diff --git a/05.recommender_system/README.cn.md b/05.recommender_system/README.cn.md index 7b4e1092c492910bcb85aa6e934c3f65b1cad819..160e24525d274df0dd7c6b794e6714dfa400ac79 100644 --- a/05.recommender_system/README.cn.md +++ b/05.recommender_system/README.cn.md @@ -215,6 +215,7 @@ print "User %s rates Movie %s with Score %s"%(user_info[uid], movie_info[mov_id] ```python +from __future__ import print_function import math import sys import numpy as np @@ -508,6 +509,11 @@ results = inferencer.infer( 'movie_title': movie_title }, return_numpy=False) + +predict_rating = np.array(results[0]) +print("Predict Rating of user id 1 on movie \"" + infer_movie_name + "\" is " + str(predict_rating[0][0])) +print("Actual Rating of user id 1 on movie \"" + infer_movie_name + "\" is 4.") + ``` ## 总结 diff --git a/05.recommender_system/README.md b/05.recommender_system/README.md index 2b1ed98e6747980921d63e5a33b6584063adb13b..939c5a433afa7fc358323f1daf8312f90b292137 100644 --- a/05.recommender_system/README.md +++ b/05.recommender_system/README.md @@ -185,6 +185,7 @@ After issuing a command `python train.py`, training will start immediately. The Our program starts with importing necessary packages and initializing some global variables: ```python +from __future__ import print_function import math import sys import numpy as np diff --git a/05.recommender_system/index.cn.html b/05.recommender_system/index.cn.html index 2fc69601d9a70e6e9f81231311e669ead5d2fc17..1180a7e512f836ef6ac0c4a8b52141df79da78c4 100644 --- a/05.recommender_system/index.cn.html +++ b/05.recommender_system/index.cn.html @@ -257,6 +257,7 @@ print "User %s rates Movie %s with Score %s"%(user_info[uid], movie_info[mov_id] ```python +from __future__ import print_function import math import sys import numpy as np @@ -550,6 +551,11 @@ results = inferencer.infer( 'movie_title': movie_title }, return_numpy=False) + +predict_rating = np.array(results[0]) +print("Predict Rating of user id 1 on movie \"" + infer_movie_name + "\" is " + str(predict_rating[0][0])) +print("Actual Rating of user id 1 on movie \"" + infer_movie_name + "\" is 4.") + ``` ## 总结 diff --git a/05.recommender_system/index.html b/05.recommender_system/index.html index 9aca3c3894cf6ed60f9386c022abb88022fcde86..461923573e3f04a38e31de86e1da858bde30466e 100644 --- a/05.recommender_system/index.html +++ b/05.recommender_system/index.html @@ -227,6 +227,7 @@ After issuing a command `python train.py`, training will start immediately. The Our program starts with importing necessary packages and initializing some global variables: ```python +from __future__ import print_function import math import sys import numpy as np diff --git a/05.recommender_system/train.py b/05.recommender_system/train.py index b04a28255a77013f8b55eccbb84f8b0a5ad8e295..564abd33d89da61cd65fee3fda2684d9ac152f90 100644 --- a/05.recommender_system/train.py +++ b/05.recommender_system/train.