A collections of helper functions to work with dataset. Load benchmark dataset, save and restore model, save and load variables.

.. automodule:: tensorlayer.files

.. autosummary::

   load_mnist_dataset
   load_fashion_mnist_dataset
   load_cifar10_dataset
   load_cropped_svhn
   load_ptb_dataset
   load_matt_mahoney_text8_dataset
   load_imdb_dataset
   load_nietzsche_dataset
   load_wmt_en_fr_dataset
   load_flickr25k_dataset
   load_flickr1M_dataset
   load_cyclegan_dataset
   load_celebA_dataset
   load_voc_dataset
   load_mpii_pose_dataset
   download_file_from_google_drive

   save_npz
   load_npz
   assign_params
   load_and_assign_npz
   save_npz_dict
   load_and_assign_npz_dict
   save_ckpt
   load_ckpt


   save_any_to_npy
   load_npy_to_any

   file_exists
   folder_exists
   del_file
   del_folder
   read_file
   load_file_list
   load_folder_list
   exists_or_mkdir
   maybe_download_and_extract

   natural_keys

   npz_to_W_pdf

.. autofunction:: load_mnist_dataset

.. autofunction:: load_fashion_mnist_dataset

.. autofunction:: load_cifar10_dataset

.. autofunction:: load_cropped_svhn

.. autofunction:: load_ptb_dataset

.. autofunction:: load_matt_mahoney_text8_dataset

.. autofunction:: load_imdb_dataset

.. autofunction:: load_nietzsche_dataset

.. autofunction:: load_wmt_en_fr_dataset

.. autofunction:: load_flickr25k_dataset

.. autofunction:: load_flickr1M_dataset

.. autofunction:: load_cyclegan_dataset

.. autofunction:: load_celebA_dataset

.. autofunction:: load_voc_dataset

.. autofunction:: load_mpii_pose_dataset

.. autofunction:: download_file_from_google_drive

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.

## 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)

.. autofunction:: save_npz

.. autofunction:: load_npz

.. autofunction:: assign_params

.. autofunction:: load_and_assign_npz

.. autofunction:: save_npz_dict

.. autofunction:: load_and_assign_npz_dict

.. autofunction:: save_ckpt

.. autofunction:: load_ckpt

.. autofunction:: save_any_to_npy

.. autofunction:: load_npy_to_any

.. autofunction:: file_exists

.. autofunction:: folder_exists

.. autofunction:: del_file

.. autofunction:: del_folder

.. autofunction:: read_file

.. autofunction:: load_file_list

.. autofunction:: load_folder_list

.. autofunction:: exists_or_mkdir

.. autofunction:: maybe_download_and_extract

.. autofunction:: natural_keys

.. autofunction:: npz_to_W_pdf