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refactor(Modify.py): 重构代码并改进模型训练流程

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- 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 #%%
# 首先我们导入所有需要的包：
#%% 导入所有需要的包
import os import os
import random import random
import numpy as np import numpy as np
import pandas as pd import pandas as pd
import deepquantum as dq
import matplotlib.pyplot as plt
import torch import torch
import torch.nn as nn import torch.nn as nn
import torch.optim as optim import torch.optim as optim
import torchvision.transforms as transforms import torchvision.transforms as transforms
from torchvision.datasets import FashionMNIST
from tqdm import tqdm from tqdm import tqdm
from sklearn.metrics import roc_auc_score
from torch.utils.data import DataLoader from torch.utils.data import DataLoader
from multiprocessing import freeze_support from torchvision.datasets import FashionMNIST
import deepquantum as dq
import matplotlib.pyplot as plt
#%% 设置随机种子以保证可复现
def seed_torch(seed=1024): def seed_torch(seed=1024):
"""
Set random seeds for reproducibility.
Args:
seed (int): Random seed number to use. Default is 1024.
"""
random.seed(seed) random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed) os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed) np.random.seed(seed)
torch.manual_seed(seed) torch.manual_seed(seed)
torch.cuda.manual_seed(seed) torch.cuda.manual_seed(seed)
# Seed all GPUs with the same seed if using multi-GPU
torch.cuda.manual_seed_all(seed) torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.benchmark = False torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True torch.backends.cudnn.deterministic = True
#%% 准确率计算函数 seed_torch(42) # 使用更常见的随机种子值
#%%
def calculate_score(y_true, y_preds): def calculate_score(y_true, y_preds):
# 将模型预测结果转为概率分布
preds_prob = torch.softmax(y_preds, dim=1) preds_prob = torch.softmax(y_preds, dim=1)
# 获得预测的类别（概率最高的一类）
preds_class = torch.argmax(preds_prob, dim=1) preds_class = torch.argmax(preds_prob, dim=1)
# 计算准确率
correct = (preds_class == y_true).float() correct = (preds_class == y_true).float()
return (correct.sum() / len(correct)).cpu().numpy() accuracy = correct.sum() / len(correct)
return accuracy.cpu().numpy()
#%% 训练与验证函数
def train_model(model, criterion, optimizer, scheduler, train_loader, valid_loader, num_epochs, device, save_path):
model.to(device)
best_acc = 0.0
metrics = {'epoch': [], 'train_acc': [], 'valid_acc': [], 'train_loss': [], 'valid_loss': []}
for epoch in range(1, num_epochs + 1): def train_model(model, criterion, optimizer, train_loader, valid_loader, num_epochs, device):
# --- 训练阶段 --- """
训练和验证模型。
Args:
model (torch.nn.Module): 要训练的模型。
criterion (torch.nn.Module): 损失函数。
optimizer (torch.optim.Optimizer): 优化器。
train_loader (torch.utils.data.DataLoader): 训练数据加载器。
valid_loader (torch.utils.data.DataLoader): 验证数据加载器。
num_epochs (int): 训练的epoch数。
Returns:
model (torch.nn.Module): 训练后的模型。
"""
model.train() model.train()
running_loss, running_acc = 0.0, 0.0 train_loss_list = []
for imgs, labels in train_loader: valid_loss_list = []
imgs, labels = imgs.to(device), labels.to(device) train_acc_list = []
valid_acc_list = []
best_valid_acc = 0.0
patience = 10 # 早停耐心值
counter = 0 # 计数器
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=10)
with tqdm(total=num_epochs) as pbar:
for epoch in range(num_epochs):
# 训练阶段
train_loss = 0.0
train_acc = 0.0
for images, labels in train_loader:
images = images.to(device)
labels = labels.to(device)
optimizer.zero_grad() optimizer.zero_grad()
outputs = model(imgs) outputs = model(images)
loss = criterion(outputs, labels) loss = criterion(outputs, labels)
loss.backward() loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step() optimizer.step()
running_loss += loss.item() train_loss += loss.item()
running_acc += calculate_score(labels, outputs) train_acc += calculate_score(labels, outputs)
train_loss = running_loss / len(train_loader) train_loss /= len(train_loader)
train_acc = running_acc / len(train_loader) train_acc /= len(train_loader)
scheduler.step()
# --- 验证阶段 --- # 验证阶段
model.eval() model.eval()
val_loss, val_acc = 0.0, 0.0 valid_loss = 0.0
valid_acc = 0.0
with torch.no_grad(): with torch.no_grad():
for imgs, labels in valid_loader: for images, labels in valid_loader:
imgs, labels = imgs.to(device), labels.to(device) images = images.to(device)
outputs = model(imgs) labels = labels.to(device)
outputs = model(images)
loss = criterion(outputs, labels) loss = criterion(outputs, labels)
val_loss += loss.item() valid_loss += loss.item()
val_acc += calculate_score(labels, outputs) valid_acc += calculate_score(labels, outputs)
valid_loss = val_loss / len(valid_loader) valid_loss /= len(valid_loader)
valid_acc = val_acc / len(valid_loader) valid_acc /= len(valid_loader)
metrics['epoch'].append(epoch) # 学习率调度器更新
metrics['train_loss'].append(train_loss) scheduler.step(valid_acc)
metrics['valid_loss'].append(valid_loss)
metrics['train_acc'].append(train_acc)
metrics['valid_acc'].append(valid_acc)
tqdm.write(f"[{save_path}] Epoch {epoch}/{num_epochs} " # 早停机制
f"Train Acc: {train_acc:.4f} Valid Acc: {valid_acc:.4f}") if valid_acc > best_valid_acc:
best_valid_acc = valid_acc
torch.save(model.state_dict(), './data/notebook2/best_model.pt')
counter = 0
else:
counter += 1
if counter >= patience:
print(f'Early stopping at epoch {epoch+1} due to no improvement in validation accuracy.')
break
pbar.set_description(f"Train loss: {train_loss:.3f} Valid Acc: {valid_acc:.3f}")
pbar.update()
train_loss_list.append(train_loss)
valid_loss_list.append(valid_loss)
train_acc_list.append(train_acc)
valid_acc_list.append(valid_acc)
# 加载最佳模型权重
if os.path.exists('./data/notebook2/best_model.pt'):
model.load_state_dict(torch.load('./data/notebook2/best_model.pt'))
# 修改metrics构建方式，确保各数组长度一致
metrics = {
'epoch': list(range(1, len(train_loss_list) + 1)),
'train_acc': train_acc_list,
'valid_acc': valid_acc_list,
'train_loss': train_loss_list,
'valid_loss': valid_loss_list
}
if valid_acc > best_acc:
best_acc = valid_acc
torch.save(model.state_dict(), save_path)
return model, metrics return model, metrics
#%% 测试函数
def test_model(model, test_loader, device): def test_model(model, test_loader, device):
model.to(device).eval() model.eval()
acc = 0.0 test_acc = 0.0
with torch.no_grad(): with torch.no_grad():
for imgs, labels in test_loader: for images, labels in test_loader:
imgs, labels = imgs.to(device), labels.to(device) images = images.to(device)
outputs = model(imgs) labels = labels.to(device)
acc += calculate_score(labels, outputs) outputs = model(images)
acc /= len(test_loader) test_acc += calculate_score(labels, outputs)
print(f"Test Accuracy: {acc:.4f}")
return acc
#%% 定义量子卷积层与模型 test_acc /= len(test_loader)
singlegate_list = ['rx','ry','rz','s','t','p','u3'] print(f'Test Acc: {test_acc:.3f}')
doublegate_list = ['rxx','ryy','rzz','swap','cnot','cp','ch','cu','ct','cz'] return test_acc
#%%
# 定义图像变换
trans1 = transforms.Compose([
transforms.RandomHorizontalFlip(), # 随机水平翻转
transforms.RandomRotation(10), # 随机旋转±10度
transforms.ColorJitter(brightness=0.2, contrast=0.2), # 颜色调整
transforms.Resize((18, 18)), # 调整大小为18x18
transforms.ToTensor(), # 转换为张量
transforms.Normalize((0.5,), (0.5,)) # 归一化到[-1, 1]
])
trans2 = transforms.Compose([
transforms.RandomHorizontalFlip(), # 随机水平翻转
transforms.RandomRotation(10), # 随机旋转±10度
transforms.ColorJitter(brightness=0.2, contrast=0.2), # 颜色调整
transforms.Resize((16, 16)), # 调整大小为16x16
transforms.ToTensor(), # 转换为张量
transforms.Normalize((0.5,), (0.5,)) # 归一化到[-1, 1]
])
train_dataset = FashionMNIST(root='./data/notebook2', train=False, transform=trans1,download=True)
test_dataset = FashionMNIST(root='./data/notebook2', train=False, transform=trans1,download=True)
# 定义训练集和测试集的比例
train_ratio = 0.8 # 训练集比例为80%，验证集比例为20%
valid_ratio = 0.2
total_samples = len(train_dataset)
train_size = int(train_ratio * total_samples)
valid_size = int(valid_ratio * total_samples)
# 分割训练集和测试集
train_dataset, valid_dataset = torch.utils.data.random_split(train_dataset, [train_size, valid_size])
# 加载随机抽取的训练数据集
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, drop_last=True)
valid_loader = DataLoader(valid_dataset, batch_size=64, shuffle=False, drop_last=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False, drop_last=True)
#%%
singlegate_list = ['rx', 'ry', 'rz', 's', 't', 'p', 'u3']
doublegate_list = ['rxx', 'ryy', 'rzz', 'swap', 'cnot', 'cp', 'ch', 'cu', 'ct', 'cz']
#%%
# 随机量子卷积层
class RandomQuantumConvolutionalLayer(nn.Module): class RandomQuantumConvolutionalLayer(nn.Module):
def __init__(self, nqubit, num_circuits, seed=1024): def __init__(self, nqubit, num_circuits, seed:int=1024):
super().__init__() super(RandomQuantumConvolutionalLayer, self).__init__()
random.seed(seed) random.seed(seed)
self.nqubit = nqubit self.nqubit = nqubit