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import print_function import math import sys import numpy as np @@ -233,7 +234,11 @@ def infer(use_cuda, inference_program, params_dirname): }, return_numpy=False) - print("infer results: ", np.array(results[0])) + predict_rating = np.array(results[0]) + print("Predict Rating of user id 1 on movie \"" + infer_movie_name + + "\" is " + str(predict_rating[0][0])) + print("Actual Rating of user id 1 on movie \"" + infer_movie_name + + "\" is 4.") def main(use_cuda): diff --git a/06.understand_sentiment/README.cn.md b/06.understand_sentiment/README.cn.md index 31c80285f1dcf36fca83a1130dd268973781dacb..88097dd3fbf3c79694136ffa8d80bfbaa3cc7da0 100644 --- a/06.understand_sentiment/README.cn.md +++ b/06.understand_sentiment/README.cn.md @@ -107,6 +107,7 @@ Paddle在`dataset/imdb.py`中提实现了imdb数据集的自动下载和读取 在该示例中，我们实现了两种文本分类算法，分别基于[推荐系统](https://github.com/PaddlePaddle/book/tree/develop/05.recommender_system)一节介绍过的文本卷积神经网络，以及[栈式双向LSTM](#栈式双向LSTM（Stacked Bidirectional LSTM）)。我们首先引入要用到的库和定义全局变量： ```python +from __future__ import print_function import paddle import paddle.fluid as fluid from functools import partial @@ -115,6 +116,7 @@ import numpy as np CLASS_DIM = 2 EMB_DIM = 128 HID_DIM = 512 +STACKED_NUM = 3 BATCH_SIZE = 128 USE_GPU = False ``` @@ -168,17 +170,12 @@ def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num): input=fc, size=hid_dim, is_reverse=(i % 2) == 0) inputs = [fc, lstm] - fc_last = paddle.layer.pooling(input=inputs[0], pooling_type=paddle.pooling.Max()) - lstm_last = paddle.layer.pooling(input=inputs[1], pooling_type=paddle.pooling.Max()) - output = paddle.layer.fc(input=[fc_last, lstm_last], - size=class_dim, - act=paddle.activation.Softmax(), - bias_attr=bias_attr, - param_attr=para_attr) - - lbl = paddle.layer.data("label", paddle.data_type.integer_value(2)) - cost = paddle.layer.classification_cost(input=output, label=lbl) - return cost, output + fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max') + lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max') + + prediction = fluid.layers.fc( + input=[fc_last, lstm_last], size=class_dim, act='softmax') + return prediction ``` 以上的栈式双向LSTM抽象出了高级特征并把其映射到和分类类别数同样大小的向量上。`paddle.activation.Softmax`函数用来计算分类属于某个类别的概率。 @@ -193,6 +190,7 @@ def inference_program(word_dict): dict_dim = len(word_dict) net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM) + # net = stacked_lstm_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM, STACKED_NUM) return net ``` @@ -301,7 +299,7 @@ trainer.train( ```python inferencer = fluid.Inferencer( - inference_program, param_path=params_dirname, place=place) + infer_func=partial(inference_program, word_dict), param_path=params_dirname, place=place) ``` ### 生成测试用输入数据 diff --git a/06.understand_sentiment/README.md b/06.understand_sentiment/README.md index f427f134de8ee0e3c9844869bb1961b6235b6c20..959d7ad600969bfc4f372cd3b582e2482cf6039e 100644 --- a/06.understand_sentiment/README.md +++ b/06.understand_sentiment/README.md @@ -103,6 +103,7 @@ After issuing a command `python train.py`, training will start immediately. The Our program starts with importing necessary packages and initializing some global variables: ```python +from __future__ import print_function import paddle import paddle.fluid as fluid from functools import partial @@ -111,6 +112,7 @@ import numpy as np CLASS_DIM = 2 EMB_DIM = 128 HID_DIM = 512 +STACKED_NUM = 3 BATCH_SIZE = 128 USE_GPU = False ``` @@ -192,6 +194,7 @@ def inference_program(word_dict): dict_dim = len(word_dict) net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM) + # net = stacked_lstm_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM, STACKED_NUM) return net ``` diff --git a/06.understand_sentiment/index.cn.html b/06.understand_sentiment/index.cn.html index 2be442eabded20f444ef8962eb25b49aab7c213c..f9935e890397460f712769a3fa61314f384a24c1 100644 --- a/06.understand_sentiment/index.cn.html +++ b/06.understand_sentiment/index.cn.html @@ -149,6 +149,7 @@ Paddle在`dataset/imdb.py`中提实现了imdb数据集的自动下载和读取 在该示例中，我们实现了两种文本分类算法，分别基于[推荐系统](https://github.com/PaddlePaddle/book/tree/develop/05.recommender_system)一节介绍过的文本卷积神经网络，以及[栈式双向LSTM](#栈式双向LSTM（Stacked Bidirectional LSTM）)。我们首先引入要用到的库和定义全局变量： ```python +from __future__ import print_function import paddle import paddle.fluid as fluid from functools import partial @@ -157,6 +158,7 @@ import numpy as np CLASS_DIM = 2 EMB_DIM = 128 HID_DIM = 512 +STACKED_NUM = 3 BATCH_SIZE = 128 USE_GPU = False ``` @@ -210,17 +212,12 @@ def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num): input=fc, size=hid_dim, is_reverse=(i % 2) == 0) inputs = [fc, lstm] - fc_last = paddle.layer.pooling(input=inputs[0], pooling_type=paddle.pooling.Max()) - lstm_last = paddle.layer.pooling(input=inputs[1], pooling_type=paddle.pooling.Max()) - output = paddle.layer.fc(input=[fc_last, lstm_last], - size=class_dim, - act=paddle.activation.Softmax(), - bias_attr=bias_attr, - param_attr=para_attr) - - lbl = paddle.layer.data("label", paddle.data_type.integer_value(2)) - cost = paddle.layer.classification_cost(input=output, label=lbl) - return cost, output + fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max') + lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max') + + prediction = fluid.layers.fc( + input=[fc_last, lstm_last], size=class_dim, act='softmax') + return prediction ``` 以上的栈式双向LSTM抽象出了高级特征并把其映射到和分类类别数同样大小的向量上。`paddle.activation.Softmax`函数用来计算分类属于某个类别的概率。 @@ -235,6 +232,7 @@ def inference_program(word_dict): dict_dim = len(word_dict) net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM) + # net = stacked_lstm_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM, STACKED_NUM) return net ``` @@ -343,7 +341,7 @@ trainer.train( ```python inferencer = fluid.Inferencer( - inference_program, param_path=params_dirname, place=place) + infer_func=partial(inference_program, word_dict), param_path=params_dirname, place=place) ``` ### 生成测试用输入数据 diff --git a/06.understand_sentiment/index.html b/06.understand_sentiment/index.html index 54f0a5b2dd4cd403074f92a967932d081396c303..2afdcfb9cc24a163eb816d4decc605cafb54e12f 100644 --- a/06.understand_sentiment/index.html +++ b/06.understand_sentiment/index.html @@ -145,6 +145,7 @@ After issuing a command `python train.py`, training will start immediately. The Our program starts with importing necessary packages and initializing some global variables: ```python +from __future__ import print_function import paddle import paddle.fluid as fluid from functools import partial @@ -153,6 +154,7 @@ import numpy as np CLASS_DIM = 2 EMB_DIM = 128 HID_DIM = 512 +STACKED_NUM = 3 BATCH_SIZE = 128 USE_GPU = False ``` @@ -234,6 +236,7 @@ def inference_program(word_dict): dict_dim = len(word_dict) net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM) + # net = stacked_lstm_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM, STACKED_NUM) return net ``` diff --git a/07.label_semantic_roles/README.cn.md b/07.label_semantic_roles/README.cn.md index e8b54ceca16fa5d0a1e10f1b1b8eb0fbe1fc22c9..bbfa6cd3e4aca0a21046791000f2c36865a13d9b 100644 --- a/07.label_semantic_roles/README.cn.md +++ b/07.label_semantic_roles/README.cn.md @@ -25,7 +25,7 @@ $$\mbox{[小明]}_{\mbox{Agent}}\mbox{[昨天]}_{\mbox{Time}}\mbox{[晚上]}_\mb 图1. 