Polish code

01.fit_a_line/index.cn.html

@@ -219,6 +219,80 @@ PaddlePaddle提供了读取数据者发生器机制来读取训练数据。读
 feed_order=['x', 'y']
 ```
 
+以及一个绘画器来进行绘制：
+
+```python
+import six
+import os
+
+
+class PlotData(object):
+    def __init__(self):
+        self.step = []
+        self.value = []
+
+    def append(self, step, value):
+        self.step.append(step)
+        self.value.append(value)
+
+    def reset(self):
+        self.step = []
+        self.value = []
+
+
+class Ploter(object):
+    def __init__(self, *args):
+        self.__args__ = args
+        self.__plot_data__ = {}
+        for title in args:
+            self.__plot_data__[title] = PlotData()
+        # demo in notebooks will use Ploter to plot figure, but when we convert
+        # the ipydb to py file for testing, the import of matplotlib will make the
+        # script crash. So we can use `export DISABLE_PLOT=True` to disable import
+        # these libs
+        self.__disable_plot__ = os.environ.get("DISABLE_PLOT")
+        if not self.__plot_is_disabled__():
+            import matplotlib.pyplot as plt
+            from IPython import display
+            self.plt = plt
+            self.display = display
+
+    def __plot_is_disabled__(self):
+        return self.__disable_plot__ == "True"
+
+    def append(self, title, step, value):
+        assert isinstance(title, six.string_types)
+        assert title in self.__plot_data__
+        data = self.__plot_data__[title]
+        assert isinstance(data, PlotData)
+        data.append(step, value)
+
+    def plot(self, path=None):
+        if self.__plot_is_disabled__():
+            return
+
+        titles = []
+        for title in self.__args__:
+            data = self.__plot_data__[title]
+            assert isinstance(data, PlotData)
+            if len(data.step) > 0:
+                titles.append(title)
+                self.plt.plot(data.step, data.value)
+        self.plt.legend(titles, loc='upper left')
+        if path is None:
+            self.display.clear_output(wait=True)
+            self.display.display(self.plt.gcf())
+        else:
+            self.plt.savefig(path)
+        self.plt.gcf().clear()
+
+    def reset(self):
+        for key in self.__plot_data__:
+            data = self.__plot_data__[key]
+            assert isinstance(data, PlotData)
+            data.reset()
+```
+
 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：
 
 ```python
@@ -226,7 +300,6 @@ feed_order=['x', 'y']
 params_dirname = "fit_a_line.inference.model"
 
 # Plot data
-from paddle.v2.plot import Ploter
 train_title = "Train cost"
 test_title = "Test cost"
 plot_cost = Ploter(train_title, test_title)
@@ -301,7 +374,7 @@ inferencer = fluid.contrib.inferencer.Inferencer(
 
 batch_size = 10
 test_reader = paddle.batch(paddle.dataset.uci_housing.test(),batch_size=batch_size)
-test_data = test_reader().next()
+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")
 

