diff --git a/01.fit_a_line/README.cn.md b/01.fit_a_line/README.cn.md
index f32aad5391383566ff8d5c0c0062b2f270057bc5..ff7349ad7c9716ac3fb8516f044e1397fbd389a0 100644
--- a/01.fit_a_line/README.cn.md
+++ b/01.fit_a_line/README.cn.md
@@ -103,17 +103,9 @@ $$MSE=\frac{1}{n}\sum_{i=1}^{n}{(\hat{Y_i}-Y_i)}^2$$
 import paddle
 import paddle.fluid as fluid
 import numpy
+import math
+import sys
 from __future__ import print_function
-try:
-    from paddle.fluid.contrib.trainer import *
-    from paddle.fluid.contrib.inferencer import *
-except ImportError:
-    print(
-        "In the fluid 1.0, the trainer and inferencer are moving to paddle.fluid.contrib",
-        file=sys.stderr)
-    from paddle.fluid.trainer import *
-    from paddle.fluid.inferencer import *
-
 ```
 
 我们通过uci_housing模块引入了数据集合[UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing)
@@ -123,7 +115,7 @@ except ImportError:
 1. 数据下载的过程。下载数据保存在~/.cache/paddle/dataset/uci_housing/housing.data。
 2. [数据预处理](#数据预处理)的过程。
 
-接下来我们定义了用于训练和测试的数据提供器。提供器每次读入一个大小为`BATCH_SIZE`的数据批次。如果用户希望加一些随机性，她可以同时定义一个批次大小和一个缓存大小。这样的话，每次数据提供器会从缓存中随机读取批次大小那么多的数据。
+接下来我们定义了用于训练的数据提供器。提供器每次读入一个大小为`BATCH_SIZE`的数据批次。如果用户希望加一些随机性，它可以同时定义一个批次大小和一个缓存大小。这样的话，每次数据提供器会从缓存中随机读取批次大小那么多的数据。
 
 ```python
 BATCH_SIZE = 20
@@ -131,29 +123,27 @@ BATCH_SIZE = 20
 train_reader = paddle.batch(
     paddle.reader.shuffle(
         paddle.dataset.uci_housing.train(), buf_size=500),
-    batch_size=BATCH_SIZE)
+        batch_size=BATCH_SIZE)
 
 test_reader = paddle.batch(
     paddle.reader.shuffle(
         paddle.dataset.uci_housing.test(), buf_size=500),
-    batch_size=BATCH_SIZE)
+        batch_size=BATCH_SIZE)
 ```
 
 ### 配置训练程序
 训练程序的目的是定义一个训练模型的网络结构。对于线性回归来讲，它就是一个从输入到输出的简单的全连接层。更加复杂的结果，比如卷积神经网络，递归神经网络等会在随后的章节中介绍。训练程序必须返回`平均损失`作为第一个返回值，因为它会被后面反向传播算法所用到。
 
 ```python
-def train_program():
-    y = fluid.layers.data(name='y', shape=[1], dtype='float32')
-
-    # feature vector of length 13
-    x = fluid.layers.data(name='x', shape=[13], dtype='float32')
-    y_predict = fluid.layers.fc(input=x, size=1, act=None)
+x = fluid.layers.data(name='x', shape=[13], dtype='float32')
+y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+y_predict = fluid.layers.fc(input=x, size=1, act=None)
 
-    loss = fluid.layers.square_error_cost(input=y_predict, label=y)
-    avg_loss = fluid.layers.mean(loss)
+main_program = fluid.default_main_program()
+startup_program = fluid.default_startup_program()
 
-    return avg_loss
+cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+avg_loss = fluid.layers.mean(cost)
 ```
 
 ### Optimizer Function 配置
@@ -161,8 +151,11 @@ def train_program():
 在下面的 `SGD optimizer`，`learning_rate` 是训练的速度，与网络的训练收敛速度有关系。
 
