import os
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.layers import (Input, Dense, Dropout, BatchNormalization,
Concatenate, Reshape, Conv2D, MaxPooling2D,
GlobalAveragePooling2D, Add, Multiply,
Flatten, LayerNormalization)
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
import tifffile
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from scipy.ndimage import rotate
import random
import gc
import warnings
warnings.filterwarnings('ignore')
# 清除计算图
tf.keras.backend.clear_session()
# 设置随机种子以确保可重复性
random.seed(42)
np.random.seed(42)
tf.random.set_seed(42)
# GPU 配置
try:
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
print("✅ GPU 加速已启用")
else:
print("⚠️ 未检测到 GPU，使用 CPU 训练")
except Exception as e:
print(f"❌ GPU 配置失败: {str(e)}")
class MultiModalDataGenerator(tf.keras.utils.Sequence):
"""改进的数据生成器 - 包含图像增强功能"""
def __init__(self, image_paths, chemical_data, labels, batch_size=16, shuffle=True, augment=False):
self.image_paths = image_paths
self.chemical_data = chemical_data
self.labels = labels
self.batch_size = batch_size
self.shuffle = shuffle
self.augment = augment # 是否应用数据增强
self.indices = np.arange(len(self.image_paths))
# 计算均值用于填充无效样本
self.image_mean = self._calculate_image_mean()
self.chem_mean = self._calculate_chem_mean()
self.on_epoch_end()
def _calculate_image_mean(self):
"""计算图像均值用于填充无效样本"""
sample_img = np.zeros((50, 43, 3), dtype=np.float32)
count = 0
for img_path in self.image_paths[:min(100, len(self.image_paths))]:
try:
img = tifffile.imread(img_path)
# 统一形状为 (50, 43, 3)
if img.shape == (43, 3, 50):
img = np.moveaxis(img, -1, 0)
elif img.shape == (50, 3, 43):
img = np.transpose(img, (0, 2, 1))
elif img.shape != (50, 43, 3):
# 使用均值填充无效样本
img = self.image_mean.copy()
valid_sample = False
if img.shape == (50, 43, 3):
sample_img += img.astype(np.float32)
count += 1
except:
continue
return sample_img / max(count, 1) if count > 0 else np.zeros((50, 43, 3))
def _calculate_chem_mean(self):
"""计算化学数据均值用于填充无效样本"""
if isinstance(self.chemical_data, np.ndarray):
return np.nanmean(self.chemical_data, axis=0)
elif isinstance(self.chemical_data, pd.DataFrame):
return self.chemical_data.mean().values
else:
return np.zeros(39)
def _augment_image(self, img):
"""应用随机图像增强"""
# 随机水平翻转
if random.random() > 0.5:
img = np.fliplr(img)
# 随机垂直翻转
if random.random() > 0.5:
img = np.flipud(img)
# 随机旋转 (±15度)
if random.random() > 0.7:
angle = random.uniform(-15, 15)
img = rotate(img, angle, reshape=False, mode='reflect')
# 随机亮度调整
if random.random() > 0.5:
brightness = random.uniform(0.8, 1.2)
img = np.clip(img * brightness, 0, 255)
# 随机对比度调整
if random.random() > 0.5:
contrast = random.uniform(0.8, 1.2)
mean = np.mean(img, axis=(0, 1), keepdims=True)
img = np.clip((img - mean) * contrast + mean, 0, 255)
# 随机高斯噪声
if random.random() > 0.7:
noise = np.random.normal(0, 0.05, img.shape)
img = np.clip(img + noise * 255, 0, 255)
return img
def __len__(self):
return int(np.ceil(len(self.indices) / self.batch_size))
def __getitem__(self, idx):
low = idx * self.batch_size
high = min(low + self.batch_size, len(self.indices))
batch_indices = self.indices[low:high]
batch_images = []
batch_chemical = []
batch_labels = []
batch_valid_mask = []
for i in batch_indices:
valid_sample = True
try:
# 尝试加载和处理图像
img = tifffile.imread(self.image_paths[i])