01.fit_a_line/index.html

@@ -238,6 +238,80 @@ for loading the training data. A reader may return multiple columns, and we need
 feed_order=['x', 'y']
 ```
 
+And a ploter to plot metrics:
+
+```python
+import six
+import os
+
+
+class PlotData(object):
+    def __init__(self):
+        self.step = []
+        self.value = []
+
+    def append(self, step, value):
+        self.step.append(step)
+        self.value.append(value)
+
+    def reset(self):
+        self.step = []
+        self.value = []
+
+
+class Ploter(object):
+    def __init__(self, *args):
+        self.__args__ = args
+        self.__plot_data__ = {}
+        for title in args:
+            self.__plot_data__[title] = PlotData()
+        # demo in notebooks will use Ploter to plot figure, but when we convert
+        # the ipydb to py file for testing, the import of matplotlib will make the
+        # script crash. So we can use `export DISABLE_PLOT=True` to disable import
+        # these libs
+        self.__disable_plot__ = os.environ.get("DISABLE_PLOT")
+        if not self.__plot_is_disabled__():
+            import matplotlib.pyplot as plt
+            from IPython import display
+            self.plt = plt
+            self.display = display
+
+    def __plot_is_disabled__(self):
+        return self.__disable_plot__ == "True"
+
+    def append(self, title, step, value):
+        assert isinstance(title, six.string_types)
+        assert title in self.__plot_data__
+        data = self.__plot_data__[title]
+        assert isinstance(data, PlotData)
+        data.append(step, value)
+
+    def plot(self, path=None):
+        if self.__plot_is_disabled__():
+            return
+
+        titles = []
+        for title in self.__args__:
+            data = self.__plot_data__[title]
+            assert isinstance(data, PlotData)
+            if len(data.step) > 0:
+                titles.append(title)
+                self.plt.plot(data.step, data.value)
+        self.plt.legend(titles, loc='upper left')
+        if path is None:
+            self.display.clear_output(wait=True)
+            self.display.display(self.plt.gcf())
+        else:
+            self.plt.savefig(path)
+        self.plt.gcf().clear()
+
+    def reset(self):
+        for key in self.__plot_data__:
+            data = self.__plot_data__[key]
+            assert isinstance(data, PlotData)
+            data.reset()
+```
+
 Moreover, an event handler is provided to print the training progress:
 
 ```python
@@ -245,7 +319,6 @@ Moreover, an event handler is provided to print the training progress:
 params_dirname = "fit_a_line.inference.model"
 
 # Plot data
-from paddle.v2.plot import Ploter
 train_title = "Train cost"
 test_title = "Test cost"
 plot_cost = Ploter(train_title, test_title)
@@ -323,7 +396,7 @@ inferencer = fluid.contrib.inferencer.Inferencer(
 
 batch_size = 10
 test_reader = paddle.batch(paddle.dataset.uci_housing.test(),batch_size=batch_size)
-test_data = test_reader().next()
+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")
 

03.image_classification/index.cn.html

@@ -324,7 +324,7 @@ def layer_warp(block_func, input, ch_in, ch_out, count, stride):
 def resnet_cifar10(ipt, depth=32):
     # depth should be one of 20, 32, 44, 56, 110, 1202
     assert (depth - 2) % 6 == 0
-    n = (depth - 2) / 6
+    n = (depth - 2) // 6
     nStages = {16, 64, 128}
     conv1 = conv_bn_layer(ipt, ch_out=16, filter_size=3, stride=1, padding=1)
     res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)

03.image_classification/index.html

@@ -324,7 +324,7 @@ Note: besides the first convolutional layer and the last fully-connected layer,
 def resnet_cifar10(ipt, depth=32):
     # depth should be one of 20, 32, 44, 56, 110, 1202
     assert (depth - 2) % 6 == 0
-    n = (depth - 2) / 6
+    n = (depth - 2) // 6
     nStages = {16, 64, 128}
     conv1 = conv_bn_layer(ipt, ch_out=16, filter_size=3, stride=1, padding=1)
     res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)

04.word2vec/index.cn.html

@@ -250,6 +250,7 @@ import numpy
 from functools import partial
 import math
 import os
+import six
 import sys
 from __future__ import print_function
 ```
@@ -436,7 +437,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
     most_possible_word_index = numpy.argmax(result[0])
     print(most_possible_word_index)
     print([
-        key for key, value in word_dict.iteritems()
+        key for key, value in six.iteritems(word_dict)
         if value == most_possible_word_index
     ][0])
 ```

04.word2vec/index.html

@@ -263,6 +263,7 @@ import numpy
 from functools import partial
 import math
 import os
+import six
 import sys
 from __future__ import print_function
 ```
@@ -454,7 +455,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
     most_possible_word_index = numpy.argmax(result[0])
     print(most_possible_word_index)
     print([
-        key for key, value in word_dict.iteritems()
+        key for key, value in six.iteritems(word_dict)
         if value == most_possible_word_index
     ][0])
 ```

