| import numpy | |
| import pylab | |
| from pylab import * | |
| from matplotlib.widgets import Slider, Button, RadioButtons | |
| # default 2d transformation matrix values | |
| x1_def, x2_def, y1_def, y2_def = 1,0,0,1 | |
| ax = subplot(111) | |
| subplots_adjust(bottom=0.25) |
| # -*- coding: utf-8 -*- | |
| import numpy as np | |
| import pylab | |
| """ | |
| Imagine you have two random variables X_1 and X_2 and both of them are normally distributed, but with different parameters. In this example, let's say distribution of X_1 is g1(x) and for X2 it is g2(x). | |
| P(X_1) ~ N(µ1,Σ1), P(X_2) ~ N(µ2, Σ2). Where Σ1 and Σ2 are variances of X_1 and X_2 consecutively which are in this example s1^2 and s2^2. | |
| export PATH=$PATH:/$HOME/bin | |
| if [ -z ${DISPLAY} ] | |
| then | |
| export DISPLAY=:0.0 | |
| fi | |
| # Terminal history settings | |
| export HISTCONTROL=ignoredups:erasedups # no duplicate entries | |
| export HISTSIZE=1000000 # big big history | |
| export HISTFILESIZE=1000000 # big big history |
| # Target OS: Linux/Mac | |
| # Prerequisites: Make sure you have installed and setup gist (https://github.com/defunkt/gist), and modified bashrc path as per your own setup which in my case was $HOME/.bash_profile. | |
| gist -u $(cat $HOME/.bashrc_gisturl) -c -p -f "my_last_bashrc" -d "`date`" $HOME/.bash_profile > $HOME/.bashrc_gisturl |
| (dp0 | |
| I0 | |
| S'tench, Tinca tinca' | |
| p1 | |
| sI1 | |
| S'goldfish, Carassius auratus' | |
| p2 | |
| sI2 | |
| S'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias' | |
| p3 |
| {0: 'tench, Tinca tinca', | |
| 1: 'goldfish, Carassius auratus', | |
| 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', | |
| 3: 'tiger shark, Galeocerdo cuvieri', | |
| 4: 'hammerhead, hammerhead shark', | |
| 5: 'electric ray, crampfish, numbfish, torpedo', | |
| 6: 'stingray', | |
| 7: 'cock', | |
| 8: 'hen', | |
| 9: 'ostrich, Struthio camelus', |
| name: "CaffeNet" | |
| layer { | |
| name: "data" | |
| type: "Input" | |
| top: "data" | |
| input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } } | |
| } | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" |
| name: "CaffeNet" | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TRAIN | |
| } | |
| transform_param { |
##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