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“ae6dae4189edd0c6c087c5b44c5fd02a16c7af77”上不存在“mobile/src/operators/kernel/sum_kernel.h”
未验证 提交 3fa53567 编写于 作者: C Chen Long 提交者： GitHub
浏览文件
操作

fix_test_loader (#889) 

* fix_test_loader

* fix_mnist_wrongs
上级 601a9386
隐藏空白更改
内联 并排
Showing with 81 addition and 256 deletion
paddle2.0_docs/image_classification/mnist_lenet_classification.ipynb
浏览文件 @ 3fa53567
......@@ -19,7 +19,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 13,
"metadata": {},
"outputs": [
{
......@@ -46,7 +46,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 14,
"metadata": {},
"outputs": [
{
......@@ -74,7 +74,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 15,
"metadata": {},
"outputs": [
{
......@@ -112,12 +112,12 @@
"metadata": {},
"source": [
"## 组网\n",
"用paddle.nn下的API，如`Conv2d`、`Pool2D`、`Linead`完成LeNet的构建。"
"用paddle.nn下的API，如`Conv2d`、`MaxPool2d`、`Linear`完成LeNet的构建。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
......@@ -141,7 +141,7 @@
" x = F.relu(x)\n",
" x = self.conv2(x)\n",
" x = self.max_pool2(x)\n",
" x = paddle.reshape(x, shape=[-1, 16*5*5])\n",
" x = paddle.flatten(x, start_axis=1,stop_axis=-1)\n",
" x = self.linear1(x)\n",
" x = F.relu(x)\n",
" x = self.linear2(x)\n",
......@@ -161,39 +161,39 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch: 0, batch_id: 0, loss is: [2.3029077], acc is: [0.15625]\n",
"epoch: 0, batch_id: 100, loss is: [1.6757016], acc is: [0.84375]\n",
"epoch: 0, batch_id: 200, loss is: [1.5340967], acc is: [0.96875]\n",
"epoch: 0, batch_id: 300, loss is: [1.4943825], acc is: [0.984375]\n",
"epoch: 0, batch_id: 400, loss is: [1.5084226], acc is: [1.]\n",
"epoch: 0, batch_id: 500, loss is: [1.5035012], acc is: [0.984375]\n",
"epoch: 0, batch_id: 600, loss is: [1.4784969], acc is: [0.984375]\n",
"epoch: 0, batch_id: 700, loss is: [1.5656701], acc is: [0.96875]\n",
"epoch: 0, batch_id: 800, loss is: [1.5226105], acc is: [1.]\n",
"epoch: 0, batch_id: 900, loss is: [1.5094678], acc is: [1.]\n",
"epoch: 1, batch_id: 0, loss is: [1.4956206], acc is: [0.984375]\n",
"epoch: 1, batch_id: 100, loss is: [1.4908005], acc is: [1.]\n",
"epoch: 1, batch_id: 200, loss is: [1.485649], acc is: [0.984375]\n",
"epoch: 1, batch_id: 300, loss is: [1.5090752], acc is: [1.]\n",
"epoch: 1, batch_id: 400, loss is: [1.5163708], acc is: [1.]\n",
"epoch: 1, batch_id: 500, loss is: [1.4863018], acc is: [0.984375]\n",
"epoch: 1, batch_id: 600, loss is: [1.4764814], acc is: [0.984375]\n",
"epoch: 1, batch_id: 700, loss is: [1.5496588], acc is: [0.984375]\n",
"epoch: 1, batch_id: 800, loss is: [1.4998187], acc is: [1.]\n",
"epoch: 1, batch_id: 900, loss is: [1.5110929], acc is: [1.]\n"
"epoch: 0, batch_id: 0, loss is: [2.3079572], acc is: [0.125]\n",
"epoch: 0, batch_id: 100, loss is: [1.7078608], acc is: [0.828125]\n",
"epoch: 0, batch_id: 200, loss is: [1.5642334], acc is: [0.90625]\n",
"epoch: 0, batch_id: 300, loss is: [1.7024238], acc is: [0.78125]\n",
"epoch: 0, batch_id: 400, loss is: [1.5536337], acc is: [0.921875]\n",
"epoch: 0, batch_id: 500, loss is: [1.6908336], acc is: [0.828125]\n",
"epoch: 0, batch_id: 600, loss is: [1.5622432], acc is: [0.921875]\n",
