From 881c3e212f7bf80adcc9b518ccad52eca79e5a93 Mon Sep 17 00:00:00 2001
From: yinkanglong <yinkanglong@163.com>
Date: Thu, 6 May 2021 21:11:25 +0800
Subject: [PATCH 1/2] =?UTF-8?q?=E4=BA=94=E6=9C=88=E4=BB=BD=E8=AE=A1?=
 =?UTF-8?q?=E5=88=92=E5=BC=80=E5=A7=8B=E6=89=A7=E8=A1=8C?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 Tensorflow/TensorFlow2.0/11 联邦学习.ipynb    | 555 ++++++++++++++++--
 .../TensorFlow2.0/12 联邦学习-文本生成.ipynb  |   0
 .../2021-05-06 20-44-37屏幕截图.png           | Bin 0 -> 92043 bytes
 工作日志/2021年3月18日-今日计划.md            |   2 +-
 工作日志/2021年3月19日-四月份计划.md          |   4 +-
 工作日志/2021年3月20日-今日计划.md            |   2 +-
 工作日志/2021年3月21日-今日计划.md            |   2 +-
 工作日志/2021年3月22日-今日计划.md            |   2 +-
 工作日志/2021年3月23日-今日计划.md            |   2 +-
 工作日志/2021年3月25日-今日计划.md            |   4 +-
 工作日志/2021年3月26日-开组会.md              |   2 +-
 工作日志/2021年3月29日-今日计划.md            |   2 +-
 工作日志/2021年3月31日-今日计划.md            |   2 +-
 工作日志/2021年4月21日-今日计划.md            |   8 +-
 工作日志/2021年4月27日-今日计划.md            |   6 +-
 工作日志/2021年4月2日-今日计划.md             |   4 +-
 ...7日-五月份计划.md => 2021年5月1日-五月份计划.md} |   5 +-
 17 files changed, 545 insertions(+), 57 deletions(-)
 create mode 100644 Tensorflow/TensorFlow2.0/12 联邦学习-文本生成.ipynb
 create mode 100644 Tensorflow/TensorFlow2.0/2021-05-06 20-44-37屏幕截图.png
 rename 工作日志/{2021年4月17日-五月份计划.md => 2021年5月1日-五月份计划.md} (76%)