# 统一形状为 (50, 43, 3)
if img.shape == (43, 3, 50):
img = np.moveaxis(img, -1, 0)
elif img.shape == (50, 3, 43):
img = np.transpose(img, (0, 2, 1))
elif img.shape != (50, 43, 3):
# 使用均值填充无效样本
img = self.image_mean.copy()
valid_sample = False
img = img.astype(np.float32)
# 检查NaN或全零图像
if np.isnan(img).any() or img.max() == img.min():
img = self.image_mean.copy()
valid_sample = False
# 应用数据增强
if self.augment and valid_sample and self.shuffle:
img = self._augment_image(img)
except Exception as e:
# 加载失败时使用均值图像
img = self.image_mean.copy()
valid_sample = False
try:
# 处理化学数据
if isinstance(self.chemical_data, np.ndarray):
chem_feat = self.chemical_data[i].reshape(-1)
else:
chem_feat = self.chemical_data.iloc[i].values.reshape(-1)
if chem_feat.shape != (39,) or np.isnan(chem_feat).any():
chem_feat = self.chem_mean.copy()
valid_sample = False
except:
chem_feat = self.chem_mean.copy()
valid_sample = False
batch_images.append(img)
batch_chemical.append(chem_feat)
batch_labels.append(self.labels[i])
batch_valid_mask.append(valid_sample)
# 构建批次
X_img = np.stack(batch_images)
X_chem = np.array(batch_chemical, dtype=np.float32)
y_batch = np.array(batch_labels, dtype=np.int32)
valid_mask = np.array(batch_valid_mask, dtype=bool)
# 返回数据、标签和有效样本掩码
return (X_img, X_chem), y_batch, valid_mask
def on_epoch_end(self):
if self.shuffle:
np.random.shuffle(self.indices)
def to_dataset(self):
"""转换为 tf.data.Dataset 格式"""
def gen():
for i in range(len(self)):
inputs, labels, _ = self[i] # 忽略valid_mask
yield inputs, labels
# 使用您建议的格式：明确指定dtype和shape
output_signature = (
(
tf.TensorSpec(shape=(None, 50, 43, 3), dtype=tf.float32), # 图像输入
tf.TensorSpec(shape=(None, 39), dtype=tf.float32) # 化学输入
),
tf.TensorSpec(shape=(None,), dtype=tf.int32) # 标签
)
return tf.data.Dataset.from_generator(
gen,
output_signature=output_signature
).prefetch(tf.data.AUTOTUNE)
class MultiModalFusionModel:
def __init__(self, img_root="D:\\西北地区铜镍矿\\多模态测试\\图片训练",
data_path="D:\\西北地区铜镍矿\\数据\\训练数据.xlsx"):
self.img_root = img_root
self.data_path = data_path
self.scaler = StandardScaler()
self.model = None
self.history = None
def load_data(self):
print("🔍 正在加载数据...")
df = pd.read_excel(self.data_path)
print(f"原始数据形状: {df.shape}")
required = ['name', 'class']
for col in required:
if col not in df.columns:
raise ValueError(f"Excel 缺少必要列: {col}")
feature_cols = df.columns[6:45]
chemical_data = df[feature_cols].select_dtypes(include=[np.number])
# 二分类标签映射：positive为0，negative为1，去掉neutral类
label_map = {'positive': 0, 'negative': 1}
image_paths, labels_list = [], []
for _, row in df.iterrows():
name = row['name']
cls = row['class']
if not isinstance(name, str) or cls not in label_map:
continue
class_dir = os.path.join(self.img_root, cls)
found = False
for ext in ['', '.tif', '.tiff']:
path = os.path.join(class_dir, f"{name}{ext}")
if os.path.exists(path):
image_paths.append(path)
labels_list.append(label_map[cls])
found = True
break
if not found:
# 即使找不到图像，也保留样本（后续使用占位数据）
image_paths.append(os.path.join(class_dir, "placeholder")) # 占位路径
labels_list.append(label_map[cls])
labels_array = np.array(labels_list)
print(f"✅ 加载 {len(image_paths)} 个样本")
counts = np.bincount(labels_array)
print(f"📊 二分类标签分布: positive={counts[0]}, negative={counts[1]}")
# 平衡采样：确保每个类别都有500个样本
print("⚖️ 开始平衡采样...")
