delete untracked file

01.fit_a_line/README.cn.md

@@ -104,6 +104,16 @@ import paddle
 import paddle.fluid as fluid
 import numpy
 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)
@@ -167,7 +177,7 @@ place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 训练器会读入一个训练程序和一些必要的其他参数：
 
 ```python
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=train_program,
     place=place,
     optimizer_func=optimizer_program)
@@ -186,22 +196,28 @@ feed_order=['x', 'y']
 # Specify the directory to save the parameters
 params_dirname = "fit_a_line.inference.model"
 
+# Plot data
+from paddle.utils import Ploter
+
 train_title = "Train cost"
 test_title = "Test cost"
+plot_cost = Ploter(train_title, test_title)
+
 
 step = 0
 
 # event_handler prints training and testing info
-def event_handler(event):
+def event_handler_plot(event):
     global step
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    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]))
+            plot_cost.append(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]))
+            plot_cost.append(test_title, step, test_metrics[0])
+            plot_cost.plot()
 
             if test_metrics[0] < 10.0:
                 # If the accuracy is good enough, we can stop the training.
@@ -209,7 +225,7 @@ def event_handler(event):
                 trainer.stop()
         step += 1
 
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    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:
@@ -226,7 +242,7 @@ def event_handler(event):
 trainer.train(
     reader=train_reader,
     num_epochs=100,
-    event_handler=event_handler,
+    event_handler=event_handler_plot,
     feed_order=feed_order)
 ```
 
@@ -249,7 +265,7 @@ def inference_program():
 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 = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
     infer_func=inference_program, param_path=params_dirname, place=place)
 
 batch_size = 10

01.fit_a_line/README.md

@@ -115,6 +115,16 @@ import paddle
 import paddle.fluid as fluid
 import numpy
 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 *
+
 ```
 
 We encapsulated the [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) in our Python module `uci_housing`.  This module can
@@ -180,7 +190,7 @@ place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 The trainer will take the `train_program` as input.
 
 ```python
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=train_program,
     place=place,
     optimizer_func=optimizer_program)
@@ -202,24 +212,28 @@ Moreover, an event handler is provided to print the training progress:
 # Specify the directory to save the parameters
 params_dirname = "fit_a_line.inference.model"
 
+# Plot data
+from paddle.utils import Ploter
+
 train_title = "Train cost"
 test_title = "Test cost"
+plot_cost = Ploter(train_title, test_title)
 
 step = 0
 
 # event_handler prints training and testing info
-def event_handler(event):
+def event_handler_plot(event):
     global step
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    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]))
-
+            plot_cost.append(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]))
-
+            plot_cost.append(test_title, step, test_metrics[0])
+            plot_cost.plot()
             if test_metrics[0] < 10.0:
+            print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0]))
                 # If the accuracy is good enough, we can stop the training.
                 print('loss is less than 10.0, stop')
                 trainer.stop()
@@ -244,7 +258,7 @@ We now can start training by calling `trainer.train()`.
 trainer.train(
     reader=train_reader,
     num_epochs=100,
-    event_handler=event_handler,
+    event_handler=event_handler_plot,
     feed_order=feed_order)
 
 ```
@@ -272,7 +286,7 @@ def inference_program():
 Inferencer will load the trained model from `params_dirname` and use it to infer the unseen data.
 
 ```python
-inferencer = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
     infer_func=inference_program, param_path=params_dirname, place=place)
 
 batch_size = 10

01.fit_a_line/index.cn.html

@@ -146,6 +146,16 @@ import paddle
 import paddle.fluid as fluid
 import numpy
 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)
@@ -209,7 +219,7 @@ place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 训练器会读入一个训练程序和一些必要的其他参数：
 
 ```python
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=train_program,
     place=place,
     optimizer_func=optimizer_program)
@@ -228,22 +238,28 @@ feed_order=['x', 'y']
 # Specify the directory to save the parameters
 params_dirname = "fit_a_line.inference.model"
 
+# Plot data
+from paddle.utils import Ploter
+
 train_title = "Train cost"
 test_title = "Test cost"
+plot_cost = Ploter(train_title, test_title)
+
 
 step = 0
 
 # event_handler prints training and testing info
-def event_handler(event):
+def event_handler_plot(event):
     global step
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    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]))
+            plot_cost.append(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]))
+            plot_cost.append(test_title, step, test_metrics[0])
+            plot_cost.plot()
 
             if test_metrics[0] < 10.0:
                 # If the accuracy is good enough, we can stop the training.
@@ -251,7 +267,7 @@ def event_handler(event):
                 trainer.stop()
         step += 1
 
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    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:
@@ -268,7 +284,7 @@ def event_handler(event):
 trainer.train(
     reader=train_reader,
     num_epochs=100,
-    event_handler=event_handler,
+    event_handler=event_handler_plot,
     feed_order=feed_order)
 ```
 
@@ -291,7 +307,7 @@ def inference_program():
 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 = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
     infer_func=inference_program, param_path=params_dirname, place=place)
 
 batch_size = 10

01.fit_a_line/index.html

@@ -157,6 +157,16 @@ import paddle
 import paddle.fluid as fluid
 import numpy
 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 *
+
 ```
 
 We encapsulated the [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) in our Python module `uci_housing`.  This module can
@@ -222,7 +232,7 @@ place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 The trainer will take the `train_program` as input.
 
