\n", " # 修改metrics构建方式,确保各数组长度一致\n", " metrics = {\n", " 'epoch': list(range(1, len(train_loss_list) + 1)),\n", " 'train_acc': train_acc_list,\n", " 'valid_acc': valid_acc_list,\n", " 'train_loss': train_loss_list,\n", " 'valid_loss': valid_loss_list\n", " }\n", "\n", "\n", " return model, metrics\n", "\n", "def test_model(model, test_loader, device):\n", " model.eval()\n", " test_acc = 0.0\n", " with torch.no_grad():\n", " for images, labels in test_loader:\n", " images = images.to(device)\n", " labels = labels.to(device)\n", " outputs = model(images)\n", " test_acc += calculate_score(labels, outputs)\n", "\n", " test_acc /= len(test_loader)\n", " print(f'Test Acc: {test_acc:.3f}')\n", " return test_acc" ], "id": "68d3cc00181d6b0f", "outputs": [], "execution_count": 17 }, { "metadata": { "ExecuteTime": { "end_time": "2025-06-26T01:24:15.272943Z", "start_time": "2025-06-26T01:24:15.238946Z" } }, "cell_type": "code", "source": [ "# 定义图像变换\n", "trans1 = transforms.Compose([\n", " transforms.RandomHorizontalFlip(), # 随机水平翻转\n", " transforms.RandomRotation(10), # 随机旋转±10度\n", " transforms.ColorJitter(brightness=0.2, contrast=0.2), # 颜色调整\n", " transforms.ToTensor(), # 转换为张量\n", " transforms.Normalize((0.5,), (0.5,)) # 归一化到[-1, 1]\n", "])\n", "\n", "trans2 = transforms.Compose([\n", " transforms.RandomHorizontalFlip(), # 随机水平翻转\n", " transforms.RandomRotation(10), # 随机旋转±10度\n", " transforms.ColorJitter(brightness=0.2, contrast=0.2), # 颜色调整\n", " transforms.ToTensor(), # 转换为张量\n", " transforms.Normalize((0.5,), (0.5,)) # 归一化到[-1, 1]\n", "])\n", "train_dataset = FashionMNIST(root='./data/notebook2', train=True, transform=trans1,download=True)\n", "test_dataset = FashionMNIST(root='./data/notebook2', train=False, transform=trans1,download=True)\n", "\n", "# 定义训练集和测试集的比例\n", "train_ratio = 0.8 # 训练集比例为80%,验证集比例为20%\n", "valid_ratio = 0.2\n", "total_samples = len(train_dataset)\n", "train_size = int(train_ratio * total_samples)\n", "valid_size = int(valid_ratio * total_samples)\n", "\n", "# 分割训练集和测试集\n", "train_dataset, valid_dataset = torch.utils.data.random_split(train_dataset, [train_size, valid_size])\n", "\n", "# 加载随机抽取的训练数据集\n", "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, drop_last=True)\n", "valid_loader = DataLoader(valid_dataset, batch_size=64, shuffle=False, drop_last=True)\n", "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False, drop_last=True)" ], "id": "1a2fb592a20d3fe1", "outputs": [], "execution_count": 18 }, { "metadata": { "ExecuteTime": { "end_time": "2025-06-26T01:24:15.287304Z", "start_time": "2025-06-26T01:24:15.284079Z" } }, "cell_type": "code", "source": [ "singlegate_list = ['rx', 'ry', 'rz', 's', 't', 'p', 'u3']\n", "doublegate_list = ['rxx', 'ryy', 'rzz', 'swap', 'cnot', 'cp', 'ch', 'cu', 'ct', 'cz']" ], "id": "8cdb66e075ac60ed", "outputs": [], "execution_count": 19 }, { "metadata": { "ExecuteTime": { "end_time": "2025-06-26T01:24:15.305490Z", "start_time": "2025-06-26T01:24:15.300543Z" } }, "cell_type": "code", "source": [ "# 随机量子卷积层\n", "class RandomQuantumConvolutionalLayer(nn.Module):\n", " def __init__(self, nqubit, num_circuits, seed:int=1024):\n", " super(RandomQuantumConvolutionalLayer, self).