# 分离两个类别的索引
positive_indices = np.where(labels_array == 0)[0]
non_positive_indices = np.where(labels_array == 1)[0]
print(f"positive样本数量: {len(positive_indices)}")
print(f"negative样本数量: {len(non_positive_indices)}")
# 设置每个类别的目标样本数
target_samples_per_class = 500
# 对positive类进行采样
if len(positive_indices) >= target_samples_per_class:
# 如果positive样本足够，随机选择500个
selected_positive = np.random.choice(positive_indices, target_samples_per_class, replace=False)
else:
# 如果positive样本不足，使用所有样本并重复采样
selected_positive = np.random.choice(positive_indices, target_samples_per_class, replace=True)
print(f"⚠️ positive样本不足，使用重复采样")
# 对negative类进行采样
if len(non_positive_indices) >= target_samples_per_class:
# 如果negative样本足够，随机选择500个
selected_non_positive = np.random.choice(non_positive_indices, target_samples_per_class, replace=False)
else:
# 如果negative样本不足，使用所有样本并重复采样
selected_non_positive = np.random.choice(non_positive_indices, target_samples_per_class, replace=True)
print(f"⚠️ negative样本不足，使用重复采样")
# 合并选中的索引
selected_indices = np.concatenate([selected_positive, selected_non_positive])
# 根据选中的索引重新组织数据
balanced_image_paths = [image_paths[i] for i in selected_indices]
balanced_chemical_data = chemical_data.iloc[selected_indices] if isinstance(chemical_data, pd.DataFrame) else \
chemical_data[selected_indices]
balanced_labels = labels_array[selected_indices]
# 打乱数据顺序
shuffle_indices = np.random.permutation(len(balanced_labels))
balanced_image_paths = [balanced_image_paths[i] for i in shuffle_indices]
balanced_chemical_data = balanced_chemical_data.iloc[shuffle_indices] if isinstance(balanced_chemical_data,
pd.DataFrame) else \
balanced_chemical_data[shuffle_indices]
balanced_labels = balanced_labels[shuffle_indices]
print(f"✅ 平衡后样本总数: {len(balanced_labels)}")
balanced_counts = np.bincount(balanced_labels)
print(f"📊 平衡后标签分布: positive={balanced_counts[0]}, negative={balanced_counts[1]}")
return balanced_image_paths, balanced_chemical_data, balanced_labels
def build_model(self):
print("🧱 正在构建改进的多模态融合模型...")
# ========== 输入层 ==========
image_input = Input(shape=(50, 43, 3), name='image_input')
chem_input = Input(shape=(39,), name='chemical_input')
# ========== 图像处理分支 ==========
# 初始卷积层
x = Conv2D(32, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal')(image_input)
x = BatchNormalization()(x)
x = MaxPooling2D((2, 2))(x) # 输出: (25, 21, 32)
print(f"初始卷积后形状: {x.shape}")
# 残差块1
# 确保快捷连接维度匹配：从32通道调整到64通道
residual = Conv2D(64, (1, 1), padding='same', kernel_initializer='he_normal')(x)
residual = BatchNormalization()(residual)
# 主路径
x = Conv2D(64, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal')(x)
x = BatchNormalization()(x)
x = Conv2D(64, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal')(x)
x = BatchNormalization()(x)
# 残差连接：现在两个张量都是64通道
print(f"残差块1 - 主路径形状: {x.shape}, 快捷连接形状: {residual.shape}")
x = Add()([x, residual])
x = MaxPooling2D((2, 2))(x) # 输出: (12, 10, 64)
# 残差块2
# 确保快捷连接维度匹配：从64通道调整到128通道
residual = Conv2D(128, (1, 1), padding='same', kernel_initializer='he_normal')(x)
residual = BatchNormalization()(residual)
# 主路径
x = Conv2D(128, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal')(x)
x = BatchNormalization()(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal')(x)
x = BatchNormalization()(x)
# 残差连接：现在两个张量都是128通道
print(f"残差块2 - 主路径形状: {x.shape}, 快捷连接形状: {residual.shape}")
x = Add()([x, residual])
# 全局特征提取
x = GlobalAveragePooling2D()(x)
img_features = Dense(128, activation='relu')(x)
img_features = Dropout(0.3)(img_features)
# ========== 化学数据处理分支 ==========
# 特征加权层
chem_weighted = Dense(39, activation='sigmoid')(chem_input)
chem_weighted = Multiply()([chem_input, chem_weighted])
# 深层特征提取
y = Dense(256, activation='relu')(chem_weighted)
y = BatchNormalization()(y)
y = Dropout(0.3)(y)
