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未验证 提交 c888f539 编写于 作者: C Chen Long 提交者： GitHub
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fix_lenet_docs test=develop (#886)

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paddle2.0_docs/image_classification/mnist_lenet_classification.ipynb
浏览文件 @ c888f539
...@@ -19,7 +19,7 @@ ...@@ -19,7 +19,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 24, "execution_count": 35,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -46,7 +46,7 @@ ...@@ -46,7 +46,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 36,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -74,7 +74,7 @@ ...@@ -74,7 +74,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 37,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -117,7 +117,7 @@ ...@@ -117,7 +117,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 19, "execution_count": 38,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -127,9 +127,9 @@ ...@@ -127,9 +127,9 @@
" def __init__(self):\n", " def __init__(self):\n",
" super(LeNet, self).__init__()\n", " super(LeNet, self).__init__()\n",
" self.conv1 = paddle.nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)\n", " self.conv1 = paddle.nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)\n",
" self.max_pool1 = paddle.nn.Pool2D(pool_size=2, pool_type='max', pool_stride=2)\n", " self.max_pool1 = paddle.nn.MaxPool2d(kernel_size=2, stride=2)\n",
" self.conv2 = paddle.nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1)\n", " self.conv2 = paddle.nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1)\n",
" self.max_pool2 = paddle.nn.Pool2D(pool_size=2, pool_type='max', pool_stride=2)\n", " self.max_pool2 = paddle.nn.MaxPool2d(kernel_size=2, stride=2)\n",
" self.linear1 = paddle.nn.Linear(in_features=16*5*5, out_features=120)\n", " self.linear1 = paddle.nn.Linear(in_features=16*5*5, out_features=120)\n",
" self.linear2 = paddle.nn.Linear(in_features=120, out_features=84)\n", " self.linear2 = paddle.nn.Linear(in_features=120, out_features=84)\n",
" self.linear3 = paddle.nn.Linear(in_features=84, out_features=10)\n", " self.linear3 = paddle.nn.Linear(in_features=84, out_features=10)\n",
...@@ -155,13 +155,141 @@ ...@@ -155,13 +155,141 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## 训练方式一\n", "# 3.训练方式一\n",
"通过`Model` 构建实例，快速完成模型训练" "组网后，开始对模型进行训练，先构建`train_loader`，加载训练数据，然后定义`train`函数，设置好损失函数后，按batch加载数据，完成模型的训练。"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 21, "execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch: 0, batch_id: 0, loss is: [2.3064885], acc is: [0.109375]\n",
"epoch: 0, batch_id: 100, loss is: [1.5477252], acc is: [1.]\n",
"epoch: 0, batch_id: 200, loss is: [1.5201148], acc is: [1.]\n",
"epoch: 0, batch_id: 300, loss is: [1.525354], acc is: [0.953125]\n",
"epoch: 0, batch_id: 400, loss is: [1.5201038], acc is: [1.]\n",
"epoch: 0, batch_id: 500, loss is: [1.4901408], acc is: [1.]\n",
"epoch: 0, batch_id: 600, loss is: [1.4925538], acc is: [0.984375]\n",
"epoch: 0, batch_id: 700, loss is: [1.5247533], acc is: [0.96875]\n",
"epoch: 0, batch_id: 800, loss is: [1.5365943], acc is: [1.]\n",
"epoch: 0, batch_id: 900, loss is: [1.5154861], acc is: [0.984375]\n",
"epoch: 1, batch_id: 0, loss is: [1.4988302], acc is: [0.984375]\n",
"epoch: 1, batch_id: 100, loss is: [1.493154], acc is: [0.984375]\n",
"epoch: 1, batch_id: 200, loss is: [1.4974915], acc is: [1.]\n",
