本文将详细介绍如何使用PyTorch框架构建一个卷积神经网络(CNN)来预测糖尿病，包含完整的代码实现、技术细节和可视化分析。
1. 项目概述
本项目使用经典的Pima Indians Diabetes数据集，通过5折交叉验证训练一个1D CNN模型，最终实现糖尿病预测。
主要技术栈包括：
- PyTorch 1.0+ (深度学习框架)
- scikit-learn (数据分割)
- Matplotlib (可视化)
- Pandas (数据处理)
2. 环境配置与数据准备
2.1 硬件配置
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")这段代码自动检测可用的计算设备，优先使用GPU加速。在Colab或配备NVIDIA显卡的机器上会自动选择CUDA。
2.2 数据加载
data = pd.read_csv('diabetes.csv')
X = data.iloc[:, :-1].values # 特征(8个医学特征)
y = data.iloc[:, -1].values # 标签(0/1)
X_tensor = torch.FloatTensor(X).unsqueeze(1) # 添加通道维度
y_tensor = torch.LongTensor(y)数据预处理关键点：
原始数据包含8个特征和1个二元标签
unsqueeze(1)将形状从[N,8]变为[N,1,8]，符合CNN输入要求没有进行标准化处理，实际应用中建议添加MinMaxScaler
3. CNN模型架构设计
3.1 网络结构
class DiabetesCNN(nn.Module):
def __init__(self, input_features):
super(DiabetesCNN, self).__init__()
self.conv1 = nn.Conv1d(1, 16, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv1d(16, 32, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool1d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(32 * (input_features//4), 64)
self.fc2 = nn.Linear(64, 2)
self.dropout = nn.Dropout(0.2)
self.relu = nn.ReLU()架构详解：
Conv1d层：处理1D序列数据(8个特征作为序列)
第一层：16个滤波器，kernel_size=3，保持维度(padding=1)
第二层：32个滤波器，同上配置
MaxPool1d：下采样因子2，序列长度从8→4→2
全连接层：
第一层：输入维度32*(8//4)=64 → 64
输出层：64 → 2 (二分类)
3.2 前向传播
def forward(self, x):
x = self.relu(self.conv1(x))
x = self.pool(x)
x = self.relu(self.conv2(x))
x = self.pool(x)
x = x.view(x.size(0), -1) # 展平
x = self.dropout(x)
x = self.relu(self.fc1(x))
x = self.fc2(x)
return x数据流维度变化：
输入: [batch, 1, 8]
Conv1 → [batch, 16, 8]
Pool → [batch, 16, 4]
Conv2 → [batch, 32, 4]
Pool → [batch, 32, 2]
Flatten → [batch, 64]
FC → [batch, 2]
4. 训练流程实现
4.1 训练函数
def train_model(model, train_loader, val_loader, criterion, optimizer, epochs=50):
# 初始化最佳准确率和记录列表
best_val_acc = 0.0
train_losses, val_losses = [], []
train_accs, val_accs = [], []
for epoch in range(epochs):
# 训练阶段
model.train()
running_loss = 0.0
train_correct = 0
train_total = 0
for inputs, labels in train_loader:
# 数据迁移到设备
inputs, labels = inputs.to(device), labels.to(device)
# 经典训练三步曲
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 统计指标
running_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
train_total += labels.size(0)
train_correct += (predicted == labels).sum().item()关键训练细节：
使用Adam优化器(学习率0.001)
交叉熵损失函数(内置softmax)
记录每个epoch的loss和accuracy
训练/验证分离
4.2 验证阶段
# 验证阶段
model.eval()
val_correct = 0
val_total = 0
val_loss = 0.0
with torch.no_grad(): # 禁用梯度计算
for inputs, labels in val_loader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
loss = criterion(outputs, labels)
val_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
val_total += labels.size(0)
val_correct += (predicted == labels).sum().item()验证阶段特点：
model.eval()：关闭Dropout等训练专用层torch.no_grad()：节省内存，加速计算不执行反向传播
5. 交叉验证实现
5.1 KFold配置
kfold = KFold(n_splits=5, shuffle=True, random_state=42)
batch_size = 32
input_features = X_tensor.shape[2] # 8
epochs = 50
# 结果存储
fold_results = []
all_train_losses, all_val_losses = [], []
all_train_accs, all_val_accs = [], []5折交叉验证优势：
充分利用小数据集
可靠评估模型性能
检测过拟合
5.2 训练循环
for fold, (train_ids, val_ids) in enumerate(kfold.split(X_tensor)):
# 数据分割
X_train, X_val = X_tensor[train_ids], X_tensor[val_ids]
y_train, y_val = y_tensor[train_ids], y_tensor[val_ids]
# 创建DataLoader
train_data = TensorDataset(X_train, y_train)
val_data = TensorDataset(X_val, y_val)
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_data, batch_size=batch_size, shuffle=False)
# 初始化模型
model = DiabetesCNN(input_features).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练并记录结果
best_val_acc, train_losses, val_losses, train_accs, val_accs = train_model(
model, train_loader, val_loader, criterion, optimizer, epochs)
fold_results.append(best_val_acc)
# 保存各fold的指标...DataLoader关键参数：
shuffle=True：训练集打乱顺序batch_size=32：小批量训练num_workers：可添加多线程加载(未显示)
6. 可视化分析
6.1 训练曲线绘制
plt.figure(figsize=(15, 10))
# 损失曲线
plt.subplot(2, 2, 1)
for i in range(len(all_train_losses)):
plt.plot(all_train_losses[i], label=f'Fold {i+1} Train')
plt.title('Training Loss Across Folds')
plt.xlabel('Epoch')
plt.ylabel('Loss')
# 准确率曲线
plt.subplot(2, 2, 3)
for i in range(len(all_train_accs)):
plt.plot(all_train_accs[i], label=f'Fold {i+1} Train')
plt.title('Training Accuracy Across Folds')
plt.xlabel('Epoch')
plt.ylabel('Accuracy (%)')曲线分析要点：
观察训练/验证曲线的收敛性
检查过拟合迹象(训练持续下降但验证波动)
比较不同fold的一致性
7. 模型保存与部署
7.1 全数据训练
full_train_loader = DataLoader(TensorDataset(X_tensor, y_tensor),
batch_size=batch_size, shuffle=True)
final_model = DiabetesCNN(input_features).to(device)
final_optimizer = optim.Adam(final_model.parameters(), lr=0.001)
train_model(final_model, full_train_loader, full_train_loader,
criterion, final_optimizer, epochs)7.2 模型保存
torch.save(final_model.state_dict(), 'diabetes_cnn_model_final.pth')部署建议：
加载模型：
model = DiabetesCNN(8) model.load_state_dict(torch.load('diabetes_cnn_model_final.pth')) model.eval()对新数据预测：
