Merge pull request #223 from helinwang/fixes

fix book examples

image_classification/README.en.md

@@ -170,6 +170,8 @@ We must import and initialize PaddlePaddle (enable/disable GPU, set the number o
 ```python
 import sys
 import paddle.v2 as paddle
+from vgg import vgg_bn_drop
+from resnet import resnet_cifar10
 
 # PaddlePaddle init
 paddle.init(use_gpu=False, trainer_count=1)
@@ -243,7 +245,9 @@ First, we use a VGG network. Since the image size and amount of CIFAR10 are rela
         The above VGG network extracts high-level features and maps them to a vector of the same size as the categories. Softmax function or classifier is then used for calculating the probability of the image belonging to each category.
 
     ```python
-    out = fc_layer(input=net, size=class_num, act=SoftmaxActivation())
+    out = paddle.layer.fc(input=net,
+                          size=classdim,
+                          act=paddle.activation.Softmax())
     ```
 
 4. Define Loss Function and Outputs
@@ -261,7 +265,7 @@ First, we use a VGG network. Since the image size and amount of CIFAR10 are rela
 The first, third and fourth steps of a ResNet are the same as a VGG. The second one is the main module.
 
 ```python
-net = resnet_cifar10(data, depth=32)
+net = resnet_cifar10(image, depth=56)
 ```
 
 Here are some basic functions used in `resnet_cifar10`:

image_classification/index.en.html

@@ -212,6 +212,8 @@ We must import and initialize PaddlePaddle (enable/disable GPU, set the number o
 ```python
 import sys
 import paddle.v2 as paddle
+from vgg import vgg_bn_drop
+from resnet import resnet_cifar10
 
 # PaddlePaddle init
 paddle.init(use_gpu=False, trainer_count=1)
@@ -285,7 +287,9 @@ First, we use a VGG network. Since the image size and amount of CIFAR10 are rela
         The above VGG network extracts high-level features and maps them to a vector of the same size as the categories. Softmax function or classifier is then used for calculating the probability of the image belonging to each category.
 
     ```python
-    out = fc_layer(input=net, size=class_num, act=SoftmaxActivation())
+    out = paddle.layer.fc(input=net,
+                          size=classdim,
+                          act=paddle.activation.Softmax())
     ```
 
 4. Define Loss Function and Outputs
@@ -303,7 +307,7 @@ First, we use a VGG network. Since the image size and amount of CIFAR10 are rela
 The first, third and fourth steps of a ResNet are the same as a VGG. The second one is the main module.
 
 ```python
-net = resnet_cifar10(data, depth=32)
+net = resnet_cifar10(image, depth=56)
 ```
 
 Here are some basic functions used in `resnet_cifar10`:

recommender_system/README.en.md

@@ -83,11 +83,6 @@ We use the [MovieLens ml-1m](http://files.grouplens.org/datasets/movielens/ml-1m
 
 `paddle.v2.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 `MovieLens` dataset.
 
-```python
-# Run this block to show dataset's documentation
-help(paddle.v2.dataset.movielens)
-```
-
 The raw `MoiveLens` contains movie ratings, relevant features from both movies and users.
 For instance, one movie's feature could be:
 
@@ -182,7 +177,6 @@ from IPython import display
 import cPickle
 
 import paddle.v2 as paddle
-
 paddle.init(use_gpu=False)
 ```
 
@@ -296,9 +290,9 @@ trainer = paddle.trainer.SGD(cost=cost, parameters=parameters,
 `paddle.dataset.movielens.train` will yield records during each pass, after shuffling, a batch input is generated for training.
 
 ```python
-reader=paddle.reader.batch(
+reader=paddle.batch(
     paddle.reader.shuffle(
-        paddle.dataset.movielens.trai(), buf_size=8192),
+        paddle.dataset.movielens.train(), buf_size=8192),
         batch_size=256)
 ```
 

recommender_system/README.md

@@ -404,7 +404,7 @@ feature = user.value() + movie.value()
 infer_dict = copy.copy(feeding)
 del infer_dict['score']
 
-prediction = paddle.infer(output=inference, parameters=parameters, input=[feature], feeding=infer_dict)
+prediction = paddle.infer(inference, parameters=parameters, input=[feature], feeding=infer_dict)
 score = (prediction[0][0] + 5.0) / 2
 print "[Predict] User %d Rating Movie %d With Score %.2f"%(user_id, movie_id, score)
 ```

recommender_system/index.en.html

@@ -125,11 +125,6 @@ We use the [MovieLens ml-1m](http://files.grouplens.org/datasets/movielens/ml-1m
 
 `paddle.v2.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 `MovieLens` dataset.
 
-```python
-# Run this block to show dataset's documentation
-help(paddle.v2.dataset.movielens)
-```
-
 The raw `MoiveLens` contains movie ratings, relevant features from both movies and users.
 For instance, one movie's feature could be:
 
@@ -224,7 +219,6 @@ from IPython import display
 import cPickle
 
 import paddle.v2 as paddle
-
 paddle.init(use_gpu=False)
 ```
 
@@ -338,9 +332,9 @@ trainer = paddle.trainer.SGD(cost=cost, parameters=parameters,
 `paddle.dataset.movielens.train` will yield records during each pass, after shuffling, a batch input is generated for training.
 
 ```python
-reader=paddle.reader.batch(
+reader=paddle.batch(
     paddle.reader.shuffle(
-        paddle.dataset.movielens.trai(), buf_size=8192),
+        paddle.dataset.movielens.train(), buf_size=8192),
         batch_size=256)
 ```
 

recommender_system/index.html

@@ -446,7 +446,7 @@ feature = user.value() + movie.value()
 infer_dict = copy.copy(feeding)
 del infer_dict['score']
 
-prediction = paddle.infer(output=inference, parameters=parameters, input=[feature], feeding=infer_dict)
+prediction = paddle.infer(inference, parameters=parameters, input=[feature], feeding=infer_dict)
 score = (prediction[0][0] + 5.0) / 2
 print "[Predict] User %d Rating Movie %d With Score %.2f"%(user_id, movie_id, score)
 ```

understand_sentiment/README.en.md

@@ -344,7 +344,7 @@ Finally, we can invoke `trainer.train` to start training:
 trainer.train(
     reader=train_reader,
     event_handler=event_handler,
-    feeding=feedig,
+    feeding=feeding,
     num_passes=10)
 ```
 

understand_sentiment/index.en.html

@@ -386,7 +386,7 @@ Finally, we can invoke `trainer.train` to start training:
 trainer.train(
     reader=train_reader,
     event_handler=event_handler,
-    feeding=feedig,
+    feeding=feeding,
     num_passes=10)
 ```