 ```python
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=train_program,
     place=place,
     optimizer_func=optimizer_program)
@@ -244,24 +254,28 @@ Moreover, an event handler is provided to print the training progress:
 # Specify the directory to save the parameters
 params_dirname = "fit_a_line.inference.model"
 
+# Plot data
+from paddle.utils import Ploter
+
 train_title = "Train cost"
 test_title = "Test cost"
+plot_cost = Ploter(train_title, test_title)
 
 step = 0
 
 # event_handler prints training and testing info
-def event_handler(event):
+def event_handler_plot(event):
     global step
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    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]))
-
+            plot_cost.append(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]))
-
+            plot_cost.append(test_title, step, test_metrics[0])
+            plot_cost.plot()
             if test_metrics[0] < 10.0:
+            print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0]))
                 # If the accuracy is good enough, we can stop the training.
                 print('loss is less than 10.0, stop')
                 trainer.stop()
@@ -286,7 +300,7 @@ We now can start training by calling `trainer.train()`.
 trainer.train(
     reader=train_reader,
     num_epochs=100,
-    event_handler=event_handler,
+    event_handler=event_handler_plot,
     feed_order=feed_order)
 
 ```
@@ -314,7 +328,7 @@ def inference_program():
 Inferencer will load the trained model from `params_dirname` and use it to infer the unseen data.
 
 ```python
-inferencer = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
     infer_func=inference_program, param_path=params_dirname, place=place)
 
 batch_size = 10

01.fit_a_line/train.py

@@ -69,25 +69,26 @@ feed_order = ['x', 'y']
 # Specify the directory to save the parameters
 params_dirname = "fit_a_line.inference.model"
 
+from paddle.utils import Ploter
+
 train_title = "Train cost"
 test_title = "Test cost"
+plot_cost = Ploter(train_title, test_title)
 
 step = 0
 
 
 # event_handler prints training and testing info
-def event_handler(event):
+def event_handler_plot(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]))
-
+            plot_cost.append(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]))
-
+            plot_cost.append(test_title, step, test_metrics[0])
+            plot_cost.plot()
             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')
@@ -105,7 +106,7 @@ def event_handler(event):
 trainer.train(
     reader=train_reader,
     num_epochs=100,
-    event_handler=event_handler,
+    event_handler=event_handler_plot,
     feed_order=feed_order)
 
 

02.recognize_digits/README.cn.md

@@ -160,6 +160,15 @@ PaddlePaddle在API中提供了自动加载[MNIST](http://yann.lecun.com/exdb/mni
 import paddle
 import paddle.fluid as fluid
 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 *
 ```
 
 ### Program Functions 配置
@@ -278,7 +287,7 @@ test_reader = paddle.batch(
 use_cuda = False # set to True if training with GPU
 place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=train_program, place=place, optimizer_func=optimizer_program)
 ```
 
@@ -294,14 +303,14 @@ Fluid API 在训练期间为回调函数提供了一个钩子。用户能够通
 params_dirname = "recognize_digits_network.inference.model"
 lists = []
 def event_handler(event):
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if event.step % 100 == 0:
             # event.metrics maps with train program return arguments.
             # event.metrics[0] will yeild avg_cost and event.metrics[1] will yeild acc in this example.
             print("Pass %d, Batch %d, Cost %f" % (
                 event.step, event.epoch, event.metrics[0]))
 
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    if isinstance(event, EndEpochEvent):
         avg_cost, acc = trainer.test(
             reader=test_reader, feed_order=['img', 'label'])
 
@@ -332,14 +341,14 @@ lists = []
 # event_handler to plot a figure
 def event_handler_plot(event):
     global step
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if step % 100 == 0:
             # event.metrics maps with train program return arguments.
             # event.metrics[0] will yeild avg_cost and event.metrics[1] will yeild acc in this example.
             cost_ploter.append(train_title, step, event.metrics[0])
             cost_ploter.plot()
         step += 1
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    if isinstance(event, EndEpochEvent):
         # save parameters
         trainer.save_params(params_dirname)
 
@@ -392,7 +401,7 @@ Test with Epoch 0, avg_cost: 0.053097883707459624, acc: 0.9822850318471338
 我们可以简单地插入在此之前定义的分类器。
 
 ```python
-inferencer = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
     # infer_func=softmax_regression, # uncomment for softmax regression
     # infer_func=multilayer_perceptron, # uncomment for MLP
     infer_func=convolutional_neural_network,  # uncomment for LeNet5

02.recognize_digits/README.md

@@ -162,6 +162,15 @@ A PaddlePaddle program starts from importing the API package:
 import paddle
 import paddle.fluid as fluid
 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 *
 ```
 