__init__()\n", " random.seed(seed)\n", " self.nqubit = nqubit\n", " self.cirs = nn.ModuleList([self.circuit(nqubit) for _ in range(num_circuits)])\n", "\n", " def circuit(self, nqubit):\n", " cir = dq.QubitCircuit(nqubit)\n", " 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\n", " cir.barrier()\n", " for iter in range(3):\n", " for i in range(nqubit):\n", " singlegate = random.choice(singlegate_list)\n", " getattr(cir, singlegate)(i)\n", " control_bit, target_bit = random.sample(range(0, nqubit - 1), 2)\n", " doublegate = random.choice(doublegate_list)\n", " if doublegate[0] in ['r', 's']:\n", " getattr(cir, doublegate)([control_bit, target_bit])\n", " else:\n", " getattr(cir, doublegate)(control_bit, target_bit)\n", " cir.barrier()\n", "\n", " cir.observable(0)\n", " return cir\n", "\n", " def forward(self, x):\n", " kernel_size, stride = 2, 2\n", " # [64, 1, 18, 18] -> [64, 1, 9, 18, 2] -> [64, 1, 9, 9, 2, 2]\n", " x_unflod = x.unfold(2, kernel_size, stride).unfold(3, kernel_size, stride)\n", " w = int((x.shape[-1] - kernel_size) / stride + 1)\n", " x_reshape = x_unflod.reshape(-1, self.nqubit)\n", "\n", " exps = []\n", " for cir in self.cirs: # out_channels\n", " cir(x_reshape)\n", " exp = cir.expectation()\n", " exps.append(exp)\n", "\n", " exps = torch.stack(exps, dim=1)\n", " exps = exps.reshape(x.shape[0], 3, w, w)\n", " return exps" ], "id": "248357d84b938ba9", "outputs": [], "execution_count": 20 }, { "metadata": { "ExecuteTime": { "end_time": "2025-06-26T01:24:15.436208Z", "start_time": "2025-06-26T01:24:15.327298Z" } }, "cell_type": "code", "source": [ "net = RandomQuantumConvolutionalLayer(nqubit=4, num_circuits=3, seed=1024)\n", "net.cirs[0].draw()" ], "id": "22520c5a291ddfb9", "outputs": [ { "data": { "text/plain": [ "
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" }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 21 }, { "metadata": { "ExecuteTime": { "end_time": "2025-06-26T01:24:15.451836Z", "start_time": "2025-06-26T01:24:15.447333Z" } }, "cell_type": "code", "source": [ "# 基于随机量子卷积层的混合模型\n", "class RandomQCCNN(nn.Module):\n", " def __init__(self):\n", " super(RandomQCCNN, self).__init__()\n", " self.conv = nn.Sequential(\n", " RandomQuantumConvolutionalLayer(nqubit=4, num_circuits=3), # num_circuits=3代表我们在quanv1层只用了3个量子卷积核\n", " nn.BatchNorm2d(3), # 添加批量归一化\n", " nn.ReLU(),\n", " nn.MaxPool2d(kernel_size=2, stride=1),\n", " # 添加形状检查层以确保尺寸正确\n", " nn.Conv2d(3, 6, kernel_size=2, stride=1),\n", " nn.BatchNorm2d(6), # 添加批量归一化\n", " nn.ReLU(),\n", " # 添加自适应池化层确保固定输出尺寸\n", " nn.AdaptiveMaxPool2d((9, 9)) # 确保输出固定尺寸\n", " )\n", " self.fc = nn.Sequential(\n", " # 根据自适应池化后的固定尺寸计算输入维度\n", " nn.Linear(6 * 9 * 9, 1024), # 确保与自适应池化输出匹配\n", " nn.BatchNorm1d(1024),\n", " nn.Dropout(0.5),\n", " nn.ReLU(),\n", " nn.Linear(1024, 512),\n", " nn.BatchNorm1d(512),\n", " nn.ReLU(),\n", " nn.Linear(512, 10)\n", " )\n", "\n", " def forward(self, x):\n", " # 添加详细的形状检查输出\n", " print(f\"Input shape: {x.shape}\")\n", " x = self.conv(x)\n", " print(f\"After conv layers: {x.shape}\") # 添加中间形状检查\n", " x = x.reshape(x.size(0), -1)\n", " print(f\"After flatten: {x.shape}\") # 添加展平后形状检查\n", " x = self.fc(x)\n", " return x" ], "id": "37c556ec625ae040", "outputs": [], "execution_count": 22 }, { "metadata": { "ExecuteTime": { "end_time": "2025-06-26T02:23:23.162303Z", "start_time": "2025-06-26T01:24:15.471352Z" } }, "cell_type": "code", "source": [ "# 修改RandomQCCNN模型的训练参数\n", "num_epochs = 100\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "print(device)\n", "seed_torch(42) # 使用相同的随机种子值\n", "model = RandomQCCNN()\n", "model.to(device)\n", "criterion = nn.CrossEntropyLoss()\n", "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\n", "optim_model, metrics = train_model(model, criterion, optimizer, train_loader, valid_loader, num_epochs, device)\n", "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\n", "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" ], "id": "15cd5b8c97c677d0", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cuda\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " 0%| | 0/100 [00:00" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 25 }, { "metadata": { "ExecuteTime": { "end_time": "2025-06-26T02:23:28.719481Z", "start_time": "2025-06-26T02:23:28.713924Z" } }, "cell_type": "code", "source": [ "class ParameterizedQuantumConvolutionalLayer(nn.Module):\n", " def __init__(self, nqubit, num_circuits):\n", " super().__init__()\n", " self.nqubit = nqubit\n", " self.cirs = nn.ModuleList([self.circuit(nqubit) for _ in range(num_circuits)])\n", "\n", " def circuit(self, nqubit):\n", " cir = dq.QubitCircuit(nqubit)\n", " cir.rxlayer(encode=True) # 数据编码\n", " cir.barrier()\n", " for iter in range(5): # 将线路深度从4增加到5\n", " cir.rylayer()\n", " cir.cnot_ring()\n", " cir.barrier()\n", "\n", " cir.observable(0)\n", " return cir\n", "\n", " def forward(self, x):\n", " kernel_size, stride = 3, 3 # 使用3x3数据块\n", " x_unflod = x.unfold(2, kernel_size, stride).unfold(3, kernel_size, stride)\n", " print(f\"Input shape: {x.shape}\") # 添加输入形状检查\n", " print(f\"Unfolded shape: {x_unflod.shape}\") # 添加展开后形状检查\n", " \n", " # 动态计算w值并验证特征图尺寸\n", " w = x_unflod.shape[2] # 使用实际展开后的尺寸\n", " # 确保展平后的总元素数与量子线路输入匹配\n", " x_reshape = x_unflod.reshape(-1, kernel_size * kernel_size) # 将每个3x3块展平为9维\n", "\n", " exps = []\n", " for cir in self.cirs:\n", " cir(x_reshape)\n", " exp = cir.expectation()\n", " exps.append(exp)\n", "\n", " exps = torch.stack(exps, dim=1)\n", " out_channels = len(self.cirs) # 使用动态计算而非硬编码值\n", " # 验证总元素数一致性\n", " assert exps.numel() == x.shape[0] * out_channels * w * w, \\\n", " f\"Element count mismatch: {exps.numel()} vs {x.shape[0] * out_channels * w * w}\"\n", " # 确保展平后的总元素数与量子线路输出匹配\n", " exps = exps.reshape(x.shape[0], out_channels, w, w)\n", " print(f\"Reshaped shape: {exps.shape}\") # 添加最终形状检查\n", " return exps" ], "id": "4b12ed743e5d80d7", "outputs": [], "execution_count": 26 }, { "metadata": { "ExecuteTime": { "end_time": "2025-06-26T02:23:29.063164Z", "start_time": "2025-06-26T02:23:28.846340Z" } }, "cell_type": "code", "source": [ "# 此处我们可视化其中一个量子卷积核的线路结构:\n", "net = ParameterizedQuantumConvolutionalLayer(nqubit=4, num_circuits=3)\n", "net.cirs[0].draw()" ], "id": "f22bb2f92a377eed", "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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}, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 27 }, { "metadata": { "ExecuteTime": { "end_time": "2025-06-26T02:23:29.186455Z", "start_time": "2025-06-26T02:23:29.182520Z" } }, "cell_type": "code", "source": [ "# QCCNN整体网络架构:\n", "class QCCNN(nn.Module):\n", " def __init__(self):\n", " super(QCCNN, self).