"epoch: 0, batch_id: 700, loss is: [1.5251796], acc is: [0.953125]\n",
"epoch: 0, batch_id: 800, loss is: [1.5698484], acc is: [0.890625]\n",
"epoch: 0, batch_id: 900, loss is: [1.5524453], acc is: [0.9375]\n",
"epoch: 1, batch_id: 0, loss is: [1.6443151], acc is: [0.84375]\n",
"epoch: 1, batch_id: 100, loss is: [1.5547533], acc is: [0.90625]\n",
"epoch: 1, batch_id: 200, loss is: [1.5019028], acc is: [1.]\n",
"epoch: 1, batch_id: 300, loss is: [1.4820204], acc is: [1.]\n",
"epoch: 1, batch_id: 400, loss is: [1.5215418], acc is: [0.984375]\n",
"epoch: 1, batch_id: 500, loss is: [1.4972374], acc is: [1.]\n",
"epoch: 1, batch_id: 600, loss is: [1.4930981], acc is: [0.984375]\n",
"epoch: 1, batch_id: 700, loss is: [1.4971689], acc is: [0.984375]\n",
"epoch: 1, batch_id: 800, loss is: [1.4611597], acc is: [1.]\n",
"epoch: 1, batch_id: 900, loss is: [1.4903957], acc is: [0.984375]\n"
]
}
],
"source": [
"import paddle\n",
"train_loader = paddle.io.DataLoader(train_dataset, places=paddle.CPUPlace(), batch_size=64)\n",
"train_loader = paddle.io.DataLoader(train_dataset, places=paddle.CPUPlace(), batch_size=64, shuffle=True)\n",
"# 加载训练集 batch_size 设为 64\n",
"def train(model):\n",
" model.train()\n",
......@@ -229,23 +229,21 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"batch_id: 0, loss is: [1.4929559], acc is: [0.984375]\n",
"batch_id: 100, loss is: [1.4921299], acc is: [0.984375]\n",
"batch_id: 200, loss is: [1.5021144], acc is: [1.]\n",
"batch_id: 300, loss is: [1.4809179], acc is: [0.984375]\n",
"batch_id: 400, loss is: [1.4768506], acc is: [1.]\n",
"batch_id: 500, loss is: [1.4768407], acc is: [1.]\n",
"batch_id: 600, loss is: [1.476671], acc is: [0.984375]\n",
"batch_id: 700, loss is: [1.5093586], acc is: [1.]\n",
"batch_id: 800, loss is: [1.5057312], acc is: [1.]\n",
"batch_id: 900, loss is: [1.4923737], acc is: [1.]\n"
"batch_id: 0, loss is: [1.4767745], acc is: [1.]\n",
"batch_id: 20, loss is: [1.4841802], acc is: [0.984375]\n",
"batch_id: 40, loss is: [1.4997194], acc is: [1.]\n",
"batch_id: 60, loss is: [1.4895413], acc is: [1.]\n",
"batch_id: 80, loss is: [1.4668798], acc is: [1.]\n",
"batch_id: 100, loss is: [1.4611752], acc is: [1.]\n",
"batch_id: 120, loss is: [1.4613602], acc is: [1.]\n",
"batch_id: 140, loss is: [1.4923686], acc is: [1.]\n"
]
}
],
......@@ -256,7 +254,7 @@
"def test(model):\n",
" model.eval()\n",
" batch_size = 64\n",
" for batch_id, data in enumerate(train_loader()):\n",
" for batch_id, data in enumerate(test_loader()):\n",
" x_data = data[0]\n",
" y_data = data[1]\n",
" predicts = model(x_data)\n",
......@@ -266,7 +264,7 @@
" avg_loss = paddle.mean(loss)\n",
" avg_acc = paddle.mean(acc)\n",
" avg_loss.backward()\n",
" if batch_id % 100 == 0:\n",
" if batch_id % 20 == 0:\n",
" print(\"batch_id: {}, loss is: {}, acc is: {}\".format(batch_id, avg_loss.numpy(), avg_acc.numpy()))\n",
"test(model)"
]
......@@ -289,7 +287,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
......@@ -317,7 +315,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 22,
"metadata": {},
"outputs": [
{
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"output_type": "stream",