 ### Program Functions Configuration
@@ -281,7 +290,7 @@ Now, we need to setup the trainer. The trainer need to take in `train_program`,
 use_cuda = False # set to True if training with GPU
 place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=train_program, place=place, optimizer_func=optimizer_program)
  ```
 
@@ -297,14 +306,14 @@ We will demonstrate two event handlers here. Please feel free to modify on the J
 params_dirname = "recognize_digits_network.inference.model"
 lists = []
 def event_handler(event):
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if event.step % 100 == 0:
             # event.metrics maps with train program return arguments.
             # event.metrics[0] will yeild avg_cost and event.metrics[1] will yeild acc in this example.
             print("Pass %d, Batch %d, Cost %f" % (
                 event.step, event.epoch, event.metrics[0]))
 
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    if isinstance(event, EndEpochEvent):
         avg_cost, acc = trainer.test(
             reader=test_reader, feed_order=['img', 'label'])
 
@@ -331,14 +340,14 @@ lists = []
 # event_handler to plot a figure
 def event_handler_plot(event):
     global step
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if step % 100 == 0:
             # event.metrics maps with train program return arguments.
             # event.metrics[0] will yeild avg_cost and event.metrics[1] will yeild acc in this example.
             cost_ploter.append(train_title, step, event.metrics[0])
             cost_ploter.plot()
         step += 1
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    if isinstance(event, EndEpochEvent):
         # save parameters
         trainer.save_params(params_dirname)
 
@@ -398,7 +407,7 @@ The `Inferencer` takes an `infer_func` and `param_path` to setup the network and
 We can simply plug-in the classifier defined earlier here.
 
 ```python
-inferencer = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
     # infer_func=softmax_regression, # uncomment for softmax regression
     # infer_func=multilayer_perceptron, # uncomment for MLP
     infer_func=convolutional_neural_network,  # uncomment for LeNet5

02.recognize_digits/index.cn.html

@@ -202,6 +202,15 @@ PaddlePaddle在API中提供了自动加载[MNIST](http://yann.lecun.com/exdb/mni
 import paddle
 import paddle.fluid as fluid
 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 *
 ```
 
 ### Program Functions 配置
@@ -320,7 +329,7 @@ test_reader = paddle.batch(
 use_cuda = False # set to True if training with GPU
 place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=train_program, place=place, optimizer_func=optimizer_program)
 ```
 
@@ -336,14 +345,14 @@ Fluid API 在训练期间为回调函数提供了一个钩子。用户能够通
 params_dirname = "recognize_digits_network.inference.model"
 lists = []
 def event_handler(event):
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if event.step % 100 == 0:
             # event.metrics maps with train program return arguments.
             # event.metrics[0] will yeild avg_cost and event.metrics[1] will yeild acc in this example.
             print("Pass %d, Batch %d, Cost %f" % (
                 event.step, event.epoch, event.metrics[0]))
 
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    if isinstance(event, EndEpochEvent):
         avg_cost, acc = trainer.test(
             reader=test_reader, feed_order=['img', 'label'])
 
@@ -374,14 +383,14 @@ lists = []
 # event_handler to plot a figure
 def event_handler_plot(event):
     global step
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if step % 100 == 0:
             # event.metrics maps with train program return arguments.
             # event.metrics[0] will yeild avg_cost and event.metrics[1] will yeild acc in this example.
             cost_ploter.append(train_title, step, event.metrics[0])
             cost_ploter.plot()
         step += 1
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    if isinstance(event, EndEpochEvent):
         # save parameters
         trainer.save_params(params_dirname)
 
@@ -434,7 +443,7 @@ Test with Epoch 0, avg_cost: 0.053097883707459624, acc: 0.9822850318471338
 我们可以简单地插入在此之前定义的分类器。
 
 ```python
-inferencer = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
     # infer_func=softmax_regression, # uncomment for softmax regression
     # infer_func=multilayer_perceptron, # uncomment for MLP
     infer_func=convolutional_neural_network,  # uncomment for LeNet5

02.recognize_digits/index.html

@@ -204,6 +204,15 @@ A PaddlePaddle program starts from importing the API package:
 import paddle
 import paddle.fluid as fluid
 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 *
 ```
 
 ### Program Functions Configuration
@@ -323,7 +332,7 @@ Now, we need to setup the trainer. The trainer need to take in `train_program`,
 use_cuda = False # set to True if training with GPU
 place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=train_program, place=place, optimizer_func=optimizer_program)
  ```
 
@@ -339,14 +348,14 @@ We will demonstrate two event handlers here. Please feel free to modify on the J
 params_dirname = "recognize_digits_network.inference.model"
 lists = []
 def event_handler(event):
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if event.step % 100 == 0:
             # event.metrics maps with train program return arguments.
             # event.metrics[0] will yeild avg_cost and event.metrics[1] will yeild acc in this example.
             print("Pass %d, Batch %d, Cost %f" % (
                 event.step, event.epoch, event.metrics[0]))
 
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    if isinstance(event, EndEpochEvent):
         avg_cost, acc = trainer.test(
             reader=test_reader, feed_order=['img', 'label'])
 
@@ -373,14 +382,14 @@ lists = []
 # event_handler to plot a figure
 def event_handler_plot(event):
     global step
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if step % 100 == 0:
             # event.metrics maps with train program return arguments.
             # event.metrics[0] will yeild avg_cost and event.metrics[1] will yeild acc in this example.
             cost_ploter.append(train_title, step, event.metrics[0])
             cost_ploter.plot()
         step += 1
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    if isinstance(event, EndEpochEvent):
         # save parameters
         trainer.save_params(params_dirname)
 
@@ -440,7 +449,7 @@ The `Inferencer` takes an `infer_func` and `param_path` to setup the network and
 We can simply plug-in the classifier defined earlier here.
 