 ```python
-def optimizer_program():
-    return fluid.optimizer.SGD(learning_rate=0.001)
+sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+sgd_optimizer.minimize(avg_loss)
+
+#clone a test_program
+test_program = main_program.clone(for_test=True)
 ```
 
 ### 定义运算场所
@@ -171,114 +164,130 @@ def optimizer_program():
 ```python
 use_cuda = False
 place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+
+exe = fluid.Executor(place)
+
 ```
 
-### 创建训练器
-训练器会读入一个训练程序和一些必要的其他参数：
+除此之外，还可以通过画图，来展现`训练进程`：
 
 ```python
-trainer = Trainer(
-    train_func=train_program,
-    place=place,
-    optimizer_func=optimizer_program)
+# Plot data
+from paddle.utils.plot import Ploter
+
+train_prompt = "Train cost"
+test_prompt = "Test cost"
+plot_prompt = Ploter(train_prompt, test_prompt)
+
 ```
 
-### 开始提供数据
-PaddlePaddle提供了读取数据者发生器机制来读取训练数据。读取数据者会一次提供多列数据，因此我们需要一个Python的list来定义读取顺序。
+### 创建训练过程
+训练需要有一个训练程序和一些必要参数，并构建了一个获取训练过程中测试误差的函数。
 
 ```python
-feed_order=['x', 'y']
+num_epochs = 100
+
+# For training test cost
+def train_test(executor, program, reader, feeder, fetch_list):
+    accumulated = 1 * [0]
+    count = 0
+    for data_test in reader():
+        outs = executor.run(program=program,
+                            feed=feeder.feed(data_test),
+                            fetch_list=fetch_list)
+        accumulated = [x_c[0] + x_c[1][0] for x_c in zip(accumulated, outs)]
+        count += 1
+    return [x_d / count for x_d in accumulated]
+
 ```
 
-除此之外，可以定义一个事件响应器来处理类似`打印训练进程`的事件：
+### 训练主循环
+PaddlePaddle提供了读取数据者发生器机制来读取训练数据。读取数据者会一次提供多列数据，因此我们需要一个Python的list来定义读取顺序。我们构建一个循环来进行训练，直到训练结果足够好或者循环次数足够多。
+如果训练顺利，可以把训练参数保存到`params_dirname`。
 
 ```python
+%matplotlib inline
 # Specify the directory to save the parameters
 params_dirname = "fit_a_line.inference.model"
-
-
-train_title = "Train cost"
-test_title = "Test cost"
-
-
+feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
+naive_exe = fluid.Executor(place)
+naive_exe.run(startup_program)
 step = 0
 
-# event_handler prints training and testing info
-def event_handler(event):
-    global step
-    if isinstance(event, EndStepEvent):
-        if step % 10 == 0:   # record a train cost every 10 batches
-            print("%s, Step %d, Cost %f" % (train_title, step, event.metrics[0]))
-
+exe_test = fluid.Executor(place)
+
+# main train loop.
+for pass_id in range(num_epochs):
+    for data_train in train_reader():
+        avg_loss_value, = exe.run(main_program,
+                                  feed=feeder.feed(data_train),
+                                  fetch_list=[avg_loss])
+        if step % 10 == 0:  # record a train cost every 10 batches
+            plot_cost.append(train_prompt, step, avg_loss_value[0])
+            plot_cost.plot()
         if step % 100 == 0:  # record a test cost every 100 batches
-            test_metrics = trainer.test(
-                reader=test_reader, feed_order=feed_order)
-            print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0]))
-
-            if test_metrics[0] < 10.0:
-                # If the accuracy is good enough, we can stop the training.
-                print('loss is less than 10.0, stop')
-                trainer.stop()
-        step += 1
-
-    if isinstance(event, EndEpochEvent):
-        if event.epoch % 10 == 0:
-            # We can save the trained parameters for the inferences later
-            if params_dirname is not None:
-                trainer.save_params(params_dirname)
-```
-
-### 开始训练
-我们现在可以通过调用`trainer.train()`来开始训练
+            test_metics = train_test(executor=exe_test,
+                                     program=test_program,
+                                     reader=test_reader,
+                                     fetch_list=[avg_loss.name],
+                                     feeder=feeder)
+            plot_cost.append(test_prompt, step, test_metics[0])
+            plot_cost.plot()
+            # If the accuracy is good enough, we can stop the training.
+            if test_metics[0] < 10.0:
+                break
 