diff --git a/Tensorflow/TensorFlow2.0/11 联邦学习.ipynb b/Tensorflow/TensorFlow2.0/11 联邦学习.ipynb
index 3040fc5f..69f42296 100644
--- a/Tensorflow/TensorFlow2.0/11 联邦学习.ipynb	
+++ b/Tensorflow/TensorFlow2.0/11 联邦学习.ipynb	
@@ -10,12 +10,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.6.9"
   },
   "orig_nbformat": 2,
   "kernelspec": {
-   "name": "python388jvsc74a57bd0238245627cfcfafd0885ed67ad6caa77217e3abcf58754ab647d5e61878a8b28",
-   "display_name": "Python 3.8.8 64-bit ('tensorflow': conda)"
+   "name": "python369jvsc74a57bd04b23302ac1afbd304ec4e8c0fc43b716576fbc7dc9e2bf77d7de3e404cbbffbc",
+   "display_name": "Python 3.6.9 64-bit ('tensorflow')"
   }
  },
  "nbformat": 4,
@@ -26,7 +26,16 @@
     "# 开始\n",
     "\n",
     "> tensorflow的学习成本有点高。而且。现在好像更多的人pytorch在做训练。改成pytorch吧。改天回来复习这一块，试一试能不能用上。\n",
-    "> 我发现我这种菜鸡，无论是什么框架都搞不出来。这几天，先把这个搞定。然后按着计划复习。"
+    "> 我发现我这种菜鸡，无论是什么框架都搞不出来。这几天，先把这个搞定。然后按着计划复习。\n",
+    "\n",
+    "## 实现过程\n",
+    "\n",
+    "1. 通过tff.simulation.datasets加载tff.simulation.ClientData类型的数据集。\n",
+    "2. 并使用tf.data.dataset处理数据集。\n",
+    "3.1. 通过tf.keras.models和tff.learning.Model构造联邦学习模型。\n",
+    "3.2. 通过tff.learning.build_federated_averaging_process实现联邦聚合算法。通过tf.keras实现前向传播算法。\n",
+    "3.3. 通过tf.kerase.loss实现损失计算方法\n",
+    "3.4. 通过tf.kerase.optimizers.SGD实现优化算法"
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -36,6 +45,18 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "WARNING:tensorflow:From /home/ykl/.virtualenvs/tensorflow/lib/python3.6/site-packages/tensorflow_federated/python/core/impl/compiler/tensorflow_computation_transformations.py:59: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
+      "WARNING:tensorflow:From /home/ykl/.virtualenvs/tensorflow/lib/python3.6/site-packages/tensorflow_federated/python/core/impl/compiler/tensorflow_computation_transformations.py:59: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use `tf.compat.v1.graph_util.extract_sub_graph`\n"
+     ]
+    },
     {
      "output_type": "execute_result",
      "data": {
@@ -63,11 +84,29 @@
     "tff.federated_computation(lambda: 'Hello, World!')()"
    ]
   },
+  {
+   "source": [
+    "## 1 加载数据\n",
+    "\n",
+    "× 通过tff.simulation.datasets加载实验数据。返回多个tff.simulation.ClientData实例。"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Downloading data from https://storage.googleapis.com/tff-datasets-public/fed_emnist_digitsonly.tar.bz2\n",
+      "97402880/97398400 [==============================] - 10s 0us/step\n"
+     ]
+    }
+   ],
    "source": [
     "# 1 加载数据\n",
     "emnist_train, emnist_test = tff.simulation.datasets.emnist.load_data()"
@@ -75,16 +114,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "3383\n",
-      "OrderedDict([('label', TensorSpec(shape=(), dtype=tf.int32, name=None)), ('pixels', TensorSpec(shape=(28, 28), dtype=tf.float32, name=None))])\n",
-      "5\n"
+      "3383\nOrderedDict([('label', TensorSpec(shape=(), dtype=tf.int32, name=None)), ('pixels', TensorSpec(shape=(28, 28), dtype=tf.float32, name=None))])\nodict_keys(['label', 'pixels'])\n5\ntf.Tensor(\n[[1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         0.99607843\n  0.99215686 0.99607843 1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         0.9882353  0.9882353  1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         0.99215686 1.         1.         1.         1.\n  0.8117647  0.91764706 1.         0.99607843 1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  0.9882353  1.         1.         0.7176471  0.3137255  0.14117648\n  0.02352941 0.47058824 1.         0.9882353  1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         0.9882353\n  1.         0.7647059  0.16078432 0.11372549 0.33333334 0.5803922\n  0.88235295 0.98039216 0.99607843 1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         0.99607843 1.         1.\n  0.6901961  0.2627451  0.64705884 0.9764706  1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         0.99607843 0.98039216 0.99607843 0.38039216\n  0.29803923 1.         1.         1.         0.9843137  0.9882353\n  0.99215686 1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         0.18431373\n  0.25882354 0.92941177 0.98039216 0.99215686 1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  0.99607843 0.99607843 0.9137255  0.5019608  0.92156863 1.\n  0.08627451 0.3882353  1.         0.9843137  1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  0.9882353  1.         0.42745098 0.01176471 0.5529412  0.56078434\n  0.05882353 0.7294118  0.99607843 0.9882353  1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  0.99607843 1.         0.92156863 0.48235294 0.23529412 0.23529412\n  0.6901961  1.         0.99607843 1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         0.99607843 1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         0.99607843 0.9843137  0.9882353  0.9882353\n  0.9882353  1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]\n [1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.         1.         1.\n  1.         1.         1.         1.        ]], shape=(28, 28), dtype=float32)\n"
      ]
     }
    ],
@@ -99,29 +136,30 @@
     "\n",
     "example_element = next(iter(example_dataset))\n",
     "\n",
-    "print(example_element['label'].numpy())"
+    "print(example_element.keys())\n",
+    "print(example_element['label'].numpy())\n",
+    "print(example_element['pixels'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "error",
-     "ename": "ImportError",
-     "evalue": "DLL load failed while importing ft2font: 找不到指定的模块。",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-6-b911da18303b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexample_element\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'pixels'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'gray'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maspect\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'equal'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mD:\\anaconda\\envs\\tensorflow\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m    172\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    173\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 174\u001b[1;33m \u001b[0m_check_versions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    175\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    176\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mD:\\anaconda\\envs\\tensorflow\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36m_check_versions\u001b[1;34m()\u001b[0m\n\u001b[0;32m    157\u001b[0m     \u001b[1;31m# Quickfix to ensure Microsoft Visual C++ redistributable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m     \u001b[1;31m# DLLs are loaded before importing kiwisolver\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 159\u001b[1;33m     \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mft2font\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    160\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    161\u001b[0m     for modname, minver in [\n",
-      "\u001b[1;31mImportError\u001b[0m: DLL load failed while importing ft2font: 找不到指定的模块。"
-     ]
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
     }
    ],
    "source": [
+    "# 1 查看数据几中的数据。\n",
     "from matplotlib import pyplot as plt\n",
     "plt.imshow(example_element['pixels'].numpy(), cmap='gray', aspect='equal')\n",
     "plt.grid(False)\n",
@@ -130,27 +168,474 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "error",
-     "ename": "ImportError",
