Merge pull request #489 from tensorlayer/docs

update docs

Author: wagamamaz

docs/modules/files.rst

@@ -1,6 +1,8 @@
 API - Files
 ===================================
 
+A collections of helper functions to work with dataset.
+Load benchmark dataset, save and restore model, save and load variables.
 
 .. automodule:: tensorlayer.files
 
@@ -50,6 +52,7 @@ API - Files
 
    npz_to_W_pdf
 
+
 Load dataset functions
 ------------------------
 
@@ -109,7 +112,7 @@ VOC 2007/2012
 ^^^^^^^^^^^^^^^^
 .. autofunction:: load_voc_dataset
 
-MPII 
+MPII
 ^^^^^^^^^^^^^^^^
 .. autofunction:: load_mpii_pose_dataset
 
@@ -159,6 +162,34 @@ Load network from ckpt
 Load and save variables
 ------------------------
 
+TensorFlow provides ``.ckpt`` file format to save and restore the models, while
+we suggest to use standard python file format ``.npz`` to save models for the
+sake of cross-platform.
+
+.. code-block:: python
+
+  ## save model as .ckpt
+  saver = tf.train.Saver()
+  save_path = saver.save(sess, "model.ckpt")
+  # restore model from .ckpt
+  saver = tf.train.Saver()
+  saver.restore(sess, "model.ckpt")
+
+  ## save model as .npz
+  tl.files.save_npz(network.all_params , name='model.npz')
+  # restore model from .npz (method 1)
+  load_params = tl.files.load_npz(name='model.npz')
+  tl.files.assign_params(sess, load_params, network)
+  # restore model from .npz (method 2)
+  tl.files.load_and_assign_npz(sess=sess, name='model.npz', network=network)
+
+  ## you can assign the pre-trained parameters as follow
+  # 1st parameter
+  tl.files.assign_params(sess, [load_params[0]], network)
+  # the first three parameters
+  tl.files.assign_params(sess, load_params[:3], network)
+
+
 Save variables as .npy
 ^^^^^^^^^^^^^^^^^^^^^^^^^
 .. autofunction:: save_any_to_npy

tensorlayer/db.py

@@ -1,10 +1,5 @@
 #! /usr/bin/python
 # -*- coding: utf-8 -*-
-"""
-Experimental Database Management System.
-
-Latest Version
-"""
 
 import inspect
 import pickle

tensorlayer/files.py

@@ -1,36 +1,4 @@
 # -*- coding: utf-8 -*-
-"""
-A collections of helper functions to work with dataset.
-
-Load benchmark dataset, save and restore model, save and load variables.
-TensorFlow provides ``.ckpt`` file format to save and restore the models, while
-we suggest to use standard python file format ``.npz`` to save models for the
-sake of cross-platform.
-
-.. code-block:: python
-
-  ## save model as .ckpt
-  saver = tf.train.Saver()
-  save_path = saver.save(sess, "model.ckpt")
-  # restore model from .ckpt
-  saver = tf.train.Saver()
-  saver.restore(sess, "model.ckpt")
-
-  ## save model as .npz
-  tl.files.save_npz(network.all_params , name='model.npz')
-  # restore model from .npz (method 1)
-  load_params = tl.files.load_npz(name='model.npz')
-  tl.files.assign_params(sess, load_params, network)
-  # restore model from .npz (method 2)
-  tl.files.load_and_assign_npz(sess=sess, name='model.npz', network=network)
-
-  ## you can assign the pre-trained parameters as follow
-  # 1st parameter
-  tl.files.assign_params(sess, [load_params[0]], network)
-  # the first three parameters
-  tl.files.assign_params(sess, load_params[:3], network)
-
-"""
 
 import gzip
 import math
@@ -1345,7 +1313,7 @@ def load_mpii_pose_dataset(path='data', is_16_pos_only=False):
     >>> import tensorlayer as tl
     >>> img_train_list, ann_train_list, img_test_list, ann_test_list = tl.files.load_mpii_pose_dataset()
     >>> image = tl.vis.read_image(img_train_list[0])
-    >>> tl.vis.draw_mpii_people_to_image(image, ann_train_list[0], 'image.png')
+    >>> tl.vis.draw_mpii_pose_to_image(image, ann_train_list[0], 'image.png')
     >>> pprint.pprint(ann_train_list[0])
 
     References

tensorlayer/visualize.py

@@ -247,7 +247,7 @@ def draw_mpii_pose_to_image(image, poses, save_name='image.png'):
     >>> import tensorlayer as tl
     >>> img_train_list, ann_train_list, img_test_list, ann_test_list = tl.files.load_mpii_pose_dataset()
     >>> image = tl.vis.read_image(img_train_list[0])
-    >>> tl.vis.draw_mpii_people_to_image(image, ann_train_list[0], 'image.png')
+    >>> tl.vis.draw_mpii_pose_to_image(image, ann_train_list[0], 'image.png')
     >>> pprint.pprint(ann_train_list[0])
 
     References