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add __all__ to all files
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‎docs/modules/distributed.rst‎

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@@ -13,6 +13,8 @@ Check this `minst example <https://github.com/zsdonghao/tensorlayer/blob/master/
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TaskSpecDef
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TaskSpec
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DistributedSession
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StopAtTimeHook
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LoadCheckpoint
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Distributed training

‎tensorlayer/activation.py‎

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import tensorflow as tf
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__all__ = [
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'identity',
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'ramp',
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'leaky_relu',
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'swish',
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'pixel_wise_softmax',
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'linear',
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'lrelu',
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]
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def identity(x):
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"""The identity activation function.

‎tensorlayer/cost.py‎

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# -*- coding: utf-8 -*-
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import logging
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import tensorflow as tf
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__all__ = [
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'cross_entropy',
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'sigmoid_cross_entropy',
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'binary_cross_entropy',
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'mean_squared_error',
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'normalized_mean_square_error',
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'absolute_difference_error',
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'dice_coe',
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'dice_hard_coe',
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'iou_coe',
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'cross_entropy_seq',
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'cross_entropy_seq_with_mask',
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'cosine_similarity',
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'li_regularizer',
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'lo_regularizer',
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'maxnorm_regularizer',
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'maxnorm_o_regularizer',
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'maxnorm_i_regularizer',
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]
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def cross_entropy(output, target, name=None):
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"""Softmax cross-entropy operation, returns the TensorFlow expression of cross-entropy for two distributions, it implements

‎tensorlayer/distributed.py‎

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#! /usr/bin/python
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# -*- coding: utf-8 -*-
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import json
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import os
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import time
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import json, os, time
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import tensorflow as tf
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from tensorflow.python.training import session_run_hook
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__all__ = ['TaskSpecDef', 'TaskSpec', 'DistributedSession', 'StopAtTimeHook', 'LoadCheckpoint']
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class TaskSpecDef(object):
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"""Specification for a distributed task.

‎tensorlayer/files.py‎

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from . import _logging as logging
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from . import nlp, utils, visualize
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__all__ = [
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'load_mnist_dataset',
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'load_fashion_mnist_dataset',
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'load_cifar10_dataset',
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'load_ptb_dataset',
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'load_matt_mahoney_text8_dataset',
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'load_imdb_dataset',
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'load_nietzsche_dataset',
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'load_wmt_en_fr_dataset',
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'load_flickr25k_dataset',
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'load_flickr1M_dataset',
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'load_cyclegan_dataset',
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'download_file_from_google_drive',
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'load_celebA_dataset',
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'load_voc_dataset',
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'save_npz',
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'load_npz',
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'assign_params',
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'load_and_assign_npz',
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'save_npz_dict',
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'load_and_assign_npz_dict',
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'save_ckpt',
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'load_ckpt',
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'save_any_to_npy',
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'load_npy_to_any',
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'file_exists',
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'folder_exists',
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'del_file',
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'del_folder',
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'read_file',
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'load_file_list',
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'load_folder_list',
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'exists_or_mkdir',
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'maybe_download_and_extract',
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'natural_keys',
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'npz_to_W_pdf',
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]
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## Load dataset functions
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def load_mnist_dataset(shape=(-1, 784), path='data'):
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"""Load the original mnist.
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Automatically download MNIST dataset and return the training, validation and test set with 50000, 10000 and 10000 digit images respectively.
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Parameters
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The shape of digit images (the default is (-1, 784), alternatively (-1, 28, 28, 1)).
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path : str
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The path that the data is downloaded to.
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Returns
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-------
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X_train, y_train, X_val, y_val, X_test, y_test: tuple
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def load_fashion_mnist_dataset(shape=(-1, 784), path='data'):
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"""Load the fashion mnist.
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Automatically download fashion-MNIST dataset and return the training, validation and test set with 50000, 10000 and 10000 fashion images respectively, `examples <http://marubon-ds.blogspot.co.uk/2017/09/fashion-mnist-exploring.html>`__.
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Parameters
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The shape of digit images (the default is (-1, 784), alternatively (-1, 28, 28, 1)).
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path : str
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The path that the data is downloaded to.
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Returns
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-------
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X_train, y_train, X_val, y_val, X_test, y_test: tuple
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def _load_mnist_dataset(shape, path, name='mnist', url='http://yann.lecun.com/exdb/mnist/'):
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"""A generic function to load mnist-like dataset.
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Parameters:
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----------
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shape : tuple

‎tensorlayer/iterate.py‎

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import numpy as np
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from six.moves import xrange
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__all__ = [
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'minibatches',
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'seq_minibatches',
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'seq_minibatches2',
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'ptb_iterator',
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]
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def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):
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"""Generate a generator that input a group of example in numpy.array and