依存句法分析句法树示例 -然而，完全句法分析需要确定句子所包含的全部句法信息，并确定句子各成分之间的关系，是一个非常困难的任务，目前技术下的句法分析准确率并不高，句法分析的细微错误都会导致SRL的错误。为了降低问题的复杂度，同时获得一定的句法结构信息，“浅层句法分析”的思想应运而生。浅层句法分析也称为部分句法分析（partial parsing）或语块划分（chunking）。和完全句法分析得到一颗完整的句法树不同，浅层句法分析只需要识别句子中某些结构相对简单的独立成分，例如：动词短语，这些被识别出来的结构称为语块。为了回避 “无法获得准确率较高的句法树” 所带来的困难，一些研究\[[1](#参考文献)\]也提出了基于语块（chunk）的SRL方法。基于语块的SRL方法将SRL作为一个序列标注问题来解决。序列标注任务一般都会采用BIO表示方式来定义序列标注的标签集，我们先来介绍这种表示方法。在BIO表示法中，B代表语块的开始，I代表语块的中间，O代表语块结束。通过B、I、O 三种标记将不同的语块赋予不同的标签，例如：对于一个角色为A的论元，将它所包含的第一个语块赋予标签B-A，将它所包含的其它语块赋予标签I-A，不属于任何论元的语块赋予标签O。 +然而，完全句法分析需要确定句子所包含的全部句法信息，并确定句子各成分之间的关系，是一个非常困难的任务，目前技术下的句法分析准确率并不高，句法分析的细微错误都会导致SRL的错误。为了降低问题的复杂度，同时获得一定的句法结构信息，“浅层句法分析”的思想应运而生。浅层句法分析也称为部分句法分析（partial parsing）或语块划分（chunking）。和完全句法分析得到一颗完整的句法树不同，浅层句法分析只需要识别句子中某些结构相对简单的独立成分，例如：动词短语，这些被识别出来的结构称为语块。为了回避 “无法获得准确率较高的句法树” 所带来的困难，一些研究\[[1](#参考文献)\]也提出了基于语块（chunk）的SRL方法。基于语块的SRL方法将SRL作为一个序列标注问题来解决。序列标注任务一般都会采用BIO表示方式来定义序列标注的标签集，我们先来介绍这种表示方法。在BIO表示法中，B代表语块的开始，I代表语块的中间，O代表语块结束。通过B、I、O 三种标记将不同的语块赋予不同的标签，例如：对于一个由角色A拓展得到的语块组，将它所包含的第一个语块赋予标签B-A，将它所包含的其它语块赋予标签I-A，不属于任何论元的语块赋予标签O。 我们继续以上面的这句话为例，图1展示了BIO表示方法。 @@ -151,14 +151,6 @@ conll05st-release/ 4. 构造以BIO法表示的标记； 5. 依据词典获取词对应的整数索引。 - -```python -# import paddle.v2.dataset.conll05 as conll05 -# conll05.corpus_reader函数完成上面第1步和第2步. -# conll05.reader_creator函数完成上面第3步到第5步. -# conll05.test函数可以获取处理之后的每条样本来供PaddlePaddle训练. -``` - 预处理完成之后一条训练样本包含9个特征，分别是：句子序列、谓词、谓词上下文（占 5 列）、谓词上下区域标志、标注序列。下表是一条训练样本的示例。 | 句子序列 | 谓词 | 谓词上下文（窗口 = 5） | 谓词上下文区域标记 | 标注序列 | @@ -187,6 +179,8 @@ conll05st-release/ 获取词典，打印词典大小： ```python +from __future__ import print_function + import math, os import numpy as np import paddle @@ -201,9 +195,9 @@ word_dict_len = len(word_dict) label_dict_len = len(label_dict) pred_dict_len = len(verb_dict) -print word_dict_len -print label_dict_len -print pred_dict_len +print('word_dict_len: ', word_dict_len) +print('label_dict_len: ', label_dict_len) +print('pred_dict_len: ', pred_dict_len) ``` ## 模型配置说明 @@ -431,7 +425,7 @@ def train(use_cuda, save_dirname=None, is_local=True): cost = cost[0] if batch_id % 10 == 0: - print("avg_cost:" + str(cost)) + print("avg_cost: " + str(cost)) if batch_id != 0: print("second per batch: " + str((time.time( ) - start_time) / batch_id)) diff --git a/07.label_semantic_roles/README.md b/07.label_semantic_roles/README.md index 36837cc3234d6770915b4c5d501058c3573dee9a..623d035da5ec71185f1269185f1dce8f366d2985 100644 --- a/07.label_semantic_roles/README.md +++ b/07.label_semantic_roles/README.md @@ -175,13 +175,6 @@ The raw data needs to be preprocessed into formats that PaddlePaddle can handle. 