with torch.no_grad(): output = model(new_data) prediction = torch.argmax(output, dim=1)
完整代码已提供所有关键实现，读者可根据实际需求进行调整和扩展。这个项目展示了如何在小规模医疗数据上应用深度学习技术，为类似的二分类问题提供了可复用的模板。
复制下载
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完整代码：
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
from sklearn.model_selection import KFold
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
data = pd.read_csv('diabetes.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
X_tensor = torch.FloatTensor(X).unsqueeze(1)
y_tensor = torch.LongTensor(y)
class DiabetesCNN(nn.Module):
def __init__(self, input_features):
super(DiabetesCNN, self).__init__()
self.conv1 = nn.Conv1d(in_channels=1, out_channels=16, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool1d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(32 * (input_features // 4), 64)
self.fc2 = nn.Linear(64, 2)
self.dropout = nn.Dropout(0.2)
self.relu = nn.ReLU()
def forward(self, x):
x = self.relu(self.conv1(x))
x = self.pool(x)
x = self.relu(self.conv2(x))
x = self.pool(x)
x = x.view(x.size(0), -1)
x = self.dropout(x)
x = self.relu(self.fc1(x))
x = self.fc2(x)
return x
def train_model(model, train_loader, val_loader, criterion, optimizer, epochs=50):
model.train()
best_val_acc = 0.0
# 用于记录训练和验证指标
train_losses = []
val_losses = []
train_accs = []
val_accs = []
for epoch in range(epochs):
model.train()
running_loss = 0.0
train_correct = 0
train_total = 0
for inputs, labels in train_loader:
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
train_total += labels.size(0)
train_correct += (predicted == labels).sum().item()
train_loss = running_loss / len(train_loader)
train_acc = 100 * train_correct / train_total
train_losses.append(train_loss)
train_accs.append(train_acc)
model.eval()
val_correct = 0
val_total = 0
val_loss = 0.0
with torch.no_grad():
for inputs, labels in val_loader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
loss = criterion(outputs, labels)
val_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
val_total += labels.size(0)
val_correct += (predicted == labels).sum().item()
val_loss = val_loss / len(val_loader)
val_acc = 100 * val_correct / val_total
val_losses.append(val_loss)
val_accs.append(val_acc)
if val_acc > best_val_acc:
best_val_acc = val_acc
print(
f'Epoch {epoch + 1}/{epochs} - Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.2f}%, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.2f}%')
return best_val_acc, train_losses, val_losses, train_accs, val_accs
# 设置交叉验证
kfold = KFold(n_splits=5, shuffle=True, random_state=42)
batch_size = 32
input_features = X_tensor.shape[2]
epochs = 50
# 用于存储每个fold的结果
fold_results = []
all_train_losses = []
all_val_losses = []
all_train_accs = []
all_val_accs = []
for fold, (train_ids, val_ids) in enumerate(kfold.split(X_tensor)):
print(f'\nFold {fold + 1}')
print('-' * 20)
X_train, X_val = X_tensor[train_ids], X_tensor[val_ids]
y_train, y_val = y_tensor[train_ids], y_tensor[val_ids]
train_data = TensorDataset(X_train, y_train)
val_data = TensorDataset(X_val, y_val)
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_data, batch_size=batch_size, shuffle=False)
model = DiabetesCNN(input_features).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
best_val_acc, train_losses, val_losses, train_accs, val_accs = train_model(
model, train_loader, val_loader, criterion, optimizer, epochs)
fold_results.append(best_val_acc)
all_train_losses.append(train_losses)
all_val_losses.append(val_losses)
all_train_accs.append(train_accs)
all_val_accs.append(val_accs)
print(f'Fold {fold + 1} Best Validation Accuracy: {best_val_acc:.2f}%')
# 绘制训练和验证曲线
plt.figure(figsize=(15, 10))
# 绘制训练和验证损失曲线
plt.subplot(2, 2, 1)
for i in range(len(all_train_losses)):
plt.plot(all_train_losses[i], label=f'Fold {i + 1} Train')
plt.title('Training Loss Across Folds')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.subplot(2, 2, 2)
for i in range(len(all_val_losses)):
plt.plot(all_val_losses[i], label=f'Fold {i + 1} Val')
plt.title('Validation Loss Across Folds')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
# 绘制训练和验证准确率曲线
plt.subplot(2, 2, 3)
for i in range(len(all_train_accs)):
plt.plot(all_train_accs[i], label=f'Fold {i + 1} Train')
plt.title('Training Accuracy Across Folds')
plt.xlabel('Epoch')
plt.ylabel('Accuracy (%)')
plt.legend()
plt.subplot(2, 2, 4)
for i in range(len(all_val_accs)):
plt.plot(all_val_accs[i], label=f'Fold {i + 1} Val')
plt.title('Validation Accuracy Across Folds')
plt.xlabel('Epoch')
plt.ylabel('Accuracy (%)')
plt.legend()
plt.tight_layout()
plt.savefig('cross_validation_curves.png')
plt.show()
# 打印交叉验证结果
print('\nCross-Validation Results:')
for i, acc in enumerate(fold_results):
print(f'Fold {i + 1}: {acc:.2f}%')
print(f'Mean Accuracy: {np.mean(fold_results):.2f}% ± {np.std(fold_results):.2f}%')
# 训练最终模型
print('\nTraining final model on all data...')