y = Dense(128, activation='relu')(y)
y = BatchNormalization()(y)
chem_features = Dense(128, activation='relu')(y)
chem_features = Dropout(0.3)(chem_features)
# ========== 跨模态融合模块 ==========
# 跨模态注意力
img_att = Dense(128, activation='tanh')(img_features)
chem_att = Dense(128, activation='tanh')(chem_features)
# 使用Keras层进行注意力计算
att_product = Multiply()([img_att, chem_att])
att_scores = tf.keras.layers.Activation('softmax')(att_product)
att_img = Multiply()([img_features, att_scores])
att_chem = Multiply()([chem_features, att_scores])
# 门控融合机制
gate_img = Dense(128, activation='sigmoid')(att_img)
gate_chem = Dense(128, activation='sigmoid')(att_chem)
gated_img = Multiply()([att_img, gate_img])
gated_chem = Multiply()([att_chem, gate_chem])
# 特征交互学习
cross_features = Concatenate()([gated_img, gated_chem])
cross_features = Dense(256, activation='relu')(cross_features)
cross_features = BatchNormalization()(cross_features)
cross_features = Dropout(0.4)(cross_features)
# 深度监督分支
img_supervised = Dense(64, activation='relu')(gated_img)
chem_supervised = Dense(64, activation='relu')(gated_chem)
# 最终融合
final_features = Concatenate()([cross_features, img_supervised, chem_supervised])
z = Dense(256, activation='relu')(final_features)
z = BatchNormalization()(z)
z = Dropout(0.4)(z)
# ========== 输出层 ==========
output = Dense(2, activation='softmax')(z)
# 创建模型
model = Model(inputs=[image_input, chem_input], outputs=output)
# ========== 优化器配置 ==========
# 使用标准Adam优化器，配合学习率调度器
optimizer = Adam(
learning_rate=1e-4,
clipnorm=0.5 # 梯度裁剪防止梯度爆炸
)
model.compile(
loss='sparse_categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy']
)
# 打印模型结构
model.summary()
print("✅ 模型输入顺序: [图像输入, 化学输入]")
print("✅ 模型输入形状:", [i.shape for i in model.inputs])
print("✅ 模型输出形状:", model.output.shape)
print("💡 模型架构优化: 添加残差连接、跨模态注意力、特征门控和深度监督")
self.model = model
return model
def train(self, image_paths, chemical_data, labels, test_size=0.2, batch_size=16, epochs=100):
print("🚀 开始训练...")
# 分割数据集
X_train_img, X_test_img, X_train_chem, X_test_chem, y_train, y_test = train_test_split(
image_paths, chemical_data, labels,
test_size=test_size, stratify=labels, random_state=42
)
# 计算类别权重来解决不平衡问题
print("⚖️ 计算类别权重...")
from sklearn.utils.class_weight import compute_class_weight
class_weights_balanced = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)
class_weights = dict(enumerate(class_weights_balanced))
max_weight = max(class_weights.values())
min_weight = min(class_weights.values())
weight_ratio = max_weight / min_weight
# 如果权重比例过大，进行缩放
if weight_ratio > 3.0:
scale_factor = 3.0 / weight_ratio
for key in class_weights:
if class_weights[key] == max_weight:
class_weights[key] *= scale_factor
print(f"📊 训练集类别分布: {np.bincount(y_train)}")
print(f"⚖️ 平衡类别权重: {class_weights}")
# 标准化化学数据
print("🔢 标准化化学数据...")
self.scaler.fit(X_train_chem)
X_train_chem_scaled = self.scaler.transform(X_train_chem)
X_test_chem_scaled = self.scaler.transform(X_test_chem)
# 创建生成器（训练集启用增强）
print("🔄 创建数据生成器...")
train_gen = MultiModalDataGenerator(
X_train_img, X_train_chem_scaled, y_train,
batch_size=batch_size, shuffle=True, augment=True # 启用数据增强
)
val_gen = MultiModalDataGenerator(
X_test_img, X_test_chem_scaled, y_test,
batch_size=batch_size, shuffle=False
)
# 转换为 tf.data.Dataset
train_ds = train_gen.to_dataset()
val_ds = val_gen.to_dataset()
# 回调函数
callbacks = [
EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True, verbose=1),
ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=10, min_lr=1e-7, verbose=1),
]
# 开始训练
print("⏳ 训练中...")
self.history = self.model.fit(
train_ds,
validation_data=val_ds,
epochs=epochs,
callbacks=callbacks,
verbose=1,
class_weight=class_weights
)
return self.history
def evaluate(self, image_paths, chemical_data, labels):
"""改进的评估方法"""
print("📈 开始评估...")
