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提交 783c4b20 编写于 作者: Y Youwei Song 提交者： Jiabin Yang
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fix Dygraph cn sample code and doc format (#1508) 

* fix Dygraph cn sample code, test=develop

* fix Dygraph doc format, test=develop
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doc/fluid/user_guides/howto/dygraph/DyGraph.md
浏览文件 @ 783c4b20
......@@ -22,35 +22,40 @@ PaddlePaddle DyGraph是一个更加灵活易用的模式，可提供：
1. 升级到最新的PaddlePaddle 1.5:
pip install -q --upgrade paddlepaddle==1.5
```
pip install -q --upgrade paddlepaddle==1.5
```
2. 使用`fluid.dygraph.guard(place=None)` 上下文：
import paddle.fluid as fluid
with fluid.dygraph.guard():
```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。
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`：
x = np.ones([2, 2], np.float32)
with fluid.dygraph.guard():
```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.]]
Process finished with exit code 0
```
[[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
......@@ -58,75 +63,75 @@ Dygraph将非常适合和Numpy一起使用，使用`fluid.dygraph.to_variable(x)
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：
loss = fluid.layers.reduce_sum(ret)
loss.backward()
print(loss.gradient())
```python
loss = fluid.layers.reduce_sum(ret)
loss.backward()
print(loss.gradient())
```
得到输出 ：
[1.]
Process finished with exit code 0
```
[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：
import paddle.fluid as fluid
import numpy as np
```python
import paddle.fluid as fluid
import numpy as np
class MyLayer(fluid.dygraph.Layer):
class MyLayer(fluid.dygraph.Layer):
def __init__(self, name_scope):
super(MyLayer, self).__init__(name_scope)
......@@ -138,16 +143,18 @@ Dygraph将非常适合和Numpy一起使用，使用`fluid.dygraph.to_variable(x)
return [x]
if __name__ == '__main__':
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)
var_inp.stop_gradient = False
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
```
## 使用DyGraph训练模型
......@@ -158,13 +165,14 @@ Dygraph将非常适合和Numpy一起使用，使用`fluid.dygraph.to_variable(x)
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,
......@@ -211,18 +219,13 @@ Dygraph将非常适合和Numpy一起使用，使用`fluid.dygraph.to_variable(x)
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)
......@@ -252,12 +255,11 @@ Dygraph将非常适合和Numpy一起使用，使用`fluid.dygraph.to_variable(x)
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]
......@@ -266,9 +268,11 @@ Dygraph将非常适合和Numpy一起使用，使用`fluid.dygraph.to_variable(x)
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 ]
......@@ -276,11 +280,11 @@ Dygraph将非常适合和Numpy一起使用，使用`fluid.dygraph.to_variable(x)
[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]]
Process finished with exit code 0
```
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
......@@ -309,9 +313,7 @@ Dygraph将非常适合和Numpy一起使用，使用`fluid.dygraph.to_variable(x)
avg_loss.backward()
adam.minimize(avg_loss)
mnist.clear_gradients()
```
6. 变量及优化器
......@@ -319,6 +321,7 @@ Dygraph将非常适合和Numpy一起使用，使用`fluid.dygraph.to_variable(x)
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
......@@ -360,9 +363,11 @@ Dygraph将非常适合和Numpy一起使用，使用`fluid.dygraph.to_variable(x)
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]
......@@ -390,17 +395,19 @@ Dygraph将非常适合和Numpy一起使用，使用`fluid.dygraph.to_variable(x)
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。
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。
place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id)
with fluid.dygraph.guard(place):
```python
place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id)
with fluid.dygraph.guard(place):
strategy = fluid.dygraph.parallel.prepare_context()
mnist = MNIST("mnist")
......@@ -436,100 +443,116 @@ Dygraph将非常适合和Numpy一起使用，使用`fluid.dygraph.to_variable(x)
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
```
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
```
----------- 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
```
.
├── 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]
```
----------- 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
......@@ -549,8 +572,8 @@ Paddle动态图多进程多卡模型训练启动时需要指定使用的GPU，
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。
with fluid.dygraph.guard():
```python
with fluid.dygraph.guard():
epoch_num = 5
BATCH_SIZE = 64
......@@ -601,11 +624,14 @@ Paddle动态图多进程多卡模型训练启动时需要指定使用的GPU，
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
......@@ -617,8 +643,8 @@ Paddle动态图多进程多卡模型训练启动时需要指定使用的GPU，
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。
def test_mnist(reader, model, batch_size):
```python
def test_mnist(reader, model, batch_size):
acc_set = []
avg_loss_set = []
for batch_id, data in enumerate(reader()):
......@@ -643,7 +669,7 @@ Paddle动态图多进程多卡模型训练启动时需要指定使用的GPU，
return avg_loss_val_mean, acc_val_mean
def inference_mnist():
def inference_mnist():
with fluid.dygraph.guard():
mnist_infer = MNIST("mnist")
# load checkpoint
......@@ -668,7 +694,7 @@ Paddle动态图多进程多卡模型训练启动时需要指定使用的GPU，
lab = np.argsort(results.numpy())
print("Inference result of image/infer_3.png is: %d" % lab[0][-1])
with fluid.dygraph.guard():
with fluid.dygraph.guard():
epoch_num = 1
BATCH_SIZE = 64
mnist = MNIST("mnist")
......@@ -717,50 +743,53 @@ Paddle动态图多进程多卡模型训练启动时需要指定使用的GPU，
print("checkpoint saved")
inference_mnist()
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
```
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())
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(
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(
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)
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())
out = exe.run(fluid.default_startup_program())
for epoch in range(epoch_num):
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)
......@@ -779,6 +808,4 @@ Paddle动态图多进程多卡模型训练启动时需要指定使用的GPU，
if batch_id % 100 == 0 and batch_id is not 0:
print("epoch: {}, batch_id: {}, loss: {}".format(epoch, batch_id, static_out))
\ No newline at end of file
```
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