API - Files

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

tensorlayer.files

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_weights load_and_assign_npz save_npz_dict load_and_assign_npz_dict save_weights_to_hdf5 load_hdf5_to_weights_in_order load_hdf5_to_weights

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

Load dataset functions

MNIST

load_mnist_dataset

Fashion-MNIST

load_fashion_mnist_dataset

CIFAR-10

load_cifar10_dataset

SVHN

load_cropped_svhn

Penn TreeBank (PTB)

load_ptb_dataset

Matt Mahoney's text8

load_matt_mahoney_text8_dataset

IMBD

load_imdb_dataset

Nietzsche

load_nietzsche_dataset

English-to-French translation data from the WMT'15 Website

load_wmt_en_fr_dataset

Flickr25k

load_flickr25k_dataset

Flickr1M

load_flickr1M_dataset

CycleGAN

load_cyclegan_dataset

CelebA

load_celebA_dataset

VOC 2007/2012

load_voc_dataset

MPII

load_mpii_pose_dataset

Google Drive

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 hdf5 to save models for the sake of cross-platform. Other file formats such as .npz are also available.

## save model as .h5
tl.files.save_weights_to_hdf5('model.h5', network.all_weights)
# restore model from .h5 (in order)
tl.files.load_hdf5_to_weights_in_order('model.h5', network.all_weights)
# restore model from .h5 (by name)
tl.files.load_hdf5_to_weights('model.h5', network.all_weights)

## save model as .npz
tl.files.save_npz(network.all_weights , name='model.npz')
# restore model from .npz (method 1)
load_params = tl.files.load_npz(name='model.npz')
tl.files.assign_weights(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_weights(sess, [load_params[0]], network)
# the first three parameters
tl.files.assign_weights(sess, load_params[:3], network)

Save network into list (npz)

save_npz

Load network from list (npz)

load_npz

Assign a list of parameters to network

assign_weights

Load and assign a list of parameters to network

load_and_assign_npz

Save network into dict (npz)

save_npz_dict

Load network from dict (npz)

load_and_assign_npz_dict

Save network into OrderedDict (hdf5)

save_weights_to_hdf5

Load network from hdf5 in order

load_hdf5_to_weights_in_order

Load network from hdf5 by name

load_hdf5_to_weights

Load and save variables

Save variables as .npy

save_any_to_npy

Load variables from .npy

load_npy_to_any

Folder/File functions

Check file exists

file_exists

Check folder exists

folder_exists

Delete file

del_file

Delete folder

del_folder

Read file

read_file

Load file list from folder

load_file_list

Load folder list from folder

load_folder_list

Check and Create folder

exists_or_mkdir

Download or extract

maybe_download_and_extract

Sort

List of string with number in human order