self.cirs = nn.ModuleList([self.circuit(nqubit) for _ in range(num_circuits)]) 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 = dq.QubitCircuit(nqubit)
cir.rxlayer(encode=True); 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
for _ in range(3):
for i in range(nqubit):
getattr(cir, random.choice(singlegate_list))(i)
c,t = random.sample(range(nqubit),2)
gate = random.choice(doublegate_list)
if gate[0] in ['r','s']:
getattr(cir, gate)([c,t])
else:
getattr(cir, gate)(c,t)
cir.barrier() cir.barrier()
for iter in range(3):
for i in range(nqubit):
singlegate = random.choice(singlegate_list)
getattr(cir, singlegate)(i)
control_bit, target_bit = random.sample(range(0, nqubit - 1), 2)
doublegate = random.choice(doublegate_list)
if doublegate[0] in ['r', 's']:
getattr(cir, doublegate)([control_bit, target_bit])
else:
getattr(cir, doublegate)(control_bit, target_bit)
cir.barrier()
cir.observable(0) cir.observable(0)
return cir 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
#%%
net = RandomQuantumConvolutionalLayer(nqubit=4, num_circuits=3, seed=1024)
net.cirs[0].draw()
#%%
# 基于随机量子卷积层的混合模型
class RandomQCCNN(nn.Module): class RandomQCCNN(nn.Module):
def __init__(self): def __init__(self):
super().__init__() super(RandomQCCNN, self).__init__()
self.conv = nn.Sequential( self.conv = nn.Sequential(
RandomQuantumConvolutionalLayer(4,3,seed=1024), 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
nn.ReLU(), nn.MaxPool2d(2,1), nn.BatchNorm2d(3), # 添加批量归一化
nn.Conv2d(3,6,2,1), nn.ReLU(), nn.MaxPool2d(2,1) nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=1),
nn.Conv2d(3, 6, kernel_size=2, stride=1),
nn.BatchNorm2d(6), # 添加批量归一化
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=1)
) )
self.fc = nn.Sequential( self.fc = nn.Sequential(
nn.Linear(6*6*6,1024), nn.Dropout(0.4), nn.Linear(6 * 6 * 6, 1024),
nn.Linear(1024,10) nn.BatchNorm1d(1024), # 添加批量归一化
nn.Dropout(0.5), # 增加dropout比例
nn.ReLU(),
nn.Linear(1024, 10)
) )
def forward(self,x):
x = self.conv(x)
x = x.view(x.size(0),-1)
return self.fc(x)
def forward(self, x):
x = self.conv(x)
x = x.reshape(x.size(0), -1)
x = self.fc(x)
return x
#%%
# 修改RandomQCCNN模型的训练参数
num_epochs = 300
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
seed_torch(42) # 使用相同的随机种子值
model = RandomQCCNN()
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/random_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/random_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/random_qccnn_weights.pt', map_location=device)
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): class ParameterizedQuantumConvolutionalLayer(nn.Module):
def __init__(self,nqubit,num_circuits): def __init__(self, nqubit, num_circuits):
super().__init__() super().__init__()
self.nqubit = nqubit self.nqubit = nqubit
self.cirs = nn.ModuleList([self.circuit(nqubit) for _ in range(num_circuits)]) 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 = dq.QubitCircuit(nqubit)
cir.rxlayer(encode=True); 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
for _ in range(4): cir.barrier()
cir.rylayer(); cir.cnot_ring(); 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) cir.observable(0)
return cir 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): class QCCNN(nn.Module):
def __init__(self): def __init__(self):
super().__init__() super(QCCNN, self).__init__()
self.conv = nn.Sequential( self.conv = nn.Sequential(
ParameterizedQuantumConvolutionalLayer(4,3), ParameterizedQuantumConvolutionalLayer(nqubit=4, num_circuits=3),
nn.ReLU(), nn.MaxPool2d(2,1) 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): self.fc = nn.Sequential(
layers = [nn.Conv2d(in_c,out_c,3,padding=1), nn.ReLU()] nn.Linear(8 * 8 * 3, 128),
for _ in range(n_convs-1): nn.BatchNorm1d(128), # 添加批量归一化
layers += [nn.Conv2d(out_c,out_c,3,padding=1), nn.ReLU()] nn.Dropout(0.5), # 增加dropout比例
layers.append(nn.MaxPool2d(2,2)) 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) return nn.Sequential(*layers)
VGG = nn.Sequential( VGG = nn.Sequential(
vgg_block(1,10,3), vgg_block(1, 32, 2), # 增加通道数和调整卷积层数量
vgg_block(10,16,3), vgg_block(32, 64, 2),
nn.Flatten(), nn.Flatten(),
nn.Linear(16*4*4,120), nn.Sigmoid(), nn.Linear(64 * 4 * 4, 256), # 调整全连接层大小
nn.Linear(120,84), nn.Sigmoid(), nn.BatchNorm1d(256), # 添加批量归一化
nn.Linear(84,10), nn.Softmax(dim=-1) 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 vgg_model = VGG
if __name__ == '__main__': vgg_model.to(device)
freeze_support() 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 vgg_test_acc = test_model(vgg_model, test_loader, device)
train_transform = transforms.Compose([ #%%
transforms.Resize((18, 18)), vgg_data = pd.read_csv('./data/notebook2/vgg_metrics.csv')
transforms.RandomRotation(15), qccnn_data = pd.read_csv('./data/notebook2/qccnn_metrics.csv')
transforms.RandomHorizontalFlip(), vgg_epoch = vgg_data['epoch']
transforms.RandomVerticalFlip(0.3), vgg_train_loss = vgg_data['train_loss']
transforms.ToTensor(), vgg_valid_loss = vgg_data['valid_loss']
transforms.Normalize((0.5,), (0.5,)) vgg_train_acc = vgg_data['train_acc']
]) vgg_valid_acc = vgg_data['valid_acc']
eval_transform = transforms.Compose([
transforms.Resize((18, 18)),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
full_train = FashionMNIST(root='./data/notebook2', train=True, transform=train_transform, download=True) qccnn_epoch = qccnn_data['epoch']
test_dataset = FashionMNIST(root='./data/notebook2', train=False, transform=eval_transform, download=True) qccnn_train_loss = qccnn_data['train_loss']
train_size = int(0.8 * len(full_train)) qccnn_valid_loss = qccnn_data['valid_loss']
valid_size = len(full_train) - train_size qccnn_train_acc = qccnn_data['train_acc']
train_ds, valid_ds = torch.utils.data.random_split(full_train, [train_size, valid_size]) qccnn_valid_acc = qccnn_data['valid_acc']
valid_ds.dataset.transform = eval_transform
batch_size = 128 # 创建图和Axes对象
train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4) fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
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)
# 三种模型配置 # 绘制训练损失曲线
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") ax1.plot(vgg_epoch, vgg_train_loss, label='VGG Train Loss')
models = { ax1.plot(vgg_epoch, vgg_valid_loss, label='VGG Valid Loss')
'random_qccnn': (RandomQCCNN(), 1e-3, './data/notebook2/random_qccnn_best.pt'), ax1.plot(qccnn_epoch, qccnn_train_loss, label='QCCNN Valid Loss')
'qccnn': (QCCNN(), 1e-4, './data/notebook2/qccnn_best.pt'), ax1.plot(qccnn_epoch, qccnn_valid_loss, label='QCCNN Valid Loss')
'vgg': (VGG, 1e-4, './data/notebook2/vgg_best.pt') ax1.set_title('Training Loss Curve')
} ax1.set_xlabel('Epoch')
ax1.set_ylabel('Loss')
ax1.legend()
all_metrics = {} # 绘制训练准确率曲线
for name, (model, lr, save_path) in models.items(): ax2.plot(vgg_epoch, vgg_train_acc, label='VGG Train Accuracy')
seed_torch(1024) ax2.plot(vgg_epoch, vgg_valid_acc, label='VGG Valid Accuracy')
model = model.to(device) ax2.plot(qccnn_epoch, qccnn_train_acc, label='QCCNN Train Accuracy')
criterion = nn.CrossEntropyLoss() ax2.plot(qccnn_epoch, qccnn_valid_acc, label='QCCNN Valid Accuracy')
optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4) ax2.set_title('Training Accuracy Curve')
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50) ax2.set_xlabel('Epoch')
ax2.set_ylabel('Accuracy')
ax2.legend()
print(f"\n=== Training {name} ===") plt.show()
_, metrics = train_model( #%%
model, criterion, optimizer, scheduler, # 这里我们对比不同模型之间可训练参数量的区别
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)
# 测试与可视化 def count_parameters(model):
plt.figure(figsize=(12,5)) """
for i,(name,metrics) in enumerate(all_metrics.items(),1): 计算模型的参数数量
model, _, save_path = models[name] """
best_model = model.to(device) return sum(p.numel() for p in model.parameters() if p.requires_grad)
best_model.load_state_dict(torch.load(save_path))
print(f"\n--- Testing {name} ---")
test_model(best_model, test_loader, device)
plt.subplot(1,3,i) number_params_VGG = count_parameters(VGG)
plt.plot(metrics['epoch'], metrics['valid_acc'], label=f'{name} Val Acc') number_params_QCCNN = count_parameters(QCCNN())
plt.xlabel('Epoch'); plt.ylabel('Valid Acc') 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}')
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)}")

332
data/notebook1/random_qccnn_metrics.csv
View File

@ -1,301 +1,33 @@
,epoch,train_acc,valid_acc,train_loss,valid_loss ,epoch,train_acc,valid_acc,train_loss,valid_loss
0,1,0.12575,0.21421370967741934,2.2937231807708742,2.2676862593620055 0,1,0.59325,0.6854838709677419,1.1784187927246095,0.8956879992638865
1,2,0.248,0.3240927419354839,2.2377592124938963,2.1976155081102924 1,2,0.713,0.7127016129032258,0.788702152967453,0.7735376723351017
2,3,0.354375,0.3815524193548387,2.129876153945923,2.044375258107339 2,3,0.755,0.7389112903225806,0.7006268813610077,0.7271041427889178
