naotokui

EncodeTS

VGG-Face model for Keras

This is the Keras model of VGG-Face.

It has been obtained through the following method:

• vgg-face-keras:directly convert the vgg-face matconvnet model to keras model
• vgg-face-keras-fc:first convert vgg-face caffe model to mxnet model,and then convert it to keras model

Details about the network architecture can be found in the following paper:

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

tomrunia

import numpy as np
from tensorflow.python.summary.event_accumulator import EventAccumulator

import matplotlib as mpl
import matplotlib.pyplot as plt

def plot_tensorflow_log(path):

    # Loading too much data is slow...
    tf_size_guidance = {

ericjang

TensorFlow development environment on Windows using Docker

Here are instructions to set up TensorFlow dev environment on Docker if you are running Windows, and configure it so that you can access Jupyter Notebook from within the VM + edit files in your text editor of choice on your Windows machine.

Installation

First, install https://www.docker.com/docker-toolbox

Since this is Windows, creating the Docker group "docker" is not necessary.

saliksyed

""" Deep Auto-Encoder implementation
	
	An auto-encoder works as follows:

	Data of dimension k is reduced to a lower dimension j using a matrix multiplication:
	softmax(W*x + b)  = x'
	
	where W is matrix from R^k --> R^j

	A reconstruction matrix W' maps back from R^j --> R^k

erikbern

# Note – this is not a bash script (some of the steps require reboot)
# I named it .sh just so Github does correct syntax highlighting.
#
# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5
#
# The CUDA part is mostly based on this excellent blog post:
# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

# Install various packages
sudo apt-get update

baraldilorenzo

##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

joyofdata

---

Preliminary Updates and Installations

(http://markus.com/install-theano-on-aws/)

sudo apt-get update

sudo apt-get -y dist-upgrade  

gaberoo

##############################################################################
# Metropolis-Hastings MCMC
#
# Runs a Metropolis-Hasting MCMC chain for a given likelihood function.
# Proposal steps are sampled from a Gaussian distribution, either in a single
# step or sequentially over the parameter space.
#
# Input:
#   theta      : starting value of the chain
#   lik.fun    : likelihood function