Merge pull request #625 from velconia/port_py3

Port current book code and doc to python3

01.fit_a_line/README.cn.md

@@ -183,26 +183,22 @@ feed_order=['x', 'y']
 # Specify the directory to save the parameters
 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)
 
 step = 0
 
 # event_handler prints training and testing info
-def event_handler_plot(event):
+def event_handler(event):
     global step
     if isinstance(event, fluid.contrib.trainer.EndStepEvent):
         if step % 10 == 0:   # record a train cost every 10 batches
-            plot_cost.append(train_title, step, event.metrics[0])
+            print("%s, Step %d, Cost %f" % (train_title, step, event.metrics[0]))
 
         if step % 100 == 0:  # record a test cost every 100 batches
             test_metrics = trainer.test(
                 reader=test_reader, feed_order=feed_order)
-            plot_cost.append(test_title, step, test_metrics[0])
-            plot_cost.plot()
+            print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0]))
 
             if test_metrics[0] < 10.0:
                 # If the accuracy is good enough, we can stop the training.
@@ -227,7 +223,7 @@ def event_handler_plot(event):
 trainer.train(
     reader=train_reader,
     num_epochs=100,
-    event_handler=event_handler_plot,
+    event_handler=event_handler,
     feed_order=feed_order)
 ```
 <div align="center">
@@ -259,7 +255,7 @@ 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

@@ -202,26 +202,22 @@ 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.v2.plot 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_plot(event):
+def event_handler(event):
     global step
     if isinstance(event, fluid.contrib.trainer.EndStepEvent):
         if step % 10 == 0:   # record a train cost every 10 batches
-            plot_cost.append(train_title, step, event.metrics[0])
+            print("%s, Step %d, Cost %f" % (train_title, step, event.metrics[0]))
 
         if step % 100 == 0:  # record a test cost every 100 batches
             test_metrics = trainer.test(
                 reader=test_reader, feed_order=feed_order)
-            plot_cost.append(test_title, step, test_metrics[0])
-            plot_cost.plot()
+            print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0]))
 
             if test_metrics[0] < 10.0:
                 # If the accuracy is good enough, we can stop the training.
@@ -229,7 +225,7 @@ def event_handler_plot(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:
@@ -248,7 +244,7 @@ We now can start training by calling `trainer.train()`.
 trainer.train(
     reader=train_reader,
     num_epochs=100,
-    event_handler=event_handler_plot,
+    event_handler=event_handler,
     feed_order=feed_order)
 
 ```
@@ -281,7 +277,7 @@ 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.cn.html

@@ -225,26 +225,22 @@ feed_order=['x', 'y']
 # Specify the directory to save the parameters
 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)
 
 step = 0
 
 # event_handler prints training and testing info
-def event_handler_plot(event):
+def event_handler(event):
     global step
     if isinstance(event, fluid.contrib.trainer.EndStepEvent):
         if step % 10 == 0:   # record a train cost every 10 batches
-            plot_cost.append(train_title, step, event.metrics[0])
+            print("%s, Step %d, Cost %f" % (train_title, step, event.metrics[0]))
 
         if step % 100 == 0:  # record a test cost every 100 batches
             test_metrics = trainer.test(
                 reader=test_reader, feed_order=feed_order)
-            plot_cost.append(test_title, step, test_metrics[0])
-            plot_cost.plot()
+            print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0]))
 
             if test_metrics[0] < 10.0:
                 # If the accuracy is good enough, we can stop the training.
@@ -269,7 +265,7 @@ def event_handler_plot(event):
 trainer.train(
     reader=train_reader,
     num_epochs=100,
-    event_handler=event_handler_plot,
+    event_handler=event_handler,
     feed_order=feed_order)
 ```
 <div align="center">
@@ -301,7 +297,7 @@ 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

@@ -244,26 +244,22 @@ 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.v2.plot 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_plot(event):
+def event_handler(event):
     global step
     if isinstance(event, fluid.contrib.trainer.EndStepEvent):
         if step % 10 == 0:   # record a train cost every 10 batches
-            plot_cost.append(train_title, step, event.metrics[0])
+            print("%s, Step %d, Cost %f" % (train_title, step, event.metrics[0]))
 
         if step % 100 == 0:  # record a test cost every 100 batches
             test_metrics = trainer.test(
                 reader=test_reader, feed_order=feed_order)
-            plot_cost.append(test_title, step, test_metrics[0])
-            plot_cost.plot()
+            print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0]))
 
             if test_metrics[0] < 10.0:
                 # If the accuracy is good enough, we can stop the training.
@@ -271,7 +267,7 @@ def event_handler_plot(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:
@@ -290,7 +286,7 @@ We now can start training by calling `trainer.train()`.
 trainer.train(
     reader=train_reader,
     num_epochs=100,
-    event_handler=event_handler_plot,
+    event_handler=event_handler,
     feed_order=feed_order)
 
 ```
@@ -323,7 +319,7 @@ 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/train.py

@@ -69,28 +69,24 @@ feed_order = ['x', 'y']
 # Specify the directory to save the parameters
 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)
 
 step = 0
 
 
 # event_handler prints training and testing info
-def event_handler_plot(event):
+def event_handler(event):
     global step
     if isinstance(event, EndStepEvent):
         if step % 10 == 0:  # record a train cost every 10 batches
-            plot_cost.append(train_title, step, event.metrics[0])
+            print("%s, Step %d, Cost %f" %
+                  (train_title, step, event.metrics[0]))
 
         if step % 100 == 0:  # record a test cost every 100 batches
             test_metrics = trainer.test(
                 reader=test_reader, feed_order=feed_order)
-            plot_cost.append(test_title, step, test_metrics[0])
-            plot_cost.plot()
+            print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0]))
 
             if test_metrics[0] < 10.0:
                 # If the accuracy is good enough, we can stop the training.
@@ -109,7 +105,7 @@ def event_handler_plot(event):
 trainer.train(
     reader=train_reader,
     num_epochs=100,
-    event_handler=event_handler_plot,
+    event_handler=event_handler,
     feed_order=feed_order)
 
 
@@ -125,7 +121,7 @@ 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/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)

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/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])
 ```

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/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, 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 +134,14 @@ 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 +128,8 @@ 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, 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 +151,14 @@ 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 +119,8 @@ 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, 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 +142,14 @@ 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/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),

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])