-```python
-%matplotlib inline
+        step += 1
 
-# The training could take up to a few minutes.
-trainer.train(
-    reader=train_reader,
-    num_epochs=100,
-    event_handler=event_handler,
-    feed_order=feed_order)
+        if math.isnan(float(avg_loss_value[0])):
+            sys.exit("got NaN loss, training failed.")
+    if params_dirname is not None:
+        # We can save the trained parameters for the inferences later
+        fluid.io.save_inference_model(params_dirname, ['x'],
+                                      [y_predict], exe)
 ```
 
-
 ## 预测
-提供一个`inference_program`和一个`params_dirname`来初始化预测器。`params_dirname`用来存储我们的参数。
-
-### 设定预测程序
-类似于`trainer.train`，预测器需要一个预测程序来做预测。我们可以稍加修改我们的训练程序来把预测值包含进来。
+需要构建一个使用训练好的参数来进行预测的程序，训练好的参数位置在`params_dirname`。
 
+### 准备预测环境
+类似于训练过程，预测器需要一个预测程序来做预测。我们可以稍加修改我们的训练程序来把预测值包含进来。
 
 ```python
-def inference_program():
-    x = fluid.layers.data(name='x', shape=[13], dtype='float32')
-    y_predict = fluid.layers.fc(input=x, size=1, act=None)
-    return y_predict
+infer_exe = fluid.Executor(place)
+inference_scope = fluid.core.Scope()
 ```
 
 ### 预测
-预测器会从`params_dirname`中读取已经训练好的模型，来对从未遇见过的数据进行预测。
+通过fluid.io.load_inference_model，预测器会从`params_dirname`中读取已经训练好的模型，来对从未遇见过的数据进行预测。
 
 ```python
-inferencer = Inferencer(
-    infer_func=inference_program, param_path=params_dirname, place=place)
-
-batch_size = 10
-test_reader = paddle.batch(paddle.dataset.uci_housing.test(),batch_size=batch_size)
-test_data = next(test_reader())
-test_x = numpy.array([data[0] for data in test_data]).astype("float32")
-test_y = numpy.array([data[1] for data in test_data]).astype("float32")
-
-results = inferencer.infer({'x': test_x})
-
-print("infer results: (House Price)")
-for idx, val in enumerate(results[0]):
-    print("%d: %.2f" % (idx, val))
-
-print("\nground truth:")
-for idx, val in enumerate(test_y):
-    print("%d: %.2f" % (idx, val))
+with fluid.scope_guard(inference_scope):
+    [inference_program, feed_target_names,
+     fetch_targets] = fluid.io.load_inference_model(params_dirname, infer_exe)
+    batch_size = 10
+
+    infer_reader = paddle.batch(
+        paddle.dataset.uci_housing.test(), batch_size=batch_size)
+
+    infer_data = next(infer_reader())
+    infer_feat = numpy.array(
+        [data[0] for data in infer_data]).astype("float32")
+    infer_label = numpy.array(
+        [data[1] for data in infer_data]).astype("float32")
+
+    assert feed_target_names[0] == 'x'
+    results = infer_exe.run(inference_program,
+                            feed={feed_target_names[0]: numpy.array(infer_feat)},
+                            fetch_list=fetch_targets)
+
+    print("infer results: (House Price)")
+    for idx, val in enumerate(results[0]):
+        print("%d: %.2f" % (idx, val))
+
+    print("\nground truth:")
+    for idx, val in enumerate(infer_label):
+        print("%d: %.2f" % (idx, val))
 ```
 
 ## 总结
diff --git a/01.fit_a_line/index.cn.html b/01.fit_a_line/index.cn.html
index 2ebfb39f609bd6a54eb3291c369444ba17af164f..73588759040b4443fa8f74c6ee72177a5ac99b79 100644
--- a/01.fit_a_line/index.cn.html
+++ b/01.fit_a_line/index.cn.html
@@ -145,17 +145,9 @@ $$MSE=\frac{1}{n}\sum_{i=1}^{n}{(\hat{Y_i}-Y_i)}^2$$
 import paddle
 import paddle.fluid as fluid
 import numpy
+import math
+import sys
 from __future__ import print_function
-try:
-    from paddle.fluid.contrib.trainer import *
-    from paddle.fluid.contrib.inferencer import *
-except ImportError:
-    print(
-        "In the fluid 1.0, the trainer and inferencer are moving to paddle.fluid.contrib",
-        file=sys.stderr)
-    from paddle.fluid.trainer import *
-    from paddle.fluid.inferencer import *
-
 ```
 