3,4,0.42625,0.4324596774193548,1.9269471073150635,1.815262294584705 3,4,0.763625,0.7273185483870968,0.668900458574295,0.7277685692233424
4,5,0.482125,0.5282258064516129,1.6988583679199218,1.6081839684517152 4,5,0.76275,0.748991935483871,0.6469288661479949,0.672559670863613
5,6,0.528375,0.5151209677419355,1.5167635107040405,1.4654438649454424 5,6,0.773625,0.7434475806451613,0.6203210880756378,0.6748099394382969
6,7,0.5465,0.5574596774193549,1.3927514476776124,1.3655932180343135 6,7,0.771625,0.7494959677419355,0.6234635796546936,0.6763338706185741
7,8,0.5705,0.5403225806451613,1.3035813112258912,1.2985444838000881 7,8,0.784125,0.7580645161290323,0.5965014040470124,0.6313219945276937
8,9,0.586875,0.5902217741935484,1.2359741706848144,1.2315824993195073 8,9,0.781,0.7419354838709677,0.5893729448318481,0.6552943125847848
9,10,0.5985,0.5967741935483871,1.1828507437705993,1.1789349471369097 9,10,0.785375,0.7600806451612904,0.582163923740387,0.6227685501498561
10,11,0.61075,0.6013104838709677,1.1400936079025268,1.146019495302631 10,11,0.790625,0.7605846774193549,0.5674120993614197,0.6145914264263646
11,12,0.61425,0.6098790322580645,1.105873393535614,1.1111935800121677 11,12,0.7995,0.765625,0.5617314722537995,0.6158498919779255
12,13,0.623,0.6234879032258065,1.076672074317932,1.089227545645929 12,13,0.7965,0.7883064516129032,0.5522617139816284,0.581030644716755
13,14,0.63475,0.6159274193548387,1.0523606872558593,1.0597389667264876 13,14,0.803125,0.7817540322580645,0.5367116575241089,0.5911272004727395
14,15,0.63575,0.5866935483870968,1.0302134299278258,1.0517296521894393 14,15,0.80025,0.7752016129032258,0.5398472018241882,0.5930967292478008
15,16,0.638625,0.625,1.0127733755111694,1.030857661078053 15,16,0.808125,0.7620967741935484,0.5326456694602967,0.6306540206555398
16,17,0.644125,0.623991935483871,0.9972539176940918,1.0212982143125227 16,17,0.8145,0.7772177419354839,0.5097175936698913,0.5843374094655437
17,18,0.647125,0.6491935483870968,0.9847306752204895,1.0031032081573241 17,18,0.817375,0.7918346774193549,0.5045695571899415,0.5643920071663395
18,19,0.651625,0.6295362903225806,0.9718224520683288,0.9922776433729357 18,19,0.81175,0.7681451612903226,0.5022978014945984,0.593349628871487
19,20,0.653375,0.655241935483871,0.9614444556236267,1.004007081831655 19,20,0.824375,0.7928427419354839,0.4974315061569214,0.5595071181174247
20,21,0.65475,0.639616935483871,0.9525526990890503,0.9718767077692093 20,21,0.818,0.7762096774193549,0.49526858401298524,0.5555580258369446
21,22,0.657125,0.6491935483870968,0.944016770362854,0.9612755986952013 21,22,0.815625,0.7862903225806451,0.4976941578388214,0.5627694274148634
22,23,0.660125,0.6512096774193549,0.9386747822761535,0.9546890701017072 22,23,0.825875,0.7908266129032258,0.4906779823303223,0.5514134312829664
23,24,0.660125,0.6607862903225806,0.9306546316146851,0.9596057341944787 23,24,0.823875,0.7953629032258065,0.4869117760658264,0.5559701804191836
24,25,0.665625,0.6466733870967742,0.9239095873832702,0.9440147549875321 24,25,0.823,0.7807459677419355,0.48527450203895567,0.5541293746040713
25,26,0.664,0.6537298387096774,0.9177467222213745,0.9464947581291199 25,26,0.824625,0.795866935483871,0.4811685870885849,0.5384060884675672
26,27,0.66675,0.6582661290322581,0.912406234741211,0.9296330482729019 26,27,0.827875,0.7938508064516129,0.48096660792827606,0.5493545234203339
27,28,0.66625,0.6607862903225806,0.9084503087997436,0.9246035218238831 27,28,0.826875,0.7998991935483871,0.477142098903656,0.5478304624557495
28,29,0.669375,0.6633064516129032,0.9031522455215454,0.9183507677047483 28,29,0.82725,0.7948588709677419,0.47373487854003904,0.547797841410483
29,30,0.66925,0.6622983870967742,0.8971806693077088,0.9155043325116557 29,30,0.825375,0.7847782258064516,0.47746446084976196,0.5636355328944421
30,31,0.669625,0.6658266129032258,0.8963782095909119,0.9107550824842146 30,31,0.828875,0.7757056451612904,0.4754366238117218,0.5671144060550197
31,32,0.6715,0.6607862903225806,0.8907575702667236,0.9062397806875168 31,32,0.8315,0.7908266129032258,0.4769072663784027,0.5523799340571126
32,33,0.672875,0.6592741935483871,0.8865971283912659,0.9067775895518642
33,34,0.6755,0.6643145161290323,0.8831153435707092,0.8996775977073177
34,35,0.6745,0.6643145161290323,0.8799522204399108,0.9008925057226612
35,36,0.67625,0.6738911290322581,0.8765962233543396,0.8957007392760246
36,37,0.67775,0.6602822580645161,0.8732238512039184,0.8957233851955783
37,38,0.677,0.6663306451612904,0.8700605201721191,0.8884582346485507
38,39,0.677625,0.6779233870967742,0.86691774892807,0.8847945544027513
39,40,0.683875,0.6683467741935484,0.8638643326759339,0.8812634848779247
40,41,0.6825,0.6703629032258065,0.8619350233078003,0.8736868096936133
41,42,0.68525,0.6769153225806451,0.8574845008850097,0.8757798306403621
42,43,0.68325,0.6829637096774194,0.8559755001068116,0.8762649028531967
43,44,0.681875,0.6794354838709677,0.8526963171958923,0.868664155083318
44,45,0.6855,0.670866935483871,0.8508440051078796,0.8656267696811307
45,46,0.687375,0.6738911290322581,0.8477729640007019,0.8647398410304901
46,47,0.689125,0.6723790322580645,0.844515609741211,0.8638435967506901
47,48,0.685,0.6779233870967742,0.8429114985466003,0.8618940441839157
48,49,0.689125,0.6794354838709677,0.8397265086174012,0.8545605559502879
49,50,0.686625,0.6804435483870968,0.8384793972969056,0.8552838506237153
50,51,0.691125,0.6774193548387096,0.8345498304367065,0.848295844370319
51,52,0.687875,0.6859879032258065,0.8335188755989075,0.855028792735069
52,53,0.691,0.6824596774193549,0.8311837863922119,0.8479280106482967
53,54,0.692875,0.6859879032258065,0.8279946088790894,0.8404466413682506
54,55,0.6925,0.6804435483870968,0.8260387935638428,0.84263696593623
55,56,0.693125,0.6849798387096774,0.8229942922592163,0.8379247784614563
56,57,0.694375,0.6844758064516129,0.8220300149917602,0.8415298577277891
57,58,0.696375,0.6880040322580645,0.8190384802818298,0.8321282882844249
58,59,0.694,0.6875,0.816288417339325,0.8343535277151293
59,60,0.694875,0.6839717741935484,0.8151030368804931,0.8336188389408973
60,61,0.69425,0.6854838709677419,0.8126006669998169,0.8352141207264315
61,62,0.697125,0.6854838709677419,0.8106505064964294,0.8332801768856664
62,63,0.69975,0.6905241935483871,0.8080810284614564,0.8234034065277346
63,64,0.70075,0.6844758064516129,0.8052171502113342,0.8192608933294973
64,65,0.69925,0.6869959677419355,0.8035496191978455,0.815050482749939
65,66,0.7005,0.6875,0.8014146156311035,0.8252233305285054
66,67,0.698125,0.6905241935483871,0.7996929430961609,0.8083832244719228
67,68,0.69975,0.6895161290322581,0.7969387445449829,0.8108954429626465
68,69,0.702125,0.6869959677419355,0.7955113248825073,0.8145994409438102
69,70,0.7,0.6935483870967742,0.792340503692627,0.807374858087109
70,71,0.702625,0.6950604838709677,0.7911754889488221,0.8086352617509903
71,72,0.703625,0.6985887096774194,0.7885409245491027,0.8008877135092213
72,73,0.70425,0.702116935483871,0.7873198952674866,0.7998276199063947
73,74,0.704125,0.6970766129032258,0.7848704538345337,0.7971179581457569
74,75,0.70625,0.6970766129032258,0.7822935285568238,0.7927621314602513
75,76,0.70475,0.6960685483870968,0.7788817420005798,0.7937883177111226
76,77,0.704875,0.6985887096774194,0.7792668890953064,0.7862545732528933
77,78,0.703625,0.7006048387096774,0.7767630572319031,0.7841528077279368
78,79,0.710875,0.6980846774193549,0.7729922785758973,0.7877849590393805
79,80,0.7085,0.7036290322580645,0.7725881729125976,0.7884936524975684
80,81,0.7145,0.6975806451612904,0.7703448913097382,0.7794140846498551
81,82,0.707875,0.703125,0.7683381514549256,0.7827766460757102
82,83,0.71425,0.704133064516129,0.7661359086036682,0.7740850121744217
83,84,0.716,0.7247983870967742,0.7635970077514649,0.7837481344899824
84,85,0.714625,0.7046370967741935,0.7620524802207946,0.7760492024883148
85,86,0.717875,0.7273185483870968,0.7587687454223633,0.7693652645234139
86,87,0.71925,0.7167338709677419,0.7571495487689972,0.7791498034231125
87,88,0.723125,0.7061491935483871,0.7559212753772736,0.7657425865050285
88,89,0.721125,0.7227822580645161,0.7540467400550842,0.7639261926374128
89,90,0.72775,0.7212701612903226,0.7519344382286072,0.7616109309657928
90,91,0.72775,0.7283266129032258,0.7487852301597595,0.7575869021877166
91,92,0.727875,0.7273185483870968,0.7474929718971253,0.7539062634591134
92,93,0.72625,0.7237903225806451,0.744426561832428,0.7515108796858019
93,94,0.7285,0.717741935483871,0.7436468710899353,0.7493124450406721
94,95,0.72875,0.7227822580645161,0.7407840843200684,0.7509921366168607
95,96,0.731875,0.7232862903225806,0.7383509948253631,0.7504203108049208
96,97,0.732125,0.7288306451612904,0.7363486151695251,0.7452502135307558
97,98,0.732875,0.7283266129032258,0.7337991933822632,0.7476337955844018
98,99,0.735625,0.7303427419354839,0.7317234919071197,0.739826298529102