"epoch: 1, batch_id: 300, loss is: [1.5089471], acc is: [0.984375]\n",
"epoch: 1, batch_id: 400, loss is: [1.5041347], acc is: [1.]\n",
"epoch: 1, batch_id: 500, loss is: [1.5145375], acc is: [1.]\n",
"epoch: 1, batch_id: 600, loss is: [1.4904011], acc is: [0.984375]\n",
"epoch: 1, batch_id: 700, loss is: [1.5121607], acc is: [0.96875]\n",
"epoch: 1, batch_id: 800, loss is: [1.5078678], acc is: [1.]\n",
"epoch: 1, batch_id: 900, loss is: [1.500349], acc is: [0.984375]\n"
]
}
],
"source": [
"import paddle\n",
"train_loader = paddle.io.DataLoader(train_dataset, places=paddle.CPUPlace(), batch_size=64)\n",
"# 加载训练集 batch_size 设为 64\n",
"def train(model):\n",
" model.train()\n",
" epochs = 2\n",
" optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())\n",
" # 用Adam作为优化函数\n",
" for epoch in range(epochs):\n",
" for batch_id, data in enumerate(train_loader()):\n",
" x_data = data[0]\n",
" y_data = data[1]\n",
" predicts = model(x_data)\n",
" loss = paddle.nn.functional.cross_entropy(predicts, y_data)\n",
" # 计算损失\n",
" acc = paddle.metric.accuracy(predicts, y_data, k=2)\n",
" avg_loss = paddle.mean(loss)\n",
" avg_acc = paddle.mean(acc)\n",
" avg_loss.backward()\n",
" if batch_id % 100 == 0:\n",
" print(\"epoch: {}, batch_id: {}, loss is: {}, acc is: {}\".format(epoch, batch_id, avg_loss.numpy(), avg_acc.numpy()))\n",
" optim.minimize(avg_loss)\n",
" model.clear_gradients()\n",
"model = LeNet()\n",
"train(model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 对模型进行验证\n",
"训练完成后，需要验证模型的效果，此时，加载测试数据集，然后用训练好的模对测试集进行预测，计算损失与精度。"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"batch_id: 0, loss is: [1.4659549], acc is: [1.]\n",
"batch_id: 100, loss is: [1.4933192], acc is: [0.984375]\n",
"batch_id: 200, loss is: [1.4779761], acc is: [1.]\n",
"batch_id: 300, loss is: [1.4919193], acc is: [0.984375]\n",
"batch_id: 400, loss is: [1.5036212], acc is: [1.]\n",
"batch_id: 500, loss is: [1.4922347], acc is: [0.984375]\n",
"batch_id: 600, loss is: [1.4765416], acc is: [0.984375]\n",
"batch_id: 700, loss is: [1.4997746], acc is: [0.984375]\n",
"batch_id: 800, loss is: [1.4831288], acc is: [1.]\n",
"batch_id: 900, loss is: [1.498342], acc is: [0.984375]\n"
]
}
],
"source": [
"import paddle\n",
"test_loader = paddle.io.DataLoader(test_dataset, places=paddle.CPUPlace(), batch_size=64)\n",
"# 加载测试数据集\n",
"def test(model):\n",
" model.eval()\n",
" batch_size = 64\n",
" for batch_id, data in enumerate(train_loader()):\n",
" x_data = data[0]\n",
" y_data = data[1]\n",
" predicts = model(x_data)\n",
" # 获取预测结果\n",
" loss = paddle.nn.functional.cross_entropy(predicts, y_data)\n",
" acc = paddle.metric.accuracy(predicts, y_data, k=2)\n",
" avg_loss = paddle.mean(loss)\n",
" avg_acc = paddle.mean(acc)\n",
" avg_loss.backward()\n",
" if batch_id % 100 == 0:\n",
" print(\"batch_id: {}, loss is: {}, acc is: {}\".format(batch_id, avg_loss.numpy(), avg_acc.numpy()))\n",
"test(model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 训练方式一结束\n",
"以上就是训练方式一，通过这种方式，可以清楚的看到训练和测试中的每一步过程。但是，这种方式句法比较复杂。因此，我们提供了训练方式二，能够更加快速、高效的完成模型的训练与测试。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.训练方式二\n",
"通过paddle提供的`Model` 构建实例，使用封装好的训练与测试接口，快速完成模型训练与测试。"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -170,10 +298,9 @@ ...@@ -170,10 +298,9 @@
"from paddle.metric import Accuracy\n", "from paddle.metric import Accuracy\n",