-     "evalue": "DLL load failed while importing ft2font: 找不到指定的模块。",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-5-a0d2faabd9e9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32mD:\\anaconda\\envs\\tensorflow\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m    172\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    173\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 174\u001b[1;33m \u001b[0m_check_versions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    175\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    176\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mD:\\anaconda\\envs\\tensorflow\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36m_check_versions\u001b[1;34m()\u001b[0m\n\u001b[0;32m    157\u001b[0m     \u001b[1;31m# Quickfix to ensure Microsoft Visual C++ redistributable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m     \u001b[1;31m# DLLs are loaded before importing kiwisolver\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 159\u001b[1;33m     \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mft2font\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    160\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    161\u001b[0m     for modname, minver in [\n",
-      "\u001b[1;31mImportError\u001b[0m: DLL load failed while importing ft2font: 找不到指定的模块。"
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 1440x288 with 40 Axes>",
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "# 1 探索数据集中的异构性。\n",
+    "# 从某一个用户的数据集中取出40条数据，验证数据内容。\n",
+    "figure = plt.figure(figsize=(20, 4))\n",
+    "j = 0\n",
+    "\n",
+    "for example in example_dataset.take(40):\n",
+    "  plt.subplot(4, 10, j+1)\n",
+    "  plt.imshow(example['pixels'].numpy(), cmap='gray', aspect='equal')\n",
+    "  plt.axis('off')\n",
+    "  j += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 864x504 with 6 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"461.998125pt\" version=\"1.1\" viewBox=\"0 0 703.725 461.998125\" width=\"703.725pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-05-06T19:50:57.939518</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.3.4, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "# 1 探索数据集中的异构行\n",
+    "# 每个客户端上数字标签的示例数量，可能不同。取决于用户的行为（不平衡的）\n",
+    "# Number of examples per layer for a sample of clients\n",
+    "f = plt.figure(figsize=(12, 7))\n",
+    "f.suptitle('Label Counts for a Sample of Clients')\n",
+    "for i in range(6):\n",
+    "  client_dataset = emnist_train.create_tf_dataset_for_client(\n",
+    "      emnist_train.client_ids[i])\n",
+    "  plot_data = collections.defaultdict(list)\n",
+    "  for example in client_dataset:\n",
+    "    # Append counts individually per label to make plots\n",
+    "    # more colorful instead of one color per plot.\n",
+    "    label = example['label'].numpy()\n",
+    "    plot_data[label].append(label)\n",
+    "  plt.subplot(2, 3, i+1)\n",
+    "  plt.title('Client {}'.format(i))\n",
+    "  for j in range(10):\n",
+    "    plt.hist(\n",
+    "        plot_data[j],\n",
+    "        density=False,\n",
+    "        bins=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 864x360 with 10 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"318.151411pt\" version=\"1.1\" viewBox=\"0 0 684 318.151411\" width=\"684pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-05-06T19:58:14.870869</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.3.4, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style 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w/scjbw3WZyeAL3PJCwcgbtPLlyVumzl6lcmp4Hf92zbZPsRUgz2ohG2Hh/ZaZHKuXn3EqaP3bWFzo8mfmvUVkwe9bB9Ttcj2GtZ8wn4sX/qZB0zun0peLOedFnvbuFKOO7tS+L6ngh7iv9dXmDzygWSv6IozbM/p3qWdT+r/jWc+bfLYDfbxXzzobyZ3dP5EuJ+Fhm9YAQAAEDUKVgAAAESNghUAAABRi66HNewhDF2w5BSTR/z0+cQyC27Z1+TF024zudF33ity8IClJv+tZHjHC7YztLCmM0NW2Iu4wLYFaeQv5pv8xk3jTA7nYWz0wQpQcIaUlCduC+dVrPNNJt+0YLrJfRcGPawnrTd5So+aYAsVCjHvanxyzdssSRXOfoa12rZ2VS6rs+t45XWT3ZGHmfyxCtvv3NH4zPW5iV0rHAffWPApkxsnJUutPYbZvvhL37Pve1W6weSxv7KPf+8j9jyZumCgrQzO95Gk6g7mDS4kHBEBAAAQNQpWAAAARI2CFQAAAFGLroc1VNtm57R7c+4eJo+b3DfxmBunzzK5Pug3C/sW64K+w3uesL0kQ+zDddjAJYltVqSY9y52HfV5hddSPvu5z5g8vmK1yT895O5O15nmGt4Fr6O5Cmva7EHg4qUnmdznPtsbtuZ4239208QH7fKpzvvoEaewr7ijn/bqtO1Hfvn0n5q8+lQ7Z+qlS041eeTFa00+5fDPmfzitN8mtllo14Qvdn3K7c//po3JXufla/qbvGJtP5N7zell8uDZdq73ivGHmnzX4gNN/ty0FxPbDHuwC61PvrD2FgAAAN0OBSsAAACiRsEKAACAqEXfw9oQXqs5aC/bPKF34jF3r7XXf5828lGTm7ztIZr+5KUmT7zxPZMXXjnA5Mv6J+d+TcvOiVZovSHdVThvavWNthds6WfsvKtHlf/J5LZgQJY6epkLzVvNtSZ/+50ZiWXe+M1Ek4c8YedQbDrSjoNeVbaH7cCyDcEa7TipaUvOmTiwpFfiNnSt2fX2uP7P+jGJZWZWvWLyyLSdN7U6bd/7n439g8lnfPirJje/YMdW/YHBSRXqqIeVntZdKVcv860T7jL5P2aelljH5pdHmFy60a5z6Bw7V3PdCbauKZtpz6/45US7zcFFePygqgIAAEDUKFgBAAAQNQpWAAAARC26HtZwDsSqlN3F/h+yc9S1zh+YWMeiGyabfEy/fUwe+KrtF9vr9eUmv37taJOfPvwGk3unuO53IQjnSO1ofs0vvfcRk3ssXmnypKtsv3OZs+Oxo2uJI251wZyqX19+sskL/jIh8ZhRt9o5EBs/PtXkhv52bA273vYtHjve9iXWTLLjZq/97TFISvY2jgt6ITnm7HzhPJWhRzfvZ/K8b05LLDNrr2NMnn7WP00+rGqxyf8559MmT5pt+6OXXGXn+G3t4JizusXOVz6ulF76rjSh1PaP/nXSnxLL1O1lj0PfXXOwyXPesnnDSXUmL5hyb7DG4p/bmSMeAAAAokbBCgAAgKhRsAIAACBq0fWwhn1Zlc7Ob3rb5Fkmn3WKvfa7JLU9a6/J23Od7flZdpxd54yf2fnOfjvY9puEPav0jhWmxmD+XUl6bLGdX9NdYt/rLw14yOSwZ5VreBe+pragL7ks2SO47Pv2ut2pyVtsTtl5V98ab/tNe79qe1zL19g8qbftW5Sk0Wnbk8ZxZ9cLX+Owp/X0vvNMvvfoQxLrGP87OzYWPzzS5vW2J3Vi4wKTF/5oisl/PfhakyucHVuSVB3dJznaC8+nkKRNQS/9Y8uDz6IBdiyeMH6+yS0Kz9HofG7YYsAREAAAAFGjYAUAAEDUKFgBAAAQNQpWAAAARM15v+2Jz9tW7Rn9rOg1rXWJ25a12HbjBm870oen7YUDRpQkLwTQXrGc7JAauig5c/5OUghj5bnG5sRtj2+xF5WYXvm6yWPTdnwNS9sTJorVrhorMY6Tda3vm7ykJTkB94CUnZh9WIldJjz5Lsyb2uwxp1SdL7+t22LT3Y8pbza/n7jtsrdONfmNt4abXN7PjoXLJv/d5PP72ItIFMI4yEd3OqbkugCFJP2+dpDJ35rzSZOnjH/H5JvH2AsFDCspzpPBOxsnxfEMAQAAULQoWAEAABA1ClYAAABEreB7WPPpFQknew+lZFsmiqUXJNTd+81q2xoSt7UGY6PSlZlcrGMhl+7Ub5aPcOLvXH2F4XEpPAaFx5yOFMLY6+7HlI50NEl8e9s7dgphHOSDY4p1T20fk+va7GfP9PIlJo8p5fyJ4vhJAAAAQNGiYAUAAEDUKFgBAAAQtXTuReKWT39Pccxihw+qMtWzq3cBBWp758IMj0scg7qPsD857F8Oe1y7yzkUsD7Za4PJ4TGm1Xc+P3x3xE8GAAAAokbBCgAAgKhRsAIAACBqBd/DCgBALOhfRj6SPavFOf/uzsQrAgAAgKhRsAIAACBqFKwAAACIGj2sAAAAXYie1dx4hQAAABA1ClYAAABEjYIVAAAAUaNgBQAAQNQoWAEAABA1ClYAAABEjYIVAAAAUaNgBQAAQNQoWAEAABA1ClYAAABEjYIVAAAAUaNgBQAAQNQoWAEAABA1ClYAAABEjYIVAAAAUaNgBQAAQNQoWAEAABA1ClYAAABEjYIVAAAAUaNgBQAAQNQoWAEAABA1ClYAAABEjYIVAAAAUaNgBQAAQNSc976r9wEAAADYJr5hBQAAQNQoWAEAABA1ClYAAABEjYIVAAAAUaNgBQAAQNQoWAEAABA1ClYAAABEjYIVAAAAUaNgBQAAQNQoWAEAABA1ClYAAABEjYIVAAAAUaNgBQAAQNQoWAEAABA1ClYAAABEjYIVAAAAUaNgBQAAQNQoWAEAABA1ClYAAABEjYIVAAAAUaNgBQAAQNQoWAEAABA1ClYAAABEjYIVAAAAUaNgBQAAQNQoWAEAABA1ClYAAABEjYIVAAAAUaNgBQAAQNQoWAEAABA1ClYAAABELd3ZnW2r9vS7a0ew66WGLnK7at2MleKyq8YK46S4cExBvjimIB+djRO+YQUAAEDUKFgBAAAQNQpWAAAARI2CFQAAAFGjYAUAAEDUKFgBAAAQNQpWAAAARI2CFQAAAFGjYAUAAEDUKFgBAAAQNQpWAAAARI2CFQAAAFGjYAUAAEDUKFgBAAAQNQpWAAAARI2CFQAAAFGjYAUAAEDUKFgBAAAQNQpWAAAARI2CFQAAAFFLd/UOAAAAIH+tvs3kElf83z8W/zMEAABAQaNgBQAAQNQoWAEAABA1eljzEPaK7Iju0F/SHeQaC7zP3VM4LtrkO10+JWcy46b7avatnd7PWOkedkadUewY+QAAAIgaBSsAAACiRsEKAACAqHXLHtZc/WZhz1A+6CvqHpa31Jm8urXc5CEltSb3LylJrKNPqjxxG+LRUS/Z9h4jSl3yfUf3FPaohmNne8dKR+OTz5/Ck6tnNfysebFxuMmvN9h8eOVCkz9cVnzjpLD3HgAAAEWPghUAAABRo2AFAABA1LpFD2uua+7WtzWYHPYUPVlfYfJ1y49ObGPJmgEmD+prexn7lNltnDl8ns1V67drn7FrhK/7+rZ6k4+bd5HJTQ2lJh8/6TWTfzhsdmIbuXraeK+7Vkevf1vwnm0OjhlPNgw2efbmiSa/U9fX5CMGvGHyub0XJbZZmeqZc18Rv/Dne2Fzo8k/W32UyfNWVpt83OjXTf7moGcS26hUmckcQ+LSUb9qTfDZcuWa6SY/9PRUk3svsnVJqtn21f9m9BEmP3rGjxPbHFdamXtnI8aoBgAAQNQoWAEAABA1ClYAAABErSh7WGta7fxlK1ptXX7L2o+Z/Jd5HzK5/F3bKzL4xSaT03XJaz+PrbXLrDtgiMmNp79r8qhSelZjkOs63ie+eq7JI27pYfKaA2zv2F5TV5lc6ez9ktToW0wOe6aZwXP3avTNJn9nzbTEMn+Ye7DJ/V+0P599l9i+xPQWezxwLfbn+w8jxphc+pPkOLywz3smc4yIT0e9iWHf+yde+JzJg6+1vcnpTXb5gUN7mfzolMNMXjmzd2KbV474s8nV6cLuVSw2S4M5VSXpnNfPMbn+QVszDN5se1RrJtvc2tPm9Pu2V/q1JttXL0lDSjaYXObsORixzx/NEQ8AAABRo2AFAABA1ChYAQAAELWC72GtDeZDlKSvvfcJk5/5o+1Rrb7P9hnuucjOiZoeNdLkjYeMMHnpp5LXEZ8ycaXJ3xr+oMlHl9vekYqU7YXkd4ddL9e1myXpiWDO3Zb7B5ncXGV7Dff/9HyTL+77ds5tJN977E7hOHivxfafPvinQxOPGT3X9h039LfHgLdn2Pd06OSNJq/bZHsKx3/Z9rTPf98eYyRJQQ8rul44dsK5NCXp8Ge+YPIe19hewyUzbQ/rRz9m5+B9avYUk3u/bR8/f+2wxDYHjrLjj37n3St8vVtkPycuXXJq4jHNv7c9q/V72GPKkRc8a/IFA+aYfO4C2wPb9Efbs7q4cWhimz2c3a8jymuDJehhBQAAAHYYBSsAAACiRsEKAACAqFGwAgAAIGoFd9JVXZudkPucJTMSyyyYPd7kXhts0/qbX7DNyUd+1J4wdeGg+00em7YnXPRO2ab5jiSb3DnRpqu1ySduW91qT5q4aPbnTZ40256gV/Pz4CIUox4PtmGb1mOfiLk7Cn82h6ftxR0un/lQ4jH/Om6UyecPfMrkSXb+bZU5e2id8eYJJvsGe7Lo8X3/ldgmJ87Eb35TVeK21Kv2tkVn2PfxtyffaPL3lp5o8tB5dvmVh9n3/df7/C6xzXC8MVZ2r/Cz5dG6fiYvnG+PH5LUPzhmXH3mHSaf2MtebOCpBntBiV7X9DG5T609gapPSfJiBZN72AsWpVWRWCZmjGoAAABEjYIVAAAAUaNgBQAAQNSi62HN1bd1y8aJJr+y3E7yL0kjDrZ9h++O6Wvybz58u8kf7mm30ertyxL2JeaD/rOu1+ztJMltSl444PxFp5k86Wp7gYfFn7WTL8/Z5yfBGmwjEj2rhafM2ffw36sWJZY5v8/y4Bb7Podjrd7bXvvGK+1k7+vOtn2zR5b/NbHNlmC8lvD9QkFo7G/ft7Yqew7E6Y/bCwvs+Ss7VtYea8fWXTOvN3lqD44xXS38fE/JTvr/brPtYS1bm3zPakfZvtdlTfYiNeevt32vL87a1+TBf59r8pKr7AVPZlS+ldhmv1S5yYVWlxTW3gIAAKDboWAFAABA1ChYAQAAELXoeljDnoqwV2R4aY1dPm17xyRpY72dJ7XkHZvPmXu+yVcc8JjJJ1S+YfLIdGUne4xCMa+xNHFb/c+Hm9x8oB1/957xU5MHl9i58MLxSe9y4ctnnuVGb/sSw3kwj1rwSZN7PPGCyV+7eYXJYQ9c5jZ6FWM3tnRz4rYRk1ebXHZlX5N7LF1r8uIv2F7FP5x5ncl7l+aew5vjzO4Vvt6NvtnkQ8pt/+h1fZPnT/RcZ9cx6+rj7TYabY/roOXv2/v3svPNH3v08yb3SRXf3O+McgAAAESNghUAAABRo2AFAABA1KLrYQ2F1+g9rcr2sA6a9tvEY36xarrJi0psn2vtvwaYfOfAg01uGGV7HT9a8abJU8uKrzekGIRzYYZ9gec+aXuXJWnySytt/uM7Jrd5u44fbxhn8pSedvkjyu31nJk7s/B01A8Y9iaH8+2+2mR72Co+Y3tcFwVzJJ5Z9bLJjT7Zix/OD4uuF34eVXdwfsOKJXY+zQn/eM7e/5XDTP75v//C5EEpO3Yk+3lDv2p8wp/V/XrY48Wlx/wl8Zi/rbNzym+orzC5skejyTW/qja5qcqeT3HVAFsLpToo7wp9nnBGPgAAAKJGwQoAAICoUbACAAAgatH1sNa2NZj8pXePNPnsQU+bfECPLYl1nD3EXmP3+cqxJs+tsHnxKyNNnjje9jW+1mjn6tynh71fot8sBmHP6stNthds9D3J38+Wn2rnQFz6dm+TXz1/ssn+pddMfuS4c0y+4kI7V96cabcntlnp7HXk6UmLX4tsj+nqVttfdso9V5i8Z8Uak//3zB+b3OztNb3TzLkapbAvPuwBPHHRsYnHTPrWYpNXfNX2rIZfE73SYHsTR6U3mTyYoVFwwmP6zKr5iWX2LFtlcqu3j3l8094mL1w80OR1/2lrpf3K7OfKulb7WSRJS1psP/TUHnZwxf5ZFPfeAQAAoNujYAUAAEDUKFgBAAAQteh6WJuD+Q6feH4fm73NfUbZfh9JunXfO03un7a9HC1Br0jFezYvbbK9In1L6kxu9XYuPknq4FLg2MXCuTFD/7tlismlW5oTy7g223s86kd2LKw7wM6zuP7zB5k84nH7xpf9uY/Jz++bnKfx8J5NJrfl6JPD7tXRuFob9Kz+2wsXmjzhxhUmD7jHXmO+Om3nWAzn84y9dwwZa4K+wM1Xj0oss/bz9pjy5bPvN/mX3znJ5DsX22PKhQfafsfEWMlvVxGRkR3M1zukxI6l1a31Jl/+tD0/YrCdAlxXTf69yWG/dVUqOV/8qBJ7HCtxyf2KGUdJAAAARI2CFQAAAFGjYAUAAEDUuryHNewX61die71UGVyje0FPkzd52zMoSactv8Rk38v2dox82HYBtdlL+mplc1+TP1ph59WrSNk5FBGnkT3Wm7xldM/kMr9+w+S2MXbO3dpjak0uW2h7fipW2r6j96bbufD2Kk32WLfJLpPi98aoNPrwWu7SV1ecaHLV7+18vW9cZo9Db1TfZHI4jyvzNheGcG7n9a021w1KfoSW1die0x/+83iTq2vtZ97kQatN7hN8voS9iSg8uc63kKQHayeZPPJv9jErjrfj6ohy+9kUfv/Y0TFmWLqwjzt8UgIAACBqFKwAAACIGgUrAAAAokbBCgAAgKh1+UlXuSbMfvqIG0w+ovwik8vmVyUeU7HKNidXrbDNyxsm2ZOups6wEzWf03eeyeNKC2ty3e4iHDthY/vHK5aafPOZ6xLrqKsZa3LFWzUm73G1PUFKKdvo/uZ59iTBW475pclDSpIn6HFhgLjl8/5UXfiOybeMuzdYwk7azUlWxWF02n5kfujifyWWefmWfU0e+IQ9hqw8z04YP2vUQya3entM4XhRnMIT+u5ecaDJm6vtWDv1oLkmp7vhJST4hhUAAABRo2AFAABA1ChYAQAAEDXnvd/mnW2r9tz2nV1kU5udqH1OQ7/EMksah5jcP237Dg/ruczkMd2kRzU1dJHLvdSOiXGshD2ta1rrEsvcvWUfkx9ZNcXk2ibbizht0HKTzxpg+4qm9rB9Rbl6tGO1q8ZKjOMkH+FYKtT3dWfrbseUUEcTwofHmQ1t9pgwodQeU7pLjyrHFCscO99d+6FOl7+w/zMmV6eLs27pbJxw1AUAAEDUKFgBAAAQNQpWAAAARK3geljz6SXrqK9oexRrf1p37zfrSDhW6n2TyVvaWkwudfYl7Jey86wWy9ih36xz9LRmdPdjygf9rJG6z9jhmNK5cCzVBOfrFOtnTYgeVgAAABQsClYAAABEjYIVAAAAUUvnXiQuua4fvyPrALaqTPU0udzRq4gkxgEkxgF2nnAsDSzp1UV7Ei9+2gAAABA1ClYAAABEjYIVAAAAUSu4HtYQPUT4IHKNH8YXAABdj09jAAAARI2CFQAAAFGjYAUAAEDUKFgBAAAQNQpWAAAARI2CFQAAAFGjYAUAAEDUKFgBAAAQNQpWAAAARI2CFQAAAFGjYAUAAEDUKFgBAAAQNQpWAAAARI2CFQAAAFGjYAUAAEDUKFgBAAAQNQpWAAAARI2CFQAAAFGjYAUAAEDUKFgBAAAQNQpWAAAARI2CFQAAAFGjYAUAAEDUKFgBAAAQNee97+p9AAAAALaJb1gBAAAQNQpWAAAARI2CFQAAAFGjYAUAAEDUKFgBAAAQNQpWAAAARO3/Ad8vsjPu9BGjAAAAAElFTkSuQmCC\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "# 1 查看数据的异构行。\n",
+    "# 用来显示每个用户的平局图像\n",
+    "# Each client has different mean images, meaning each client will be nudging\n",
+    "# the model in their own directions locally.\n",
+    "\n",
+    "for i in range(5):\n",
+    "  client_dataset = emnist_train.create_tf_dataset_for_client(\n",
+    "      emnist_train.client_ids[i])\n",
+    "  plot_data = collections.defaultdict(list)\n",
+    "  for example in client_dataset:\n",
+    "    plot_data[example['label'].numpy()].append(example['pixels'].numpy())\n",
+    "  f = plt.figure(i, figsize=(12, 5))\n",
+    "  f.suptitle(\"Client #{}'s Mean Image Per Label\".format(i))\n",
+    "  for j in range(10):\n",
+    "    mean_img = np.mean(plot_data[j], 0)\n",
+    "    plt.subplot(2, 5, j+1)\n",
+    "    plt.imshow(mean_img.reshape((28, 28)))\n",
+    "    plt.axis('off')"
+   ]
+  },
+  {
+   "source": [
+    "# 2 数据处理\n",
+    "\n",
+    "主要使用tf.dataset方法，将原始数据转换为list，方便输入和训练。"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 2 对数据进行预处理\n",
+    "# 将数据分成多个不同的批次(某一个客户端的数据)\n",
+    "\n",
+    "NUM_CLIENTS = 10\n",
+    "NUM_EPOCHS = 5\n",
+    "BATCH_SIZE = 20\n",
+    "SHUFFLE_BUFFER = 100\n",
+    "PREFETCH_BUFFER = 10\n",
+    "\n",
+    "def preprocess(dataset):\n",
+    "\n",
+    "  def batch_format_fn(element):\n",
+    "    \"\"\"Flatten a batch `pixels` and return the features as an `OrderedDict`.\"\"\"\n",
+    "    return collections.OrderedDict(\n",
+    "        x=tf.reshape(element['pixels'], [-1, 784]),\n",
+    "        y=tf.reshape(element['label'], [-1, 1]))\n",
+    "\n",
+    "  return dataset.repeat(NUM_EPOCHS).shuffle(SHUFFLE_BUFFER).batch(BATCH_SIZE).map(batch_format_fn).prefetch(PREFETCH_BUFFER)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "OrderedDict([('x',\n",
+       "              array([[1., 1., 1., ..., 1., 1., 1.],\n",
+       "                     [1., 1., 1., ..., 1., 1., 1.],\n",
+       "                     [1., 1., 1., ..., 1., 1., 1.],\n",
+       "                     ...,\n",
+       "                     [1., 1., 1., ..., 1., 1., 1.],\n",
+       "                     [1., 1., 1., ..., 1., 1., 1.],\n",
+       "                     [1., 1., 1., ..., 1., 1., 1.]], dtype=float32)),\n",
+       "             ('y',\n",
+       "              array([[7],\n",
+       "                     [2],\n",
+       "                     [8],\n",
+       "                     [6],\n",
+       "                     [6],\n",
+       "                     [3],\n",
+       "                     [1],\n",
+       "                     [7],\n",
+       "                     [1],\n",
+       "                     [8],\n",
+       "                     [3],\n",
+       "                     [7],\n",
+       "                     [4],\n",
+       "                     [3],\n",
+       "                     [6],\n",
+       "                     [3],\n",
+       "                     [0],\n",
+       "                     [3],\n",
+       "                     [1],\n",
+       "                     [5]], dtype=int32))])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "# 2 检验数据处理的结果\n",
+    "preprocessed_example_dataset = preprocess(example_dataset)\n",
+    "\n",
+    "sample_batch = tf.nest.map_structure(lambda x: x.numpy(),\n",
+    "                                     next(iter(preprocessed_example_dataset)))\n",
+    "\n",
+    "sample_batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 2 通过simulation dataset构建训练数据的列表.\n",
+    "def make_federated_data(client_data, client_ids):\n",
+    "  return [\n",
+    "      preprocess(client_data.create_tf_dataset_for_client(x))\n",
+    "      for x in client_ids\n",
+    "  ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Number of client datasets: 10\nFirst dataset: <PrefetchDataset shapes: OrderedDict([(x, (None, 784)), (y, (None, 1))]), types: OrderedDict([(x, tf.float32), (y, tf.int32)])>\n"
      ]
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt"
+    "# 2 实现客户端的选择\n",
+    "sample_clients = emnist_train.client_ids[0:NUM_CLIENTS]\n",
+    "\n",
+    "federated_train_data = make_federated_data(emnist_train, sample_clients)\n",
+    "\n",
+    "print('Number of client datasets: {l}'.format(l=len(federated_train_data)))\n",
+    "print('First dataset: {d}'.format(d=federated_train_data[0]))"
    ]
   },
+  {
+   "source": [
+    "# 3 模型训练\n",
+    "1. 构建了keras模型，转换为tff模型。\n",
+    "2. 进行正向传播，实现联邦聚合算法。\n",
+    "3. 定义损失函数。\n",
+    "4. 定义优化方法。\n",
