diff --git a/手写数字识别1.py b/手写数字识别1.py
deleted file mode 100644
index d91fd00..0000000
--- a/手写数字识别1.py
+++ /dev/null
@@ -1,93 +0,0 @@
-#调用 TensorFlow
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import tensorflow as tf
-
-from tensorflow.keras.layers import Dense, Flatten, Conv2D
-from tensorflow.keras import Model
-
-#载入并准备好 MNIST 数据集：
-mnist = tf.keras.datasets.mnist
-
-(x_train, y_train), (x_test, y_test) = mnist.load_data()
-x_train, x_test = x_train / 255.0, x_test / 255.0
-
-# Add a channels dimension
-x_train = x_train[..., tf.newaxis]
-x_test = x_test[..., tf.newaxis]
-
-
-#使用 tf.data 来将数据集切分为 batch 以及混淆数据集：
-train_ds = tf.data.Dataset.from_tensor_slices(
-    (x_train, y_train)).shuffle(10000).batch(32)
-test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)
-
-#使用 Keras 模型子类化（model subclassing） API 构建 tf.keras 模型：
-class MyModel(Model):
-    def __init__(self):
-        super(MyModel, self).__init__()
-        self.conv1 = Conv2D(32, 3, activation='relu')
-        self.flatten = Flatten()
-        self.d1 = Dense(128, activation='relu')
-        self.d2 = Dense(10, activation='softmax')
-
-    def call(self, x):
-        x = self.conv1(x)
-        x = self.flatten(x)
-        x = self.d1(x)
-        return self.d2(x)
-
-
-model = MyModel()
-
-#为训练选择优化器与损失函数：
-loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
-
-optimizer = tf.keras.optimizers.Adam()
-
-#选择衡量指标来度量模型的损失值（loss）和准确率（accuracy）。这些指标在 epoch 上累积值，然后打印出整体结果
-train_loss = tf.keras.metrics.Mean(name='train_loss')
-train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(
-    name='train_accuracy')
-
-test_loss = tf.keras.metrics.Mean(name='test_loss')
-test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(
-    name='test_accuracy')
-
-#使用 tf.GradientTape 来训练模型
-@tf.function
-def train_step(images, labels):
-    with tf.GradientTape() as tape:
-        predictions = model(images)
-        loss = loss_object(labels, predictions)
-    gradients = tape.gradient(loss, model.trainable_variables)
-    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
-
-    train_loss(loss)
-    train_accuracy(labels, predictions)
-
-#测试模型：
-@tf.function
-def test_step(images, labels):
-    predictions = model(images)
-    t_loss = loss_object(labels, predictions)
-
-    test_loss(t_loss)
-    test_accuracy(labels, predictions)
-
-
-EPOCHS = 5
-
-for epoch in range(EPOCHS):
-    for images, labels in train_ds:
-        train_step(images, labels)
-
-    for test_images, test_labels in test_ds:
-        test_step(test_images, test_labels)
-
-    template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
-    print(template.format(epoch + 1,
-                          train_loss.result(),
-                          train_accuracy.result() * 100,
-                          test_loss.result(),
-                          test_accuracy.result() * 100))