使用 catboost 提高 CNN 模型的准确性

如何解决使用 catboost 提高 CNN 模型的准确性

如何提高我的 cnn 模型的准确性？目前我的准确率为 70%。如何使用 Catboost 或 XGBoost 获得更好的准确性？还有什么我可以使用的，也许是 Keras 调谐器？

import tensorflow as tf

import pandas as pd
import category_encoders as ce

from tensorflow.keras import datasets,layers,models
import matplotlib.pyplot as plt

#from catboost import CatBoostClassifier
#catboost = CatBoostClassifier()


(train_images,train_labels),(test_images,test_labels) = datasets.cifar10.load_data()

train_images,test_images = train_images/255.0,test_images/255.0

class_names = ['airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck',]

model = models.Sequential()
model.add(layers.Conv2D(32,(3,3),activation='relu',input_shape=(32,32,3)))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64,activation='relu'))
model.add(layers.MaxPooling2D((2,activation='relu'))

model.add(layers.Flatten())
model.add(layers.Dense(64,activation='relu'))
model.add(layers.Dense(10))

model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy'])

history = model.fit(train_images,train_labels,epochs=10,validation_data=(test_images,test_labels))


plt.plot(history.history['accuracy'],label='accuracy')
plt.plot(history.history['val_accuracy'],label = 'val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5,1])
plt.legend(loc='lower right')

test_loss,test_acc = model.evaluate(test_images,test_labels,verbose=2)

print(test_acc)