 我们通过uci_housing模块引入了数据集合[UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing)
@@ -165,7 +157,7 @@ except ImportError:
 1. 数据下载的过程。下载数据保存在~/.cache/paddle/dataset/uci_housing/housing.data。
 2. [数据预处理](#数据预处理)的过程。
 
-接下来我们定义了用于训练和测试的数据提供器。提供器每次读入一个大小为`BATCH_SIZE`的数据批次。如果用户希望加一些随机性，她可以同时定义一个批次大小和一个缓存大小。这样的话，每次数据提供器会从缓存中随机读取批次大小那么多的数据。
+接下来我们定义了用于训练的数据提供器。提供器每次读入一个大小为`BATCH_SIZE`的数据批次。如果用户希望加一些随机性，它可以同时定义一个批次大小和一个缓存大小。这样的话，每次数据提供器会从缓存中随机读取批次大小那么多的数据。
 
 ```python
 BATCH_SIZE = 20
@@ -173,29 +165,27 @@ BATCH_SIZE = 20
 train_reader = paddle.batch(
     paddle.reader.shuffle(
         paddle.dataset.uci_housing.train(), buf_size=500),
-    batch_size=BATCH_SIZE)
+        batch_size=BATCH_SIZE)
 
 test_reader = paddle.batch(
     paddle.reader.shuffle(
         paddle.dataset.uci_housing.test(), buf_size=500),
-    batch_size=BATCH_SIZE)
+        batch_size=BATCH_SIZE)
 ```
 
 ### 配置训练程序
 训练程序的目的是定义一个训练模型的网络结构。对于线性回归来讲，它就是一个从输入到输出的简单的全连接层。更加复杂的结果，比如卷积神经网络，递归神经网络等会在随后的章节中介绍。训练程序必须返回`平均损失`作为第一个返回值，因为它会被后面反向传播算法所用到。
 
 ```python
-def train_program():
-    y = fluid.layers.data(name='y', shape=[1], dtype='float32')
-
-    # feature vector of length 13
-    x = fluid.layers.data(name='x', shape=[13], dtype='float32')
-    y_predict = fluid.layers.fc(input=x, size=1, act=None)
+x = fluid.layers.data(name='x', shape=[13], dtype='float32')
+y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+y_predict = fluid.layers.fc(input=x, size=1, act=None)
 
-    loss = fluid.layers.square_error_cost(input=y_predict, label=y)
-    avg_loss = fluid.layers.mean(loss)
+main_program = fluid.default_main_program()
+startup_program = fluid.default_startup_program()
 
-    return avg_loss
+cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+avg_loss = fluid.layers.mean(cost)
 ```
 
 ### Optimizer Function 配置
@@ -203,8 +193,11 @@ def train_program():
 在下面的 `SGD optimizer`，`learning_rate` 是训练的速度，与网络的训练收敛速度有关系。
 
 ```python
-def optimizer_program():
-    return fluid.optimizer.SGD(learning_rate=0.001)
+sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+sgd_optimizer.minimize(avg_loss)
+
+#clone a test_program
+test_program = main_program.clone(for_test=True)
 ```
 
