diff --git a/number_train.py b/number_train.py
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index 0000000..822b6c3
--- /dev/null
+++ b/number_train.py
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+import os
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, Conv2D, MaxPooling2D, Flatten
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
+
+import tensorflow as tf
+print(tf.__version__)
+
+# 导入数据
+mnist = tf.keras.datasets.mnist
+(train_data, train_target), (test_data, test_target) = mnist.load_data()
+
+# 改变数据维度
+# 改变数据维度
+train_data = train_data.reshape(-1, 28, 28, 1)
+test_data = test_data.reshape(-1, 28, 28, 1)
+# 注：在TensorFlow中，在做卷积的时候需要把数据变成4维的格式
+# 这4个维度分别是：数据数量，图片高度，图片宽度，图片通道数
+
+# 归一化（有助于提升训练速度）
+train_data = train_data / 255.0
+test_data = test_data / 255.0
+
+# 独热编码
+train_target = tf.keras.utils.to_categorical(train_target, num_classes=10)
+test_target = tf.keras.utils.to_categorical(test_target, num_classes=10)  # 10种结果
+
+# 配置早停
+early_stopping = EarlyStopping(monitor='val_loss', patience=3, verbose=1, restore_best_weights=True)
+
+# 配置GPU加速
+strategy = tf.distribute.MirroredStrategy()
+print('Number of devices: {}'.format(strategy.num_replicas_in_sync))
+
+with strategy.scope():
+    # 构建更复杂的模型
+    model = tf.keras.Sequential([
+        Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1), padding = 'same'),
+        MaxPooling2D((2, 2), padding = 'same'),
+        Conv2D(64, (3, 3), activation='relu', padding = 'same'),
+        MaxPooling2D((2, 2), padding = 'same'),
+        Flatten(),
+        Dense(1024, activation='relu'),
+        Dropout(0.5),
+        Dense(10, activation='softmax')
+    ])
+
+    # 编译模型
+    optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
+    model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
+
+# # 配置模型检查点，保存最优模型
+# checkpoint_path = "./model/number_model.h5"
+# model_checkpoint = ModelCheckpoint(checkpoint_path, monitor='val_loss',
+#                                      save_best_only=True, save_weights_only=False, verbose=1)
+
+# 训练模型
+model.fit(train_data, train_target, epochs=5, validation_data=(test_data, test_target), callbacks=[early_stopping])
+
+# 保存模型为 .h5 文件
+model.save("./model/number_model.h5", save_format='h5')
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