DyGraph.md

# 动态图机制-DyGraph



PaddlePaddle的DyGraph模式是一种动态的图执行机制，可以立即执行结果，无需构建整个图。同时，和以往静态的执行计算图不同，DyGraph模式下您的所有操作可以立即获得执行结果，而不必等待所构建的计算图全部执行完成，这样可以让您更加直观地构建PaddlePaddle下的深度学习任务，以及进行模型的调试，同时还减少了大量用于构建静态计算图的代码，使得您编写、调试网络的过程变得更加便捷。



PaddlePaddle DyGraph是一个更加灵活易用的模式，可提供：      

*	更加灵活便捷的代码组织结构： 使用python的执行控制流程和面向对象的模型设计


* 	更加便捷的调试功能： 直接使用python的打印方法即时打印所需要的结果，从而检查正在运行的模型结果便于测试更改


*  和静态执行图通用的模型代码：同样的模型代码可以使用更加便捷的DyGraph调试，执行，同时也支持使用原有的静态图模式执行

有关的动态图机制更多的实际模型示例请参考[Paddle/models/dygraph](https://github.com/PaddlePaddle/models/tree/develop/dygraph)

## 设置和基本用法

1. 升级到最新的PaddlePaddle 1.5:		

```
pip install -q --upgrade paddlepaddle==1.5
```



2. 使用`fluid.dygraph.guard(place=None)` 上下文：

```python
import paddle.fluid as fluid
with fluid.dygraph.guard():
    # write your executable dygraph code here             
```

现在您就可以在`fluid.dygraph.guard()`上下文环境中使用DyGraph的模式运行网络了，DyGraph将改变以往PaddlePaddle的执行方式： 现在他们将会立即执行，并且将计算结果返回给Python。	
	
Dygraph将非常适合和Numpy一起使用，使用`fluid.dygraph.to_variable(x)`将会将ndarray转换为`fluid.Variable`，而使用`fluid.Variable.numpy()`将可以把任意时刻获取到的计算结果转换为Numpy`ndarray`：         
		
```python
x = np.ones([2, 2], np.float32)
with fluid.dygraph.guard():
    inputs = []
    for _ in range(10):
        inputs.append(fluid.dygraph.to_variable(x))
    ret = fluid.layers.sums(inputs)
    print(ret.numpy())
```

得到输出：

```
[[10. 10.]
[10. 10.]]
```
	
	
>	这里创建了一系列`ndarray`的输入，执行了一个`sum`操作之后，我们可以直接将运行的结果打印出来
		
然后通过调用`reduce_sum`后使用`Variable.backward()`方法执行反向，使用`Variable.gradient()`方法即可获得反向网络执行完成后的梯度值的`ndarray`形式：
	
	
```python
loss = fluid.layers.reduce_sum(ret)
loss.backward()
print(loss.gradient())
```
	
			
得到输出 ：

```
[1.]
```

## 基于DyGraph构建网络
		
1. 编写一段用于DyGraph执行的Object-Oriented-Designed, PaddlePaddle模型代码主要由以下**三个部分**组成： **请注意，如果您设计的这一层结构是包含参数的，则必须要使用继承自`fluid.dygraph.Layer`的Object-Oriented-Designed的类来描述该层的行为。**

	1. 建立一个可以在DyGraph模式中执行的，Object-Oriented的网络，需要继承自`fluid.dygraph.Layer`，其中需要调用基类的`__init__`方法，并且实现带有参数`name_scope`（用来标识本层的名字）的`__init__`构造函数，在构造函数中，我们通常会执行一些例如参数初始化，子网络初始化的操作，执行这些操作时不依赖于输入的动态信息:
        
        ```python
        class MyLayer(fluid.dygraph.Layer):
            def __init__(self, name_scope):
                super(MyLayer, self).__init__(name_scope)
        ```
    
    2. 实现一个`forward(self, *inputs)`的执行函数，该函数将负责执行实际运行时网络的执行逻辑， 该函数将会在每一轮训练/预测中被调用，这里我们将执行一个简单的`relu` -> `elementwise add` -> `reduce sum`：

        ```python
            def forward(self, inputs):
                x = fluid.layers.relu(inputs)
                self._x_for_debug = x
                x = fluid.layers.elementwise_mul(x, x)
                x = fluid.layers.reduce_sum(x)
                return [x]
        ```

2. 在`fluid.dygraph.guard()`中执行：
    
    1. 使用Numpy构建输入：

        ```python
        np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
        ```