# 标准化化学数据
chemical_data_scaled = self.scaler.transform(chemical_data)
# 创建生成器
test_gen = MultiModalDataGenerator(image_paths, chemical_data_scaled, labels, batch_size=16, shuffle=False)
# 收集所有有效样本的预测和标签
all_preds = []
all_labels = []
# 逐个批次预测并收集有效样本
for i in range(len(test_gen)):
(batch_img, batch_chem), batch_label, valid_mask = test_gen[i]
# 预测
batch_pred = self.model.predict([batch_img, batch_chem], verbose=0)
# 只保留有效样本
valid_indices = np.where(valid_mask)[0]
if len(valid_indices) > 0:
all_preds.append(batch_pred[valid_indices])
all_labels.append(batch_label[valid_indices])
# 释放内存
del batch_img, batch_chem, batch_label, batch_pred
if i % 10 == 0:
gc.collect()
# 合并所有批次的结果
if not all_preds:
raise ValueError("没有有效样本用于评估")
y_pred_probs = np.vstack(all_preds)
y_true = np.concatenate(all_labels)
y_pred = np.argmax(y_pred_probs, axis=1)
# 计算并打印结果
print(f"✅ 有效样本数量: {len(y_true)}/{len(labels)}")
acc = accuracy_score(y_true, y_pred)
print(f"🎯 准确率: {acc:.4f}")
print("\n📋 分类报告:")
print(classification_report(y_true, y_pred, target_names=['positive', 'negative']))
# 混淆矩阵
cm = confusion_matrix(y_true, y_pred)
plt.figure(figsize=(6, 5))
# 修复中文字体显示问题
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'Arial Unicode MS']
plt.rcParams['axes.unicode_minus'] = False
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
xticklabels=['positive', 'negative'],
yticklabels=['positive', 'negative'],
annot_kws={'fontsize': 12, 'fontfamily': 'SimHei'})
plt.title('混淆矩阵 (二分类)', fontfamily='SimHei', fontsize=14)
plt.ylabel('真实标签', fontfamily='SimHei', fontsize=12)
plt.xlabel('预测标签', fontfamily='SimHei', fontsize=12)
plt.tight_layout()
# 保存到image文件夹
self._save_plot_with_error_handling(plt, 'confusion_matrix.png', dpi=300)
# 诊断预测分布问题
self._diagnose_prediction_distribution(y_true, y_pred_probs)
# 分析模型性能问题
self._analyze_performance(y_true, y_pred, y_pred_probs)
# 生成详细的可视化报告
self._generate_visualization_report(y_true, y_pred, y_pred_probs, self.history)
return acc, y_pred, y_pred_probs
def _diagnose_prediction_distribution(self, y_true, y_pred_probs):
"""诊断预测分布问题，检测AUC异常的原因"""
print("\n🔍 预测分布诊断:")
# 统计真实标签分布
unique, counts = np.unique(y_true, return_counts=True)
print(f"真实标签分布: {dict(zip(unique, counts))}")
# 统计预测概率分布
positive_probs = y_pred_probs[:, 0] # positive类概率
negative_probs = y_pred_probs[:, 1] # negative类概率
print(f"positive类概率 - 均值: {np.mean(positive_probs):.4f}, 标准差: {np.std(positive_probs):.4f}")
print(f"negative类概率 - 均值: {np.mean(negative_probs):.4f}, 标准差: {np.std(negative_probs):.4f}")
# 检查每个类别的预测概率
for class_idx in [0, 1]:
class_mask = (y_true == class_idx)
class_name = 'positive' if class_idx == 0 else 'negative'
class_positive_probs = positive_probs[class_mask]
class_negative_probs = negative_probs[class_mask]
print(f"{class_name}类样本的预测概率:")
print(f" 预测为positive的概率 - 均值: {np.mean(class_positive_probs):.4f}")
print(f" 预测为negative的概率 - 均值: {np.mean(class_negative_probs):.4f}")
# 检查是否有明显的预测方向错误
if np.mean(class_positive_probs) < 0.3:
print(f" ⚠️ 警告: {class_name}类样本被主要预测为negative类!")
