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May 11, 2022
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update practises of 2.3.0 version #4735

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8dc8fdd
update practises of 2.3.0 version
yang131313 May 10, 2022
fa239bd
modify hello_paddle
yang131313 May 10, 2022
8435e02
Merge branch 'develop' of https://github.com/PaddlePaddle/docs into u…
yang131313 May 10, 2022
406a8a9
update practises of 2.3.0 version
yang131313 May 11, 2022
135d826
Merge branch 'develop' of https://github.com/PaddlePaddle/docs into u…
yang131313 May 11, 2022
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88 changes: 44 additions & 44 deletions docs/practices/cv/convnet_image_classification.ipynb
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104 changes: 43 additions & 61 deletions docs/practices/cv/image_classification.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -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。"
]
},
Expand All @@ -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
},
Expand All @@ -35,7 +35,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"2.3.0-rc0\n"
"2.3.0\n"
]
}
],
Expand All @@ -58,7 +58,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {
"collapsed": false
},
Expand Down Expand Up @@ -87,7 +87,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {
"collapsed": false
},
Expand All @@ -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": [
Expand All @@ -122,7 +132,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {
"collapsed": false
},
Expand Down Expand Up @@ -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",
Expand All @@ -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",
Expand All @@ -251,7 +233,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"metadata": {
"collapsed": false
},
Expand All @@ -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"
}
Expand Down Expand Up @@ -303,7 +285,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"metadata": {
"collapsed": false
},
Expand All @@ -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"
]
}
],
Expand Down Expand Up @@ -361,7 +343,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"metadata": {
"collapsed": false
},
Expand All @@ -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"
]
}
],
Expand Down
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