提交 e9f2c0a8 编写于 作者: Y Yi Wang 提交者: GitHub

Merge pull request #193 from helinwang/sentiment_fix

fix according to comments for PR 175
......@@ -118,9 +118,9 @@ Figure 4. Stacked Bidirectional LSTM for NLP modeling.
We use [IMDB](http://ai.stanford.edu/%7Eamaas/data/sentiment/) dataset for sentiment analysis in this tutorial, which consists of 50,000 movie reviews split evenly into 25k train and 25k test sets. In the labeled train/test sets, a negative review has a score <= 4 out of 10, and a positive review has a score >= 7 out of 10.
`paddle.datasets` package encapsulates multiple public datasets, including `cifar`, `imdb`, `mnist`, `moivelens` and `wmt14`, etc. There's no need for us to manually download and preprocess IMDB.
`paddle.datasets` package encapsulates multiple public datasets, including `cifar`, `imdb`, `mnist`, `moivelens`, and `wmt14`, etc. There's no need for us to manually download and preprocess IMDB.
After issuing a command `python train.py`, training will starting immediately. The details will be unpacked by the following sessions to see how it works.
After issuing a command `python train.py`, training will start immediately. The details will be unpacked by the following sessions to see how it works.
## Model Structure
......@@ -172,7 +172,7 @@ def convolution_net(input_dim, class_dim=2, emb_dim=128, hid_dim=128):
1. Define Loss Function
In the context of supervised learning, labels of training set are defined in `paddle.layer.data`, too. During training, cross-entropy is used as loss function in `paddle.layer.classification_cost` and as the output of the network; During testing, the outputs are the probabilities calculated in the classifier.
In the context of supervised learning, labels of the training set are defined in `paddle.layer.data`, too. During training, cross-entropy is used as loss function in `paddle.layer.classification_cost` and as the output of the network; During testing, the outputs are the probabilities calculated in the classifier.
#### Stacked bidirectional LSTM
......@@ -257,7 +257,7 @@ def stacked_lstm_net(input_dim,
1. Define Loss Function
In the context of supervised learning, labels of training set are defined in `paddle.layer.data`, too. During training, cross-entropy is used as loss function in `paddle.layer.classification_cost` and as the output of the network; During testing, the outputs are the probabilities calculated in the classifier.
In the context of supervised learning, labels of the training set are defined in `paddle.layer.data`, too. During training, cross-entropy is used as loss function in `paddle.layer.classification_cost` and as the output of the network; During testing, the outputs are the probabilities calculated in the classifier.
To reiterate, we can either invoke `convolution_net` or `stacked_lstm_net`.
......@@ -322,7 +322,7 @@ test_reader = paddle.batch(
feeding = {'word': 0, 'label': 1}
```
Callback function `event_handler` will be invoked to track training and testing process when a pre-defined event happens.
Callback function `event_handler` will be invoked to track training progress when a pre-defined event happens.
```python
def event_handler(event):
......@@ -348,11 +348,10 @@ trainer.train(
num_passes=10)
```
After training is done, the model from each pass is saved in `output/pass-%05d`. For example, the model of Pass 300 is saved in `output/pass-00299`.
## Conclusion
In this chapter, we use sentiment analysis as an example to introduce applying deep learning models on end-to-end short text classification, as well as how to use PaddlePaddle to implement the model. Meanwhile, we briefly introduce two models for text processing: CNN and RNN. In following chapters we will see how these models can be applied in other tasks.
In this chapter, we use sentiment analysis as an example to introduce applying deep learning models on end-to-end short text classification, as well as how to use PaddlePaddle to implement the model. Meanwhile, we briefly introduce two models for text processing: CNN and RNN. In following chapters, we will see how these models can be applied in other tasks.
## Reference
......
......@@ -160,9 +160,9 @@ Figure 4. Stacked Bidirectional LSTM for NLP modeling.
We use [IMDB](http://ai.stanford.edu/%7Eamaas/data/sentiment/) dataset for sentiment analysis in this tutorial, which consists of 50,000 movie reviews split evenly into 25k train and 25k test sets. In the labeled train/test sets, a negative review has a score <= 4 out of 10, and a positive review has a score >= 7 out of 10.
`paddle.datasets` package encapsulates multiple public datasets, including `cifar`, `imdb`, `mnist`, `moivelens` and `wmt14`, etc. There's no need for us to manually download and preprocess IMDB.
`paddle.datasets` package encapsulates multiple public datasets, including `cifar`, `imdb`, `mnist`, `moivelens`, and `wmt14`, etc. There's no need for us to manually download and preprocess IMDB.
After issuing a command `python train.py`, training will starting immediately. The details will be unpacked by the following sessions to see how it works.
After issuing a command `python train.py`, training will start immediately. The details will be unpacked by the following sessions to see how it works.
## Model Structure
......@@ -214,7 +214,7 @@ def convolution_net(input_dim, class_dim=2, emb_dim=128, hid_dim=128):
1. Define Loss Function
In the context of supervised learning, labels of training set are defined in `paddle.layer.data`, too. During training, cross-entropy is used as loss function in `paddle.layer.classification_cost` and as the output of the network; During testing, the outputs are the probabilities calculated in the classifier.
In the context of supervised learning, labels of the training set are defined in `paddle.layer.data`, too. During training, cross-entropy is used as loss function in `paddle.layer.classification_cost` and as the output of the network; During testing, the outputs are the probabilities calculated in the classifier.
#### Stacked bidirectional LSTM
......@@ -299,7 +299,7 @@ def stacked_lstm_net(input_dim,
1. Define Loss Function
In the context of supervised learning, labels of training set are defined in `paddle.layer.data`, too. During training, cross-entropy is used as loss function in `paddle.layer.classification_cost` and as the output of the network; During testing, the outputs are the probabilities calculated in the classifier.
In the context of supervised learning, labels of the training set are defined in `paddle.layer.data`, too. During training, cross-entropy is used as loss function in `paddle.layer.classification_cost` and as the output of the network; During testing, the outputs are the probabilities calculated in the classifier.
To reiterate, we can either invoke `convolution_net` or `stacked_lstm_net`.
......@@ -364,7 +364,7 @@ test_reader = paddle.batch(
feeding = {'word': 0, 'label': 1}
```
Callback function `event_handler` will be invoked to track training and testing process when a pre-defined event happens.
Callback function `event_handler` will be invoked to track training progress when a pre-defined event happens.
```python
def event_handler(event):
......@@ -390,11 +390,10 @@ trainer.train(
num_passes=10)
```
After training is done, the model from each pass is saved in `output/pass-%05d`. For example, the model of Pass 300 is saved in `output/pass-00299`.
## Conclusion
In this chapter, we use sentiment analysis as an example to introduce applying deep learning models on end-to-end short text classification, as well as how to use PaddlePaddle to implement the model. Meanwhile, we briefly introduce two models for text processing: CNN and RNN. In following chapters we will see how these models can be applied in other tasks.
In this chapter, we use sentiment analysis as an example to introduce applying deep learning models on end-to-end short text classification, as well as how to use PaddlePaddle to implement the model. Meanwhile, we briefly introduce two models for text processing: CNN and RNN. In following chapters, we will see how these models can be applied in other tasks.
## Reference
......
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