下面是我的代码：
import os
import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers, models, applications
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.optimizers.legacy import Adam
from sklearn.model_selection import train_test_split
import tensorflow_addons as tfa # 新增用于高级优化器
# 配置文件路径
YIZHUIHE_PATH = "yizhuihe"
STATS_PATH = "zhuihetongji.xlsx"
PREPROCESSED_DATA_PATH = "preprocessed_data4" # 保存预处理数据的目录
MODEL_PATH = "bamboo_matching_model4.h5" # 模型保存路径
EPOCHS = 10
BATCH_SIZE = 32
# 创建保存预处理数据的目录
os.makedirs(PREPROCESSED_DATA_PATH, exist_ok=True)
# -------------------- 数据预处理模块 --------------------
class BambooPreprocessor:
def __init__(self, target_size=(256, 64)):
self.target_size = target_size
self.region_size = (64, 64)
def _enhance_texture(self, img):
"""增强竹简纹理特征"""
# 转换为RGB格式保持一致性
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# 1. 自适应直方图均衡化
lab = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2LAB)
l, a, b = cv2.split(lab)
clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
l = clahe.apply(l)
lab = cv2.merge((l, a, b))
enhanced = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB)
# 2. 纹理方向滤波
kernel = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
textured = cv2.filter2D(enhanced, -1, kernel)
return textured
def _detect_fracture(self, img):
"""改进的断口检测方法"""
# 灰度化+二值化
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU)
# 边缘检测（自适应阈值）
low_thresh = np.percentile(gray, 25)
high_thresh = np.percentile(gray, 75)
edges = cv2.Canny(gray, low_thresh, high_thresh)
# 轮廓分析（OpenCV版本兼容处理）
if cv2.__version__.startswith('4'):
contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
else:
_, contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if not contours:
return None
main_contour = max(contours, key=cv2.contourArea)
# 提取边界区域
x, y, w, h = cv2.boundingRect(main_contour)
boundary_width = 10 # 统一边界宽度
# 确保边界在图像范围内
top = img[max(y, 0):min(y+boundary_width, img.shape[0]),
max(x, 0):min(x+boundary_width, img.shape[1])]
bottom = img[max(y+h-boundary_width, 0):min(y+h, img.shape[0]),
max(x, 0):min(x+boundary_width, img.shape[1])]
left = img[max(y, 0):min(y+h, img.shape[0]),
max(x, 0):min(x+boundary_width, img.shape[1])]
right = img[max(y, 0):min(y+h, img.shape[0]),
max(x+w-boundary_width, 0):min(x+w, img.shape[1])]
# 方向特征
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=5)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=5)
orientation = np.arctan2(sobely, sobelx)
orientation_region = orientation[max(y, 0):min(y+h, img.shape[0]),
max(x, 0):min(x+w, img.shape[1])]
return {
'top': top,
'bottom': bottom,
'left': left,
'right': right,
'orientation': orientation_region
}
def preprocess(self, img_path):
img = cv2.imread(img_path)
if img is None:
return None
# 纹理增强
img = self._enhance_texture(img)
# 提取断口区域
fracture_regions = self._detect_fracture(img)
if not fracture_regions:
return None
# 统一所有区域的大小
resized_regions = {}
for region_name in ['top', 'bottom', 'left', 'right']:
region = fracture_regions[region_name]
# 确保是单通道灰度图
if len(region.shape) == 3:
region = cv2.cvtColor(region, cv2.COLOR_BGR2GRAY)
resized_regions[region_name] = cv2.resize(region, self.region_size)
# 处理方向特征图
orientation = fracture_regions['orientation']
orientation = cv2.normalize(orientation, None, 0, 255, cv2.NORM_MINMAX)
orientation = cv2.resize(orientation, self.region_size)
# 创建多通道特征图
channels = [
resized_regions['top'],
resized_regions['bottom'],
resized_regions['left'],
resized_regions['right'],
orientation
]
# 堆叠通道并调整最终尺寸
processed = np.stack(channels, axis=-1)
return cv2.resize(processed, self.target_size)
# --------------------
# 数据加载器
# --------------------
class BambooDataset:
def __init__(self, data_root):
self.data_root = data_root
self.groups = self._load_groups()
self.preprocessor = BambooPreprocessor()
def _load_groups(self):
"""加载已缀合组信息"""
groups = {}
for group_id in os.listdir(self.data_root):
group_path = os.path.join(self.data_root, group_id)
if os.path.isdir(group_path):
groups[group_id] = [
os.path.join(group_path, f)
for f in os.listdir(group_path)
if f.endswith(('.jpg', '.png'))
]
return groups
def _generate_pairs(self):
"""生成训练样本对（正负样本）"""
positive_pairs = []
