From f176cc8f79a500e8fe0da52437147d40154d6e18 Mon Sep 17 00:00:00 2001
From: Nicky Chan <nicky@baidu.com>
Date: Wed, 25 Apr 2018 22:33:11 -0700
Subject: [PATCH] Paddle tutorial cn (#428)

---
 demo/demo_cn.rst           |   1 +
 demo/paddle/TUTORIAL_CN.md | 173 +++++++++++++++++++++++++++++++++++++
 2 files changed, 174 insertions(+)
 create mode 100644 demo/paddle/TUTORIAL_CN.md

diff --git a/demo/demo_cn.rst b/demo/demo_cn.rst
index fa8a1924..c796c996 100644
--- a/demo/demo_cn.rst
+++ b/demo/demo_cn.rst
@@ -7,6 +7,7 @@ VisualDL 支持 Python 和 C++ 基于 DL 框架，
 .. toctree::
     :maxdepth: 1
 
+    paddle/TUTORIAL_CN.md
     keras/TUTORIAL_CN.md
     mxnet/TUTORIAL_CN.md
     pytorch/TUTORIAL_CN.md
\ No newline at end of file
diff --git a/demo/paddle/TUTORIAL_CN.md b/demo/paddle/TUTORIAL_CN.md
new file mode 100644
index 00000000..e0ee5dd7
--- /dev/null
+++ b/demo/paddle/TUTORIAL_CN.md
@@ -0,0 +1,173 @@
+# 如何在PaddlePaddle中使用VisualDL
+
+
+下面我们演示一下如何在PaddlePaddle中使用VisualDL，从而可以把PaddlePaddle的训练过程可视化出来。我们将以PaddlePaddle用卷积神经网络(CNN, Convolutional Neural Network)来训练
+[Cifar10](https://www.cs.toronto.edu/~kriz/cifar.html) 数据集作为例子。
+
+以下范例是在官方Paddle Book [Example](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification)
+的基础上用PaddlePaddle's Fluid API修改。
+
+完整的演示程序可以在[这里](https://github.com/PaddlePaddle/VisualDL/blob/develop/demo/paddle/paddle_cifar10.py)下载。
+
+这程序是在Paddle v2 0.11版本上开发。可以用```pip install paddlepaddle``` 或 ```docker pull paddlepaddle/paddle:0.11.0```来安装。注意Paddle还没支持Python3。
+安装详细流程请看[这里](http://paddlepaddle.org/docs/0.11.0/documentation/cn/getstarted/build_and_install/index_en.html)
+
+首先我们创建Loggers来记录不同种类的数据：
+
+```python
+# create VisualDL logger and directory
+logdir = "./tmp"
+logwriter = LogWriter(logdir, sync_cycle=10)
+
+# create 'train' run
+with logwriter.mode("train") as writer:
+    # create 'loss' scalar tag to keep track of loss function
+    loss_scalar = writer.scalar("loss")
+
+with logwriter.mode("train") as writer:
+    acc_scalar = writer.scalar("acc")
+
+num_samples = 4
+with logwriter.mode("train") as writer:
+    conv_image = writer.image("conv_image", num_samples, 1) #show 4 samples for every 1 step
+    input_image = writer.image("input_image", num_samples, 1)
+
+with logwriter.mode("train") as writer:
+    param1_histgram = writer.histogram("param1", 100) #100 buckets, e.g 100 data sets in a histograms
+
+```
+
+我们再来用Paddle v2 Fluid APIs创建VGG CNN模型:
+
+
+```python
+def vgg16_bn_drop(input):
+    def conv_block(input, num_filter, groups, dropouts):
+        return fluid.nets.img_conv_group(
+            input=input,
+            pool_size=2,
+            pool_stride=2,
+            conv_num_filter=[num_filter] * groups,
+            conv_filter_size=3,
+            conv_act='relu',
+            conv_with_batchnorm=True,
+            conv_batchnorm_drop_rate=dropouts,
+            pool_type='max')
+
+    conv1 = conv_block(input, 64, 2, [0.3, 0])
+    conv2 = conv_block(conv1, 128, 2, [0.4, 0])
+    conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
+    conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
+    conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
+
+    drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
+    fc1 = fluid.layers.fc(input=drop, size=512, act=None)
+    bn = fluid.layers.batch_norm(input=fc1, act='relu')
+    drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