 ```python
-inferencer = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
     # infer_func=softmax_regression, # uncomment for softmax regression
     # infer_func=multilayer_perceptron, # uncomment for MLP
     infer_func=convolutional_neural_network,  # uncomment for LeNet5

03.image_classification/README.cn.md

@@ -169,6 +169,15 @@ import paddle.fluid as fluid
 import numpy
 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 *
 ```
 
 本教程中我们提供了VGG和ResNet两个模型的配置。
@@ -346,7 +355,7 @@ def optimizer_program():
 ```python
 use_cuda = False
 place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=train_program,
     optimizer_func=optimizer_program,
     place=place)
@@ -394,12 +403,12 @@ cost_ploter = Ploter(train_title, test_title)
 step = 0
 def event_handler_plot(event):
     global step
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if step % 1 == 0:
             cost_ploter.append(train_title, step, event.metrics[0])
             cost_ploter.plot()
         step += 1
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    if isinstance(event, EndEpochEvent):
         avg_cost, accuracy = trainer.test(
             reader=test_reader,
             feed_order=['pixel', 'label'])
@@ -417,7 +426,7 @@ params_dirname = "image_classification_resnet.inference.model"
 
 # event handler to track training and testing process
 def event_handler(event):
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if event.step % 100 == 0:
             print("\nPass %d, Batch %d, Cost %f, Acc %f" %
                   (event.step, event.epoch, event.metrics[0],
@@ -426,7 +435,7 @@ def event_handler(event):
             sys.stdout.write('.')
             sys.stdout.flush()
 
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    if isinstance(event, EndEpochEvent):
         # Test against with the test dataset to get accuracy.
         avg_cost, accuracy = trainer.test(
             reader=test_reader, feed_order=['pixel', 'label'])
@@ -513,7 +522,7 @@ img = load_image(cur_dir + '/image/dog.png')
 现在我们准备做预测。
 
 ```python
-inferencer = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
     infer_func=inference_program, param_path=params_dirname, place=place)
 label_list = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]
 # inference

03.image_classification/README.md

@@ -172,6 +172,15 @@ import paddle.fluid as fluid
 import numpy
 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 *
 ```
 
 Now we are going to walk you through the implementations of the VGG and ResNet.
@@ -348,7 +357,7 @@ Here we specify `Adam` optimization algorithm via `fluid.optimizer`.
 ```python
 use_cuda = False
 place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=train_program,
     optimizer_func=optimizer_program,
     place=place)
@@ -392,12 +401,12 @@ cost_ploter = Ploter(train_title, test_title)
 step = 0
 def event_handler_plot(event):
     global step
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if step % 1 == 0:
             cost_ploter.append(train_title, step, event.metrics[0])
             cost_ploter.plot()
         step += 1
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    if isinstance(event, EndEpochEvent):
         avg_cost, accuracy = trainer.test(
             reader=test_reader,
             feed_order=['pixel', 'label'])
@@ -415,7 +424,7 @@ params_dirname = "image_classification_resnet.inference.model"
 
 # event handler to track training and testing process
 def event_handler(event):
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if event.step % 100 == 0:
             print("\nPass %d, Batch %d, Cost %f, Acc %f" %
                   (event.step, event.epoch, event.metrics[0],
@@ -424,7 +433,7 @@ def event_handler(event):
             sys.stdout.write('.')
             sys.stdout.flush()
 
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    if isinstance(event, EndEpochEvent):
         # Test against with the test dataset to get accuracy.
         avg_cost, accuracy = trainer.test(
             reader=test_reader, feed_order=['pixel', 'label'])
@@ -512,7 +521,7 @@ We can simply plug-in the inference_program defined earlier here.
 Now we are ready to do inference.
 
 ```python
-inferencer = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
     infer_func=inference_program, param_path=params_dirname, place=place)
 
 label_list = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]

03.image_classification/index.cn.html

@@ -211,6 +211,15 @@ import paddle.fluid as fluid
 import numpy
 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 *
 ```
 
 本教程中我们提供了VGG和ResNet两个模型的配置。
@@ -388,7 +397,7 @@ def optimizer_program():
 ```python
 use_cuda = False
 place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=train_program,
     optimizer_func=optimizer_program,
     place=place)
@@ -436,12 +445,12 @@ cost_ploter = Ploter(train_title, test_title)
 step = 0
 def event_handler_plot(event):
     global step
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if step % 1 == 0:
             cost_ploter.append(train_title, step, event.metrics[0])
             cost_ploter.plot()
         step += 1
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    if isinstance(event, EndEpochEvent):
         avg_cost, accuracy = trainer.test(
             reader=test_reader,
             feed_order=['pixel', 'label'])
@@ -459,7 +468,7 @@ params_dirname = "image_classification_resnet.inference.model"
 
