微调模型中的Keras精度差异

from tensorflow.keras.applications import VGG16
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam

# 加载预训练模型
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# 添加自定义层
x = base_model.output
x = Dense(256, activation='relu')(x)
predictions = Dense(10, activation='softmax')(x)  # 假设有10个类别

# 构建最终模型
model = Model(inputs=base_model.input, outputs=predictions)

# 冻结预训练模型的层
for layer in base_model.layers:
    layer.trainable = False

# 编译模型
model.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(train_data, train_labels, epochs=10, batch_size=32, validation_data=(val_data, val_labels))