test

.idea/.gitignore

@@ -0,0 +1,3 @@
+# Default ignored files
+/shelf/
+/workspace.xml

.idea/ML.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (tf2.0)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/ML.iml" filepath="$PROJECT_DIR$/.idea/ML.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

main.py

@@ -0,0 +1,61 @@
+import os
+
+import tensorflow as tf
+from tensorflow import keras
+import matplotlib.pyplot as plt
+
+print(tf.version.VERSION)
+
+(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()
+
+plt.imshow(train_images[0])
+
+train_labels = train_labels[:1000]
+test_labels = test_labels[:1000]
+
+train_images = train_images[:1000].reshape(-1, 28 * 28) / 255.0
+test_images = test_images[:1000].reshape(-1, 28 * 28) / 255.0
+
+# 定义一个简单的序列模型
+def create_model():
+  model = tf.keras.models.Sequential([
+    keras.layers.Dense(512, activation='relu', input_shape=(784,)),
+    keras.layers.Dropout(0.2),
+    keras.layers.Dense(10)
+  ])
+
+  model.compile(optimizer='adam',
+                loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),
+                metrics=['accuracy'])
+
+  return model
+
+# 创建一个基本的模型实例
+model = create_model()
+
+# 显示模型的结构
+model.summary()
+
+checkpoint_path = "training_1/cp.ckpt"
+checkpoint_dir = os.path.dirname(checkpoint_path)
+
+# 创建一个保存模型权重的回调
+cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
+                                                 save_weights_only=True,
+                                                 verbose=1)
+
+# 使用新的回调训练模型
+model.fit(train_images,
+            train_labels,
+            epochs=10,
+            batch_size=8,
+            validation_data=(test_images,test_labels),
+            callbacks=[cp_callback])  # 通过回调训练
+
+# 这可能会生成与保存优化程序状态相关的警告。
+# 这些警告（以及整个笔记本中的类似警告）
+# 是防止过时使用，可以忽略。
+
+results = model.evaluate(test_images,  test_labels, verbose=2)
+
+print(results)

training_1/checkpoint

@@ -0,0 +1,2 @@
+model_checkpoint_path: "cp.ckpt"
+all_model_checkpoint_paths: "cp.ckpt"