99,100,0.733625,0.7283266129032258,0.7287046048641205,0.739542922665996
100,101,0.73375,0.7147177419354839,0.7274810273647309,0.7344657682603405
101,102,0.7355,0.7338709677419355,0.7247261893749237,0.7346700814462477
102,103,0.73225,0.733366935483871,0.7240956840515137,0.7285718571755194
103,104,0.737,0.7247983870967742,0.720921181678772,0.7279678198599047
104,105,0.73725,0.7162298387096774,0.7193985497951507,0.7334578460262667
105,106,0.73975,0.7232862903225806,0.7166938002109527,0.728367522839577
106,107,0.737875,0.7363911290322581,0.715068681716919,0.7223057727659902
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208,209,0.7595,0.7439516129032258,0.6450260980129242,0.66756044376281
209,210,0.762375,0.7525201612903226,0.6450640928745269,0.6618116507607121
210,211,0.764125,0.7479838709677419,0.6432662625312805,0.6611761902609179
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218,219,0.759875,0.751008064516129,0.6410156943798065,0.6790805785886703
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222,223,0.761,0.7570564516129032,0.6397264943122863,0.6680223576484188
223,224,0.76425,0.7520161290322581,0.6378453469276428,0.6630678580653283
224,225,0.763875,0.7520161290322581,0.6382321789264679,0.662065879952523
225,226,0.762,0.7479838709677419,0.639366191625595,0.661988039170542
226,227,0.765,0.7525201612903226,0.6383702094554902,0.6641764746558282
227,228,0.763125,0.751008064516129,0.6380119869709014,0.6607372049362429
228,229,0.759375,0.7520161290322581,0.6383262400627137,0.6545132811992399
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236,237,0.762625,0.7520161290322581,0.6365966260433197,0.6692352737149885
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247,248,0.7655,0.7525201612903226,0.6310685338973999,0.6538672639477637
248,249,0.766,0.7570564516129032,0.6316399626731872,0.6516222578863944
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250,251,0.764625,0.7479838709677419,0.6300767264366149,0.6660763275238776
251,252,0.768375,0.7525201612903226,0.6311213612556458,0.6618637481043416
252,253,0.764375,0.75,0.6319745321273804,0.6553429893908962
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255,256,0.766125,0.7575604838709677,0.6310848982334137,0.6525870225121898
256,257,0.767625,0.7520161290322581,0.6288257777690888,0.6681103860178301
257,258,0.7655,0.7550403225806451,0.6306543848514556,0.659768590042668
258,259,0.764,0.7555443548387096,0.6302573113441468,0.6550669179808709
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263,264,0.76825,0.7515120967741935,0.628407103061676,0.6540230772187633
264,265,0.7675,0.7520161290322581,0.630276577949524,0.6641475250644069
265,266,0.767875,0.7530241935483871,0.6273887372016906,0.6583489189224858
266,267,0.76625,0.7535282258064516,0.6282406325340271,0.6604514862260511
267,268,0.76925,0.7535282258064516,0.6271086316108704,0.6536620384262454
268,269,0.76825,0.7469758064516129,0.6269893596172332,0.6587162335072795
269,270,0.767125,0.7479838709677419,0.6261151025295257,0.6642194390296936
270,271,0.768,0.7545362903225806,0.62709636926651,0.659077537636603
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272,273,0.768625,0.7570564516129032,0.6237564489841462,0.6504895312170829
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275,276,0.768375,0.7605846774193549,0.6264821493625641,0.6480650353816247
276,277,0.767375,0.7585685483870968,0.6253943295478821,0.6529869091126227
277,278,0.768,0.7585685483870968,0.6250380325317383,0.6509808676858102
278,279,0.77225,0.7560483870967742,0.6249497833251954,0.652328065326137
279,280,0.768375,0.7610887096774194,0.6253566663265229,0.6481388095886477
280,281,0.769,0.7631048387096774,0.6269658114910126,0.6511666505567489
281,282,0.77225,0.7615927419354839,0.6219663026332856,0.6503843261349586
282,283,0.768375,0.7530241935483871,0.6239040396213531,0.6559025366460124
283,284,0.773125,0.7565524193548387,0.6230207705497741,0.6521498032154576
284,285,0.77225,0.7575604838709677,0.6224600894451141,0.6498911640336437
285,286,0.772375,0.7535282258064516,0.6230463716983795,0.6594464038648913
286,287,0.772125,0.7454637096774194,0.623299633026123,0.6596774737681111
287,288,0.7705,0.7575604838709677,0.6226675305366516,0.6503997896948168
288,289,0.771875,0.7615927419354839,0.6228007516860962,0.6582970378860351
289,290,0.769,0.7610887096774194,0.6228819963932037,0.6468615512694081
290,291,0.77075,0.7560483870967742,0.6219233012199402,0.6543603664444338
291,292,0.770125,0.7570564516129032,0.6231648538112641,0.6516289316838787
292,293,0.772625,0.7555443548387096,0.6210650091171265,0.6526086061231552
293,294,0.7735,0.7580645161290323,0.6205819368362426,0.650269466061746
294,295,0.76975,0.7600806451612904,0.6209871962070465,0.6479213949172727
295,296,0.770125,0.7565524193548387,0.6204864876270294,0.6524337001385228
296,297,0.76875,0.7464717741935484,0.622816385269165,0.6591307309366041
297,298,0.771375,0.7585685483870968,0.620723108291626,0.647264729584417
298,299,0.768625,0.7580645161290323,0.621616400718689,0.6502204921937758
299,300,0.7725,0.7555443548387096,0.6198663051128388,0.6479197202190277

1 epoch train_acc valid_acc train_loss valid_loss
2 0 1 0.12575 0.59325 0.21421370967741934 0.6854838709677419 2.2937231807708742 1.1784187927246095 2.2676862593620055 0.8956879992638865
3 1 2 0.248 0.713 0.3240927419354839 0.7127016129032258 2.2377592124938963 0.788702152967453 2.1976155081102924 0.7735376723351017
4 2 3 0.354375 0.755 0.3815524193548387 0.7389112903225806 2.129876153945923 0.7006268813610077 2.044375258107339 0.7271041427889178
5 3 4 0.42625 0.763625 0.4324596774193548 0.7273185483870968 1.9269471073150635 0.668900458574295 1.815262294584705 0.7277685692233424
6 4 5 0.482125 0.76275 0.5282258064516129 0.748991935483871 1.6988583679199218 0.6469288661479949 1.6081839684517152 0.672559670863613
7 5 6 0.528375 0.773625 0.5151209677419355 0.7434475806451613 1.5167635107040405 0.6203210880756378 1.4654438649454424 0.6748099394382969
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286 287 0.772125 0.7454637096774194 0.623299633026123 0.6596774737681111
287 288 0.7705 0.7575604838709677 0.6226675305366516 0.6503997896948168
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299 300 0.7725 0.7555443548387096 0.6198663051128388 0.6479197202190277

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@ -1,51 +1,49 @@
epoch,train_acc,valid_acc,train_loss,valid_loss ,epoch,train_acc,valid_acc,train_loss,valid_loss
1,0.3112916666666667,0.5076444892473119,2.1129255460103353,1.7863053237238238 0,1,0.391375,0.5882056451612904,1.7793689422607422,1.3356874796652025
2,0.46210416666666665,0.5547715053763441,1.5605127541224162,1.3499009532313193 1,2,0.617125,0.6365927419354839,1.059556163787842,0.9690603344671188
3,0.5200833333333333,0.5892137096774194,1.298180630683899,1.1617528520604616 2,3,0.663125,0.6577620967741935,0.9102170023918151,0.9078671547674364
4,0.5641666666666667,0.6311323924731183,1.161390997727712,1.0419180438082705 3,4,0.69,0.6819556451612904,0.8537670917510987,0.8649420180628377
5,0.6055833333333334,0.6598622311827957,1.062113331158956,0.9560088053826363 4,5,0.71125,0.7056451612903226,0.8021836678981781,0.8317571128568342
6,0.6316666666666667,0.6804435483870968,0.9944041889508565,0.8958990977656457 5,6,0.719375,0.6844758064516129,0.7773117737770081,0.8293971457789021
7,0.6513958333333333,0.6928763440860215,0.9395476338068645,0.8504296951396491 6,7,0.733,0.7247983870967742,0.7432277894020081,0.7570091389840649
8,0.6677708333333333,0.7007728494623656,0.8985106336275737,0.8148919709267155 7,8,0.727125,0.7051411290322581,0.7392586808204651,0.7708722833664187
9,0.6809375,0.7095934139784946,0.8673048818906148,0.7897684433126962 8,9,0.740375,0.7091733870967742,0.716008551120758,0.7547545788749572
10,0.6876666666666666,0.7185819892473119,0.8408271819750468,0.7671139176173877 9,10,0.74775,0.7091733870967742,0.6988923935890198,0.7634256799374858
11,0.697625,0.7247143817204301,0.8202435002326965,0.7502269674372929 10,11,0.75025,0.7328629032258065,0.6836859595775604,0.7220065324537216
12,0.7030625,0.7303427419354839,0.8024592914581299,0.7343912464316174 11,12,0.75525,0.7313508064516129,0.6790840411186219,0.7320531125991575
13,0.7118958333333333,0.7316868279569892,0.7869214735031128,0.7222210591839205 12,13,0.755875,0.734375,0.6686462206840516,0.7067360877990723
14,0.7163958333333333,0.7404233870967742,0.7718908907572428,0.7093277560767307 13,14,0.755875,0.7338709677419355,0.659578008890152,0.6924379564100697
15,0.7211041666666667,0.7437836021505376,0.7589795832633972,0.6981546148177116 14,15,0.758625,0.7217741935483871,0.6591809678077698,0.7092515037905786
16,0.724875,0.748067876344086,0.7483940289815267,0.6881407947950465 15,16,0.770125,0.7474798387096774,0.6366513178348542,0.6689942498360911
17,0.7293958333333334,0.75,0.7373875479698181,0.6788085179944192 16,17,0.76975,0.75,0.64143789935112,0.6781372427940369