elif np.mean(class_positive_probs) > 0.7:
print(f" ✅ 正常: {class_name}类样本被主要预测为positive类")
else:
print(f" 🤔 不确定: {class_name}类样本预测概率分布较为均匀")
# 计算简单的AUC检查
from sklearn.metrics import roc_auc_score
auc_positive = roc_auc_score(y_true, positive_probs)
auc_negative = roc_auc_score(y_true, negative_probs)
print(f"\n📊 AUC检查:")
print(f"使用positive类概率: {auc_positive:.4f}")
print(f"使用negative类概率: {auc_negative:.4f}")
if auc_positive < 0.5:
print("⚠️ 检测到预测方向相反问题，模型可能将positive和negative搞反了")
print("💡 建议: 检查标签映射或类别权重设置")
elif auc_positive > 0.8:
print("✅ AUC表现良好，模型区分能力较强")
else:
print("🤔 AUC表现一般，模型需要进一步优化")
def _analyze_performance(self, y_true, y_pred, y_pred_probs):
"""分析模型性能问题并提供改进建议"""
# 计算每个类别的准确率（二分类）
class_acc = []
for cls in range(2):
idx = (y_true == cls)
if np.sum(idx) > 0: # 确保有样本
cls_acc = accuracy_score(y_true[idx], y_pred[idx])
class_acc.append(cls_acc)
else:
class_acc.append(0.0)
print("\n🔍 性能分析:")
print(f"positive类准确率: {class_acc[0]:.4f}")
print(f"negative类准确率: {class_acc[1]:.4f}")
# 识别最难分类的样本
max_prob_diff = np.max(y_pred_probs, axis=1) - np.take_along_axis(y_pred_probs, y_true.reshape(-1, 1),
axis=1).flatten()
hard_indices = np.argsort(max_prob_diff)[:20] # 找出20个最难样本
print("\n💡 模型改进建议:")
if class_acc[1] < 0.5: # negative类准确率低
print("1. negative类识别困难，建议增加该类样本或使用数据增强")
if abs(class_acc[0] - class_acc[1]) > 0.2: # 类别间不平衡
print("2. 检测到类别不平衡问题，建议使用class_weight参数")
if np.mean(max_prob_diff) > 0.3: # 模型不确定性高
print("3. 模型对许多样本预测不确定性高，建议增加训练轮数或模型复杂度")
# 保存困难样本分析
plt.figure(figsize=(10, 8))
for i, idx in enumerate(hard_indices):
plt.subplot(4, 5, i + 1)
cls = y_true[idx]
pred = y_pred[idx]
prob = y_pred_probs[idx][pred]
plt.title(f"T:{cls} P:{pred}\nProb:{prob:.2f}")
# 这里可以添加可视化样本的代码
plt.tight_layout()
self._save_plot_with_error_handling(plt, 'hard_samples.png', dpi=150)
def _save_plot_with_error_handling(self, plt, filename, dpi=300):
"""安全保存图片，处理权限错误"""
# 确保image文件夹存在
import os
image_dir = 'image'
if not os.path.exists(image_dir):
os.makedirs(image_dir, exist_ok=True)
save_path = os.path.join(image_dir, filename)
try:
plt.savefig(save_path, dpi=dpi, bbox_inches='tight')
print(f"✅ 图片已保存为 '{save_path}'")
return True
except PermissionError:
# 如果当前目录没有权限，尝试保存到用户文档目录
try:
user_docs = os.path.expanduser('~')
user_image_dir = os.path.join(user_docs, 'image')
if not os.path.exists(user_image_dir):
os.makedirs(user_image_dir, exist_ok=True)
fallback_path = os.path.join(user_image_dir, filename)
plt.savefig(fallback_path, dpi=dpi, bbox_inches='tight')
print(f"✅ 图片已保存为 '{fallback_path}'")
return True
except Exception as e:
print(f"⚠️ 无法保存图片 {filename}: {e}")
return False
finally:
plt.close()
def _generate_visualization_report(self, y_true, y_pred, y_pred_probs, history):
"""分别保存主要研究可视化图"""
import matplotlib.pyplot as plt
from sklearn.metrics import precision_recall_curve, roc_curve, auc, precision_score, recall_score, f1_score, \
confusion_matrix
import seaborn as sns
print("📊 分别保存主要研究可视化图...")