 # event handler to track training and testing process
 def event_handler(event):
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if event.step % 100 == 0:
             print("\nPass %d, Batch %d, Cost %f, Acc %f" %
                   (event.step, event.epoch, event.metrics[0],
@@ -468,7 +477,7 @@ def event_handler(event):
             sys.stdout.write('.')
             sys.stdout.flush()
 
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    if isinstance(event, EndEpochEvent):
         # Test against with the test dataset to get accuracy.
         avg_cost, accuracy = trainer.test(
             reader=test_reader, feed_order=['pixel', 'label'])
@@ -555,7 +564,7 @@ img = load_image(cur_dir + '/image/dog.png')
 现在我们准备做预测。
 
 ```python
-inferencer = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
     infer_func=inference_program, param_path=params_dirname, place=place)
 label_list = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]
 # inference

03.image_classification/index.html

@@ -214,6 +214,15 @@ import paddle.fluid as fluid
 import numpy
 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 *
 ```
 
 Now we are going to walk you through the implementations of the VGG and ResNet.
@@ -390,7 +399,7 @@ Here we specify `Adam` optimization algorithm via `fluid.optimizer`.
 ```python
 use_cuda = False
 place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=train_program,
     optimizer_func=optimizer_program,
     place=place)
@@ -434,12 +443,12 @@ cost_ploter = Ploter(train_title, test_title)
 step = 0
 def event_handler_plot(event):
     global step
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if step % 1 == 0:
             cost_ploter.append(train_title, step, event.metrics[0])
             cost_ploter.plot()
         step += 1
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    if isinstance(event, EndEpochEvent):
         avg_cost, accuracy = trainer.test(
             reader=test_reader,
             feed_order=['pixel', 'label'])
@@ -457,7 +466,7 @@ params_dirname = "image_classification_resnet.inference.model"
 
 # event handler to track training and testing process
 def event_handler(event):
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if event.step % 100 == 0:
             print("\nPass %d, Batch %d, Cost %f, Acc %f" %
                   (event.step, event.epoch, event.metrics[0],
@@ -466,7 +475,7 @@ def event_handler(event):
             sys.stdout.write('.')
             sys.stdout.flush()
 
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    if isinstance(event, EndEpochEvent):
         # Test against with the test dataset to get accuracy.
         avg_cost, accuracy = trainer.test(
             reader=test_reader, feed_order=['pixel', 'label'])
@@ -554,7 +563,7 @@ We can simply plug-in the inference_program defined earlier here.
 Now we are ready to do inference.
 
 ```python
-inferencer = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
     infer_func=inference_program, param_path=params_dirname, place=place)
 
 label_list = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]

04.word2vec/README.cn.md

@@ -211,6 +211,15 @@ import os
 import six
 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 *
 ```
 
 然后，定义参数：
@@ -310,7 +319,7 @@ def train(use_cuda, train_program, params_dirname):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 
     def event_handler(event):
-        if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+        if isinstance(event, EndStepEvent):
             # We output cost every 10 steps.
             if event.step % 10 == 0:
                 outs = trainer.test(
@@ -330,7 +339,7 @@ def train(use_cuda, train_program, params_dirname):
                 if math.isnan(avg_cost):
                     sys.exit("got NaN loss, training failed.")
 
-    trainer = fluid.contrib.trainer.Trainer(
+    trainer = Trainer(
         train_func=train_program,
         optimizer_func=optimizer_func,
         place=place)
@@ -361,7 +370,7 @@ Step 20: Average Cost 5.766995
 ```python
 def infer(use_cuda, inference_program, params_dirname=None):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    inferencer = fluid.contrib.inferencer.Inferencer(
+    inferencer = Inferencer(
         infer_func=inference_program, param_path=params_dirname, place=place)
 
     # Setup inputs by creating 4 LoDTensors representing 4 words. Here each word

04.word2vec/README.md

@@ -224,6 +224,15 @@ import os
 import six
 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 *
 ```
 
 - Configure parameters and build word dictionary.
@@ -322,7 +331,7 @@ def train(use_cuda, train_program, params_dirname):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 
     def event_handler(event):
-        if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+        if isinstance(event, EndStepEvent):
             outs = trainer.test(
                 reader=test_reader,
                 feed_order=['firstw', 'secondw', 'thirdw', 'fourthw', 'nextw'])
@@ -342,9 +351,9 @@ def train(use_cuda, train_program, params_dirname):
             if math.isnan(avg_cost):
                 sys.exit("got NaN loss, training failed.")
 
-    trainer = fluid.contrib.trainer.Trainer(
+    trainer = Trainer(
         train_func=train_program,
-        # Note here we need to choose more sophisticated optimizer
+        # Note here we need to chse more sophisticated optimizer
         # such as AdaGrad with a decay rate. The normal SGD converges
         # very slowly.
         # optimizer=fluid.optimizer.SGD(learning_rate=0.001),
@@ -379,7 +388,7 @@ We can use our trained model to predict the next word given its previous N-gram.
 ```python
 def infer(use_cuda, inference_program, params_dirname=None):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    inferencer = fluid.contrib.inferencer.Inferencer(
+    inferencer = Inferencer(
         infer_func=inference_program, param_path=params_dirname, place=place)
 