full_train_loader = DataLoader(TensorDataset(X_tensor, y_tensor), batch_size=batch_size, shuffle=True)
final_model = DiabetesCNN(input_features).to(device)
final_optimizer = optim.Adam(final_model.parameters(), lr=0.001)
train_model(final_model, full_train_loader, full_train_loader, criterion, final_optimizer, epochs)
torch.save(final_model.state_dict(), 'diabetes_cnn_model_final.pth')
训练过程
Using device: cuda
Fold 1
--------------------
Epoch 1/50 - Train Loss: 0.6533, Train Acc: 66.01%, Val Loss: 0.6610, Val Acc: 63.16%
Epoch 2/50 - Train Loss: 0.6341, Train Acc: 66.01%, Val Loss: 0.6441, Val Acc: 63.16%
Epoch 3/50 - Train Loss: 0.6177, Train Acc: 66.01%, Val Loss: 0.6188, Val Acc: 63.16%
Epoch 4/50 - Train Loss: 0.5901, Train Acc: 66.17%, Val Loss: 0.5904, Val Acc: 63.16%
Epoch 5/50 - Train Loss: 0.5680, Train Acc: 69.97%, Val Loss: 0.5498, Val Acc: 74.34%
Epoch 6/50 - Train Loss: 0.5460, Train Acc: 71.95%, Val Loss: 0.5323, Val Acc: 75.00%
Epoch 7/50 - Train Loss: 0.5334, Train Acc: 72.77%, Val Loss: 0.5177, Val Acc: 75.00%
Epoch 8/50 - Train Loss: 0.5261, Train Acc: 74.09%, Val Loss: 0.5050, Val Acc: 76.32%
Epoch 9/50 - Train Loss: 0.5198, Train Acc: 73.43%, Val Loss: 0.5086, Val Acc: 76.32%
Epoch 10/50 - Train Loss: 0.5082, Train Acc: 75.25%, Val Loss: 0.4906, Val Acc: 76.97%
Epoch 11/50 - Train Loss: 0.5016, Train Acc: 74.59%, Val Loss: 0.4775, Val Acc: 78.95%
Epoch 12/50 - Train Loss: 0.4964, Train Acc: 75.41%, Val Loss: 0.4701, Val Acc: 78.95%
Epoch 13/50 - Train Loss: 0.4874, Train Acc: 75.25%, Val Loss: 0.4933, Val Acc: 75.00%
Epoch 14/50 - Train Loss: 0.4999, Train Acc: 74.92%, Val Loss: 0.4645, Val Acc: 78.95%
Epoch 15/50 - Train Loss: 0.4832, Train Acc: 77.39%, Val Loss: 0.4611, Val Acc: 78.95%
Epoch 16/50 - Train Loss: 0.4851, Train Acc: 77.56%, Val Loss: 0.4607, Val Acc: 78.29%
Epoch 17/50 - Train Loss: 0.4845, Train Acc: 75.58%, Val Loss: 0.4560, Val Acc: 79.61%
Epoch 18/50 - Train Loss: 0.4760, Train Acc: 76.40%, Val Loss: 0.4783, Val Acc: 78.29%
Epoch 19/50 - Train Loss: 0.4852, Train Acc: 75.41%, Val Loss: 0.4561, Val Acc: 78.95%
Epoch 20/50 - Train Loss: 0.4848, Train Acc: 76.90%, Val Loss: 0.4688, Val Acc: 78.95%
Epoch 21/50 - Train Loss: 0.4812, Train Acc: 75.58%, Val Loss: 0.4629, Val Acc: 78.29%
Epoch 22/50 - Train Loss: 0.4777, Train Acc: 75.58%, Val Loss: 0.4560, Val Acc: 78.95%
Epoch 23/50 - Train Loss: 0.4705, Train Acc: 76.07%, Val Loss: 0.4537, Val Acc: 78.95%
Epoch 24/50 - Train Loss: 0.4657, Train Acc: 76.73%, Val Loss: 0.4534, Val Acc: 78.29%
Epoch 25/50 - Train Loss: 0.4727, Train Acc: 76.73%, Val Loss: 0.4526, Val Acc: 77.63%
Epoch 26/50 - Train Loss: 0.4620, Train Acc: 78.55%, Val Loss: 0.4530, Val Acc: 78.29%
Epoch 27/50 - Train Loss: 0.4614, Train Acc: 77.89%, Val Loss: 0.4533, Val Acc: 77.63%
Epoch 28/50 - Train Loss: 0.4719, Train Acc: 75.58%, Val Loss: 0.4545, Val Acc: 79.61%
Epoch 29/50 - Train Loss: 0.4619, Train Acc: 78.38%, Val Loss: 0.4526, Val Acc: 76.97%
Epoch 30/50 - Train Loss: 0.4621, Train Acc: 76.90%, Val Loss: 0.4580, Val Acc: 78.95%
Epoch 31/50 - Train Loss: 0.4729, Train Acc: 78.05%, Val Loss: 0.4515, Val Acc: 76.32%
Epoch 32/50 - Train Loss: 0.4667, Train Acc: 76.07%, Val Loss: 0.4650, Val Acc: 76.97%
Epoch 33/50 - Train Loss: 0.4734, Train Acc: 77.39%, Val Loss: 0.4517, Val Acc: 75.66%
Epoch 34/50 - Train Loss: 0.4675, Train Acc: 76.90%, Val Loss: 0.4502, Val Acc: 76.97%
Epoch 35/50 - Train Loss: 0.4666, Train Acc: 77.23%, Val Loss: 0.4518, Val Acc: 76.97%
Epoch 36/50 - Train Loss: 0.4593, Train Acc: 77.39%, Val Loss: 0.4494, Val Acc: 76.32%
Epoch 37/50 - Train Loss: 0.4668, Train Acc: 77.89%, Val Loss: 0.4643, Val Acc: 76.97%
Epoch 38/50 - Train Loss: 0.4535, Train Acc: 78.55%, Val Loss: 0.4543, Val Acc: 78.29%
Epoch 39/50 - Train Loss: 0.4668, Train Acc: 75.91%, Val Loss: 0.4497, Val Acc: 75.66%
Epoch 40/50 - Train Loss: 0.4543, Train Acc: 79.04%, Val Loss: 0.4661, Val Acc: 76.97%
Epoch 41/50 - Train Loss: 0.4861, Train Acc: 77.56%, Val Loss: 0.4613, Val Acc: 77.63%
Epoch 42/50 - Train Loss: 0.4618, Train Acc: 78.05%, Val Loss: 0.4803, Val Acc: 73.68%
Epoch 43/50 - Train Loss: 0.4620, Train Acc: 78.55%, Val Loss: 0.4524, Val Acc: 77.63%
Epoch 44/50 - Train Loss: 0.4521, Train Acc: 79.37%, Val Loss: 0.4543, Val Acc: 76.32%
Epoch 45/50 - Train Loss: 0.4604, Train Acc: 76.57%, Val Loss: 0.4503, Val Acc: 76.32%
Epoch 46/50 - Train Loss: 0.4644, Train Acc: 76.57%, Val Loss: 0.4499, Val Acc: 76.32%
Epoch 47/50 - Train Loss: 0.4656, Train Acc: 77.06%, Val Loss: 0.4528, Val Acc: 76.32%
