| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| # this is a quick implementation of http://arxiv.org/abs/1508.06576 | |
| # BUT! This is kind of dirty. Lots of hard coding. | |
| import numpy as np | |
| import math | |
| from chainer import cuda, Function, FunctionSet, gradient_check, Variable, optimizers | |
| import chainer.functions as Fu |
##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
| [{ | |
| "lat": "40.79142677512476", | |
| "lng": "-73.93807411193848", | |
| "name": "1 Ave @ 110 St", | |
| "camera": "http://207.251.86.238/cctv261.jpg" | |
| }, { | |
| "lat": "40.800426144169315", | |
| "lng": "-73.93155097961426", | |
| "name": "1 Ave @ 124 St", | |
| "camera": "http://207.251.86.238/cctv254.jpg" |
| import sys | |
| import os | |
| import numpy as np | |
| import theano | |
| import theano.tensor as T | |
| import lasagne as nn | |
| import time | |
| from PIL import Image | |
| from scipy.stats import norm | |
| from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams |
| Sources: | |
| =============== | |
| https://www.digitalocean.com/community/tutorials/initial-server-setup-with-ubuntu-14-04 | |
| https://www.digitalocean.com/community/tutorials/how-to-set-up-a-node-js-application-for-production-on-ubuntu-14-04 | |
| https://github.com/yyx990803/pod | |
| https://www.namecheap.com/support/knowledgebase/article.aspx/1162/46/how-can-i-point-my-domain-name-to-my-home-servers-ip | |
| Basic setup |
| raise ValueError("DEPRECATED/FROZEN - see https://github.com/kastnerkyle/tools for the latest") | |
| # License: BSD 3-clause | |
| # Authors: Kyle Kastner | |
| # Harvest, Cheaptrick, D4C, WORLD routines based on MATLAB code from M. Morise | |
| # http://ml.cs.yamanashi.ac.jp/world/english/ | |
| # MGC code based on r9y9 (Ryuichi Yamamoto) MelGeneralizedCepstrums.jl | |
| # Pieces also adapted from SPTK | |
| from __future__ import division | |
| import numpy as np |
The Batch Normalization paper describes a method to address the various issues related to training of Deep Neural Networks. It makes normalization a part of the architecture itself and reports significant improvements in terms of the number of iterations required to train the network.
Covariate shift refers to the change in the input distribution to a learning system. In the case of deep networks, the input to each layer is affected by parameters in all the input layers. So even small changes to the network get amplified down the network. This leads to change in the input distribution to internal layers of the deep network and is known as internal covariate shift.
It is well established that networks converge faster if the inputs have been whitened (ie zero mean, unit variances) and are uncorrelated and internal covariate shift leads to just the opposite.
Current version: 1.0.19 1.0.15 (as of 2018-12-10)
