Adapt to pre-commit (0.16.0)

.travis.yml

@@ -17,9 +17,11 @@ addons:
       - python-pip
       - python2.7-dev
   ssh_known_hosts: 52.76.173.135
+
 before_install:
   -  sudo pip install -U virtualenv pre-commit pip
   -  docker pull paddlepaddle/paddle:latest
+
 script:
   - exit_code=0
   - .travis/precommit.sh || exit_code=$(( exit_code | $? ))
@@ -34,6 +36,7 @@ script:
     curl $DEPLOY_DOCS_SH | bash -s $CONTENT_DEC_PASSWD $TRAVIS_BRANCH $MODELS_DIR
     exit_code=$(( exit_code | $? ))
     exit $exit_code
+
 notifications:
   email:
     on_success: change

conv_seq2seq/README.md

@@ -7,51 +7,51 @@ Jonas Gehring, Micheal Auli, David Grangier, et al. Convolutional Sequence to Se
 
 - In this tutorial, each line in a data file contains one sample and each sample consists of a source sentence and a target sentence. And the two sentences are seperated by '\t'. So, to use your own data, it should be organized as follows:
 
-	```
-	<source sentence>\t<target sentence>
-	```
+  ```
+  <source sentence>\t<target sentence>
+  ```
 
 # Training a Model
 - Modify the following script if needed and then run:
 
-	```bash
-	python train.py \
-	  --train_data_path ./data/train_data \
-	  --test_data_path ./data/test_data \
-	  --src_dict_path ./data/src_dict \
-	  --trg_dict_path ./data/trg_dict \
-	  --enc_blocks "[(256, 3)] * 5" \
-	  --dec_blocks "[(256, 3)] * 3" \
-	  --emb_size 256 \
-	  --pos_size 200 \
-	  --drop_rate 0.1 \
-	  --use_gpu False \
-	  --trainer_count 1 \
-	  --batch_size 32 \
-	  --num_passes 20 \
-	  >train.log 2>&1
-	```
+    ```bash
+    python train.py \
+      --train_data_path ./data/train_data \
+      --test_data_path ./data/test_data \
+      --src_dict_path ./data/src_dict \
+      --trg_dict_path ./data/trg_dict \
+      --enc_blocks "[(256, 3)] * 5" \
+      --dec_blocks "[(256, 3)] * 3" \
+      --emb_size 256 \
+      --pos_size 200 \
+      --drop_rate 0.1 \
+      --use_gpu False \
+      --trainer_count 1 \
+      --batch_size 32 \
+      --num_passes 20 \
+      >train.log 2>&1
+    ```
 
 # Inferring by a Trained Model
 - Infer by a trained model by running:
 
-	```bash
-	python infer.py \
-	  --infer_data_path ./data/infer_data \
-	  --src_dict_path ./data/src_dict \
-	  --trg_dict_path ./data/trg_dict \
-	  --enc_blocks "[(256, 3)] * 5" \
-	  --dec_blocks "[(256, 3)] * 3" \
-	  --emb_size 256 \
-	  --pos_size 200 \
-	  --drop_rate 0.1 \
-	  --use_gpu False \
-	  --trainer_count 1 \
-	  --max_len 100 \
-	  --beam_size 1 \
-	  --model_path ./params.pass-0.tar.gz \
-	  1>infer_result 2>infer.log
-	```
+    ```bash
+    python infer.py \
+      --infer_data_path ./data/infer_data \
+      --src_dict_path ./data/src_dict \
+      --trg_dict_path ./data/trg_dict \
+      --enc_blocks "[(256, 3)] * 5" \
+      --dec_blocks "[(256, 3)] * 3" \
+      --emb_size 256 \
+      --pos_size 200 \
+      --drop_rate 0.1 \
+      --use_gpu False \
+      --trainer_count 1 \
+      --max_len 100 \
+      --beam_size 1 \
+      --model_path ./params.pass-0.tar.gz \
+      1>infer_result 2>infer.log
+    ```
 
 # Notes
 

conv_seq2seq/model.py

@@ -147,7 +147,8 @@ def encoder(token_emb,
     encoded_sum = paddle.layer.addto(input=[encoded_vec, embedding])
 
     # halve the variance of the sum
-    encoded_sum = paddle.layer.slope_intercept(input=encoded_sum, slope=math.sqrt(0.5))
+    encoded_sum = paddle.layer.slope_intercept(
+        input=encoded_sum, slope=math.sqrt(0.5))
 
     return encoded_vec, encoded_sum