 ### 定义运算场所
@@ -213,114 +206,130 @@ def optimizer_program():
 ```python
 use_cuda = False
 place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+
+exe = fluid.Executor(place)
+
 ```
 
-### 创建训练器
-训练器会读入一个训练程序和一些必要的其他参数：
+除此之外，还可以通过画图，来展现`训练进程`：
 
 ```python
-trainer = Trainer(
-    train_func=train_program,
-    place=place,
-    optimizer_func=optimizer_program)
+# Plot data
+from paddle.utils.plot import Ploter
+
+train_prompt = "Train cost"
+test_prompt = "Test cost"
+plot_prompt = Ploter(train_prompt, test_prompt)
+
 ```
 
-### 开始提供数据
-PaddlePaddle提供了读取数据者发生器机制来读取训练数据。读取数据者会一次提供多列数据，因此我们需要一个Python的list来定义读取顺序。
+### 创建训练过程
+训练需要有一个训练程序和一些必要参数，并构建了一个获取训练过程中测试误差的函数。
 
 ```python
-feed_order=['x', 'y']
+num_epochs = 100
+
+# For training test cost
+def train_test(executor, program, reader, feeder, fetch_list):
+    accumulated = 1 * [0]
+    count = 0
+    for data_test in reader():
+        outs = executor.run(program=program,
+                            feed=feeder.feed(data_test),
+                            fetch_list=fetch_list)
+        accumulated = [x_c[0] + x_c[1][0] for x_c in zip(accumulated, outs)]
+        count += 1
+    return [x_d / count for x_d in accumulated]
+
 ```
 
-除此之外，可以定义一个事件响应器来处理类似`打印训练进程`的事件：
+### 训练主循环
+PaddlePaddle提供了读取数据者发生器机制来读取训练数据。读取数据者会一次提供多列数据，因此我们需要一个Python的list来定义读取顺序。我们构建一个循环来进行训练，直到训练结果足够好或者循环次数足够多。
+如果训练顺利，可以把训练参数保存到`params_dirname`。
 
 ```python
+%matplotlib inline
 # Specify the directory to save the parameters
 params_dirname = "fit_a_line.inference.model"
-
-
-train_title = "Train cost"
-test_title = "Test cost"
-
-
+feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
+naive_exe = fluid.Executor(place)
+naive_exe.run(startup_program)
 step = 0
 
-# event_handler prints training and testing info
-def event_handler(event):
-    global step
-    if isinstance(event, EndStepEvent):
-        if step % 10 == 0:   # record a train cost every 10 batches
-            print("%s, Step %d, Cost %f" % (train_title, step, event.metrics[0]))
-
+exe_test = fluid.Executor(place)
+
+# main train loop.
+for pass_id in range(num_epochs):
+    for data_train in train_reader():
+        avg_loss_value, = exe.run(main_program,
+                                  feed=feeder.feed(data_train),
+                                  fetch_list=[avg_loss])
+        if step % 10 == 0:  # record a train cost every 10 batches
+            plot_cost.append(train_prompt, step, avg_loss_value[0])
+            plot_cost.plot()
         if step % 100 == 0:  # record a test cost every 100 batches
-            test_metrics = trainer.test(
-                reader=test_reader, feed_order=feed_order)
-            print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0]))
-
-            if test_metrics[0] < 10.0:
-                # If the accuracy is good enough, we can stop the training.
-                print('loss is less than 10.0, stop')
-                trainer.stop()
-        step += 1
-
-    if isinstance(event, EndEpochEvent):
-        if event.epoch % 10 == 0:
-            # We can save the trained parameters for the inferences later
-            if params_dirname is not None:
-                trainer.save_params(params_dirname)
-```
-
-### 开始训练
-我们现在可以通过调用`trainer.train()`来开始训练
+            test_metics = train_test(executor=exe_test,
+                                     program=test_program,
+                                     reader=test_reader,
+                                     fetch_list=[avg_loss.name],
+                                     feeder=feeder)
+            plot_cost.append(test_prompt, step, test_metics[0])
+            plot_cost.plot()
+            # If the accuracy is good enough, we can stop the training.
+            if test_metics[0] < 10.0:
+                break
 
-```python
-%matplotlib inline
+        step += 1
 
-# The training could take up to a few minutes.
-trainer.train(
-    reader=train_reader,
-    num_epochs=100,
-    event_handler=event_handler,
-    feed_order=feed_order)
+        if math.isnan(float(avg_loss_value[0])):
+            sys.exit("got NaN loss, training failed.")
+    if params_dirname is not None:
+        # We can save the trained parameters for the inferences later
+        fluid.io.save_inference_model(params_dirname, ['x'],
+                                      [y_predict], exe)
 ```
 