"inputs = InputSpec([None, 784], 'float32', 'x')\n", "inputs = InputSpec([None, 784], 'float32', 'x')\n",
"labels = InputSpec([None, 10], 'float32', 'x')\n", "labels = InputSpec([None, 10], 'float32', 'x')\n",
"model = paddle.hapi.Model(LeNet(), inputs, labels)\n", "model = paddle.Model(LeNet(), inputs, labels)\n",
"optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())\n", "optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())\n",
"\n", "\n",
"\n",
"model.prepare(\n", "model.prepare(\n",
" optim,\n", " optim,\n",
" paddle.nn.loss.CrossEntropyLoss(),\n", " paddle.nn.loss.CrossEntropyLoss(),\n",
...@@ -190,7 +317,7 @@ ...@@ -190,7 +317,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 22, "execution_count": 42,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -198,266 +325,209 @@ ...@@ -198,266 +325,209 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Epoch 1/2\n", "Epoch 1/2\n",
"step 10/938 - loss: 2.2369 - acc_top1: 0.3281 - acc_top2: 0.4172 - 18ms/step\n" "step 10/938 - loss: 2.2434 - acc_top1: 0.1344 - acc_top2: 0.3719 - 14ms/step\n",
] "step 20/938 - loss: 2.0292 - acc_top1: 0.2836 - acc_top2: 0.4633 - 14ms/step\n",
}, "step 30/938 - loss: 1.9341 - acc_top1: 0.3755 - acc_top2: 0.5214 - 14ms/step\n",
{ "step 40/938 - loss: 1.8009 - acc_top1: 0.4469 - acc_top2: 0.5727 - 14ms/step\n",
"name": "stderr", "step 50/938 - loss: 1.8000 - acc_top1: 0.4975 - acc_top2: 0.6125 - 13ms/step\n",
"output_type": "stream", "step 60/938 - loss: 1.6335 - acc_top1: 0.5417 - acc_top2: 0.6438 - 14ms/step\n",
"text": [ "step 70/938 - loss: 1.7931 - acc_top1: 0.5708 - acc_top2: 0.6643 - 13ms/step\n",
"/Library/Python/3.7/site-packages/paddle/fluid/layers/utils.py:76: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n", "step 80/938 - loss: 1.6699 - acc_top1: 0.5961 - acc_top2: 0.6846 - 13ms/step\n",
" return (isinstance(seq, collections.Sequence) and\n" "step 90/938 - loss: 1.6832 - acc_top1: 0.6189 - acc_top2: 0.7069 - 13ms/step\n",
] "step 100/938 - loss: 1.6336 - acc_top1: 0.6409 - acc_top2: 0.7245 - 14ms/step\n",
}, "step 110/938 - loss: 1.6598 - acc_top1: 0.6557 - acc_top2: 0.7376 - 13ms/step\n",
{ "step 120/938 - loss: 1.6348 - acc_top1: 0.6708 - acc_top2: 0.7488 - 13ms/step\n",
"name": "stdout", "step 130/938 - loss: 1.6223 - acc_top1: 0.6851 - acc_top2: 0.7601 - 13ms/step\n",
"output_type": "stream", "step 140/938 - loss: 1.5622 - acc_top1: 0.6970 - acc_top2: 0.7694 - 13ms/step\n",
"text": [ "step 150/938 - loss: 1.6455 - acc_top1: 0.7065 - acc_top2: 0.7767 - 14ms/step\n",
"step 20/938 - loss: 2.0185 - acc_top1: 0.3656 - acc_top2: 0.4328 - 17ms/step\n", "step 160/938 - loss: 1.6127 - acc_top1: 0.7154 - acc_top2: 0.7837 - 14ms/step\n",
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"step 800/938 - loss: 1.5858 - acc_top1: 0.8377 - acc_top2: 0.8719 - 15ms/step\n", "step 938/938 - loss: 1.4942 - acc_top1: 0.8473 - acc_top2: 0.8778 - 14ms/step\n",
"step 810/938 - loss: 1.5800 - acc_top1: 0.8384 - acc_top2: 0.8724 - 15ms/step\n",
"step 820/938 - loss: 1.6312 - acc_top1: 0.8390 - acc_top2: 0.8727 - 15ms/step\n",
"step 830/938 - loss: 1.5812 - acc_top1: 0.8398 - acc_top2: 0.8732 - 15ms/step\n",
"step 840/938 - loss: 1.5661 - acc_top1: 0.8402 - acc_top2: 0.8734 - 15ms/step\n",
"step 850/938 - loss: 1.5379 - acc_top1: 0.8409 - acc_top2: 0.8739 - 15ms/step\n",
"step 860/938 - loss: 1.5266 - acc_top1: 0.8413 - acc_top2: 0.8740 - 15ms/step\n",
