I hereby claim:
- I am phreeza on github.
- I am tmco (https://keybase.io/tmco) on keybase.
- I have a public key whose fingerprint is CD4C 0BFB 64C8 D681 4227 561E 5924 70A9 0C46 A0EE
To claim this, I am signing this object:
import tensorflow as tf | |
node1 = tf.constant(3.0, tf.float32) | |
node2 = tf.constant(4.0) # also tf.float32 implicitly | |
with tf.Session() as sess: | |
W = tf.Variable([.3], tf.float32, name='W') | |
b = tf.Variable([-.3], tf.float32, name='b') | |
x = tf.placeholder(tf.float32, name='x') | |
with tf.name_scope("LinearModel"): |
{ | |
"embeddings": [ | |
{ | |
"tensorName": "Arxiv Titles", | |
"tensorShape": [ | |
10000, | |
256 | |
], | |
"tensorPath": "https://gist.githubusercontent.com/phreeza/e6a8f136fcda76bb7820f788e87e8681/raw/967badf2f7794a32e862c4ee2a8a65b59b118fb8/embeddings.tsv", | |
"metadataPath": "https://gist.github.com/phreeza/e6a8f136fcda76bb7820f788e87e8681/raw/967badf2f7794a32e862c4ee2a8a65b59b118fb8/metadata.tsv" |
import glob | |
entries = [] | |
for n,fname in enumerate(glob.glob('/Users/tom/Downloads/data/*/*.txt')): | |
f = open(fname) | |
s = f.readlines() | |
x = [g.split('\t') for g in ' '.join(s).strip().split('\n ----------\n')][:-1] | |
if n%1000 == 0: | |
print n,fname | |
for raw_entry in x: |
# adapted from http://stackoverflow.com/a/12502560/379300 | |
# Output should be valid markdown, so it can be turned into a nice pdf with pandoc | |
import poppler | |
import sys | |
import urllib | |
import os | |
def main(): | |
input_filename = sys.argv[1] | |
# http://blog.hartwork.org/?p=612 |
# This script is released 'as is' into the public domain | |
from math import cos,sin | |
import os | |
from time import sleep | |
def y(p): | |
return (sin(p)**3) | |
def x(p): | |
return -(13*cos(p)-5*cos(2*p)-2*cos(3*p)-cos(4*t))/16 | |
while True: | |
for r in range(14): |
# -*- coding: utf-8 -*- | |
# -*- mode: python -*- | |
# Adapted from mpl_toolkits.axes_grid2 | |
# LICENSE: Python Software Foundation (http://docs.python.org/license.html) | |
from matplotlib.offsetbox import AnchoredOffsetbox | |
class AnchoredScaleBar(AnchoredOffsetbox): | |
def __init__(self, transform, sizex=0, sizey=0, labelx=None, labely=None, loc=4, | |
pad=0.1, borderpad=0.1, sep=2, prop=None, **kwargs): | |
""" |
I hereby claim:
To claim this, I am signing this object:
import urllib | |
import datetime as dt | |
from datetime import timedelta | |
import pytz | |
from PIL import Image | |
import numpy as np | |
import subprocess | |
import socket | |
import os |
# Test the regularisation parameter on the convolutional Layer | |
from keras.datasets import mnist | |
from keras.models import Sequential | |
from keras.layers.convolutional import Convolution2D | |
from keras.layers.core import Dense,Flatten | |
from keras.utils import np_utils | |
from keras.regularizers import l2 | |
import numpy as np |