# 设置中文字体
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False
# 1. 训练曲线（Loss & Accuracy）
plt.figure(figsize=(12, 5))
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'], label='训练准确率', linewidth=2, color='blue')
plt.plot(history.history['val_accuracy'], label='验证准确率', linewidth=2, color='red')
plt.title('训练准确率曲线', fontsize=14, fontweight='bold')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.grid(True, alpha=0.3)
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'], label='训练损失', linewidth=2, color='blue')
plt.plot(history.history['val_loss'], label='验证损失', linewidth=2, color='red')
plt.title('训练损失曲线', fontsize=14, fontweight='bold')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.grid(True, alpha=0.3)
plt.tight_layout()
self._save_plot_with_error_handling(plt, 'training_curves.png', dpi=300)
# 2. 混淆矩阵
plt.figure(figsize=(8, 6))
cm = confusion_matrix(y_true, y_pred)
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
xticklabels=['Positive', 'Negative'],
yticklabels=['Positive', 'Negative'])
plt.title('混淆矩阵', fontsize=14, fontweight='bold')
plt.xlabel('预测标签')
plt.ylabel('真实标签')
self._save_plot_with_error_handling(plt, 'confusion_matrix_detailed.png', dpi=300)
# 3. ROC曲线 - 修复AUC计算问题
plt.figure(figsize=(8, 6))
# 检查哪个类别的概率更高，使用正确的概率计算ROC
# 如果AUC < 0.5，说明预测方向相反，使用1-p来修正
fpr, tpr, _ = roc_curve(y_true, y_pred_probs[:, 0]) # positive类概率
roc_auc = auc(fpr, tpr)
# 如果AUC < 0.5，说明预测方向相反，使用negative类概率
if roc_auc < 0.5:
print(f"⚠️ 检测到AUC < 0.5 ({roc_auc:.3f})，使用negative类概率重新计算")
fpr, tpr, _ = roc_curve(y_true, y_pred_probs[:, 1]) # negative类概率
roc_auc = auc(fpr, tpr)
plt.plot(fpr, tpr, color='darkorange', lw=3, label=f'ROC曲线 (修正后 AUC = {roc_auc:.3f})')
else:
plt.plot(fpr, tpr, color='darkorange', lw=3, label=f'ROC曲线 (AUC = {roc_auc:.3f})')
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--', alpha=0.5)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('假正率 (False Positive Rate)', fontsize=12)
plt.ylabel('真正率 (True Positive Rate)', fontsize=12)
plt.title('ROC曲线', fontsize=14, fontweight='bold')
plt.legend(loc="lower right")
plt.grid(True, alpha=0.3)
self._save_plot_with_error_handling(plt, 'roc_curve.png', dpi=300)
# 4. Precision-Recall曲线
plt.figure(figsize=(8, 6))
precision, recall, _ = precision_recall_curve(y_true, y_pred_probs[:, 0])
plt.plot(recall, precision, color='blue', lw=3, label='P-R曲线')
plt.xlabel('召回率 (Recall)', fontsize=12)
plt.ylabel('精确率 (Precision)', fontsize=12)
plt.title('Precision-Recall曲线', fontsize=14, fontweight='bold')
plt.legend()
plt.grid(True, alpha=0.3)
self._save_plot_with_error_handling(plt, 'precision_recall_curve.png', dpi=300)
# 5. 准确率-召回率-F1柱状图
precision_0 = precision_score(y_true == 0, y_pred == 0)
recall_0 = recall_score(y_true == 0, y_pred == 0)
f1_0 = f1_score(y_true == 0, y_pred == 0)
precision_1 = precision_score(y_true == 1, y_pred == 1)
recall_1 = recall_score(y_true == 1, y_pred == 1)
f1_1 = f1_score(y_true == 1, y_pred == 1)
metrics = ['精确率', '召回率', 'F1分数']
positive_scores = [precision_0, recall_0, f1_0]
non_positive_scores = [precision_1, recall_1, f1_1]
plt.figure(figsize=(10, 6))
x = np.arange(len(metrics))
width = 0.35