+    "5. 进行反向传播。"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 3.1 使用keras创建模型\n",
+    "def create_keras_model():\n",
+    "  return tf.keras.models.Sequential([\n",
+    "      tf.keras.layers.InputLayer(input_shape=(784,)),\n",
+    "      tf.keras.layers.Dense(10, kernel_initializer='zeros'),\n",
+    "      tf.keras.layers.Softmax(),\n",
+    "  ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 3.1 通过keras模型转换为tff模型\n",
+    "def model_fn():\n",
+    "  # We _must_ create a new model here, and _not_ capture it from an external\n",
+    "  # scope. TFF will call this within different graph contexts.\n",
+    "  keras_model = create_keras_model()\n",
+    "  return tff.learning.from_keras_model(\n",
+    "      keras_model,\n",
+    "      input_spec=preprocessed_example_dataset.element_spec,\n",
+    "      loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
+    "      metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 3.2-3.3-3.4定义前像传播过程、定义损失计算方法、定义优化方法\n",
+    "iterative_process = tff.learning.build_federated_averaging_process(\n",
+    "    model_fn,\n",
+    "    client_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=0.02),\n",
+    "    server_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=1.0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "( -> <model=<trainable=<float32[784,10],float32[10]>,non_trainable=<>>,optimizer_state=<int64>,delta_aggregate_state=<value_sum_process=<>,weight_sum_process=<>>,model_broadcast_state=<>>@SERVER)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 3 查看联邦计算过程\n",
+    "print(str(iterative_process.initialize.type_signature))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 3 服务器初始化状态\n",
+    "\n",
+    "state = iterative_process.initialize()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "round  1, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.119135804), ('loss', 3.0766096)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 3 客户端进行单轮训练\n",
+    "state, metrics = iterative_process.next(state, federated_train_data)\n",
+    "print('round  1, metrics={}'.format(metrics))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "round  2, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.35864198), ('loss', 1.9986831)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round  3, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.38539094), ('loss', 1.8443733)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round  4, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.43024692), ('loss', 1.7397194)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round  5, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.3962963), ('loss', 1.8008647)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round  6, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.44547325), ('loss', 1.675535)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round  7, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.48209876), ('loss', 1.6074551)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round  8, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.4973251), ('loss', 1.5451111)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round  9, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.5067901), ('loss', 1.5109721)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 10, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.5388889), ('loss', 1.4563706)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 11, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.5617284), ('loss', 1.3862591)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 12, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.58312756), ('loss', 1.3402764)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 13, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.58312756), ('loss', 1.3353041)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 14, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.6238683), ('loss', 1.253434)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 15, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.63580245), ('loss', 1.2053648)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 16, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.6393004), ('loss', 1.207883)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 17, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.6674897), ('loss', 1.1293923)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 18, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.6895062), ('loss', 1.0926687)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 19, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.677572), ('loss', 1.0902609)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 20, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.70308644), ('loss', 1.0715235)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 21, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.6995885), ('loss', 1.0483558)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 22, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.7074074), ('loss', 1.0288506)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 23, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.7308642), ('loss', 0.99303496)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 24, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.73209876), ('loss', 0.98611385)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 25, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.75226337), ('loss', 0.930803)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 26, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.7547325), ('loss', 0.91434574)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 27, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.74053496), ('loss', 0.93025583)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 28, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.75781894), ('loss', 0.9082405)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n",
+      "round 29, metrics=OrderedDict([('broadcast', ()), ('aggregation', OrderedDict([('mean_value', ()), ('mean_weight', ())])), ('train', OrderedDict([('sparse_categorical_accuracy', 0.7660494), ('loss', 0.87998044)])), ('stat', OrderedDict([('num_examples', 4860)]))])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 3 客户端进行多伦训练\n",
+    "NUM_ROUNDS = 30\n",
+    "for round_num in range(2, NUM_ROUNDS):\n",
+    "  state, metrics = iterative_process.next(state, federated_train_data)\n",
+    "  print('round {:2d}, metrics={}'.format(round_num, metrics))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 4 使用tensorboard进行可视化，并对过程进行评估\n",
+    "logdir = \"/tmp/logs/scalars/training/\"\n",
+    "summary_writer = tf.summary.create_file_writer(logdir)\n",
+    "state = iterative_process.initialize()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 4 绘制相关的标量，包括accurate和loss\n",
+    "with summary_writer.as_default():\n",
+    "  for round_num in range(1, NUM_ROUNDS):\n",
+    "    state, metrics = iterative_process.next(state, federated_train_data)\n",
+    "    for name, value in metrics['train'].items():\n",
+    "      tf.summary.scalar(name, value, step=round_num)"
+   ]
+  },
+  {
+   "source": [
+    "!ls {logdir}\n",
+    "\n",
+    "%tensorboard --logdir {logdir} --port=0\n",
+    "\n",
+    "![tensorboard过程](2021-05-06 20-44-37屏幕截图.png)"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/Tensorflow/TensorFlow2.0/12 联邦学习-文本生成.ipynb b/Tensorflow/TensorFlow2.0/12 联邦学习-文本生成.ipynb
new file mode 100644
index 00000000..e69de29b
diff --git a/Tensorflow/TensorFlow2.0/2021-05-06 20-44-37屏幕截图.png b/Tensorflow/TensorFlow2.0/2021-05-06 20-44-37屏幕截图.png
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diff --git a/工作日志/2021年3月18日-今日计划.md b/工作日志/2021年3月18日-今日计划.md
index ab932140..37ae7f30 100644
--- a/工作日志/2021年3月18日-今日计划.md
+++ b/工作日志/2021年3月18日-今日计划.md
@@ -3,7 +3,7 @@
 > 完成昨天的任务。
 
