update practises of 2.3.0 version (#4735)

* update practises of 2.3.0 version

* modify hello_paddle

* update practises of 2.3.0 version

Author: yang131313

docs/practices/cv/convnet_image_classification.ipynb

@@ -9,7 +9,7 @@
     "# 使用LeNet在MNIST数据集实现图像分类\n",
     "\n",
     "**作者:** [PaddlePaddle](https://github.com/PaddlePaddle) <br>\n",
-    "**日期:** 2022.4 <br>\n",
+    "**日期:** 2022.5 <br>\n",
     "**摘要:** 本示例教程演示如何在MNIST数据集上用LeNet进行图像分类。"
    ]
   },
@@ -21,12 +21,12 @@
    "source": [
     "## 一、环境配置\n",
     "\n",
-    "本教程基于PaddlePaddle 2.3.0-rc0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) PaddlePaddle 2.3.0-rc0。"
+    "本教程基于PaddlePaddle 2.3.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) PaddlePaddle 2.3.0。"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -35,7 +35,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.3.0-rc0\n"
+      "2.3.0\n"
      ]
     }
    ],
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -98,6 +98,16 @@
      "text": [
       "train_data0 label is: [5]\n"
      ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 144x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -122,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -178,20 +188,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "W0422 18:56:10.020583 19533 gpu_context.cc:244] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
-      "W0422 18:56:10.026566 19533 gpu_context.cc:272] device: 0, cuDNN Version: 7.6.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from paddle.metric import Accuracy\n",
     "model = paddle.Model(LeNet())   # 用Model封装模型\n",
@@ -207,30 +208,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n",
-      "Epoch 1/2\n",
-      "step  20/938 [..............................] - loss: 1.4646 - acc: 0.3828 - ETA: 17s - 19ms/ste"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "step  30/938 [..............................] - loss: 1.1068 - acc: 0.4672 - ETA: 14s - 16ms/stepstep 938/938 [==============================] - loss: 0.1653 - acc: 0.9273 - 11ms/step         \n",
-      "Epoch 2/2\n",
-      "step 938/938 [==============================] - loss: 0.0199 - acc: 0.9767 - 11ms/step        \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# 训练模型\n",
     "model.fit(train_dataset,\n",
@@ -251,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -261,17 +243,17 @@
      "output_type": "stream",
      "text": [
       "Eval begin...\n",
-      "step 157/157 [==============================] - loss: 0.0048 - acc: 0.9780 - 8ms/step         \n",
+      "step 157/157 [==============================] - loss: 4.2854e-04 - acc: 0.9841 - 7ms/step     \n",
       "Eval samples: 10000\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "{'loss': [0.0047780997], 'acc': 0.978}"
+       "{'loss': [0.00042853763], 'acc': 0.9841}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -303,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -312,14 +294,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 0, batch_id: 0, loss is: [3.7514806], acc is: [0.21875]\n",
-      "epoch: 0, batch_id: 300, loss is: [0.19029362], acc is: [0.953125]\n",
-      "epoch: 0, batch_id: 600, loss is: [0.12201739], acc is: [0.953125]\n",
-      "epoch: 0, batch_id: 900, loss is: [0.03218058], acc is: [0.984375]\n",
-      "epoch: 1, batch_id: 0, loss is: [0.114471], acc is: [0.953125]\n",
-      "epoch: 1, batch_id: 300, loss is: [0.00857661], acc is: [1.]\n",
-      "epoch: 1, batch_id: 600, loss is: [0.10740176], acc is: [0.96875]\n",
-      "epoch: 1, batch_id: 900, loss is: [0.19590104], acc is: [0.9375]\n"
+      "epoch: 0, batch_id: 0, loss is: [2.9878871], acc is: [0.140625]\n",
+      "epoch: 0, batch_id: 300, loss is: [0.22775462], acc is: [0.921875]\n",
+      "epoch: 0, batch_id: 600, loss is: [0.06251755], acc is: [0.984375]\n",
+      "epoch: 0, batch_id: 900, loss is: [0.1097075], acc is: [0.96875]\n",
+      "epoch: 1, batch_id: 0, loss is: [0.04311676], acc is: [0.984375]\n",
+      "epoch: 1, batch_id: 300, loss is: [0.00150577], acc is: [1.]\n",
+      "epoch: 1, batch_id: 600, loss is: [0.08764459], acc is: [0.96875]\n",
+      "epoch: 1, batch_id: 900, loss is: [0.14419323], acc is: [0.9375]\n"
      ]
     }
    ],
@@ -361,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -370,14 +352,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "batch_id: 0, loss is: [0.04440754], acc is: [0.984375]\n",
-      "batch_id: 20, loss is: [0.19196557], acc is: [0.9375]\n",
-      "batch_id: 40, loss is: [0.09817676], acc is: [0.984375]\n",
-      "batch_id: 60, loss is: [0.16782945], acc is: [0.953125]\n",
-      "batch_id: 80, loss is: [0.05786889], acc is: [0.96875]\n",
-      "batch_id: 100, loss is: [0.00799548], acc is: [1.]\n",
-      "batch_id: 120, loss is: [0.00511317], acc is: [1.]\n",
-      "batch_id: 140, loss is: [0.01672031], acc is: [1.]\n"
+      "batch_id: 0, loss is: [0.01201783], acc is: [1.]\n",
+      "batch_id: 20, loss is: [0.09013407], acc is: [0.984375]\n",
+      "batch_id: 40, loss is: [0.07025866], acc is: [0.96875]\n",
+      "batch_id: 60, loss is: [0.08602518], acc is: [0.984375]\n",
+      "batch_id: 80, loss is: [0.00779913], acc is: [1.]\n",
+      "batch_id: 100, loss is: [0.00508764], acc is: [1.]\n",
+      "batch_id: 120, loss is: [0.00401443], acc is: [1.]\n",
+      "batch_id: 140, loss is: [0.03930391], acc is: [0.96875]\n"
      ]
     }
    ],