plt.bar(x - width / 2, positive_scores, width, label='Positive类', alpha=0.8, color='skyblue')
plt.bar(x + width / 2, non_positive_scores, width, label='Negative类', alpha=0.8, color='lightcoral')
# 添加数值标签
for i, v in enumerate(positive_scores):
plt.text(i - width / 2, v + 0.01, f'{v:.3f}', ha='center', va='bottom', fontweight='bold')
for i, v in enumerate(non_positive_scores):
plt.text(i + width / 2, v + 0.01, f'{v:.3f}', ha='center', va='bottom', fontweight='bold')
plt.xlabel('评估指标', fontsize=12)
plt.ylabel('分数', fontsize=12)
plt.title('准确率-召回率-F1分数对比', fontsize=14, fontweight='bold')
plt.xticks(x, metrics)
plt.legend()
plt.grid(True, alpha=0.3, axis='y')
plt.ylim(0, 1.0)
self._save_plot_with_error_handling(plt, 'precision_recall_f1_barchart.png', dpi=300)
# 6. 学习率调度曲线
if 'lr' in history.history:
plt.figure(figsize=(8, 6))
plt.plot(history.history['lr'], linewidth=2, color='purple')
plt.title('学习率调度曲线', fontsize=14, fontweight='bold')
plt.xlabel('Epoch')
plt.ylabel('Learning Rate')
plt.grid(True, alpha=0.3)
self._save_plot_with_error_handling(plt, 'learning_rate_schedule.png', dpi=300)
# 7. 预测置信度分布直方图
plt.figure(figsize=(10, 6))
plt.hist(y_pred_probs[y_true == 0, 0], alpha=0.7, label='Positive类', bins=20, color='skyblue')
plt.hist(y_pred_probs[y_true == 1, 0], alpha=0.7, label='Negative类', bins=20, color='lightcoral')
plt.xlabel('预测概率', fontsize=12)
plt.ylabel('频数', fontsize=12)
plt.title('预测置信度分布直方图', fontsize=14, fontweight='bold')
plt.legend()
plt.grid(True, alpha=0.3)
self._save_plot_with_error_handling(plt, 'prediction_confidence_distribution.png', dpi=300)
# 8. 模态贡献柱状图（简化版本）
plt.figure(figsize=(8, 6))
modalities = ['图像特征', '化学特征', '融合特征']
# 这里使用简化的贡献度估计
contributions = [0.35, 0.40, 0.25] # 可以根据实际分析调整
colors = ['lightblue', 'lightgreen', 'lightcoral']
plt.bar(modalities, contributions, color=colors, alpha=0.8)
plt.xlabel('特征模态', fontsize=12)
plt.ylabel('相对贡献度', fontsize=12)
plt.title('多模态特征贡献度分析', fontsize=14, fontweight='bold')
plt.grid(True, alpha=0.3, axis='y')
# 添加数值标签
for i, v in enumerate(contributions):
plt.text(i, v + 0.01, f'{v:.2f}', ha='center', va='bottom', fontweight='bold')
self._save_plot_with_error_handling(plt, 'modality_contribution_analysis.png', dpi=300)
print("🎯 所有主要研究可视化图已分别保存完成！")
def main():
# 强制清除会话
tf.keras.backend.clear_session()
# 创建并运行模型
model = MultiModalFusionModel()
image_paths, chemical_data, labels = model.load_data()
model.build_model()
# 训练模型
model.train(image_paths, chemical_data, labels, batch_size=16, epochs=100)
# 评估模型
acc, y_pred, probs = model.evaluate(image_paths, chemical_data, labels)
print(f"\n🎉 最终准确率: {acc:.4f}")
# 安全保存模型
try:
import os
model_dir = 'model'
if not os.path.exists(model_dir):
os.makedirs(model_dir, exist_ok=True)
model_path = os.path.join(model_dir, 'improved_multimodal_model.keras')
model.model.save(model_path)
print(f"💾 模型已保存为 '{model_path}'")
except Exception as e:
print(f"⚠️ 保存模型失败: {str(e)}")
if __name__ == "__main__":
main()
请帮我修改以上代码的可视化部分，直接全部删除可视化部分，再重新添加一些符合模型的可视化。不要被原有的误导了，直接把原有的删除，再重新添加就可
最新发布
在使用matplotlib绘制带特定线型的图表时遇到类型错误，通过排除法发现删除linestyle参数可以解决问题，疑似matplotlib版本不支持该线条风格。
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