Epoch 48/50 - Train Loss: 0.4666, Train Acc: 76.57%, Val Loss: 0.4548, Val Acc: 76.32%
Epoch 49/50 - Train Loss: 0.4543, Train Acc: 77.89%, Val Loss: 0.4594, Val Acc: 76.97%
Epoch 50/50 - Train Loss: 0.4581, Train Acc: 77.06%, Val Loss: 0.4497, Val Acc: 76.97%
Fold 1 Best Validation Accuracy: 79.61%
Fold 2
--------------------
Epoch 1/50 - Train Loss: 0.6640, Train Acc: 65.35%, Val Loss: 0.6394, Val Acc: 65.79%
Epoch 2/50 - Train Loss: 0.6409, Train Acc: 65.35%, Val Loss: 0.6258, Val Acc: 65.79%
Epoch 3/50 - Train Loss: 0.6235, Train Acc: 65.35%, Val Loss: 0.6071, Val Acc: 65.79%
Epoch 4/50 - Train Loss: 0.5903, Train Acc: 65.51%, Val Loss: 0.5726, Val Acc: 65.79%
Epoch 5/50 - Train Loss: 0.5389, Train Acc: 73.43%, Val Loss: 0.5337, Val Acc: 71.71%
Epoch 6/50 - Train Loss: 0.5072, Train Acc: 75.91%, Val Loss: 0.5123, Val Acc: 73.03%
Epoch 7/50 - Train Loss: 0.4944, Train Acc: 76.73%, Val Loss: 0.5127, Val Acc: 72.37%
Epoch 8/50 - Train Loss: 0.4844, Train Acc: 77.06%, Val Loss: 0.5140, Val Acc: 72.37%
Epoch 9/50 - Train Loss: 0.4836, Train Acc: 77.23%, Val Loss: 0.5103, Val Acc: 73.68%
Epoch 10/50 - Train Loss: 0.4751, Train Acc: 78.88%, Val Loss: 0.5065, Val Acc: 74.34%
Epoch 11/50 - Train Loss: 0.4788, Train Acc: 77.72%, Val Loss: 0.5186, Val Acc: 73.68%
Epoch 12/50 - Train Loss: 0.4762, Train Acc: 77.39%, Val Loss: 0.5045, Val Acc: 74.34%
Epoch 13/50 - Train Loss: 0.4756, Train Acc: 77.39%, Val Loss: 0.5042, Val Acc: 74.34%
Epoch 14/50 - Train Loss: 0.4722, Train Acc: 78.22%, Val Loss: 0.5114, Val Acc: 74.34%
Epoch 15/50 - Train Loss: 0.4661, Train Acc: 77.56%, Val Loss: 0.4998, Val Acc: 72.37%
Epoch 16/50 - Train Loss: 0.4609, Train Acc: 78.55%, Val Loss: 0.5048, Val Acc: 75.00%
Epoch 17/50 - Train Loss: 0.4646, Train Acc: 77.72%, Val Loss: 0.5072, Val Acc: 75.00%
Epoch 18/50 - Train Loss: 0.4552, Train Acc: 78.88%, Val Loss: 0.5006, Val Acc: 74.34%
Epoch 19/50 - Train Loss: 0.4650, Train Acc: 77.23%, Val Loss: 0.5076, Val Acc: 73.68%
Epoch 20/50 - Train Loss: 0.4602, Train Acc: 78.55%, Val Loss: 0.4982, Val Acc: 73.68%
Epoch 21/50 - Train Loss: 0.4453, Train Acc: 78.88%, Val Loss: 0.4963, Val Acc: 72.37%
Epoch 22/50 - Train Loss: 0.4563, Train Acc: 78.22%, Val Loss: 0.4963, Val Acc: 72.37%
Epoch 23/50 - Train Loss: 0.4509, Train Acc: 77.56%, Val Loss: 0.4944, Val Acc: 72.37%
Epoch 24/50 - Train Loss: 0.4514, Train Acc: 78.05%, Val Loss: 0.4942, Val Acc: 73.68%
Epoch 25/50 - Train Loss: 0.4573, Train Acc: 77.89%, Val Loss: 0.4957, Val Acc: 72.37%
Epoch 26/50 - Train Loss: 0.4485, Train Acc: 79.37%, Val Loss: 0.4927, Val Acc: 72.37%
Epoch 27/50 - Train Loss: 0.4527, Train Acc: 78.71%, Val Loss: 0.5123, Val Acc: 75.00%
Epoch 28/50 - Train Loss: 0.4556, Train Acc: 77.72%, Val Loss: 0.4931, Val Acc: 73.68%
Epoch 29/50 - Train Loss: 0.4553, Train Acc: 78.55%, Val Loss: 0.4974, Val Acc: 73.03%
Epoch 30/50 - Train Loss: 0.4515, Train Acc: 76.90%, Val Loss: 0.4936, Val Acc: 72.37%
Epoch 31/50 - Train Loss: 0.4510, Train Acc: 77.89%, Val Loss: 0.4923, Val Acc: 73.03%
Epoch 32/50 - Train Loss: 0.4494, Train Acc: 78.88%, Val Loss: 0.4925, Val Acc: 73.03%
Epoch 33/50 - Train Loss: 0.4491, Train Acc: 77.72%, Val Loss: 0.4975, Val Acc: 72.37%
Epoch 34/50 - Train Loss: 0.4485, Train Acc: 77.72%, Val Loss: 0.4951, Val Acc: 73.03%
Epoch 35/50 - Train Loss: 0.4502, Train Acc: 78.38%, Val Loss: 0.4985, Val Acc: 73.68%
Epoch 36/50 - Train Loss: 0.4482, Train Acc: 77.23%, Val Loss: 0.4959, Val Acc: 73.03%
Epoch 37/50 - Train Loss: 0.4556, Train Acc: 77.06%, Val Loss: 0.4963, Val Acc: 73.03%
Epoch 38/50 - Train Loss: 0.4464, Train Acc: 78.55%, Val Loss: 0.4935, Val Acc: 72.37%
Epoch 39/50 - Train Loss: 0.4383, Train Acc: 78.88%, Val Loss: 0.5050, Val Acc: 73.68%
Epoch 40/50 - Train Loss: 0.4405, Train Acc: 79.21%, Val Loss: 0.4977, Val Acc: 72.37%
Epoch 41/50 - Train Loss: 0.4412, Train Acc: 78.38%, Val Loss: 0.4950, Val Acc: 73.03%
Epoch 42/50 - Train Loss: 0.4395, Train Acc: 78.05%, Val Loss: 0.4951, Val Acc: 73.03%
Epoch 43/50 - Train Loss: 0.4321, Train Acc: 78.55%, Val Loss: 0.4933, Val Acc: 72.37%
Epoch 44/50 - Train Loss: 0.4350, Train Acc: 78.71%, Val Loss: 0.4980, Val Acc: 72.37%
Epoch 45/50 - Train Loss: 0.4406, Train Acc: 78.88%, Val Loss: 0.5048, Val Acc: 73.68%
Epoch 46/50 - Train Loss: 0.4460, Train Acc: 78.05%, Val Loss: 0.5150, Val Acc: 73.03%
Epoch 47/50 - Train Loss: 0.4367, Train Acc: 78.38%, Val Loss: 0.4979, Val Acc: 71.71%
Epoch 48/50 - Train Loss: 0.4368, Train Acc: 78.71%, Val Loss: 0.5003, Val Acc: 73.03%