-
 ## 预测
-提供一个`inference_program`和一个`params_dirname`来初始化预测器。`params_dirname`用来存储我们的参数。
-
-### 设定预测程序
-类似于`trainer.train`，预测器需要一个预测程序来做预测。我们可以稍加修改我们的训练程序来把预测值包含进来。
+需要构建一个使用训练好的参数来进行预测的程序，训练好的参数位置在`params_dirname`。
 
+### 准备预测环境
+类似于训练过程，预测器需要一个预测程序来做预测。我们可以稍加修改我们的训练程序来把预测值包含进来。
 
 ```python
-def inference_program():
-    x = fluid.layers.data(name='x', shape=[13], dtype='float32')
-    y_predict = fluid.layers.fc(input=x, size=1, act=None)
-    return y_predict
+infer_exe = fluid.Executor(place)
+inference_scope = fluid.core.Scope()
 ```
 
 ### 预测
-预测器会从`params_dirname`中读取已经训练好的模型，来对从未遇见过的数据进行预测。
+通过fluid.io.load_inference_model，预测器会从`params_dirname`中读取已经训练好的模型，来对从未遇见过的数据进行预测。
 
 ```python
-inferencer = Inferencer(
-    infer_func=inference_program, param_path=params_dirname, place=place)
-
-batch_size = 10
-test_reader = paddle.batch(paddle.dataset.uci_housing.test(),batch_size=batch_size)
-test_data = next(test_reader())
-test_x = numpy.array([data[0] for data in test_data]).astype("float32")
-test_y = numpy.array([data[1] for data in test_data]).astype("float32")
-
-results = inferencer.infer({'x': test_x})
-
-print("infer results: (House Price)")
-for idx, val in enumerate(results[0]):
-    print("%d: %.2f" % (idx, val))
-
-print("\nground truth:")
-for idx, val in enumerate(test_y):
-    print("%d: %.2f" % (idx, val))
+with fluid.scope_guard(inference_scope):
+    [inference_program, feed_target_names,
+     fetch_targets] = fluid.io.load_inference_model(params_dirname, infer_exe)
+    batch_size = 10
+
+    infer_reader = paddle.batch(
+        paddle.dataset.uci_housing.test(), batch_size=batch_size)
+
+    infer_data = next(infer_reader())
+    infer_feat = numpy.array(
+        [data[0] for data in infer_data]).astype("float32")
+    infer_label = numpy.array(
+        [data[1] for data in infer_data]).astype("float32")
+
+    assert feed_target_names[0] == 'x'
+    results = infer_exe.run(inference_program,
+                            feed={feed_target_names[0]: numpy.array(infer_feat)},
+                            fetch_list=fetch_targets)
+
+    print("infer results: (House Price)")
+    for idx, val in enumerate(results[0]):
+        print("%d: %.2f" % (idx, val))
+
+    print("\nground truth:")
+    for idx, val in enumerate(infer_label):
+        print("%d: %.2f" % (idx, val))
 ```
 