 - [x] 学习、复习图算法，动手实现所有的图算法。 
-- [ ] ~~看完数据结构与算法的三本书！！！对相关的原理进行复习和总结。~~
+- [x] ~~看完数据结构与算法的三本书！！！对相关的原理进行复习和总结。~~
 - [x] 学习机器学习的实现方案。毕设计划真正的开始执行。
 - [x] 关于字符串分割。字符串格式化方法的总结。转换成流，作为流对象处理。转换为容器。作为容器对象处理，使用泛型算法。
 
diff --git a/工作日志/2021年3月19日-四月份计划.md b/工作日志/2021年3月19日-四月份计划.md
index 692854f0..220e80d3 100644
--- a/工作日志/2021年3月19日-四月份计划.md
+++ b/工作日志/2021年3月19日-四月份计划.md
@@ -29,8 +29,8 @@
 - tensorflow
   - [x] 学习 TensorFlow2.0 基础
   - [x] TensorFlow 恶意软件神经网络分类
-  - [ ] tensorflow federated学习
-  - [ ] TensorFlow 联邦学习实现
+  - [x] tensorflow federated学习
+  - [x] TensorFlow 联邦学习实现
 - pytorch
   - [x] pytoch学习
   - [x] pytorch 恶意软件神经网络分类
diff --git a/工作日志/2021年3月20日-今日计划.md b/工作日志/2021年3月20日-今日计划.md
index 779a0ed7..e36d10e4 100644
--- a/工作日志/2021年3月20日-今日计划.md
+++ b/工作日志/2021年3月20日-今日计划.md
@@ -17,7 +17,7 @@
 2. 每天一个联邦学习框架
    - [x] pytorch学习
 3. 每天一篇联邦学习的文章。
-   - [ ] ~~deepAMD~~
+   - [x] ~~deepAMD~~
 4. 每天数据集处理一小步。
    - [X] 获取2020年的两个超大型数据集，并存到云盘当中
    - [X] 获取CIC关于恶意软件分析的论文
diff --git a/工作日志/2021年3月21日-今日计划.md b/工作日志/2021年3月21日-今日计划.md
index 582197d4..690defe6 100644
--- a/工作日志/2021年3月21日-今日计划.md
+++ b/工作日志/2021年3月21日-今日计划.md
@@ -4,7 +4,7 @@
 2. 每天一个联邦学习框架
    - [x] pytorch
 3. 每天一篇联邦学习的文章。
-   - [ ] ~~联邦半监督学习~~
+   - [x] ~~联邦半监督学习~~
 