	2. 转换输入的`ndarray`为`Variable`, 并执行前向网络获取返回值： 使用`fluid.dygraph.to_variable(np_inp)`转换Numpy输入为DyGraph接收的输入，然后使用`my_layer(var_inp)[0]`调用callable object并且获取了`x`作为返回值，利用`x.numpy()`方法直接获取了执行得到的`x`的`ndarray`返回值。

        ```python
        with fluid.dygraph.guard():
            var_inp = fluid.dygraph.to_variable(np_inp)
            my_layer = MyLayer("my_layer")
            x = my_layer(var_inp)[0]
            dy_out = x.numpy()
        ```
	
	3. 计算梯度：自动微分对于实现机器学习算法（例如用于训练神经网络的反向传播）来说很有用， 使用`x.backward()`方法可以从某个`fluid.Varaible`开始执行反向网络，同时利用`my_layer._x_for_debug.gradient()`获取了网络中`x`梯度的`ndarray` 返回值：

        ```python
            x.backward()
            dy_grad = my_layer._x_for_debug.gradient()
        ```

完整代码如下：

```python
import paddle.fluid as fluid
import numpy as np


class MyLayer(fluid.dygraph.Layer):
    def __init__(self, name_scope):
        super(MyLayer, self).__init__(name_scope)
        self.fc = fluid.dygraph.nn.FC(self.full_name(), size=12)
    
    def forward(self, inputs):
        x = self.fc(inputs)
        x = fluid.layers.relu(x)
        self._x_for_debug = x
        x = fluid.layers.elementwise_mul(x, x)
        x = fluid.layers.reduce_sum(x)
        return [x]


if __name__ == '__main__':
    np_inp = np.array([[1.0, 2.0, -1.0]], dtype=np.float32)
    with fluid.dygraph.guard():
        var_inp = fluid.dygraph.to_variable(np_inp)
        my_layer = MyLayer("my_layer")
        x = my_layer(var_inp)[0]
        dy_out = x.numpy()
        x.backward()
        dy_grad = my_layer._x_for_debug.gradient()
        my_layer.clear_gradients()  # 将参数梯度清零以保证下一轮训练的正确性
```

### 关于自动剪枝

每个 ``Variable`` 都有一个 ``stop_gradient`` 属性，可以用于细粒度地在反向梯度计算时排除部分子图，以提高效率。

如果OP只有一个输入需要梯度，那么该OP的输出也需要梯度。
相反，只有当OP的所有输入都不需要梯度时，该OP的输出也不需要梯度。
在所有的 ``Variable`` 都不需要梯度的子图中，反向计算就不会进行计算了。

在动态图模式下，除参数以外的所有 ``Variable`` 的 ``stop_gradient`` 属性默认值都为 ``True``，而参数的 ``stop_gradient`` 属性默认值为 ``False``。
该属性用于自动剪枝，避免不必要的反向运算。

例如：

```python
import paddle.fluid as fluid
import numpy as np

with fluid.dygraph.guard():
    x = fluid.dygraph.to_variable(np.random.randn(5, 5))  # 默认stop_gradient=True
    y = fluid.dygraph.to_variable(np.random.randn(5, 5))  # 默认stop_gradient=True
    z = fluid.dygraph.to_variable(np.random.randn(5, 5))
    z.stop_gradient = False
    a = x + y
    a.stop_gradient  # True
    b = a + z
    b.stop_gradient  # False
```

当你想冻结你的模型的一部分，或者你事先知道你不会使用某些参数的梯度的时候，这个功能是非常有用的。

例如：

```python
import paddle.fluid as fluid
import numpy as np

with fluid.dygraph.guard():
    value0 = np.arange(26).reshape(2, 13).astype("float32")
    value1 = np.arange(6).reshape(2, 3).astype("float32")
    value2 = np.arange(10).reshape(2, 5).astype("float32")
    fc = fluid.FC("fc1", size=5, dtype="float32")
    fc2 = fluid.FC("fc2", size=3, dtype="float32")
    a = fluid.dygraph.to_variable(value0)
    b = fluid.dygraph.to_variable(value1)
    c = fluid.dygraph.to_variable(value2)
    out1 = fc(a)
    out2 = fc2(b)
    out1.stop_gradient = True  # 将不会对out1这部分子图做反向计算
    out = fluid.layers.concat(input=[out1, out2, c], axis=1)
    out.backward()
    # 可以发现这里fc参数的梯度都为0
    assert (fc._w.gradient() == 0).all()
    assert (out1.gradient() == 0).all()
```

## 使用DyGraph训练模型

接下来我们将以“手写数字识别”这个最基础的模型为例，展示如何利用DyGraph模式搭建并训练一个模型：

有关手写数字识别的相关理论知识请参考[PaddleBook](https://github.com/PaddlePaddle/book/tree/develop/02.recognize_digits)中的内容，我们在这里默认您已经了解了该模型所需的深度学习理论知识。


