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update to 2.2 (#4100)
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‎docs/practices/cv/convnet_image_classification.ipynb‎

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‎docs/practices/cv/image_classification.ipynb‎

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"# 使用LeNet在MNIST数据集实现图像分类\n",
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"\n",
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"**作者:** [PaddlePaddle](https://github.com/PaddlePaddle) <br>\n",
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"**日期:** 2021.10 <br>\n",
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"**日期:** 2021.11 <br>\n",
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"**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。"
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"source": [
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"## 一、环境配置\n",
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"本教程基于Paddle 2.2.0-rc0 编写，如果您的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.1 。"
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"本教程基于Paddle 2.2.0 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) Paddle 2.2.0 。"
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"name": "stdout",
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"2.2.0-rc0\n"
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"2.2.0\n"
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"text": [
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"The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n",
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"Epoch 1/2\n",
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"step 938/938 [==============================] - loss: 0.0329 - acc: 0.9399 - 10ms/step \n",
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"step 938/938 [==============================] - loss: 0.0763 - acc: 0.9526 - 11ms/step \n",
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"Epoch 2/2\n",
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"step 938/938 [==============================] - loss: 0.0092 - acc: 0.9798 - 10ms/step \n"
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"step 938/938 [==============================] - loss: 0.0075 - acc: 0.9835 - 10ms/step \n"
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"output_type": "stream",
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"text": [
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"Eval begin...\n",
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"step 157/157 [==============================] - loss: 4.4728e-04 - acc: 0.9857 - 8ms/step \n",
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"step 157/157 [==============================] - loss: 2.0455e-04 - acc: 0.9864 - 8ms/step \n",
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"Eval samples: 10000\n"
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]
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"data": {
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"{'loss': [0.0004472804], 'acc': 0.9857}"
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"{'loss': [0.00020454898], 'acc': 0.9864}"
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"execution_count": 11,
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"execution_count": 9,
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"metadata": {},
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"name": "stdout",
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"epoch: 0, batch_id: 0, loss is: [3.2611141], acc is: [0.078125]\n",
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"epoch: 0, batch_id: 300, loss is: [0.24404016], acc is: [0.921875]\n",
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"epoch: 0, batch_id: 600, loss is: [0.03953885], acc is: [1.]\n",
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"epoch: 0, batch_id: 900, loss is: [0.03700985], acc is: [0.984375]\n",
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"epoch: 1, batch_id: 0, loss is: [0.05806625], acc is: [0.96875]\n",
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"epoch: 1, batch_id: 300, loss is: [0.06538856], acc is: [0.953125]\n",
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"epoch: 1, batch_id: 600, loss is: [0.03884572], acc is: [0.984375]\n",
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"epoch: 1, batch_id: 900, loss is: [0.01922364], acc is: [0.984375]\n"
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"epoch: 0, batch_id: 0, loss is: [3.0316443], acc is: [0.0625]\n",
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"epoch: 0, batch_id: 300, loss is: [0.16949166], acc is: [0.9375]\n",
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"epoch: 0, batch_id: 600, loss is: [0.04333997], acc is: [0.984375]\n",
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"epoch: 0, batch_id: 900, loss is: [0.0382758], acc is: [0.984375]\n",
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"epoch: 1, batch_id: 0, loss is: [0.05184244], acc is: [0.96875]\n",
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"epoch: 1, batch_id: 300, loss is: [0.04323502], acc is: [0.984375]\n",
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"epoch: 1, batch_id: 600, loss is: [0.06236228], acc is: [0.984375]\n",
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"epoch: 1, batch_id: 900, loss is: [0.03451318], acc is: [0.96875]\n"
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‎docs/practices/cv/image_search.ipynb‎

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‎docs/practices/cv/image_segmentation.ipynb‎