 
 > 明天实现动态数据处理的脚本
diff --git a/工作日志/2021年3月22日-今日计划.md b/工作日志/2021年3月22日-今日计划.md
index 1e58abff..1f49fc36 100644
--- a/工作日志/2021年3月22日-今日计划.md
+++ b/工作日志/2021年3月22日-今日计划.md
@@ -4,7 +4,7 @@
    - [x] 支持向量机（学习原理、sklearn实践
    - [x] 集成方法（学习原理、sklearn实践
 2. 每天一个联邦学习框架
-   - [ ] pysyft学习
+   - [x] pysyft学习
 
 
 ## 收获
diff --git a/工作日志/2021年3月23日-今日计划.md b/工作日志/2021年3月23日-今日计划.md
index be26ed7d..6b37592b 100644
--- a/工作日志/2021年3月23日-今日计划.md
+++ b/工作日志/2021年3月23日-今日计划.md
@@ -1,7 +1,7 @@
 ## 计划
 
 1. 每天一个联邦学习框架
-   - [ ] pysft学习
+   - [x] pysft学习
 
 
 ## 收获
diff --git a/工作日志/2021年3月25日-今日计划.md b/工作日志/2021年3月25日-今日计划.md
index 80c5cb8c..e0081596 100644
--- a/工作日志/2021年3月25日-今日计划.md
+++ b/工作日志/2021年3月25日-今日计划.md
@@ -1,9 +1,9 @@
 ## 计划
 
 1. 每天数据集处理一小步。
-   - [ ] 实现静态数据处理脚本
+   - [x] 实现静态数据处理脚本
 2. 每天数据集处理一小步。
-   - [ ] 实现动态数据处理脚本
+   - [x] 实现动态数据处理脚本
 
 > 阅读完之前的论文
 
diff --git a/工作日志/2021年3月26日-开组会.md b/工作日志/2021年3月26日-开组会.md
index c1e5ef0b..03ade338 100644
--- a/工作日志/2021年3月26日-开组会.md
+++ b/工作日志/2021年3月26日-开组会.md
@@ -3,7 +3,7 @@
 ## 计划
 
 > 执行之前的计划
-- [ ] 整理会议记录发到群里。
+- [x] 整理会议记录发到群里。
 
 ## 收获
 
diff --git a/工作日志/2021年3月29日-今日计划.md b/工作日志/2021年3月29日-今日计划.md
index 136de95f..ce3731fe 100644
--- a/工作日志/2021年3月29日-今日计划.md
+++ b/工作日志/2021年3月29日-今日计划.md
@@ -3,6 +3,6 @@
 
 - [x] 数据结构预算法——蛮力法整理完成
 - [x] 基础知识与项目经历——操作系统整理开始part1
-- [ ] 联邦学习与恶意软件——pysyft学习完成
+- [x] 联邦学习与恶意软件——pysyft学习完成
 
 ## 收获
\ No newline at end of file
diff --git a/工作日志/2021年3月31日-今日计划.md b/工作日志/2021年3月31日-今日计划.md
index c7dc09f1..e6dc1383 100644
--- a/工作日志/2021年3月31日-今日计划.md
+++ b/工作日志/2021年3月31日-今日计划.md
@@ -4,6 +4,6 @@
 
 - [x] 数据结构与算法——动态规划和贪心整理完成
 - [x] 基础知识与项目经历——数据库整理完成
-- [ ] 联邦学习与恶意软件——TensorFlow学习完成part2
+- [x] 联邦学习与恶意软件——TensorFlow学习完成part2
 
