shaybensasson

~ 50-character subject line ~

~ 72-character wrapped longer description. ~

# Description of why, not how.
# Feel free to be detailed.
# nano: use `ctrl+c` to get line/character position

shaybensasson

#!/bin/bash

# this script installs GCC 4.9.4 
# to use it navigate to your home directory and type:
# sh install-gcc-4.9.4.sh

# download and install gcc 4.9.4
wget https://ftp.gnu.org/gnu/gcc/gcc-4.9.4/gcc-4.9.4.tar.gz
tar xzf gcc-4.9.4.tar.gz
cd gcc-4.9.4

shaybensasson

import numpy
print('numpy.__version__')
print(numpy.__version__)
import theano
print('theano.__version__')
print(theano.__version__)
import tensorflow
print('tensorflow.__version__')
print(tensorflow.__version__)
import keras

shaybensasson

description "Jupyter Upstart script"

start on filesystem or runlevel [2345]
stop on shutdown

script
    export HOME="/home/shay" cd $HOME/
    echo $$ > /var/run/jupyter_start.pid
    #exec jupyter-notebook --config='$HOME/.jupyter/jupyter_notebook.config.py'
    #exec ipython notebook

shaybensasson

'''
trying keras to be random seeded

Switch backends by changin ~/.keras/keras.json:
Theano: https://drive.google.com/open?id=0B-FcStylmYuVU1E5S25QX2JXcGs
TensorFlow: https://drive.google.com/open?id=0B-FcStylmYuVNnpfZERoeUo0QnM 

Keras Backend:
*Theano (1 epoch, GPU): 
Test loss: 0.111780489241

shaybensasson

import time
from timeit import default_timer as timer
from datetime import timedelta

t_start = timer()

time.sleep(1)

#print(timer()-t_start)
print(timedelta(seconds=timer()-t_start))

shaybensasson

# revisiting the XOR and donut problems to show how features
# can be learned automatically using neural networks.
#
# the notes for this class can be found at: 
# https://www.udemy.com/data-science-deep-learning-in-python

import numpy as np
import matplotlib.pyplot as plt

# for binary classification! no softmax here

shaybensasson

#OBSOLETE:
#http://stackoverflow.com/questions/32565829/simple-way-to-measure-cell-execution-time-in-ipython-notebook
#%install_ext https://raw.github.com/cpcloud/ipython-autotime/master/autotime.py

#use: pip install ipython-autotime
%load_ext autotime

shaybensasson

import os,random

os.environ["KERAS_BACKEND"] = "theano"
os.environ["THEANO_FLAGS"]  = "device=gpu,lib.cnmem=0.6"
#os.environ["THEANO_FLAGS"]  = "device=cpu"

!nvidia-smi

shaybensasson

function [ret] = translate(value, leftMin, leftMax, rightMin, rightMax)
        % Figure out how 'wide' each range is
        leftSpan = leftMax - leftMin;
        rightSpan = rightMax - rightMin;
        
        % Convert the left range into a 0-1 range (float)
        valueScaled = (value - leftMin) / (leftSpan);
        
        % Convert the 0-1 range into a value in the right range.
        ret = rightMin + (valueScaled * rightSpan);