18,0.7345,0.7517641129032258,0.7301613558133443,0.6724013622089099 17,18,0.766625,0.7469758064516129,0.6327295961380005,0.6749444469328849
19,0.7350416666666667,0.7550403225806451,0.7210039290587107,0.6651292238184201 18,19,0.772125,0.7298387096774194,0.6213552060127259,0.6950428524324971
20,0.7391458333333333,0.758736559139785,0.711990216255188,0.6584682823509298 19,20,0.771625,0.7560483870967742,0.619529750585556,0.6620656597998834
21,0.7433958333333334,0.7608366935483871,0.7047773049672444,0.6513981505106854 20,21,0.77425,0.7459677419354839,0.6170263500213623,0.6944894117693747
22,0.7442916666666667,0.7634408602150538,0.6997552577654521,0.6454382868864204 21,22,0.77375,0.7379032258064516,0.610548350572586,0.698592597438443
23,0.7481458333333333,0.7653729838709677,0.6937046423753103,0.6401395256160408 22,23,0.774125,0.7494959677419355,0.6073116481304168,0.6649176555295144
24,0.7498333333333334,0.7683131720430108,0.6873725473880767,0.6349253362865859 23,24,0.78,0.75,0.601892656326294,0.6502023070089279
25,0.7529166666666667,0.769573252688172,0.6828897857666015,0.6308066611007977 24,25,0.777875,0.7671370967741935,0.5965238115787506,0.6416350141648324
26,0.7547291666666667,0.7708333333333334,0.6747500312328338,0.6260690948655528 25,26,0.7875,0.751008064516129,0.584319313287735,0.6557927843063108
27,0.7547291666666667,0.7722614247311828,0.6727884339491527,0.6230032514500362 26,27,0.784625,0.7651209677419355,0.5858220131397247,0.6265111680953733
28,0.7580208333333334,0.7736055107526881,0.6705719958146413,0.6201616948650729 27,28,0.77975,0.7610887096774194,0.5928270950317382,0.6353864400617538
29,0.757125,0.7748655913978495,0.6678872625033061,0.6165956912502166 28,29,0.780625,0.7520161290322581,0.5824392430782318,0.6569667564284417
30,0.76025,0.7759576612903226,0.6641453323364258,0.6141596103227267 29,30,0.78475,0.7686491935483871,0.5810435285568237,0.6306867743692091
31,0.7609583333333333,0.7767137096774194,0.6619020007451375,0.6114715427480718 30,31,0.789,0.7706653225806451,0.5672282783985138,0.6261125274242894
32,0.7618333333333334,0.776377688172043,0.6576849890549977,0.609050533463878 31,32,0.78525,0.7560483870967742,0.5757509377002716,0.6505500414679127
33,0.7629583333333333,0.7783938172043011,0.6566993356545766,0.6069687149857962 32,33,0.792,0.7681451612903226,0.5613697295188904,0.629849144527989
34,0.7631666666666667,0.7791498655913979,0.6553838003476461,0.6052348261238426 33,34,0.793875,0.7620967741935484,0.5625830183029175,0.6189906856706066
35,0.7626666666666667,0.7785618279569892,0.6523663277626037,0.6037159392269709 34,35,0.791875,0.7681451612903226,0.5602230775356293,0.6212261732547514
36,0.7653333333333333,0.780325940860215,0.6526273953914642,0.6023694485105494 35,36,0.794625,0.7615927419354839,0.5545250961780548,0.619811047469416
37,0.7639791666666667,0.7810819892473119,0.6524330813884736,0.6012957778669172 36,37,0.794625,0.7701612903225806,0.5573954427242279,0.6205428954093687
38,0.7673958333333334,0.7799059139784946,0.6500039516290029,0.6002496516191831 37,38,0.79425,0.764616935483871,0.5514744529724122,0.628905875067557
39,0.764875,0.7815860215053764,0.6476710465749105,0.5992967845291219 38,39,0.792375,0.7842741935483871,0.5618353080749512,0.5954909113145643
40,0.7661041666666667,0.782258064516129,0.6484741485118866,0.5987250285763894 39,40,0.796375,0.7711693548387096,0.5491654114723206,0.6137347759739045
41,0.7668333333333334,0.7818380376344086,0.64447620677948,0.5979425240588444 40,41,0.799,0.7641129032258065,0.5372960684299469,0.6470560056547965
42,0.7662291666666666,0.7817540322580645,0.6458657967249553,0.5976644568545844 41,42,0.800375,0.7671370967741935,0.5395989503860473,0.6211921880322118
43,0.76675,0.7824260752688172,0.6449048202037811,0.5971484975789183 42,43,0.806,0.7671370967741935,0.5370515692234039,0.6075864828401997
44,0.7677916666666667,0.7825100806451613,0.6437487976551056,0.5968063899906733 43,44,0.801875,0.7605846774193549,0.5388010408878326,0.5891174308715328
45,0.7677083333333333,0.7825940860215054,0.6432626353104909,0.5964810960395361 44,45,0.800875,0.766633064516129,0.539761929512024,0.610026998865989
46,0.7666041666666666,0.7826780913978495,0.6452286802132925,0.5963560240243071 45,46,0.802375,0.780241935483871,0.5270701496601105,0.6000283591208919
47,0.768875,0.7827620967741935,0.6432473388512929,0.5962550809947393 46,47,0.799875,0.7772177419354839,0.5320828959941865,0.5864833074231302
48,0.7674583333333334,0.7826780913978495,0.6433716455300649,0.5961945524779699 47,48,0.807,0.7837701612903226,0.5309389193058014,0.5761975809451072
49,0.7678333333333334,0.7827620967741935,0.6440556212266286,0.596176440036425
50,0.7677083333333333,0.7825940860215054,0.6438165396849315,0.5961719805835396

1 epoch train_acc valid_acc train_loss valid_loss
2 0 1 0.3112916666666667 0.391375 0.5076444892473119 0.5882056451612904 2.1129255460103353 1.7793689422607422 1.7863053237238238 1.3356874796652025
3 1 2 0.46210416666666665 0.617125 0.5547715053763441 0.6365927419354839 1.5605127541224162 1.059556163787842 1.3499009532313193 0.9690603344671188
4 2 3 0.5200833333333333 0.663125 0.5892137096774194 0.6577620967741935 1.298180630683899 0.9102170023918151 1.1617528520604616 0.9078671547674364
5 3 4 0.5641666666666667 0.69 0.6311323924731183 0.6819556451612904 1.161390997727712 0.8537670917510987 1.0419180438082705 0.8649420180628377
6 4 5 0.6055833333333334 0.71125 0.6598622311827957 0.7056451612903226 1.062113331158956 0.8021836678981781 0.9560088053826363 0.8317571128568342
7 5 6 0.6316666666666667 0.719375 0.6804435483870968 0.6844758064516129 0.9944041889508565 0.7773117737770081 0.8958990977656457 0.8293971457789021
8 6 7 0.6513958333333333 0.733 0.6928763440860215 0.7247983870967742 0.9395476338068645 0.7432277894020081 0.8504296951396491 0.7570091389840649
9 7 8 0.6677708333333333 0.727125 0.7007728494623656 0.7051411290322581 0.8985106336275737 0.7392586808204651 0.8148919709267155 0.7708722833664187
10 8 9 0.6809375 0.740375 0.7095934139784946 0.7091733870967742 0.8673048818906148 0.716008551120758 0.7897684433126962 0.7547545788749572
11 9 10 0.6876666666666666 0.74775 0.7185819892473119 0.7091733870967742 0.8408271819750468 0.6988923935890198 0.7671139176173877 0.7634256799374858
12 10 11 0.697625 0.75025 0.7247143817204301 0.7328629032258065 0.8202435002326965 0.6836859595775604 0.7502269674372929 0.7220065324537216
13 11 12 0.7030625 0.75525 0.7303427419354839 0.7313508064516129 0.8024592914581299 0.6790840411186219 0.7343912464316174 0.7320531125991575
14 12 13 0.7118958333333333 0.755875 0.7316868279569892 0.734375 0.7869214735031128 0.6686462206840516 0.7222210591839205 0.7067360877990723
15 13 14 0.7163958333333333 0.755875 0.7404233870967742 0.7338709677419355 0.7718908907572428 0.659578008890152 0.7093277560767307 0.6924379564100697
16 14 15 0.7211041666666667 0.758625 0.7437836021505376 0.7217741935483871 0.7589795832633972 0.6591809678077698 0.6981546148177116 0.7092515037905786
17 15 16 0.724875 0.770125 0.748067876344086 0.7474798387096774 0.7483940289815267 0.6366513178348542 0.6881407947950465 0.6689942498360911
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20 18 19 0.7350416666666667 0.772125 0.7550403225806451 0.7298387096774194 0.7210039290587107 0.6213552060127259 0.6651292238184201 0.6950428524324971
21 19 20 0.7391458333333333 0.771625 0.758736559139785 0.7560483870967742 0.711990216255188 0.619529750585556 0.6584682823509298 0.6620656597998834
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25 23 24 0.7498333333333334 0.78 0.7683131720430108 0.75 0.6873725473880767 0.601892656326294 0.6349253362865859 0.6502023070089279
26 24 25 0.7529166666666667 0.777875 0.769573252688172 0.7671370967741935 0.6828897857666015 0.5965238115787506 0.6308066611007977 0.6416350141648324
27 25 26 0.7547291666666667 0.7875 0.7708333333333334 0.751008064516129 0.6747500312328338 0.584319313287735 0.6260690948655528 0.6557927843063108
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30 28 29 0.757125 0.780625 0.7748655913978495 0.7520161290322581 0.6678872625033061 0.5824392430782318 0.6165956912502166 0.6569667564284417
31 29 30 0.76025 0.78475 0.7759576612903226 0.7686491935483871 0.6641453323364258 0.5810435285568237 0.6141596103227267 0.6306867743692091
32 30 31 0.7609583333333333 0.789 0.7767137096774194 0.7706653225806451 0.6619020007451375 0.5672282783985138 0.6114715427480718 0.6261125274242894
33 31 32 0.7618333333333334 0.78525 0.776377688172043 0.7560483870967742 0.6576849890549977 0.5757509377002716 0.609050533463878 0.6505500414679127
34 32 33 0.7629583333333333 0.792 0.7783938172043011 0.7681451612903226 0.6566993356545766 0.5613697295188904 0.6069687149857962 0.629849144527989
35 33 34 0.7631666666666667 0.793875 0.7791498655913979 0.7620967741935484 0.6553838003476461 0.5625830183029175 0.6052348261238426 0.6189906856706066