     # Setup inputs by creating 4 LoDTensors representing 4 words. Here each word

04.word2vec/index.cn.html

@@ -253,6 +253,15 @@ import os
 import six
 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 *
 ```
 
 然后，定义参数：
@@ -352,7 +361,7 @@ def train(use_cuda, train_program, params_dirname):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 
     def event_handler(event):
-        if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+        if isinstance(event, EndStepEvent):
             # We output cost every 10 steps.
             if event.step % 10 == 0:
                 outs = trainer.test(
@@ -372,7 +381,7 @@ def train(use_cuda, train_program, params_dirname):
                 if math.isnan(avg_cost):
                     sys.exit("got NaN loss, training failed.")
 
-    trainer = fluid.contrib.trainer.Trainer(
+    trainer = Trainer(
         train_func=train_program,
         optimizer_func=optimizer_func,
         place=place)
@@ -403,7 +412,7 @@ Step 20: Average Cost 5.766995
 ```python
 def infer(use_cuda, inference_program, params_dirname=None):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    inferencer = fluid.contrib.inferencer.Inferencer(
+    inferencer = Inferencer(
         infer_func=inference_program, param_path=params_dirname, place=place)
 
     # Setup inputs by creating 4 LoDTensors representing 4 words. Here each word

04.word2vec/index.html

@@ -266,6 +266,15 @@ import os
 import six
 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 *
 ```
 
 - Configure parameters and build word dictionary.
@@ -364,7 +373,7 @@ def train(use_cuda, train_program, params_dirname):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 
     def event_handler(event):
-        if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+        if isinstance(event, EndStepEvent):
             outs = trainer.test(
                 reader=test_reader,
                 feed_order=['firstw', 'secondw', 'thirdw', 'fourthw', 'nextw'])
@@ -384,9 +393,9 @@ def train(use_cuda, train_program, params_dirname):
             if math.isnan(avg_cost):
                 sys.exit("got NaN loss, training failed.")
 
-    trainer = fluid.contrib.trainer.Trainer(
+    trainer = Trainer(
         train_func=train_program,
-        # Note here we need to choose more sophisticated optimizer
+        # Note here we need to chse more sophisticated optimizer
         # such as AdaGrad with a decay rate. The normal SGD converges
         # very slowly.
         # optimizer=fluid.optimizer.SGD(learning_rate=0.001),
@@ -421,7 +430,7 @@ We can use our trained model to predict the next word given its previous N-gram.
 ```python
 def infer(use_cuda, inference_program, params_dirname=None):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    inferencer = fluid.contrib.inferencer.Inferencer(
+    inferencer = Inferencer(
         infer_func=inference_program, param_path=params_dirname, place=place)
 
     # Setup inputs by creating 4 LoDTensors representing 4 words. Here each word

04.word2vec/train.py

@@ -38,7 +38,7 @@ HIDDEN_SIZE = 256
 N = 5
 BATCH_SIZE = 100
 
-use_cuda = False  # set to True if training with GPU
+use_cuda = True  # set to True if training with GPU
 
 word_dict = paddle.dataset.imikolov.build_dict()
 dict_size = len(word_dict)

05.recommender_system/README.cn.md

@@ -225,6 +225,15 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 import paddle.fluid.nets as nets
+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 *
 
 IS_SPARSE = True
 USE_GPU = False
@@ -409,7 +418,7 @@ test_reader = paddle.batch(
 训练器需要一个训练程序和一个训练优化函数。
 
 ```python
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=train_program, place=place, optimizer_func=optimizer_func)
 ```
 
@@ -437,7 +446,7 @@ plot_cost = Ploter(test_title)
 
 
 def event_handler(event):
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         avg_cost_set = trainer.test(
             reader=test_reader, feed_order=feed_order)
 
@@ -473,7 +482,7 @@ trainer.train(
 传入`inference_program`和`params_dirname`来初始化一个预测器, `params_dirname`用来存放训练过程中的各个参数。
 
 ```python
-inferencer = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
         inference_program, param_path=params_dirname, place=place)
 ```
 

05.recommender_system/README.md

@@ -193,6 +193,15 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 import paddle.fluid.nets as nets
+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 *
 
 IS_SPARSE = True
 USE_GPU = False
@@ -382,7 +391,7 @@ test_reader = paddle.batch(
 Create a trainer that takes `train_program` as input and specify optimizer function.
 
 ```python
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=train_program, place=place, optimizer_func=optimizer_func)
 ```
 
@@ -412,7 +421,7 @@ plot_cost = Ploter(test_title)
 
 
 def event_handler(event):
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         avg_cost_set = trainer.test(
             reader=test_reader, feed_order=feed_order)
 