‎tensorlayer/nlp.py‎

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# -*- coding: utf-8 -*-
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import collections
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import os
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import random
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import re
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# Metric
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import subprocess
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import tempfile
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import warnings
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import collections, os, random
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import re, subprocess, tempfile, warnings
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import numpy as np
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import tensorflow as tf
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from six.moves import urllib, xrange
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from tensorflow.python.platform import gfile
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from . import _logging as logging
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__all__ = [
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'generate_skip_gram_batch',
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'sample',
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'sample_top',
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'SimpleVocabulary',
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'Vocabulary',
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'process_sentence',
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'create_vocab',
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'simple_read_words',
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'read_words',
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'read_analogies_file',
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'build_vocab',
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'build_reverse_dictionary',
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'build_words_dataset',
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'words_to_word_ids',
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'word_ids_to_words',
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'save_vocab',
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'basic_tokenizer',
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'create_vocabulary',
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'initialize_vocabulary',
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'sentence_to_token_ids',
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'data_to_token_ids',
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'moses_multi_bleu',
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]
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def generate_skip_gram_batch(data, batch_size, num_skips, skip_window, data_index=0):
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"""Generate a training batch for the Skip-Gram model.

‎tensorlayer/prepro.py‎

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# -*- coding: utf-8 -*-
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import threading
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import time
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import threading, time, scipy, skimage
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import numpy as np
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import scipy
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import scipy.ndimage as ndi
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import skimage
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# import tensorlayer as tl
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from scipy import linalg
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from scipy.ndimage.filters import gaussian_filter
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# linalg https://docs.scipy.org/doc/scipy/reference/linalg.html
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# ndimage https://docs.scipy.org/doc/scipy/reference/ndimage.html
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__all__ = [
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'threading_data',
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'rotation',
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'rotation_multi',
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'crop',
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'crop_multi',
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'flip_axis',
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'flip_axis_multi',
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'shift',
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'shift_multi',
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'shear',
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'shear_multi',
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'shear2',
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'shear_multi2',
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'swirl',
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'swirl_multi',
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'elastic_transform',
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'elastic_transform_multi',
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'zoom',
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'zoom_multi',
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'brightness',
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'brightness_multi',
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'illumination',
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'rgb_to_hsv',
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'hsv_to_rgb',
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'adjust_hue',
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'imresize',
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'pixel_value_scale',
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'samplewise_norm',
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'featurewise_norm',
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'get_zca_whitening_principal_components_img',
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'zca_whitening',
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'channel_shift',
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'channel_shift_multi',
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'drop',
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'transform_matrix_offset_center',
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'apply_transform',
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'projective_transform_by_points',
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'array_to_img',
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'find_contours',
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'pt2map',
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'binary_dilation',
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'dilation',
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'binary_erosion',
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'erosion',
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'obj_box_coords_rescale',
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'obj_box_coord_rescale',
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'obj_box_coord_scale_to_pixelunit',
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'obj_box_coord_centroid_to_upleft_butright',
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'obj_box_coord_upleft_butright_to_centroid',
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'obj_box_coord_centroid_to_upleft',
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'obj_box_coord_upleft_to_centroid',
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'parse_darknet_ann_str_to_list',
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'parse_darknet_ann_list_to_cls_box',
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'obj_box_left_right_flip',
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'obj_box_imresize',
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'obj_box_crop',
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'obj_box_shift',
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'obj_box_zoom',
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'pad_sequences',
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'remove_pad_sequences',
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'process_sequences',
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'sequences_add_start_id',
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'sequences_add_end_id',
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'sequences_add_end_id_after_pad',
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'sequences_get_mask',
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]
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def threading_data(data=None, fn=None, thread_count=None, **kwargs):
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"""Process a batch of data by given function by threading.
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# elastic_transform
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def elastic_transform(x, alpha, sigma, mode="constant", cval=0, is_random=False):
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"""Elastic transformation for image as described in `[Simard2003] <http://deeplearning.cs.cmu.edu/pdfs/Simard.pdf>`__.
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‎tensorlayer/rein.py‎

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import tensorflow as tf
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from six.moves import xrange
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__all__ = [
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'discount_episode_rewards',
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'cross_entropy_reward_loss',
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'log_weight',
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'choice_action_by_probs',
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]
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def discount_episode_rewards(rewards=None, gamma=0.99, mode=0):
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"""Take 1D float array of rewards and compute discounted rewards for an

‎tensorlayer/utils.py‎

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# -*- coding: utf-8 -*-
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import os
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import random
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import subprocess
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import sys
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import time
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import os, random, subprocess, sys, time
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from sys import exit as _exit
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from sys import platform as _platform
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import numpy as np
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import tensorflow as tf
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import tensorlayer as tl
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from . import _logging as logging
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from . import iterate
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__all__ = [
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'fit',
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'test',
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'predict',
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'evaluation',
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'dict_to_one',
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'flatten_list',
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'class_balancing_oversample',
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'get_random_int',
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'list_string_to_dict',
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'exit_tensorflow',
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'open_tensorboard',
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'clear_all_placeholder_variables',
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'set_gpu_fraction',
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]
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def fit(sess,
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network,

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