Michael Dietz mjdietzx

## reduce_lr_keras_issue.py
'''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

## vis_rekognition_gfs_overlapping_indices.py
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

## rekognition_video_face_search_uncertainty.py
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']:

## uport.js
<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',

## main.py
import io

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


s3_client = boto3.client('s3')

## dynamic_ec2.py
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 "{}" >> {}

## ResNeXt_gan.py
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)

## pytorch-lambda-deploy.sh
#
# 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
#

## improved_wGAN_loss.py
"""
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


#

## install-tesla-driver-ubuntu.sh
# http://www.nvidia.com/download/driverResults.aspx/117079/en-us
wget http://us.download.nvidia.com/tesla/375.51/nvidia-driver-local-repo-ubuntu1604_375.51-1_amd64.deb
sudo dpkg -i nvidia-driver-local-repo-ubuntu1604_375.51-1_amd64.deb
sudo apt-get update
sudo apt-get -y install cuda-drivers

echo "Reboot required."
	'''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
	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
	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']:
	<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',
	import io

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


	s3_client = boto3.client('s3')
	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 "{}" >> {}
	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)
	#
	# 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
	#
	"""
	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


	#
	# http://www.nvidia.com/download/driverResults.aspx/117079/en-us
	wget http://us.download.nvidia.com/tesla/375.51/nvidia-driver-local-repo-ubuntu1604_375.51-1_amd64.deb
	sudo dpkg -i nvidia-driver-local-repo-ubuntu1604_375.51-1_amd64.deb
	sudo apt-get update
	sudo apt-get -y install cuda-drivers

	echo "Reboot required."