"text": [
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"step 100/938 - loss: 1.5644 - acc_top1: 0.6281 - acc_top2: 0.7145 - 14ms/step\n",
"step 200/938 - loss: 1.6221 - acc_top1: 0.7634 - acc_top2: 0.8380 - 13ms/step\n",
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"step 700/938 - loss: 1.4642 - acc_top1: 0.8983 - acc_top2: 0.9417 - 13ms/step\n",
"step 800/938 - loss: 1.4789 - acc_top1: 0.9069 - acc_top2: 0.9477 - 13ms/step\n",
"step 900/938 - loss: 1.4773 - acc_top1: 0.9135 - acc_top2: 0.9523 - 13ms/step\n",
"step 938/938 - loss: 1.4714 - acc_top1: 0.9157 - acc_top2: 0.9538 - 13ms/step\n",
"save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/0\n",
"Epoch 2/2\n",
"step 10/938 - loss: 1.5034 - acc_top1: 0.9688 - acc_top2: 0.9891 - 16ms/step\n",
"step 20/938 - loss: 1.4879 - acc_top1: 0.9711 - acc_top2: 0.9898 - 16ms/step\n",
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"step 60/938 - loss: 1.4924 - acc_top1: 0.9719 - acc_top2: 0.9898 - 15ms/step\n",
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"step 90/938 - loss: 1.4628 - acc_top1: 0.9740 - acc_top2: 0.9915 - 15ms/step\n",
"step 100/938 - loss: 1.4794 - acc_top1: 0.9745 - acc_top2: 0.9919 - 15ms/step\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step 110/938 - loss: 1.4665 - acc_top1: 0.9749 - acc_top2: 0.9918 - 15ms/step\n",
"step 120/938 - loss: 1.4769 - acc_top1: 0.9755 - acc_top2: 0.9917 - 15ms/step\n",
"step 130/938 - loss: 1.4883 - acc_top1: 0.9755 - acc_top2: 0.9918 - 15ms/step\n",
"step 140/938 - loss: 1.4779 - acc_top1: 0.9757 - acc_top2: 0.9920 - 15ms/step\n",
"step 150/938 - loss: 1.4926 - acc_top1: 0.9751 - acc_top2: 0.9922 - 15ms/step\n",
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"step 900/938 - loss: 1.4813 - acc_top1: 0.9766 - acc_top2: 0.9936 - 14ms/step\n",
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"step 930/938 - loss: 1.4890 - acc_top1: 0.9769 - acc_top2: 0.9936 - 14ms/step\n",
"step 938/938 - loss: 1.4617 - acc_top1: 0.9769 - acc_top2: 0.9937 - 14ms/step\n",
"step 100/938 - loss: 1.4863 - acc_top1: 0.9695 - acc_top2: 0.9897 - 13ms/step\n",
"step 200/938 - loss: 1.4883 - acc_top1: 0.9707 - acc_top2: 0.9912 - 13ms/step\n",
"step 300/938 - loss: 1.4695 - acc_top1: 0.9720 - acc_top2: 0.9910 - 13ms/step\n",
"step 400/938 - loss: 1.4628 - acc_top1: 0.9720 - acc_top2: 0.9915 - 13ms/step\n",
"step 500/938 - loss: 1.5079 - acc_top1: 0.9727 - acc_top2: 0.9918 - 13ms/step\n",
"step 600/938 - loss: 1.4803 - acc_top1: 0.9727 - acc_top2: 0.9919 - 13ms/step\n",
"step 700/938 - loss: 1.4612 - acc_top1: 0.9732 - acc_top2: 0.9923 - 13ms/step\n",
"step 800/938 - loss: 1.4755 - acc_top1: 0.9732 - acc_top2: 0.9923 - 13ms/step\n",
"step 900/938 - loss: 1.4698 - acc_top1: 0.9732 - acc_top2: 0.9922 - 13ms/step\n",
"step 938/938 - loss: 1.4764 - acc_top1: 0.9734 - acc_top2: 0.9923 - 13ms/step\n",
"save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/1\n",
"save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/final\n"
]
......@@ -530,6 +354,7 @@
"model.fit(train_dataset,\n",
" epochs=2,\n",
" batch_size=64,\n",
" log_freq=100,\n",
