mjdietzx

'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function
import numpy as np
np.random.seed(1337)  # for reproducibility

mjdietzx

import collections
import json


def duplicates():
    #
    # organize raw Rekognition `boto3.client('rekognition').get_face_search()` response for debugging this issue
    #

    with open('duplicated_index_bug.json', 'r') as f:  # https://s3.us-east-2.amazonaws.com/brayniac-waya-ai/duplicated_index_bug.json

mjdietzx

import collections


def sort_raw_rekognition_results(results):
    indices = collections.defaultdict(list)  # unique people detected in video to a `list` of their `PersonMatch` objects
    
    timestamps = collections.defaultdict(list)  # `lists` maintain order so we can keep track of people and their indices
    timestamps_indices = collections.defaultdict(list)

    for p in self.results['Persons']:

mjdietzx

<script src="https://unpkg.com/uport-connect/dist/uport-connect.min.js"></script>
<script src="https://unpkg.com/axios/dist/axios.min.js"></script>


<script type="text/javascript">
  function uport() {
    try {
      const u = new window.uportconnect.Connect('blockimmo', {
        clientId: '2ohEPzgzsh7gm68BUcHQMkfaQs8BA4ysatY',
        network: 'rinkeby',

mjdietzx

import io

import boto3
import PIL.Image
import torch
from torch.utils import model_zoo
import torchvision


s3_client = boto3.client('s3')

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ec2_client = boto3.client('ec2')

user_data = """#!/bin/bash
sudo apt-get update
curl "https://bootstrap.pypa.io/get-pip.py" -o "get-pip.py"
sudo python3 get-pip.py
sudo pip3 install boto3
sudo apt-get install -y libgtk2.0-dev
sudo pip3 install opencv-python
echo "{}" >> {}

mjdietzx

from keras import layers
from keras import models
import tensorflow as tf


#
# generator input params
#

rand_dim = (1, 1, 2048)  # dimension of the generator's input tensor (gaussian noise)

mjdietzx

#
# written for Amazon Linux AMI
# creates an AWS Lambda deployment package for pytorch deep learning models (Python 3.6.1)
# assumes lambda function defined in ~/main.py
# deployment package created at ~/waya-ai-lambda.zip
#

#
# install python 3.6.1
#

mjdietzx

"""
wGAN implemented on top of tensorflow as described in: [Wasserstein GAN](https://arxiv.org/pdf/1701.07875.pdf)
with improvements as described in: [Improved Training of Wasserstein GANs](https://arxiv.org/pdf/1704.00028.pdf).

"""

import tensorflow as tf


#

mjdietzx

#!/bin/bash

# creates a GitHub release (draft) and adds pre-built artifacts to the release
# after running this script user should manually check the release in GitHub, optionally edit it, and publish it

# args: :version_number (the version number of this release), :body (text describing the contents of the tag)
# example usage: ./gh_release_bamboo.sh "1.0.0" "Release notes: ..."
# => name: nRF5-ble-driver_<platform_name>_1.0.0_compiled-binaries.zip example: nRF5-ble-driver_win-64_2.0.1_compiled-binaries.zip

# to ensure that bash is used: https://answers.atlassian.com/questions/28625/making-a-bamboo-script-execute-using-binbash