06.understand_sentiment/index.cn.html

@@ -316,7 +316,7 @@ params_dirname = "understand_sentiment_conv.inference.model"
 def event_handler(event):
     if isinstance(event, fluid.contrib.trainer.EndStepEvent):
         print("Step {0}, Epoch {1} Metrics {2}".format(
-                event.step, event.epoch, map(np.array, event.metrics)))
+                event.step, event.epoch, list(map(np.array, event.metrics))))
 
         if event.step == 10:
             trainer.save_params(params_dirname)

06.understand_sentiment/index.html

@@ -323,7 +323,7 @@ params_dirname = "understand_sentiment_conv.inference.model"
 def event_handler(event):
     if isinstance(event, fluid.contrib.trainer.EndStepEvent):
         print("Step {0}, Epoch {1} Metrics {2}".format(
-                event.step, event.epoch, map(np.array, event.metrics)))
+                event.step, event.epoch, list(map(np.array, event.metrics))))
 
         if event.step == 10:
             trainer.save_params(params_dirname)

06.understand_sentiment/train_conv.py

@@ -111,7 +111,8 @@ def train(use_cuda, train_program, params_dirname):
                     event.step, avg_cost, acc))
 
                 print("Step {0}, Epoch {1} Metrics {2}".format(
-                    event.step, event.epoch, list(map(np.array, event.metrics))))
+                    event.step, event.epoch, list(map(np.array,
+                                                      event.metrics))))
 
         elif isinstance(event, EndEpochEvent):
             trainer.save_params(params_dirname)

06.understand_sentiment/train_dyn_rnn.py

@@ -128,7 +128,8 @@ def train(use_cuda, train_program, params_dirname):
                     event.step, avg_cost, acc))
 
                 print("Step {0}, Epoch {1} Metrics {2}".format(
-                    event.step, event.epoch, list(map(np.array, event.metrics))))
+                    event.step, event.epoch, list(map(np.array,
+                                                      event.metrics))))
 
         elif isinstance(event, EndEpochEvent):
             trainer.save_params(params_dirname)

06.understand_sentiment/train_stacked_lstm.py

@@ -119,7 +119,8 @@ def train(use_cuda, train_program, params_dirname):
                     event.step, avg_cost, acc))
 
                 print("Step {0}, Epoch {1} Metrics {2}".format(
-                    event.step, event.epoch, list(map(np.array, event.metrics))))
+                    event.step, event.epoch, list(map(np.array,
+                                                      event.metrics))))
 
         elif isinstance(event, EndEpochEvent):
             trainer.save_params(params_dirname)

07.label_semantic_roles/index.cn.html

@@ -226,8 +226,9 @@ from __future__ import print_function
 import math, os
 import numpy as np
 import paddle
-import paddle.v2.dataset.conll05 as conll05
+import paddle.dataset.conll05 as conll05
 import paddle.fluid as fluid
+import six
 import time
 
 with_gpu = os.getenv('WITH_GPU', '0') != '0'
@@ -459,7 +460,7 @@ def train(use_cuda, save_dirname=None, is_local=True):
 
         start_time = time.time()
         batch_id = 0
-        for pass_id in xrange(PASS_NUM):
+        for pass_id in six.moves.xrange(PASS_NUM):
             for data in train_data():
                 cost = exe.run(main_program,
                                feed=feeder.feed(data),

07.label_semantic_roles/index.html

@@ -249,8 +249,9 @@ from __future__ import print_function
 import math, os
 import numpy as np
 import paddle
-import paddle.v2.dataset.conll05 as conll05
+import paddle.dataset.conll05 as conll05
 import paddle.fluid as fluid
+import six
 import time
 
 with_gpu = os.getenv('WITH_GPU', '0') != '0'
@@ -469,7 +470,7 @@ def train(use_cuda, save_dirname=None, is_local=True):
 
         start_time = time.time()
         batch_id = 0
-        for pass_id in xrange(PASS_NUM):
+        for pass_id in six.moves.xrange(PASS_NUM):
             for data in train_data():
                 cost = exe.run(main_program,
                                feed=feeder.feed(data),