提交 2b795845 编写于 作者: L Luo Tao 提交者: Yu Yang

fix dead link

Change-Id: Id0804d03fc15529a8cf0b13c9e97d628103f5cec
上级 291318e5
......@@ -93,7 +93,7 @@ where `train.sh` is almost the same as `demo/seqToseq/translation/train.sh`, the
- `--init_model_path`: path of the initialization model, here is `data/paraphrase_model`
- `--load_missing_parameter_strategy`: operations when model file is missing, here use a normal distibution to initialize the other parameters except for the embedding layer
For users who want to understand the dataset format, model architecture and training procedure in detail, please refer to [Text generation Tutorial](text_generation.md).
For users who want to understand the dataset format, model architecture and training procedure in detail, please refer to [Text generation Tutorial](../text_generation/text_generation.md).
## Optional Function ##
### Embedding Parameters Observation
......
......@@ -291,7 +291,7 @@ It just start a paddle training process, write the log to `log.txt`,
then print it on screen.
Each command line argument in :code:`run.sh`, please refer to the `command line
arguments <TBD>`_ page. The short description of these arguments is shown as follow.
arguments <../../ui/index.html#command-line-argument>`_ page. The short description of these arguments is shown as follow.
* config\: Tell paddle which file is neural network configuration.
* save_dir\: Tell paddle save model into './output'
......@@ -303,8 +303,6 @@ arguments <TBD>`_ page. The short description of these arguments is shown as fol
* dot_period\: Print a :code:`.` after train :code:`dot_period` batches.
* num_passes\: Train at most :code:`num_passes`.
If training process starts successfully, the output likes follow:
.. code-block:: text
......
......@@ -4,23 +4,23 @@
图像
''''
* `图像分类 <http://www.paddlepaddle.org/doc/demo/image_classification/index.html>`_
* `图像分类 <../..doc/demo/image_classification/index.html>`_
自然语言处理
''''''''''''
* `情感分析 <http://www.paddlepaddle.org/doc/demo/sentiment_analysis/index.html>`_
* `文本生成 <http://www.paddlepaddle.org/doc/demo/text_generation/index.html>`_
* `词性标注 <http://www.paddlepaddle.org/doc/demo/semantic_role_labeling/index.html>`_
* `情感分析 <../../doc/demo/sentiment_analysis/index.html>`_
* `文本生成 <../../doc/demo/text_generation/index.html>`_
* `词性标注 <../../doc/demo/semantic_role_labeling/index.html>`_
推荐
''''
* `MovieLens数据集 <http://www.paddlepaddle.org/doc/demo/rec/ml_dataset.html>`_
* `MovieLens评分回归 <http://www.paddlepaddle.org/doc/demo/rec/ml_regression.html>`_
* `MovieLens数据集 <../../doc/demo/rec/ml_dataset.html>`_
* `MovieLens评分回归 <../../doc/demo/rec/ml_regression.html>`_
常用模型
''''''''
* `ImageNet: ResNet <http://www.paddlepaddle.org/doc/demo/imagenet_model/index.html>`_
* `Embedding: Chinese Word <http://www.paddlepaddle.org/doc/demo/embedding_model/index.html>`_
* `ImageNet: ResNet <../../doc/demo/imagenet_model/resnet_model.html>`_
* `Embedding: Chinese Word <../../doc/demo/embedding_model/index.html>`_
......@@ -14,10 +14,6 @@
"""
Data Sources are helpers to define paddle training data or testing data.
There are several data attributes will be used by paddle:
- Data ProviderType\: such as Python, Protobuf
- Data File list\: a single file that contains all data file paths
"""
from paddle.trainer.config_parser import *
from .utils import deprecated
......@@ -27,8 +23,7 @@ try:
except ImportError:
import pickle
__all__ = ['define_py_data_sources',
'define_py_data_sources2']
__all__ = ['define_py_data_sources2']
def define_py_data_source(file_list, cls, module,
......@@ -50,11 +45,8 @@ def define_py_data_source(file_list, cls, module,
define_py_data_source("train.list", TrainData, "data_provider", "process",
args={"dictionary": dict_name})
The related data provider can refer to
`here <data_provider/python_case.html#quick-start>`__.
:param data_cls:
:param file_list: file list name.
:param file_list: file list name, which contains all data file paths
:type file_list: basestring
:param cls: Train or Test Class.
:type cls: TrainData or TestData
......@@ -105,27 +97,10 @@ def define_py_data_source(file_list, cls, module,
def define_py_data_sources(train_list, test_list, module, obj, args=None,
train_async=False, data_cls=PyData):
"""
Define python Train/Test data sources in one method. If train/test use
the same Data Provider configuration, module/obj/args contain one argument,
otherwise contain a list or tuple of arguments. For example\:
.. code-block:: python
define_py_data_sources("train.list", "test.list", module="data_provider"
obj="process", args={"dictionary": dict_name})
Or.
.. code-block:: python
The annotation is almost the same as define_py_data_sources2, except that
it can specific train_async and data_cls.
define_py_data_sources("train.list", "test.list", module="data_provider"
obj=["process_train", "process_test"],
args=[{"dictionary": dict_train}, {"dictionary": dict_test}])
The related data provider can refer to
`here <data_provider/python_case.html#sequence-example>`__.
:param data_cls:
:param data_cls:
:param train_list: Train list name.
:type train_list: basestring
:param test_list: Test list name.
......@@ -183,6 +158,43 @@ def define_py_data_sources(train_list, test_list, module, obj, args=None,
def define_py_data_sources2(train_list, test_list, module, obj, args=None):
"""
Define python Train/Test data sources in one method. If train/test use
the same Data Provider configuration, module/obj/args contain one argument,
otherwise contain a list or tuple of arguments. For example\:
.. code-block:: python
define_py_data_sources2(train_list="train.list",
test_list="test.list",
module="data_provider"
# if train/test use different configurations,
# obj=["process_train", "process_test"]
obj="process",
args={"dictionary": dict_name})
The related data provider can refer to
`here <../../data_provider/pydataprovider2.html#dataprovider-for-the-sequential-model>`__.
:param train_list: Train list name.
:type train_list: basestring
:param test_list: Test list name.
:type test_list: basestring
:param module: python module name. If train and test is different, then
pass a tuple or list to this argument.
:type module: basestring or tuple or list
:param obj: python object name. May be a function name if using
PyDataProviderWrapper. If train and test is different, then pass
a tuple or list to this argument.
:type obj: basestring or tuple or list
:param args: The best practice is using dict() to pass arguments into
DataProvider, and use :code:`@init_hook_wrapper` to receive
arguments. If train and test is different, then pass a tuple
or list to this argument.
:type args: string or picklable object or list or tuple.
:return: None
:rtype: None
"""
define_py_data_sources(train_list=train_list,
test_list=test_list,
module=module,
......
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