" save_dir='mnist_checkpoint')"
]
},
......@@ -542,7 +367,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 23,
"metadata": {},
"outputs": [
{
......@@ -550,32 +375,32 @@
"output_type": "stream",
"text": [
"Eval begin...\n",
"step 10/157 - loss: 1.5023 - acc_top1: 0.9781 - acc_top2: 0.9969 - 7ms/step\n",
"step 20/157 - loss: 1.5326 - acc_top1: 0.9750 - acc_top2: 0.9953 - 7ms/step\n",
"step 30/157 - loss: 1.4881 - acc_top1: 0.9745 - acc_top2: 0.9943 - 7ms/step\n",
"step 40/157 - loss: 1.4703 - acc_top1: 0.9715 - acc_top2: 0.9934 - 6ms/step\n",
"step 50/157 - loss: 1.4793 - acc_top1: 0.9728 - acc_top2: 0.9934 - 6ms/step\n",
"step 60/157 - loss: 1.5338 - acc_top1: 0.9721 - acc_top2: 0.9924 - 6ms/step\n",
"step 70/157 - loss: 1.4801 - acc_top1: 0.9721 - acc_top2: 0.9922 - 6ms/step\n",
"step 80/157 - loss: 1.4763 - acc_top1: 0.9725 - acc_top2: 0.9928 - 6ms/step\n",
"step 90/157 - loss: 1.4682 - acc_top1: 0.9747 - acc_top2: 0.9936 - 6ms/step\n",
"step 100/157 - loss: 1.4780 - acc_top1: 0.9758 - acc_top2: 0.9939 - 6ms/step\n",
"step 110/157 - loss: 1.4686 - acc_top1: 0.9763 - acc_top2: 0.9942 - 6ms/step\n",
"step 120/157 - loss: 1.4624 - acc_top1: 0.9780 - acc_top2: 0.9947 - 6ms/step\n",
"step 130/157 - loss: 1.4968 - acc_top1: 0.9787 - acc_top2: 0.9948 - 6ms/step\n",
"step 140/157 - loss: 1.4612 - acc_top1: 0.9798 - acc_top2: 0.9952 - 6ms/step\n",
"step 150/157 - loss: 1.4613 - acc_top1: 0.9806 - acc_top2: 0.9955 - 6ms/step\n",
"step 157/157 - loss: 1.4612 - acc_top1: 0.9803 - acc_top2: 0.9955 - 6ms/step\n",
"step 10/157 - loss: 1.5238 - acc_top1: 0.9750 - acc_top2: 0.9938 - 7ms/step\n",
"step 20/157 - loss: 1.5143 - acc_top1: 0.9727 - acc_top2: 0.9922 - 7ms/step\n",
"step 30/157 - loss: 1.5290 - acc_top1: 0.9698 - acc_top2: 0.9932 - 7ms/step\n",
"step 40/157 - loss: 1.4624 - acc_top1: 0.9684 - acc_top2: 0.9930 - 7ms/step\n",
"step 50/157 - loss: 1.4771 - acc_top1: 0.9697 - acc_top2: 0.9925 - 7ms/step\n",
"step 60/157 - loss: 1.5066 - acc_top1: 0.9701 - acc_top2: 0.9922 - 6ms/step\n",
"step 70/157 - loss: 1.4804 - acc_top1: 0.9699 - acc_top2: 0.9920 - 6ms/step\n",
"step 80/157 - loss: 1.4718 - acc_top1: 0.9707 - acc_top2: 0.9930 - 6ms/step\n",
"step 90/157 - loss: 1.4874 - acc_top1: 0.9726 - acc_top2: 0.9934 - 6ms/step\n",
"step 100/157 - loss: 1.4612 - acc_top1: 0.9736 - acc_top2: 0.9936 - 6ms/step\n",
"step 110/157 - loss: 1.4612 - acc_top1: 0.9746 - acc_top2: 0.9938 - 6ms/step\n",
"step 120/157 - loss: 1.4763 - acc_top1: 0.9763 - acc_top2: 0.9941 - 6ms/step\n",
"step 130/157 - loss: 1.4786 - acc_top1: 0.9764 - acc_top2: 0.9935 - 6ms/step\n",
"step 140/157 - loss: 1.4612 - acc_top1: 0.9775 - acc_top2: 0.9939 - 6ms/step\n",
"step 150/157 - loss: 1.4894 - acc_top1: 0.9785 - acc_top2: 0.9943 - 6ms/step\n",
"step 157/157 - loss: 1.4612 - acc_top1: 0.9777 - acc_top2: 0.9941 - 6ms/step\n",
"Eval samples: 10000\n"
]
},
{
"data": {
"text/plain": [
"{'loss': [1.4611506], 'acc_top1': 0.9803, 'acc_top2': 0.9955}"
"{'loss': [1.4611504], 'acc_top1': 0.9777, 'acc_top2': 0.9941}"
]
},
"execution_count": 12,
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
......
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