from datetime import datetime
import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.callbacks import LearningRateScheduler, EarlyStopping, TensorBoard
from model_core import LeNet5Custom


# 打印模型框架基本信息
print(tf.sysconfig.get_build_info())


def load_and_preprocess_data():
    # 加载MNIST数据集
    (train_images, train_labels), (test_images, test_labels) = mnist.load_data()

    # 反转像素值：255 - x
    train_images = 255 - train_images
    test_images = 255 - test_images

    # 归一化像素值到 [0, 1] 区间
    train_images = train_images.astype("float32") / 255
    test_images = test_images.astype("float32") / 255

    # 由于 MNIST 的图像是灰度图像，需要增加一个颜色通道
    train_images = train_images.reshape(-1, 28, 28, 1)
    test_images = test_images.reshape(-1, 28, 28, 1)

    # 对标签进行分类编码
    train_labels = to_categorical(train_labels, 10)
    test_labels = to_categorical(test_labels, 10)

    return train_images, train_labels, test_images, test_labels


def lr_schedule(epoch):
    lr = 1e-3
    if epoch > 15:
        lr *= 0.1
    elif epoch > 55:
        lr *= 0.01
    return lr


def train_model_dp(train_images, train_labels, test_images, test_labels, dropout_rates, epochs):
    lr_scheduler = LearningRateScheduler(lr_schedule)
    early_stopping = EarlyStopping(monitor='val_loss',
                                   patience=(epochs // 10 if epochs // 10 > 5 else 5),
                                   restore_best_weights=True)

    best_accuracy = 0.0
    best_dropout_rate = None
    best_model = None

    for dropout_rate in dropout_rates:
        print(f"Training model with dropout rate: {dropout_rate}")

        model = LeNet5Custom(dropout_rate)
        model = model.compile_model()

        model.summary()

        model.fit(train_images,
                  train_labels,
                  epochs=epochs,
                  batch_size=256,
                  validation_data=(test_images, test_labels),
                  callbacks=[lr_scheduler, early_stopping])

        # 评估模型
        test_loss, test_acc = model.evaluate(test_images, test_labels)
        print(f'Test Accuracy with dropout rate {dropout_rate}: {test_acc:.4f} with loss {test_loss:.4f}')

        if test_acc > best_accuracy:
            best_accuracy = test_acc
            best_dropout_rate = dropout_rate
            best_model = model

    return best_model, best_dropout_rate, best_accuracy


def save_model(model, accuracy):
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")  # 获取当前时间戳并格式化为字符串
    filepath = f'./model/lenet5_model_best_{timestamp}.h5'
    model.save(filepath=filepath)
    print(f"Best model weights saved to {filepath} with test accuracy: {accuracy:.4f}")


if __name__ == "__main__":
    tf.keras.backend.clear_session()
    tf.compat.v1.reset_default_graph()

    train_images, train_labels, test_images, test_labels = load_and_preprocess_data()

    dropout_rates = [0.1, 0.2, 0.3, 0.4, 0.5] # 0.3 is the best of [0.1, 0.2, 0.3, 0.4, 0.5]
    epochs = 100

    best_model, best_dropout_rate, best_accuracy = train_model_dp(train_images,
                                                                  train_labels,
                                                                  test_images,
                                                                  test_labels,
                                                                  dropout_rates,
                                                                  epochs)
    print(f'Best dropout rate: {best_dropout_rate} with test accuracy: {best_accuracy:.4f}')

    save_model(best_model, best_accuracy)