From f4c259b3bee0aefa90990802e34dac62f4786b0c Mon Sep 17 00:00:00 2001
From: zsdonghao <dhsig552@163.com>
Date: Sat, 14 Apr 2018 12:23:09 +0100
Subject: [PATCH] update docs save model;

---
 docs/modules/files.rst | 64 ++++++++++++++++++++++++------------------
 1 file changed, 36 insertions(+), 28 deletions(-)

diff --git a/docs/modules/files.rst b/docs/modules/files.rst
index 272c8c829..6861d8d1b 100644
--- a/docs/modules/files.rst
+++ b/docs/modules/files.rst
@@ -120,9 +120,40 @@ Google Drive
 ^^^^^^^^^^^^^^^^
 .. autofunction:: download_file_from_google_drive
 
+
+
+
+
 Load and save network
 ----------------------
 
+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 network into list (npz)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 .. autofunction:: save_npz
@@ -159,37 +190,10 @@ 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
@@ -199,6 +203,8 @@ Load variables from .npy
 .. autofunction:: load_npy_to_any
 
 
+
+
 Folder/File functions
 ------------------------
 
@@ -238,6 +244,8 @@ Download or extract
 ^^^^^^^^^^^^^^^^^^^^^^^^^
 .. autofunction:: maybe_download_and_extract
 
+
+
 Sort
 -------