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"# 基于U-Net卷积神经网络实现宠物图像分割\n",
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"\n",
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"**作者:** [PaddlePaddle](https://github.com/PaddlePaddle)<br>\n",
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"**日期:** 2021.10<br>\n",
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"**日期:** 2021.11<br>\n",
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"**摘要:** 本示例教程使用U-Net实现图像分割。"
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"'2.2.0-rc0'"
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"'2.2.0'"
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"outputs": [
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{
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"name": "stderr",
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"text": [
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"W1108 15:37:35.553402 159 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
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"W1108 15:37:35.558030 159 device_context.cc:465] device: 0, cuDNN Version: 7.6.\n"
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]
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},
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"The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n",
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"Epoch 1/15\n",
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"step 197/197 [==============================] - loss: 0.7315 - 255ms/step \n",
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"Eval begin...\n",
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"step 35/35 [==============================] - loss: 0.6949 - 232ms/step \n",
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"Eval samples: 1108\n",
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"Epoch 2/15\n",
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"step 197/197 [==============================] - loss: 0.4554 - 249ms/step \n",
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"Eval begin...\n",
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"step 35/35 [==============================] - loss: 0.5985 - 232ms/step \n",
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"Eval samples: 1108\n",
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"Epoch 3/15\n",
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"step 197/197 [==============================] - loss: 0.4946 - 273ms/step \n",
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"Eval begin...\n",
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"step 35/35 [==============================] - loss: 0.5212 - 264ms/step \n",
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"Eval samples: 1108\n",
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"Epoch 4/15\n",
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"step 197/197 [==============================] - loss: 0.6329 - 263ms/step \n",
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"Eval begin...\n",
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"step 35/35 [==============================] - loss: 0.6810 - 233ms/step \n",
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"Eval samples: 1108\n",
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"Epoch 5/15\n",
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"step 197/197 [==============================] - loss: 0.5155 - 247ms/step \n",
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"Eval begin...\n",
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"step 35/35 [==============================] - loss: 0.4560 - 234ms/step \n",
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"Eval samples: 1108\n",
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"Epoch 6/15\n",
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"step 197/197 [==============================] - loss: 0.3929 - 251ms/step \n",
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"Eval begin...\n",
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"step 35/35 [==============================] - loss: 0.4077 - 233ms/step \n",
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"Eval samples: 1108\n",
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"Epoch 7/15\n",
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"step 197/197 [==============================] - loss: 0.2830 - 260ms/step \n",
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"Eval begin...\n",
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"step 35/35 [==============================] - loss: 0.3842 - 232ms/step \n",
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"Eval samples: 1108\n",
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"Epoch 8/15\n",
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"step 197/197 [==============================] - loss: 0.2933 - 259ms/step \n",
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"Eval begin...\n",
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"step 35/35 [==============================] - loss: 0.4037 - 231ms/step \n",
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"Eval samples: 1108\n",
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"Epoch 9/15\n",
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"step 197/197 [==============================] - loss: 0.4570 - 252ms/step \n",
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"Eval begin...\n",
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"step 35/35 [==============================] - loss: 0.5587 - 230ms/step \n",
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"Eval samples: 1108\n",
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"Epoch 10/15\n",
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"step 197/197 [==============================] - loss: 0.3355 - 256ms/step \n",
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"Eval begin...\n",
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"step 35/35 [==============================] - loss: 0.4318 - 231ms/step \n",
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"Eval samples: 1108\n",
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"Epoch 11/15\n",
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"step 197/197 [==============================] - loss: 0.3249 - 247ms/step \n",
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"Eval begin...\n",
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"step 35/35 [==============================] - loss: 0.4173 - 232ms/step \n",
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"step 197/197 [==============================] - loss: 0.2620 - 258ms/step \n",
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"Eval begin...\n",
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"step 35/35 [==============================] - loss: 0.3748 - 235ms/step \n",
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"Eval samples: 1108\n",
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"Epoch 14/15\n",
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"step 197/197 [==============================] - loss: 0.3658 - 248ms/step \n",
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"Eval begin...\n",
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"step 35/35 [==============================] - loss: 0.4121 - 239ms/step \n",
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"Eval samples: 1108\n",
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"Epoch 15/15\n",
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"step 197/197 [==============================] - loss: 0.3259 - 247ms/step \n",
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"step 80/197 [===========>..................] - loss: 1.0866 - ETA: 29s - 248ms/st"
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