negative_pairs = []
# 正样本：同组内竹简
for group_id, paths in self.groups.items():
if len(paths) < 2:
continue
for i in range(len(paths)):
for j in range(i+1, len(paths)):
positive_pairs.append((paths[i], paths[j], 1))
# 负样本：不同组竹简
all_paths = [p for paths in self.groups.values() for p in paths]
group_ids = list(self.groups.keys())
# 平衡正负样本数量
num_neg = min(len(positive_pairs), len(all_paths) * 2)
for _ in range(num_neg):
while True:
path1 = np.random.choice(all_paths)
path2 = np.random.choice(all_paths)
group1 = next(g for g, ps in self.groups.items() if path1 in ps)
group2 = next(g for g, ps in self.groups.items() if path2 in ps)
if group1 != group2:
negative_pairs.append((path1, path2, 0))
break
return positive_pairs + negative_pairs
def load_dataset(self):
"""加载预处理后的数据集"""
pairs = self._generate_pairs()
X1, X2, y = [], [], []
for path1, path2, label in pairs:
img1 = self.preprocessor.preprocess(path1)
img2 = self.preprocessor.preprocess(path2)
if img1 is not None and img2 is not None:
X1.append(img1)
X2.append(img2)
y.append(label)
return np.array(X1), np.array(X2), np.array(y)
def create_improved_siamese_network(input_shape):
"""改进的孪生网络架构"""
def spatial_attention(input_tensor):
"""空间注意力机制"""
avg_pool = layers.Lambda(lambda x: tf.reduce_mean(x, axis=3, keepdims=True))(input_tensor)
max_pool = layers.Lambda(lambda x: tf.reduce_max(x, axis=3, keepdims=True))(input_tensor)
concat = layers.Concatenate(axis=3)([avg_pool, max_pool])
cbam_feature = layers.Conv2D(1, kernel_size=7, padding='same', activation='sigmoid')(concat)
return layers.Multiply()([input_tensor, cbam_feature])
# 输入层
base_input = layers.Input(shape=input_shape)
# 通道适配层：将5通道转换为3通道
x = layers.Conv2D(3, (1, 1), padding='same', name='channel_adapter')(base_input)
# 共享特征提取器（使用ResNet50）
base_model = applications.ResNet50(
weights='imagenet',
include_top=False,
input_shape=(input_shape[0], input_shape[1], 3)
)
x = base_model(x)
# 添加注意力机制
x = spatial_attention(x)
# 多尺度特征融合
branch1 = layers.GlobalAveragePooling2D()(x)
branch2 = layers.GlobalMaxPooling2D()(x)
x = layers.Concatenate()([branch1, branch2])
# 增强特征表示
x = layers.Dense(512, activation='relu', kernel_regularizer=tf.keras.regularizers.l1_l2(0.01, 0.01))(x)
x = layers.Dropout(0.3)(x)
feature_extractor = models.Model(inputs=base_input, outputs=x)
# 孪生架构
input_a = layers.Input(shape=input_shape)
input_b = layers.Input(shape=input_shape)
features_a = feature_extractor(input_a)
features_b = feature_extractor(input_b)
# 改进的特征差异度量
diff = layers.Subtract()([features_a, features_b])
abs_diff = layers.Lambda(lambda x: tf.abs(x))(diff)
squared_diff = layers.Lambda(lambda x: tf.square(x))(diff)
concat_diff = layers.Concatenate()([abs_diff, squared_diff])
# 相似性分类
x = layers.Dense(256, activation='relu')(concat_diff)
x = layers.Dropout(0.2)(x)
classification_output = layers.Dense(1, activation='sigmoid', name='classification')(x)
# 添加特征差异作为额外输出
feat_diff = layers.Lambda(
lambda x: tf.reduce_mean(tf.abs(x), axis=1),
name='feature_difference'
)(concat_diff)
# 创建具有两个输出的模型
siamese_model = models.Model(
inputs=[input_a, input_b],
outputs=[classification_output, feat_diff]
)
return siamese_model
# 配对数据增强生成器
class PairedDataGenerator:
def __init__(self, X1, X2, y, datagen, batch_size=32):
self.X1 = X1
self.X2 = X2
self.y = y
self.datagen = datagen
self.batch_size = batch_size
# 初始化数据生成器
self.genX1 = datagen.flow(X1, y, batch_size=batch_size, shuffle=False)
self.genX2 = datagen.flow(X2, y, batch_size=batch_size, shuffle=False)
def __iter__(self):
return self
def __next__(self):
X1_batch, y_batch = next(self.genX1)
X2_batch, _ = next(self.genX2)
return [X1_batch, X2_batch], y_batch
# 困难样本挖掘回调
class HardExampleMiner(tf.keras.callbacks.Callback):
def __init__(self, train_data, threshold=0.2):
self.X1_train, self.X2_train, self.y_train = train_data
self.threshold = threshold
def on_epoch_end(self, epoch, logs=None):
# 获取当前批次预测结果
y_pred, _ = self.model.predict([self.X1_train, self.X2_train], verbose=0)
# 选择预测概率接近0.5的困难样本
hard_indices = np.where(np.abs(y_pred.squeeze() - 0.5) < self.threshold)[0]
# 增强训练（仅训练困难样本）
if len(hard_indices) > 0:
self.model.fit(
[self.X1_train[hard_indices], self.X2_train[hard_indices]],