36 34 35 0.7626666666666667 0.791875 0.7785618279569892 0.7681451612903226 0.6523663277626037 0.5602230775356293 0.6037159392269709 0.6212261732547514
37 35 36 0.7653333333333333 0.794625 0.780325940860215 0.7615927419354839 0.6526273953914642 0.5545250961780548 0.6023694485105494 0.619811047469416
38 36 37 0.7639791666666667 0.794625 0.7810819892473119 0.7701612903225806 0.6524330813884736 0.5573954427242279 0.6012957778669172 0.6205428954093687
39 37 38 0.7673958333333334 0.79425 0.7799059139784946 0.764616935483871 0.6500039516290029 0.5514744529724122 0.6002496516191831 0.628905875067557
40 38 39 0.764875 0.792375 0.7815860215053764 0.7842741935483871 0.6476710465749105 0.5618353080749512 0.5992967845291219 0.5954909113145643
41 39 40 0.7661041666666667 0.796375 0.782258064516129 0.7711693548387096 0.6484741485118866 0.5491654114723206 0.5987250285763894 0.6137347759739045
42 40 41 0.7668333333333334 0.799 0.7818380376344086 0.7641129032258065 0.64447620677948 0.5372960684299469 0.5979425240588444 0.6470560056547965
43 41 42 0.7662291666666666 0.800375 0.7817540322580645 0.7671370967741935 0.6458657967249553 0.5395989503860473 0.5976644568545844 0.6211921880322118
44 42 43 0.76675 0.806 0.7824260752688172 0.7671370967741935 0.6449048202037811 0.5370515692234039 0.5971484975789183 0.6075864828401997
45 43 44 0.7677916666666667 0.801875 0.7825100806451613 0.7605846774193549 0.6437487976551056 0.5388010408878326 0.5968063899906733 0.5891174308715328
46 44 45 0.7677083333333333 0.800875 0.7825940860215054 0.766633064516129 0.6432626353104909 0.539761929512024 0.5964810960395361 0.610026998865989
47 45 46 0.7666041666666666 0.802375 0.7826780913978495 0.780241935483871 0.6452286802132925 0.5270701496601105 0.5963560240243071 0.6000283591208919
48 46 47 0.768875 0.799875 0.7827620967741935 0.7772177419354839 0.6432473388512929 0.5320828959941865 0.5962550809947393 0.5864833074231302
49 47 48 0.7674583333333334 0.807 0.7826780913978495 0.7837701612903226 0.6433716455300649 0.5309389193058014 0.5961945524779699 0.5761975809451072
49 0.7678333333333334 0.7827620967741935 0.6440556212266286 0.596176440036425
50 0.7677083333333333 0.7825940860215054 0.6438165396849315 0.5961719805835396

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@ -1,51 +1,34 @@
epoch,train_acc,valid_acc,train_loss,valid_loss ,epoch,train_acc,valid_acc,train_loss,valid_loss
1,0.6377291666666667,0.7426075268817204,1.0222952149709066,0.6658745436899124 0,1,0.561125,0.6476814516129032,1.271985122203827,0.9911825291572078
2,0.7588541666666667,0.7799059139784946,0.6436424539883931,0.5876969707909451 1,2,0.676375,0.6668346774193549,0.9134431419372558,0.8937955517922679
3,0.7829791666666667,0.7955309139784946,0.5888768948713938,0.5508138095178912 2,3,0.699125,0.6824596774193549,0.8243759956359863,0.8520721235582905
4,0.7927916666666667,0.8009912634408602,0.5641531114578247,0.5473136597423143 3,4,0.716625,0.6995967741935484,0.7818560593128204,0.8005917687569896
5,0.7997291666666667,0.8065356182795699,0.5444657148520152,0.515406842834206 4,5,0.725125,0.7101814516129032,0.745261854171753,0.8037946531849522
6,0.8043541666666667,0.8140120967741935,0.5316140202681223,0.5052012169873843 5,6,0.733375,0.7061491935483871,0.7344184167385102,0.7732808570707997
7,0.80975,0.8156922043010753,0.521293937365214,0.4983856466508681 6,7,0.737875,0.7253024193548387,0.7150477197170257,0.74149090051651
8,0.8155208333333334,0.8162802419354839,0.5079634555180867,0.49958501611986467 7,8,0.74325,0.7253024193548387,0.7022454526424408,0.7516527022084882
9,0.8161875,0.8159442204301075,0.5036936206022898,0.4907228536503289 8,9,0.742625,0.7298387096774194,0.7041031284332275,0.7247120468847214
10,0.816875,0.8160282258064516,0.497530157327652,0.4895895427914076 9,10,0.747875,0.7116935483870968,0.6772661633491516,0.7338270487323884
11,0.8210416666666667,0.8269489247311828,0.4916797243754069,0.47517218673101036 10,11,0.757875,0.7212701612903226,0.6562292041778565,0.7387931039256435
12,0.8210416666666667,0.8266129032258065,0.4892559293905894,0.468310099135163 11,12,0.761625,0.7328629032258065,0.6542983632087708,0.725050816612859
13,0.8241041666666666,0.8211525537634409,0.4825246704419454,0.4856182368852759 12,13,0.75925,0.7318548387096774,0.6493379819393158,0.7001086867624714
14,0.8239791666666667,0.8264448924731183,0.48390908201535543,0.46895075997998636 13,14,0.767375,0.7379032258064516,0.6460576868057251,0.6988139988914612
15,0.8245,0.8230846774193549,0.4795244421164195,0.47176380727880746 14,15,0.765875,0.7288306451612904,0.6339491362571716,0.7128441218406923
16,0.8270416666666667,0.829133064516129,0.47462198424339297,0.4671747069205007 15,16,0.763125,0.7368951612903226,0.6262373595237732,0.714297366719092
17,0.8273958333333333,0.8297211021505376,0.4733834793567657,0.4658442559421703 16,17,0.76925,0.7227822580645161,0.6279029569625855,0.7181522269402781
18,0.828125,0.8337533602150538,0.47195579528808596,0.46035799896845253 17,18,0.77025,0.7449596774193549,0.6159816448688507,0.6757595616002237
19,0.8294583333333333,0.829133064516129,0.4668528276284536,0.46208705472689804 18,19,0.7745,0.7283266129032258,0.6136245548725128,0.6998546681096477
20,0.830625,0.8303931451612904,0.46666145197550457,0.4616392642580053 19,20,0.775375,0.7469758064516129,0.6000997524261474,0.6749713065162781
21,0.8320416666666667,0.8280409946236559,0.46406093080838523,0.4690516353935324 20,21,0.7805,0.748991935483871,0.5928600332736969,0.6656959902855658
22,0.8315833333333333,0.8293010752688172,0.4628266294002533,0.4590829178210228 21,22,0.77525,0.7444556451612904,0.599046837568283,0.6857193170055267
23,0.8315416666666666,0.8298051075268817,0.46202420779069264,0.4582436110383721 22,23,0.785,0.7384072580645161,0.5875316572189331,0.6785462142959717
24,0.8328541666666667,0.8282090053763441,0.4594616918563843,0.46520241454083433 23,24,0.778375,0.765625,0.588378502368927,0.627939272311426
25,0.8321458333333334,0.8313172043010753,0.45899707794189454,0.4611524093535639 24,25,0.78575,0.7459677419354839,0.5700427904129028,0.6502643679418871
26,0.835,0.8311491935483871,0.4561348853111267,0.460902286793596 25,26,0.7805,0.75,0.5785817315578461,0.6712307555060233
27,0.8342708333333333,0.8335013440860215,0.45571692689259846,0.4524733103731627 26,27,0.78675,0.7474798387096774,0.5676946561336518,0.6555941143343526
28,0.83475,0.831989247311828,0.45314634958902994,0.4535403392648184 27,28,0.787875,0.7505040322580645,0.575938116312027,0.6507430134281036
29,0.8353958333333333,0.8333333333333334,0.4508692183494568,0.4509869323622796 28,29,0.789,0.7605846774193549,0.5655779435634612,0.6520830373610219
30,0.8358958333333333,0.8314012096774194,0.4508490650653839,0.4559444804345408 29,30,0.790625,0.7434475806451613,0.5578647639751434,0.6789213486256138
31,0.8379791666666667,0.8347614247311828,0.44810916344324747,0.45132114586009775 30,31,0.787375,0.7565524193548387,0.5687701859474182,0.649224087115257
32,0.8369375,0.832997311827957,0.4488534228801727,0.4531173186917459 31,32,0.79575,0.7560483870967742,0.5432633152008056,0.6388471222692921
33,0.8375208333333334,0.8366935483870968,0.4460577855904897,0.4518213977095901 32,33,0.7895,0.7595766129032258,0.5557173223495483,0.6391933618053314
34,0.8372708333333333,0.8314852150537635,0.4453533016045888,0.4571616124081355
35,0.8381458333333334,0.8351814516129032,0.4446527805328369,0.4513627043975297
36,0.8392916666666667,0.8373655913978495,0.4431237142880758,0.44869983228304056
37,0.838375,0.8347614247311828,0.4424218458334605,0.4486082660895522
38,0.8397916666666667,0.8355174731182796,0.4412206415732702,0.44703892129723743
39,0.8401875,0.8365255376344086,0.4396123299598694,0.4470836206149029
40,0.8399791666666667,0.8345934139784946,0.43971687642733254,0.4466231196157394
41,0.8410833333333333,0.8373655913978495,0.43810279977321626,0.4461495735312021
42,0.8408958333333333,0.8372815860215054,0.4371747978528341,0.4456534408113008
43,0.8414583333333333,0.8363575268817204,0.43774009283383686,0.4454879440287108
44,0.841375,0.8358534946236559,0.4363077602386475,0.4460145914426414
45,0.8408958333333333,0.8363575268817204,0.43665687982241314,0.44518788110825325
46,0.841875,0.8371135752688172,0.43598757874965666,0.44520101848468985
47,0.8422083333333333,0.836945564516129,0.4349166991710663,0.44520143988311933
48,0.8421041666666667,0.8360215053763441,0.43520630804697674,0.44510498354511874
49,0.8416875,0.836861559139785,0.4354708949327469,0.44491737311886204
50,0.8408125,0.836861559139785,0.43524290529886883,0.4449239748139535

1 epoch train_acc valid_acc train_loss valid_loss
2 0 1 0.6377291666666667 0.561125 0.7426075268817204 0.6476814516129032 1.0222952149709066 1.271985122203827 0.6658745436899124 0.9911825291572078
