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

已完成理解「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」