[self.y_train[hard_indices], np.zeros(len(hard_indices))], # 为特征差异输出提供伪标签
epochs=1,
batch_size=BATCH_SIZE,
verbose=0
)
def hybrid_loss(y_true, y_pred):
"""混合损失函数 - 同时处理分类输出和特征差异"""
# 解包预测值：y_pred 包含两个输出
classification_output = y_pred[0]
feat_diff = y_pred[1]
# 确保y_true是正确形状 (batch_size, 1)
y_true = tf.reshape(tf.cast(y_true, tf.float32), [-1, 1])
# 计算二元交叉熵损失 - 确保维度匹配
bce = tf.keras.losses.binary_crossentropy(
y_true,
classification_output,
from_logits=False
)
# 特征差异正则化 - 确保维度匹配
reg_loss = 0.1 * tf.maximum(0.5 - feat_diff, 0)
return bce + reg_loss
def train_and_evaluate():
# 检查预处理数据文件是否存在
x1_path = os.path.join(PREPROCESSED_DATA_PATH, "X1.npy")
x2_path = os.path.join(PREPROCESSED_DATA_PATH, "X2.npy")
y_path = os.path.join(PREPROCESSED_DATA_PATH, "y.npy")
if os.path.exists(x1_path) and os.path.exists(x2_path) and os.path.exists(y_path):
# 加载预处理数据
X1 = np.load(x1_path)
X2 = np.load(x2_path)
y = np.load(y_path)
print("预处理数据已加载")
else:
# 加载并预处理数据
dataset = BambooDataset(YIZHUIHE_PATH)
X1, X2, y = dataset.load_dataset()
# 保存预处理数据
np.save(x1_path, X1)
np.save(x2_path, X2)
np.save(y_path, y)
print("预处理数据已保存")
# 划分数据集
X1_train, X1_val, X2_train, X2_val, y_train, y_val = train_test_split(
X1, X2, y, test_size=0.2, random_state=42
)
# 数据增强配置
datagen = ImageDataGenerator(
rotation_range=10,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=True,
fill_mode='nearest'
)
# 创建配对数据生成器
train_generator = PairedDataGenerator(
X1_train, X2_train, y_train, datagen, batch_size=BATCH_SIZE
)
# 创建模型
siamese_model = create_improved_siamese_network(X1_train.shape[1:])
# 优化器（使用Lookahead优化器提升收敛性）
optimizer = tfa.optimizers.Lookahead(
Adam(learning_rate=1e-4),
sync_period=6,
slow_step_size=0.5
)
# 编译模型 - 使用自定义混合损失函数
siamese_model.compile(
optimizer=optimizer,
loss=hybrid_loss,
metrics={'classification': 'accuracy'} # 只为分类输出计算准确率
)
# 早停策略 - 监控分类输出的准确率
early_stopping = EarlyStopping(
monitor='val_classification_accuracy',
patience=10,
restore_best_weights=True,
min_delta=0.001
)
# 困难样本挖掘
hard_miner = HardExampleMiner(
train_data=(X1_train, X2_train, y_train),
threshold=0.2
)
# 模型训练 - 注意：验证数据需要两个输出
history = siamese_model.fit(
train_generator,
steps_per_epoch=len(X1_train) // BATCH_SIZE,
validation_data=([X1_val, X2_val], [y_val, np.zeros(len(y_val))]), # 为特征差异输出提供伪标签
epochs=EPOCHS,
callbacks=[early_stopping, hard_miner],
class_weight={0: 1.0, 1: 1.5}
)
# 保存模型
siamese_model.save(MODEL_PATH)
# 模型评估 - 只关注分类输出
loss = siamese_model.evaluate([X1_val, X2_val], [y_val, np.zeros(len(y_val))])
# 损失函数返回总损失，但我们关心分类准确率
# 在评估中，metrics 会返回分类准确率
print(f"验证准确率: {history.history['val_classification_accuracy'][-1]*100:.2f}%")
return history
if __name__ == "__main__":
train_and_evaluate()在训练完毕后遇到了报错ifference_loss: 1.1556 - classification_accuracy: 0.4956 - val_loss: 36.8123 - val_classification_loss: 0.6945 -
val_feature_difference_loss: 0.1284 - val_classification_accuracy: 0.4487
Epoch 10/10
1/32 [..............................] - ETA: 6:00 - loss: 30.5024 - classification_loss: 0.8720 - feature_differ 2/32 [>.............................] - ETA: 6:02 - loss: 30.2336 - classification_loss: 0.8529 - feature_differ 3/32 [=>............................] - ETA: 5:56 - loss: 30.5036 - classification_loss: 0.8665 - feature_differ 4/32 [==>...........................] - ETA: 5:47 - loss: 30.2310 - classification_loss: 0.8640 - feature_differ 5/32 [===>..........................] - ETA: 5:35 - loss: 29.9351 - classification_loss: 0.8612 - feature_differ 6/32 [====>.........................] - ETA: 5:23 - loss: 29.8819 - classification_loss: 0.8636 - feature_differ 7/32 [=====>........................] - ETA: 5:11 - loss: 29.8167 - classification_loss: 0.8773 - feature_differ 8/32 [======>.......................] - ETA: 4:58 - loss: 29.7443 - classification_loss: 0.9530 - feature_differ 9/32 [=======>......................] - ETA: 4:39 - loss: 29.6304 - classification_loss: 0.9469 - feature_differ10/32 [========>.....................] - ETA: 4:27 - loss: 29.4564 - classification_loss: 0.9363 - feature_differ11/32 [=========>....................] - ETA: 4:16 - loss: 29.2758 - classification_loss: 