3 1 2 0.7588541666666667 0.676375 0.7799059139784946 0.6668346774193549 0.6436424539883931 0.9134431419372558 0.5876969707909451 0.8937955517922679
4 2 3 0.7829791666666667 0.699125 0.7955309139784946 0.6824596774193549 0.5888768948713938 0.8243759956359863 0.5508138095178912 0.8520721235582905
5 3 4 0.7927916666666667 0.716625 0.8009912634408602 0.6995967741935484 0.5641531114578247 0.7818560593128204 0.5473136597423143 0.8005917687569896
6 4 5 0.7997291666666667 0.725125 0.8065356182795699 0.7101814516129032 0.5444657148520152 0.745261854171753 0.515406842834206 0.8037946531849522
7 5 6 0.8043541666666667 0.733375 0.8140120967741935 0.7061491935483871 0.5316140202681223 0.7344184167385102 0.5052012169873843 0.7732808570707997
8 6 7 0.80975 0.737875 0.8156922043010753 0.7253024193548387 0.521293937365214 0.7150477197170257 0.4983856466508681 0.74149090051651
9 7 8 0.8155208333333334 0.74325 0.8162802419354839 0.7253024193548387 0.5079634555180867 0.7022454526424408 0.49958501611986467 0.7516527022084882
10 8 9 0.8161875 0.742625 0.8159442204301075 0.7298387096774194 0.5036936206022898 0.7041031284332275 0.4907228536503289 0.7247120468847214
11 9 10 0.816875 0.747875 0.8160282258064516 0.7116935483870968 0.497530157327652 0.6772661633491516 0.4895895427914076 0.7338270487323884
12 10 11 0.8210416666666667 0.757875 0.8269489247311828 0.7212701612903226 0.4916797243754069 0.6562292041778565 0.47517218673101036 0.7387931039256435
13 11 12 0.8210416666666667 0.761625 0.8266129032258065 0.7328629032258065 0.4892559293905894 0.6542983632087708 0.468310099135163 0.725050816612859
14 12 13 0.8241041666666666 0.75925 0.8211525537634409 0.7318548387096774 0.4825246704419454 0.6493379819393158 0.4856182368852759 0.7001086867624714
15 13 14 0.8239791666666667 0.767375 0.8264448924731183 0.7379032258064516 0.48390908201535543 0.6460576868057251 0.46895075997998636 0.6988139988914612
16 14 15 0.8245 0.765875 0.8230846774193549 0.7288306451612904 0.4795244421164195 0.6339491362571716 0.47176380727880746 0.7128441218406923
17 15 16 0.8270416666666667 0.763125 0.829133064516129 0.7368951612903226 0.47462198424339297 0.6262373595237732 0.4671747069205007 0.714297366719092
18 16 17 0.8273958333333333 0.76925 0.8297211021505376 0.7227822580645161 0.4733834793567657 0.6279029569625855 0.4658442559421703 0.7181522269402781
19 17 18 0.828125 0.77025 0.8337533602150538 0.7449596774193549 0.47195579528808596 0.6159816448688507 0.46035799896845253 0.6757595616002237
20 18 19 0.8294583333333333 0.7745 0.829133064516129 0.7283266129032258 0.4668528276284536 0.6136245548725128 0.46208705472689804 0.6998546681096477
21 19 20 0.830625 0.775375 0.8303931451612904 0.7469758064516129 0.46666145197550457 0.6000997524261474 0.4616392642580053 0.6749713065162781
22 20 21 0.8320416666666667 0.7805 0.8280409946236559 0.748991935483871 0.46406093080838523 0.5928600332736969 0.4690516353935324 0.6656959902855658
23 21 22 0.8315833333333333 0.77525 0.8293010752688172 0.7444556451612904 0.4628266294002533 0.599046837568283 0.4590829178210228 0.6857193170055267
24 22 23 0.8315416666666666 0.785 0.8298051075268817 0.7384072580645161 0.46202420779069264 0.5875316572189331 0.4582436110383721 0.6785462142959717
25 23 24 0.8328541666666667 0.778375 0.8282090053763441 0.765625 0.4594616918563843 0.588378502368927 0.46520241454083433 0.627939272311426
26 24 25 0.8321458333333334 0.78575 0.8313172043010753 0.7459677419354839 0.45899707794189454 0.5700427904129028 0.4611524093535639 0.6502643679418871
27 25 26 0.835 0.7805 0.8311491935483871 0.75 0.4561348853111267 0.5785817315578461 0.460902286793596 0.6712307555060233
28 26 27 0.8342708333333333 0.78675 0.8335013440860215 0.7474798387096774 0.45571692689259846 0.5676946561336518 0.4524733103731627 0.6555941143343526
29 27 28 0.83475 0.787875 0.831989247311828 0.7505040322580645 0.45314634958902994 0.575938116312027 0.4535403392648184 0.6507430134281036
30 28 29 0.8353958333333333 0.789 0.8333333333333334 0.7605846774193549 0.4508692183494568 0.5655779435634612 0.4509869323622796 0.6520830373610219
31 29 30 0.8358958333333333 0.790625 0.8314012096774194 0.7434475806451613 0.4508490650653839 0.5578647639751434 0.4559444804345408 0.6789213486256138
32 30 31 0.8379791666666667 0.787375 0.8347614247311828 0.7565524193548387 0.44810916344324747 0.5687701859474182 0.45132114586009775 0.649224087115257
33 31 32 0.8369375 0.79575 0.832997311827957 0.7560483870967742 0.4488534228801727 0.5432633152008056 0.4531173186917459 0.6388471222692921
34 32 33 0.8375208333333334 0.7895 0.8366935483870968 0.7595766129032258 0.4460577855904897 0.5557173223495483 0.4518213977095901 0.6391933618053314
34 0.8372708333333333 0.8314852150537635 0.4453533016045888 0.4571616124081355
35 0.8381458333333334 0.8351814516129032 0.4446527805328369 0.4513627043975297
36 0.8392916666666667 0.8373655913978495 0.4431237142880758 0.44869983228304056
37 0.838375 0.8347614247311828 0.4424218458334605 0.4486082660895522
38 0.8397916666666667 0.8355174731182796 0.4412206415732702 0.44703892129723743
39 0.8401875 0.8365255376344086 0.4396123299598694 0.4470836206149029
40 0.8399791666666667 0.8345934139784946 0.43971687642733254 0.4466231196157394
41 0.8410833333333333 0.8373655913978495 0.43810279977321626 0.4461495735312021
42 0.8408958333333333 0.8372815860215054 0.4371747978528341 0.4456534408113008
43 0.8414583333333333 0.8363575268817204 0.43774009283383686 0.4454879440287108
44 0.841375 0.8358534946236559 0.4363077602386475 0.4460145914426414
45 0.8408958333333333 0.8363575268817204 0.43665687982241314 0.44518788110825325
46 0.841875 0.8371135752688172 0.43598757874965666 0.44520101848468985
47 0.8422083333333333 0.836945564516129 0.4349166991710663 0.44520143988311933
48 0.8421041666666667 0.8360215053763441 0.43520630804697674 0.44510498354511874
49 0.8416875 0.836861559139785 0.4354708949327469 0.44491737311886204
50 0.8408125 0.836861559139785 0.43524290529886883 0.4449239748139535

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@ -1,51 +1,86 @@
epoch,train_acc,valid_acc,train_loss,valid_loss ,epoch,train_acc,valid_acc,train_loss,valid_loss
1,0.2535833333333333,0.41952284946236557,2.274448096593221,2.2001695325297694 0,1,0.575125,0.7011088709677419,2.031563010215759,1.8535375249001287
2,0.4691875,0.5073084677419355,2.116311640103658,2.051948335862929 1,2,0.723875,0.7515120967741935,1.7430778923034669,1.713461822079074
3,0.5270416666666666,0.5266297043010753,2.009436710357666,1.9777893276624783 2,3,0.76425,0.7525201612903226,1.6991203842163085,1.7051894049490652
4,0.5353541666666667,0.535114247311828,1.9530798486073813,1.9356736265203005 3,4,0.779875,0.7716733870967742,1.6807003259658813,1.690080665772961
5,0.5935416666666666,0.6175235215053764,1.9118746633529664,1.891348486305565 4,5,0.79525,0.7696572580645161,1.6654855661392212,1.6926762519344207
6,0.636625,0.6625504032258065,1.8629151741663614,1.847881397893352 5,6,0.80175,0.7888104838709677,1.6598792200088501,1.6711357831954956
7,0.686625,0.7302587365591398,1.8228577140172322,1.8057919330494379 6,7,0.80875,0.7721774193548387,1.6529147624969482,1.6847506146277151
8,0.7415416666666667,0.7511760752688172,1.7803055089314779,1.7647134245082896 7,8,0.812375,0.8004032258064516,1.649049828529358,1.6597587523921844
9,0.7542291666666666,0.7582325268817204,1.7502256110509236,1.7423059594246648 8,9,0.815375,0.7893145161290323,1.6458229179382324,1.6707303101016628
10,0.7607708333333333,0.764616935483871,1.7317150996526083,1.724781782396378 9,10,0.825,0.811491935483871,1.6370085287094116,1.6518691432091497
11,0.7677291666666667,0.7719254032258065,1.7192926041285197,1.713816902970755 10,11,0.832375,0.795866935483871,1.630222158432007,1.6616583139665666
12,0.7704791666666667,0.7731014784946236,1.71117463239034,1.7070557173862253 11,12,0.83225,0.8145161290322581,1.6293829317092896,1.6454557026586225
13,0.771625,0.7736055107526881,1.7053865385055542,1.7027398668309695 12,13,0.834875,0.8125,1.6268642024993896,1.6500283018235238
14,0.7747708333333333,0.7749495967741935,1.7004834445317587,1.6978983327906618 13,14,0.840875,0.8104838709677419,1.6199358901977539,1.652194688397069
15,0.7763333333333333,0.7751176075268817,1.6967166414260864,1.6960316857983988 14,15,0.83975,0.8069556451612904,1.6210605192184449,1.6520756982987927
16,0.7790416666666666,0.7767977150537635,1.6929608987172444,1.692371742699736 15,16,0.84175,0.8004032258064516,1.6195698108673096,1.6582853217278757
17,0.780125,0.7801579301075269,1.6910814901987712,1.6917471321680213 16,17,0.838625,0.8049395161290323,1.6228710346221924,1.656198097813514
18,0.7817916666666667,0.7827620967741935,1.6882368882497152,1.6861866725388395 17,18,0.83775,0.8140120967741935,1.6226149969100951,1.6462942131104008
19,0.7827291666666667,0.7815860215053764,1.6857908573150635,1.6868788478195027 18,19,0.845375,0.8251008064516129,1.6150365982055663,1.6358921681680987
