andrei-pokrovsky

#
# A minimal sample implementing a single sparse convolution layer with synthetic data using SBNet primitives.
#

import numpy as np
import tensorflow as tf

sbnet_module = tf.load_op_library('../libsbnet.so')

def divup(a, b):

keithweaver

'''
Using OpenCV takes a mp4 video and produces a number of images.

Requirements
----
You require OpenCV 3.2 to be installed.

Run
----
Open the main.py and edit the path to the video. Then run:

indykish

# Install Docker on Xenial 16.04.1 x64
# Ref https://docs.docker.com/engine/installation/linux/ubuntulinux/
# No interactive for now.
export DEBIAN_FRONTEND=noninteractive
# Update your APT package index.
sudo apt-get -y update
# Update package information, ensure that APT works with the https method, and that CA certificates are installed.
sudo apt-get -y install apt-transport-https ca-certificates
# Add the new GPG key.
sudo apt-key adv --keyserver hkp://p80.pool.sks-keyservers.net:80 --recv-keys 58118E89F3A912897C070ADBF76221572C52609D

vinhkhuc

# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
# where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
# Similarly, for h * W_2 + b_2
import tensorflow as tf
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

karpathy

"""
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)

psycharo-zz

def read_seq(path):
    
    def read_header(ifile):
        feed = ifile.read(4)
        norpix = ifile.read(24)
        version = struct.unpack('@i', ifile.read(4))
        length = struct.unpack('@i', ifile.read(4))
        assert(length != 1024)
        descr = ifile.read(512)
        params = [struct.unpack('@i', ifile.read(4))[0] for i in range(0,9)]