0.9282 - feature_differ12/32 [==========>...................] - ETA: 4:04 - loss: 29.1919 - classification_loss: 0.9231 - feature_differ13/32 [===========>..................] - ETA: 3:53 - loss: 29.1008 - classification_loss: 0.9268 - feature_differ14/32 [============>.................] - ETA: 3:41 - loss: 28.9858 - classification_loss: 0.9246 - feature_differ15/32 [=============>................] - ETA: 3:29 - loss: 28.8968 - classification_loss: 0.9279 - feature_differ16/32 [==============>...............] - ETA: 3:17 - loss: 28.7870 - classification_loss: 0.9224 - feature_differ17/32 [==============>...............] - ETA: 3:05 - loss: 28.6540 - classification_loss: 0.9196 - feature_differ18/32 [===============>..............] - ETA: 2:53 - loss: 28.5789 - classification_loss: 0.9091 - feature_differ19/32 [================>.............] - ETA: 2:41 - loss: 28.4827 - classification_loss: 0.9090 - feature_differ20/32 [=================>............] - ETA: 2:30 - loss: 28.3714 - classification_loss: 0.9066 - feature_differ21/32 [==================>...........] - ETA: 2:17 - loss: 28.2615 - classification_loss: 0.9061 - feature_differ22/32 [===================>..........] - ETA: 2:05 - loss: 28.1563 - classification_loss: 0.9246 - feature_differ23/32 [====================>.........] - ETA: 1:52 - loss: 28.0344 - classification_loss: 0.9217 - feature_differ24/32 [=====================>........] - ETA: 1:40 - loss: 27.9423 - classification_loss: 0.9150 - feature_differ25/32 [======================>.......] - ETA: 1:27 - loss: 27.8502 - classification_loss: 0.9217 - feature_differ26/32 [=======================>......] - ETA: 1:15 - loss: 27.7519 - classification_loss: 0.9186 - feature_differ27/32 [========================>.....] - ETA: 1:02 - loss: 27.6517 - classification_loss: 0.9175 - feature_differ28/32 [=========================>....] - ETA: 50s - loss: 27.5349 - classification_loss: 0.9154 - feature_differe29/32 [==========================>...] - ETA: 37s - loss: 27.4168 - classification_loss: 0.9140 - feature_differe30/32 [===========================>..] - ETA: 25s - loss: 27.3513 - classification_loss: 0.9132 - feature_differe31/32 [============================>.] - ETA: 12s - loss: 27.2566 - classification_loss: 0.9135 - feature_differe32/32 [==============================] - ETA: 0s - loss: 27.1861 - classification_loss: 0.9117 - feature_difference_loss: 1.1294 - classification_accuracy: 0.4956 2025-06-22 14:05:17.364362: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at fused_batch_norm_op.cc:1565 : RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[32,4,16,1024] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator cpu
2025-06-22 14:05:17.396860: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at conv_ops_fused_impl.h:772 : RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[32,4,16,1024] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator cpu
Traceback (most recent call last):
File "c:\Users\lin13\Desktop\data\lab9-8.py", line 436, in <module>
train_and_evaluate()
File "c:\Users\lin13\Desktop\data\lab9-8.py", line 415, in train_and_evaluate
history = siamese_model.fit(
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "c:\Users\lin13\Desktop\data\lab9-8.py", line 310, in on_epoch_end
self.model.fit(
tensorflow.python.framework.errors_impl.ResourceExhaustedError: Graph execution error:
Detected at node model_1/model/resnet50/conv4_block6_3_bn/FusedBatchNormV3 defined at (most recent call last):
File "c:\Users\lin13\Desktop\data\lab9-8.py", line 436, in <module>
File "c:\Users\lin13\Desktop\data\lab9-8.py", line 415, in train_and_evaluate
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\utils\traceback_utils.py", line 65, in error_handler
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\training.py", line 1850, in fit
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\callbacks.py", line 453, in on_epoch_end
File "c:\Users\lin13\Desktop\data\lab9-8.py", line 310, in on_epoch_end
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\utils\traceback_utils.py", line 65, in error_handler