@@ -451,7 +460,7 @@ trainer.train(
 Initialize Inferencer with `inference_program` and `params_dirname` which is where we save params from training.
 
 ```python
-inferencer = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
         inference_program, param_path=params_dirname, place=place)  
 ```
 

05.recommender_system/index.cn.html

@@ -267,6 +267,15 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 import paddle.fluid.nets as nets
+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 *
 
 IS_SPARSE = True
 USE_GPU = False
@@ -451,7 +460,7 @@ test_reader = paddle.batch(
 训练器需要一个训练程序和一个训练优化函数。
 
 ```python
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=train_program, place=place, optimizer_func=optimizer_func)
 ```
 
@@ -479,7 +488,7 @@ plot_cost = Ploter(test_title)
 
 
 def event_handler(event):
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         avg_cost_set = trainer.test(
             reader=test_reader, feed_order=feed_order)
 
@@ -515,7 +524,7 @@ trainer.train(
 传入`inference_program`和`params_dirname`来初始化一个预测器, `params_dirname`用来存放训练过程中的各个参数。
 
 ```python
-inferencer = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
         inference_program, param_path=params_dirname, place=place)
 ```
 

05.recommender_system/index.html

@@ -235,6 +235,15 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 import paddle.fluid.nets as nets
+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 *
 
 IS_SPARSE = True
 USE_GPU = False
@@ -424,7 +433,7 @@ test_reader = paddle.batch(
 Create a trainer that takes `train_program` as input and specify optimizer function.
 
 ```python
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=train_program, place=place, optimizer_func=optimizer_func)
 ```
 
@@ -454,7 +463,7 @@ plot_cost = Ploter(test_title)
 
 
 def event_handler(event):
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         avg_cost_set = trainer.test(
             reader=test_reader, feed_order=feed_order)
 
@@ -493,7 +502,7 @@ trainer.train(
 Initialize Inferencer with `inference_program` and `params_dirname` which is where we save params from training.
 
 ```python
-inferencer = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
         inference_program, param_path=params_dirname, place=place)  
 ```
 

06.understand_sentiment/README.cn.md

@@ -112,6 +112,15 @@ import paddle
 import paddle.fluid as fluid
 from functools import partial
 import numpy as np
+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 *
 
 CLASS_DIM = 2
 EMB_DIM = 128
@@ -249,7 +258,7 @@ train_reader = paddle.batch(
 训练器需要一个训练程序和一个训练优化函数。
 
 ```python
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=partial(train_program, word_dict),
     place=place,
     optimizer_func=optimizer_func)
@@ -272,7 +281,7 @@ feed_order = ['words', 'label']
 params_dirname = "understand_sentiment_conv.inference.model"
 
 def event_handler(event):
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         print("Step {0}, Epoch {1} Metrics {2}".format(
                 event.step, event.epoch, list(map(np.array, event.metrics))))
 
@@ -300,7 +309,7 @@ trainer.train(
 传入`inference_program`和`params_dirname`来初始化一个预测器, `params_dirname`用来存放训练过程中的各个参数。
 
 ```python
-inferencer = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
         infer_func=partial(inference_program, word_dict), param_path=params_dirname, place=place)
 ```
 

06.understand_sentiment/README.md

@@ -108,6 +108,15 @@ import paddle
 import paddle.fluid as fluid
 from functools import partial
 import numpy as np
+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 *
 
 CLASS_DIM = 2
 EMB_DIM = 128
@@ -255,7 +264,7 @@ train_reader = paddle.batch(
 Create a trainer that takes `train_program` as input and specify optimizer function.
 
 ```python
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=partial(train_program, word_dict),
     place=place,
     optimizer_func=optimizer_func)
@@ -279,7 +288,7 @@ For example, we can check the cost by `trainer.test` when `EndStepEvent` occurs
 params_dirname = "understand_sentiment_conv.inference.model"
 
 def event_handler(event):
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         print("Step {0}, Epoch {1} Metrics {2}".format(
                 event.step, event.epoch, list(map(np.array, event.metrics))))
 
@@ -307,7 +316,7 @@ trainer.train(
 Initialize Inferencer with `inference_program` and `params_dirname` which is where we save params from training.
 
 ```python
-inferencer = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
         infer_func=partial(inference_program, word_dict),
         param_path=params_dirname,
         place=place)

06.understand_sentiment/index.cn.html

@@ -154,6 +154,15 @@ import paddle
 import paddle.fluid as fluid
 from functools import partial
 import numpy as np
+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 *
 
 CLASS_DIM = 2
 EMB_DIM = 128
@@ -291,7 +300,7 @@ train_reader = paddle.batch(
 训练器需要一个训练程序和一个训练优化函数。
 
 ```python
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=partial(train_program, word_dict),
     place=place,
     optimizer_func=optimizer_func)
@@ -314,7 +323,7 @@ feed_order = ['words', 'label']
 params_dirname = "understand_sentiment_conv.inference.model"
 
 def event_handler(event):
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         print("Step {0}, Epoch {1} Metrics {2}".format(
                 event.step, event.epoch, list(map(np.array, event.metrics))))
 
