eerwitt

# Typical setup to include TensorFlow.
import tensorflow as tf

# Make a queue of file names including all the JPEG images files in the relative
# image directory.
filename_queue = tf.train.string_input_producer(
    tf.train.match_filenames_once("./images/*.jpg"))

# Read an entire image file which is required since they're JPEGs, if the images
# are too large they could be split in advance to smaller files or use the Fixed

albertomontesg

awjuliani

import tensorflow as tf
import numpy as np
import tensorflow.contrib.slim as slim

total_layers = 25 #Specify how deep we want our network
units_between_stride = total_layers / 5

def resUnit(input_layer,i):
    with tf.variable_scope("res_unit"+str(i)):
        part1 = slim.batch_norm(input_layer,activation_fn=None)

symisc

import requests
import json

# Mimic the two famous Snapchat filters: The flower crown & the dog facial parts filter.
# The target image must contain at least one human face. The more faces you got, the more funny it should be!
# 
# Only three commands are actually needed in order to mimic the Snapchat filters:
# face landmarks:         https://pixlab.io/cmd?id=facelandmarks
# smart resize:           https://pixlab.io/cmd?id=smartresize
# merge:                  https://pixlab.io/cmd?id=merge

thomwolf

##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang, Rutgers University, Email: [email protected]
## Modified by Thomas Wolf, HuggingFace Inc., Email: [email protected]
## Copyright (c) 2017-2018
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

"""Encoding Data Parallel"""