__init__()\n", " self.conv = nn.Sequential(\n", " ParameterizedQuantumConvolutionalLayer(nqubit=4, num_circuits=3), # 恢复为4量子比特\n", " nn.BatchNorm2d(3), # 恢复原始通道数\n", " nn.ReLU(),\n", " nn.MaxPool2d(kernel_size=2, stride=1),\n", " nn.Conv2d(3, 6, kernel_size=1), # 添加1x1卷积层增强通道间信息交互\n", " nn.BatchNorm2d(6),\n", " nn.ReLU(),\n", " nn.AdaptiveMaxPool2d((9, 9)) # 确保输出固定尺寸\n", " )\n", "\n", " self.fc = nn.Sequential(\n", " # 根据新的特征图大小调整输入维度:6通道、9x9特征图 => 6*9*9=486\n", " nn.Linear(6 * 9 * 9, 1024), # 修改为正确的输入维度\n", " nn.BatchNorm1d(1024),\n", " nn.Dropout(0.5),\n", " nn.ReLU(),\n", " nn.Linear(1024, 512),\n", " nn.BatchNorm1d(512),\n", " nn.ReLU(),\n", " nn.Linear(512, 10)\n", " )\n", "\n", " def forward(self, x):\n", " x = self.conv(x)\n", " x = x.reshape(x.size(0), -1)\n", " x = self.fc(x)\n", " return x" ], "id": "689b98ee8ef5a139", "outputs": [], "execution_count": 28 }, { "metadata": { "ExecuteTime": { "end_time": "2025-06-26T03:24:45.098535Z", "start_time": "2025-06-26T02:23:29.191976Z" } }, "cell_type": "code", "source": [ "# 修改QCCNN模型的训练参数\n", "num_epochs = 100\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "\n", "model = QCCNN()\n", "model.to(device)\n", "criterion = nn.CrossEntropyLoss()\n", "optimizer = optim.AdamW(model.parameters(), lr=5e-4, weight_decay=5e-5, amsgrad=True) # 优化学习率和weight_decay参数\n", "optim_model, metrics = train_model(model, criterion, optimizer, train_loader, valid_loader, num_epochs, device)\n", "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\n", "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" ], "id": "2433c2ae15fb60e6", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 0%| | 0/100 [00:00= 1\n", " layers.append(nn.Conv2d(in_channel,out_channel,kernel_size=3,padding=1))\n", " layers.append(nn.ReLU())\n", " for _ in range(num_convs-1):\n", " layers.append(nn.Conv2d(out_channel,out_channel,kernel_size=3,padding=1))\n", " layers.append(nn.ReLU())\n", " layers.append(nn.MaxPool2d(kernel_size=2,stride=2))\n", " return nn.Sequential(*layers)\n", "\n", "VGG = nn.Sequential(\n", " vgg_block(1, 32, 2), # 增加通道数和调整卷积层数量\n", " vgg_block(32, 64, 2),\n", " nn.Flatten(),\n", " nn.Linear(64 * 7 * 7, 256), # 调整全连接层大小\n", " nn.BatchNorm1d(256), # 添加批量归一化\n", " nn.ReLU(),\n", " nn.Dropout(0.5), # 增加dropout比例\n", " nn.Linear(256, 128),\n", " nn.BatchNorm1d(128), # 添加批量归一化\n", " nn.ReLU(),\n", " nn.Dropout(0.5),\n", " nn.Linear(128, 10),\n", " nn.Softmax(dim=-1)\n", ")" ], "id": "f72e03c426bd658b", "outputs": [], "execution_count": 33 }, { "metadata": { "ExecuteTime": { "end_time": "2025-06-26T04:00:50.810478Z", "start_time": "2025-06-26T03:27:58.648817Z" } }, "cell_type": "code", "source": [ "# 修改VGG模型的训练参数\n", "num_epochs = 100\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "\n", "vgg_model = VGG\n", "vgg_model.to(device)\n", "criterion = nn.CrossEntropyLoss()\n", "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\n", "vgg_model, metrics = train_model(vgg_model, criterion, optimizer, train_loader, valid_loader, num_epochs, device)\n", "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\n", "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" ], "id": "234337eef155a6de", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Train