| ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ | |
| ## 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""" |
| 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 |
| 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) |
| # 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 |
##VGG19 model for Keras
This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| import math | |
| import chainer | |
| import chainer.links as L | |
| import chainer.functions as F | |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
