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Akram Zaytar Akramz

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Akramz / min-char-rnn.py
Created January 20, 2020 15:46 — forked from karpathy/min-char-rnn.py
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np
# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)
@Akramz
Akramz / generate_gif.py
Created February 11, 2019 19:33 — forked from vaclavcadek/generate_gif.py
How to generate animation using numpy arrays.
from matplotlib.animation import FuncAnimation
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(figsize=(5, 8))
def update(i):
im_normed = np.random.random((64, 64))
ax.imshow(im_normed)
@Akramz
Akramz / bobp-python.md
Created October 9, 2018 09:25 — forked from sloria/bobp-python.md
A "Best of the Best Practices" (BOBP) guide to developing in Python.

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

  • "Build tools for others that you want to be built for you." - Kenneth Reitz
  • "Simplicity is alway better than functionality." - Pieter Hintjens
@Akramz
Akramz / install-tensorflow.sh
Created March 29, 2017 16:55 — forked from erikbern/install-tensorflow.sh
Installing TensorFlow on EC2
# 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
import folium
import pandas as pd
# Map init.
map_osm = folium.Map()
data = pd.read_csv('data.csv')
data_ = data[['LATITUDE (HIGH ACCURACY)','LONGITUDE (HIGH ACCURACY)']].astype(float)
for index, row in data_.iterrows():
import cv2
import os
import numpy as np
import pandas as pd
from sys import exit
from collections import Counter
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
from keras.utils.np_utils import to_categorical
from keras.models import Sequential
from sys import exit
import time
import os.path
from sys import exit
from subprocess import call
import datetime
import urllib
# years
years = [2016]
import os
os.environ["THEANO_FLAGS"] = "device=gpu,floatX=float32"
from theano import function, config, shared, sandbox
import theano.tensor as T
import numpy
import time
vlen = 10 * 30 * 768 # 10 x #cores x # threads per core
iters = 1000
installation guide for spot GPU instances :
* MUSTS :
- sudo apt-get update
- sudo apt-get -y dist-upgrade
- sudo apt-get install -y gcc g++ gfortran build-essential git wget linux-image-generic libopenblas-dev python-dev python-pip python-nose python-numpy python-scipy
- sudo apt-get install -y liblapack-dev
- sudo apt-get install -y libblas-dev
C H T W DP DateUTC Type CON
Fes-Sais 46.018711076 24.4734074096 12.8436834369 13.6771496248 2016-06-25 20:00:00 72 Clear
Fes-Sais 41.8126554348 26.3298100303 12.7266287124 14.6923411172 2016-06-25 21:00:00 72 Clear
Fes-Sais 43.505598531 25.7060682843 12.3009745827 14.4071879905 2016-06-25 22:00:00 72 Clear
Fes-Sais 48.0412325718 24.0021621803 11.7006364947 13.6104086947 2016-06-25 23:00:00 72 Clear
Fes-Sais 53.3471927502 22.0347349117 10.9991886353 12.7041734618 2016-06-26 00:00:00 72 Clear
Fes-Sais 58.11953424 20.2713587875 10.2604195077 11.8952656355 2016-06-26 01:00:00 72 Clear
Fes-Sais 61.617390857 18.9699977333 9.55777011478 11.2934759047 2016-06-26 02:00:00 72 Clear
Fes-Sais 63.4916280367 18.2593624401 8.97471670473 10.9576880474 2016-06-26 03:00:00 72 Clear
Fes-Sais 63.6550310709 18.1831970433 8.59613211566 10.9142032575 2016-06-26 04:00:00 72 Clear