"step 870/938 - loss: 1.5264 - acc_top1: 0.8420 - acc_top2: 0.8745 - 15ms/step\n",
"step 880/938 - loss: 1.5688 - acc_top1: 0.8425 - acc_top2: 0.8748 - 15ms/step\n",
"step 890/938 - loss: 1.5707 - acc_top1: 0.8429 - acc_top2: 0.8751 - 15ms/step\n",
"step 900/938 - loss: 1.5564 - acc_top1: 0.8432 - acc_top2: 0.8752 - 15ms/step\n",
"step 910/938 - loss: 1.4924 - acc_top1: 0.8438 - acc_top2: 0.8757 - 15ms/step\n",
"step 920/938 - loss: 1.5514 - acc_top1: 0.8443 - acc_top2: 0.8760 - 15ms/step\n",
"step 930/938 - loss: 1.5850 - acc_top1: 0.8446 - acc_top2: 0.8762 - 15ms/step\n",
"step 938/938 - loss: 1.4915 - acc_top1: 0.8448 - acc_top2: 0.8764 - 15ms/step\n",
"save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/0\n", "save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/0\n",
"Eval begin...\n",
"step 10/157 - loss: 1.5984 - acc_top1: 0.8797 - acc_top2: 0.8953 - 5ms/step\n",
"step 20/157 - loss: 1.6266 - acc_top1: 0.8789 - acc_top2: 0.9000 - 5ms/step\n",
"step 30/157 - loss: 1.6475 - acc_top1: 0.8771 - acc_top2: 0.8984 - 5ms/step\n",
"step 40/157 - loss: 1.6329 - acc_top1: 0.8730 - acc_top2: 0.8957 - 5ms/step\n",
"step 50/157 - loss: 1.5399 - acc_top1: 0.8712 - acc_top2: 0.8934 - 5ms/step\n",
"step 60/157 - loss: 1.6322 - acc_top1: 0.8750 - acc_top2: 0.8961 - 5ms/step\n",
"step 70/157 - loss: 1.5818 - acc_top1: 0.8721 - acc_top2: 0.8931 - 5ms/step\n",
"step 80/157 - loss: 1.5522 - acc_top1: 0.8760 - acc_top2: 0.8979 - 5ms/step\n",
"step 90/157 - loss: 1.6085 - acc_top1: 0.8785 - acc_top2: 0.8984 - 5ms/step\n",
"step 100/157 - loss: 1.5661 - acc_top1: 0.8784 - acc_top2: 0.8980 - 5ms/step\n",
"step 110/157 - loss: 1.5694 - acc_top1: 0.8805 - acc_top2: 0.8996 - 5ms/step\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step 120/157 - loss: 1.6012 - acc_top1: 0.8824 - acc_top2: 0.9003 - 5ms/step\n",
"step 130/157 - loss: 1.5378 - acc_top1: 0.8844 - acc_top2: 0.9017 - 5ms/step\n",
"step 140/157 - loss: 1.5068 - acc_top1: 0.8858 - acc_top2: 0.9022 - 5ms/step\n",
"step 150/157 - loss: 1.5424 - acc_top1: 0.8873 - acc_top2: 0.9029 - 5ms/step\n",
"step 157/157 - loss: 1.5862 - acc_top1: 0.8872 - acc_top2: 0.9035 - 5ms/step\n",
"Eval samples: 10000\n",
"Epoch 2/2\n", "Epoch 2/2\n",
"step 10/938 - loss: 1.5988 - acc_top1: 0.8859 - acc_top2: 0.9016 - 15ms/step\n", "step 10/938 - loss: 1.5919 - acc_top1: 0.8875 - acc_top2: 0.9047 - 14ms/step\n",
"step 20/938 - loss: 1.5702 - acc_top1: 0.8852 - acc_top2: 0.9047 - 15ms/step\n", "step 20/938 - loss: 1.5900 - acc_top1: 0.8875 - acc_top2: 0.9062 - 14ms/step\n",
"step 30/938 - loss: 1.5999 - acc_top1: 0.8833 - acc_top2: 0.9021 - 15ms/step\n", "step 30/938 - loss: 1.5929 - acc_top1: 0.8891 - acc_top2: 0.9036 - 13ms/step\n",
"step 40/938 - loss: 1.5652 - acc_top1: 0.8816 - acc_top2: 0.9000 - 15ms/step\n", "step 40/938 - loss: 1.5855 - acc_top1: 0.8883 - acc_top2: 0.9027 - 13ms/step\n",
"step 50/938 - loss: 1.6163 - acc_top1: 0.8853 - acc_top2: 0.9047 - 15ms/step\n", "step 50/938 - loss: 1.6197 - acc_top1: 0.8916 - acc_top2: 0.9072 - 13ms/step\n",
"step 60/938 - loss: 1.5307 - acc_top1: 0.8849 - acc_top2: 0.9049 - 15ms/step\n", "step 60/938 - loss: 1.5084 - acc_top1: 0.8914 - acc_top2: 0.9078 - 13ms/step\n",
"step 70/938 - loss: 1.5542 - acc_top1: 0.8846 - acc_top2: 0.9029 - 15ms/step\n", "step 70/938 - loss: 1.5552 - acc_top1: 0.8904 - acc_top2: 0.9067 - 13ms/step\n",