@@ -342,7 +351,7 @@ trainer.train(
 传入`inference_program`和`params_dirname`来初始化一个预测器, `params_dirname`用来存放训练过程中的各个参数。
 
 ```python
-inferencer = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
         infer_func=partial(inference_program, word_dict), param_path=params_dirname, place=place)
 ```
 

06.understand_sentiment/index.html

@@ -150,6 +150,15 @@ import paddle
 import paddle.fluid as fluid
 from functools import partial
 import numpy as np
+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 *
 
 CLASS_DIM = 2
 EMB_DIM = 128
@@ -297,7 +306,7 @@ train_reader = paddle.batch(
 Create a trainer that takes `train_program` as input and specify optimizer function.
 
 ```python
-trainer = fluid.contrib.trainer.Trainer(
+trainer = Trainer(
     train_func=partial(train_program, word_dict),
     place=place,
     optimizer_func=optimizer_func)
@@ -321,7 +330,7 @@ For example, we can check the cost by `trainer.test` when `EndStepEvent` occurs
 params_dirname = "understand_sentiment_conv.inference.model"
 
 def event_handler(event):
-    if isinstance(event, fluid.contrib.trainer.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         print("Step {0}, Epoch {1} Metrics {2}".format(
                 event.step, event.epoch, list(map(np.array, event.metrics))))
 
@@ -349,7 +358,7 @@ trainer.train(
 Initialize Inferencer with `inference_program` and `params_dirname` which is where we save params from training.
 
 ```python
-inferencer = fluid.contrib.inferencer.Inferencer(
+inferencer = Inferencer(
         infer_func=partial(inference_program, word_dict),
         param_path=params_dirname,
         place=place)

08.machine_translation/README.cn.md

@@ -150,6 +150,15 @@ import paddle.fluid.layers as pd
 from paddle.fluid.executor import Executor
 from functools import partial
 import os
+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 *
 
 dict_size = 30000
 source_dict_dim = target_dict_dim = dict_size
@@ -338,7 +347,7 @@ train_reader = paddle.batch(
 
 ```python
 is_sparse = False
-trainer = fluid.Trainer(
+trainer = Trainer(
         train_func=partial(train_program, is_sparse),
         place=place,
         optimizer_func=optimizer_func)
@@ -359,7 +368,7 @@ feed_order = [
 
 ```python
 def event_handler(event):
-    if isinstance(event, fluid.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if event.step % 10 == 0:
             print('pass_id=' + str(event.epoch) + ' batch=' + str(event.step))
 

08.machine_translation/README.md

@@ -180,6 +180,15 @@ import paddle.fluid.layers as pd
 from paddle.fluid.executor import Executor
 from functools import partial
 import os
+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 *
 
 dict_size = 30000
 source_dict_dim = target_dict_dim = dict_size
@@ -374,7 +383,7 @@ Create a trainer that takes `train_program` as input and specify optimizer funct
 
 ```python
 is_sparse = False
-trainer = fluid.Trainer(
+trainer = Trainer(
         train_func=partial(train_program, is_sparse),
         place=place,
         optimizer_func=optimizer_func)
@@ -397,7 +406,7 @@ For example, we can check the cost by `trainer.test` when `EndStepEvent` occurs
 
 ```python
 def event_handler(event):
-    if isinstance(event, fluid.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if event.step % 10 == 0:
             print('pass_id=' + str(event.epoch) + ' batch=' + str(event.step))
 

08.machine_translation/index.cn.html

@@ -192,6 +192,15 @@ import paddle.fluid.layers as pd
 from paddle.fluid.executor import Executor
 from functools import partial
 import os
+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 *
 
 dict_size = 30000
 source_dict_dim = target_dict_dim = dict_size
@@ -380,7 +389,7 @@ train_reader = paddle.batch(
 
 ```python
 is_sparse = False
-trainer = fluid.Trainer(
+trainer = Trainer(
         train_func=partial(train_program, is_sparse),
         place=place,
         optimizer_func=optimizer_func)
@@ -401,7 +410,7 @@ feed_order = [
 
 ```python
 def event_handler(event):
-    if isinstance(event, fluid.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if event.step % 10 == 0:
             print('pass_id=' + str(event.epoch) + ' batch=' + str(event.step))
 

08.machine_translation/index.html

@@ -222,6 +222,15 @@ import paddle.fluid.layers as pd
 from paddle.fluid.executor import Executor
 from functools import partial
 import os
+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 *
 
 dict_size = 30000
 source_dict_dim = target_dict_dim = dict_size
@@ -416,7 +425,7 @@ Create a trainer that takes `train_program` as input and specify optimizer funct
 
 ```python
 is_sparse = False
-trainer = fluid.Trainer(
+trainer = Trainer(
         train_func=partial(train_program, is_sparse),
         place=place,
         optimizer_func=optimizer_func)
@@ -439,7 +448,7 @@ For example, we can check the cost by `trainer.test` when `EndStepEvent` occurs
 
 ```python
 def event_handler(event):
-    if isinstance(event, fluid.EndStepEvent):
+    if isinstance(event, EndStepEvent):
         if event.step % 10 == 0:
             print('pass_id=' + str(event.epoch) + ' batch=' + str(event.step))