Epoch 49/50 - Train Loss: 0.4306, Train Acc: 79.87%, Val Loss: 0.5001, Val Acc: 72.37%
Epoch 50/50 - Train Loss: 0.4299, Train Acc: 78.55%, Val Loss: 0.5014, Val Acc: 73.03%
Fold 2 Best Validation Accuracy: 75.00%
Fold 3
--------------------
Epoch 1/50 - Train Loss: 0.6494, Train Acc: 65.51%, Val Loss: 0.6422, Val Acc: 65.13%
Epoch 2/50 - Train Loss: 0.6302, Train Acc: 65.51%, Val Loss: 0.6254, Val Acc: 65.13%
Epoch 3/50 - Train Loss: 0.6113, Train Acc: 65.51%, Val Loss: 0.5903, Val Acc: 65.13%
Epoch 4/50 - Train Loss: 0.5717, Train Acc: 65.84%, Val Loss: 0.5344, Val Acc: 72.37%
Epoch 5/50 - Train Loss: 0.5309, Train Acc: 73.10%, Val Loss: 0.5028, Val Acc: 79.61%
Epoch 6/50 - Train Loss: 0.5104, Train Acc: 74.42%, Val Loss: 0.4957, Val Acc: 75.00%
Epoch 7/50 - Train Loss: 0.4976, Train Acc: 75.08%, Val Loss: 0.4904, Val Acc: 76.32%
Epoch 8/50 - Train Loss: 0.4892, Train Acc: 75.91%, Val Loss: 0.4909, Val Acc: 77.63%
Epoch 9/50 - Train Loss: 0.4842, Train Acc: 77.89%, Val Loss: 0.4917, Val Acc: 76.97%
Epoch 10/50 - Train Loss: 0.4772, Train Acc: 77.06%, Val Loss: 0.4918, Val Acc: 75.00%
Epoch 11/50 - Train Loss: 0.4692, Train Acc: 77.23%, Val Loss: 0.4856, Val Acc: 75.66%
Epoch 12/50 - Train Loss: 0.4725, Train Acc: 77.39%, Val Loss: 0.4877, Val Acc: 75.66%
Epoch 13/50 - Train Loss: 0.4696, Train Acc: 78.22%, Val Loss: 0.4906, Val Acc: 75.66%
Epoch 14/50 - Train Loss: 0.4563, Train Acc: 77.39%, Val Loss: 0.4978, Val Acc: 76.32%
Epoch 15/50 - Train Loss: 0.4770, Train Acc: 78.71%, Val Loss: 0.4913, Val Acc: 74.34%
Epoch 16/50 - Train Loss: 0.4558, Train Acc: 77.06%, Val Loss: 0.4873, Val Acc: 75.00%
Epoch 17/50 - Train Loss: 0.4651, Train Acc: 76.07%, Val Loss: 0.4836, Val Acc: 75.66%
Epoch 18/50 - Train Loss: 0.4612, Train Acc: 76.73%, Val Loss: 0.4977, Val Acc: 76.97%
Epoch 19/50 - Train Loss: 0.4715, Train Acc: 76.90%, Val Loss: 0.4852, Val Acc: 75.66%
Epoch 20/50 - Train Loss: 0.4554, Train Acc: 78.22%, Val Loss: 0.4901, Val Acc: 75.00%
Epoch 21/50 - Train Loss: 0.4498, Train Acc: 78.55%, Val Loss: 0.4899, Val Acc: 75.66%
Epoch 22/50 - Train Loss: 0.4596, Train Acc: 77.23%, Val Loss: 0.4937, Val Acc: 74.34%
Epoch 23/50 - Train Loss: 0.4470, Train Acc: 77.39%, Val Loss: 0.4918, Val Acc: 75.66%
Epoch 24/50 - Train Loss: 0.4565, Train Acc: 77.06%, Val Loss: 0.4913, Val Acc: 75.66%
Epoch 25/50 - Train Loss: 0.4567, Train Acc: 77.89%, Val Loss: 0.4889, Val Acc: 75.66%
Epoch 26/50 - Train Loss: 0.4606, Train Acc: 76.40%, Val Loss: 0.5028, Val Acc: 76.32%
Epoch 27/50 - Train Loss: 0.4487, Train Acc: 77.89%, Val Loss: 0.4901, Val Acc: 74.34%
Epoch 28/50 - Train Loss: 0.4459, Train Acc: 78.22%, Val Loss: 0.4908, Val Acc: 75.00%
Epoch 29/50 - Train Loss: 0.4413, Train Acc: 78.38%, Val Loss: 0.4937, Val Acc: 76.32%
Epoch 30/50 - Train Loss: 0.4524, Train Acc: 77.06%, Val Loss: 0.4896, Val Acc: 74.34%
Epoch 31/50 - Train Loss: 0.4493, Train Acc: 78.55%, Val Loss: 0.4888, Val Acc: 76.32%
Epoch 32/50 - Train Loss: 0.4405, Train Acc: 77.56%, Val Loss: 0.4917, Val Acc: 75.00%
Epoch 33/50 - Train Loss: 0.4388, Train Acc: 78.71%, Val Loss: 0.4969, Val Acc: 75.00%
Epoch 34/50 - Train Loss: 0.4374, Train Acc: 78.55%, Val Loss: 0.4886, Val Acc: 75.66%
Epoch 35/50 - Train Loss: 0.4364, Train Acc: 78.22%, Val Loss: 0.4927, Val Acc: 76.32%
Epoch 36/50 - Train Loss: 0.4422, Train Acc: 78.88%, Val Loss: 0.4885, Val Acc: 74.34%
Epoch 37/50 - Train Loss: 0.4411, Train Acc: 77.23%, Val Loss: 0.4929, Val Acc: 74.34%
Epoch 38/50 - Train Loss: 0.4419, Train Acc: 78.38%, Val Loss: 0.5028, Val Acc: 74.34%
Epoch 39/50 - Train Loss: 0.4322, Train Acc: 78.71%, Val Loss: 0.4894, Val Acc: 76.97%
Epoch 40/50 - Train Loss: 0.4336, Train Acc: 78.55%, Val Loss: 0.4869, Val Acc: 73.68%
Epoch 41/50 - Train Loss: 0.4388, Train Acc: 78.05%, Val Loss: 0.4958, Val Acc: 74.34%
Epoch 42/50 - Train Loss: 0.4402, Train Acc: 79.21%, Val Loss: 0.4871, Val Acc: 73.68%
Epoch 43/50 - Train Loss: 0.4409, Train Acc: 79.21%, Val Loss: 0.4876, Val Acc: 74.34%
Epoch 44/50 - Train Loss: 0.4325, Train Acc: 79.87%, Val Loss: 0.5009, Val Acc: 74.34%
Epoch 45/50 - Train Loss: 0.4363, Train Acc: 77.89%, Val Loss: 0.4843, Val Acc: 73.03%
Epoch 46/50 - Train Loss: 0.4277, Train Acc: 80.69%, Val Loss: 0.5052, Val Acc: 74.34%
Epoch 47/50 - Train Loss: 0.4287, Train Acc: 78.22%, Val Loss: 0.4915, Val Acc: 75.00%
Epoch 48/50 - Train Loss: 0.4254, Train Acc: 79.04%, Val Loss: 0.4881, Val Acc: 75.00%
Epoch 49/50 - Train Loss: 0.4176, Train Acc: 80.86%, Val Loss: 0.4984, Val Acc: 73.68%
Epoch 50/50 - Train Loss: 0.4282, Train Acc: 79.04%, Val Loss: 0.4924, Val Acc: 75.00%