 ## 收获
\ No newline at end of file
diff --git a/工作日志/2021年4月21日-今日计划.md b/工作日志/2021年4月21日-今日计划.md
index b8600574..f6ddd1d9 100644
--- a/工作日志/2021年4月21日-今日计划.md
+++ b/工作日志/2021年4月21日-今日计划.md
@@ -7,13 +7,13 @@
 
 ### sklearn
 
-- [ ] 复习教程（包括tutorial、API、code）
+- [x] 复习教程（包括tutorial、API、code）
 
 ### pytorch
 
-- [ ] 复习教程（包括tutorial、API、code）
-- [ ] pytorch 数据流水线技术
-- [ ] 学习pysyft框架并应用
+- [x] 复习教程（包括tutorial、API、code）
+- [x] pytorch 数据流水线技术
+- [x] 学习pysyft框架并应用
 
 
 ### TensorFlow
diff --git a/工作日志/2021年4月27日-今日计划.md b/工作日志/2021年4月27日-今日计划.md
index acca1340..2cfd0c2e 100644
--- a/工作日志/2021年4月27日-今日计划.md
+++ b/工作日志/2021年4月27日-今日计划.md
@@ -1,8 +1,8 @@
 ## 计划
 
-- [ ] pysyft框架学习（一个小时）
-- [ ] pysyft框架实验（一个小时）快点搞完这个吧，弄完这个就可以真正的开始论文的时间了。五一劳动节，需要多花点时间，补一下拉下的进度了。
-- [ ] 整理相关的论文（）
+- [x] pysyft框架学习（一个小时）
+- [x] pysyft框架实验（一个小时）快点搞完这个吧，弄完这个就可以真正的开始论文的时间了。五一劳动节，需要多花点时间，补一下拉下的进度了。
+- [x] 整理相关的论文（）
 
 
 ## 收获
diff --git a/工作日志/2021年4月2日-今日计划.md b/工作日志/2021年4月2日-今日计划.md
index 077e4641..172f29f5 100644
--- a/工作日志/2021年4月2日-今日计划.md
+++ b/工作日志/2021年4月2日-今日计划.md
@@ -3,5 +3,5 @@
 > 每日计划包括三方面的内容：数据结构与算法、基础知识与项目经历、联邦学习与恶意软件
 
 - [x] 数据结构预算法——图算法和随机化算法整理完成
-- [ ] 基础知识与项目经历——项目经历整理完成
-- [ ] 联邦学习与恶意软件——TensorFlow federated学习完成part2
+- [x] 基础知识与项目经历——项目经历整理完成
+- [x] 联邦学习与恶意软件——TensorFlow federated学习完成part2
diff --git a/工作日志/2021年4月17日-五月份计划.md b/工作日志/2021年5月1日-五月份计划.md
similarity index 76%
rename from 工作日志/2021年4月17日-五月份计划.md
rename to 工作日志/2021年5月1日-五月份计划.md
index 8e80ff77..9ec57701 100644
--- a/工作日志/2021年4月17日-五月份计划.md
+++ b/工作日志/2021年5月1日-五月份计划.md
@@ -1,8 +1,11 @@
 ## 任务
 
 > 四月份计划主要进行基础知识的学习和复习。完成了初步的数据处理。接下来的主要任务是阅读论文并完成论文复习计划。
+> 开始执行五月份计划，与四月份计划进行衔接。完成工程上与学术上的推进工作。
+
+### **阅读论文**（两周）
+> （100篇计划）蒋师兄前前后后分享了估计也有一百篇了，现在重新开始读论文，在复现文章的同时进行广泛的阅读。利用citavi这个工具。
 
-### **阅读论文计划（100篇计划）**（两周）
 
 ### **联邦学习文章复现**（四周）
 

From d560d25d1e42f0632c35a079ae712c0fa04e1c7c Mon Sep 17 00:00:00 2001
From: yinkanglong <yinkanglong@163.com>
Date: Thu, 6 May 2021 21:22:20 +0800
Subject: [PATCH 2/2] =?UTF-8?q?=E8=AE=A1=E5=88=92?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 工作日志/2021年5月6日-今日计划.md | 12 ++++++++++++
 工作日志/2021年5月7日-今日计划.md |  6 ++++++
 工作日志/2021年5月8日-今日计划.md |  6 ++++++
 工作日志/2021年5月9日-今日计划.md |  6 ++++++
 4 files changed, 30 insertions(+)
 create mode 100644 工作日志/2021年5月6日-今日计划.md
 create mode 100644 工作日志/2021年5月7日-今日计划.md
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 create mode 100644 工作日志/2021年5月9日-今日计划.md

diff --git a/工作日志/2021年5月6日-今日计划.md b/工作日志/2021年5月6日-今日计划.md
new file mode 100644
index 00000000..fb0c3bbe
--- /dev/null
+++ b/工作日志/2021年5月6日-今日计划.md
@@ -0,0 +1,12 @@
+## 任务
+
+- [x] 四月份计划——tensorflow federated
+- [ ] 四月份计划——pysyft
+
+
+
+## 收获
+
+1. 我发现python机器学习这一套在linux下更好运行，windows配置环境果然要麻烦一百倍。从今天开始将主要的工作环境转移到linux上边。算法的运行和学习都在linux上执行。去Windows上做一下收尾工作。
+2. 当前的主要任务包括两个，一个是四月份未完成的计划。一个是五月份新开始的计划。
+3. tensorflow federated已经学习完成了，能够完成基本的联邦学习过程。因为与学弟合作的部分还是pytorch。今后最好使用pytorch进行开发吧。除非由绝对的优势，不会回到tensorflow上了。
\ No newline at end of file
diff --git a/工作日志/2021年5月7日-今日计划.md b/工作日志/2021年5月7日-今日计划.md
new file mode 100644
index 00000000..fae7c26e
--- /dev/null
+++ b/工作日志/2021年5月7日-今日计划.md
@@ -0,0 +1,6 @@
+## 计划
+
+- [ ] 四月份计划——pytorch 两个数据集训练
+- [ ] 四月份计划——pysyft 两个数据集训练
+
+## 收获
\ No newline at end of file
diff --git a/工作日志/2021年5月8日-今日计划.md b/工作日志/2021年5月8日-今日计划.md
new file mode 100644
index 00000000..823ebea9
--- /dev/null
+++ b/工作日志/2021年5月8日-今日计划.md
@@ -0,0 +1,6 @@
+## 计划
+
+- [ ] 五月份计划——ida 实现
+- [ ] 五月份计划——prox实现
+
+## 收获
\ No newline at end of file
diff --git a/工作日志/2021年5月9日-今日计划.md b/工作日志/2021年5月9日-今日计划.md
new file mode 100644
index 00000000..844a5ece
--- /dev/null
+++ b/工作日志/2021年5月9日-今日计划.md
@@ -0,0 +1,6 @@
+## 计划
+
+- [ ] 五月份计划——静态数据处理脚本
+- [ ] 五月份计划——动态数据处理脚本
+
+## 收获
\ No newline at end of file