1. 准备数据，我们使用`paddle.dataset.mnist`作为训练所需要的数据集：

    ```python
    train_reader = paddle.batch(
    paddle.dataset.mnist.train(), batch_size=BATCH_SIZE, drop_last=True)
    ```

2. 构建网络，虽然您可以根据之前的介绍自己定义所有的网络结构，但是您也可以直接使用`fluid.dygraph.Layer`当中我们为您定制好的一些基础网络结构，这里我们利用`fluid.dygraph.Conv2D`以及`fluid.dygraph.Pool2d`构建了基础的`SimpleImgConvPool`：

    ```python
    class SimpleImgConvPool(fluid.dygraph.Layer):
        def __init__(self,
                     name_scope,
                     num_filters,
                     filter_size,
                     pool_size,
                     pool_stride,
                     pool_padding=0,
                     pool_type='max',
                     global_pooling=False,
                     conv_stride=1,
                     conv_padding=0,
                     conv_dilation=1,
                     conv_groups=1,
                     act=None,
                     use_cudnn=False,
                     param_attr=None,
                     bias_attr=None):
            super(SimpleImgConvPool, self).__init__(name_scope)
    
            self._conv2d = fluid.dygraph.Conv2D(
                self.full_name(),
                num_filters=num_filters,
                filter_size=filter_size,
                stride=conv_stride,
                padding=conv_padding,
                dilation=conv_dilation,
                groups=conv_groups,
                param_attr=param_attr,
                bias_attr=bias_attr,
                act=act,
                use_cudnn=use_cudnn)
    
            self._pool2d = fluid.dygraph.Pool2D(
                self.full_name(),
                pool_size=pool_size,
                pool_type=pool_type,
                pool_stride=pool_stride,
                pool_padding=pool_padding,
                global_pooling=global_pooling,
                use_cudnn=use_cudnn)
    
        def forward(self, inputs):
            x = self._conv2d(inputs)
            x = self._pool2d(x)
            return x
    ```

    > 注意: 构建网络时子网络的定义和使用请在`__init__`中进行， 而子网络的执行则在`forward`函数中进行

3. 利用已经构建好的`SimpleImgConvPool`组成最终的`MNIST`网络：

    ```python
    class MNIST(fluid.dygraph.Layer):
        def __init__(self, name_scope):
            super(MNIST, self).__init__(name_scope)
    
            self._simple_img_conv_pool_1 = SimpleImgConvPool(
                self.full_name(), 20, 5, 2, 2, act="relu")
    
            self._simple_img_conv_pool_2 = SimpleImgConvPool(
                self.full_name(), 50, 5, 2, 2, act="relu")
    
            pool_2_shape = 50 * 4 * 4
            SIZE = 10
            scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5
            self._fc = fluid.dygraph.FC(self.full_name(),
                                        10,
                                        param_attr=fluid.param_attr.ParamAttr(
                                            initializer=fluid.initializer.NormalInitializer(
                                                loc=0.0, scale=scale)),
                                        act="softmax")
    
        def forward(self, inputs, label=None):
            x = self._simple_img_conv_pool_1(inputs)
            x = self._simple_img_conv_pool_2(x)
            x = self._fc(x)
            if label is not None:
                acc = fluid.layers.accuracy(input=x, label=label)
                return x, acc
            else:
                return x
   ```
			
4. 在`fluid.dygraph.guard()`中定义配置好的`MNIST`网络结构，此时即使没有训练也可以在`fluid.dygraph.guard()`中调用模型并且检查输出：

    ```python
    with fluid.dygraph.guard():
        mnist = MNIST("mnist")
        id, data = list(enumerate(train_reader()))[0]
        dy_x_data = np.array(
            [x[0].reshape(1, 28, 28)
             for x in data]).astype('float32')
        img = fluid.dygraph.to_variable(dy_x_data)
        print("result is: {}".format(mnist(img).numpy()))
   ```
   
   输出：
   
   ```
   result is: [[0.10135901 0.1051138  0.1027941  ... 0.0972859  0.10221873 0.10165327]
           [0.09735426 0.09970362 0.10198303 ... 0.10134517 0.10179105 0.10025002]
           [0.09539858 0.10213123 0.09543551 ... 0.10613529 0.10535969 0.097991  ]
           ...
           [0.10120598 0.0996111  0.10512722 ... 0.10067689 0.10088114 0.10071224]
           [0.09889644 0.10033772 0.10151272 ... 0.10245881 0.09878646 0.101483  ]
           [0.09097178 0.10078511 0.10198414 ... 0.10317434 0.10087223 0.09816764]]
   ```