4. Construct the markings in BIO format; 5. Obtain the integer index corresponding to the word according to the dictionary. -```python -# import paddle.v2.dataset.conll05 as conll05 -# conll05.corpus_reader does step 1 and 2 as mentioned above. -# conll05.reader_creator does step 3 to 5. -# conll05.test gets preprocessed training instances. -``` - After preprocessing, a training sample contains nine features, namely: word sequence, predicate, predicate context (5 columns), region mark sequence, label sequence. The following table is an example of a training sample. | word sequence | predicate | predicate context（5 columns） | region mark sequence | label sequence| @@ -209,6 +202,8 @@ We trained a language model on the English Wikipedia to get a word vector lookup Here we fetch the dictionary, and print its size: ```python +from __future__ import print_function + import math, os import numpy as np import paddle @@ -223,9 +218,9 @@ word_dict_len = len(word_dict) label_dict_len = len(label_dict) pred_dict_len = len(verb_dict) -print word_dict_len -print label_dict_len -print pred_dict_len +print('word_dict_len: ', word_dict_len) +print('label_dict_len: ', label_dict_len) +print('pred_dict_len: ', pred_dict_len) ``` ## Model Configuration @@ -440,7 +435,7 @@ def train(use_cuda, save_dirname=None, is_local=True): cost = cost[0] if batch_id % 10 == 0: - print("avg_cost:" + str(cost)) + print("avg_cost: " + str(cost)) if batch_id != 0: print("second per batch: " + str((time.time( ) - start_time) / batch_id)) diff --git a/07.label_semantic_roles/index.cn.html b/07.label_semantic_roles/index.cn.html index 0a3276359a5f5deb40df536b249936d10fa4eba7..c19befb1b6a0a4ee679d52da838167c5f716b349 100644 --- a/07.label_semantic_roles/index.cn.html +++ b/07.label_semantic_roles/index.cn.html @@ -67,7 +67,7 @@ $$\mbox{[小明]}_{\mbox{Agent}}\mbox{[昨天]}_{\mbox{Time}}\mbox{[晚上]}_\mb 图1. 依存句法分析句法树示例 -然而，完全句法分析需要确定句子所包含的全部句法信息，并确定句子各成分之间的关系，是一个非常困难的任务，目前技术下的句法分析准确率并不高，句法分析的细微错误都会导致SRL的错误。为了降低问题的复杂度，同时获得一定的句法结构信息，“浅层句法分析”的思想应运而生。浅层句法分析也称为部分句法分析（partial parsing）或语块划分（chunking）。和完全句法分析得到一颗完整的句法树不同，浅层句法分析只需要识别句子中某些结构相对简单的独立成分，例如：动词短语，这些被识别出来的结构称为语块。为了回避 “无法获得准确率较高的句法树” 所带来的困难，一些研究\[[1](#参考文献)\]也提出了基于语块（chunk）的SRL方法。基于语块的SRL方法将SRL作为一个序列标注问题来解决。序列标注任务一般都会采用BIO表示方式来定义序列标注的标签集，我们先来介绍这种表示方法。在BIO表示法中，B代表语块的开始，I代表语块的中间，O代表语块结束。通过B、I、O 三种标记将不同的语块赋予不同的标签，例如：对于一个角色为A的论元，将它所包含的第一个语块赋予标签B-A，将它所包含的其它语块赋予标签I-A，不属于任何论元的语块赋予标签O。 +然而，完全句法分析需要确定句子所包含的全部句法信息，并确定句子各成分之间的关系，是一个非常困难的任务，目前技术下的句法分析准确率并不高，句法分析的细微错误都会导致SRL的错误。为了降低问题的复杂度，同时获得一定的句法结构信息，“浅层句法分析”的思想应运而生。浅层句法分析也称为部分句法分析（partial parsing）或语块划分（chunking）。和完全句法分析得到一颗完整的句法树不同，浅层句法分析只需要识别句子中某些结构相对简单的独立成分，例如：动词短语，这些被识别出来的结构称为语块。为了回避 “无法获得准确率较高的句法树” 所带来的困难，一些研究\[[1](#参考文献)\]也提出了基于语块（chunk）的SRL方法。基于语块的SRL方法将SRL作为一个序列标注问题来解决。序列标注任务一般都会采用BIO表示方式来定义序列标注的标签集，我们先来介绍这种表示方法。在BIO表示法中，B代表语块的开始，I代表语块的中间，O代表语块结束。通过B、I、O 三种标记将不同的语块赋予不同的标签，例如：对于一个由角色A拓展得到的语块组，将它所包含的第一个语块赋予标签B-A，将它所包含的其它语块赋予标签I-A，不属于任何论元的语块赋予标签O。 我们继续以上面的这句话为例，图1展示了BIO表示方法。 @@ -193,14 +193,6 @@ conll05st-release/ 4. 构造以BIO法表示的标记； 5. 依据词典获取词对应的整数索引。 - -```python -# import paddle.v2.dataset.conll05 as conll05 -# conll05.corpus_reader函数完成上面第1步和第2步. -# conll05.reader_creator函数完成上面第3步到第5步. -# conll05.test函数可以获取处理之后的每条样本来供PaddlePaddle训练. -``` - 预处理完成之后一条训练样本包含9个特征，分别是：句子序列、谓词、谓词上下文（占 5 列）、谓词上下区域标志、标注序列。下表是一条训练样本的示例。 | 句子序列 | 谓词 | 谓词上下文（窗口 = 5） | 谓词上下文区域标记 | 标注序列 | @@ -229,6 +221,8 @@ conll05st-release/ 获取词典，打印词典大小： ```python +from __future__ import print_function + import math, os import numpy as np import paddle @@ -243,9 +237,9 @@ word_dict_len = len(word_dict) label_dict_len = len(label_dict) pred_dict_len = len(verb_dict) -print word_dict_len -print label_dict_len -print pred_dict_len +print('word_dict_len: ', word_dict_len) +print('label_dict_len: ', label_dict_len) +print('pred_dict_len: ', pred_dict_len) ``` ## 模型配置说明 @@ -473,7 +467,7 @@ def train(use_cuda, save_dirname=None, is_local=True): cost = cost[0] if batch_id % 10 == 0: - print("avg_cost:" + str(cost)) + print("avg_cost: " + str(cost)) if batch_id != 0: print("second per batch: " + str((time.time( ) - start_time) / batch_id)) diff --git a/07.label_semantic_roles/index.html b/07.label_semantic_roles/index.html index 7d84612aa653746fdf247f2dbc44072d85937d0b..170c6b6f5916415bfbbb564680dc1efa12ef6de5 100644 --- a/07.label_semantic_roles/index.html +++ b/07.label_semantic_roles/index.html @@ -217,13 +217,6 @@ The raw data needs to be preprocessed into formats that PaddlePaddle can handle. 4. Construct the markings in BIO format; 5. Obtain the integer index corresponding to the word according to the dictionary. -```python -# import paddle.v2.dataset.conll05 as conll05 -# conll05.corpus_reader does step 1 and 2 as mentioned above. -# conll05.reader_creator does step 3 to 5. -# conll05.test gets preprocessed training instances. -``` - After preprocessing, a training sample contains nine features, namely: word sequence, predicate, predicate context (5 columns), region mark sequence, label sequence. The following table is an example of a training sample. | word sequence | predicate | predicate context（5 columns） | region mark sequence | label sequence| @@ -251,6 +244,8 @@ We trained a language model on the English Wikipedia to get a word vector lookup Here we fetch the dictionary, and print its size: ```python +from __future__ import print_function + import math, os import numpy as np import paddle @@ -265,9 +260,9 @@ word_dict_len = len(word_dict) label_dict_len = len(label_dict) pred_dict_len = len(verb_dict) -print word_dict_len -print label_dict_len -print pred_dict_len +print('word_dict_len: ', word_dict_len) +print('label_dict_len: ', label_dict_len) +print('pred_dict_len: ', pred_dict_len) ``` ## Model Configuration @@ -482,7 +477,7 @@ def train(use_cuda, save_dirname=None, is_local=True): cost = cost[0] if batch_id % 10 == 0: - print("avg_cost:" + str(cost)) + print("avg_cost: " + str(cost)) if batch_id != 0: print("second per batch: " + str((time.time( ) - start_time) / batch_id)) diff --git a/07.label_semantic_roles/train.py b/07.label_semantic_roles/train.py index f09ee33282aae551e746fa4458c7efd8a66cd991..780167a2cfa7b25d2d064efb700cac324cde3f6b 100644 --- a/07.label_semantic_roles/train.py +++ b/07.label_semantic_roles/train.py @@ -1,3 +1,5 @@ +from __future__ import print_function + import math, os import numpy as np import paddle diff --git a/08.machine_translation/README.cn.md b/08.machine_translation/README.cn.md index 014c9d79580bff9198bf26a0d38e03ac637dd84e..7457383100696b71cf3e8898a92c784dcfe030ee 100644 --- a/08.machine_translation/README.cn.md +++ b/08.machine_translation/README.cn.md @@ -85,7 +85,7 @@ 2. 将$z_{i+1}$通过`softmax`归一化，得到目标语言序列的第$i+1$个单词的概率分布$p_{i+1}$。概率分布公式如下： - $$p\left ( u_{i+1}|u_{<i+1},\mathbf{x} \right )=softmax(W_sz_{i+1}+b_z)$$ + $$p\left ( u_{i+1}|u_{