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"step 690/938 - loss: 1.4820 - acc_top1: 0.8891 - acc_top2: 0.9060 - 15ms/step\n",
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"step 710/938 - loss: 1.5245 - acc_top1: 0.8911 - acc_top2: 0.9083 - 15ms/step\n",
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"step 790/938 - loss: 1.4850 - acc_top1: 0.8986 - acc_top2: 0.9166 - 15ms/step\n",
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"step 810/938 - loss: 1.4783 - acc_top1: 0.9005 - acc_top2: 0.9186 - 15ms/step\n",
"step 820/938 - loss: 1.5256 - acc_top1: 0.9011 - acc_top2: 0.9194 - 15ms/step\n",
"step 830/938 - loss: 1.4801 - acc_top1: 0.9019 - acc_top2: 0.9202 - 15ms/step\n",
"step 840/938 - loss: 1.4873 - acc_top1: 0.9026 - acc_top2: 0.9211 - 15ms/step\n",
"step 850/938 - loss: 1.5093 - acc_top1: 0.9034 - acc_top2: 0.9219 - 15ms/step\n",
"step 860/938 - loss: 1.4727 - acc_top1: 0.9042 - acc_top2: 0.9227 - 15ms/step\n",
"step 870/938 - loss: 1.4917 - acc_top1: 0.9050 - acc_top2: 0.9235 - 15ms/step\n",
"step 880/938 - loss: 1.4792 - acc_top1: 0.9058 - acc_top2: 0.9243 - 15ms/step\n",
"step 890/938 - loss: 1.4854 - acc_top1: 0.9066 - acc_top2: 0.9251 - 15ms/step\n",
"step 900/938 - loss: 1.4616 - acc_top1: 0.9074 - acc_top2: 0.9258 - 15ms/step\n",
"step 910/938 - loss: 1.4954 - acc_top1: 0.9081 - acc_top2: 0.9265 - 15ms/step\n",
"step 920/938 - loss: 1.4875 - acc_top1: 0.9087 - acc_top2: 0.9272 - 15ms/step\n",
"step 930/938 - loss: 1.5037 - acc_top1: 0.9094 - acc_top2: 0.9279 - 15ms/step\n",
"step 938/938 - loss: 1.4964 - acc_top1: 0.9099 - acc_top2: 0.9284 - 15ms/step\n",
"save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/1\n",
"Eval begin...\n",
"step 10/157 - loss: 1.5196 - acc_top1: 0.9719 - acc_top2: 0.9969 - 5ms/step\n",
"step 20/157 - loss: 1.5393 - acc_top1: 0.9672 - acc_top2: 0.9945 - 6ms/step\n",
"step 30/157 - loss: 1.4928 - acc_top1: 0.9630 - acc_top2: 0.9906 - 5ms/step\n",
"step 40/157 - loss: 1.4765 - acc_top1: 0.9617 - acc_top2: 0.9902 - 5ms/step\n",
"step 50/157 - loss: 1.4646 - acc_top1: 0.9631 - acc_top2: 0.9903 - 5ms/step\n"
] ]
}, },
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"step 60/157 - loss: 1.5646 - acc_top1: 0.9641 - acc_top2: 0.9906 - 5ms/step\n", "step 110/938 - loss: 1.5484 - acc_top1: 0.8848 - acc_top2: 0.9017 - 14ms/step\n",
"step 70/157 - loss: 1.5167 - acc_top1: 0.9618 - acc_top2: 0.9900 - 5ms/step\n", "step 120/938 - loss: 1.5904 - acc_top1: 0.8840 - acc_top2: 0.9020 - 14ms/step\n",
"step 80/157 - loss: 1.4728 - acc_top1: 0.9635 - acc_top2: 0.9906 - 5ms/step\n", "step 130/938 - loss: 1.5108 - acc_top1: 0.8852 - acc_top2: 0.9025 - 14ms/step\n",
"step 90/157 - loss: 1.5030 - acc_top1: 0.9668 - acc_top2: 0.9917 - 5ms/step\n", "step 140/938 - loss: 1.6199 - acc_top1: 0.8840 - acc_top2: 0.9016 - 14ms/step\n",
"step 100/157 - loss: 1.4612 - acc_top1: 0.9677 - acc_top2: 0.9914 - 5ms/step\n", "step 150/938 - loss: 1.5337 - acc_top1: 0.8842 - acc_top2: 0.9019 - 13ms/step\n",
"step 110/157 - loss: 1.4612 - acc_top1: 0.9689 - acc_top2: 0.9913 - 5ms/step\n", "step 160/938 - loss: 1.6094 - acc_top1: 0.8846 - acc_top2: 0.9023 - 13ms/step\n",
"step 120/157 - loss: 1.4612 - acc_top1: 0.9707 - acc_top2: 0.9919 - 5ms/step\n", "step 170/938 - loss: 1.5653 - acc_top1: 0.8843 - acc_top2: 0.9019 - 13ms/step\n",
"step 130/157 - loss: 1.4621 - acc_top1: 0.9719 - acc_top2: 0.9923 - 5ms/step\n", "step 180/938 - loss: 1.5978 - acc_top1: 0.8835 - acc_top2: 0.9011 - 13ms/step\n",
"step 140/157 - loss: 1.4612 - acc_top1: 0.9734 - acc_top2: 0.9929 - 5ms/step\n", "step 190/938 - loss: 1.5950 - acc_top1: 0.8833 - acc_top2: 0.9012 - 13ms/step\n",
"step 150/157 - loss: 1.4660 - acc_top1: 0.9748 - acc_top2: 0.9933 - 5ms/step\n", "step 200/938 - loss: 1.6422 - acc_top1: 0.8828 - acc_top2: 0.9002 - 13ms/step\n",
"step 157/157 - loss: 1.5215 - acc_top1: 0.9731 - acc_top2: 0.9930 - 5ms/step\n", "step 210/938 - loss: 1.5752 - acc_top1: 0.8831 - acc_top2: 0.9004 - 13ms/step\n",
"Eval samples: 10000\n", "step 220/938 - loss: 1.6635 - acc_top1: 0.8832 - acc_top2: 0.9001 - 13ms/step\n",
"step 230/938 - loss: 1.5726 - acc_top1: 0.8823 - acc_top2: 0.8991 - 13ms/step\n",
"step 240/938 - loss: 1.5702 - acc_top1: 0.8814 - acc_top2: 0.8981 - 13ms/step\n",
"step 250/938 - loss: 1.5748 - acc_top1: 0.8814 - acc_top2: 0.8981 - 14ms/step\n",
"step 260/938 - loss: 1.5589 - acc_top1: 0.8822 - acc_top2: 0.8988 - 14ms/step\n",
"step 270/938 - loss: 1.5902 - acc_top1: 0.8823 - acc_top2: 0.8988 - 14ms/step\n",
"step 280/938 - loss: 1.5646 - acc_top1: 0.8817 - acc_top2: 0.8982 - 14ms/step\n",
"step 290/938 - loss: 1.6280 - acc_top1: 0.8819 - acc_top2: 0.8985 - 14ms/step\n",
"step 300/938 - loss: 1.5697 - acc_top1: 0.8815 - acc_top2: 0.8982 - 14ms/step\n",
"step 310/938 - loss: 1.5540 - acc_top1: 0.8814 - acc_top2: 0.8981 - 14ms/step\n",
"step 320/938 - loss: 1.5598 - acc_top1: 0.8821 - acc_top2: 0.8988 - 14ms/step\n",
"step 330/938 - loss: 1.5498 - acc_top1: 0.8824 - acc_top2: 0.8991 - 14ms/step\n",
"step 340/938 - loss: 1.6276 - acc_top1: 0.8818 - acc_top2: 0.8984 - 14ms/step\n",
"step 350/938 - loss: 1.5129 - acc_top1: 0.8821 - acc_top2: 0.8988 - 14ms/step\n",
"step 360/938 - loss: 1.6158 - acc_top1: 0.8818 - acc_top2: 0.8984 - 14ms/step\n",
"step 370/938 - loss: 1.5300 - acc_top1: 0.8820 - acc_top2: 0.8986 - 14ms/step\n",
"step 380/938 - loss: 1.5718 - acc_top1: 0.8822 - acc_top2: 0.8988 - 14ms/step\n",
"step 390/938 - loss: 1.5898 - acc_top1: 0.8825 - acc_top2: 0.8990 - 14ms/step\n",
"step 400/938 - loss: 1.5177 - acc_top1: 0.8834 - acc_top2: 0.9000 - 14ms/step\n",
"step 410/938 - loss: 1.6493 - acc_top1: 0.8831 - acc_top2: 0.8997 - 14ms/step\n",
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"step 450/938 - loss: 1.5555 - acc_top1: 0.8844 - acc_top2: 0.9005 - 14ms/step\n",
"step 460/938 - loss: 1.5536 - acc_top1: 0.8845 - acc_top2: 0.9005 - 14ms/step\n",
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"step 520/938 - loss: 1.5588 - acc_top1: 0.8857 - acc_top2: 0.9016 - 14ms/step\n",
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"step 790/938 - loss: 1.6029 - acc_top1: 0.8879 - acc_top2: 0.9032 - 14ms/step\n",
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"step 830/938 - loss: 1.5911 - acc_top1: 0.8879 - acc_top2: 0.9033 - 14ms/step\n",
"step 840/938 - loss: 1.5845 - acc_top1: 0.8881 - acc_top2: 0.9035 - 14ms/step\n",
"step 850/938 - loss: 1.5628 - acc_top1: 0.8880 - acc_top2: 0.9035 - 14ms/step\n",
"step 860/938 - loss: 1.5596 - acc_top1: 0.8880 - acc_top2: 0.9035 - 14ms/step\n",
"step 870/938 - loss: 1.5843 - acc_top1: 0.8882 - acc_top2: 0.9036 - 14ms/step\n",
"step 880/938 - loss: 1.5393 - acc_top1: 0.8883 - acc_top2: 0.9036 - 14ms/step\n",
"step 890/938 - loss: 1.5382 - acc_top1: 0.8882 - acc_top2: 0.9035 - 14ms/step\n",
"step 900/938 - loss: 1.5910 - acc_top1: 0.8884 - acc_top2: 0.9036 - 14ms/step\n",
"step 910/938 - loss: 1.5682 - acc_top1: 0.8886 - acc_top2: 0.9038 - 14ms/step\n",
"step 920/938 - loss: 1.5736 - acc_top1: 0.8889 - acc_top2: 0.9039 - 14ms/step\n",
"step 930/938 - loss: 1.5283 - acc_top1: 0.8888 - acc_top2: 0.9038 - 14ms/step\n",
"step 938/938 - loss: 1.5582 - acc_top1: 0.8888 - acc_top2: 0.9038 - 14ms/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" "save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/final\n"
] ]
} }
], ],
"source": [ "source": [
"model.fit(train_dataset,\n", "model.fit(train_dataset,\n",
" test_dataset,\n",
" epochs=2,\n", " epochs=2,\n",
" batch_size=64,\n", " batch_size=64,\n",
" save_dir='mnist_checkpoint')" " save_dir='mnist_checkpoint')"
...@@ -467,131 +537,59 @@ ...@@ -467,131 +537,59 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"### 训练方式1结束\n", "### 使用model.evaluate来预测模型"
"以上就是训练方式1，可以非常快速的完成网络模型训练。此外，paddle还可以用下面的方式来完成模型的训练。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3.训练方式2\n",
"方式1可以快速便捷的完成训练，隐藏了训练时的细节。而方式2则可以用最基本的方式，完成模型的训练。具体如下。"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 23, "execution_count": 43,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"epoch: 0, batch_id: 0, loss is: [2.300888], acc is: [0.28125]\n", "Eval begin...\n",
"epoch: 0, batch_id: 100, loss is: [1.6948285], acc is: [0.8125]\n", "step 10/157 - loss: 1.5447 - acc_top1: 0.8953 - acc_top2: 0.9078 - 5ms/step\n",
"epoch: 0, batch_id: 200, loss is: [1.5282547], acc is: [0.96875]\n", "step 20/157 - loss: 1.6185 - acc_top1: 0.8930 - acc_top2: 0.9078 - 5ms/step\n",
"epoch: 0, batch_id: 300, loss is: [1.509404], acc is: [0.96875]\n", "step 30/157 - loss: 1.6497 - acc_top1: 0.8917 - acc_top2: 0.9057 - 5ms/step\n",
"epoch: 0, batch_id: 400, loss is: [1.4973292], acc is: [1.]\n", "step 40/157 - loss: 1.6318 - acc_top1: 0.8902 - acc_top2: 0.9055 - 5ms/step\n",
"epoch: 0, batch_id: 500, loss is: [1.5063374], acc is: [0.984375]\n", "step 50/157 - loss: 1.5533 - acc_top1: 0.8856 - acc_top2: 0.9012 - 5ms/step\n",
"epoch: 0, batch_id: 600, loss is: [1.490077], acc is: [0.984375]\n", "step 60/157 - loss: 1.6212 - acc_top1: 0.8878 - acc_top2: 0.9036 - 5ms/step\n",
"epoch: 0, batch_id: 700, loss is: [1.5206413], acc is: [0.984375]\n", "step 70/157 - loss: 1.5674 - acc_top1: 0.8839 - acc_top2: 0.9002 - 5ms/step\n",
"epoch: 0, batch_id: 800, loss is: [1.5104291], acc is: [1.]\n", "step 80/157 - loss: 1.5409 - acc_top1: 0.8891 - acc_top2: 0.9043 - 5ms/step\n",
"epoch: 0, batch_id: 900, loss is: [1.5216607], acc is: [0.96875]\n", "step 90/157 - loss: 1.6133 - acc_top1: 0.8903 - acc_top2: 0.9045 - 5ms/step\n",
"epoch: 1, batch_id: 0, loss is: [1.4949667], acc is: [0.984375]\n", "step 100/157 - loss: 1.5535 - acc_top1: 0.8909 - acc_top2: 0.9044 - 5ms/step\n",
"epoch: 1, batch_id: 100, loss is: [1.4923338], acc is: [0.96875]\n", "step 110/157 - loss: 1.5690 - acc_top1: 0.8916 - acc_top2: 0.9054 - 5ms/step\n",
"epoch: 1, batch_id: 200, loss is: [1.5026703], acc is: [1.]\n", "step 120/157 - loss: 1.6147 - acc_top1: 0.8926 - acc_top2: 0.9055 - 5ms/step\n",
"epoch: 1, batch_id: 300, loss is: [1.4965419], acc is: [0.984375]\n", "step 130/157 - loss: 1.5203 - acc_top1: 0.8944 - acc_top2: 0.9066 - 5ms/step\n",
"epoch: 1, batch_id: 400, loss is: [1.5270758], acc is: [1.]\n", "step 140/157 - loss: 1.5066 - acc_top1: 0.8952 - acc_top2: 0.9068 - 5ms/step\n",
"epoch: 1, batch_id: 500, loss is: [1.4774603], acc is: [1.]\n", "step 150/157 - loss: 1.5536 - acc_top1: 0.8958 - acc_top2: 0.9072 - 5ms/step\n",
"epoch: 1, batch_id: 600, loss is: [1.4762554], acc is: [0.984375]\n", "step 157/157 - loss: 1.5855 - acc_top1: 0.8956 - acc_top2: 0.9076 - 5ms/step\n",
"epoch: 1, batch_id: 700, loss is: [1.4773959], acc is: [0.984375]\n", "Eval samples: 10000\n"
"epoch: 1, batch_id: 800, loss is: [1.5044193], acc is: [1.]\n",
"epoch: 1, batch_id: 900, loss is: [1.4986757], acc is: [0.96875]\n"
] ]
} },
],
"source": [
"import paddle\n",
"train_loader = paddle.io.DataLoader(train_dataset, places=paddle.CPUPlace(), batch_size=64)\n",
"def train(model):\n",
" model.train()\n",
" epochs = 2\n",
" batch_size = 64\n",
" optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())\n",
" for epoch in range(epochs):\n",
" for batch_id, data in enumerate(train_loader()):\n",
" x_data = data[0]\n",
" y_data = data[1]\n",
" predicts = model(x_data)\n",
" loss = paddle.nn.functional.cross_entropy(predicts, y_data)\n",
" acc = paddle.metric.accuracy(predicts, y_data, k=2)\n",
" avg_loss = paddle.mean(loss)\n",
" avg_acc = paddle.mean(acc)\n",
" avg_loss.backward()\n",
" if batch_id % 100 == 0:\n",
" print(\"epoch: {}, batch_id: {}, loss is: {}, acc is: {}\".format(epoch, batch_id, avg_loss.numpy(), avg_acc.numpy()))\n",
" optim.minimize(avg_loss)\n",
" model.clear_gradients()\n",
"model = LeNet()\n",
"train(model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 对模型进行验证"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{ {
"name": "stdout", "data": {
"output_type": "stream", "text/plain": [
"text": [ "{'loss': [1.585474], 'acc_top1': 0.8956, 'acc_top2': 0.9076}"
"batch_id: 0, loss is: [1.5017498], acc is: [1.]\n", ]
"batch_id: 100, loss is: [1.4783669], acc is: [0.984375]\n", },
"batch_id: 200, loss is: [1.4958509], acc is: [1.]\n", "execution_count": 43,
"batch_id: 300, loss is: [1.4924574], acc is: [1.]\n", "metadata": {},
"batch_id: 400, loss is: [1.4762049], acc is: [1.]\n", "output_type": "execute_result"
"batch_id: 500, loss is: [1.4817208], acc is: [0.984375]\n",
"batch_id: 600, loss is: [1.4763825], acc is: [0.984375]\n",
"batch_id: 700, loss is: [1.4954926], acc is: [1.]\n",
"batch_id: 800, loss is: [1.5220823], acc is: [0.984375]\n",
"batch_id: 900, loss is: [1.4945463], acc is: [0.984375]\n"
]
} }
], ],
"source": [ "source": [
"import paddle\n", "model.evaluate(test_dataset, batch_size=64)"
"test_loader = paddle.io.DataLoader(test_dataset, places=paddle.CPUPlace(), batch_size=64)\n",
"def test(model):\n",
" model.eval()\n",
" batch_size = 64\n",
" for batch_id, data in enumerate(train_loader()):\n",
" x_data = data[0]\n",
" y_data = data[1]\n",
" predicts = model(x_data)\n",
" loss = paddle.nn.functional.cross_entropy(predicts, y_data)\n",
" acc = paddle.metric.accuracy(predicts, y_data, k=2)\n",
" avg_loss = paddle.mean(loss)\n",
" avg_acc = paddle.mean(acc)\n",
" avg_loss.backward()\n",
" if batch_id % 100 == 0:\n",
" print(\"batch_id: {}, loss is: {}, acc is: {}\".format(batch_id, avg_loss.numpy(), avg_acc.numpy()))\n",
"test(model)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"### 训练方式2结束\n", "### 训练方式二结束\n",
"以上就是训练方式2，通过这种方式，可以清楚的看到训练和测试中的每一步过程。" "以上就是训练方式二，可以快速、高效的完成网络模型训练与预测。"
] ]
}, },
{ {
......
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