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merged 3 commits into from
Apr 16, 2018
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update docs save model; #490

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f4c259b
update docs save model;
zsdonghao Apr 14, 2018
ce51e5b
Merge branch 'master' into docs
zsdonghao Apr 14, 2018
ca99b65
Merge branch 'master' into docs
zsdonghao Apr 15, 2018
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64 changes: 36 additions & 28 deletions docs/modules/files.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand All @@ -199,6 +203,8 @@ Load variables from .npy
.. autofunction:: load_npy_to_any




Folder/File functions
------------------------

Expand Down Expand Up @@ -238,6 +244,8 @@ Download or extract
^^^^^^^^^^^^^^^^^^^^^^^^^
.. autofunction:: maybe_download_and_extract



Sort
-------

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