5. 构建训练循环，在每一轮参数更新完成后我们调用`mnist.clear_gradients()`来重置梯度：

    ```python
    with fluid.dygraph.guard():
        epoch_num = 5		
        BATCH_SIZE = 64
        train_reader = paddle.batch(
            paddle.dataset.mnist.train(), batch_size=32, drop_last=True)
        mnist = MNIST("mnist")
        adam = fluid.optimizer.AdamOptimizer(learning_rate=0.001)
        for epoch in range(epoch_num):
            for batch_id, data in enumerate(train_reader()):
                dy_x_data = np.array([x[0].reshape(1, 28, 28)
                                      for x in data]).astype('float32')
                y_data = np.array(
                    [x[1] for x in data]).astype('int64').reshape(-1, 1)
    
                img = fluid.dygraph.to_variable(dy_x_data)
                label = fluid.dygraph.to_variable(y_data)
    
                cost = mnist(img)
    
                loss = fluid.layers.cross_entropy(cost, label)
                avg_loss = fluid.layers.mean(loss)
    
                if batch_id % 100 == 0 and batch_id is not 0:
                    print("epoch: {}, batch_id: {}, loss is: {}".format(epoch, batch_id, avg_loss.numpy()))
                avg_loss.backward()
                adam.minimize(avg_loss)
                mnist.clear_gradients()
    ```

6. 变量及优化器

	模型的参数或者任何您希望检测的值可以作为变量封装在类中，然后通过对象获取并使用`numpy()`方法获取其`ndarray`的输出， 在训练过程中您可以使用`mnist.parameters()`来获取到网络中所有的参数，也可以指定某一个`Layer`的某个参数或者`parameters()`来获取该层的所有参数，使用`numpy()`方法随时查看参数的值

	反向运行后调用之前定义的`Adam`优化器对象的`minimize`方法进行参数更新:
		
    ```python
    with fluid.dygraph.guard():
        epoch_num = 5
        BATCH_SIZE = 64
    
        mnist = MNIST("mnist")
        adam = fluid.optimizer.AdamOptimizer(learning_rate=0.001)
        train_reader = paddle.batch(
            paddle.dataset.mnist.train(), batch_size= BATCH_SIZE, drop_last=True)
    
        np.set_printoptions(precision=3, suppress=True)
        for epoch in range(epoch_num):
            for batch_id, data in enumerate(train_reader()):
                dy_x_data = np.array(
                    [x[0].reshape(1, 28, 28)
                     for x in data]).astype('float32')
                y_data = np.array(
                    [x[1] for x in data]).astype('int64').reshape(BATCH_SIZE, 1)
    
                img = fluid.dygraph.to_variable(dy_x_data)
                label = fluid.dygraph.to_variable(y_data)
                label.stop_gradient = True
    
                cost = mnist(img)
                loss = fluid.layers.cross_entropy(cost, label)
                avg_loss = fluid.layers.mean(loss)
    
                dy_out = avg_loss.numpy()
    
                avg_loss.backward()
                adam.minimize(avg_loss)
                mnist.clear_gradients()
    
                dy_param_value = {}
                for param in mnist.parameters():
                    dy_param_value[param.name] = param.numpy()
    
                if batch_id % 20 == 0:
                    print("Loss at step {}: {}".format(batch_id, avg_loss.numpy()))
        print("Final loss: {}".format(avg_loss.numpy()))
        print("_simple_img_conv_pool_1_conv2d W's mean is: {}".format(mnist._simple_img_conv_pool_1._conv2d._filter_param.numpy().mean()))
        print("_simple_img_conv_pool_1_conv2d Bias's mean is: {}".format(mnist._simple_img_conv_pool_1._conv2d._bias_param.numpy().mean()))
    ```

    输出：
    
        ```
        Loss at step 0: [2.302]
        Loss at step 20: [1.616]
        Loss at step 40: [1.244]
        Loss at step 60: [1.142]
        Loss at step 80: [0.911]
        Loss at step 100: [0.824]
        Loss at step 120: [0.774]
        Loss at step 140: [0.626]
        Loss at step 160: [0.609]
        Loss at step 180: [0.627]
        Loss at step 200: [0.466]
        Loss at step 220: [0.499]
        Loss at step 240: [0.614]
        Loss at step 260: [0.585]
        Loss at step 280: [0.503]
        Loss at step 300: [0.423]
        Loss at step 320: [0.509]
        Loss at step 340: [0.348]
        Loss at step 360: [0.452]
        Loss at step 380: [0.397]
        Loss at step 400: [0.54]
        Loss at step 420: [0.341]
        Loss at step 440: [0.337]
        Loss at step 460: [0.155]
        Final loss: [0.164]
        _simple_img_conv_pool_1_conv2d W's mean is: 0.00606656912714
        _simple_img_conv_pool_1_conv2d Bias's mean is: -3.4576318285e-05
        ```