loss: 1.510 Valid Acc: 0.925: 100%|██████████| 100/100 [32:52<00:00, 19.72s/it]\n" ] } ], "execution_count": 34 }, { "metadata": { "ExecuteTime": { "end_time": "2025-06-26T04:01:03.995494Z", "start_time": "2025-06-26T04:01:01.239374Z" } }, "cell_type": "code", "source": [ "state_dict = torch.load('./data/notebook2/vgg_weights.pt', map_location=device)\n", "vgg_model = VGG\n", "vgg_model.load_state_dict(state_dict)\n", "vgg_model.to(device)\n", "\n", "vgg_test_acc = test_model(vgg_model, test_loader, device)" ], "id": "ef857e4ec99a951a", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test Acc: 0.920\n" ] } ], "execution_count": 35 }, { "metadata": { "ExecuteTime": { "end_time": "2025-06-26T04:01:05.563661Z", "start_time": "2025-06-26T04:01:05.416700Z" } }, "cell_type": "code", "source": [ "vgg_data = pd.read_csv('./data/notebook2/vgg_metrics.csv')\n", "qccnn_data = pd.read_csv('./data/notebook2/qccnn_metrics.csv')\n", "vgg_epoch = vgg_data['epoch']\n", "vgg_train_loss = vgg_data['train_loss']\n", "vgg_valid_loss = vgg_data['valid_loss']\n", "vgg_train_acc = vgg_data['train_acc']\n", "vgg_valid_acc = vgg_data['valid_acc']\n", "\n", "qccnn_epoch = qccnn_data['epoch']\n", "qccnn_train_loss = qccnn_data['train_loss']\n", "qccnn_valid_loss = qccnn_data['valid_loss']\n", "qccnn_train_acc = qccnn_data['train_acc']\n", "qccnn_valid_acc = qccnn_data['valid_acc']\n", "\n", "# 创建图和Axes对象\n", "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n", "\n", "# 绘制训练损失曲线\n", "ax1.plot(vgg_epoch, vgg_train_loss, label='VGG Train Loss')\n", "ax1.plot(vgg_epoch, vgg_valid_loss, label='VGG Valid Loss')\n", "ax1.plot(qccnn_epoch, qccnn_train_loss, label='QCCNN Valid Loss')\n", "ax1.plot(qccnn_epoch, qccnn_valid_loss, label='QCCNN Valid Loss')\n", "ax1.set_title('Training Loss Curve')\n", "ax1.set_xlabel('Epoch')\n", "ax1.set_ylabel('Loss')\n", "ax1.legend()\n", "\n", "# 绘制训练准确率曲线\n", "ax2.plot(vgg_epoch, vgg_train_acc, label='VGG Train Accuracy')\n", "ax2.plot(vgg_epoch, vgg_valid_acc, label='VGG Valid Accuracy')\n", "ax2.plot(qccnn_epoch, qccnn_train_acc, label='QCCNN Train Accuracy')\n", "ax2.plot(qccnn_epoch, qccnn_valid_acc, label='QCCNN Valid Accuracy')\n", "ax2.set_title('Training Accuracy Curve')\n", "ax2.set_xlabel('Epoch')\n", "ax2.set_ylabel('Accuracy')\n", "ax2.legend()\n", "\n", "plt.show()" ], "id": "5d20475f38028031", "outputs": [ { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 36 }, { "metadata": { "ExecuteTime": { "end_time": "2025-06-26T04:01:12.832423Z", "start_time": "2025-06-26T04:01:12.811096Z" } }, "cell_type": "code", "source": [ "# 这里我们对比不同模型之间可训练参数量的区别\n", "\n", "def count_parameters(model):\n", " \"\"\"\n", " 计算模型的参数数量\n", " \"\"\"\n", " return sum(p.numel() for p in model.parameters() if p.requires_grad)\n", "\n", "number_params_VGG = count_parameters(VGG)\n", "number_params_QCCNN = count_parameters(QCCNN())\n", "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}')" ], "id": "72451dcf013280ac", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "VGG 模型可训练参数量:903018\t QCCNN模型可训练参数量:1031792\n" ] } ], "execution_count": 37 } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }