From 5e48b05e1375360ffe471bcb610038cb3286fc78 Mon Sep 17 00:00:00 2001 From: Chen Long Date: Tue, 8 Sep 2020 19:39:41 +0800 Subject: [PATCH] add_tutorial_for_paddle test=develop (#2567) * add_totorial_for_paddle test=develop * fix_index test=develop * fix_index_tutorial test=develop * fix_index test=develop * fix_index test=develop * fix_index test=develop * fix index test=develop * change_md_to_rst test=develop * fix image path test=develop * fix index test=develop * fix image test=develop * fix_image test=develop * fix image test=develop * change title test=develop --- doc/paddle/index_cn.rst | 1 + doc/paddle/tutorial/cv_case/index_cn.rst | 15 + .../mnist_lenet_classification.rst | 503 ++++++++++++++++++ .../mnist_lenet_classification_6_1.png | Bin 0 -> 2309 bytes doc/paddle/tutorial/index_cn.rst | 19 + 5 files changed, 538 insertions(+) create mode 100644 doc/paddle/tutorial/cv_case/index_cn.rst create mode 100644 doc/paddle/tutorial/cv_case/mnist_lenet_classification/mnist_lenet_classification.rst create mode 100644 doc/paddle/tutorial/cv_case/mnist_lenet_classification/mnist_lenet_classification_files/mnist_lenet_classification_6_1.png create mode 100644 doc/paddle/tutorial/index_cn.rst diff --git a/doc/paddle/index_cn.rst b/doc/paddle/index_cn.rst index 8132b7879..de09e58d9 100644 --- a/doc/paddle/index_cn.rst +++ b/doc/paddle/index_cn.rst @@ -13,6 +13,7 @@ install/index_cn.rst guides/index_cn.rst + tutorial/index_cn.rst api/index_cn.rst faq/index_cn.rst release_note_cn.md diff --git a/doc/paddle/tutorial/cv_case/index_cn.rst b/doc/paddle/tutorial/cv_case/index_cn.rst new file mode 100644 index 000000000..2bf44a2bd --- /dev/null +++ b/doc/paddle/tutorial/cv_case/index_cn.rst @@ -0,0 +1,15 @@ +################ +计算机视觉 +################ + + +在这里PaddlePaddle为大家提供了一篇cv的教程供大家学习： + + - `图像分类 <./mnist_lenet_classification/mnist_lenet_classification.html>`_ ：介绍使用 Paddle 在MNIST数据集上完成图像分类。 + +.. toctree:: + :hidden: + :titlesonly: + + mnist_lenet_classification/mnist_lenet_classification.rst + diff --git a/doc/paddle/tutorial/cv_case/mnist_lenet_classification/mnist_lenet_classification.rst b/doc/paddle/tutorial/cv_case/mnist_lenet_classification/mnist_lenet_classification.rst new file mode 100644 index 000000000..0a6e13854 --- /dev/null +++ b/doc/paddle/tutorial/cv_case/mnist_lenet_classification/mnist_lenet_classification.rst @@ -0,0 +1,503 @@ +MNIST数据集使用LeNet进行图像分类 +================================ + +本示例教程演示如何在MNIST数据集上用LeNet进行图像分类。 +手写数字的MNIST数据集，包含60,000个用于训练的示例和10,000个用于测试的示例。这些数字已经过尺寸标准化并位于图像中心，图像是固定大小(28x28像素)，其值为0到1。该数据集的官方地址为：http://yann.lecun.com/exdb/mnist/ + +环境 +---- + +本教程基于paddle-develop编写，如果您的环境不是本版本，请先安装paddle-develop版本。 + +.. code:: ipython3 + + import paddle + print(paddle.__version__) + paddle.disable_static() + + +.. parsed-literal:: + + 0.0.0 + + +加载数据集 +---------- + +我们使用飞桨自带的paddle.dataset完成mnist数据集的加载。 + +.. code:: ipython3 + + print('download training data and load training data') + train_dataset = paddle.vision.datasets.MNIST(mode='train') + test_dataset = paddle.vision.datasets.MNIST(mode='test') + print('load finished') + + +.. parsed-literal:: + + /Library/Python/3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. + and should_run_async(code) + + +.. parsed-literal:: + + download training data and load training data + load finished + + +取训练集中的一条数据看一下。 + +.. code:: ipython3 + + import numpy as np + import matplotlib.pyplot as plt + train_data0, train_label_0 = train_dataset[0][0],train_dataset[0][1] + train_data0 = train_data0.reshape([28,28]) + plt.figure(figsize=(2,2)) + plt.imshow(train_data0, cmap=plt.cm.binary) + print('train_data0 label is: ' + str(train_label_0)) + + +.. parsed-literal:: + + train_data0 label is: [5] + + +.. image:: https://github.com/PaddlePaddle/FluidDoc/blob/develop/doc/paddle/user_guides/cv_case/image_classification/image/cifar.png?raw=true + +2.组网 +------ + +用paddle.nn下的API，如\ ``Conv2d``\ 、\ ``Pool2D``\ 、\ ``Linead``\ 完成LeNet的构建。 + +.. code:: ipython3 + + import paddle + import paddle.nn.functional as F + class LeNet(paddle.nn.Layer): + def __init__(self): + super(LeNet, self).__init__() + self.conv1 = paddle.nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2) + self.max_pool1 = paddle.nn.MaxPool2d(kernel_size=2, stride=2) + self.conv2 = paddle.nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1) + self.max_pool2 = paddle.nn.MaxPool2d(kernel_size=2, stride=2) + self.linear1 = paddle.nn.Linear(in_features=16*5*5, out_features=120) + self.linear2 = paddle.nn.Linear(in_features=120, out_features=84) + self.linear3 = paddle.nn.Linear(in_features=84, out_features=10) + + def forward(self, x): + x = self.conv1(x) + x = F.relu(x) + x = self.max_pool1(x) + x = F.relu(x) + x = self.conv2(x) + x = self.max_pool2(x) + x = paddle.reshape(x, shape=[-1, 16*5*5]) + x = self.linear1(x) + x = F.relu(x) + x = self.linear2(x) + x = F.relu(x) + x = self.linear3(x) + x = F.softmax(x) + return x + + +.. parsed-literal:: + + /Library/Python/3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. + and should_run_async(code) + + +3.训练方式一 +------------ + +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。 + +.. code:: ipython3 + + import paddle + train_loader = paddle.io.DataLoader(train_dataset, places=paddle.CPUPlace(), batch_size=64) + # 加载训练集 batch_size 设为 64 + def train(model): + model.train() + epochs = 2 + optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters()) + # 用Adam作为优化函数 + for epoch in range(epochs): + for batch_id, data in enumerate(train_loader()): + x_data = data[0] + y_data = data[1] + predicts = model(x_data) + loss = paddle.nn.functional.cross_entropy(predicts, y_data) + # 计算损失 + acc = paddle.metric.accuracy(predicts, y_data, k=2) + avg_loss = paddle.mean(loss) + avg_acc = paddle.mean(acc) + avg_loss.backward() + if batch_id % 100 == 0: + print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, avg_loss.numpy(), avg_acc.numpy())) + optim.minimize(avg_loss) + model.clear_gradients() + model = LeNet() + train(model) + + +.. parsed-literal:: + + epoch: 0, batch_id: 0, loss is: [2.3062382], acc is: [0.109375] + epoch: 0, batch_id: 100, loss is: [1.6826601], acc is: [0.84375] + epoch: 0, batch_id: 200, loss is: [1.685574], acc is: [0.796875] + epoch: 0, batch_id: 300, loss is: [1.5752499], acc is: [0.96875] + epoch: 0, batch_id: 400, loss is: [1.5006541], acc is: [1.] + epoch: 0, batch_id: 500, loss is: [1.5343401], acc is: [0.984375] + epoch: 0, batch_id: 600, loss is: [1.4875913], acc is: [0.984375] + epoch: 0, batch_id: 700, loss is: [1.5139006], acc is: [0.984375] + epoch: 0, batch_id: 800, loss is: [1.5227785], acc is: [0.984375] + epoch: 0, batch_id: 900, loss is: [1.4938308], acc is: [1.] + epoch: 1, batch_id: 0, loss is: [1.4826943], acc is: [0.984375] + epoch: 1, batch_id: 100, loss is: [1.4852213], acc is: [0.984375] + epoch: 1, batch_id: 200, loss is: [1.5008337], acc is: [1.] + epoch: 1, batch_id: 300, loss is: [1.505826], acc is: [1.] + epoch: 1, batch_id: 400, loss is: [1.4768786], acc is: [1.] + epoch: 1, batch_id: 500, loss is: [1.4950027], acc is: [0.984375] + epoch: 1, batch_id: 600, loss is: [1.4762383], acc is: [0.984375] + epoch: 1, batch_id: 700, loss is: [1.5276604], acc is: [0.96875] + epoch: 1, batch_id: 800, loss is: [1.4897399], acc is: [1.] + epoch: 1, batch_id: 900, loss is: [1.4927337], acc is: [1.] + + +对模型进行验证 +~~~~~~~~~~~~~~ + +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。 + +.. code:: ipython3 + + import paddle + test_loader = paddle.io.DataLoader(test_dataset, places=paddle.CPUPlace(), batch_size=64) + # 加载测试数据集 + def test(model): + model.eval() + batch_size = 64 + for batch_id, data in enumerate(train_loader()): + x_data = data[0] + y_data = data[1] + predicts = model(x_data) + # 获取预测结果 + loss = paddle.nn.functional.cross_entropy(predicts, y_data) + acc = paddle.metric.accuracy(predicts, y_data, k=2) + avg_loss = paddle.mean(loss) + avg_acc = paddle.mean(acc) + avg_loss.backward() + if batch_id % 100 == 0: + print("batch_id: {}, loss is: {}, acc is: {}".format(batch_id, avg_loss.numpy(), avg_acc.numpy())) + test(model) + + +.. parsed-literal:: + + batch_id: 0, loss is: [1.4630548], acc is: [1.] + batch_id: 100, loss is: [1.4789999], acc is: [0.984375] + batch_id: 200, loss is: [1.4621592], acc is: [1.] + batch_id: 300, loss is: [1.486401], acc is: [1.] + batch_id: 400, loss is: [1.4767764], acc is: [1.] + batch_id: 500, loss is: [1.4987783], acc is: [0.984375] + batch_id: 600, loss is: [1.4767168], acc is: [1.] + batch_id: 700, loss is: [1.4876428], acc is: [0.984375] + batch_id: 800, loss is: [1.4924926], acc is: [0.984375] + batch_id: 900, loss is: [1.4799261], acc is: [1.] + + +训练方式一结束 +~~~~~~~~~~~~~~ + +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。 + +3.训练方式二 +------------ + +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。 + +.. code:: ipython3 + + import paddle + from paddle.static import InputSpec + from paddle.metric import Accuracy + inputs = InputSpec([None, 784], 'float32', 'x') + labels = InputSpec([None, 10], 'float32', 'x') + model = paddle.Model(LeNet(), inputs, labels) + optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters()) + + model.prepare( + optim, + paddle.nn.loss.CrossEntropyLoss(), + Accuracy(topk=(1, 2)) + ) + +使用model.fit来训练模型 +~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: ipython3 + + model.fit(train_dataset, + epochs=2, + batch_size=64, + save_dir='mnist_checkpoint') + + +.. parsed-literal:: + + Epoch 1/2 + step 10/938 - 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14ms/step + step 920/938 - loss: 1.4768 - acc_top1: 0.9751 - acc_top2: 0.9927 - 14ms/step + step 930/938 - loss: 1.4806 - acc_top1: 0.9752 - acc_top2: 0.9928 - 14ms/step + step 938/938 - loss: 1.4910 - acc_top1: 0.9752 - acc_top2: 0.9928 - 14ms/step + save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/1 + save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/final + + +使用model.evaluate来预测模型 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: ipython3 + + model.evaluate(test_dataset, batch_size=64) + + +.. parsed-literal:: + + Eval begin... + step 10/157 - loss: 1.5014 - acc_top1: 0.9766 - acc_top2: 0.9953 - 6ms/step + step 20/157 - loss: 1.5239 - acc_top1: 0.9742 - acc_top2: 0.9922 - 6ms/step + step 30/157 - loss: 1.4926 - acc_top1: 0.9740 - acc_top2: 0.9932 - 6ms/step + step 40/157 - loss: 1.4612 - acc_top1: 0.9734 - acc_top2: 0.9938 - 6ms/step + step 50/157 - loss: 1.4612 - acc_top1: 0.9719 - 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