20,0.7827083333333333,0.7840221774193549,1.6843910643259685,1.6850647259784002 19,20,0.85125,0.8296370967741935,1.6097205333709717,1.6333312757553593
21,0.7848541666666666,0.7857862903225806,1.6823169787724812,1.6813292862266622 20,21,0.851875,0.8210685483870968,1.609424132347107,1.6388044895664338
22,0.7876666666666666,0.7848622311827957,1.6801685171127319,1.6815461574062225 21,22,0.850375,0.8210685483870968,1.6101643466949462,1.6369191561975787
23,0.7879791666666667,0.7848622311827957,1.6788572374979656,1.6818229216401295 22,23,0.858125,0.8185483870967742,1.6031983375549317,1.6421872877305554
24,0.7890833333333334,0.7859543010752689,1.6776897859573365,1.6799169009731663 23,24,0.857125,0.8361895161290323,1.6038059520721435,1.62519553015309
25,0.7902708333333334,0.7883904569892473,1.6763870159784953,1.6777271570697907 24,25,0.854,0.8301411290322581,1.606280044555664,1.6290803609355804
26,0.791,0.789986559139785,1.6751194190979004,1.6777111407249206 25,26,0.858375,0.8119959677419355,1.6011180095672608,1.648349738890125
27,0.7918541666666666,0.7886424731182796,1.674490571975708,1.6776156912567795 26,27,0.861375,0.8371975806451613,1.6000273780822754,1.6237776010267195
28,0.7936666666666666,0.7905745967741935,1.6728278172810873,1.6744540147883917 27,28,0.8705,0.8351814516129032,1.5907322645187378,1.6240986316434798
29,0.793375,0.7876344086021505,1.671903525352478,1.6776354030896259 28,29,0.87175,0.828125,1.5896439476013184,1.6316479982868317
30,0.7945833333333333,0.7901545698924731,1.6710031661987306,1.6751063805754467 29,30,0.867375,0.8266129032258065,1.5936143741607667,1.632056209348863
31,0.7959166666666667,0.7932627688172043,1.6700964994430543,1.6719800926023913 30,31,0.86575,0.8377016129032258,1.5949567575454713,1.6240303862479426
32,0.7955833333333333,0.7933467741935484,1.6697869466145834,1.6723043623790945 31,32,0.86975,0.8336693548387096,1.5907692136764526,1.6251203167823054
33,0.7971458333333333,0.792002688172043,1.668815224647522,1.6722341519530102 32,33,0.86975,0.8397177419354839,1.5919580411911012,1.6214879328204739
34,0.7974375,0.7931787634408602,1.6682115157445272,1.6723163063808153 33,34,0.872375,0.8422379032258065,1.5886561880111694,1.6194973222671016
35,0.7979791666666667,0.7947748655913979,1.6676976483662924,1.670254216399244 34,35,0.876,0.8331653225806451,1.585767653465271,1.6247131862948019
36,0.7984583333333334,0.7950268817204301,1.6669070380528768,1.6704239499184392 35,36,0.871875,0.8392137096774194,1.5895709590911866,1.6215527211466143
37,0.799,0.795866935483871,1.6664897476832072,1.6691849411174815 36,37,0.871125,0.8245967741935484,1.590462643623352,1.6370407227546937
38,0.7991875,0.7948588709677419,1.6660230029424032,1.6700230785595473 37,38,0.87325,0.8301411290322581,1.5876847248077393,1.6306157150576193
39,0.800125,0.795950940860215,1.6655609397888183,1.6688298884258475 38,39,0.8705,0.8240927419354839,1.590844289779663,1.634965923524672
40,0.8006458333333333,0.7956989247311828,1.6652250595092772,1.6690674840763051 39,40,0.88125,0.8402217741935484,1.5803495874404907,1.6220719968118975
41,0.8006875,0.7962869623655914,1.664971160888672,1.6688041135828982 40,41,0.881875,0.8462701612903226,1.5788077726364136,1.614263488400367
42,0.8015416666666667,0.795866935483871,1.6647087678909303,1.668453808753721 41,42,0.87625,0.8487903225806451,1.5843929862976074,1.6131737078389814
43,0.8013958333333333,0.7962029569892473,1.6643381754557292,1.6682484457569737 42,43,0.88175,0.842741935483871,1.5789586782455445,1.6184405088424683
44,0.8016458333333333,0.7965389784946236,1.6641663211186728,1.668392535178892 43,44,0.880375,0.8417338709677419,1.5802030544281005,1.6189708786626016
45,0.8017708333333333,0.7957829301075269,1.6640429185231527,1.6681856429705055 44,45,0.880375,0.8492943548387096,1.5803771505355835,1.610948174230514
46,0.8021041666666666,0.7965389784946236,1.663849822362264,1.6680005634984663 45,46,0.877625,0.8523185483870968,1.5828620948791503,1.6089561793111986
47,0.8024583333333334,0.7966229838709677,1.663770879427592,1.6679569816076627 46,47,0.882125,0.8442540322580645,1.578727219581604,1.6160847948443504
48,0.8025833333333333,0.7962029569892473,1.6636734215418498,1.6680130330465173 47,48,0.872875,0.8392137096774194,1.5874832077026366,1.6228746021947553
49,0.8024583333333334,0.7970430107526881,1.6635978577931723,1.6679544077124646 48,49,0.884125,0.8371975806451613,1.5771635084152222,1.6237398655183855
50,0.8025,0.7970430107526881,1.6635685895284016,1.6679527682642783 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

1 epoch train_acc valid_acc train_loss valid_loss
2 0 1 0.2535833333333333 0.575125 0.41952284946236557 0.7011088709677419 2.274448096593221 2.031563010215759 2.2001695325297694 1.8535375249001287
3 1 2 0.4691875 0.723875 0.5073084677419355 0.7515120967741935 2.116311640103658 1.7430778923034669 2.051948335862929 1.713461822079074
4 2 3 0.5270416666666666 0.76425 0.5266297043010753 0.7525201612903226 2.009436710357666 1.6991203842163085 1.9777893276624783 1.7051894049490652
5 3 4 0.5353541666666667 0.779875 0.535114247311828 0.7716733870967742 1.9530798486073813 1.6807003259658813 1.9356736265203005 1.690080665772961
6 4 5 0.5935416666666666 0.79525 0.6175235215053764 0.7696572580645161 1.9118746633529664 1.6654855661392212 1.891348486305565 1.6926762519344207
7 5 6 0.636625 0.80175 0.6625504032258065 0.7888104838709677 1.8629151741663614 1.6598792200088501 1.847881397893352 1.6711357831954956
8 6 7 0.686625 0.80875 0.7302587365591398 0.7721774193548387 1.8228577140172322 1.6529147624969482 1.8057919330494379 1.6847506146277151
9 7 8 0.7415416666666667 0.812375 0.7511760752688172 0.8004032258064516 1.7803055089314779 1.649049828529358 1.7647134245082896 1.6597587523921844
10 8 9 0.7542291666666666 0.815375 0.7582325268817204 0.7893145161290323 1.7502256110509236 1.6458229179382324 1.7423059594246648 1.6707303101016628
11 9 10 0.7607708333333333 0.825 0.764616935483871 0.811491935483871 1.7317150996526083 1.6370085287094116 1.724781782396378 1.6518691432091497
12 10 11 0.7677291666666667 0.832375 0.7719254032258065 0.795866935483871 1.7192926041285197 1.630222158432007 1.713816902970755 1.6616583139665666
13 11 12 0.7704791666666667 0.83225 0.7731014784946236 0.8145161290322581 1.71117463239034 1.6293829317092896 1.7070557173862253 1.6454557026586225
14 12 13 0.771625 0.834875 0.7736055107526881 0.8125 1.7053865385055542 1.6268642024993896 1.7027398668309695 1.6500283018235238
15 13 14 0.7747708333333333 0.840875 0.7749495967741935 0.8104838709677419 1.7004834445317587 1.6199358901977539 1.6978983327906618 1.652194688397069
16 14 15 0.7763333333333333 0.83975 0.7751176075268817 0.8069556451612904 1.6967166414260864 1.6210605192184449 1.6960316857983988 1.6520756982987927
17 15 16 0.7790416666666666 0.84175 0.7767977150537635 0.8004032258064516 1.6929608987172444 1.6195698108673096 1.692371742699736 1.6582853217278757
18 16 17 0.780125 0.838625 0.7801579301075269 0.8049395161290323 1.6910814901987712 1.6228710346221924 1.6917471321680213 1.656198097813514
19 17 18 0.7817916666666667 0.83775 0.7827620967741935 0.8140120967741935 1.6882368882497152 1.6226149969100951 1.6861866725388395 1.6462942131104008
20 18 19 0.7827291666666667 0.845375 0.7815860215053764 0.8251008064516129 1.6857908573150635 1.6150365982055663 1.6868788478195027 1.6358921681680987
21 19 20 0.7827083333333333 0.85125 0.7840221774193549 0.8296370967741935 1.6843910643259685 1.6097205333709717 1.6850647259784002 1.6333312757553593
22 20 21 0.7848541666666666 0.851875 0.7857862903225806 0.8210685483870968 1.6823169787724812 1.609424132347107 1.6813292862266622 1.6388044895664338
23 21 22 0.7876666666666666 0.850375 0.7848622311827957 0.8210685483870968 1.6801685171127319 1.6101643466949462 1.6815461574062225 1.6369191561975787
24 22 23 0.7879791666666667 0.858125 0.7848622311827957 0.8185483870967742 1.6788572374979656 1.6031983375549317 1.6818229216401295 1.6421872877305554
25 23 24 0.7890833333333334 0.857125 0.7859543010752689 0.8361895161290323 1.6776897859573365 1.6038059520721435 1.6799169009731663 1.62519553015309
26 24 25 0.7902708333333334 0.854 0.7883904569892473 0.8301411290322581 1.6763870159784953 1.606280044555664 1.6777271570697907 1.6290803609355804
27 25 26 0.791 0.858375 0.789986559139785 0.8119959677419355 1.6751194190979004 1.6011180095672608 1.6777111407249206 1.648349738890125
28 26 27 0.7918541666666666 0.861375 0.7886424731182796 0.8371975806451613 1.674490571975708 1.6000273780822754 1.6776156912567795 1.6237776010267195
29 27 28 0.7936666666666666 0.8705 0.7905745967741935 0.8351814516129032 1.6728278172810873 1.5907322645187378 1.6744540147883917 1.6240986316434798
30 28 29 0.793375 0.87175 0.7876344086021505 0.828125 1.671903525352478 1.5896439476013184 1.6776354030896259 1.6316479982868317
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