 ## 总结
diff --git a/01.fit_a_line/train.py b/01.fit_a_line/train.py
index 6077a16b2b663d020035597d1d3b0276238d9e30..5b4625e07e4b00045f9689f061816f5212389ba4 100644
--- a/01.fit_a_line/train.py
+++ b/01.fit_a_line/train.py
@@ -13,122 +13,134 @@
 # limitations under the License.
 
 from __future__ import print_function
+
 import paddle
 import paddle.fluid as fluid
-import sys
-
-try:
-    from paddle.fluid.contrib.trainer import *
-    from paddle.fluid.contrib.inferencer import *
-except ImportError:
-    print(
-        "In the fluid 1.0, the trainer and inferencer are moving to paddle.fluid.contrib",
-        file=sys.stderr)
-    from paddle.fluid.trainer import *
-    from paddle.fluid.inferencer import *
-
 import numpy
+import math
+import sys
 
-BATCH_SIZE = 20
-
-train_reader = paddle.batch(
-    paddle.reader.shuffle(paddle.dataset.uci_housing.train(), buf_size=500),
-    batch_size=BATCH_SIZE)
 
-test_reader = paddle.batch(
-    paddle.reader.shuffle(paddle.dataset.uci_housing.test(), buf_size=500),
-    batch_size=BATCH_SIZE)
+# For training test cost
+def train_test(executor, program, reader, feeder, fetch_list):
+    accumulated = 1 * [0]
+    count = 0
+    for data_test in reader():
+        outs = executor.run(
+            program=program, feed=feeder.feed(data_test), fetch_list=fetch_list)
+        accumulated = [x_c[0] + x_c[1][0] for x_c in zip(accumulated, outs)]
+        count += 1
+    return [x_d / count for x_d in accumulated]
 
 
-def train_program():
-    y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+def main():
+    batch_size = 20
+    train_reader = paddle.batch(
+        paddle.reader.shuffle(paddle.dataset.uci_housing.train(), buf_size=500),
+        batch_size=batch_size)
+    test_reader = paddle.batch(
+        paddle.reader.shuffle(paddle.dataset.uci_housing.test(), buf_size=500),
+        batch_size=batch_size)
 
     # feature vector of length 13
     x = fluid.layers.data(name='x', shape=[13], dtype='float32')
+    y = fluid.layers.data(name='y', shape=[1], dtype='float32')
     y_predict = fluid.layers.fc(input=x, size=1, act=None)
 
-    loss = fluid.layers.square_error_cost(input=y_predict, label=y)
-    avg_loss = fluid.layers.mean(loss)
-
-    return avg_loss
-
-
-def optimizer_program():
-    return fluid.optimizer.SGD(learning_rate=0.001)
-
-
-# can use CPU or GPU
-use_cuda = False
-place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-
-trainer = Trainer(
-    train_func=train_program, place=place, optimizer_func=optimizer_program)
-
-feed_order = ['x', 'y']
-
-# Specify the directory to save the parameters
-params_dirname = "fit_a_line.inference.model"
-
-train_title = "Train cost"
-test_title = "Test cost"
-
-step = 0
-
-
-# event_handler prints training and testing info
-def event_handler(event):
-    global step
-    if isinstance(event, EndStepEvent):
-        if step % 10 == 0:  # record a train cost every 10 batches
-            print("%s, Step %d, Cost %f" %
-                  (train_title, step, event.metrics[0]))
-        if step % 100 == 0:  # record a test cost every 100 batches
-            test_metrics = trainer.test(
-                reader=test_reader, feed_order=feed_order)
-            print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0]))
-            if test_metrics[0] < 10.0:
+    main_program = fluid.default_main_program()
+    startup_program = fluid.default_startup_program()
+
+    cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+    avg_loss = fluid.layers.mean(cost)
+
+    sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+    sgd_optimizer.minimize(avg_loss)
+
+    test_program = main_program.clone(for_test=True)
+
+    # can use CPU or GPU
+    use_cuda = False
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    # Specify the directory to save the parameters
+    params_dirname = "fit_a_line.inference.model"
+    num_epochs = 100
+
+    # main train loop.
+    feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
+    exe.run(startup_program)
+
+    train_prompt = "Train cost"
+    test_prompt = "Test cost"
+    step = 0
+
+    exe_test = fluid.Executor(place)
+
+    for pass_id in range(num_epochs):
+        for data_train in train_reader():
+            avg_loss_value, = exe.run(
+                main_program,
+                feed=feeder.feed(data_train),
+                fetch_list=[avg_loss])
+            if step % 10 == 0:  # record a train cost every 10 batches
+                print("%s, Step %d, Cost %f" %
+                      (train_prompt, step, avg_loss_value[0]))
+
+            if step % 100 == 0:  # record a test cost every 100 batches
+                test_metics = train_test(
+                    executor=exe_test,
+                    program=test_program,
+                    reader=test_reader,
+                    fetch_list=[avg_loss],
+                    feeder=feeder)
+                print("%s, Step %d, Cost %f" %
+                      (test_prompt, step, test_metics[0]))
                 # If the accuracy is good enough, we can stop the training.
-                print('loss is less than 10.0, stop')
-                trainer.stop()
-        step += 1
+                if test_metics[0] < 10.0:
+                    break
 
