diff --git a/train.py b/train.py
new file mode 100644
index 0000000..71ac9ce
--- /dev/null
+++ b/train.py
@@ -0,0 +1,106 @@
+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)