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\training.py", line 1783, in fit
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\training.py", line 1377, in train_function
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\training.py", line 1360, in step_function
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\training.py", line 1349, in run_step
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\training.py", line 1126, in train_step
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\utils\traceback_utils.py", line 65, in error_handler
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\training.py", line 589, in __call__
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\utils\traceback_utils.py", line 65, in error_handler
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\base_layer.py",
line 1149, in __call__
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\utils\traceback_utils.py", line 96, in error_handler
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\functional.py",
line 515, in call
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\functional.py",
line 672, in _run_internal_graph
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\utils\traceback_utils.py", line 65, in error_handler
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\training.py", line 589, in __call__
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\utils\traceback_utils.py", line 65, in error_handler
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\base_layer.py",
line 1149, in __call__
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\utils\traceback_utils.py", line 96, in error_handler
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\functional.py",
line 515, in call
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\functional.py",
line 672, in _run_internal_graph
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\utils\traceback_utils.py", line 65, in error_handler
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\training.py", line 589, in __call__
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\utils\traceback_utils.py", line 65, in error_handler
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\base_layer.py",
line 1149, in __call__
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\utils\traceback_utils.py", line 96, in error_handler
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\functional.py",
line 515, in call
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\functional.py",
line 672, in _run_internal_graph
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\utils\traceback_utils.py", line 65, in error_handler
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\engine\base_layer.py",
line 1149, in __call__
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\utils\traceback_utils.py", line 96, in error_handler
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\layers\normalization\batch_normalization.py", line 597, in call
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\layers\normalization\batch_normalization.py", line 990, in _fused_batch_norm
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\utils\control_flow_util.py", line 108, in smart_cond
File "C:\Users\lin13\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\src\layers\normalization\batch_normalization.py", line 964, in _fused_batch_norm_training
OOM when allocating tensor with shape[32,4,16,1024] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator cpu
[[{{node model_1/model/resnet50/conv4_block6_3_bn/FusedBatchNormV3}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to
RunOptions for current allocation info. This isn't available when running in Eager mode.
[Op:__inference_train_function_66233]
2025-06-22 14:05:32.873794: W tensorflow/core/kernels/data/generator_dataset_op.cc:108] Error occurred when finalizing GeneratorDataset iterator: FAILED_PRECONDITION: Python interpreter state is not initialized. The process may be terminated.
[[{{node PyFunc}}]]
PS C:\Users\lin13\Desktop\data> 帮我看看为什么
最新发布
本文解释了TensorFlow中tf.where()函数的工作原理及其在损失函数中的应用。当条件a为真时，该函数返回b；当条件a为假时，则返回c。
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