-    if isinstance(event, EndEpochEvent):
-        if event.epoch % 10 == 0:
-            # We can save the trained parameters for the inferences later
-            if params_dirname is not None:
-                trainer.save_params(params_dirname)
+            step += 1
 
+            if math.isnan(float(avg_loss_value[0])):
+                sys.exit("got NaN loss, training failed.")
+        if params_dirname is not None:
+            # We can save the trained parameters for the inferences later
+            fluid.io.save_inference_model(params_dirname, ['x'], [y_predict],
+                                          exe)
 
-# The training could take up to a few minutes.
-trainer.train(
-    reader=train_reader,
-    num_epochs=100,
-    event_handler=event_handler,
-    feed_order=feed_order)
+    infer_exe = fluid.Executor(place)
+    inference_scope = fluid.core.Scope()
 
+    # infer
+    with fluid.scope_guard(inference_scope):
+        [inference_program, feed_target_names, fetch_targets
+         ] = fluid.io.load_inference_model(params_dirname, infer_exe)
+        batch_size = 10
 
-def inference_program():
-    x = fluid.layers.data(name='x', shape=[13], dtype='float32')
-    y_predict = fluid.layers.fc(input=x, size=1, act=None)
-    return y_predict
+        infer_reader = paddle.batch(
+            paddle.dataset.uci_housing.test(), batch_size=batch_size)
 
+        infer_data = next(infer_reader())
+        infer_feat = numpy.array(
+            [data[0] for data in infer_data]).astype("float32")
+        infer_label = numpy.array(
+            [data[1] for data in infer_data]).astype("float32")
 
-inferencer = Inferencer(
-    infer_func=inference_program, param_path=params_dirname, place=place)
+        assert feed_target_names[0] == 'x'
+        results = infer_exe.run(
+            inference_program,
+            feed={feed_target_names[0]: numpy.array(infer_feat)},
+            fetch_list=fetch_targets)
 
-batch_size = 10
-test_reader = paddle.batch(
-    paddle.dataset.uci_housing.test(), batch_size=batch_size)
-test_data = next(test_reader())
-test_x = numpy.array([data[0] for data in test_data]).astype("float32")
-test_y = numpy.array([data[1] for data in test_data]).astype("float32")
+        print("infer results: (House Price)")
+        for idx, val in enumerate(results[0]):
+            print("%d: %.2f" % (idx, val))
 
-results = inferencer.infer({'x': test_x})
+        print("\nground truth:")
+        for idx, val in enumerate(infer_label):
+            print("%d: %.2f" % (idx, val))
 
-print("infer results: (House Price)")
-for idx, val in enumerate(results[0]):
-    print("%d: %.2f" % (idx, val))
 
-print("\nground truth:")
-for idx, val in enumerate(test_y):
-    print("%d: %.2f" % (idx, val))
+if __name__ == '__main__':
+    main()