Fold 3 Best Validation Accuracy: 79.61%
Fold 4
--------------------
Epoch 1/50 - Train Loss: 0.6630, Train Acc: 65.24%, Val Loss: 0.6393, Val Acc: 65.56%
Epoch 2/50 - Train Loss: 0.6328, Train Acc: 65.40%, Val Loss: 0.6170, Val Acc: 65.56%
Epoch 3/50 - Train Loss: 0.6088, Train Acc: 65.40%, Val Loss: 0.5834, Val Acc: 65.56%
Epoch 4/50 - Train Loss: 0.5712, Train Acc: 65.90%, Val Loss: 0.5335, Val Acc: 72.85%
Epoch 5/50 - Train Loss: 0.5322, Train Acc: 73.97%, Val Loss: 0.5047, Val Acc: 77.48%
Epoch 6/50 - Train Loss: 0.5108, Train Acc: 75.78%, Val Loss: 0.4954, Val Acc: 78.15%
Epoch 7/50 - Train Loss: 0.4963, Train Acc: 75.45%, Val Loss: 0.4927, Val Acc: 76.16%
Epoch 8/50 - Train Loss: 0.4993, Train Acc: 74.79%, Val Loss: 0.5034, Val Acc: 75.50%
Epoch 9/50 - Train Loss: 0.4875, Train Acc: 77.10%, Val Loss: 0.4910, Val Acc: 76.16%
Epoch 10/50 - Train Loss: 0.4803, Train Acc: 76.11%, Val Loss: 0.4888, Val Acc: 75.50%
Epoch 11/50 - Train Loss: 0.4776, Train Acc: 77.27%, Val Loss: 0.4890, Val Acc: 75.50%
Epoch 12/50 - Train Loss: 0.4692, Train Acc: 76.77%, Val Loss: 0.4994, Val Acc: 75.50%
Epoch 13/50 - Train Loss: 0.4684, Train Acc: 77.59%, Val Loss: 0.4964, Val Acc: 74.83%
Epoch 14/50 - Train Loss: 0.4727, Train Acc: 76.77%, Val Loss: 0.4945, Val Acc: 76.16%
Epoch 15/50 - Train Loss: 0.4748, Train Acc: 76.94%, Val Loss: 0.4969, Val Acc: 74.83%
Epoch 16/50 - Train Loss: 0.4744, Train Acc: 75.62%, Val Loss: 0.4988, Val Acc: 74.17%
Epoch 17/50 - Train Loss: 0.4664, Train Acc: 77.43%, Val Loss: 0.4955, Val Acc: 73.51%
Epoch 18/50 - Train Loss: 0.4537, Train Acc: 76.94%, Val Loss: 0.5028, Val Acc: 74.83%
Epoch 19/50 - Train Loss: 0.4553, Train Acc: 78.42%, Val Loss: 0.4979, Val Acc: 73.51%
Epoch 20/50 - Train Loss: 0.4581, Train Acc: 77.59%, Val Loss: 0.5114, Val Acc: 75.50%
Epoch 21/50 - Train Loss: 0.4518, Train Acc: 77.59%, Val Loss: 0.5026, Val Acc: 74.83%
Epoch 22/50 - Train Loss: 0.4467, Train Acc: 77.43%, Val Loss: 0.5110, Val Acc: 76.16%
Epoch 23/50 - Train Loss: 0.4523, Train Acc: 76.94%, Val Loss: 0.5065, Val Acc: 73.51%
Epoch 24/50 - Train Loss: 0.4566, Train Acc: 76.61%, Val Loss: 0.5082, Val Acc: 74.83%
Epoch 25/50 - Train Loss: 0.4521, Train Acc: 77.76%, Val Loss: 0.5120, Val Acc: 74.83%
Epoch 26/50 - Train Loss: 0.4452, Train Acc: 76.94%, Val Loss: 0.5121, Val Acc: 76.82%
Epoch 27/50 - Train Loss: 0.4458, Train Acc: 77.27%, Val Loss: 0.5089, Val Acc: 74.17%
Epoch 28/50 - Train Loss: 0.4402, Train Acc: 78.25%, Val Loss: 0.5059, Val Acc: 74.83%
Epoch 29/50 - Train Loss: 0.4457, Train Acc: 77.59%, Val Loss: 0.5144, Val Acc: 76.16%
Epoch 30/50 - Train Loss: 0.4384, Train Acc: 78.75%, Val Loss: 0.5100, Val Acc: 74.17%
Epoch 31/50 - Train Loss: 0.4437, Train Acc: 78.58%, Val Loss: 0.5081, Val Acc: 74.17%
Epoch 32/50 - Train Loss: 0.4391, Train Acc: 78.09%, Val Loss: 0.5129, Val Acc: 74.83%
Epoch 33/50 - Train Loss: 0.4287, Train Acc: 78.75%, Val Loss: 0.5157, Val Acc: 75.50%
Epoch 34/50 - Train Loss: 0.4325, Train Acc: 79.41%, Val Loss: 0.5123, Val Acc: 75.50%
Epoch 35/50 - Train Loss: 0.4422, Train Acc: 77.92%, Val Loss: 0.5269, Val Acc: 76.82%
Epoch 36/50 - Train Loss: 0.4359, Train Acc: 77.92%, Val Loss: 0.5165, Val Acc: 76.16%
Epoch 37/50 - Train Loss: 0.4300, Train Acc: 78.25%, Val Loss: 0.5255, Val Acc: 75.50%
Epoch 38/50 - Train Loss: 0.4376, Train Acc: 76.77%, Val Loss: 0.5212, Val Acc: 74.83%
Epoch 39/50 - Train Loss: 0.4353, Train Acc: 78.91%, Val Loss: 0.5305, Val Acc: 76.82%
Epoch 40/50 - Train Loss: 0.4313, Train Acc: 80.07%, Val Loss: 0.5250, Val Acc: 75.50%
Epoch 41/50 - Train Loss: 0.4280, Train Acc: 79.74%, Val Loss: 0.5210, Val Acc: 74.83%
Epoch 42/50 - Train Loss: 0.4172, Train Acc: 79.90%, Val Loss: 0.5238, Val Acc: 76.16%
Epoch 43/50 - Train Loss: 0.4194, Train Acc: 80.40%, Val Loss: 0.5227, Val Acc: 76.16%
Epoch 44/50 - Train Loss: 0.4211, Train Acc: 78.91%, Val Loss: 0.5325, Val Acc: 74.83%
Epoch 45/50 - Train Loss: 0.4264, Train Acc: 80.07%, Val Loss: 0.5316, Val Acc: 75.50%
Epoch 46/50 - Train Loss: 0.4271, Train Acc: 80.07%, Val Loss: 0.5367, Val Acc: 74.17%
Epoch 47/50 - Train Loss: 0.4203, Train Acc: 78.42%, Val Loss: 0.5378, Val Acc: 74.83%
Epoch 48/50 - Train Loss: 0.4254, Train Acc: 78.58%, Val Loss: 0.5564, Val Acc: 75.50%
Epoch 49/50 - Train Loss: 0.4312, Train Acc: 79.74%, Val Loss: 0.5338, Val Acc: 74.17%
Epoch 50/50 - Train Loss: 0.4215, Train Acc: 77.76%, Val Loss: 0.5374, Val Acc: 75.50%
Fold 4 Best Validation Accuracy: 78.15%
Fold 5
--------------------