7.	性能

在使用`fluid.dygraph.guard()`时可以通过传入`fluid.CUDAPlace(0)`或者`fluid.CPUPlace()`来选择执行DyGraph的设备，通常如果不做任何处理将会自动适配您的设备。

## 使用多卡训练模型

目前PaddlePaddle支持通过多进程方式进行多卡训练，即每个进程对应一张卡。训练过程中，在第一次执行前向操作时，如果该操作需要参数，则会将0号卡的参数Broadcast到其他卡上，确保各个卡上的参数一致；在计算完反向操作之后，将产生的参数梯度在所有卡之间进行聚合；最后在各个GPU卡上分别进行参数更新。

```python
place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id)
with fluid.dygraph.guard(place):

    strategy = fluid.dygraph.parallel.prepare_context()
    mnist = MNIST("mnist")
    adam = AdamOptimizer(learning_rate=0.001)
    mnist = fluid.dygraph.parallel.DataParallel(mnist, strategy)

    train_reader = paddle.batch(
        paddle.dataset.mnist.train(), batch_size=BATCH_SIZE, drop_last=True)
    train_reader = fluid.contrib.reader.distributed_batch_reader(
            train_reader)

    for epoch in range(epoch_num):
        for batch_id, data in enumerate(train_reader()):
            dy_x_data = np.array([x[0].reshape(1, 28, 28)
                                  for x in data]).astype('float32')
            y_data = np.array(
                [x[1] for x in data]).astype('int64').reshape(-1, 1)

            img = to_variable(dy_x_data)
            label = to_variable(y_data)
            label.stop_gradient = True

            cost, acc = mnist(img, label)

            loss = fluid.layers.cross_entropy(cost, label)
            avg_loss = fluid.layers.mean(loss)

            avg_loss = mnist.scale_loss(avg_loss)
            avg_loss.backward()
            mnist.apply_collective_grads()
            
            adam.minimize(avg_loss)
            mnist.clear_gradients()
            if batch_id % 100 == 0 and batch_id is not 0:
                print("epoch: {}, batch_id: {}, loss is: {}".format(epoch, batch_id, avg_loss.numpy()))
```

动态图单卡训练转多卡训练需要修改的地方主要有四处：
1. 需要从环境变量获取设备的ID，即：

    ```python
    place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id)
    ```

2. 需要对原模型做一些预处理，即：

    ```python
    strategy = fluid.dygraph.parallel.prepare_context()
    mnist = MNIST("mnist")
    adam = AdamOptimizer(learning_rate=0.001)
    mnist = fluid.dygraph.parallel.DataParallel(mnist, strategy)
    ```

3. 数据读取，必须确保每个进程读取的数据是不同的，即所有进程读取数据的交集为空，所有进程读取数据的并集是完整的数据集：

    ```python
    train_reader = paddle.batch(
        paddle.dataset.mnist.train(), batch_size=BATCH_SIZE, drop_last=True)
    train_reader = fluid.contrib.reader.distributed_batch_reader(
        train_reader)
    ```

4. 需要对loss进行调整，以及对参数的梯度进行聚合，即：

    ```python
    avg_loss = mnist.scale_loss(avg_loss)
    avg_loss.backward()
    mnist.apply_collective_grads()
    ```

Paddle动态图多进程多卡模型训练启动时需要指定使用的GPU，即如果使用`0,1,2,3`卡，启动方式如下：

```
python -m paddle.distributed.launch --selected_gpus=0,1,2,3 --log_dir ./mylog train.py 
```

输出结果为：

```
-----------  Configuration Arguments -----------
cluster_node_ips: 127.0.0.1
log_dir: ./mylog
node_ip: 127.0.0.1
print_config: True
selected_gpus: 0,1,2,3
started_port: 6170
training_script: train.py
training_script_args: ['--use_data_parallel', '1']
use_paddlecloud: True
------------------------------------------------
trainers_endpoints: 127.0.0.1:6170,127.0.0.1:6171,127.0.0.1:6172,127.0.0.1:6173 , node_id: 0 , current_node_ip: 127.0.0.1 , num_nodes: 1 , node_ips: ['127.0.0.1'] , nranks: 4
```

