Port current book code and doc to python3

01.fit_a_line/README.cn.md

@@ -177,6 +177,80 @@ 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
@@ -184,7 +258,6 @@ 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)
@@ -259,7 +332,7 @@ 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/README.md

@@ -196,6 +196,80 @@ 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
@@ -203,7 +277,6 @@ 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)
@@ -281,7 +354,7 @@ 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/plot.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+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()

01.fit_a_line/train.py

@@ -70,7 +70,7 @@ feed_order = ['x', 'y']
 params_dirname = "fit_a_line.inference.model"
 
 # Plot data
-from paddle.v2.plot import Ploter
+from plot import Ploter
 
 train_title = "Train cost"
 test_title = "Test cost"
@@ -125,7 +125,7 @@ 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/README.cn.md

@@ -282,7 +282,7 @@ 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/README.md

@@ -282,7 +282,7 @@ 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)

03.image_classification/resnet.py

@@ -70,7 +70,7 @@ 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/train.py

@@ -102,7 +102,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
     inferencer = Inferencer(
         infer_func=inference_program, param_path=params_dirname, place=place)
 
-    # Prepare testing data. 
+    # Prepare testing data.
     from PIL import Image
     import numpy as np
     import os

04.word2vec/README.cn.md

@@ -208,6 +208,7 @@ import numpy
 from functools import partial
 import math
 import os
+import six
 import sys
 from __future__ import print_function
 ```
@@ -394,7 +395,7 @@ 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/README.md

@@ -221,6 +221,7 @@ import numpy
 from functools import partial
 import math
 import os
+import six
 import sys
 from __future__ import print_function
 ```
@@ -412,7 +413,7 @@ 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/train.py

@@ -12,8 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from __future__ import print_function
-import paddle.v2 as paddle
+import paddle as paddle
 import paddle.fluid as fluid
+import six
 import sys
 
 try:
@@ -176,7 +177,7 @@ 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/README.cn.md

@@ -274,7 +274,7 @@ 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/README.md

@@ -281,7 +281,7 @@ 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 +111,7 @@ 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, map(np.array, event.metrics)))
+                    event.step, event.epoch, list(map(np.array, event.metrics))))
 
         elif isinstance(event, EndEpochEvent):
             trainer.save_params(params_dirname)
@@ -133,14 +133,14 @@ def infer(use_cuda, inference_program, params_dirname=None):
         place=place)
 
     # Setup input by creating LoDTensor to represent sequence of words.
-    # Here each word is the basic element of the LoDTensor and the shape of 
-    # each word (base_shape) should be [1] since it is simply an index to 
+    # Here each word is the basic element of the LoDTensor and the shape of
+    # each word (base_shape) should be [1] since it is simply an index to
     # look up for the corresponding word vector.
     # Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]],
-    # which has only one lod level. Then the created LoDTensor will have only 
-    # one higher level structure (sequence of words, or sentence) than the basic 
-    # element (word). Hence the LoDTensor will hold data for three sentences of 
-    # length 3, 4 and 2, respectively. 
+    # which has only one lod level. Then the created LoDTensor will have only
+    # one higher level structure (sequence of words, or sentence) than the basic
+    # element (word). Hence the LoDTensor will hold data for three sentences of
+    # length 3, 4 and 2, respectively.
     # Note that lod info should be a list of lists.
 
     reviews_str = [

06.understand_sentiment/train_dyn_rnn.py

@@ -128,7 +128,7 @@ 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, map(np.array, event.metrics)))
+                    event.step, event.epoch, list(map(np.array, event.metrics))))
 
         elif isinstance(event, EndEpochEvent):
             trainer.save_params(params_dirname)
@@ -150,14 +150,14 @@ def infer(use_cuda, inference_program, params_dirname=None):
         place=place)
 
     # Setup input by creating LoDTensor to represent sequence of words.
-    # Here each word is the basic element of the LoDTensor and the shape of 
-    # each word (base_shape) should be [1] since it is simply an index to 
+    # Here each word is the basic element of the LoDTensor and the shape of
+    # each word (base_shape) should be [1] since it is simply an index to
     # look up for the corresponding word vector.
     # Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]],
-    # which has only one lod level. Then the created LoDTensor will have only 
-    # one higher level structure (sequence of words, or sentence) than the basic 
-    # element (word). Hence the LoDTensor will hold data for three sentences of 
-    # length 3, 4 and 2, respectively. 
+    # which has only one lod level. Then the created LoDTensor will have only
+    # one higher level structure (sequence of words, or sentence) than the basic
+    # element (word). Hence the LoDTensor will hold data for three sentences of
+    # length 3, 4 and 2, respectively.
     # Note that lod info should be a list of lists.
 
     reviews_str = [

06.understand_sentiment/train_stacked_lstm.py

@@ -119,7 +119,7 @@ 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, map(np.array, event.metrics)))
+                    event.step, event.epoch, list(map(np.array, event.metrics))))
 
         elif isinstance(event, EndEpochEvent):
             trainer.save_params(params_dirname)
@@ -141,14 +141,14 @@ def infer(use_cuda, inference_program, params_dirname=None):
         place=place)
 
     # Setup input by creating LoDTensor to represent sequence of words.
-    # Here each word is the basic element of the LoDTensor and the shape of 
-    # each word (base_shape) should be [1] since it is simply an index to 
+    # Here each word is the basic element of the LoDTensor and the shape of
+    # each word (base_shape) should be [1] since it is simply an index to
     # look up for the corresponding word vector.
     # Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]],
-    # which has only one lod level. Then the created LoDTensor will have only 
-    # one higher level structure (sequence of words, or sentence) than the basic 
-    # element (word). Hence the LoDTensor will hold data for three sentences of 
-    # length 3, 4 and 2, respectively. 
+    # which has only one lod level. Then the created LoDTensor will have only
+    # one higher level structure (sequence of words, or sentence) than the basic
+    # element (word). Hence the LoDTensor will hold data for three sentences of
+    # length 3, 4 and 2, respectively.
     # Note that lod info should be a list of lists.
 
     reviews_str = [

07.label_semantic_roles/README.cn.md

@@ -184,8 +184,9 @@ 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'
@@ -417,7 +418,7 @@ 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/README.md

@@ -207,8 +207,9 @@ 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'
@@ -427,7 +428,7 @@ 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/train.py

@@ -3,8 +3,9 @@ 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'
@@ -167,7 +168,7 @@ 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), fetch_list=[avg_cost])