Epoch 1/50 - Train Loss: 0.6627, Train Acc: 65.24%, Val Loss: 0.6267, Val Acc: 67.55%
Epoch 2/50 - Train Loss: 0.6166, Train Acc: 64.91%, Val Loss: 0.5772, Val Acc: 67.55%
Epoch 3/50 - Train Loss: 0.5680, Train Acc: 68.53%, Val Loss: 0.5351, Val Acc: 74.83%
Epoch 4/50 - Train Loss: 0.5262, Train Acc: 74.30%, Val Loss: 0.5070, Val Acc: 73.51%
Epoch 5/50 - Train Loss: 0.5130, Train Acc: 74.96%, Val Loss: 0.4970, Val Acc: 72.85%
Epoch 6/50 - Train Loss: 0.5019, Train Acc: 76.44%, Val Loss: 0.4944, Val Acc: 75.50%
Epoch 7/50 - Train Loss: 0.4911, Train Acc: 76.11%, Val Loss: 0.4868, Val Acc: 76.82%
Epoch 8/50 - Train Loss: 0.4987, Train Acc: 76.44%, Val Loss: 0.4904, Val Acc: 77.48%
Epoch 9/50 - Train Loss: 0.4776, Train Acc: 76.28%, Val Loss: 0.4836, Val Acc: 77.48%
Epoch 10/50 - Train Loss: 0.4686, Train Acc: 78.75%, Val Loss: 0.4831, Val Acc: 77.48%
Epoch 11/50 - Train Loss: 0.4710, Train Acc: 76.61%, Val Loss: 0.4853, Val Acc: 77.48%
Epoch 12/50 - Train Loss: 0.4687, Train Acc: 76.61%, Val Loss: 0.4788, Val Acc: 78.81%
Epoch 13/50 - Train Loss: 0.4642, Train Acc: 77.76%, Val Loss: 0.4840, Val Acc: 77.48%
Epoch 14/50 - Train Loss: 0.4745, Train Acc: 77.43%, Val Loss: 0.4764, Val Acc: 80.13%
Epoch 15/50 - Train Loss: 0.4625, Train Acc: 77.59%, Val Loss: 0.4807, Val Acc: 78.15%
Epoch 16/50 - Train Loss: 0.4713, Train Acc: 77.10%, Val Loss: 0.4799, Val Acc: 77.48%
Epoch 17/50 - Train Loss: 0.4714, Train Acc: 77.92%, Val Loss: 0.4779, Val Acc: 77.48%
Epoch 18/50 - Train Loss: 0.4769, Train Acc: 76.94%, Val Loss: 0.4726, Val Acc: 79.47%
Epoch 19/50 - Train Loss: 0.4655, Train Acc: 76.94%, Val Loss: 0.4719, Val Acc: 80.79%
Epoch 20/50 - Train Loss: 0.4779, Train Acc: 77.59%, Val Loss: 0.5113, Val Acc: 74.17%
Epoch 21/50 - Train Loss: 0.4785, Train Acc: 76.61%, Val Loss: 0.4762, Val Acc: 80.13%
Epoch 22/50 - Train Loss: 0.4626, Train Acc: 76.94%, Val Loss: 0.4739, Val Acc: 79.47%
Epoch 23/50 - Train Loss: 0.4605, Train Acc: 76.61%, Val Loss: 0.4751, Val Acc: 78.81%
Epoch 24/50 - Train Loss: 0.4572, Train Acc: 77.10%, Val Loss: 0.4788, Val Acc: 79.47%
Epoch 25/50 - Train Loss: 0.4560, Train Acc: 76.94%, Val Loss: 0.4760, Val Acc: 80.13%
Epoch 26/50 - Train Loss: 0.4508, Train Acc: 78.58%, Val Loss: 0.4761, Val Acc: 78.81%
Epoch 27/50 - Train Loss: 0.4465, Train Acc: 79.24%, Val Loss: 0.4785, Val Acc: 78.81%
Epoch 28/50 - Train Loss: 0.4634, Train Acc: 76.94%, Val Loss: 0.4888, Val Acc: 76.16%
Epoch 29/50 - Train Loss: 0.4708, Train Acc: 77.27%, Val Loss: 0.4783, Val Acc: 79.47%
Epoch 30/50 - Train Loss: 0.4641, Train Acc: 77.92%, Val Loss: 0.4778, Val Acc: 80.79%
Epoch 31/50 - Train Loss: 0.4507, Train Acc: 78.25%, Val Loss: 0.4802, Val Acc: 78.15%
Epoch 32/50 - Train Loss: 0.4472, Train Acc: 77.92%, Val Loss: 0.4800, Val Acc: 79.47%
Epoch 33/50 - Train Loss: 0.4566, Train Acc: 76.94%, Val Loss: 0.4782, Val Acc: 78.15%
Epoch 34/50 - Train Loss: 0.4451, Train Acc: 80.07%, Val Loss: 0.4798, Val Acc: 80.79%
Epoch 35/50 - Train Loss: 0.4543, Train Acc: 77.27%, Val Loss: 0.4804, Val Acc: 78.15%
Epoch 36/50 - Train Loss: 0.4409, Train Acc: 79.41%, Val Loss: 0.4821, Val Acc: 80.79%
Epoch 37/50 - Train Loss: 0.4566, Train Acc: 77.27%, Val Loss: 0.4916, Val Acc: 77.48%
Epoch 38/50 - Train Loss: 0.4543, Train Acc: 77.76%, Val Loss: 0.4823, Val Acc: 80.13%
Epoch 39/50 - Train Loss: 0.4439, Train Acc: 78.58%, Val Loss: 0.4920, Val Acc: 76.82%
Epoch 40/50 - Train Loss: 0.4469, Train Acc: 76.77%, Val Loss: 0.4888, Val Acc: 78.15%
Epoch 41/50 - Train Loss: 0.4495, Train Acc: 77.92%, Val Loss: 0.4895, Val Acc: 77.48%
Epoch 42/50 - Train Loss: 0.4534, Train Acc: 77.59%, Val Loss: 0.4834, Val Acc: 80.13%
Epoch 43/50 - Train Loss: 0.4397, Train Acc: 78.09%, Val Loss: 0.4973, Val Acc: 78.15%
Epoch 44/50 - Train Loss: 0.4358, Train Acc: 77.92%, Val Loss: 0.4930, Val Acc: 79.47%
Epoch 45/50 - Train Loss: 0.4422, Train Acc: 78.42%, Val Loss: 0.4865, Val Acc: 79.47%
Epoch 46/50 - Train Loss: 0.4337, Train Acc: 78.09%, Val Loss: 0.4867, Val Acc: 78.81%
Epoch 47/50 - Train Loss: 0.4344, Train Acc: 79.41%, Val Loss: 0.5084, Val Acc: 78.81%
Epoch 48/50 - Train Loss: 0.4415, Train Acc: 78.25%, Val Loss: 0.4896, Val Acc: 80.13%
Epoch 49/50 - Train Loss: 0.4452, Train Acc: 78.75%, Val Loss: 0.5051, Val Acc: 76.82%
Epoch 50/50 - Train Loss: 0.4461, Train Acc: 77.92%, Val Loss: 0.4913, Val Acc: 80.13%
Fold 5 Best Validation Accuracy: 80.79%
Cross-Validation Results:
Fold 1: 79.61%
Fold 2: 75.00%
Fold 3: 79.61%
Fold 4: 78.15%
Fold 5: 80.79%
Mean Accuracy: 78.63% ± 2.00%
Training final model on all data...