此时，程序会将每个进程的输出log导出到./mylog路径下：

```
.
├── mylog
│   ├── workerlog.0
│   ├── workerlog.1
│   ├── workerlog.2
│   └── workerlog.3
└── train.py
```

如果不指定`--log_dir`，程序会将打印出所有进程的输出，即：

```
-----------  Configuration Arguments -----------
cluster_node_ips: 127.0.0.1
log_dir: None
node_ip: 127.0.0.1
print_config: True
selected_gpus: 0,1,2,3
started_port: 6170
training_script: train.py
training_script_args: ['--use_data_parallel', '1']
use_paddlecloud: True
------------------------------------------------
trainers_endpoints: 127.0.0.1:6170,127.0.0.1:6171,127.0.0.1:6172,127.0.0.1:6173 , node_id: 0 , current_node_ip: 127.0.0.1 , num_nodes: 1 , node_ips: ['127.0.0.1'] , nranks: 4
grep: warning: GREP_OPTIONS is deprecated; please use an alias or script
grep: warning: GREP_OPTIONS is deprecated; please use an alias or script
grep: warning: GREP_OPTIONS is deprecated; please use an alias or script
grep: warning: GREP_OPTIONS is deprecated; please use an alias or script
I0923 09:32:36.423513 56410 nccl_context.cc:120] init nccl context nranks: 4 local rank: 1 gpu id: 1
I0923 09:32:36.425287 56411 nccl_context.cc:120] init nccl context nranks: 4 local rank: 2 gpu id: 2
I0923 09:32:36.429337 56409 nccl_context.cc:120] init nccl context nranks: 4 local rank: 0 gpu id: 0
I0923 09:32:36.429440 56412 nccl_context.cc:120] init nccl context nranks: 4 local rank: 3 gpu id: 3
W0923 09:32:42.594097 56412 device_context.cc:198] Please NOTE: device: 3, CUDA Capability: 70, Driver API Version: 9.0, Runtime API Version: 9.0
W0923 09:32:42.605836 56412 device_context.cc:206] device: 3, cuDNN Version: 7.5.
W0923 09:32:42.632463 56410 device_context.cc:198] Please NOTE: device: 1, CUDA Capability: 70, Driver API Version: 9.0, Runtime API Version: 9.0
W0923 09:32:42.637948 56410 device_context.cc:206] device: 1, cuDNN Version: 7.5.
W0923 09:32:42.648674 56411 device_context.cc:198] Please NOTE: device: 2, CUDA Capability: 70, Driver API Version: 9.0, Runtime API Version: 9.0
W0923 09:32:42.654021 56411 device_context.cc:206] device: 2, cuDNN Version: 7.5.
W0923 09:32:43.048696 56409 device_context.cc:198] Please NOTE: device: 0, CUDA Capability: 70, Driver API Version: 9.0, Runtime API Version: 9.0
W0923 09:32:43.053236 56409 device_context.cc:206] device: 0, cuDNN Version: 7.5.
start data reader (trainers_num: 4, trainer_id: 2)
start data reader (trainers_num: 4, trainer_id: 3)
start data reader (trainers_num: 4, trainer_id: 1)
start data reader (trainers_num: 4, trainer_id: 0)
Loss at epoch 0 step 0: [0.57390565]
Loss at epoch 0 step 0: [0.57523954]
Loss at epoch 0 step 0: [0.575606]
Loss at epoch 0 step 0: [0.5767452]
```

## 模型参数的保存



在模型训练中可以使用`                    fluid.dygraph.save_persistables(your_model_object.state_dict(), "save_dir", optimizers=None)`来保存`your_model_object`中所有的模型参数, 以及使用`learning rate decay`的优化器。也可以自定义需要保存的“参数名” - “参数对象”的Python Dictionary传入。

同样可以使用`models，optimizers =     fluid.dygraph.load_persistables("save_dir")`获取保存的模型参数和优化器。