+    fc2 = fluid.layers.fc(input=drop2, size=512, act=None)
+    return fc2, conv1
+
+
+classdim = 10
+data_shape = [3, 32, 32]
+
+images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
+label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+net, conv1 = vgg16_bn_drop(images)
+
+predict = fluid.layers.fc(
+    input=net,
+    size=classdim,
+    act='softmax',
+    param_attr=ParamAttr(name="param1", initializer=NormalInitializer()))
+cost = fluid.layers.cross_entropy(input=predict, label=label)
+avg_cost = fluid.layers.mean(x=cost)
+
+optimizer = fluid.optimizer.Adam(learning_rate=0.001)
+opts = optimizer.minimize(avg_cost)
+
+accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
+
+BATCH_SIZE = 16
+PASS_NUM = 1
+
+train_reader = paddle.batch(
+    paddle.reader.shuffle(paddle.dataset.cifar.train10(), buf_size=128 * 10),
+    batch_size=BATCH_SIZE)
+
+place = fluid.CPUPlace()
+exe = fluid.Executor(place)
+feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
+exe.run(fluid.default_startup_program())
+```
+
+接下来我们开始训练并且同时用 VisualDL 来采集相关数据
+
+
+```python
+for pass_id in range(PASS_NUM):
+    accuracy.reset(exe)
+    for data in train_reader():
+        loss, conv1_out, param1, acc = exe.run(
+            fluid.default_main_program(),
+            feed=feeder.feed(data),
+            fetch_list=[avg_cost, conv1, param1_var] + accuracy.metrics)
+        pass_acc = accuracy.eval(exe)
+
+        # all code below is for VisualDL
+
+        # start picking sample from beginning
+        if sample_num == 0:
+            input_image.start_sampling()
+            conv_image.start_sampling()
+
+        idx1 = input_image.is_sample_taken()
+        idx2 = conv_image.is_sample_taken()
+        assert idx1 == idx2
+        idx = idx1
+        if idx != -1:
+            image_data = data[0][0]
+            # reshape the image to 32x32 and 3 channels
+            input_image_data = np.transpose(
+                image_data.reshape(data_shape), axes=[1, 2, 0])
+            # add sample to VisualDL Image Writer to view input image
+            input_image.set_sample(idx, input_image_data.shape,
+                                   input_image_data.flatten())
+
+
+            conv_image_data = conv1_out[0][0]
+            # add sample to view conv image
+            conv_image.set_sample(idx, conv_image_data.shape,
+                                  conv_image_data.flatten())
+
+            sample_num += 1
+            # when we have enough samples, call finish sampling()
+            if sample_num % num_samples == 0:
+                input_image.finish_sampling()
+                conv_image.finish_sampling()
+                sample_num = 0
+
+        # add record for loss and accuracy to scalar
+        loss_scalar.add_record(step, loss)
+        acc_scalar.add_record(step, acc)
+        param1_histgram.add_record(step, param1.flatten())
+
+        print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
+            pass_acc))
+        step += 1
+```
+
+训练结束后，各个组件的可视化结果如下：
+
+关于accuracy和loss的数值图的如下：
+
+<p align=center>
+<img width="70%" src="https://github.com/daming-lu/large_files/blob/master/paddle_demo_figs/paddle_scalar.png?raw=true" />
+</p>
+
+训练过后的来源图和卷积权重图的各四个样本如下：
+
+<p align=center>
+<img width="70%" src="https://github.com/daming-lu/large_files/blob/master/paddle_demo_figs/paddle_image.png?raw=true" />
+</p>
-- 
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