Epoch 1/50 - Train Loss: 0.6579, Train Acc: 65.44%, Val Loss: 0.6412, Val Acc: 65.44%
Epoch 2/50 - Train Loss: 0.6326, Train Acc: 65.44%, Val Loss: 0.6137, Val Acc: 65.30%
Epoch 3/50 - Train Loss: 0.5963, Train Acc: 67.68%, Val Loss: 0.5641, Val Acc: 68.21%
Epoch 4/50 - Train Loss: 0.5485, Train Acc: 72.82%, Val Loss: 0.5203, Val Acc: 74.67%
Epoch 5/50 - Train Loss: 0.5089, Train Acc: 75.07%, Val Loss: 0.4883, Val Acc: 76.52%
Epoch 6/50 - Train Loss: 0.5042, Train Acc: 75.20%, Val Loss: 0.4894, Val Acc: 74.67%
Epoch 7/50 - Train Loss: 0.4966, Train Acc: 75.20%, Val Loss: 0.4889, Val Acc: 75.99%
Epoch 8/50 - Train Loss: 0.4998, Train Acc: 75.59%, Val Loss: 0.4727, Val Acc: 77.44%
Epoch 9/50 - Train Loss: 0.4800, Train Acc: 77.18%, Val Loss: 0.4772, Val Acc: 76.91%
Epoch 10/50 - Train Loss: 0.4759, Train Acc: 76.91%, Val Loss: 0.4655, Val Acc: 77.57%
Epoch 11/50 - Train Loss: 0.4859, Train Acc: 75.59%, Val Loss: 0.4699, Val Acc: 76.65%
Epoch 12/50 - Train Loss: 0.4742, Train Acc: 77.04%, Val Loss: 0.4592, Val Acc: 77.44%
Epoch 13/50 - Train Loss: 0.4719, Train Acc: 77.70%, Val Loss: 0.4567, Val Acc: 77.44%
Epoch 14/50 - Train Loss: 0.4647, Train Acc: 76.91%, Val Loss: 0.4554, Val Acc: 76.78%
Epoch 15/50 - Train Loss: 0.4702, Train Acc: 77.57%, Val Loss: 0.4592, Val Acc: 77.04%
Epoch 16/50 - Train Loss: 0.4590, Train Acc: 77.57%, Val Loss: 0.4624, Val Acc: 77.97%
Epoch 17/50 - Train Loss: 0.4595, Train Acc: 76.91%, Val Loss: 0.4510, Val Acc: 77.04%
Epoch 18/50 - Train Loss: 0.4603, Train Acc: 77.97%, Val Loss: 0.4477, Val Acc: 77.57%
Epoch 19/50 - Train Loss: 0.4571, Train Acc: 77.44%, Val Loss: 0.4526, Val Acc: 77.44%
Epoch 20/50 - Train Loss: 0.4683, Train Acc: 76.78%, Val Loss: 0.4433, Val Acc: 78.50%
Epoch 21/50 - Train Loss: 0.4516, Train Acc: 78.23%, Val Loss: 0.4423, Val Acc: 77.31%
Epoch 22/50 - Train Loss: 0.4614, Train Acc: 77.31%, Val Loss: 0.4445, Val Acc: 77.84%
Epoch 23/50 - Train Loss: 0.4556, Train Acc: 77.84%, Val Loss: 0.4442, Val Acc: 77.84%
Epoch 24/50 - Train Loss: 0.4539, Train Acc: 78.50%, Val Loss: 0.4390, Val Acc: 78.63%
Epoch 25/50 - Train Loss: 0.4505, Train Acc: 77.44%, Val Loss: 0.4401, Val Acc: 79.02%
Epoch 26/50 - Train Loss: 0.4488, Train Acc: 77.57%, Val Loss: 0.4440, Val Acc: 78.63%
Epoch 27/50 - Train Loss: 0.4586, Train Acc: 77.31%, Val Loss: 0.4377, Val Acc: 78.76%
Epoch 28/50 - Train Loss: 0.4456, Train Acc: 78.63%, Val Loss: 0.4363, Val Acc: 77.97%
Epoch 29/50 - Train Loss: 0.4520, Train Acc: 77.97%, Val Loss: 0.4350, Val Acc: 78.63%
Epoch 30/50 - Train Loss: 0.4484, Train Acc: 77.57%, Val Loss: 0.4327, Val Acc: 78.76%
Epoch 31/50 - Train Loss: 0.4411, Train Acc: 79.16%, Val Loss: 0.4286, Val Acc: 78.89%
Epoch 32/50 - Train Loss: 0.4417, Train Acc: 79.68%, Val Loss: 0.4295, Val Acc: 78.89%
Epoch 33/50 - Train Loss: 0.4361, Train Acc: 78.50%, Val Loss: 0.4324, Val Acc: 78.89%
Epoch 34/50 - Train Loss: 0.4443, Train Acc: 77.44%, Val Loss: 0.4270, Val Acc: 79.68%
Epoch 35/50 - Train Loss: 0.4349, Train Acc: 77.97%, Val Loss: 0.4343, Val Acc: 78.76%
Epoch 36/50 - Train Loss: 0.4441, Train Acc: 77.84%, Val Loss: 0.4323, Val Acc: 78.76%
Epoch 37/50 - Train Loss: 0.4460, Train Acc: 78.76%, Val Loss: 0.4289, Val Acc: 79.55%
Epoch 38/50 - Train Loss: 0.4376, Train Acc: 78.50%, Val Loss: 0.4222, Val Acc: 79.68%
Epoch 39/50 - Train Loss: 0.4421, Train Acc: 78.36%, Val Loss: 0.4200, Val Acc: 79.29%
Epoch 40/50 - Train Loss: 0.4316, Train Acc: 79.16%, Val Loss: 0.4294, Val Acc: 79.82%
Epoch 41/50 - Train Loss: 0.4409, Train Acc: 78.76%, Val Loss: 0.4213, Val Acc: 79.95%
Epoch 42/50 - Train Loss: 0.4304, Train Acc: 78.89%, Val Loss: 0.4472, Val Acc: 77.57%
Epoch 43/50 - Train Loss: 0.4352, Train Acc: 80.21%, Val Loss: 0.4219, Val Acc: 79.82%
Epoch 44/50 - Train Loss: 0.4413, Train Acc: 79.29%, Val Loss: 0.4146, Val Acc: 79.29%
Epoch 45/50 - Train Loss: 0.4246, Train Acc: 79.16%, Val Loss: 0.4167, Val Acc: 80.21%
Epoch 46/50 - Train Loss: 0.4297, Train Acc: 78.23%, Val Loss: 0.4129, Val Acc: 79.55%
Epoch 47/50 - Train Loss: 0.4254, Train Acc: 79.16%, Val Loss: 0.4069, Val Acc: 80.08%
Epoch 48/50 - Train Loss: 0.4260, Train Acc: 79.55%, Val Loss: 0.4094, Val Acc: 80.47%
Epoch 49/50 - Train Loss: 0.4189, Train Acc: 80.21%, Val Loss: 0.4158, Val Acc: 79.82%
Epoch 50/50 - Train Loss: 0.4270, Train Acc: 79.82%, Val Loss: 0.4058, Val Acc: 80.08%曲线：