再使用`your_modle_object.load_dict(models)`接口来恢复保存的模型参数从而达到继续训练的目的。

以及使用`your_optimizer_object.load(optimizers)`接口来恢复保存的优化器中的`learning rate decay`值




下面的代码展示了如何在“手写数字识别”任务中保存参数并且读取已经保存的参数来继续训练。


```python
with fluid.dygraph.guard():
    epoch_num = 5
    BATCH_SIZE = 64

    mnist = MNIST("mnist")
    adam = fluid.optimizer.Adam(learning_rate=0.001)
    train_reader = paddle.batch(
        paddle.dataset.mnist.train(), batch_size= BATCH_SIZE, drop_last=True)

    np.set_printoptions(precision=3, suppress=True)
    dy_param_init_value={}
    for epoch in range(epoch_num):
        for batch_id, data in enumerate(train_reader()):
            dy_x_data = np.array(
                [x[0].reshape(1, 28, 28)
                 for x in data]).astype('float32')
            y_data = np.array(
                [x[1] for x in data]).astype('int64').reshape(BATCH_SIZE, 1)

            img = fluid.dygraph.to_variable(dy_x_data)
            label = fluid.dygraph.to_variable(y_data)
            label.stop_gradient = True

            cost = mnist(img)
            loss = fluid.layers.cross_entropy(cost, label)
            avg_loss = fluid.layers.mean(loss)

            dy_out = avg_loss.numpy()

            avg_loss.backward()
            adam.minimize(avg_loss)
            if batch_id == 20:
                fluid.dygraph.save_persistables(mnist.state_dict(), "save_dir", adam)
            mnist.clear_gradients()

            if batch_id == 20:
                for param in mnist.parameters():
                    dy_param_init_value[param.name] = param.numpy()
                model, _ = fluid.dygraph.load_persistables("save_dir")
                mnist.load_dict(model)
                break
        if epoch == 0:
            break
    restore = mnist.parameters()
    # check save and load

    success = True
    for value in restore:
        if (not np.array_equal(value.numpy(), dy_param_init_value[value.name])) or (not np.isfinite(value.numpy().all())) or (np.isnan(value.numpy().any())):
            success = False
    print("model save and load success? {}".format(success))
```

需要注意的是，如果采用多卡训练，只需要一个进程对模型参数进行保存，因此在保存模型参数时，需要进行指定保存哪个进程的参数，比如

```python
    if fluid.dygraph.parallel.Env().local_rank == 0:
        fluid.dygraph.save_persistables(mnist.state_dict(), "save_dir")
```

## 模型评估

当我们需要在DyGraph模式下利用搭建的模型进行预测任务，可以使用`YourModel.eval()`接口，在之前的手写数字识别模型中我们使用`mnist.eval()`来启动预测模式（我们默认在`fluid.dygraph.guard()`上下文中是训练模式），在预测的模式下，DyGraph将只会执行前向的预测网络，而不会进行自动求导并执行反向网络：

下面的代码展示了如何使用DyGraph模式训练一个用于执行“手写数字识别”任务的模型并保存，并且利用已经保存好的模型进行预测。

我们在`fluid.dygraph.guard()`上下文中进行了模型的保存和训练，值得注意的是，当我们需要在训练的过程中进行预测时需要使用`YourModel.eval()`切换到预测模式，并且在预测完成后使用`YourModel.train()`切换回训练模式继续训练。

我们在`inference_mnist `中启用另一个`fluid.dygraph.guard()`，并在其上下文中`load`之前保存的`checkpoint`进行预测，同样的在执行预测前需要使用`YourModel.eval()`来切换到预测模式。
			
