update docs

docs/zh_cn/quick_start/nas_tutorial_example.md

+# 图像分类网络结构搜索-快速开始
+
+该教程以图像分类模型MobileNetV2为例，说明如何在cifar10数据集上快速使用[网络结构搜索接口](../api/nas_api.md)。
+Tips: 运行该示例前请确认已正确安装Paddle和PaddleSlim。
+
+该示例包含以下步骤：
+
+1. 加载示例数据
+2. 构建模型
+3. 开始训练当前网络结构
+4. 开始评估当前网络结构
+5. 回传当前网络结构的得分
+
+
+以下章节依次介绍每个步骤的内容。
+
+# 1. 加载示例数据
+使用的示例数据集为cifar10，paddle框架中`paddle.dataset.cifar`包括了cifar数据集的下载和读取，代码如下：
+```python
+import paddle
+import paddle.fluid as fluid
+train_reader = paddle.batch(paddle.reader.shuffle(paddle.dataset.cifar.train10(cycle=False), buf_size=1024),batch_size=256)
+train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())
+eval_reader = paddle.batch(paddle.dataset.cifar.test10(cycle=False), batch_size=256)
+eval_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())
+```
+其他数据集读取请参考[自定义数据集]()
+
+
+# 2. 构建模型
+```python
+### 初始化SANAS实例
+sanas = slim.nas.SANAS(configs=[('MobileNetV2Space')], server_addr=("", 8337), save_checkpoint=None)
+### 获取当前搜索到的模型结构
+archs = sanas.next_archs()[0]
+### 根据模型结构构造训练program和测试program
+train_program = fluid.Program()
+startup_program = fluid.Program()
+with fluid.program_guard(train_program, startup_program):
+    data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32')
+    label = fluid.data(name='label', shape=[None, 1], dtype='int64')
+    output = archs(data)
+    output = fluid.layers.fc(input=output, size=10)
+
+    ### 定义优化器和损失函数
+    softmax_out = fluid.layers.softmax(input=output, use_cudnn=False)
+    cost = fluid.layers.cross_entropy(input=softmax_out, label=label)
+    avg_cost = fluid.layers.mean(cost)
+    acc_top1 = fluid.layers.accuracy(input=softmax_out, label=label, k=1)
+    acc_top5 = fluid.layers.accuracy(input=softmax_out, label=label, k=5)
+    ### 克隆训练program得到测试program，这一步必须放在定义优化器之前
+    eval_program = fluid.default_main_program().clone(for_test=True)
+
+    optimizer = fluid.optimizer.Adam(learning_rate=0.1)
+    optimizer.minimize(avg_cost)
+
+    place = fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    exe.run(startup_program)
+```
+
+# 3. 开始训练当前网络结构
+```python
+outputs = [avg_cost.name, acc_top1.name, acc_top5.name]
+for data in train_reader():
+    batch_reward = exe.run(train_program, feed=train_feeder.feed(data), fetch_list = outputs)
+    print("TRAIN: loss: {}, acc1: {}, acc5:{}".format(batch_reward[0], batch_reward[1], batch_reward[2]))
+```
+
+# 4. 开始评估当前网络结构
+```python
+reward = []
+for data in eval_reader():
+    batch_reward = exe.run(eval_program, feed=eval_feeder.feed(data), fetch_list = outputs)
+    reward_avg = np.mean(np.array(batch_reward), axis=1)
+    reward.append(reward_avg)
+    print("TEST: loss: {}, acc1: {}, acc5:{}".format(batch_reward[0], batch_reward[1], batch_reward[2]))
+finally_reward = np.mean(np.array(reward), axis=0)
+print("FINAL TEST: avg_cost: {}, acc1: {}, acc5: {}".format(finally_reward[0], finally_reward[1], finally_reward[2]))
+```
+
+# 5. 回传当前网络结构的得分
+```python
+sanas.reward(float(finally_reward[1]))
+```