import matplotlib.pyplot as plt import tensorflow as tf from keras.applications import InceptionV3, ResNet50, InceptionResNetV2, VGG19, MobileNetV3Large, MobileNetV3Small, ResNet50V2, resnet_v2, mobilenet_v3 from keras.models import Model from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Concatenate, Input, Flatten, Dropout from tensorflow.keras.optimizers import Adam, SGD from tensorflow.keras import optimizers, regularizers from keras.callbacks import EarlyStopping from image_match_labels8 import train_ds, label_names from image_match_labels81 import val_ds moblie_net = MobileNetV3Large(input_shape=(192, 192, 3), include_top=False, weights='imagenet') num_classes = len(label_names) model = tf.keras.Sequential([ moblie_net, GlobalAveragePooling2D(), Dense(256, activation='relu', kernel_regularizer=regularizers.l2(0.05)), Dropout(0.5), # 添加Dropout层 Dense(num_classes, activation='softmax') ]) optimizer = Adam(learning_rate=0.0001) model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy']) reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, min_lr=1e-6) early_stopping = EarlyStopping(monitor='val_accuracy', patience=5, restore_best_weights=True) history = model.fit(train_ds, epochs=60, validation_data=val_ds, callbacks=[reduce_lr，early_stopping])该代码loss: 0.0545 - accuracy: 0.9990 - val_loss: 0.8392 - val_accuracy: 0.8233，怎么修改代码提高验证集准确率，完整代码

• 优化模型以提高验证集准确率。

优化策略

• 增加模型复杂度：尝试增加更多的隐藏层或隐藏单元的数量来捕捉数据中的复杂关系。1
• 调整参数：包括学习率、批次大小和训练轮次等，尝试不同的超参数组合。1
• 数据增强：通过在训练集中应用变换（如旋转、平移和缩放）增加数据多样性。1
• 正则化技术：使用L1或L2正则化限制模型复杂度，减少过拟合。1
• 扩大训练集：获取更多训练数据，使模型有更多样本进行学习。1
• 尝试不同的优化器：除了SGD，尝试Adam、RMSprop等优化器。1
• 模型集成：使用集成学习方法，组合多个模型的预测结果。1

代码修改建议

• 增加Dropout比率：提高模型泛化能力，减少过拟合。
• 调整L2正则化系数：可能需要调整L2正则化系数以找到最佳值。
• 更换优化器：尝试使用不同的优化器，如SGD或RMSprop。
• 增加数据增强：在训练过程中应用数据增强技术。
• 模型集成：训练多个模型并集成它们的预测结果。

完整代码示例

import matplotlib.pyplot as plt
import tensorflow as tf
from keras.applications import MobileNetV3Large
from keras.models import Model
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout
from tensorflow.keras.optimizers import Adam, SGD
from tensorflow.keras import regularizers
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
from image_match_labels8 import train_ds, label_names
from image_match_labels81 import val_ds

num_classes = len(label_names)
moblie_net = MobileNetV3Large(input_shape=(192, 192, 3), include_top=False, weights='imagenet')

model = tf.keras.Sequential([
    moblie_net,
    GlobalAveragePooling2D(),
    Dense(256, activation='relu', kernel_regularizer=regularizers.l2(0.05)),
    Dropout(0.6),  # 增加Dropout比率
    Dense(num_classes, activation='softmax')
])

optimizer = Adam(learning_rate=0.0001)
model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])

reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, min_lr=1e-6)
early_stopping = EarlyStopping(monitor='val_accuracy', patience=5, restore_best_weights=True)

history = model.fit(train_ds, epochs=60, validation_data=val_ds, callbacks=[reduce_lr, early_stopping])