```python
def test_mnist(reader, model, batch_size):
    acc_set = []
    avg_loss_set = []
    for batch_id, data in enumerate(reader()):
        dy_x_data = np.array([x[0].reshape(1, 28, 28)
                              for x in data]).astype('float32')
        y_data = np.array(
            [x[1] for x in data]).astype('int64').reshape(batch_size, 1)

        img = fluid.dygraph.to_variable(dy_x_data)
        label = fluid.dygraph.to_variable(y_data)
        label.stop_gradient = True
        prediction, acc = model(img, label)
        loss = fluid.layers.cross_entropy(input=prediction, label=label)
        avg_loss = fluid.layers.mean(loss)
        acc_set.append(float(acc.numpy()))
        avg_loss_set.append(float(avg_loss.numpy()))

        # get test acc and loss
    acc_val_mean = np.array(acc_set).mean()
    avg_loss_val_mean = np.array(avg_loss_set).mean()

    return avg_loss_val_mean, acc_val_mean


def inference_mnist():
    with fluid.dygraph.guard():
        mnist_infer = MNIST("mnist")
        # load checkpoint
        model_dict, _ = fluid.dygraph.load_persistables("save_dir")
        mnist_infer.load_dict(model_dict)
        print("checkpoint loaded")

        # start evaluate mode
        mnist_infer.eval()

        def load_image(file):
            im = Image.open(file).convert('L')
            im = im.resize((28, 28), Image.ANTIALIAS)
            im = np.array(im).reshape(1, 1, 28, 28).astype(np.float32)
            im = im / 255.0 * 2.0 - 1.0
            return im

        cur_dir = os.path.dirname(os.path.realpath(__file__))
        tensor_img = load_image(cur_dir + '/image/infer_3.png')

        results = mnist_infer(fluid.dygraph.to_variable(tensor_img))
        lab = np.argsort(results.numpy())
        print("Inference result of image/infer_3.png is: %d" % lab[0][-1])

with fluid.dygraph.guard():
    epoch_num = 1
    BATCH_SIZE = 64
    mnist = MNIST("mnist")
    adam = fluid.optimizer.AdamOptimizer(learning_rate=0.001)
    test_reader = paddle.batch(
        paddle.dataset.mnist.test(), batch_size=BATCH_SIZE, drop_last=True)

    train_reader = paddle.batch(
        paddle.dataset.mnist.train(),
        batch_size=BATCH_SIZE,
        drop_last=True)

    for epoch in range(epoch_num):
        for batch_id, data in enumerate(train_reader()):
            dy_x_data = np.array([x[0].reshape(1, 28, 28)
                                  for x in data]).astype('float32')
            y_data = np.array(
                [x[1] for x in data]).astype('int64').reshape(-1, 1)

            img = fluid.dygraph.to_variable(dy_x_data)
            label = fluid.dygraph.to_variable(y_data)
            label.stop_gradient = True

            cost, acc = mnist(img, label)

            loss = fluid.layers.cross_entropy(cost, label)
            avg_loss = fluid.layers.mean(loss)


            avg_loss.backward()

            adam.minimize(avg_loss)
            # save checkpoint
            mnist.clear_gradients()
            if batch_id % 100 == 0:
                print("Loss at epoch {} step {}: {:}".format(
                    epoch, batch_id, avg_loss.numpy()))

        mnist.eval()
        test_cost, test_acc = test_mnist(test_reader, mnist, BATCH_SIZE)
        mnist.train()
        print("Loss at epoch {} , Test avg_loss is: {}, acc is: {}".format(
            epoch, test_cost, test_acc))

    fluid.dygraph.save_persistables(mnist.state_dict(), "save_dir")
    print("checkpoint saved")

    inference_mnist()
```

输出：

```
Loss at epoch 0 step 0: [2.2991252]
Loss at epoch 0 step 100: [0.15491392]
Loss at epoch 0 step 200: [0.13315125]
Loss at epoch 0 step 300: [0.10253005]
Loss at epoch 0 step 400: [0.04266362]
Loss at epoch 0 step 500: [0.08894891]
Loss at epoch 0 step 600: [0.08999012]
Loss at epoch 0 step 700: [0.12975612]
Loss at epoch 0 step 800: [0.15257305]
Loss at epoch 0 step 900: [0.07429226]
Loss at epoch 0 , Test avg_loss is: 0.05995981965082674, acc is: 0.9794671474358975
checkpoint saved
No optimizer loaded. If you didn't save optimizer, please ignore this. The program can still work with new optimizer. 
checkpoint loaded
Inference result of image/infer_3.png is: 3
```


## 编写兼容的模型

以上一步中手写数字识别的例子为例，动态图的模型代码可以直接用于静态图中作为模型代码，执行时，直接使用PaddlePaddle静态图执行方式即可，这里以静态图中的`executor`为例, 模型代码可以直接使用之前的模型代码，执行时使用`Executor`执行即可
	
```python
epoch_num = 1
BATCH_SIZE = 64
exe = fluid.Executor(fluid.CPUPlace())

mnist = MNIST("mnist")
sgd = fluid.optimizer.SGDOptimizer(learning_rate=1e-3)
train_reader = paddle.batch(
    paddle.dataset.mnist.train(), batch_size=BATCH_SIZE, drop_last=True)
img = fluid.layers.data(
    name='pixel', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
cost = mnist(img)
loss = fluid.layers.cross_entropy(cost, label)
avg_loss = fluid.layers.mean(loss)
sgd.minimize(avg_loss)

out = exe.run(fluid.default_startup_program())

for epoch in range(epoch_num):
    for batch_id, data in enumerate(train_reader()):
        static_x_data = np.array(
            [x[0].reshape(1, 28, 28)
             for x in data]).astype('float32')
        y_data = np.array(
            [x[1] for x in data]).astype('int64').reshape([BATCH_SIZE, 1])

        fetch_list = [avg_loss.name]
        out = exe.run(
            fluid.default_main_program(),
            feed={"pixel": static_x_data,
                  "label": y_data},
            fetch_list=fetch_list)

        static_out = out[0]

        if batch_id % 100 == 0 and batch_id is not 0:
            print("epoch: {}, batch_id: {}, loss: {}".format(epoch, batch_id, static_out))
```